code
stringlengths 82
54.1k
| code_codestyle
int64 0
699
| style_context
stringlengths 111
35.6k
| style_context_codestyle
int64 0
699
| label
int64 0
1
|
|---|---|---|---|---|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Optional[Any] =logging.get_logger(__name__)
__snake_case : str ={
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class lowerCamelCase__ ( lowerCamelCase__):
'''simple docstring'''
snake_case_ ="""sew-d"""
def __init__(self ,__lowerCamelCase=32 ,__lowerCamelCase=7_68 ,__lowerCamelCase=12 ,__lowerCamelCase=12 ,__lowerCamelCase=30_72 ,__lowerCamelCase=2 ,__lowerCamelCase=5_12 ,__lowerCamelCase=2_56 ,__lowerCamelCase=True ,__lowerCamelCase=True ,__lowerCamelCase=("p2c", "c2p") ,__lowerCamelCase="layer_norm" ,__lowerCamelCase="gelu_python" ,__lowerCamelCase=0.1 ,__lowerCamelCase=0.1 ,__lowerCamelCase=0.1 ,__lowerCamelCase=0.0 ,__lowerCamelCase=0.1 ,__lowerCamelCase=0.02 ,__lowerCamelCase=1e-7 ,__lowerCamelCase=1e-5 ,__lowerCamelCase="group" ,__lowerCamelCase="gelu" ,__lowerCamelCase=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12) ,__lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,__lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,__lowerCamelCase=False ,__lowerCamelCase=1_28 ,__lowerCamelCase=16 ,__lowerCamelCase=True ,__lowerCamelCase=0.05 ,__lowerCamelCase=10 ,__lowerCamelCase=2 ,__lowerCamelCase=0.0 ,__lowerCamelCase=10 ,__lowerCamelCase=0 ,__lowerCamelCase="mean" ,__lowerCamelCase=False ,__lowerCamelCase=False ,__lowerCamelCase=2_56 ,__lowerCamelCase=0 ,__lowerCamelCase=1 ,__lowerCamelCase=2 ,**__lowerCamelCase ,) -> int:
"""simple docstring"""
super().__init__(**__a ,pad_token_id=__a ,bos_token_id=__a ,eos_token_id=__a )
lowerCAmelCase__ : Dict = hidden_size
lowerCAmelCase__ : Any = feat_extract_norm
lowerCAmelCase__ : List[str] = feat_extract_activation
lowerCAmelCase__ : Optional[Any] = list(__a )
lowerCAmelCase__ : Union[str, Any] = list(__a )
lowerCAmelCase__ : Tuple = list(__a )
lowerCAmelCase__ : Tuple = conv_bias
lowerCAmelCase__ : List[Any] = num_conv_pos_embeddings
lowerCAmelCase__ : Optional[Any] = num_conv_pos_embedding_groups
lowerCAmelCase__ : str = len(self.conv_dim )
lowerCAmelCase__ : List[str] = num_hidden_layers
lowerCAmelCase__ : List[str] = intermediate_size
lowerCAmelCase__ : List[str] = squeeze_factor
lowerCAmelCase__ : str = max_position_embeddings
lowerCAmelCase__ : Dict = position_buckets
lowerCAmelCase__ : List[Any] = share_att_key
lowerCAmelCase__ : Optional[int] = relative_attention
lowerCAmelCase__ : int = norm_rel_ebd
lowerCAmelCase__ : Tuple = list(__a )
lowerCAmelCase__ : List[Any] = hidden_act
lowerCAmelCase__ : Union[str, Any] = num_attention_heads
lowerCAmelCase__ : List[Any] = hidden_dropout
lowerCAmelCase__ : List[str] = attention_dropout
lowerCAmelCase__ : Dict = activation_dropout
lowerCAmelCase__ : str = feat_proj_dropout
lowerCAmelCase__ : List[str] = final_dropout
lowerCAmelCase__ : Dict = layer_norm_eps
lowerCAmelCase__ : Optional[int] = feature_layer_norm_eps
lowerCAmelCase__ : List[Any] = initializer_range
lowerCAmelCase__ : Union[str, Any] = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect.'''
'''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
f"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
f"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowerCAmelCase__ : Any = apply_spec_augment
lowerCAmelCase__ : Any = mask_time_prob
lowerCAmelCase__ : Optional[Any] = mask_time_length
lowerCAmelCase__ : Tuple = mask_time_min_masks
lowerCAmelCase__ : List[str] = mask_feature_prob
lowerCAmelCase__ : Optional[int] = mask_feature_length
lowerCAmelCase__ : List[Any] = mask_feature_min_masks
# ctc loss
lowerCAmelCase__ : Tuple = ctc_loss_reduction
lowerCAmelCase__ : int = ctc_zero_infinity
# sequence classification
lowerCAmelCase__ : Tuple = use_weighted_layer_sum
lowerCAmelCase__ : int = classifier_proj_size
@property
def lowerCAmelCase__ (self ) -> int:
"""simple docstring"""
return functools.reduce(operator.mul ,self.conv_stride ,1 )
| 647 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = 'wavlm'
def __init__( self , __a=32 , __a=7_68 , __a=12 , __a=12 , __a=30_72 , __a="gelu" , __a=0.1 , __a=0.1 , __a=0.1 , __a=0.0 , __a=0.1 , __a=0.1 , __a=0.02 , __a=1e-5 , __a="group" , __a="gelu" , __a=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __a=(5, 2, 2, 2, 2, 2, 2) , __a=(10, 3, 3, 3, 3, 2, 2) , __a=False , __a=1_28 , __a=16 , __a=3_20 , __a=8_00 , __a=False , __a=True , __a=0.05 , __a=10 , __a=2 , __a=0.0 , __a=10 , __a=3_20 , __a=2 , __a=0.1 , __a=1_00 , __a=2_56 , __a=2_56 , __a=0.1 , __a="mean" , __a=False , __a=False , __a=2_56 , __a=(5_12, 5_12, 5_12, 5_12, 15_00) , __a=(5, 3, 3, 1, 1) , __a=(1, 2, 3, 1, 1) , __a=5_12 , __a=80 , __a=0 , __a=1 , __a=2 , __a=False , __a=3 , __a=2 , __a=3 , __a=None , **__a , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**__a , pad_token_id=__a , bos_token_id=__a , eos_token_id=__a)
_UpperCamelCase = hidden_size
_UpperCamelCase = feat_extract_norm
_UpperCamelCase = feat_extract_activation
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = conv_bias
_UpperCamelCase = num_buckets
_UpperCamelCase = max_bucket_distance
_UpperCamelCase = num_conv_pos_embeddings
_UpperCamelCase = num_conv_pos_embedding_groups
_UpperCamelCase = len(self.conv_dim)
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = num_attention_heads
_UpperCamelCase = hidden_dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = feat_proj_dropout
_UpperCamelCase = final_dropout
_UpperCamelCase = layerdrop
_UpperCamelCase = layer_norm_eps
_UpperCamelCase = initializer_range
_UpperCamelCase = num_ctc_classes
_UpperCamelCase = vocab_size
_UpperCamelCase = do_stable_layer_norm
_UpperCamelCase = use_weighted_layer_sum
_UpperCamelCase = classifier_proj_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F''' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,'''
F''' `len(config.conv_kernel) = {len(self.conv_kernel)}`.''')
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_UpperCamelCase = apply_spec_augment
_UpperCamelCase = mask_time_prob
_UpperCamelCase = mask_time_length
_UpperCamelCase = mask_time_min_masks
_UpperCamelCase = mask_feature_prob
_UpperCamelCase = mask_feature_length
# parameters for pretraining with codevector quantized representations
_UpperCamelCase = num_codevectors_per_group
_UpperCamelCase = num_codevector_groups
_UpperCamelCase = contrastive_logits_temperature
_UpperCamelCase = num_negatives
_UpperCamelCase = codevector_dim
_UpperCamelCase = proj_codevector_dim
_UpperCamelCase = diversity_loss_weight
# ctc loss
_UpperCamelCase = ctc_loss_reduction
_UpperCamelCase = ctc_zero_infinity
# adapter
_UpperCamelCase = add_adapter
_UpperCamelCase = adapter_kernel_size
_UpperCamelCase = adapter_stride
_UpperCamelCase = num_adapter_layers
_UpperCamelCase = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_UpperCamelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = xvector_output_dim
@property
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1)
| 19 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
@property
def __lowercase ( self : Tuple ):
'''simple docstring'''
torch.manual_seed(0 )
_a : Union[str, Any] = UNetaDModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=3 ,out_channels=3 ,down_block_types=('DownBlock2D', 'AttnDownBlock2D') ,up_block_types=('AttnUpBlock2D', 'UpBlock2D') ,)
return model
@property
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
_a : Optional[Any] = VQModel(
block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,latent_channels=3 ,)
return model
@property
def __lowercase ( self : List[str] ):
'''simple docstring'''
torch.manual_seed(0 )
_a : Any = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1E-05 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=1000 ,)
return CLIPTextModel(__a )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
_a : Any = self.dummy_uncond_unet
_a : Union[str, Any] = DDIMScheduler()
_a : int = self.dummy_vq_model
_a : int = LDMPipeline(unet=__a ,vqvae=__a ,scheduler=__a )
ldm.to(__a )
ldm.set_progress_bar_config(disable=__a )
_a : Optional[Any] = torch.manual_seed(0 )
_a : Dict = ldm(generator=__a ,num_inference_steps=2 ,output_type='numpy' ).images
_a : List[Any] = torch.manual_seed(0 )
_a : List[str] = ldm(generator=__a ,num_inference_steps=2 ,output_type='numpy' ,return_dict=__a )[0]
_a : str = image[0, -3:, -3:, -1]
_a : Tuple = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_a : str = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
_a : Any = 1E-2 if torch_device != 'mps' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self : Dict ):
'''simple docstring'''
_a : Union[str, Any] = LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
ldm.to(__a )
ldm.set_progress_bar_config(disable=__a )
_a : Any = torch.manual_seed(0 )
_a : Union[str, Any] = ldm(generator=__a ,num_inference_steps=5 ,output_type='numpy' ).images
_a : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
_a : Tuple = np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
_a : Optional[Any] = 1E-2 if torch_device != 'mps' else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 229 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
_a = """bart"""
_a = True
@st.cache(allow_output_mutation=__snake_case )
def lowerCamelCase__ ( ) -> Dict:
"""simple docstring"""
if LOAD_DENSE_INDEX:
_UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' )
_UpperCamelCase = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' )
_UpperCamelCase = qar_model.eval()
else:
_UpperCamelCase , _UpperCamelCase = (None, None)
if MODEL_TYPE == "bart":
_UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' )
_UpperCamelCase = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' )
_UpperCamelCase = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' )
sas_model.load_state_dict(save_dict['''model'''] )
_UpperCamelCase = sas_model.eval()
else:
_UpperCamelCase , _UpperCamelCase = make_qa_sas_model(
model_name='''t5-small''', from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''', device='''cuda:0''' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=__snake_case )
def lowerCamelCase__ ( ) -> Tuple:
"""simple docstring"""
if LOAD_DENSE_INDEX:
_UpperCamelCase = faiss.StandardGpuResources()
_UpperCamelCase = datasets.load_dataset(path='''wiki_snippets''', name='''wiki40b_en_100_0''' )['''train''']
_UpperCamelCase = np.memmap(
'''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''', dtype='''float32''', mode='''r''', shape=(wikiaab_passages.num_rows, 1_28), )
_UpperCamelCase = faiss.IndexFlatIP(1_28 )
_UpperCamelCase = faiss.index_cpu_to_gpu(__snake_case, 1, __snake_case )
wikiaab_gpu_index_flat.add(__snake_case ) # TODO fix for larger GPU
else:
_UpperCamelCase , _UpperCamelCase = (None, None)
_UpperCamelCase = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=__snake_case )
def lowerCamelCase__ ( ) -> int:
"""simple docstring"""
_UpperCamelCase = datasets.load_dataset('''eli5''', name='''LFQA_reddit''' )
_UpperCamelCase = elia['''train_eli5''']
_UpperCamelCase = np.memmap(
'''eli5_questions_reps.dat''', dtype='''float32''', mode='''r''', shape=(elia_train.num_rows, 1_28) )
_UpperCamelCase = faiss.IndexFlatIP(1_28 )
eli5_train_q_index.add(__snake_case )
return (elia_train, eli5_train_q_index)
_a , _a , _a = load_indexes()
_a , _a , _a , _a = load_models()
_a , _a = load_train_data()
def lowerCamelCase__ ( __snake_case, __snake_case=10 ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = embed_questions_for_retrieval([question], __snake_case, __snake_case )
_UpperCamelCase , _UpperCamelCase = eli5_train_q_index.search(__snake_case, __snake_case )
_UpperCamelCase = [elia_train[int(__snake_case )] for i in I[0]]
return nn_examples
def lowerCamelCase__ ( __snake_case, __snake_case="wiki40b", __snake_case="dense", __snake_case=10 ) -> List[str]:
"""simple docstring"""
if source == "none":
_UpperCamelCase , _UpperCamelCase = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
_UpperCamelCase , _UpperCamelCase = query_qa_dense_index(
__snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case )
else:
_UpperCamelCase , _UpperCamelCase = query_es_index(
__snake_case, __snake_case, index_name='''english_wiki40b_snippets_100w''', n_results=__snake_case, )
_UpperCamelCase = [
(res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
]
_UpperCamelCase = '''question: {} context: {}'''.format(__snake_case, __snake_case )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda __snake_case : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __snake_case : None),
} )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=64, __snake_case=2_56, __snake_case=False, __snake_case=2, __snake_case=0.95, __snake_case=0.8 ) -> Dict:
"""simple docstring"""
with torch.no_grad():
_UpperCamelCase = qa_sas_generate(
__snake_case, __snake_case, __snake_case, num_answers=1, num_beams=__snake_case, min_len=__snake_case, max_len=__snake_case, do_sample=__snake_case, temp=__snake_case, top_p=__snake_case, top_k=__snake_case, max_input_length=10_24, device='''cuda:0''', )[0]
return (answer, support_list)
st.title("""Long Form Question Answering with ELI5""")
# Start sidebar
_a = """<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
_a = """
<html>
<head>
<style>
.img-container {
padding-left: 90px;
padding-right: 90px;
padding-top: 50px;
padding-bottom: 50px;
background-color: #f0f3f9;
}
</style>
</head>
<body>
<span class=\"img-container\"> <!-- Inline parent element -->
%s
</span>
</body>
</html>
""" % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
_a = """
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
"""
st.sidebar.markdown(description, unsafe_allow_html=True)
_a = [
"""Answer the question""",
"""View the retrieved document only""",
"""View the most similar ELI5 question and answer""",
"""Show me everything, please!""",
]
_a = st.sidebar.checkbox("""Demo options""")
if demo_options:
_a = st.sidebar.selectbox(
"""""",
action_list,
index=3,
)
_a = action_list.index(action_st)
_a = st.sidebar.selectbox(
"""""",
["""Show full text of passages""", """Show passage section titles"""],
index=0,
)
_a = show_type == """Show full text of passages"""
else:
_a = 3
_a = True
_a = st.sidebar.checkbox("""Retrieval options""")
if retrieval_options:
_a = """
### Information retriever options
The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
"""
st.sidebar.markdown(retriever_info)
_a = st.sidebar.selectbox("""Which Wikipedia format should the model use?""", ["""wiki40b""", """none"""])
_a = st.sidebar.selectbox("""Which Wikipedia indexer should the model use?""", ["""dense""", """sparse""", """mixed"""])
else:
_a = """wiki40b"""
_a = """dense"""
_a = """beam"""
_a = 2
_a = 64
_a = 256
_a = None
_a = None
_a = st.sidebar.checkbox("""Generation options""")
if generate_options:
_a = """
### Answer generation options
The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
**beam** search, or **sample** from the decoder's output probabilities.
"""
st.sidebar.markdown(generate_info)
_a = st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
_a = st.sidebar.slider(
"""Minimum generation length""", min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
_a = st.sidebar.slider(
"""Maximum generation length""", min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
_a = st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
_a = st.sidebar.slider(
"""Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
_a = st.sidebar.slider(
"""Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
_a = None
# start main text
_a = [
"""<MY QUESTION>""",
"""How do people make chocolate?""",
"""Why do we get a fever when we are sick?""",
"""How can different animals perceive different colors?""",
"""What is natural language processing?""",
"""What's the best way to treat a sunburn?""",
"""What exactly are vitamins ?""",
"""How does nuclear energy provide electricity?""",
"""What's the difference between viruses and bacteria?""",
"""Why are flutes classified as woodwinds when most of them are made out of metal ?""",
"""Why do people like drinking coffee even though it tastes so bad?""",
"""What happens when wine ages? How does it make the wine taste better?""",
"""If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?""",
"""How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?""",
"""How does New Zealand have so many large bird predators?""",
]
_a = st.selectbox(
"""What would you like to ask? ---- select <MY QUESTION> to enter a new query""",
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
_a = st.text_input("""Enter your question here:""", """""")
else:
_a = question_s
if st.button("""Show me!"""):
if action in [0, 1, 3]:
if index_type == "mixed":
_a , _a = make_support(question, source=wiki_source, method="""dense""", n_results=10)
_a , _a = make_support(question, source=wiki_source, method="""sparse""", n_results=10)
_a = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
_a = support_list[:10]
_a = """<P> """ + """ <P> """.join([res[-1] for res in support_list])
else:
_a , _a = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
_a , _a = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == """sampled"""),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown("""### The model generated answer is:""")
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown("""--- \n ### The model is drawing information from the following Wikipedia passages:""")
for i, res in enumerate(support_list):
_a = """https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_"""))
_a = res[1].strip()
if sec_titles == "":
_a = """[{}]({})""".format(res[0], wiki_url)
else:
_a = sec_titles.split(""" & """)
_a = """ & """.join(
["""[{}]({}#{})""".format(sec.strip(), wiki_url, sec.strip().replace(""" """, """_""")) for sec in sec_list]
)
st.markdown(
"""{0:02d} - **Article**: {1:<18} <br> _Section_: {2}""".format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
"""> <span style=\"font-family:arial; font-size:10pt;\">""" + res[-1] + """</span>""", unsafe_allow_html=True
)
if action in [2, 3]:
_a = find_nearest_training(question)
_a = nn_train_list[0]
st.markdown(
"""--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""])
)
_a = [
"""{}. {}""".format(i + 1, """ \n""".join([line.strip() for line in ans.split("""\n""") if line.strip() != """"""]))
for i, (ans, sc) in enumerate(zip(train_exple["""answers"""]["""text"""], train_exple["""answers"""]["""score"""]))
if i == 0 or sc > 2
]
st.markdown("""##### Its answers were: \n\n {}""".format("""\n""".join(answers_st)))
_a = """
---
**Disclaimer**
*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
"""
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 19 | 0 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __lowerCamelCase (UpperCAmelCase__ : Any ):
SCREAMING_SNAKE_CASE = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(__snake_case , __snake_case )
def __lowerCamelCase (UpperCAmelCase__ : Union[str, Any] ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = emb.weight.shape
SCREAMING_SNAKE_CASE = nn.Linear(__snake_case , __snake_case , bias=__snake_case )
SCREAMING_SNAKE_CASE = emb.weight.data
return lin_layer
def __lowerCamelCase (UpperCAmelCase__ : str ):
SCREAMING_SNAKE_CASE = torch.load(__snake_case , map_location="cpu" )
SCREAMING_SNAKE_CASE = mam_aaa["args"] or mam_aaa["cfg"]["model"]
SCREAMING_SNAKE_CASE = mam_aaa["model"]
remove_ignore_keys_(__snake_case )
SCREAMING_SNAKE_CASE = state_dict["encoder.embed_tokens.weight"].shape[0]
SCREAMING_SNAKE_CASE = MaMaaaConfig(
vocab_size=__snake_case , max_position_embeddings=1_0_2_4 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="relu" , )
SCREAMING_SNAKE_CASE = state_dict["decoder.embed_tokens.weight"]
SCREAMING_SNAKE_CASE = MaMaaaForConditionalGeneration(__snake_case )
model.model.load_state_dict(__snake_case , strict=__snake_case )
SCREAMING_SNAKE_CASE = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
_lowerCamelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
_lowerCamelCase : List[Any] = parser.parse_args()
_lowerCamelCase : Dict = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 403 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
_a = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
_a = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> Tuple:
"""simple docstring"""
for attribute in key.split('''.''' ):
_UpperCamelCase = getattr(__snake_case, __snake_case )
if weight_type is not None:
_UpperCamelCase = getattr(__snake_case, __snake_case ).shape
else:
_UpperCamelCase = hf_pointer.shape
assert hf_shape == value.shape, (
F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
_UpperCamelCase = value
elif weight_type == "weight_g":
_UpperCamelCase = value
elif weight_type == "weight_v":
_UpperCamelCase = value
elif weight_type == "bias":
_UpperCamelCase = value
else:
_UpperCamelCase = value
logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def lowerCamelCase__ ( __snake_case, __snake_case ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = []
_UpperCamelCase = fairseq_model.state_dict()
_UpperCamelCase = hf_model.feature_extractor
_UpperCamelCase = hf_model.adapter
for name, value in fairseq_dict.items():
_UpperCamelCase = False
if "conv_layers" in name:
load_conv_layer(
__snake_case, __snake_case, __snake_case, __snake_case, hf_model.config.feat_extract_norm == '''group''', )
_UpperCamelCase = True
elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.'''] ):
load_adapter(__snake_case, __snake_case, __snake_case, __snake_case )
_UpperCamelCase = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
_UpperCamelCase = True
if "*" in mapped_key:
_UpperCamelCase = name.split(__snake_case )[0].split('''.''' )[-2]
_UpperCamelCase = mapped_key.replace('''*''', __snake_case )
if "weight_g" in name:
_UpperCamelCase = '''weight_g'''
elif "weight_v" in name:
_UpperCamelCase = '''weight_v'''
elif "bias" in name:
_UpperCamelCase = '''bias'''
elif "weight" in name:
_UpperCamelCase = '''weight'''
else:
_UpperCamelCase = None
set_recursively(__snake_case, __snake_case, __snake_case, __snake_case, __snake_case )
continue
if not is_used:
unused_weights.append(__snake_case )
logger.warning(F'''Unused weights: {unused_weights}''' )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = full_name.split('''conv_layers.''' )[-1]
_UpperCamelCase = name.split('''.''' )
_UpperCamelCase = int(items[0] )
_UpperCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
_UpperCamelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
_UpperCamelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
_UpperCamelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
_UpperCamelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__snake_case )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> Dict:
"""simple docstring"""
_UpperCamelCase = full_name.split('''adaptor.''' )[-1]
_UpperCamelCase = name.split('''.''' )
if items[1].isdigit():
_UpperCamelCase = int(items[1] )
else:
_UpperCamelCase = None
if "adaptor" not in full_name:
if "proj_ln" in full_name:
# has to be layer norm
if "bias" in name:
assert (
value.shape == adapter.proj_layer_norm.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj_layer_norm.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
_UpperCamelCase = value
else:
# has to be projection layer
if "bias" in name:
assert (
value.shape == adapter.proj.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter proj layer bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter proj layer weight was initialized from {full_name}.''' )
elif isinstance(__snake_case, __snake_case ):
if "bias" in name:
assert (
value.shape == adapter.layers[layer_id].conv.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
elif "weight" in name:
assert (
value.shape == adapter.layers[layer_id].conv.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
else:
unused_weights.append(__snake_case )
def lowerCamelCase__ ( __snake_case ) -> str:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase = emb.weight.shape
_UpperCamelCase = nn.Linear(__snake_case, __snake_case, bias=__snake_case )
_UpperCamelCase = emb.weight.data
return lin_layer
@torch.no_grad()
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = WavaVecaConfig.from_pretrained(
__snake_case, add_adapter=__snake_case, adapter_stride=__snake_case, adapter_kernel_size=__snake_case, use_auth_token=__snake_case, output_hidden_size=__snake_case, )
_UpperCamelCase = MBartConfig.from_pretrained(__snake_case )
# load model
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path], arg_overrides={
'''config_yaml''': config_yaml_path,
'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ),
'''w2v_path''': checkpoint_path,
'''load_pretrained_decoder_from''': None,
}, )
_UpperCamelCase = model[0].eval()
# load feature extractor
_UpperCamelCase = WavaVecaFeatureExtractor.from_pretrained(__snake_case, use_auth_token=__snake_case )
# set weights for wav2vec2 encoder
_UpperCamelCase = WavaVecaModel(__snake_case )
recursively_load_weights_wavaveca(model.encoder, __snake_case )
# load decoder weights
_UpperCamelCase = MBartForCausalLM(__snake_case )
_UpperCamelCase , _UpperCamelCase = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(), strict=__snake_case )
logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}''' )
logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' )
_UpperCamelCase = SpeechEncoderDecoderModel(encoder=__snake_case, decoder=__snake_case )
_UpperCamelCase = False
_UpperCamelCase = MBartaaTokenizer(__snake_case )
tokenizer.save_pretrained(__snake_case )
_UpperCamelCase = hf_wavavec.config.to_dict()
_UpperCamelCase = tokenizer.pad_token_id
_UpperCamelCase = tokenizer.bos_token_id
_UpperCamelCase = tokenizer.eos_token_id
_UpperCamelCase = '''mbart50'''
_UpperCamelCase = '''wav2vec2'''
_UpperCamelCase = tokenizer.eos_token_id
_UpperCamelCase = 25_00_04
_UpperCamelCase = tokenizer.eos_token_id
_UpperCamelCase = SpeechEncoderDecoderConfig.from_dict(__snake_case )
hf_wavavec.save_pretrained(__snake_case )
feature_extractor.save_pretrained(__snake_case )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
parser.add_argument(
"""--encoder_config_path""",
default="""facebook/wav2vec2-xls-r-1b""",
type=str,
help="""Path to hf encoder wav2vec2 checkpoint config""",
)
parser.add_argument(
"""--decoder_config_path""",
default="""facebook/mbart-large-50-one-to-many-mmt""",
type=str,
help="""Path to hf decoder checkpoint config""",
)
parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""")
parser.add_argument("""--start_token_id""", default=25_0004, type=int, help="""`decoder_start_token_id` of model config""")
_a = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
args.config_yaml_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
add_adapter=args.add_adapter,
adapter_kernel_size=args.adapter_kernel_size,
adapter_stride=args.adapter_stride,
decoder_start_token_id=args.start_token_id,
encoder_output_dim=args.encoder_output_dim,
)
| 19 | 0 |
'''simple docstring'''
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError
from .utils import require_pil
class A ( _a ):
def __lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
_a = pa.array(TypedSequence([1, 2, 3] ) )
self.assertEqual(arr.type , pa.intaa() )
def __lowerCAmelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
with self.assertRaises(__a ):
_a = pa.array(TypedSequence([1, 2, 3] ) , type=pa.intaa() )
def __lowerCAmelCase ( self : List[str] ) -> Any:
"""simple docstring"""
with self.assertRaises(__a ):
_a = pa.array(TypedSequence([1, 2, 3] , try_type=Value('''bool''' ) , type=Value('''int64''' ) ) )
def __lowerCAmelCase ( self : List[str] ) -> Tuple:
"""simple docstring"""
_a = pa.array(TypedSequence([1, 2, 3] , type=Value('''int32''' ) ) )
self.assertEqual(arr.type , pa.intaa() )
def __lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
_a = pa.array(TypedSequence(['''foo''', '''bar'''] , type=Value('''int64''' ) ) )
def __lowerCAmelCase ( self : str ) -> int:
"""simple docstring"""
_a = pa.array(TypedSequence([1, 2, 3] , try_type=Value('''int32''' ) ) )
self.assertEqual(arr.type , pa.intaa() )
def __lowerCAmelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
_a = pa.array(TypedSequence(['''foo''', '''bar'''] , try_type=Value('''int64''' ) ) )
self.assertEqual(arr.type , pa.string() )
def __lowerCAmelCase ( self : Any ) -> Any:
"""simple docstring"""
_a = pa.array(TypedSequence([[[1, 2, 3]]] , type=ArrayaD((1, 3) , '''int64''' ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , '''int64''' ) )
def __lowerCAmelCase ( self : str ) -> Optional[int]:
"""simple docstring"""
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
_a = pa.array(TypedSequence(['''foo''', '''bar'''] , type=ArrayaD((1, 3) , '''int64''' ) ) )
def __lowerCAmelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
_a = pa.array(TypedSequence([[[1, 2, 3]]] , try_type=ArrayaD((1, 3) , '''int64''' ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , '''int64''' ) )
def __lowerCAmelCase ( self : Dict ) -> Tuple:
"""simple docstring"""
_a = pa.array(TypedSequence(['''foo''', '''bar'''] , try_type=ArrayaD((1, 3) , '''int64''' ) ) )
self.assertEqual(arr.type , pa.string() )
@require_pil
def __lowerCAmelCase ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
import PIL.Image
_a = PIL.Image.fromarray(np.arange(10 , dtype=np.uinta ).reshape(2 , 5 ) )
with patch(
'''datasets.arrow_writer.cast_to_python_objects''' , side_effect=__a ) as mock_cast_to_python_objects:
_a = pa.array(TypedSequence([{'''path''': None, '''bytes''': B'''image_bytes'''}, pil_image] , type=Image() ) )
_a , _a = mock_cast_to_python_objects.call_args_list[-1]
self.assertIn('''optimize_list_casting''' , __a )
self.assertFalse(kwargs['''optimize_list_casting'''] )
def snake_case_ (UpperCamelCase : Optional[Any] , UpperCamelCase : Any ):
'''simple docstring'''
_a = pa.BufferReader(__snake_case ) if isinstance(__snake_case , pa.Buffer ) else pa.memory_map(__snake_case )
_a = pa.ipc.open_stream(__snake_case )
_a = f.read_all()
assert len(pa_table.to_batches() ) == expected_num_chunks
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
del pa_table
@pytest.mark.parametrize('''writer_batch_size''' , [None, 1, 10] )
@pytest.mark.parametrize(
'''fields''' , [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] )
def snake_case_ (UpperCamelCase : Dict , UpperCamelCase : Any ):
'''simple docstring'''
_a = pa.BufferOutputStream()
_a = pa.schema(__snake_case ) if fields else None
with ArrowWriter(stream=__snake_case , schema=__snake_case , writer_batch_size=__snake_case ) as writer:
writer.write({'''col_1''': '''foo''', '''col_2''': 1} )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_a = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
assert writer._schema == pa.schema(__snake_case , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def snake_case_ ():
'''simple docstring'''
_a = pa.BufferOutputStream()
_a = Features({'''labels''': ClassLabel(names=['''neg''', '''pos'''] )} )
with ArrowWriter(stream=__snake_case , features=__snake_case ) as writer:
writer.write({'''labels''': 0} )
writer.write({'''labels''': 1} )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == features.arrow_schema
assert writer._schema.metadata == features.arrow_schema.metadata
_a = pa.BufferReader(output.getvalue() )
_a = pa.ipc.open_stream(__snake_case )
_a = f.read_all()
_a = pa_table.schema
assert pa_table.num_rows == 2
assert schema == features.arrow_schema
assert schema.metadata == features.arrow_schema.metadata
assert features == Features.from_arrow_schema(__snake_case )
@pytest.mark.parametrize('''writer_batch_size''' , [None, 1, 10] )
def snake_case_ (UpperCamelCase : Dict ):
'''simple docstring'''
_a = pa.BufferOutputStream()
with ArrowWriter(
stream=__snake_case , writer_batch_size=__snake_case , hash_salt='''split_name''' , check_duplicates=__snake_case , ) as writer:
with pytest.raises(__snake_case ):
writer.write({'''col_1''': '''foo''', '''col_2''': 1} , key=[1, 2] )
_a , _a = writer.finalize()
@pytest.mark.parametrize('''writer_batch_size''' , [None, 2, 10] )
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
_a = pa.BufferOutputStream()
with ArrowWriter(
stream=__snake_case , writer_batch_size=__snake_case , hash_salt='''split_name''' , check_duplicates=__snake_case , ) as writer:
with pytest.raises(__snake_case ):
writer.write({'''col_1''': '''foo''', '''col_2''': 1} , key=10 )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} , key=10 )
_a , _a = writer.finalize()
@pytest.mark.parametrize('''writer_batch_size''' , [None, 2, 10] )
def snake_case_ (UpperCamelCase : List[Any] ):
'''simple docstring'''
_a = pa.BufferOutputStream()
with ArrowWriter(
stream=__snake_case , writer_batch_size=__snake_case , hash_salt='''split_name''' , check_duplicates=__snake_case , ) as writer:
writer.write({'''col_1''': '''foo''', '''col_2''': 1} , key=1 )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} , key=2 )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('''writer_batch_size''' , [None, 1, 10] )
@pytest.mark.parametrize(
'''fields''' , [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] )
def snake_case_ (UpperCamelCase : int , UpperCamelCase : Dict ):
'''simple docstring'''
_a = pa.BufferOutputStream()
_a = pa.schema(__snake_case ) if fields else None
with ArrowWriter(stream=__snake_case , schema=__snake_case , writer_batch_size=__snake_case ) as writer:
writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]} )
writer.write_batch({'''col_1''': [], '''col_2''': []} )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_a = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
assert writer._schema == pa.schema(__snake_case , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('''writer_batch_size''' , [None, 1, 10] )
@pytest.mark.parametrize(
'''fields''' , [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] )
def snake_case_ (UpperCamelCase : Tuple , UpperCamelCase : str ):
'''simple docstring'''
_a = pa.BufferOutputStream()
_a = pa.schema(__snake_case ) if fields else None
with ArrowWriter(stream=__snake_case , schema=__snake_case , writer_batch_size=__snake_case ) as writer:
writer.write_table(pa.Table.from_pydict({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]} ) )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_a = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
assert writer._schema == pa.schema(__snake_case , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('''writer_batch_size''' , [None, 1, 10] )
@pytest.mark.parametrize(
'''fields''' , [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] )
def snake_case_ (UpperCamelCase : Optional[Any] , UpperCamelCase : int ):
'''simple docstring'''
_a = pa.BufferOutputStream()
_a = pa.schema(__snake_case ) if fields else None
with ArrowWriter(stream=__snake_case , schema=__snake_case , writer_batch_size=__snake_case ) as writer:
writer.write_row(pa.Table.from_pydict({'''col_1''': ['''foo'''], '''col_2''': [1]} ) )
writer.write_row(pa.Table.from_pydict({'''col_1''': ['''bar'''], '''col_2''': [2]} ) )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
_a = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
assert writer._schema == pa.schema(__snake_case , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def snake_case_ ():
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
_a = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
_a = os.path.join(__snake_case , '''test.arrow''' )
with ArrowWriter(path=__snake_case , schema=pa.schema(__snake_case ) ) as writer:
writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]} )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == pa.schema(__snake_case , metadata=writer._schema.metadata )
_check_output(__snake_case , 1 )
def snake_case_ (UpperCamelCase : List[str] ):
'''simple docstring'''
if pa.types.is_list(__snake_case ):
return get_base_dtype(arr_type.value_type )
else:
return arr_type
def snake_case_ (UpperCamelCase : List[str] , UpperCamelCase : Tuple ):
'''simple docstring'''
if isinstance(lst[0] , __snake_case ):
change_first_primitive_element_in_list(lst[0] , __snake_case )
else:
_a = value
@pytest.mark.parametrize('''optimized_int_type, expected_dtype''' , [(None, pa.intaa()), (Value('''int32''' ), pa.intaa())] )
@pytest.mark.parametrize('''sequence''' , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def snake_case_ (UpperCamelCase : Optional[Any] , UpperCamelCase : Union[str, Any] , UpperCamelCase : List[Any] ):
'''simple docstring'''
_a = pa.array(TypedSequence(__snake_case , optimized_int_type=__snake_case ) )
assert get_base_dtype(arr.type ) == expected_dtype
@pytest.mark.parametrize(
'''col, expected_dtype''' , [
('''attention_mask''', pa.inta()),
('''special_tokens_mask''', pa.inta()),
('''token_type_ids''', pa.inta()),
('''input_ids''', pa.intaa()),
('''other''', pa.intaa()),
] , )
@pytest.mark.parametrize('''sequence''' , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def snake_case_ (UpperCamelCase : int , UpperCamelCase : str , UpperCamelCase : Tuple ):
'''simple docstring'''
_a = pa.array(OptimizedTypedSequence(__snake_case , col=__snake_case ) )
assert get_base_dtype(arr.type ) == expected_dtype
# not in range
if col != "other":
# avoids errors due to in-place modifications
_a = copy.deepcopy(__snake_case )
_a = np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
change_first_primitive_element_in_list(__snake_case , __snake_case )
_a = pa.array(OptimizedTypedSequence(__snake_case , col=__snake_case ) )
assert get_base_dtype(arr.type ) == pa.intaa()
@pytest.mark.parametrize('''raise_exception''' , [False, True] )
def snake_case_ (UpperCamelCase : Dict , UpperCamelCase : Optional[int] ):
'''simple docstring'''
_a = str(tmp_path / '''dataset-train.arrow''' )
try:
with ArrowWriter(path=__snake_case ) as writer:
if raise_exception:
raise pa.lib.ArrowInvalid()
else:
writer.stream.close()
except pa.lib.ArrowInvalid:
pass
finally:
assert writer.stream.closed
def snake_case_ (UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
_a = '''mock://dataset-train.arrow'''
with ArrowWriter(path=__snake_case , storage_options=mockfs.storage_options ) as writer:
assert isinstance(writer._fs , type(__snake_case ) )
assert writer._fs.storage_options == mockfs.storage_options
writer.write({'''col_1''': '''foo''', '''col_2''': 1} )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert mockfs.exists(__snake_case )
def snake_case_ ():
'''simple docstring'''
_a = pa.BufferOutputStream()
with ParquetWriter(stream=__snake_case ) as writer:
writer.write({'''col_1''': '''foo''', '''col_2''': 1} )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} )
_a , _a = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_a = pa.BufferReader(output.getvalue() )
_a = pq.read_table(__snake_case )
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize('''embed_local_files''' , [False, True] )
def snake_case_ (UpperCamelCase : List[Any] , UpperCamelCase : str ):
'''simple docstring'''
import PIL.Image
_a = str(tmp_path / '''test_image_rgb.jpg''' )
PIL.Image.fromarray(np.zeros((5, 5) , dtype=np.uinta ) ).save(__snake_case , format='''png''' )
_a = pa.BufferOutputStream()
with ParquetWriter(
stream=__snake_case , features=Features({'''image''': Image()} ) , embed_local_files=__snake_case ) as writer:
writer.write({'''image''': image_path} )
writer.finalize()
_a = pa.BufferReader(output.getvalue() )
_a = pq.read_table(__snake_case )
_a = pa_table.to_pydict()
if embed_local_files:
assert isinstance(out['''image'''][0]['''path'''] , __snake_case )
with open(__snake_case , '''rb''' ) as f:
assert out["image"][0]["bytes"] == f.read()
else:
assert out["image"][0]["path"] == image_path
assert out["image"][0]["bytes"] is None
def snake_case_ ():
'''simple docstring'''
_a = pa.schema([pa.field('''col_1''' , pa.string() , nullable=__snake_case )] )
_a = pa.BufferOutputStream()
with ArrowWriter(stream=__snake_case ) as writer:
writer._build_writer(inferred_schema=__snake_case )
assert writer._schema == pa.schema([pa.field('''col_1''' , pa.string() )] )
| 22 |
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case=None, **__snake_case ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()]
_UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()][: len(__snake_case )]
_UpperCamelCase = calculate_rouge(__snake_case, __snake_case, **__snake_case )
if save_path is not None:
save_json(__snake_case, __snake_case, indent=__snake_case )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 19 | 0 |
'''simple docstring'''
from __future__ import annotations
def __a ( A__ , A__ , A__ ) -> tuple[float, list[float]]:
lowerCAmelCase = list(range(len(__snake_case ) ) )
lowerCAmelCase = [v / w for v, w in zip(__snake_case , __snake_case )]
index.sort(key=lambda A__ : ratio[i] , reverse=__snake_case )
lowerCAmelCase = 0
lowerCAmelCase = [0] * len(__snake_case )
for i in index:
if weight[i] <= capacity:
lowerCAmelCase = 1
max_value += value[i]
capacity -= weight[i]
else:
lowerCAmelCase = capacity / weight[i]
max_value += value[i] * capacity / weight[i]
break
return max_value, fractions
if __name__ == "__main__":
import doctest
doctest.testmod()
| 649 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = ['image_processor', 'tokenizer']
lowercase__ = 'ViTImageProcessor'
lowercase__ = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self , __a=None , __a=None , **__a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __a , )
_UpperCamelCase = kwargs.pop('''feature_extractor''')
_UpperCamelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''')
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''')
super().__init__(__a , __a)
def __call__( self , __a=None , __a=None , __a=None , __a=None , **__a) -> Tuple:
'''simple docstring'''
if text is None and visual_prompt is None and images is None:
raise ValueError('''You have to specify either text, visual prompt or images.''')
if text is not None and visual_prompt is not None:
raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''')
if text is not None:
_UpperCamelCase = self.tokenizer(__a , return_tensors=__a , **__a)
if visual_prompt is not None:
_UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a)
if images is not None:
_UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a)
if visual_prompt is not None and images is not None:
_UpperCamelCase = {
'''pixel_values''': image_features.pixel_values,
'''conditional_pixel_values''': prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
_UpperCamelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
_UpperCamelCase = {
'''conditional_pixel_values''': prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**__a) , tensor_type=__a)
def UpperCAmelCase ( self , *__a , **__a) -> Any:
'''simple docstring'''
return self.tokenizer.batch_decode(*__a , **__a)
def UpperCAmelCase ( self , *__a , **__a) -> List[str]:
'''simple docstring'''
return self.tokenizer.decode(*__a , **__a)
@property
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __a , )
return self.image_processor_class
@property
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __a , )
return self.image_processor
| 19 | 0 |
'''simple docstring'''
import sys
from pathlib import Path
UpperCamelCase__ : Optional[Any] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import deepcopy # noqa
from parameterized import parameterized # noqa
from transformers import TrainingArguments, is_torch_available # noqa
from transformers.deepspeed import is_deepspeed_available # noqa
from transformers.file_utils import WEIGHTS_NAME # noqa
from transformers.testing_utils import ( # noqa
CaptureLogger,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
mockenv_context,
require_deepspeed,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
from transformers.trainer_utils import set_seed # noqa
set_seed(42)
UpperCamelCase__ : Optional[Any] = {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}
UpperCamelCase__ : Optional[Any] = 'zero2'
UpperCamelCase__ : int = 'zero3'
UpperCamelCase__ : Union[str, Any] = [ZEROa, ZEROa]
def __UpperCamelCase( _A : List[Any] , _A : Optional[int] , _A : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Dict = parameterized.to_safe_name('''_'''.join(str(__snake_case ) for x in param.args ) )
return F'''{func.__name__}_{param_based_name}'''
# Cartesian-product of zero stages with models to test
UpperCamelCase__ : List[Any] = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class _lowercase ( lowerCAmelCase ):
'''simple docstring'''
@parameterized.expand(__a ,name_func=__a )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ) -> List[str]:
'''simple docstring'''
self.run_and_check(
stage=__a ,model=__a ,distributed=__a ,fpaa=__a ,)
@require_torch_multi_gpu
@parameterized.expand(__a ,name_func=__a )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ) -> int:
'''simple docstring'''
self.run_and_check(
stage=__a ,model=__a ,distributed=__a ,fpaa=__a ,)
@parameterized.expand(__a ,name_func=__a )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ) -> List[str]:
'''simple docstring'''
self.run_and_check(
stage=__a ,model=__a ,distributed=__a ,fpaa=__a ,)
@require_torch_multi_gpu
@parameterized.expand(__a ,name_func=__a )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ) -> Dict:
'''simple docstring'''
self.run_and_check(
stage=__a ,model=__a ,distributed=__a ,fpaa=__a ,)
def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> Tuple:
'''simple docstring'''
pass
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = 10 ,lowerCamelCase_ = True ,lowerCamelCase_ = True ,lowerCamelCase_ = True ,) -> Dict:
'''simple docstring'''
UpperCAmelCase__ : List[str] = models[model]
UpperCAmelCase__ : Optional[int] = self.run_trainer(
stage=__a ,model_name=__a ,eval_steps=__a ,num_train_epochs=1 ,distributed=__a ,fpaa=__a ,)
self.do_checks(__a )
return output_dir
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = 10 ,lowerCamelCase_ = 1 ,lowerCamelCase_ = True ,lowerCamelCase_ = True ,) -> int:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.get_auto_remove_tmp_dir('''./xxx''' ,after=__a )
UpperCAmelCase__ : str = f'''
--model_name_or_path {model_name}
--dataset_name hf-internal-testing/librispeech_asr_dummy
--dataset_config_name clean
--train_split_name validation
--validation_split_name validation
--output_dir {output_dir}
--num_train_epochs {str(__a )}
--per_device_train_batch_size 2
--per_device_eval_batch_size 2
--evaluation_strategy steps
--learning_rate 5e-4
--warmup_steps 8
--orthography timit
--preprocessing_num_workers 1
--group_by_length
--freeze_feature_extractor
--report_to none
--save_steps 0
--eval_steps {eval_steps}
--report_to none
'''.split()
if fpaa:
args.extend(['''--fp16'''] )
# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
# hence the separate config files
UpperCAmelCase__ : Optional[int] = f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
UpperCAmelCase__ : Optional[Any] = [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
UpperCAmelCase__ : Optional[int] = self.get_launcher(__a )
UpperCAmelCase__ : Any = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(__a ,env=self.get_env() )
return output_dir
def lowerCAmelCase__ ( self ,lowerCamelCase_=False ) -> Tuple:
'''simple docstring'''
UpperCAmelCase__ : List[str] = min(2 ,get_gpu_count() ) if distributed else 1
return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()
| 614 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import UperNetForSemanticSegmentation
from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCAmelCase:
def __init__( self , __a , __a=13 , __a=32 , __a=3 , __a=4 , __a=[10, 20, 30, 40] , __a=[2, 2, 3, 2] , __a=True , __a=True , __a=37 , __a="gelu" , __a=10 , __a=0.02 , __a=["stage2", "stage3", "stage4"] , __a=3 , __a=None , ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = image_size
_UpperCamelCase = num_channels
_UpperCamelCase = num_stages
_UpperCamelCase = hidden_sizes
_UpperCamelCase = depths
_UpperCamelCase = is_training
_UpperCamelCase = use_labels
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = out_features
_UpperCamelCase = num_labels
_UpperCamelCase = scope
_UpperCamelCase = num_stages
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_UpperCamelCase = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return ConvNextConfig(
num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , )
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return UperNetConfig(
backbone_config=self.get_backbone_config() , hidden_size=5_12 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=__a , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=2_56 , auxiliary_num_convs=1 , auxiliary_concat_input=__a , loss_ignore_index=2_55 , num_labels=self.num_labels , )
def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = UperNetForSemanticSegmentation(config=__a)
model.to(__a)
model.eval()
_UpperCamelCase = model(__a)
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size))
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase__ = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
lowercase__ = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = UperNetModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(__a)
_UpperCamelCase = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCamelCase = [*signature.parameters.keys()]
_UpperCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__a)
@unittest.skip(reason='''UperNet does not use inputs_embeds''')
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason='''UperNet does not support input and output embeddings''')
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''UperNet does not have a base model''')
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason='''UperNet does not have a base model''')
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''')
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
pass
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
def check_hidden_states_output(__a , __a , __a):
_UpperCamelCase = model_class(__a)
model.to(__a)
model.eval()
with torch.no_grad():
_UpperCamelCase = model(**self._prepare_for_class(__a , __a))
_UpperCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_UpperCamelCase = self.model_tester.num_stages
self.assertEqual(len(__a) , expected_num_stages + 1)
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = True
check_hidden_states_output(__a , __a , __a)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCamelCase = True
check_hidden_states_output(__a , __a , __a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCamelCase = _config_zero_init(__a)
_UpperCamelCase = _config_zero_init(configs_no_init.backbone_config)
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(config=__a)
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@unittest.skip(reason='''UperNet does not have tied weights''')
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
pass
@slow
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained(__a)
self.assertIsNotNone(__a)
def lowerCamelCase__ ( ) -> int:
"""simple docstring"""
_UpperCamelCase = hf_hub_download(
repo_id='''hf-internal-testing/fixtures_ade20k''', repo_type='''dataset''', filename='''ADE_val_00000001.jpg''' )
_UpperCamelCase = Image.open(__snake_case ).convert('''RGB''' )
return image
@require_torch
@require_vision
@slow
class _UpperCAmelCase( unittest.TestCase ):
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny''')
_UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny''').to(__a)
_UpperCamelCase = prepare_img()
_UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a)
with torch.no_grad():
_UpperCamelCase = model(**__a)
_UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12))
self.assertEqual(outputs.logits.shape , __a)
_UpperCamelCase = torch.tensor(
[[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]).to(__a)
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4))
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny''')
_UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny''').to(__a)
_UpperCamelCase = prepare_img()
_UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a)
with torch.no_grad():
_UpperCamelCase = model(**__a)
_UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12))
self.assertEqual(outputs.logits.shape , __a)
_UpperCamelCase = torch.tensor(
[[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]).to(__a)
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4))
| 19 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_keras_nlp_available():
from transformers.models.gpta import TFGPTaTokenizer
lowercase =['gpt2']
lowercase ='gpt2'
if is_tf_available():
class __magic_name__ ( tf.Module ):
def __init__( self , snake_case) -> Optional[Any]:
'''simple docstring'''
super().__init__()
_UpperCAmelCase : int =tokenizer
_UpperCAmelCase : Dict =AutoConfig.from_pretrained(__a)
_UpperCAmelCase : Optional[int] =TFGPTaLMHeadModel.from_config(__a)
@tf.function(input_signature=(tf.TensorSpec((None,) , tf.string , name='text'),))
def lowerCAmelCase ( self , snake_case) -> List[str]:
'''simple docstring'''
_UpperCAmelCase : Tuple =self.tokenizer(__a)
_UpperCAmelCase : List[str] =tokenized['input_ids'].to_tensor()
_UpperCAmelCase : Any =tf.cast(input_ids_dense > 0 , tf.intaa)
# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])
_UpperCAmelCase : Union[str, Any] =self.model(input_ids=__a , attention_mask=__a)['logits']
return outputs
@require_tf
@require_keras_nlp
class __magic_name__ ( unittest.TestCase ):
def lowerCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
super().setUp()
_UpperCAmelCase : Tuple =[GPTaTokenizer.from_pretrained(__a) for checkpoint in (TOKENIZER_CHECKPOINTS)]
_UpperCAmelCase : str =[TFGPTaTokenizer.from_pretrained(__a) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers) == len(self.tf_tokenizers)
_UpperCAmelCase : Any =[
'This is a straightforward English test sentence.',
'This one has some weird characters\rto\nsee\r\nif those\u00E9break things.',
'Now we\'re going to add some Chinese: 一 二 三 一二三',
'And some much more rare Chinese: 齉 堃 齉堃',
'Je vais aussi écrire en français pour tester les accents',
'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
]
_UpperCAmelCase : Optional[Any] =list(zip(self.test_sentences , self.test_sentences[::-1]))
def lowerCAmelCase ( self) -> Dict:
'''simple docstring'''
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers):
for test_inputs in self.test_sentences:
_UpperCAmelCase : List[Any] =tokenizer([test_inputs] , return_tensors='tf')
_UpperCAmelCase : str =tf_tokenizer([test_inputs])
for key in python_outputs.keys():
# convert them to numpy to avoid messing with ragged tensors
_UpperCAmelCase : Union[str, Any] =python_outputs[key].numpy()
_UpperCAmelCase : Tuple =tf_outputs[key].numpy()
self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape))
self.assertTrue(tf.reduce_all(tf.cast(__a , tf.intaa) == tf_outputs_values))
@slow
def lowerCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
_UpperCAmelCase : Dict =tf.function(__a)
for test_inputs in self.test_sentences:
_UpperCAmelCase : Dict =tf.constant(__a)
_UpperCAmelCase : Union[str, Any] =compiled_tokenizer(__a)
_UpperCAmelCase : Dict =tf_tokenizer(__a)
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))
@slow
def lowerCAmelCase ( self) -> List[Any]:
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
_UpperCAmelCase : str =ModelToSave(tokenizer=__a)
_UpperCAmelCase : List[str] =tf.convert_to_tensor([self.test_sentences[0]])
_UpperCAmelCase : Tuple =model.serving(__a) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
_UpperCAmelCase : List[str] =Path(__a) / 'saved.model'
tf.saved_model.save(__a , __a , signatures={'serving_default': model.serving})
_UpperCAmelCase : List[Any] =tf.saved_model.load(__a)
_UpperCAmelCase : Union[str, Any] =loaded_model.signatures['serving_default'](__a)['output_0']
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertTrue(tf.reduce_all(out == loaded_output))
@slow
def lowerCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
_UpperCAmelCase : Dict =tf.convert_to_tensor([self.test_sentences[0]])
_UpperCAmelCase : List[Any] =tf_tokenizer(__a) # Build model with some sample inputs
_UpperCAmelCase : Union[str, Any] =tf_tokenizer.get_config()
_UpperCAmelCase : List[Any] =TFGPTaTokenizer.from_config(__a)
_UpperCAmelCase : Optional[int] =model_from_config(__a)
for key in from_config_output.keys():
self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]))
@slow
def lowerCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
# for the test to run
_UpperCAmelCase : List[Any] =1_2_3_1_2_3
for max_length in [3, 5, 1_0_2_4]:
_UpperCAmelCase : int =tf.convert_to_tensor([self.test_sentences[0]])
_UpperCAmelCase : Dict =tf_tokenizer(__a , max_length=__a)
_UpperCAmelCase : Any =out['input_ids'].numpy().shape[1]
assert out_length == max_length
| 446 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = (DDPMScheduler,)
def UpperCAmelCase ( self , **__a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = {
'''num_train_timesteps''': 10_00,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''variance_type''': '''fixed_small''',
'''clip_sample''': True,
}
config.update(**__a)
return config
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
for timesteps in [1, 5, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__a)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2]):
self.check_over_configs(beta_start=__a , beta_end=__a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__a)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=__a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
self.check_over_configs(thresholding=__a)
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=__a , prediction_type=__a , sample_max_value=__a , )
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=__a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
for t in [0, 5_00, 9_99]:
self.check_over_forward(time_step=__a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0979)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1e-5
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = len(__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter
_UpperCamelCase = torch.manual_seed(0)
for t in reversed(range(__a)):
# 1. predict noise residual
_UpperCamelCase = model(__a , __a)
# 2. predict previous mean of sample x_t-1
_UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCamelCase = pred_prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
assert abs(result_sum.item() - 258.9606) < 1e-2
assert abs(result_mean.item() - 0.3372) < 1e-3
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''')
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = len(__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter
_UpperCamelCase = torch.manual_seed(0)
for t in reversed(range(__a)):
# 1. predict noise residual
_UpperCamelCase = model(__a , __a)
# 2. predict previous mean of sample x_t-1
_UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCamelCase = pred_prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
assert abs(result_sum.item() - 202.0296) < 1e-2
assert abs(result_mean.item() - 0.2631) < 1e-3
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [1_00, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=__a)
_UpperCamelCase = scheduler.timesteps
for i, timestep in enumerate(__a):
if i == len(__a) - 1:
_UpperCamelCase = -1
else:
_UpperCamelCase = timesteps[i + 1]
_UpperCamelCase = scheduler.previous_timestep(__a)
_UpperCamelCase = prev_t.item()
self.assertEqual(__a , __a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [1_00, 87, 50, 51, 0]
with self.assertRaises(__a , msg='''`custom_timesteps` must be in descending order.'''):
scheduler.set_timesteps(timesteps=__a)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [1_00, 87, 50, 1, 0]
_UpperCamelCase = len(__a)
with self.assertRaises(__a , msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
scheduler.set_timesteps(num_inference_steps=__a , timesteps=__a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
__a , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ):
scheduler.set_timesteps(timesteps=__a)
| 19 | 0 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 663 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
_a = 100
_a = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_a = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=1_00 )
def lowerCamelCase__ ( __snake_case ) -> set[int]:
"""simple docstring"""
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
_UpperCamelCase = set()
_UpperCamelCase = 42
_UpperCamelCase = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def lowerCamelCase__ ( __snake_case = 50_00 ) -> int | None:
"""simple docstring"""
for number_to_partition in range(1, __snake_case ):
if len(partition(__snake_case ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 19 | 0 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
)
from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST
class __magic_name__ :
def __init__( self : List[Any] , UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any]=13 , UpperCamelCase__ : Tuple=7 , UpperCamelCase__ : Tuple=True , UpperCamelCase__ : Dict=True , UpperCamelCase__ : Union[str, Any]=True , UpperCamelCase__ : Union[str, Any]=True , UpperCamelCase__ : str=99 , UpperCamelCase__ : List[Any]=32 , UpperCamelCase__ : Dict=5 , UpperCamelCase__ : List[str]=4 , UpperCamelCase__ : List[Any]=37 , UpperCamelCase__ : Any="gelu" , UpperCamelCase__ : int=0.1 , UpperCamelCase__ : str=0.1 , UpperCamelCase__ : List[str]=1_28 , UpperCamelCase__ : Optional[Any]=32 , UpperCamelCase__ : int=16 , UpperCamelCase__ : int=2 , UpperCamelCase__ : Optional[int]=0.02 , UpperCamelCase__ : Optional[Any]=3 , UpperCamelCase__ : Tuple=4 , UpperCamelCase__ : List[str]=None , ) -> List[str]:
'''simple docstring'''
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = seq_length
UpperCAmelCase = is_training
UpperCAmelCase = use_input_mask
UpperCAmelCase = use_token_type_ids
UpperCAmelCase = use_labels
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = type_vocab_size
UpperCAmelCase = type_sequence_label_size
UpperCAmelCase = initializer_range
UpperCAmelCase = num_labels
UpperCAmelCase = num_choices
UpperCAmelCase = scope
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase = None
if self.use_input_mask:
UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase = None
if self.use_token_type_ids:
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Any:
'''simple docstring'''
return NezhaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__a , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
(
(
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) ,
) = self.prepare_config_and_inputs()
UpperCAmelCase = True
UpperCAmelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def SCREAMING_SNAKE_CASE_ ( self : Any , UpperCamelCase__ : List[str] , UpperCamelCase__ : int , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : str ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase = NezhaModel(config=__a )
model.to(__a )
model.eval()
UpperCAmelCase = model(__a , attention_mask=__a , token_type_ids=__a )
UpperCAmelCase = model(__a , token_type_ids=__a )
UpperCAmelCase = model(__a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : List[Any] , UpperCamelCase__ : int , UpperCamelCase__ : Union[str, Any] , ) -> Dict:
'''simple docstring'''
UpperCAmelCase = True
UpperCAmelCase = NezhaModel(__a )
model.to(__a )
model.eval()
UpperCAmelCase = model(
__a , attention_mask=__a , token_type_ids=__a , encoder_hidden_states=__a , encoder_attention_mask=__a , )
UpperCAmelCase = model(
__a , attention_mask=__a , token_type_ids=__a , encoder_hidden_states=__a , )
UpperCAmelCase = model(__a , attention_mask=__a , token_type_ids=__a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , UpperCamelCase__ : int , UpperCamelCase__ : List[str] , UpperCamelCase__ : int , UpperCamelCase__ : Any , UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : Union[str, Any] ) -> Dict:
'''simple docstring'''
UpperCAmelCase = NezhaForMaskedLM(config=__a )
model.to(__a )
model.eval()
UpperCAmelCase = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Tuple ) -> Any:
'''simple docstring'''
UpperCAmelCase = NezhaForNextSentencePrediction(config=__a )
model.to(__a )
model.eval()
UpperCAmelCase = model(
__a , attention_mask=__a , token_type_ids=__a , labels=__a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def SCREAMING_SNAKE_CASE_ ( self : Any , UpperCamelCase__ : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : Any , UpperCamelCase__ : Dict , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : str ) -> Dict:
'''simple docstring'''
UpperCAmelCase = NezhaForPreTraining(config=__a )
model.to(__a )
model.eval()
UpperCAmelCase = model(
__a , attention_mask=__a , token_type_ids=__a , labels=__a , next_sentence_label=__a , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : int , UpperCamelCase__ : Tuple ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase = NezhaForQuestionAnswering(config=__a )
model.to(__a )
model.eval()
UpperCAmelCase = model(
__a , attention_mask=__a , token_type_ids=__a , start_positions=__a , end_positions=__a , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , UpperCamelCase__ : int , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int ) -> Dict:
'''simple docstring'''
UpperCAmelCase = self.num_labels
UpperCAmelCase = NezhaForSequenceClassification(__a )
model.to(__a )
model.eval()
UpperCAmelCase = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : int , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Any ) -> List[str]:
'''simple docstring'''
UpperCAmelCase = self.num_labels
UpperCAmelCase = NezhaForTokenClassification(config=__a )
model.to(__a )
model.eval()
UpperCAmelCase = model(__a , attention_mask=__a , token_type_ids=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Dict , UpperCamelCase__ : Dict , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase = self.num_choices
UpperCAmelCase = NezhaForMultipleChoice(config=__a )
model.to(__a )
model.eval()
UpperCAmelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCAmelCase = model(
__a , attention_mask=__a , token_type_ids=__a , labels=__a , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE_ ( self : int ) -> Tuple:
'''simple docstring'''
UpperCAmelCase = self.prepare_config_and_inputs()
(
(
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) ,
) = config_and_inputs
UpperCAmelCase = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class __magic_name__ ( A__, A__, A__, unittest.TestCase ):
lowercase : Dict =(
(
NezhaModel,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
)
if is_torch_available()
else ()
)
lowercase : Dict =(
{
'''feature-extraction''': NezhaModel,
'''fill-mask''': NezhaForMaskedLM,
'''question-answering''': NezhaForQuestionAnswering,
'''text-classification''': NezhaForSequenceClassification,
'''token-classification''': NezhaForTokenClassification,
'''zero-shot''': NezhaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase : Any =True
def SCREAMING_SNAKE_CASE_ ( self : Dict , UpperCamelCase__ : int , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int=False ) -> str:
'''simple docstring'''
UpperCAmelCase = super()._prepare_for_class(__a , __a , return_labels=__a )
if return_labels:
if model_class in get_values(__a ):
UpperCAmelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__a )
UpperCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__a )
return inputs_dict
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> int:
'''simple docstring'''
UpperCAmelCase = NezhaModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=__a , hidden_size=37 )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self : Dict ) -> str:
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*__a )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Any:
'''simple docstring'''
(
(
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) ,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
UpperCAmelCase = None
self.model_tester.create_and_check_model_as_decoder(
__a , __a , __a , __a , __a , __a , __a , __a , __a , )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__a )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__a )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Tuple:
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_next_sequence_prediction(*__a )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__a )
def SCREAMING_SNAKE_CASE_ ( self : Dict ) -> str:
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__a )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Any:
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__a )
def SCREAMING_SNAKE_CASE_ ( self : int ) -> List[str]:
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__a )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase = NezhaModel.from_pretrained(__a )
self.assertIsNotNone(__a )
@slow
@require_torch_gpu
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# NezhaForMultipleChoice behaves incorrectly in JIT environments.
if model_class == NezhaForMultipleChoice:
return
UpperCAmelCase = True
UpperCAmelCase = model_class(config=__a )
UpperCAmelCase = self._prepare_for_class(__a , __a )
UpperCAmelCase = torch.jit.trace(
__a , (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(__a , os.path.join(__a , "bert.pt" ) )
UpperCAmelCase = torch.jit.load(os.path.join(__a , "bert.pt" ) , map_location=__a )
loaded(inputs_dict["input_ids"].to(__a ) , inputs_dict["attention_mask"].to(__a ) )
@require_torch
class __magic_name__ ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase = NezhaModel.from_pretrained("sijunhe/nezha-cn-base" )
UpperCAmelCase = torch.tensor([[0, 1, 2, 3, 4, 5]] )
UpperCAmelCase = torch.tensor([[0, 1, 1, 1, 1, 1]] )
with torch.no_grad():
UpperCAmelCase = model(__a , attention_mask=__a )[0]
UpperCAmelCase = torch.Size((1, 6, 7_68) )
self.assertEqual(output.shape , __a )
UpperCAmelCase = torch.tensor([[[0.06_85, 0.24_41, 0.11_02], [0.06_00, 0.19_06, 0.13_49], [0.02_21, 0.08_19, 0.05_86]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __a , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
UpperCAmelCase = NezhaForMaskedLM.from_pretrained("sijunhe/nezha-cn-base" )
UpperCAmelCase = torch.tensor([[0, 1, 2, 3, 4, 5]] )
UpperCAmelCase = torch.tensor([[1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
UpperCAmelCase = model(__a , attention_mask=__a )[0]
UpperCAmelCase = torch.Size((1, 6, 2_11_28) )
self.assertEqual(output.shape , __a )
UpperCAmelCase = torch.tensor(
[[-2.79_39, -1.79_02, -2.21_89], [-2.85_85, -1.89_08, -2.37_23], [-2.64_99, -1.77_50, -2.25_58]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __a , atol=1e-4 ) )
| 323 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> np.array:
"""simple docstring"""
_UpperCamelCase = int(np.ceil((x_end - xa) / step_size ) )
_UpperCamelCase = np.zeros((n + 1,) )
_UpperCamelCase = ya
_UpperCamelCase = xa
for k in range(__snake_case ):
_UpperCamelCase = y[k] + step_size * ode_func(__snake_case, y[k] )
_UpperCamelCase = y[k] + (
(step_size / 2) * (ode_func(__snake_case, y[k] ) + ode_func(x + step_size, __snake_case ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 19 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
__a: Any = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
""" Distillation"""
)
)
parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
parser.add_argument("""--vocab_transform""", action="""store_true""")
__a: int = parser.parse_args()
if args.model_type == "bert":
__a: Optional[int] = BertForMaskedLM.from_pretrained(args.model_name)
__a: Dict = """bert"""
else:
raise ValueError("""args.model_type should be \"bert\".""")
__a: Tuple = model.state_dict()
__a: Union[str, Any] = {}
for w in ["word_embeddings", "position_embeddings"]:
__a: Optional[Any] = state_dict[F'{prefix}.embeddings.{w}.weight']
for w in ["weight", "bias"]:
__a: List[Any] = state_dict[F'{prefix}.embeddings.LayerNorm.{w}']
__a: Optional[int] = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
__a: int = state_dict[
F'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'
]
__a: List[str] = state_dict[
F'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'
]
__a: Tuple = state_dict[
F'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'
]
__a: Union[str, Any] = state_dict[
F'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'
]
__a: Union[str, Any] = state_dict[
F'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'
]
__a: Tuple = state_dict[
F'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'
]
__a: str = state_dict[
F'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'
]
__a: int = state_dict[
F'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'
]
std_idx += 1
__a: Optional[int] = state_dict["""cls.predictions.decoder.weight"""]
__a: Union[str, Any] = state_dict["""cls.predictions.bias"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
__a: Any = state_dict[F'cls.predictions.transform.dense.{w}']
__a: Any = state_dict[F'cls.predictions.transform.LayerNorm.{w}']
print(F'N layers selected for distillation: {std_idx}')
print(F'Number of params transferred for distillation: {len(compressed_sd.keys())}')
print(F'Save transferred checkpoint to {args.dump_checkpoint}.')
torch.save(compressed_sd, args.dump_checkpoint)
| 152 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_a = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
""" Distillation"""
)
)
parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
parser.add_argument("""--vocab_transform""", action="""store_true""")
_a = parser.parse_args()
if args.model_type == "bert":
_a = BertForMaskedLM.from_pretrained(args.model_name)
_a = """bert"""
else:
raise ValueError("""args.model_type should be \"bert\".""")
_a = model.state_dict()
_a = {}
for w in ["word_embeddings", "position_embeddings"]:
_a = state_dict[F"""{prefix}.embeddings.{w}.weight"""]
for w in ["weight", "bias"]:
_a = state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]
_a = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
]
std_idx += 1
_a = state_dict["""cls.predictions.decoder.weight"""]
_a = state_dict["""cls.predictions.bias"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
_a = state_dict[F"""cls.predictions.transform.dense.{w}"""]
_a = state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]
print(F"""N layers selected for distillation: {std_idx}""")
print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 19 | 0 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
CTRLTokenizer,
GPTaTokenizer,
GPTaTokenizerFast,
PreTrainedTokenizerFast,
RobertaTokenizer,
RobertaTokenizerFast,
is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
TOKENIZER_MAPPING,
get_tokenizer_config,
tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
DUMMY_DIFF_TOKENIZER_IDENTIFIER,
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tokenizers,
slow,
)
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Any = 0
@slow
def _snake_case ( self ):
"""simple docstring"""
for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
lowerCamelCase : int = AutoTokenizer.from_pretrained(__a )
self.assertIsNotNone(__a )
self.assertIsInstance(__a , (BertTokenizer, BertTokenizerFast) )
self.assertGreater(len(__a ) , 0 )
for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
lowerCamelCase : Any = AutoTokenizer.from_pretrained(__a )
self.assertIsNotNone(__a )
self.assertIsInstance(__a , (GPTaTokenizer, GPTaTokenizerFast) )
self.assertGreater(len(__a ) , 0 )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained(__a )
self.assertIsInstance(__a , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 12 )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained(__a )
self.assertIsInstance(__a , (RobertaTokenizer, RobertaTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 20 )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Tuple = AutoConfig.from_pretrained(__a )
self.assertIsInstance(__a , __a )
# Check that tokenizer_type ≠ model_type
lowerCamelCase : Optional[int] = AutoTokenizer.from_pretrained(__a , config=__a )
self.assertIsInstance(__a , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 12 )
def _snake_case ( self ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy("./tests/fixtures/vocab.txt" , os.path.join(__a , "vocab.txt" ) )
lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained(__a , tokenizer_type="bert" , use_fast=__a )
self.assertIsInstance(__a , __a )
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy("./tests/fixtures/vocab.json" , os.path.join(__a , "vocab.json" ) )
shutil.copy("./tests/fixtures/merges.txt" , os.path.join(__a , "merges.txt" ) )
lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained(__a , tokenizer_type="gpt2" , use_fast=__a )
self.assertIsInstance(__a , __a )
@require_tokenizers
def _snake_case ( self ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy("./tests/fixtures/vocab.txt" , os.path.join(__a , "vocab.txt" ) )
lowerCamelCase : Dict = AutoTokenizer.from_pretrained(__a , tokenizer_type="bert" )
self.assertIsInstance(__a , __a )
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy("./tests/fixtures/vocab.json" , os.path.join(__a , "vocab.json" ) )
shutil.copy("./tests/fixtures/merges.txt" , os.path.join(__a , "merges.txt" ) )
lowerCamelCase : int = AutoTokenizer.from_pretrained(__a , tokenizer_type="gpt2" )
self.assertIsInstance(__a , __a )
def _snake_case ( self ):
"""simple docstring"""
with pytest.raises(__a ):
AutoTokenizer.from_pretrained("./" , tokenizer_type="xxx" )
@require_tokenizers
def _snake_case ( self ):
"""simple docstring"""
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
lowerCamelCase : List[str] = tokenizer_class.from_pretrained("wietsedv/bert-base-dutch-cased" )
self.assertIsInstance(__a , (BertTokenizer, BertTokenizerFast) )
if isinstance(__a , __a ):
self.assertEqual(tokenizer.basic_tokenizer.do_lower_case , __a )
else:
self.assertEqual(tokenizer.do_lower_case , __a )
self.assertEqual(tokenizer.model_max_length , 512 )
@require_tokenizers
def _snake_case ( self ):
"""simple docstring"""
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
with self.assertRaisesRegex(
__a , "julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier" , ):
lowerCamelCase : Tuple = tokenizer_class.from_pretrained("julien-c/herlolip-not-exists" )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Tuple = TOKENIZER_MAPPING.values()
lowerCamelCase : int = []
for slow_tok, fast_tok in tokenizers:
if slow_tok is not None:
tokenizer_names.append(slow_tok.__name__ )
if fast_tok is not None:
tokenizer_names.append(fast_tok.__name__ )
for tokenizer_name in tokenizer_names:
# must find the right class
tokenizer_class_from_name(__a )
@require_tokenizers
def _snake_case ( self ):
"""simple docstring"""
self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased" , use_fast=__a ) , __a )
self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased" ) , __a )
@require_tokenizers
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Optional[int] = AutoTokenizer.from_pretrained("distilbert-base-uncased" , do_lower_case=__a )
lowerCamelCase : List[str] = "Hello, world. How are you?"
lowerCamelCase : str = tokenizer.tokenize(__a )
self.assertEqual("[UNK]" , tokens[0] )
lowerCamelCase : Tuple = AutoTokenizer.from_pretrained("microsoft/mpnet-base" , do_lower_case=__a )
lowerCamelCase : Union[str, Any] = tokenizer.tokenize(__a )
self.assertEqual("[UNK]" , tokens[0] )
@require_tokenizers
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Optional[int] = AutoTokenizer.from_pretrained("robot-test/dummy-tokenizer-fast-with-model-config" )
self.assertEqual(type(__a ) , __a )
self.assertEqual(tokenizer.model_max_length , 512 )
self.assertEqual(tokenizer.vocab_size , 3_0000 )
self.assertEqual(tokenizer.unk_token , "[UNK]" )
self.assertEqual(tokenizer.padding_side , "right" )
self.assertEqual(tokenizer.truncation_side , "right" )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Any = AutoTokenizer.from_pretrained(__a )
self.assertIsInstance(__a , (BertTokenizer, BertTokenizerFast) )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__a )
lowerCamelCase : List[str] = AutoTokenizer.from_pretrained(__a )
self.assertIsInstance(__a , tokenizer.__class__ )
self.assertEqual(tokenizera.vocab_size , 12 )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : str = AutoTokenizer.from_pretrained("ctrl" )
# There is no fast CTRL so this always gives us a slow tokenizer.
self.assertIsInstance(__a , __a )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = get_tokenizer_config("bert-base-cased" )
lowerCamelCase : Union[str, Any] = config.pop("_commit_hash" , __a )
# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
self.assertEqual(__a , {"do_lower_case": False} )
# This model does not have a tokenizer_config so we get back an empty dict.
lowerCamelCase : Optional[Any] = get_tokenizer_config(__a )
self.assertDictEqual(__a , {} )
# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
lowerCamelCase : str = AutoTokenizer.from_pretrained(__a )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__a )
lowerCamelCase : Optional[Any] = get_tokenizer_config(__a )
# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
self.assertEqual(config["tokenizer_class"] , "BertTokenizer" )
def _snake_case ( self ):
"""simple docstring"""
try:
AutoConfig.register("custom" , __a )
AutoTokenizer.register(__a , slow_tokenizer_class=__a )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__a ):
AutoTokenizer.register(__a , slow_tokenizer_class=__a )
lowerCamelCase : Tuple = CustomTokenizer.from_pretrained(__a )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__a )
lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained(__a )
self.assertIsInstance(__a , __a )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
@require_tokenizers
def _snake_case ( self ):
"""simple docstring"""
try:
AutoConfig.register("custom" , __a )
# Can register in two steps
AutoTokenizer.register(__a , slow_tokenizer_class=__a )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, None) )
AutoTokenizer.register(__a , fast_tokenizer_class=__a )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast) )
del TOKENIZER_MAPPING._extra_content[CustomConfig]
# Can register in one step
AutoTokenizer.register(
__a , slow_tokenizer_class=__a , fast_tokenizer_class=__a )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast) )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(__a ):
AutoTokenizer.register(__a , fast_tokenizer_class=__a )
# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
# and that model does not have a tokenizer.json
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCamelCase : List[str] = BertTokenizerFast.from_pretrained(__a )
bert_tokenizer.save_pretrained(__a )
lowerCamelCase : Optional[Any] = CustomTokenizerFast.from_pretrained(__a )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__a )
lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained(__a )
self.assertIsInstance(__a , __a )
lowerCamelCase : int = AutoTokenizer.from_pretrained(__a , use_fast=__a )
self.assertIsInstance(__a , __a )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def _snake_case ( self ):
"""simple docstring"""
with self.assertRaises(__a ):
lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(__a ):
lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=__a )
lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=__a )
self.assertTrue(tokenizer.special_attribute_present )
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__a )
lowerCamelCase : Tuple = AutoTokenizer.from_pretrained(__a , trust_remote_code=__a )
self.assertTrue(reloaded_tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , "NewTokenizerFast" )
# Test we can also load the slow version
lowerCamelCase : Optional[Any] = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=__a , use_fast=__a )
self.assertTrue(tokenizer.special_attribute_present )
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(__a )
lowerCamelCase : Optional[int] = AutoTokenizer.from_pretrained(__a , trust_remote_code=__a , use_fast=__a )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , "NewTokenizer" )
self.assertTrue(reloaded_tokenizer.special_attribute_present )
else:
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , "NewTokenizer" )
@require_tokenizers
def _snake_case ( self ):
"""simple docstring"""
class UpperCAmelCase_ ( UpperCamelCase ):
'''simple docstring'''
__A : List[Any] = False
class UpperCAmelCase_ ( UpperCamelCase ):
'''simple docstring'''
__A : str = NewTokenizer
__A : Optional[int] = False
try:
AutoConfig.register("custom" , __a )
AutoTokenizer.register(__a , slow_tokenizer_class=__a )
AutoTokenizer.register(__a , fast_tokenizer_class=__a )
# If remote code is not set, the default is to use local
lowerCamelCase : List[str] = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" )
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" )
self.assertFalse(tokenizer.special_attribute_present )
lowerCamelCase : str = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" , use_fast=__a )
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
self.assertFalse(tokenizer.special_attribute_present )
# If remote code is disabled, we load the local one.
lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=__a )
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" )
self.assertFalse(tokenizer.special_attribute_present )
lowerCamelCase : int = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=__a , use_fast=__a )
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
self.assertFalse(tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub
lowerCamelCase : List[Any] = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=__a )
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" )
self.assertTrue(tokenizer.special_attribute_present )
lowerCamelCase : List[str] = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer" , trust_remote_code=__a , use_fast=__a )
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
self.assertTrue(tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Tuple = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer_legacy" , trust_remote_code=__a )
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" )
# Test we can also load the slow version
lowerCamelCase : Any = AutoTokenizer.from_pretrained(
"hf-internal-testing/test_dynamic_tokenizer_legacy" , trust_remote_code=__a , use_fast=__a )
self.assertTrue(tokenizer.special_attribute_present )
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
else:
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
def _snake_case ( self ):
"""simple docstring"""
with self.assertRaisesRegex(
__a , "bert-base is not a local folder and is not a valid model identifier" ):
lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained("bert-base" )
def _snake_case ( self ):
"""simple docstring"""
with self.assertRaisesRegex(
__a , R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ):
lowerCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained(__a , revision="aaaaaa" )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : List[str] = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
with RequestCounter() as counter:
lowerCamelCase : Tuple = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
| 340 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
_a = """platform"""
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class _UpperCAmelCase:
lowercase__ = PegasusConfig
lowercase__ = {}
lowercase__ = 'gelu'
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=False , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a=0.1 , __a=0.1 , __a=20 , __a=2 , __a=1 , __a=0 , ) -> int:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = eos_token_id
_UpperCamelCase = pad_token_id
_UpperCamelCase = bos_token_id
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size).clip(3 , self.vocab_size)
_UpperCamelCase = np.expand_dims(np.array([self.eos_token_id] * self.batch_size) , 1)
_UpperCamelCase = np.concatenate([input_ids, eos_tensor] , axis=1)
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCamelCase = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_UpperCamelCase = prepare_pegasus_inputs_dict(__a , __a , __a)
return config, inputs_dict
def UpperCAmelCase ( self , __a , __a , __a) -> Tuple:
'''simple docstring'''
_UpperCamelCase = 20
_UpperCamelCase = model_class_name(__a)
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''])
_UpperCamelCase , _UpperCamelCase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
_UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a)
_UpperCamelCase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''')
_UpperCamelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCamelCase = model.decode(
decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , )
_UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''')
_UpperCamelCase = model.decode(
decoder_input_ids[:, -1:] , __a , decoder_attention_mask=__a , past_key_values=outputs_cache.past_key_values , decoder_position_ids=__a , )
_UpperCamelCase = model.decode(__a , __a)
_UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''')
def UpperCAmelCase ( self , __a , __a , __a) -> Tuple:
'''simple docstring'''
_UpperCamelCase = 20
_UpperCamelCase = model_class_name(__a)
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''])
_UpperCamelCase , _UpperCamelCase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
_UpperCamelCase = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
_UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a)
_UpperCamelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCamelCase = model.decode(
decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , )
_UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''')
_UpperCamelCase = model.decode(
decoder_input_ids[:, -1:] , __a , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=__a , decoder_position_ids=__a , )
_UpperCamelCase = model.decode(__a , __a , decoder_attention_mask=__a)
_UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''')
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=None, __snake_case=None, ) -> Union[str, Any]:
"""simple docstring"""
if attention_mask is None:
_UpperCamelCase = np.not_equal(__snake_case, config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
_UpperCamelCase = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape, dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:], config.pad_token_id ).astype(np.inta ),
], axis=-1, )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class _UpperCAmelCase( lowerCamelCase , unittest.TestCase ):
lowercase__ = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
lowercase__ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
lowercase__ = True
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = FlaxPegasusModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(__a , __a , __a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(__a , __a , __a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
_UpperCamelCase = self._prepare_for_class(__a , __a)
_UpperCamelCase = model_class(__a)
@jax.jit
def encode_jitted(__a , __a=None , **__a):
return model.encode(input_ids=__a , attention_mask=__a)
with self.subTest('''JIT Enabled'''):
_UpperCamelCase = encode_jitted(**__a).to_tuple()
with self.subTest('''JIT Disabled'''):
with jax.disable_jit():
_UpperCamelCase = encode_jitted(**__a).to_tuple()
self.assertEqual(len(__a) , len(__a))
for jitted_output, output in zip(__a , __a):
self.assertEqual(jitted_output.shape , output.shape)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
_UpperCamelCase = model_class(__a)
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask'''])
_UpperCamelCase = {
'''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
'''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
'''encoder_outputs''': encoder_outputs,
}
@jax.jit
def decode_jitted(__a , __a , __a):
return model.decode(
decoder_input_ids=__a , decoder_attention_mask=__a , encoder_outputs=__a , )
with self.subTest('''JIT Enabled'''):
_UpperCamelCase = decode_jitted(**__a).to_tuple()
with self.subTest('''JIT Disabled'''):
with jax.disable_jit():
_UpperCamelCase = decode_jitted(**__a).to_tuple()
self.assertEqual(len(__a) , len(__a))
for jitted_output, output in zip(__a , __a):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
for model_class_name in self.all_model_classes:
_UpperCamelCase = model_class_name.from_pretrained('''google/pegasus-large''' , from_pt=__a)
_UpperCamelCase = np.ones((1, 1))
_UpperCamelCase = model(__a)
self.assertIsNotNone(__a)
@slow
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''')
_UpperCamelCase = PegasusTokenizer.from_pretrained('''google/pegasus-xsum''')
_UpperCamelCase = [
''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
]
_UpperCamelCase = [
'''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
'''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
]
_UpperCamelCase = tokenizer(__a , return_tensors='''np''' , truncation=__a , max_length=5_12 , padding=__a)
_UpperCamelCase = model.generate(**__a , num_beams=2).sequences
_UpperCamelCase = tokenizer.batch_decode(__a , skip_special_tokens=__a)
assert tgt_text == decoded
| 19 | 0 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
__snake_case : Optional[Any] =HUGGINGFACE_HUB_CACHE
__snake_case : str ='config.json'
__snake_case : Optional[int] ='diffusion_pytorch_model.bin'
__snake_case : Dict ='diffusion_flax_model.msgpack'
__snake_case : Any ='model.onnx'
__snake_case : int ='diffusion_pytorch_model.safetensors'
__snake_case : Optional[int] ='weights.pb'
__snake_case : int ='https://huggingface.co'
__snake_case : Optional[int] =default_cache_path
__snake_case : Tuple ='diffusers_modules'
__snake_case : Dict =os.getenv('HF_MODULES_CACHE', os.path.join(hf_cache_home, 'modules'))
__snake_case : str =['fp16', 'non-ema']
__snake_case : int ='.self_attn'
| 647 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
BertConfig,
DPRConfig,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
class _UpperCAmelCase:
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=2 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=5_12 , __a=16 , __a=2 , __a=0.02 , __a=3 , __a=4 , __a=None , __a=0 , ) -> Any:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_mask
_UpperCamelCase = use_token_type_ids
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = type_vocab_size
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = num_labels
_UpperCamelCase = num_choices
_UpperCamelCase = scope
_UpperCamelCase = projection_dim
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCamelCase = None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length])
_UpperCamelCase = None
if self.use_token_type_ids:
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
_UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices)
_UpperCamelCase = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__a , initializer_range=self.initializer_range , )
_UpperCamelCase = DPRConfig(projection_dim=self.projection_dim , **config.to_dict())
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = TFDPRContextEncoder(config=__a)
_UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a)
_UpperCamelCase = model(__a , token_type_ids=__a)
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size))
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = TFDPRQuestionEncoder(config=__a)
_UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a)
_UpperCamelCase = model(__a , token_type_ids=__a)
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size))
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Dict:
'''simple docstring'''
_UpperCamelCase = TFDPRReader(config=__a)
_UpperCamelCase = model(__a , attention_mask=__a)
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,))
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {'''input_ids''': input_ids}
return config, inputs_dict
@require_tf
class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase__ = (
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
lowercase__ = {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = TFDPRModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a , hidden_size=37)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*__a)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*__a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*__a)
@slow
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRQuestionEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRReader.from_pretrained(__a)
self.assertIsNotNone(__a)
@require_tf
class _UpperCAmelCase( unittest.TestCase ):
@slow
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base''')
_UpperCamelCase = tf.constant(
[[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]]) # [CLS] hello, is my dog cute? [SEP]
_UpperCamelCase = model(__a)[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
_UpperCamelCase = tf.constant(
[
[
0.0323_6253,
0.1275_3335,
0.1681_8509,
0.0027_9786,
0.389_6933,
0.2426_4945,
0.217_8971,
-0.0233_5227,
-0.0848_1959,
-0.1432_4117,
]
])
self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1e-4))
| 19 | 0 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class UpperCAmelCase__ :
"""simple docstring"""
@staticmethod
def __lowercase ( *_a : Union[str, Any] ,**_a : str ):
'''simple docstring'''
pass
def UpperCAmelCase_ (__a : int ):
"""simple docstring"""
_a : Tuple = hashlib.mda(image.tobytes() )
return m.hexdigest()[:1_0]
def UpperCAmelCase_ (__a : Optional[Any] ):
"""simple docstring"""
_a : Optional[Any] = np.array(__snake_case )
_a : Optional[Any] = npimg.shape
return {"hash": hashimage(__snake_case ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : List[str] = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
__UpperCAmelCase : List[str] = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def __lowercase ( self : str ,_a : Optional[int] ,_a : List[str] ,_a : Optional[int] ):
'''simple docstring'''
_a : Tuple = MaskGenerationPipeline(model=__a ,image_processor=__a )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def __lowercase ( self : Optional[int] ,_a : str ,_a : int ):
'''simple docstring'''
pass
@require_tf
@unittest.skip('Image segmentation not implemented in TF' )
def __lowercase ( self : int ):
'''simple docstring'''
pass
@slow
@require_torch
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
_a : Optional[Any] = pipeline('mask-generation' ,model='facebook/sam-vit-huge' )
_a : Any = image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg' ,points_per_batch=256 )
# Shortening by hashing
_a : Optional[int] = []
for i, o in enumerate(outputs['masks'] ):
new_outupt += [{"mask": mask_to_test_readable(__a ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(__a ,decimals=4 ) ,[
{'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
{'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.021},
{'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
{'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
{'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
{'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9967},
{'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.993},
{'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9909},
{'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9879},
{'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9834},
{'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9716},
{'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9612},
{'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9599},
{'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9552},
{'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9532},
{'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9516},
{'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9499},
{'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9483},
{'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9464},
{'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.943},
{'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.943},
{'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9408},
{'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9335},
{'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9326},
{'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9262},
{'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8999},
{'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8986},
{'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8984},
{'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8873},
{'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8871}
] ,)
# fmt: on
@require_torch
@slow
def __lowercase ( self : int ):
'''simple docstring'''
_a : Dict = 'facebook/sam-vit-huge'
_a : Union[str, Any] = pipeline('mask-generation' ,model=__a )
_a : str = image_segmenter(
'http://images.cocodataset.org/val2017/000000039769.jpg' ,pred_iou_thresh=1 ,points_per_batch=256 )
# Shortening by hashing
_a : Union[str, Any] = []
for i, o in enumerate(outputs['masks'] ):
new_outupt += [{"mask": mask_to_test_readable(__a ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(__a ,decimals=4 ) ,[
{'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
{'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0210},
{'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
{'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
{'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
] ,)
| 229 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowerCamelCase__ ( __snake_case ) -> Optional[Any]:
"""simple docstring"""
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
or (cp >= 0x3400 and cp <= 0x4DBF) #
or (cp >= 0x2_0000 and cp <= 0x2_A6DF) #
or (cp >= 0x2_A700 and cp <= 0x2_B73F) #
or (cp >= 0x2_B740 and cp <= 0x2_B81F) #
or (cp >= 0x2_B820 and cp <= 0x2_CEAF) #
or (cp >= 0xF900 and cp <= 0xFAFF)
or (cp >= 0x2_F800 and cp <= 0x2_FA1F) #
): #
return True
return False
def lowerCamelCase__ ( __snake_case ) -> Optional[Any]:
"""simple docstring"""
for char in word:
_UpperCamelCase = ord(__snake_case )
if not _is_chinese_char(__snake_case ):
return 0
return 1
def lowerCamelCase__ ( __snake_case ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = set()
for token in tokens:
_UpperCamelCase = len(__snake_case ) > 1 and is_chinese(__snake_case )
if chinese_word:
word_set.add(__snake_case )
_UpperCamelCase = list(__snake_case )
return word_list
def lowerCamelCase__ ( __snake_case, __snake_case ) -> int:
"""simple docstring"""
if not chinese_word_set:
return bert_tokens
_UpperCamelCase = max([len(__snake_case ) for w in chinese_word_set] )
_UpperCamelCase = bert_tokens
_UpperCamelCase , _UpperCamelCase = 0, len(__snake_case )
while start < end:
_UpperCamelCase = True
if is_chinese(bert_word[start] ):
_UpperCamelCase = min(end - start, __snake_case )
for i in range(__snake_case, 1, -1 ):
_UpperCamelCase = ''''''.join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1, start + i ):
_UpperCamelCase = '''##''' + bert_word[j]
_UpperCamelCase = start + i
_UpperCamelCase = False
break
if single_word:
start += 1
return bert_word
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = []
for i in range(0, len(__snake_case ), 1_00 ):
_UpperCamelCase = ltp_tokenizer.pipeline(lines[i : i + 1_00], tasks=['''cws'''] ).cws
_UpperCamelCase = [get_chinese_word(__snake_case ) for r in res]
ltp_res.extend(__snake_case )
assert len(__snake_case ) == len(__snake_case )
_UpperCamelCase = []
for i in range(0, len(__snake_case ), 1_00 ):
_UpperCamelCase = bert_tokenizer(lines[i : i + 1_00], add_special_tokens=__snake_case, truncation=__snake_case, max_length=5_12 )
bert_res.extend(res['''input_ids'''] )
assert len(__snake_case ) == len(__snake_case )
_UpperCamelCase = []
for input_ids, chinese_word in zip(__snake_case, __snake_case ):
_UpperCamelCase = []
for id in input_ids:
_UpperCamelCase = bert_tokenizer._convert_id_to_token(__snake_case )
input_tokens.append(__snake_case )
_UpperCamelCase = add_sub_symbol(__snake_case, __snake_case )
_UpperCamelCase = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__snake_case ):
if token[:2] == "##":
_UpperCamelCase = token[2:]
# save chinese tokens' pos
if len(__snake_case ) == 1 and _is_chinese_char(ord(__snake_case ) ):
ref_id.append(__snake_case )
ref_ids.append(__snake_case )
assert len(__snake_case ) == len(__snake_case )
return ref_ids
def lowerCamelCase__ ( __snake_case ) -> Optional[int]:
"""simple docstring"""
with open(args.file_name, '''r''', encoding='''utf-8''' ) as f:
_UpperCamelCase = f.readlines()
_UpperCamelCase = [line.strip() for line in data if len(__snake_case ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
_UpperCamelCase = LTP(args.ltp ) # faster in GPU device
_UpperCamelCase = BertTokenizer.from_pretrained(args.bert )
_UpperCamelCase = prepare_ref(__snake_case, __snake_case, __snake_case )
with open(args.save_path, '''w''', encoding='''utf-8''' ) as f:
_UpperCamelCase = [json.dumps(__snake_case ) + '''\n''' for ref in ref_ids]
f.writelines(__snake_case )
if __name__ == "__main__":
_a = argparse.ArgumentParser(description="""prepare_chinese_ref""")
parser.add_argument(
"""--file_name""",
required=False,
type=str,
default="""./resources/chinese-demo.txt""",
help="""file need process, same as training data in lm""",
)
parser.add_argument(
"""--ltp""",
required=False,
type=str,
default="""./resources/ltp""",
help="""resources for LTP tokenizer, usually a path""",
)
parser.add_argument(
"""--bert""",
required=False,
type=str,
default="""./resources/robert""",
help="""resources for Bert tokenizer""",
)
parser.add_argument(
"""--save_path""",
required=False,
type=str,
default="""./resources/ref.txt""",
help="""path to save res""",
)
_a = parser.parse_args()
main(args)
| 19 | 0 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowercase ( a ):
lowercase__ : Any = (DPMSolverSDEScheduler,)
lowercase__ : int = 10
def __snake_case( self : Optional[int] , **_UpperCamelCase : Tuple ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {
"num_train_timesteps": 1_100,
"beta_start": 0.0_0_0_1,
"beta_end": 0.0_2,
"beta_schedule": "linear",
"noise_sampler_seed": 0,
}
config.update(**__a )
return config
def __snake_case( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
for timesteps in [10, 50, 100, 1_000]:
self.check_over_configs(num_train_timesteps=__a )
def __snake_case( self : Any ) -> Dict:
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1] , [0.0_0_0_2, 0.0_0_2, 0.0_2] ):
self.check_over_configs(beta_start=__a , beta_end=__a )
def __snake_case( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__a )
def __snake_case( self : Union[str, Any] ) -> str:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__a )
def __snake_case( self : int ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE = self.get_scheduler_config()
SCREAMING_SNAKE_CASE = scheduler_class(**__a )
scheduler.set_timesteps(self.num_inference_steps )
SCREAMING_SNAKE_CASE = self.dummy_model()
SCREAMING_SNAKE_CASE = self.dummy_sample_deter * scheduler.init_noise_sigma
SCREAMING_SNAKE_CASE = sample.to(__a )
for i, t in enumerate(scheduler.timesteps ):
SCREAMING_SNAKE_CASE = scheduler.scale_model_input(__a , __a )
SCREAMING_SNAKE_CASE = model(__a , __a )
SCREAMING_SNAKE_CASE = scheduler.step(__a , __a , __a )
SCREAMING_SNAKE_CASE = output.prev_sample
SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__a ) )
SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5 ) < 1e-2
assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6 ) < 1e-2
assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2 ) < 1e-3
else:
assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2 ) < 1e-2
assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6 ) < 1e-3
def __snake_case( self : Optional[int] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE = self.get_scheduler_config(prediction_type="v_prediction" )
SCREAMING_SNAKE_CASE = scheduler_class(**__a )
scheduler.set_timesteps(self.num_inference_steps )
SCREAMING_SNAKE_CASE = self.dummy_model()
SCREAMING_SNAKE_CASE = self.dummy_sample_deter * scheduler.init_noise_sigma
SCREAMING_SNAKE_CASE = sample.to(__a )
for i, t in enumerate(scheduler.timesteps ):
SCREAMING_SNAKE_CASE = scheduler.scale_model_input(__a , __a )
SCREAMING_SNAKE_CASE = model(__a , __a )
SCREAMING_SNAKE_CASE = scheduler.step(__a , __a , __a )
SCREAMING_SNAKE_CASE = output.prev_sample
SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__a ) )
SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3 ) < 1e-2
assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3 ) < 1e-2
assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7 ) < 1e-3
else:
assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5 ) < 1e-2
assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1 ) < 1e-3
def __snake_case( self : List[str] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE = self.get_scheduler_config()
SCREAMING_SNAKE_CASE = scheduler_class(**__a )
scheduler.set_timesteps(self.num_inference_steps , device=__a )
SCREAMING_SNAKE_CASE = self.dummy_model()
SCREAMING_SNAKE_CASE = self.dummy_sample_deter.to(__a ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
SCREAMING_SNAKE_CASE = scheduler.scale_model_input(__a , __a )
SCREAMING_SNAKE_CASE = model(__a , __a )
SCREAMING_SNAKE_CASE = scheduler.step(__a , __a , __a )
SCREAMING_SNAKE_CASE = output.prev_sample
SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__a ) )
SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8 ) < 1e-2
assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2 ) < 1e-2
assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1 ) < 1e-3
else:
assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2 ) < 1e-2
assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6 ) < 1e-3
def __snake_case( self : List[Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE = self.get_scheduler_config()
SCREAMING_SNAKE_CASE = scheduler_class(**__a , use_karras_sigmas=__a )
scheduler.set_timesteps(self.num_inference_steps , device=__a )
SCREAMING_SNAKE_CASE = self.dummy_model()
SCREAMING_SNAKE_CASE = self.dummy_sample_deter.to(__a ) * scheduler.init_noise_sigma
SCREAMING_SNAKE_CASE = sample.to(__a )
for t in scheduler.timesteps:
SCREAMING_SNAKE_CASE = scheduler.scale_model_input(__a , __a )
SCREAMING_SNAKE_CASE = model(__a , __a )
SCREAMING_SNAKE_CASE = scheduler.step(__a , __a , __a )
SCREAMING_SNAKE_CASE = output.prev_sample
SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__a ) )
SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8 ) < 1e-2
assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1 ) < 1e-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5 ) < 1e-2
assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1 ) < 1e-2
else:
assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2 ) < 1e-2
assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1 ) < 1e-2
| 403 |
"""simple docstring"""
import heapq
def lowerCamelCase__ ( __snake_case ) -> set[int]:
"""simple docstring"""
_UpperCamelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(__snake_case, [-1 * len(__snake_case ), (key, value)] )
# chosen_vertices = set of chosen vertices
_UpperCamelCase = set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
_UpperCamelCase = heapq.heappop(__snake_case )[1][0]
chosen_vertices.add(__snake_case )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
_UpperCamelCase = elem[1][1].index(__snake_case )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(__snake_case )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
_a = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
| 19 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class A ( _a ):
lowercase_ = 42
@flax_register_to_config
class A ( nn.Module ,_a ,_a ):
lowercase_ = 32
lowercase_ = 4
lowercase_ = 4
lowercase_ = (
'CrossAttnDownBlock2D',
'CrossAttnDownBlock2D',
'CrossAttnDownBlock2D',
'DownBlock2D',
)
lowercase_ = ('UpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D')
lowercase_ = False
lowercase_ = (320, 640, 1280, 1280)
lowercase_ = 2
lowercase_ = 8
lowercase_ = None
lowercase_ = 1280
lowercase_ = 0.0
lowercase_ = False
lowercase_ = jnp.floataa
lowercase_ = True
lowercase_ = 0
lowercase_ = False
def __lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : Optional[Any] ) -> FrozenDict:
"""simple docstring"""
_a = (1, self.in_channels, self.sample_size, self.sample_size)
_a = jnp.zeros(__a , dtype=jnp.floataa )
_a = jnp.ones((1,) , dtype=jnp.intaa )
_a = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
_a , _a = jax.random.split(__a )
_a = {'''params''': params_rng, '''dropout''': dropout_rng}
return self.init(__a , __a , __a , __a )["params"]
def __lowerCAmelCase ( self : List[Any] ) -> str:
"""simple docstring"""
_a = self.block_out_channels
_a = block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
'''At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.''' )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
_a = self.num_attention_heads or self.attention_head_dim
# input
_a = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
_a = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
_a = FlaxTimestepEmbedding(__a , dtype=self.dtype )
_a = self.only_cross_attention
if isinstance(__a , __a ):
_a = (only_cross_attention,) * len(self.down_block_types )
if isinstance(__a , __a ):
_a = (num_attention_heads,) * len(self.down_block_types )
# down
_a = []
_a = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
_a = output_channel
_a = block_out_channels[i]
_a = i == len(__a ) - 1
if down_block_type == "CrossAttnDownBlock2D":
_a = FlaxCrossAttnDownBlockaD(
in_channels=__a , out_channels=__a , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
_a = FlaxDownBlockaD(
in_channels=__a , out_channels=__a , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(__a )
_a = down_blocks
# mid
_a = FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
# up
_a = []
_a = list(reversed(__a ) )
_a = list(reversed(__a ) )
_a = list(reversed(__a ) )
_a = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
_a = output_channel
_a = reversed_block_out_channels[i]
_a = reversed_block_out_channels[min(i + 1 , len(__a ) - 1 )]
_a = i == len(__a ) - 1
if up_block_type == "CrossAttnUpBlock2D":
_a = FlaxCrossAttnUpBlockaD(
in_channels=__a , out_channels=__a , prev_output_channel=__a , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
_a = FlaxUpBlockaD(
in_channels=__a , out_channels=__a , prev_output_channel=__a , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(__a )
_a = output_channel
_a = up_blocks
# out
_a = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_a = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : Optional[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : str=None , lowerCAmelCase_ : List[str]=None , lowerCAmelCase_ : str = True , lowerCAmelCase_ : List[Any] = False , ) -> Union[FlaxUNetaDConditionOutput, Tuple]:
"""simple docstring"""
if not isinstance(__a , jnp.ndarray ):
_a = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(__a , jnp.ndarray ) and len(timesteps.shape ) == 0:
_a = timesteps.astype(dtype=jnp.floataa )
_a = jnp.expand_dims(__a , 0 )
_a = self.time_proj(__a )
_a = self.time_embedding(__a )
# 2. pre-process
_a = jnp.transpose(__a , (0, 2, 3, 1) )
_a = self.conv_in(__a )
# 3. down
_a = (sample,)
for down_block in self.down_blocks:
if isinstance(__a , __a ):
_a , _a = down_block(__a , __a , __a , deterministic=not train )
else:
_a , _a = down_block(__a , __a , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
_a = ()
for down_block_res_sample, down_block_additional_residual in zip(
__a , __a ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
_a = new_down_block_res_samples
# 4. mid
_a = self.mid_block(__a , __a , __a , deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
_a = down_block_res_samples[-(self.layers_per_block + 1) :]
_a = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(__a , __a ):
_a = up_block(
__a , temb=__a , encoder_hidden_states=__a , res_hidden_states_tuple=__a , deterministic=not train , )
else:
_a = up_block(__a , temb=__a , res_hidden_states_tuple=__a , deterministic=not train )
# 6. post-process
_a = self.conv_norm_out(__a )
_a = nn.silu(__a )
_a = self.conv_out(__a )
_a = jnp.transpose(__a , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=__a )
| 22 |
"""simple docstring"""
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def lowerCamelCase__ ( ) -> List[str]:
"""simple docstring"""
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
_UpperCamelCase = '''__test_patch_submodule_mock__'''
with patch_submodule(_test_patching, '''os.path.join''', __snake_case ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os, _PatchedModuleObj )
assert isinstance(_test_patching.os.path, _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path, _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os, _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path, _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path, _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def lowerCamelCase__ ( ) -> List[str]:
"""simple docstring"""
assert _test_patching.open is open
_UpperCamelCase = '''__test_patch_submodule_builtin_mock__'''
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching, '''open''', __snake_case ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def lowerCamelCase__ ( ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_missing_mock__'''
with patch_submodule(_test_patching, '''pandas.read_csv''', __snake_case ):
pass
def lowerCamelCase__ ( ) -> Dict:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_missing_builtin_mock__'''
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching, '''len''', __snake_case ) is None
with patch_submodule(_test_patching, '''len''', __snake_case ):
assert _test_patching.len is mock
assert _test_patching.len is len
def lowerCamelCase__ ( ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_start_and_stop_mock__'''
_UpperCamelCase = patch_submodule(_test_patching, '''open''', __snake_case )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def lowerCamelCase__ ( ) -> Optional[int]:
"""simple docstring"""
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
_UpperCamelCase = '''__test_patch_submodule_successive_join__'''
_UpperCamelCase = '''__test_patch_submodule_successive_dirname__'''
_UpperCamelCase = '''__test_patch_submodule_successive_rename__'''
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching, '''os.path.join''', __snake_case ):
with patch_submodule(_test_patching, '''os.rename''', __snake_case ):
with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching, '''os.rename''', __snake_case ):
with patch_submodule(_test_patching, '''os.path.join''', __snake_case ):
with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def lowerCamelCase__ ( ) -> str:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_doesnt_exist_mock__'''
with patch_submodule(_test_patching, '''__module_that_doesn_exist__.__attribute_that_doesn_exist__''', __snake_case ):
pass
with patch_submodule(_test_patching, '''os.__attribute_that_doesn_exist__''', __snake_case ):
pass
| 19 | 0 |
'''simple docstring'''
def __a ( A__ , A__ ) -> int:
lowerCAmelCase = ""
for i in table:
res += inp[i - 1]
return res
def __a ( A__ ) -> str:
return data[1:] + data[0]
def __a ( A__ , A__ ) -> Tuple:
lowerCAmelCase = ""
for i in range(len(__snake_case ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def __a ( A__ , A__ ) -> List[Any]:
lowerCAmelCase = int("0b" + data[0] + data[-1] , 2 )
lowerCAmelCase = int("0b" + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def __a ( A__ , A__ , A__ , A__ , A__ ) -> Dict:
lowerCAmelCase = message[:4]
lowerCAmelCase = message[4:]
lowerCAmelCase = apply_table(__snake_case , __snake_case )
lowerCAmelCase = xor(__snake_case , __snake_case )
lowerCAmelCase = apply_sbox(__snake_case , temp[:4] ) # noqa: E741
lowerCAmelCase = apply_sbox(__snake_case , temp[4:] )
lowerCAmelCase = "0" * (2 - len(__snake_case )) + l # noqa: E741
lowerCAmelCase = "0" * (2 - len(__snake_case )) + r
lowerCAmelCase = apply_table(l + r , __snake_case )
lowerCAmelCase = xor(__snake_case , __snake_case )
return temp + right
if __name__ == "__main__":
lowercase : str = input('Enter 10 bit key: ')
lowercase : str = input('Enter 8 bit message: ')
lowercase : int = [6, 3, 7, 4, 8, 5, 1_0, 9]
lowercase : List[Any] = [3, 5, 2, 7, 4, 1_0, 1, 9, 8, 6]
lowercase : int = [2, 4, 3, 1]
lowercase : Dict = [2, 6, 3, 1, 4, 8, 5, 7]
lowercase : Union[str, Any] = [4, 1, 3, 5, 7, 2, 8, 6]
lowercase : Tuple = [4, 1, 2, 3, 2, 3, 4, 1]
lowercase : Dict = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
lowercase : Tuple = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
lowercase : Optional[Any] = apply_table(key, paa_table)
lowercase : Union[str, Any] = temp[:5]
lowercase : Union[str, Any] = temp[5:]
lowercase : str = left_shift(left)
lowercase : Any = left_shift(right)
lowercase : List[Any] = apply_table(left + right, pa_table)
lowercase : str = left_shift(left)
lowercase : Any = left_shift(right)
lowercase : List[Any] = left_shift(left)
lowercase : List[Any] = left_shift(right)
lowercase : Any = apply_table(left + right, pa_table)
# encryption
lowercase : Tuple = apply_table(message, IP)
lowercase : Union[str, Any] = function(expansion, sa, sa, keya, temp)
lowercase : List[Any] = temp[4:] + temp[:4]
lowercase : Optional[Any] = function(expansion, sa, sa, keya, temp)
lowercase : List[Any] = apply_table(temp, IP_inv)
print('Cipher text is:', CT)
# decryption
lowercase : str = apply_table(CT, IP)
lowercase : Tuple = function(expansion, sa, sa, keya, temp)
lowercase : Optional[int] = temp[4:] + temp[:4]
lowercase : Any = function(expansion, sa, sa, keya, temp)
lowercase : str = apply_table(temp, IP_inv)
print('Plain text after decypting is:', PT)
| 649 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = original_name.split('''.''' )[0]
_UpperCamelCase = key.split('''.''' )
_UpperCamelCase = int(key_list[key_list.index(__snake_case ) - 2] )
_UpperCamelCase = int(key_list[key_list.index(__snake_case ) - 1] )
_UpperCamelCase = orig_block_num - offset
_UpperCamelCase = key.replace(F'''{orig_block_num}.{layer_num}.{original_name}''', F'''block.{new_block_num}.{layer_num}.{new_name}''' )
return key
def lowerCamelCase__ ( __snake_case ) -> str:
"""simple docstring"""
_UpperCamelCase = OrderedDict()
_UpperCamelCase , _UpperCamelCase = 0, 0
for key, value in state_dict.items():
if key.startswith('''network''' ):
_UpperCamelCase = key.replace('''network''', '''poolformer.encoder''' )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith('''bias''' ) and "patch_embed" not in key:
patch_emb_offset += 1
_UpperCamelCase = key[: key.find('''proj''' )]
_UpperCamelCase = key.replace(__snake_case, F'''patch_embeddings.{total_embed_found}.''' )
_UpperCamelCase = key.replace('''proj''', '''projection''' )
if key.endswith('''bias''' ):
total_embed_found += 1
if "patch_embeddings" in key:
_UpperCamelCase = '''poolformer.encoder.''' + key
if "mlp.fc1" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''mlp.fc1''', '''output.conv1''' )
if "mlp.fc2" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''mlp.fc2''', '''output.conv2''' )
if "norm1" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''norm1''', '''before_norm''' )
if "norm2" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''norm2''', '''after_norm''' )
if "layer_scale_1" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''layer_scale_1''', '''layer_scale_1''' )
if "layer_scale_2" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''layer_scale_2''', '''layer_scale_2''' )
if "head" in key:
_UpperCamelCase = key.replace('''head''', '''classifier''' )
_UpperCamelCase = value
return new_state_dict
def lowerCamelCase__ ( ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_UpperCamelCase = Image.open(requests.get(__snake_case, stream=__snake_case ).raw )
return image
@torch.no_grad()
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> int:
"""simple docstring"""
_UpperCamelCase = PoolFormerConfig()
# set attributes based on model_name
_UpperCamelCase = '''huggingface/label-files'''
_UpperCamelCase = model_name[-3:]
_UpperCamelCase = 10_00
_UpperCamelCase = '''imagenet-1k-id2label.json'''
_UpperCamelCase = (1, 10_00)
# set config attributes
_UpperCamelCase = json.load(open(hf_hub_download(__snake_case, __snake_case, repo_type='''dataset''' ), '''r''' ) )
_UpperCamelCase = {int(__snake_case ): v for k, v in idalabel.items()}
_UpperCamelCase = idalabel
_UpperCamelCase = {v: k for k, v in idalabel.items()}
if size == "s12":
_UpperCamelCase = [2, 2, 6, 2]
_UpperCamelCase = [64, 1_28, 3_20, 5_12]
_UpperCamelCase = 4.0
_UpperCamelCase = 0.9
elif size == "s24":
_UpperCamelCase = [4, 4, 12, 4]
_UpperCamelCase = [64, 1_28, 3_20, 5_12]
_UpperCamelCase = 4.0
_UpperCamelCase = 0.9
elif size == "s36":
_UpperCamelCase = [6, 6, 18, 6]
_UpperCamelCase = [64, 1_28, 3_20, 5_12]
_UpperCamelCase = 4.0
_UpperCamelCase = 1e-6
_UpperCamelCase = 0.9
elif size == "m36":
_UpperCamelCase = [6, 6, 18, 6]
_UpperCamelCase = [96, 1_92, 3_84, 7_68]
_UpperCamelCase = 4.0
_UpperCamelCase = 1e-6
_UpperCamelCase = 0.95
elif size == "m48":
_UpperCamelCase = [8, 8, 24, 8]
_UpperCamelCase = [96, 1_92, 3_84, 7_68]
_UpperCamelCase = 4.0
_UpperCamelCase = 1e-6
_UpperCamelCase = 0.95
else:
raise ValueError(F'''Size {size} not supported''' )
# load image processor
_UpperCamelCase = PoolFormerImageProcessor(crop_pct=__snake_case )
# Prepare image
_UpperCamelCase = prepare_img()
_UpperCamelCase = image_processor(images=__snake_case, return_tensors='''pt''' ).pixel_values
logger.info(F'''Converting model {model_name}...''' )
# load original state dict
_UpperCamelCase = torch.load(__snake_case, map_location=torch.device('''cpu''' ) )
# rename keys
_UpperCamelCase = rename_keys(__snake_case )
# create HuggingFace model and load state dict
_UpperCamelCase = PoolFormerForImageClassification(__snake_case )
model.load_state_dict(__snake_case )
model.eval()
# Define image processor
_UpperCamelCase = PoolFormerImageProcessor(crop_pct=__snake_case )
_UpperCamelCase = image_processor(images=prepare_img(), return_tensors='''pt''' ).pixel_values
# forward pass
_UpperCamelCase = model(__snake_case )
_UpperCamelCase = outputs.logits
# define expected logit slices for different models
if size == "s12":
_UpperCamelCase = torch.tensor([-0.3045, -0.6758, -0.4869] )
elif size == "s24":
_UpperCamelCase = torch.tensor([0.4402, -0.1374, -0.8045] )
elif size == "s36":
_UpperCamelCase = torch.tensor([-0.6080, -0.5133, -0.5898] )
elif size == "m36":
_UpperCamelCase = torch.tensor([0.3952, 0.2263, -1.2668] )
elif size == "m48":
_UpperCamelCase = torch.tensor([0.1167, -0.0656, -0.3423] )
else:
raise ValueError(F'''Size {size} not supported''' )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3], __snake_case, atol=1e-2 )
# finally, save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(__snake_case ).mkdir(exist_ok=__snake_case )
model.save_pretrained(__snake_case )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__snake_case )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument(
"""--model_name""",
default="""poolformer_s12""",
type=str,
help="""Name of the model you'd like to convert.""",
)
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
_a = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 19 | 0 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
UpperCamelCase__ : int = 100
UpperCamelCase__ : List[Any] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
UpperCamelCase__ : Dict = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=1_00 )
def __UpperCamelCase( _A : Optional[int] ):
'''simple docstring'''
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
UpperCAmelCase__ : Optional[Any] = set()
UpperCAmelCase__ : List[Any] = 42
UpperCAmelCase__ : int = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def __UpperCamelCase( _A : Optional[int] = 50_00 ):
'''simple docstring'''
for number_to_partition in range(1 , __snake_case ):
if len(partition(__snake_case ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(f"""{solution() = }""")
| 614 |
"""simple docstring"""
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = (DPMSolverSDEScheduler,)
lowercase__ = 10
def UpperCAmelCase ( self , **__a) -> int:
'''simple docstring'''
_UpperCamelCase = {
'''num_train_timesteps''': 11_00,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''noise_sampler_seed''': 0,
}
config.update(**__a)
return config
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02]):
self.check_over_configs(beta_start=__a , beta_end=__a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__a)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__a)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
scheduler.set_timesteps(self.num_inference_steps)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCamelCase = sample.to(__a)
for i, t in enumerate(scheduler.timesteps):
_UpperCamelCase = scheduler.scale_model_input(__a , __a)
_UpperCamelCase = model(__a , __a)
_UpperCamelCase = scheduler.step(__a , __a , __a)
_UpperCamelCase = output.prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47_8210_4492_1875) < 1e-2
assert abs(result_mean.item() - 0.2178_7059_6456_5277) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3521_1181_6406) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9068_9229_9652) < 1e-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''')
_UpperCamelCase = scheduler_class(**__a)
scheduler.set_timesteps(self.num_inference_steps)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCamelCase = sample.to(__a)
for i, t in enumerate(scheduler.timesteps):
_UpperCamelCase = scheduler.scale_model_input(__a , __a)
_UpperCamelCase = model(__a , __a)
_UpperCamelCase = scheduler.step(__a , __a , __a)
_UpperCamelCase = output.prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77_1492_0043_9453) < 1e-2
assert abs(result_mean.item() - 0.1_6226_2890_1481_6284) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1_6633_6059_5703) < 1e-2
assert abs(result_mean.item() - 0.1_6688_3260_0116_7297) < 1e-3
else:
assert abs(result_sum.item() - 119.8_4875_4882_8125) < 1e-2
assert abs(result_mean.item() - 0.1560_5306_6253_6621) < 1e-3
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
scheduler.set_timesteps(self.num_inference_steps , device=__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
_UpperCamelCase = scheduler.scale_model_input(__a , __a)
_UpperCamelCase = model(__a , __a)
_UpperCamelCase = scheduler.step(__a , __a , __a)
_UpperCamelCase = output.prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46_9573_9746_0938) < 1e-2
assert abs(result_mean.item() - 0.2_1805_9346_0798_2635) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3536_3769_5312) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9083_8241_5771) < 1e-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a , use_karras_sigmas=__a)
scheduler.set_timesteps(self.num_inference_steps , device=__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma
_UpperCamelCase = sample.to(__a)
for t in scheduler.timesteps:
_UpperCamelCase = scheduler.scale_model_input(__a , __a)
_UpperCamelCase = model(__a , __a)
_UpperCamelCase = scheduler.step(__a , __a , __a)
_UpperCamelCase = output.prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66_9741_3574_2188) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63_6535_6445_3125) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
else:
assert abs(result_sum.item() - 170.3_1352_2338_8672) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
| 19 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowercase =logging.get_logger(__name__)
lowercase ='▁'
lowercase ={'vocab_file': 'sentencepiece.bpe.model'}
lowercase ={
'vocab_file': {
'facebook/mbart-large-en-ro': (
'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
),
'facebook/mbart-large-cc25': (
'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
),
}
}
lowercase ={
'facebook/mbart-large-en-ro': 1024,
'facebook/mbart-large-cc25': 1024,
}
# fmt: off
lowercase =['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']
class __magic_name__ ( lowerCAmelCase ):
UpperCAmelCase =VOCAB_FILES_NAMES
UpperCAmelCase =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase =PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase =["input_ids", "attention_mask"]
UpperCAmelCase =[]
UpperCAmelCase =[]
def __init__( self , snake_case , snake_case="<s>" , snake_case="</s>" , snake_case="</s>" , snake_case="<s>" , snake_case="<unk>" , snake_case="<pad>" , snake_case="<mask>" , snake_case=None , snake_case=None , snake_case=None , snake_case = None , snake_case=None , **snake_case , ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[int] =AddedToken(__a , lstrip=__a , rstrip=__a) if isinstance(__a , __a) else mask_token
_UpperCAmelCase : List[Any] ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__a , eos_token=__a , unk_token=__a , sep_token=__a , cls_token=__a , pad_token=__a , mask_token=__a , tokenizer_file=__a , src_lang=__a , tgt_lang=__a , additional_special_tokens=__a , sp_model_kwargs=self.sp_model_kwargs , **__a , )
_UpperCAmelCase : str =spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(str(__a))
_UpperCAmelCase : Tuple =vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
_UpperCAmelCase : Tuple ={'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
_UpperCAmelCase : Optional[int] =1
_UpperCAmelCase : Any =len(self.sp_model)
_UpperCAmelCase : List[Any] ={
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__a)
}
_UpperCAmelCase : List[Any] ={v: k for k, v in self.lang_code_to_id.items()}
_UpperCAmelCase : Optional[Any] =len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
_UpperCAmelCase : List[Any] ={v: k for k, v in self.fairseq_tokens_to_ids.items()}
_UpperCAmelCase : List[str] =list(self.lang_code_to_id.keys())
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens])
_UpperCAmelCase : Tuple =src_lang if src_lang is not None else 'en_XX'
_UpperCAmelCase : List[Any] =self.lang_code_to_id[self._src_lang]
_UpperCAmelCase : Optional[Any] =tgt_lang
self.set_src_lang_special_tokens(self._src_lang)
def __getstate__( self) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Dict =self.__dict__.copy()
_UpperCAmelCase : Optional[int] =None
_UpperCAmelCase : int =self.sp_model.serialized_model_proto()
return state
def __setstate__( self , snake_case) -> int:
'''simple docstring'''
_UpperCAmelCase : List[Any] =d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs'):
_UpperCAmelCase : int ={}
_UpperCAmelCase : int =spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
@property
def lowerCAmelCase ( self) -> Dict:
'''simple docstring'''
return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def lowerCAmelCase ( self) -> str:
'''simple docstring'''
return self._src_lang
@src_lang.setter
def lowerCAmelCase ( self , snake_case) -> None:
'''simple docstring'''
_UpperCAmelCase : List[Any] =new_src_lang
self.set_src_lang_special_tokens(self._src_lang)
def lowerCAmelCase ( self , snake_case , snake_case = None , snake_case = False) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__a , token_ids_a=__a , already_has_special_tokens=__a)
_UpperCAmelCase : Any =[1] * len(self.prefix_tokens)
_UpperCAmelCase : Union[str, Any] =[1] * len(self.suffix_tokens)
if token_ids_a is None:
return prefix_ones + ([0] * len(__a)) + suffix_ones
return prefix_ones + ([0] * len(__a)) + ([0] * len(__a)) + suffix_ones
def lowerCAmelCase ( self , snake_case , snake_case = None) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def lowerCAmelCase ( self , snake_case , snake_case = None) -> List[int]:
'''simple docstring'''
_UpperCAmelCase : List[str] =[self.sep_token_id]
_UpperCAmelCase : Dict =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]
def lowerCAmelCase ( self , snake_case , snake_case , snake_case , snake_case , **snake_case) -> List[Any]:
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model')
_UpperCAmelCase : str =src_lang
_UpperCAmelCase : str =self(__a , add_special_tokens=__a , return_tensors=__a , **__a)
_UpperCAmelCase : Union[str, Any] =self.convert_tokens_to_ids(__a)
_UpperCAmelCase : Tuple =tgt_lang_id
return inputs
def lowerCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : int ={self.convert_ids_to_tokens(__a): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def lowerCAmelCase ( self , snake_case) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(__a , out_type=__a)
def lowerCAmelCase ( self , snake_case) -> List[Any]:
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
_UpperCAmelCase : Tuple =self.sp_model.PieceToId(__a)
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def lowerCAmelCase ( self , snake_case) -> Union[str, Any]:
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset)
def lowerCAmelCase ( self , snake_case) -> int:
'''simple docstring'''
_UpperCAmelCase : Optional[int] =''.join(__a).replace(__a , ' ').strip()
return out_string
def lowerCAmelCase ( self , snake_case , snake_case = None) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(__a):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
_UpperCAmelCase : Optional[Any] =os.path.join(
__a , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
if os.path.abspath(self.vocab_file) != os.path.abspath(__a) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , __a)
elif not os.path.isfile(self.vocab_file):
with open(__a , 'wb') as fi:
_UpperCAmelCase : Tuple =self.sp_model.serialized_model_proto()
fi.write(__a)
return (out_vocab_file,)
def lowerCAmelCase ( self , snake_case , snake_case = "en_XX" , snake_case = None , snake_case = "ro_RO" , **snake_case , ) -> BatchEncoding:
'''simple docstring'''
_UpperCAmelCase : Any =src_lang
_UpperCAmelCase : int =tgt_lang
return super().prepare_seqaseq_batch(__a , __a , **__a)
def lowerCAmelCase ( self) -> str:
'''simple docstring'''
return self.set_src_lang_special_tokens(self.src_lang)
def lowerCAmelCase ( self) -> str:
'''simple docstring'''
return self.set_tgt_lang_special_tokens(self.tgt_lang)
def lowerCAmelCase ( self , snake_case) -> None:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] =self.lang_code_to_id[src_lang]
_UpperCAmelCase : Optional[Any] =[]
_UpperCAmelCase : Dict =[self.eos_token_id, self.cur_lang_code]
def lowerCAmelCase ( self , snake_case) -> None:
'''simple docstring'''
_UpperCAmelCase : List[str] =self.lang_code_to_id[lang]
_UpperCAmelCase : Tuple =[]
_UpperCAmelCase : Union[str, Any] =[self.eos_token_id, self.cur_lang_code]
| 446 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_a = logging.get_logger(__name__)
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = ['pixel_values']
def __init__( self , __a = True , __a = None , __a = PILImageResampling.BICUBIC , __a = True , __a = True , __a = 1 / 2_55 , __a = None , __a = True , __a = None , __a = None , **__a , ) -> None:
'''simple docstring'''
super().__init__(**__a)
_UpperCamelCase = size if size is not None else {'''height''': 2_24, '''width''': 2_24}
_UpperCamelCase = get_size_dict(__a)
_UpperCamelCase = crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
_UpperCamelCase = get_size_dict(__a , default_to_square=__a , param_name='''crop_size''')
_UpperCamelCase = do_resize
_UpperCamelCase = do_rescale
_UpperCamelCase = do_normalize
_UpperCamelCase = do_center_crop
_UpperCamelCase = crop_size
_UpperCamelCase = size
_UpperCamelCase = resample
_UpperCamelCase = rescale_factor
_UpperCamelCase = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
_UpperCamelCase = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCAmelCase ( self , __a , __a , __a = PILImageResampling.BILINEAR , __a = None , **__a , ) -> np.ndarray:
'''simple docstring'''
_UpperCamelCase = get_size_dict(__a)
if "shortest_edge" in size:
_UpperCamelCase = get_resize_output_image_size(__a , size=size['''shortest_edge'''] , default_to_square=__a)
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
_UpperCamelCase = (size['''height'''], size['''width'''])
else:
raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''')
return resize(__a , size=__a , resample=__a , data_format=__a , **__a)
def UpperCAmelCase ( self , __a , __a , __a = None , **__a , ) -> np.ndarray:
'''simple docstring'''
_UpperCamelCase = get_size_dict(__a)
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''')
return center_crop(__a , size=(size['''height'''], size['''width''']) , data_format=__a , **__a)
def UpperCAmelCase ( self , __a , __a , __a = None , **__a) -> np.ndarray:
'''simple docstring'''
return rescale(__a , scale=__a , data_format=__a , **__a)
def UpperCAmelCase ( self , __a , __a , __a , __a = None , **__a , ) -> np.ndarray:
'''simple docstring'''
return normalize(__a , mean=__a , std=__a , data_format=__a , **__a)
def UpperCAmelCase ( self , __a , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = ChannelDimension.FIRST , **__a , ) -> BatchFeature:
'''simple docstring'''
_UpperCamelCase = do_resize if do_resize is not None else self.do_resize
_UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
_UpperCamelCase = crop_size if crop_size is not None else self.crop_size
_UpperCamelCase = get_size_dict(__a , param_name='''crop_size''' , default_to_square=__a)
_UpperCamelCase = resample if resample is not None else self.resample
_UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCamelCase = image_mean if image_mean is not None else self.image_mean
_UpperCamelCase = image_std if image_std is not None else self.image_std
_UpperCamelCase = size if size is not None else self.size
_UpperCamelCase = get_size_dict(__a)
if not is_batched(__a):
_UpperCamelCase = [images]
if not valid_images(__a):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''')
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''')
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''')
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''')
# All transformations expect numpy arrays.
_UpperCamelCase = [to_numpy_array(__a) for image in images]
if do_resize:
_UpperCamelCase = [self.resize(image=__a , size=__a , resample=__a) for image in images]
if do_center_crop:
_UpperCamelCase = [self.center_crop(image=__a , size=__a) for image in images]
if do_rescale:
_UpperCamelCase = [self.rescale(image=__a , scale=__a) for image in images]
if do_normalize:
_UpperCamelCase = [self.normalize(image=__a , mean=__a , std=__a) for image in images]
_UpperCamelCase = [to_channel_dimension_format(__a , __a) for image in images]
_UpperCamelCase = {'''pixel_values''': images}
return BatchFeature(data=__a , tensor_type=__a)
| 19 | 0 |
class lowerCamelCase :
"""simple docstring"""
def __init__( self : List[Any], _UpperCAmelCase : List[Any], _UpperCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = name
SCREAMING_SNAKE_CASE__ : Optional[int] = val
def __str__( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
return F'''{self.__class__.__name__}({self.name}, {self.val})'''
def __lt__( self : List[Any], _UpperCAmelCase : int ) -> Union[str, Any]:
"""simple docstring"""
return self.val < other.val
class lowerCamelCase :
"""simple docstring"""
def __init__( self : int, _UpperCAmelCase : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {}
SCREAMING_SNAKE_CASE__ : Optional[int] = {}
SCREAMING_SNAKE_CASE__ : Tuple = self.build_heap(__a )
def __getitem__( self : List[str], _UpperCAmelCase : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return self.get_value(__a )
def A_ ( self : Union[str, Any], _UpperCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
return (idx - 1) // 2
def A_ ( self : Optional[Any], _UpperCAmelCase : Optional[Any] ) -> Any:
"""simple docstring"""
return idx * 2 + 1
def A_ ( self : Any, _UpperCAmelCase : str ) -> int:
"""simple docstring"""
return idx * 2 + 2
def A_ ( self : int, _UpperCAmelCase : int ) -> Tuple:
"""simple docstring"""
return self.heap_dict[key]
def A_ ( self : Any, _UpperCAmelCase : Tuple ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = len(__a ) - 1
SCREAMING_SNAKE_CASE__ : str = self.get_parent_idx(__a )
for idx, i in enumerate(__a ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = idx
SCREAMING_SNAKE_CASE__ : str = i.val
for i in range(__a, -1, -1 ):
self.sift_down(__a, __a )
return array
def A_ ( self : Union[str, Any], _UpperCAmelCase : Union[str, Any], _UpperCAmelCase : List[Any] ) -> int:
"""simple docstring"""
while True:
SCREAMING_SNAKE_CASE__ : int = self.get_left_child_idx(__a ) # noqa: E741
SCREAMING_SNAKE_CASE__ : Tuple = self.get_right_child_idx(__a )
SCREAMING_SNAKE_CASE__ : Dict = idx
if l < len(__a ) and array[l] < array[idx]:
SCREAMING_SNAKE_CASE__ : str = l
if r < len(__a ) and array[r] < array[smallest]:
SCREAMING_SNAKE_CASE__ : int = r
if smallest != idx:
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Dict = array[smallest], array[idx]
(
(
SCREAMING_SNAKE_CASE__
) ,(
SCREAMING_SNAKE_CASE__
) ,
) : Union[str, Any] = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
SCREAMING_SNAKE_CASE__ : List[str] = smallest
else:
break
def A_ ( self : int, _UpperCAmelCase : Optional[int] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_parent_idx(__a )
while p >= 0 and self.heap[p] > self.heap[idx]:
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Optional[int] = self.heap[idx], self.heap[p]
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Dict = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
SCREAMING_SNAKE_CASE__ : Any = p
SCREAMING_SNAKE_CASE__ : Tuple = self.get_parent_idx(__a )
def A_ ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return self.heap[0]
def A_ ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Optional[Any] = self.heap[-1], self.heap[0]
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Tuple = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
SCREAMING_SNAKE_CASE__ : List[Any] = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0, self.heap )
return x
def A_ ( self : Any, _UpperCAmelCase : List[Any] ) -> Tuple:
"""simple docstring"""
self.heap.append(__a )
SCREAMING_SNAKE_CASE__ : int = len(self.heap ) - 1
SCREAMING_SNAKE_CASE__ : Optional[Any] = node.val
self.sift_up(len(self.heap ) - 1 )
def A_ ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return len(self.heap ) == 0
def A_ ( self : Dict, _UpperCAmelCase : Optional[Any], _UpperCAmelCase : int ) -> List[Any]:
"""simple docstring"""
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
SCREAMING_SNAKE_CASE__ : Tuple = new_value
SCREAMING_SNAKE_CASE__ : List[Any] = new_value
self.sift_up(self.idx_of_element[node] )
_lowerCamelCase : str = Node('''R''', -1)
_lowerCamelCase : Optional[int] = Node('''B''', 6)
_lowerCamelCase : int = Node('''A''', 3)
_lowerCamelCase : str = Node('''X''', 1)
_lowerCamelCase : List[Any] = Node('''E''', 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
_lowerCamelCase : Dict = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
print(i)
print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -1_7)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 663 |
"""simple docstring"""
# Imports
import numpy as np
class _UpperCAmelCase:
def __init__( self , __a=None , __a=None , __a=None , __a=None , __a=None) -> Dict:
'''simple docstring'''
self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a)
def UpperCAmelCase ( self , __a=None , __a=None , __a=None , __a=None , __a=None) -> Dict:
'''simple docstring'''
if red is not None:
_UpperCamelCase = red
if green is not None:
_UpperCamelCase = green
if blue is not None:
_UpperCamelCase = blue
if red_edge is not None:
_UpperCamelCase = red_edge
if nir is not None:
_UpperCamelCase = nir
return True
def UpperCAmelCase ( self , __a="" , __a=None , __a=None , __a=None , __a=None , __a=None) -> List[str]:
'''simple docstring'''
self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a)
_UpperCamelCase = {
'''ARVI2''': self.arvaa,
'''CCCI''': self.ccci,
'''CVI''': self.cvi,
'''GLI''': self.gli,
'''NDVI''': self.ndvi,
'''BNDVI''': self.bndvi,
'''redEdgeNDVI''': self.red_edge_ndvi,
'''GNDVI''': self.gndvi,
'''GBNDVI''': self.gbndvi,
'''GRNDVI''': self.grndvi,
'''RBNDVI''': self.rbndvi,
'''PNDVI''': self.pndvi,
'''ATSAVI''': self.atsavi,
'''BWDRVI''': self.bwdrvi,
'''CIgreen''': self.ci_green,
'''CIrededge''': self.ci_rededge,
'''CI''': self.ci,
'''CTVI''': self.ctvi,
'''GDVI''': self.gdvi,
'''EVI''': self.evi,
'''GEMI''': self.gemi,
'''GOSAVI''': self.gosavi,
'''GSAVI''': self.gsavi,
'''Hue''': self.hue,
'''IVI''': self.ivi,
'''IPVI''': self.ipvi,
'''I''': self.i,
'''RVI''': self.rvi,
'''MRVI''': self.mrvi,
'''MSAVI''': self.m_savi,
'''NormG''': self.norm_g,
'''NormNIR''': self.norm_nir,
'''NormR''': self.norm_r,
'''NGRDI''': self.ngrdi,
'''RI''': self.ri,
'''S''': self.s,
'''IF''': self._if,
'''DVI''': self.dvi,
'''TVI''': self.tvi,
'''NDRE''': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('''Index not in the list!''')
return False
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return self.nir * (self.red / (self.green**2))
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
return (self.nir - self.red) / (self.nir + self.red)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
return (self.nir - self.blue) / (self.nir + self.blue)
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return (self.redEdge - self.red) / (self.redEdge + self.red)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return (self.nir - self.green) / (self.nir + self.green)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def UpperCAmelCase ( self , __a=0.08 , __a=1.22 , __a=0.03) -> Optional[Any]:
'''simple docstring'''
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
return (self.nir / self.green) - 1
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return (self.nir / self.redEdge) - 1
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
return (self.red - self.blue) / self.red
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5))) * (abs(ndvi + 0.5) ** (1 / 2))
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return self.nir - self.green
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)
def UpperCAmelCase ( self , __a=0.16) -> Optional[Any]:
'''simple docstring'''
return (self.nir - self.green) / (self.nir + self.green + y)
def UpperCAmelCase ( self , __a=0.5) -> Dict:
'''simple docstring'''
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue))
def UpperCAmelCase ( self , __a=None , __a=None) -> Any:
'''simple docstring'''
return (self.nir - b) / (a * self.red)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
return (self.red + self.green + self.blue) / 30.5
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return self.nir / self.red
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return (self.rvi() - 1) / (self.rvi() + 1)
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return self.green / (self.nir + self.red + self.green)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
return self.nir / (self.nir + self.red + self.green)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return self.red / (self.nir + self.red + self.green)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return (self.green - self.red) / (self.green + self.red)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
return (self.red - self.green) / (self.red + self.green)
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = np.max([np.max(self.red), np.max(self.green), np.max(self.blue)])
_UpperCamelCase = np.min([np.min(self.red), np.min(self.green), np.min(self.blue)])
return (max_value - min_value) / max_value
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return self.nir / self.red
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return (self.ndvi() + 0.5) ** (1 / 2)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 19 | 0 |
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ ) -> int:
return int(input_a == input_a == 0 )
def lowerCamelCase_() -> None:
print("Truth Table of NOR Gate:" )
print("| Input 1 | Input 2 | Output |" )
print(F'| 0 | 0 | {nor_gate(0 , 0 )} |' )
print(F'| 0 | 1 | {nor_gate(0 , 1 )} |' )
print(F'| 1 | 0 | {nor_gate(1 , 0 )} |' )
print(F'| 1 | 1 | {nor_gate(1 , 1 )} |' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 323 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class _UpperCAmelCase:
def __init__( self , __a , __a=13 , __a=64 , __a=2 , __a=3 , __a=True , __a=True , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=10 , __a=0.02 , __a=[1, 16, 4, 4] , __a=None , ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = image_size
_UpperCamelCase = patch_size
_UpperCamelCase = num_channels
_UpperCamelCase = is_training
_UpperCamelCase = use_labels
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = scope
_UpperCamelCase = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
_UpperCamelCase = (self.image_size // 32) ** 2
_UpperCamelCase = num_patches + 1
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_UpperCamelCase = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [4, 8, 16, 32],
'''num_groups''': 2,
}
return ViTHybridConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__a , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=__a , )
def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = ViTHybridModel(config=__a)
model.to(__a)
model.eval()
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCAmelCase ( self , __a , __a , __a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = self.type_sequence_label_size
_UpperCamelCase = ViTHybridForImageClassification(__a)
model.to(__a)
model.eval()
_UpperCamelCase = model(__a , labels=__a)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = self.prepare_config_and_inputs()
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = config_and_inputs
_UpperCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase__ = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
lowercase__ = (
{'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
if is_torch_available()
else {}
)
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = ViTHybridModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37)
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''')
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
pass
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(__a)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
_UpperCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__a , nn.Linear))
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(__a)
_UpperCamelCase = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCamelCase = [*signature.parameters.keys()]
_UpperCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __a)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__a)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCamelCase = _config_zero_init(__a)
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(config=__a)
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
_UpperCamelCase = [F'''{name}.{key}''' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@slow
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = ViTHybridModel.from_pretrained(__a)
self.assertIsNotNone(__a)
def lowerCamelCase__ ( ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class _UpperCAmelCase( unittest.TestCase ):
@cached_property
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
if is_vision_available()
else None
)
@slow
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
__a)
_UpperCamelCase = self.default_image_processor
_UpperCamelCase = prepare_img()
_UpperCamelCase = image_processor(images=__a , return_tensors='''pt''').to(__a)
# forward pass
with torch.no_grad():
_UpperCamelCase = model(**__a)
# verify the logits
_UpperCamelCase = torch.Size((1, 10_00))
self.assertEqual(outputs.logits.shape , __a)
_UpperCamelCase = torch.tensor([-1.9090, -0.4993, -0.2389]).to(__a)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __a , atol=1e-4))
@slow
@require_accelerate
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''')
_UpperCamelCase = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''' , device_map='''auto''')
_UpperCamelCase = prepare_img()
_UpperCamelCase = image_processor(images=__a , return_tensors='''pt''')
_UpperCamelCase = model(**__a)
_UpperCamelCase = outputs.logits
# model predicts one of the 1000 ImageNet classes
_UpperCamelCase = logits.argmax(-1).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] , '''tabby, tabby cat''')
| 19 | 0 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__a: Optional[Any] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.14.0""", """To fix: pip install -r examples/pytorch/audio-classification/requirements.txt""")
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 1_6000 ):
lowercase__ : Any = int(round(sample_rate * max_length ) )
if len(__snake_case ) <= sample_length:
return wav
lowercase__ : List[Any] = randint(0 , len(__snake_case ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class UpperCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE = field(default=a__ , metadata={"help": "Name of a dataset from the datasets package"} )
SCREAMING_SNAKE_CASE = field(
default=a__ , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
SCREAMING_SNAKE_CASE = field(
default=a__ , metadata={"help": "A file containing the training audio paths and labels."} )
SCREAMING_SNAKE_CASE = field(
default=a__ , metadata={"help": "A file containing the validation audio paths and labels."} )
SCREAMING_SNAKE_CASE = field(
default="train" , metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to \'train\'"
} , )
SCREAMING_SNAKE_CASE = field(
default="validation" , metadata={
"help": (
"The name of the training data set split to use (via the datasets library). Defaults to \'validation\'"
)
} , )
SCREAMING_SNAKE_CASE = field(
default="audio" , metadata={"help": "The name of the dataset column containing the audio data. Defaults to \'audio\'"} , )
SCREAMING_SNAKE_CASE = field(
default="label" , metadata={"help": "The name of the dataset column containing the labels. Defaults to \'label\'"} )
SCREAMING_SNAKE_CASE = field(
default=a__ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
SCREAMING_SNAKE_CASE = field(
default=a__ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
} , )
SCREAMING_SNAKE_CASE = field(
default=2_0 , metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."} , )
@dataclass
class UpperCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE = field(
default="facebook/wav2vec2-base" , metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} , )
SCREAMING_SNAKE_CASE = field(
default=a__ , metadata={"help": "Pretrained config name or path if not the same as model_name"} )
SCREAMING_SNAKE_CASE = field(
default=a__ , metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"} )
SCREAMING_SNAKE_CASE = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
SCREAMING_SNAKE_CASE = field(
default=a__ , metadata={"help": "Name or path of preprocessor config."} )
SCREAMING_SNAKE_CASE = field(
default=a__ , metadata={"help": "Whether to freeze the feature encoder layers of the model."} )
SCREAMING_SNAKE_CASE = field(
default=a__ , metadata={"help": "Whether to generate an attention mask in the feature extractor."} )
SCREAMING_SNAKE_CASE = field(
default=a__ , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
SCREAMING_SNAKE_CASE = field(
default=a__ , metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
SCREAMING_SNAKE_CASE = field(
default=a__ , metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."} , )
def _lowerCAmelCase( self ) -> Union[str, Any]:
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''will be removed in a future version. Use `--freeze_feature_encoder`'''
'''instead. Setting `freeze_feature_encoder==True`.''' , __a , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''should not be used in combination with `--freeze_feature_encoder`.'''
'''Only make use of `--freeze_feature_encoder`.''' )
def __UpperCamelCase ( ):
lowercase__ : List[str] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase__ , lowercase__ , lowercase__ : int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase__ , lowercase__ , lowercase__ : List[Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_audio_classification''' , __snake_case , __snake_case )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowercase__ : List[Any] = training_args.get_process_log_level()
logger.setLevel(__snake_case )
transformers.utils.logging.set_verbosity(__snake_case )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} """
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
lowercase__ : Optional[int] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase__ : Optional[int] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to train from scratch.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Initialize our dataset and prepare it for the audio classification task.
lowercase__ : Optional[Any] = DatasetDict()
lowercase__ : Optional[int] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.train_split_name , use_auth_token=True if model_args.use_auth_token else None , )
lowercase__ : Any = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=data_args.eval_split_name , use_auth_token=True if model_args.use_auth_token else None , )
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"""--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. """
'''Make sure to set `--audio_column_name` to the correct audio column - one of '''
F"""{', '.join(raw_datasets['train'].column_names )}.""" )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
F"""--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. """
'''Make sure to set `--label_column_name` to the correct text column - one of '''
F"""{', '.join(raw_datasets['train'].column_names )}.""" )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
lowercase__ : List[Any] = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path , return_attention_mask=model_args.attention_mask , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
lowercase__ : str = raw_datasets.cast_column(
data_args.audio_column_name , datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
lowercase__ : Tuple = feature_extractor.model_input_names[0]
def train_transforms(UpperCAmelCase ):
lowercase__ : Optional[int] = []
for audio in batch[data_args.audio_column_name]:
lowercase__ : str = random_subsample(
audio['''array'''] , max_length=data_args.max_length_seconds , sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(__snake_case )
lowercase__ : Tuple = feature_extractor(__snake_case , sampling_rate=feature_extractor.sampling_rate )
lowercase__ : Dict = {model_input_name: inputs.get(__snake_case )}
lowercase__ : List[Any] = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(UpperCAmelCase ):
lowercase__ : Optional[int] = [audio['''array'''] for audio in batch[data_args.audio_column_name]]
lowercase__ : Tuple = feature_extractor(__snake_case , sampling_rate=feature_extractor.sampling_rate )
lowercase__ : Optional[int] = {model_input_name: inputs.get(__snake_case )}
lowercase__ : Optional[int] = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
lowercase__ : str = raw_datasets['''train'''].features[data_args.label_column_name].names
lowercase__ , lowercase__ : Any = {}, {}
for i, label in enumerate(__snake_case ):
lowercase__ : int = str(__snake_case )
lowercase__ : List[str] = label
# Load the accuracy metric from the datasets package
lowercase__ : Any = evaluate.load('''accuracy''' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(UpperCAmelCase ):
lowercase__ : List[str] = np.argmax(eval_pred.predictions , axis=1 )
return metric.compute(predictions=__snake_case , references=eval_pred.label_ids )
lowercase__ : List[Any] = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path , num_labels=len(__snake_case ) , labelaid=__snake_case , idalabel=__snake_case , finetuning_task='''audio-classification''' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowercase__ : Optional[int] = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__snake_case , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
lowercase__ : Any = (
raw_datasets['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(__snake_case , output_all_columns=__snake_case )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
lowercase__ : Dict = (
raw_datasets['''eval'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(__snake_case , output_all_columns=__snake_case )
# Initialize our trainer
lowercase__ : str = Trainer(
model=__snake_case , args=__snake_case , train_dataset=raw_datasets['''train'''] if training_args.do_train else None , eval_dataset=raw_datasets['''eval'''] if training_args.do_eval else None , compute_metrics=__snake_case , tokenizer=__snake_case , )
# Training
if training_args.do_train:
lowercase__ : int = None
if training_args.resume_from_checkpoint is not None:
lowercase__ : Any = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase__ : Optional[Any] = last_checkpoint
lowercase__ : int = trainer.train(resume_from_checkpoint=__snake_case )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
lowercase__ : Dict = trainer.evaluate()
trainer.log_metrics('''eval''' , __snake_case )
trainer.save_metrics('''eval''' , __snake_case )
# Write model card and (optionally) push to hub
lowercase__ : Optional[int] = {
'''finetuned_from''': model_args.model_name_or_path,
'''tasks''': '''audio-classification''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''audio-classification'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__snake_case )
else:
trainer.create_model_card(**__snake_case )
if __name__ == "__main__":
main()
| 152 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = ['vqvae']
def __init__( self , __a , __a , __a , __a , ) -> List[str]:
'''simple docstring'''
super().__init__()
self.register_modules(unet=__a , scheduler=__a , mel=__a , vqvae=__a)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return 50 if isinstance(self.scheduler , __a) else 10_00
@torch.no_grad()
def __call__( self , __a = 1 , __a = None , __a = None , __a = 0 , __a = 0 , __a = None , __a = None , __a = 0 , __a = 0 , __a = None , __a = 0 , __a = None , __a = None , __a=True , ) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
'''simple docstring'''
_UpperCamelCase = steps or self.get_default_steps()
self.scheduler.set_timesteps(__a)
_UpperCamelCase = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size) == int:
_UpperCamelCase = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
_UpperCamelCase = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=__a , device=self.device , )
_UpperCamelCase = noise
_UpperCamelCase = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(__a , __a)
_UpperCamelCase = self.mel.audio_slice_to_image(__a)
_UpperCamelCase = np.frombuffer(input_image.tobytes() , dtype='''uint8''').reshape(
(input_image.height, input_image.width))
_UpperCamelCase = (input_image / 2_55) * 2 - 1
_UpperCamelCase = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float).to(self.device)
if self.vqvae is not None:
_UpperCamelCase = self.vqvae.encode(torch.unsqueeze(__a , 0)).latent_dist.sample(
generator=__a)[0]
_UpperCamelCase = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
_UpperCamelCase = self.scheduler.add_noise(__a , __a , self.scheduler.timesteps[start_step - 1])
_UpperCamelCase = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
_UpperCamelCase = int(mask_start_secs * pixels_per_second)
_UpperCamelCase = int(mask_end_secs * pixels_per_second)
_UpperCamelCase = self.scheduler.add_noise(__a , __a , torch.tensor(self.scheduler.timesteps[start_step:]))
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
if isinstance(self.unet , __a):
_UpperCamelCase = self.unet(__a , __a , __a)['''sample''']
else:
_UpperCamelCase = self.unet(__a , __a)['''sample''']
if isinstance(self.scheduler , __a):
_UpperCamelCase = self.scheduler.step(
model_output=__a , timestep=__a , sample=__a , eta=__a , generator=__a , )['''prev_sample''']
else:
_UpperCamelCase = self.scheduler.step(
model_output=__a , timestep=__a , sample=__a , generator=__a , )['''prev_sample''']
if mask is not None:
if mask_start > 0:
_UpperCamelCase = mask[:, step, :, :mask_start]
if mask_end > 0:
_UpperCamelCase = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
_UpperCamelCase = 1 / self.vqvae.config.scaling_factor * images
_UpperCamelCase = self.vqvae.decode(__a)['''sample''']
_UpperCamelCase = (images / 2 + 0.5).clamp(0 , 1)
_UpperCamelCase = images.cpu().permute(0 , 2 , 3 , 1).numpy()
_UpperCamelCase = (images * 2_55).round().astype('''uint8''')
_UpperCamelCase = list(
(Image.fromarray(_[:, :, 0]) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(__a , mode='''RGB''').convert('''L''') for _ in images))
_UpperCamelCase = [self.mel.image_to_audio(__a) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(__a)[:, np.newaxis, :]) , **ImagePipelineOutput(__a))
@torch.no_grad()
def UpperCAmelCase ( self , __a , __a = 50) -> np.ndarray:
'''simple docstring'''
assert isinstance(self.scheduler , __a)
self.scheduler.set_timesteps(__a)
_UpperCamelCase = np.array(
[np.frombuffer(image.tobytes() , dtype='''uint8''').reshape((1, image.height, image.width)) for image in images])
_UpperCamelCase = (sample / 2_55) * 2 - 1
_UpperCamelCase = torch.Tensor(__a).to(self.device)
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,))):
_UpperCamelCase = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
_UpperCamelCase = self.scheduler.alphas_cumprod[t]
_UpperCamelCase = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
_UpperCamelCase = 1 - alpha_prod_t
_UpperCamelCase = self.unet(__a , __a)['''sample''']
_UpperCamelCase = (1 - alpha_prod_t_prev) ** 0.5 * model_output
_UpperCamelCase = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
_UpperCamelCase = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def UpperCAmelCase ( __a , __a , __a) -> torch.Tensor:
'''simple docstring'''
_UpperCamelCase = acos(torch.dot(torch.flatten(__a) , torch.flatten(__a)) / torch.norm(__a) / torch.norm(__a))
return sin((1 - alpha) * theta) * xa / sin(__a) + sin(alpha * theta) * xa / sin(__a)
| 19 | 0 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
_snake_case = 1E-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , __A , __A=16 , __A=13 , __A=7 , __A=14 , __A=10 , __A=19 , __A=5 , __A=4 , __A=True , __A=16 , __A=2 , __A=4 , __A=4 , __A="gelu" , __A=0.1 , __A=0.1 , __A=[1, 2, 3, 4, 5] , __A=25 , __A=5 , ):
"""simple docstring"""
lowerCamelCase : str = d_model
lowerCamelCase : Any = parent
lowerCamelCase : Any = batch_size
lowerCamelCase : Union[str, Any] = prediction_length
lowerCamelCase : Any = context_length
lowerCamelCase : Tuple = cardinality
lowerCamelCase : int = num_time_features
lowerCamelCase : Optional[int] = lags_sequence
lowerCamelCase : List[str] = embedding_dimension
lowerCamelCase : Optional[Any] = is_training
lowerCamelCase : str = hidden_size
lowerCamelCase : Tuple = num_hidden_layers
lowerCamelCase : List[Any] = num_attention_heads
lowerCamelCase : Optional[int] = intermediate_size
lowerCamelCase : Any = hidden_act
lowerCamelCase : str = hidden_dropout_prob
lowerCamelCase : Optional[int] = attention_probs_dropout_prob
lowerCamelCase : int = context_length
lowerCamelCase : Union[str, Any] = prediction_length + label_length
lowerCamelCase : Optional[int] = label_length
lowerCamelCase : Optional[Any] = moving_average
lowerCamelCase : List[Any] = autocorrelation_factor
def _snake_case ( self ):
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def _snake_case ( self , __A ):
"""simple docstring"""
lowerCamelCase : Optional[int] = config.context_length + max(config.lags_sequence )
lowerCamelCase : Tuple = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
lowerCamelCase : Dict = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
lowerCamelCase : Union[str, Any] = floats_tensor([self.batch_size, _past_length] )
lowerCamelCase : Optional[int] = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
lowerCamelCase : int = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
lowerCamelCase : str = floats_tensor([self.batch_size, config.prediction_length] )
lowerCamelCase : Tuple = {
"past_values": past_values,
"static_categorical_features": static_categorical_features,
"past_time_features": past_time_features,
"past_observed_mask": past_observed_mask,
"future_time_features": future_time_features,
"future_values": future_values,
}
return inputs_dict
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Optional[int] = self.get_config()
lowerCamelCase : List[str] = self.prepare_autoformer_inputs_dict(__a )
return config, inputs_dict
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase : Optional[int] = self.prepare_config_and_inputs()
return config, inputs_dict
def _snake_case ( self , __A , __A ):
"""simple docstring"""
lowerCamelCase : Any = AutoformerModel(config=__a ).to(__a ).eval()
lowerCamelCase : Optional[int] = model(**__a )
lowerCamelCase : int = outputs.encoder_last_hidden_state
lowerCamelCase : List[Any] = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase : List[str] = model.get_encoder()
encoder.save_pretrained(__a )
lowerCamelCase : int = AutoformerEncoder.from_pretrained(__a ).to(__a )
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : List[str] = model.create_network_inputs(**__a )
lowerCamelCase , lowerCamelCase : Optional[Any] = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
lowerCamelCase : Any = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
lowerCamelCase : Optional[Any] = encoder(inputs_embeds=__a )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
lowerCamelCase : Tuple = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
lowerCamelCase : Optional[int] = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
lowerCamelCase : Dict = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
lowerCamelCase : List[str] = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase : Dict = model.get_decoder()
decoder.save_pretrained(__a )
lowerCamelCase : Union[str, Any] = AutoformerDecoder.from_pretrained(__a ).to(__a )
lowerCamelCase : Optional[int] = decoder(
trend=__a , inputs_embeds=__a , encoder_hidden_states=__a , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class UpperCAmelCase_ ( UpperCamelCase , UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
__A : Tuple = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
__A : Any = (AutoformerForPrediction,) if is_torch_available() else ()
__A : int = {"feature-extraction": AutoformerModel} if is_torch_available() else {}
__A : int = False
__A : Optional[int] = False
__A : Tuple = False
__A : str = False
__A : str = False
__A : Tuple = False
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : str = AutoformerModelTester(self )
lowerCamelCase : int = ConfigTester(self , config_class=__a , has_text_modality=__a )
def _snake_case ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
lowerCamelCase : str = model_class(__a )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__a )
lowerCamelCase , lowerCamelCase : Tuple = model_class.from_pretrained(__a , output_loading_info=__a )
self.assertEqual(info["missing_keys"] , [] )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*__a )
@unittest.skip(reason="Model has no tokens embeddings" )
def _snake_case ( self ):
"""simple docstring"""
pass
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = inspect.signature(getattr(__a , "forward" ) )
# The main input is the name of the argument after `self`
lowerCamelCase : str = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , __a )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase : Tuple = model_class(__a )
lowerCamelCase : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase : Dict = [*signature.parameters.keys()]
lowerCamelCase : Dict = [
"past_values",
"past_time_features",
"past_observed_mask",
"static_categorical_features",
"static_real_features",
"future_values",
"future_time_features",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("future_observed_mask" )
expected_arg_names.extend(
[
"decoder_attention_mask",
"head_mask",
"decoder_head_mask",
"cross_attn_head_mask",
"encoder_outputs",
"past_key_values",
"output_hidden_states",
"output_attentions",
"use_cache",
"return_dict",
] )
self.assertListEqual(arg_names[: len(__a )] , __a )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase : Union[str, Any] = True
lowerCamelCase : str = getattr(self.model_tester , "seq_length" , __a )
lowerCamelCase : str = getattr(self.model_tester , "decoder_seq_length" , __a )
lowerCamelCase : List[Any] = getattr(self.model_tester , "encoder_seq_length" , __a )
lowerCamelCase : Any = getattr(self.model_tester , "d_model" , __a )
lowerCamelCase : Any = getattr(self.model_tester , "num_attention_heads" , __a )
lowerCamelCase : Any = d_model // num_attention_heads
for model_class in self.all_model_classes:
lowerCamelCase : List[str] = True
lowerCamelCase : int = False
lowerCamelCase : Union[str, Any] = True
lowerCamelCase : List[str] = model_class(__a )
model.to(__a )
model.eval()
with torch.no_grad():
lowerCamelCase : str = model(**self._prepare_for_class(__a , __a ) )
lowerCamelCase : List[str] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCamelCase : int = True
lowerCamelCase : Tuple = model_class(__a )
model.to(__a )
model.eval()
with torch.no_grad():
lowerCamelCase : Any = model(**self._prepare_for_class(__a , __a ) )
lowerCamelCase : Union[str, Any] = outputs.encoder_attentions
self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
lowerCamelCase : Optional[Any] = len(__a )
lowerCamelCase : List[str] = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(__a , __a )
# decoder attentions
lowerCamelCase : Union[str, Any] = outputs.decoder_attentions
self.assertIsInstance(__a , (list, tuple) )
self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
lowerCamelCase : str = outputs.cross_attentions
self.assertIsInstance(__a , (list, tuple) )
self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
lowerCamelCase : str = True
lowerCamelCase : Any = True
lowerCamelCase : Optional[int] = model_class(__a )
model.to(__a )
model.eval()
with torch.no_grad():
lowerCamelCase : Any = model(**self._prepare_for_class(__a , __a ) )
self.assertEqual(out_len + 2 , len(__a ) )
lowerCamelCase : Union[str, Any] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def _snake_case ( self ):
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def lowercase_( SCREAMING_SNAKE_CASE_="train-batch.pt" ):
'''simple docstring'''
lowerCamelCase : Any = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" , filename=__snake_case , repo_type="dataset" )
lowerCamelCase : str = torch.load(__snake_case , map_location=__snake_case )
return batch
@require_torch
@slow
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Any = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(__a )
lowerCamelCase : Optional[int] = prepare_batch()
with torch.no_grad():
lowerCamelCase : Union[str, Any] = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , future_values=batch["future_values"] , future_time_features=batch["future_time_features"] , )[0]
lowerCamelCase : int = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , __a )
lowerCamelCase : List[str] = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=__a )
self.assertTrue(torch.allclose(output[0, :3, :3] , __a , atol=__a ) )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Optional[int] = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(__a )
lowerCamelCase : List[str] = prepare_batch("val-batch.pt" )
with torch.no_grad():
lowerCamelCase : Any = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , ).encoder_last_hidden_state
lowerCamelCase : Dict = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , __a )
lowerCamelCase : Tuple = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=__a )
self.assertTrue(torch.allclose(output[0, :3, :3] , __a , atol=__a ) )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Dict = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(__a )
lowerCamelCase : str = prepare_batch("val-batch.pt" )
with torch.no_grad():
lowerCamelCase : Any = model.generate(
static_categorical_features=batch["static_categorical_features"] , past_time_features=batch["past_time_features"] , past_values=batch["past_values"] , future_time_features=batch["future_time_features"] , past_observed_mask=batch["past_observed_mask"] , )
lowerCamelCase : List[Any] = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , __a )
lowerCamelCase : int = torch.tensor([3130.6763, 4056.5293, 7053.0786] , device=__a )
lowerCamelCase : Optional[int] = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , __a , rtol=1e-1 ) )
| 340 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_a = logging.get_logger(__name__)
_a = {
"""facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = 'detr'
lowercase__ = ['past_key_values']
lowercase__ = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , __a=True , __a=None , __a=3 , __a=1_00 , __a=6 , __a=20_48 , __a=8 , __a=6 , __a=20_48 , __a=8 , __a=0.0 , __a=0.0 , __a=True , __a="relu" , __a=2_56 , __a=0.1 , __a=0.0 , __a=0.0 , __a=0.02 , __a=1.0 , __a=False , __a="sine" , __a="resnet50" , __a=True , __a=False , __a=1 , __a=5 , __a=2 , __a=1 , __a=1 , __a=5 , __a=2 , __a=0.1 , **__a , ) -> int:
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
_UpperCamelCase = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
elif isinstance(__a , __a):
_UpperCamelCase = backbone_config.get('''model_type''')
_UpperCamelCase = CONFIG_MAPPING[backbone_model_type]
_UpperCamelCase = config_class.from_dict(__a)
# set timm attributes to None
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None, None, None
_UpperCamelCase = use_timm_backbone
_UpperCamelCase = backbone_config
_UpperCamelCase = num_channels
_UpperCamelCase = num_queries
_UpperCamelCase = d_model
_UpperCamelCase = encoder_ffn_dim
_UpperCamelCase = encoder_layers
_UpperCamelCase = encoder_attention_heads
_UpperCamelCase = decoder_ffn_dim
_UpperCamelCase = decoder_layers
_UpperCamelCase = decoder_attention_heads
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = activation_function
_UpperCamelCase = init_std
_UpperCamelCase = init_xavier_std
_UpperCamelCase = encoder_layerdrop
_UpperCamelCase = decoder_layerdrop
_UpperCamelCase = encoder_layers
_UpperCamelCase = auxiliary_loss
_UpperCamelCase = position_embedding_type
_UpperCamelCase = backbone
_UpperCamelCase = use_pretrained_backbone
_UpperCamelCase = dilation
# Hungarian matcher
_UpperCamelCase = class_cost
_UpperCamelCase = bbox_cost
_UpperCamelCase = giou_cost
# Loss coefficients
_UpperCamelCase = mask_loss_coefficient
_UpperCamelCase = dice_loss_coefficient
_UpperCamelCase = bbox_loss_coefficient
_UpperCamelCase = giou_loss_coefficient
_UpperCamelCase = eos_coefficient
super().__init__(is_encoder_decoder=__a , **__a)
@property
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return self.d_model
@classmethod
def UpperCAmelCase ( cls , __a , **__a) -> int:
'''simple docstring'''
return cls(backbone_config=__a , **__a)
def UpperCAmelCase ( self) -> Dict[str, any]:
'''simple docstring'''
_UpperCamelCase = copy.deepcopy(self.__dict__)
if output["backbone_config"] is not None:
_UpperCamelCase = self.backbone_config.to_dict()
_UpperCamelCase = self.__class__.model_type
return output
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = version.parse('1.11' )
@property
def UpperCAmelCase ( self) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
])
@property
def UpperCAmelCase ( self) -> float:
'''simple docstring'''
return 1e-5
@property
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return 12
| 19 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCAmelCase__ ( ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = ArgumentParser(
description=(
'''PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'''
))
# Optional arguments for the launch helper
parser.add_argument('''--num_cores''' ,type=__snake_case ,default=1 ,help='''Number of TPU cores to use (1 or 8).''')
# positional
parser.add_argument(
'''training_script''' ,type=__snake_case ,help=(
'''The full path to the single TPU training '''
'''program/script to be launched in parallel, '''
'''followed by all the arguments for the '''
'''training script'''
) ,)
# rest from the training program
parser.add_argument('''training_script_args''' ,nargs=__snake_case)
return parser.parse_args()
def lowerCAmelCase__ ( ):
'''simple docstring'''
lowerCAmelCase__ : Union[str, Any] = parse_args()
# Import training_script as a module.
lowerCAmelCase__ : Optional[int] = Path(args.training_script)
sys.path.append(str(script_fpath.parent.resolve()))
lowerCAmelCase__ : List[str] = script_fpath.stem
lowerCAmelCase__ : List[Any] = importlib.import_module(__snake_case)
# Patch sys.argv
lowerCAmelCase__ : str = [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores)]
xmp.spawn(mod._mp_fn ,args=() ,nprocs=args.num_cores)
if __name__ == "__main__":
main()
| 647 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = 'wavlm'
def __init__( self , __a=32 , __a=7_68 , __a=12 , __a=12 , __a=30_72 , __a="gelu" , __a=0.1 , __a=0.1 , __a=0.1 , __a=0.0 , __a=0.1 , __a=0.1 , __a=0.02 , __a=1e-5 , __a="group" , __a="gelu" , __a=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __a=(5, 2, 2, 2, 2, 2, 2) , __a=(10, 3, 3, 3, 3, 2, 2) , __a=False , __a=1_28 , __a=16 , __a=3_20 , __a=8_00 , __a=False , __a=True , __a=0.05 , __a=10 , __a=2 , __a=0.0 , __a=10 , __a=3_20 , __a=2 , __a=0.1 , __a=1_00 , __a=2_56 , __a=2_56 , __a=0.1 , __a="mean" , __a=False , __a=False , __a=2_56 , __a=(5_12, 5_12, 5_12, 5_12, 15_00) , __a=(5, 3, 3, 1, 1) , __a=(1, 2, 3, 1, 1) , __a=5_12 , __a=80 , __a=0 , __a=1 , __a=2 , __a=False , __a=3 , __a=2 , __a=3 , __a=None , **__a , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**__a , pad_token_id=__a , bos_token_id=__a , eos_token_id=__a)
_UpperCamelCase = hidden_size
_UpperCamelCase = feat_extract_norm
_UpperCamelCase = feat_extract_activation
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = conv_bias
_UpperCamelCase = num_buckets
_UpperCamelCase = max_bucket_distance
_UpperCamelCase = num_conv_pos_embeddings
_UpperCamelCase = num_conv_pos_embedding_groups
_UpperCamelCase = len(self.conv_dim)
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = num_attention_heads
_UpperCamelCase = hidden_dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = feat_proj_dropout
_UpperCamelCase = final_dropout
_UpperCamelCase = layerdrop
_UpperCamelCase = layer_norm_eps
_UpperCamelCase = initializer_range
_UpperCamelCase = num_ctc_classes
_UpperCamelCase = vocab_size
_UpperCamelCase = do_stable_layer_norm
_UpperCamelCase = use_weighted_layer_sum
_UpperCamelCase = classifier_proj_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F''' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,'''
F''' `len(config.conv_kernel) = {len(self.conv_kernel)}`.''')
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_UpperCamelCase = apply_spec_augment
_UpperCamelCase = mask_time_prob
_UpperCamelCase = mask_time_length
_UpperCamelCase = mask_time_min_masks
_UpperCamelCase = mask_feature_prob
_UpperCamelCase = mask_feature_length
# parameters for pretraining with codevector quantized representations
_UpperCamelCase = num_codevectors_per_group
_UpperCamelCase = num_codevector_groups
_UpperCamelCase = contrastive_logits_temperature
_UpperCamelCase = num_negatives
_UpperCamelCase = codevector_dim
_UpperCamelCase = proj_codevector_dim
_UpperCamelCase = diversity_loss_weight
# ctc loss
_UpperCamelCase = ctc_loss_reduction
_UpperCamelCase = ctc_zero_infinity
# adapter
_UpperCamelCase = add_adapter
_UpperCamelCase = adapter_kernel_size
_UpperCamelCase = adapter_stride
_UpperCamelCase = num_adapter_layers
_UpperCamelCase = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_UpperCamelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = xvector_output_dim
@property
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1)
| 19 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class UpperCAmelCase__ ( lowercase__ ):
"""simple docstring"""
__UpperCAmelCase : str = '''wavlm'''
def __init__( self : Dict ,_a : str=32 ,_a : List[str]=768 ,_a : Optional[Any]=12 ,_a : int=12 ,_a : str=3072 ,_a : List[Any]="gelu" ,_a : Any=0.1 ,_a : Optional[int]=0.1 ,_a : Optional[Any]=0.1 ,_a : List[Any]=0.0 ,_a : Dict=0.1 ,_a : Any=0.1 ,_a : Optional[Any]=0.02 ,_a : Union[str, Any]=1E-5 ,_a : str="group" ,_a : Optional[int]="gelu" ,_a : List[Any]=(512, 512, 512, 512, 512, 512, 512) ,_a : Tuple=(5, 2, 2, 2, 2, 2, 2) ,_a : List[str]=(10, 3, 3, 3, 3, 2, 2) ,_a : Tuple=False ,_a : Tuple=128 ,_a : List[str]=16 ,_a : Optional[int]=320 ,_a : Optional[int]=800 ,_a : int=False ,_a : Optional[int]=True ,_a : Optional[int]=0.05 ,_a : Optional[int]=10 ,_a : Any=2 ,_a : Tuple=0.0 ,_a : str=10 ,_a : str=320 ,_a : Tuple=2 ,_a : List[str]=0.1 ,_a : List[Any]=100 ,_a : Dict=256 ,_a : Union[str, Any]=256 ,_a : List[str]=0.1 ,_a : List[str]="mean" ,_a : Optional[int]=False ,_a : str=False ,_a : Any=256 ,_a : List[Any]=(512, 512, 512, 512, 1500) ,_a : List[Any]=(5, 3, 3, 1, 1) ,_a : Union[str, Any]=(1, 2, 3, 1, 1) ,_a : List[str]=512 ,_a : List[Any]=80 ,_a : Tuple=0 ,_a : Optional[int]=1 ,_a : List[Any]=2 ,_a : Union[str, Any]=False ,_a : str=3 ,_a : Tuple=2 ,_a : int=3 ,_a : Union[str, Any]=None ,**_a : Optional[int] ,):
'''simple docstring'''
super().__init__(**__a ,pad_token_id=__a ,bos_token_id=__a ,eos_token_id=__a )
_a : int = hidden_size
_a : List[Any] = feat_extract_norm
_a : Optional[Any] = feat_extract_activation
_a : Optional[Any] = list(__a )
_a : List[str] = list(__a )
_a : Optional[int] = list(__a )
_a : str = conv_bias
_a : int = num_buckets
_a : Union[str, Any] = max_bucket_distance
_a : Union[str, Any] = num_conv_pos_embeddings
_a : Optional[Any] = num_conv_pos_embedding_groups
_a : int = len(self.conv_dim )
_a : str = num_hidden_layers
_a : List[str] = intermediate_size
_a : Tuple = hidden_act
_a : str = num_attention_heads
_a : Tuple = hidden_dropout
_a : int = attention_dropout
_a : Optional[int] = activation_dropout
_a : int = feat_proj_dropout
_a : Tuple = final_dropout
_a : Optional[int] = layerdrop
_a : Union[str, Any] = layer_norm_eps
_a : Optional[int] = initializer_range
_a : Union[str, Any] = num_ctc_classes
_a : Tuple = vocab_size
_a : Dict = do_stable_layer_norm
_a : int = use_weighted_layer_sum
_a : List[Any] = classifier_proj_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
F""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
F""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_a : List[Any] = apply_spec_augment
_a : Tuple = mask_time_prob
_a : Any = mask_time_length
_a : Optional[Any] = mask_time_min_masks
_a : Tuple = mask_feature_prob
_a : int = mask_feature_length
# parameters for pretraining with codevector quantized representations
_a : Optional[Any] = num_codevectors_per_group
_a : Optional[Any] = num_codevector_groups
_a : str = contrastive_logits_temperature
_a : List[Any] = num_negatives
_a : Union[str, Any] = codevector_dim
_a : Any = proj_codevector_dim
_a : int = diversity_loss_weight
# ctc loss
_a : Optional[int] = ctc_loss_reduction
_a : Tuple = ctc_zero_infinity
# adapter
_a : int = add_adapter
_a : List[str] = adapter_kernel_size
_a : Dict = adapter_stride
_a : Optional[Any] = num_adapter_layers
_a : Union[str, Any] = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_a : Optional[int] = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_a : Union[str, Any] = list(__a )
_a : int = list(__a )
_a : List[Any] = list(__a )
_a : Any = xvector_output_dim
@property
def __lowercase ( self : Tuple ):
'''simple docstring'''
return functools.reduce(operator.mul ,self.conv_stride ,1 )
| 229 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
_a = """bart"""
_a = True
@st.cache(allow_output_mutation=__snake_case )
def lowerCamelCase__ ( ) -> Dict:
"""simple docstring"""
if LOAD_DENSE_INDEX:
_UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' )
_UpperCamelCase = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' )
_UpperCamelCase = qar_model.eval()
else:
_UpperCamelCase , _UpperCamelCase = (None, None)
if MODEL_TYPE == "bart":
_UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' )
_UpperCamelCase = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' )
_UpperCamelCase = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' )
sas_model.load_state_dict(save_dict['''model'''] )
_UpperCamelCase = sas_model.eval()
else:
_UpperCamelCase , _UpperCamelCase = make_qa_sas_model(
model_name='''t5-small''', from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''', device='''cuda:0''' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=__snake_case )
def lowerCamelCase__ ( ) -> Tuple:
"""simple docstring"""
if LOAD_DENSE_INDEX:
_UpperCamelCase = faiss.StandardGpuResources()
_UpperCamelCase = datasets.load_dataset(path='''wiki_snippets''', name='''wiki40b_en_100_0''' )['''train''']
_UpperCamelCase = np.memmap(
'''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''', dtype='''float32''', mode='''r''', shape=(wikiaab_passages.num_rows, 1_28), )
_UpperCamelCase = faiss.IndexFlatIP(1_28 )
_UpperCamelCase = faiss.index_cpu_to_gpu(__snake_case, 1, __snake_case )
wikiaab_gpu_index_flat.add(__snake_case ) # TODO fix for larger GPU
else:
_UpperCamelCase , _UpperCamelCase = (None, None)
_UpperCamelCase = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=__snake_case )
def lowerCamelCase__ ( ) -> int:
"""simple docstring"""
_UpperCamelCase = datasets.load_dataset('''eli5''', name='''LFQA_reddit''' )
_UpperCamelCase = elia['''train_eli5''']
_UpperCamelCase = np.memmap(
'''eli5_questions_reps.dat''', dtype='''float32''', mode='''r''', shape=(elia_train.num_rows, 1_28) )
_UpperCamelCase = faiss.IndexFlatIP(1_28 )
eli5_train_q_index.add(__snake_case )
return (elia_train, eli5_train_q_index)
_a , _a , _a = load_indexes()
_a , _a , _a , _a = load_models()
_a , _a = load_train_data()
def lowerCamelCase__ ( __snake_case, __snake_case=10 ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = embed_questions_for_retrieval([question], __snake_case, __snake_case )
_UpperCamelCase , _UpperCamelCase = eli5_train_q_index.search(__snake_case, __snake_case )
_UpperCamelCase = [elia_train[int(__snake_case )] for i in I[0]]
return nn_examples
def lowerCamelCase__ ( __snake_case, __snake_case="wiki40b", __snake_case="dense", __snake_case=10 ) -> List[str]:
"""simple docstring"""
if source == "none":
_UpperCamelCase , _UpperCamelCase = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
_UpperCamelCase , _UpperCamelCase = query_qa_dense_index(
__snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case )
else:
_UpperCamelCase , _UpperCamelCase = query_es_index(
__snake_case, __snake_case, index_name='''english_wiki40b_snippets_100w''', n_results=__snake_case, )
_UpperCamelCase = [
(res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
]
_UpperCamelCase = '''question: {} context: {}'''.format(__snake_case, __snake_case )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda __snake_case : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __snake_case : None),
} )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=64, __snake_case=2_56, __snake_case=False, __snake_case=2, __snake_case=0.95, __snake_case=0.8 ) -> Dict:
"""simple docstring"""
with torch.no_grad():
_UpperCamelCase = qa_sas_generate(
__snake_case, __snake_case, __snake_case, num_answers=1, num_beams=__snake_case, min_len=__snake_case, max_len=__snake_case, do_sample=__snake_case, temp=__snake_case, top_p=__snake_case, top_k=__snake_case, max_input_length=10_24, device='''cuda:0''', )[0]
return (answer, support_list)
st.title("""Long Form Question Answering with ELI5""")
# Start sidebar
_a = """<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
_a = """
<html>
<head>
<style>
.img-container {
padding-left: 90px;
padding-right: 90px;
padding-top: 50px;
padding-bottom: 50px;
background-color: #f0f3f9;
}
</style>
</head>
<body>
<span class=\"img-container\"> <!-- Inline parent element -->
%s
</span>
</body>
</html>
""" % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
_a = """
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
"""
st.sidebar.markdown(description, unsafe_allow_html=True)
_a = [
"""Answer the question""",
"""View the retrieved document only""",
"""View the most similar ELI5 question and answer""",
"""Show me everything, please!""",
]
_a = st.sidebar.checkbox("""Demo options""")
if demo_options:
_a = st.sidebar.selectbox(
"""""",
action_list,
index=3,
)
_a = action_list.index(action_st)
_a = st.sidebar.selectbox(
"""""",
["""Show full text of passages""", """Show passage section titles"""],
index=0,
)
_a = show_type == """Show full text of passages"""
else:
_a = 3
_a = True
_a = st.sidebar.checkbox("""Retrieval options""")
if retrieval_options:
_a = """
### Information retriever options
The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
"""
st.sidebar.markdown(retriever_info)
_a = st.sidebar.selectbox("""Which Wikipedia format should the model use?""", ["""wiki40b""", """none"""])
_a = st.sidebar.selectbox("""Which Wikipedia indexer should the model use?""", ["""dense""", """sparse""", """mixed"""])
else:
_a = """wiki40b"""
_a = """dense"""
_a = """beam"""
_a = 2
_a = 64
_a = 256
_a = None
_a = None
_a = st.sidebar.checkbox("""Generation options""")
if generate_options:
_a = """
### Answer generation options
The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
**beam** search, or **sample** from the decoder's output probabilities.
"""
st.sidebar.markdown(generate_info)
_a = st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
_a = st.sidebar.slider(
"""Minimum generation length""", min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
_a = st.sidebar.slider(
"""Maximum generation length""", min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
_a = st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
_a = st.sidebar.slider(
"""Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
_a = st.sidebar.slider(
"""Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
_a = None
# start main text
_a = [
"""<MY QUESTION>""",
"""How do people make chocolate?""",
"""Why do we get a fever when we are sick?""",
"""How can different animals perceive different colors?""",
"""What is natural language processing?""",
"""What's the best way to treat a sunburn?""",
"""What exactly are vitamins ?""",
"""How does nuclear energy provide electricity?""",
"""What's the difference between viruses and bacteria?""",
"""Why are flutes classified as woodwinds when most of them are made out of metal ?""",
"""Why do people like drinking coffee even though it tastes so bad?""",
"""What happens when wine ages? How does it make the wine taste better?""",
"""If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?""",
"""How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?""",
"""How does New Zealand have so many large bird predators?""",
]
_a = st.selectbox(
"""What would you like to ask? ---- select <MY QUESTION> to enter a new query""",
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
_a = st.text_input("""Enter your question here:""", """""")
else:
_a = question_s
if st.button("""Show me!"""):
if action in [0, 1, 3]:
if index_type == "mixed":
_a , _a = make_support(question, source=wiki_source, method="""dense""", n_results=10)
_a , _a = make_support(question, source=wiki_source, method="""sparse""", n_results=10)
_a = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
_a = support_list[:10]
_a = """<P> """ + """ <P> """.join([res[-1] for res in support_list])
else:
_a , _a = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
_a , _a = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == """sampled"""),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown("""### The model generated answer is:""")
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown("""--- \n ### The model is drawing information from the following Wikipedia passages:""")
for i, res in enumerate(support_list):
_a = """https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_"""))
_a = res[1].strip()
if sec_titles == "":
_a = """[{}]({})""".format(res[0], wiki_url)
else:
_a = sec_titles.split(""" & """)
_a = """ & """.join(
["""[{}]({}#{})""".format(sec.strip(), wiki_url, sec.strip().replace(""" """, """_""")) for sec in sec_list]
)
st.markdown(
"""{0:02d} - **Article**: {1:<18} <br> _Section_: {2}""".format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
"""> <span style=\"font-family:arial; font-size:10pt;\">""" + res[-1] + """</span>""", unsafe_allow_html=True
)
if action in [2, 3]:
_a = find_nearest_training(question)
_a = nn_train_list[0]
st.markdown(
"""--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""])
)
_a = [
"""{}. {}""".format(i + 1, """ \n""".join([line.strip() for line in ans.split("""\n""") if line.strip() != """"""]))
for i, (ans, sc) in enumerate(zip(train_exple["""answers"""]["""text"""], train_exple["""answers"""]["""score"""]))
if i == 0 or sc > 2
]
st.markdown("""##### Its answers were: \n\n {}""".format("""\n""".join(answers_st)))
_a = """
---
**Disclaimer**
*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
"""
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 19 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCamelCase : List[Any] = {
'''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : List[Any] = [
'''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MegaForCausalLM''',
'''MegaForMaskedLM''',
'''MegaForMultipleChoice''',
'''MegaForQuestionAnswering''',
'''MegaForSequenceClassification''',
'''MegaForTokenClassification''',
'''MegaModel''',
'''MegaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
_lowerCamelCase : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 403 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
_a = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
_a = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> Tuple:
"""simple docstring"""
for attribute in key.split('''.''' ):
_UpperCamelCase = getattr(__snake_case, __snake_case )
if weight_type is not None:
_UpperCamelCase = getattr(__snake_case, __snake_case ).shape
else:
_UpperCamelCase = hf_pointer.shape
assert hf_shape == value.shape, (
F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
_UpperCamelCase = value
elif weight_type == "weight_g":
_UpperCamelCase = value
elif weight_type == "weight_v":
_UpperCamelCase = value
elif weight_type == "bias":
_UpperCamelCase = value
else:
_UpperCamelCase = value
logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def lowerCamelCase__ ( __snake_case, __snake_case ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = []
_UpperCamelCase = fairseq_model.state_dict()
_UpperCamelCase = hf_model.feature_extractor
_UpperCamelCase = hf_model.adapter
for name, value in fairseq_dict.items():
_UpperCamelCase = False
if "conv_layers" in name:
load_conv_layer(
__snake_case, __snake_case, __snake_case, __snake_case, hf_model.config.feat_extract_norm == '''group''', )
_UpperCamelCase = True
elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.'''] ):
load_adapter(__snake_case, __snake_case, __snake_case, __snake_case )
_UpperCamelCase = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
_UpperCamelCase = True
if "*" in mapped_key:
_UpperCamelCase = name.split(__snake_case )[0].split('''.''' )[-2]
_UpperCamelCase = mapped_key.replace('''*''', __snake_case )
if "weight_g" in name:
_UpperCamelCase = '''weight_g'''
elif "weight_v" in name:
_UpperCamelCase = '''weight_v'''
elif "bias" in name:
_UpperCamelCase = '''bias'''
elif "weight" in name:
_UpperCamelCase = '''weight'''
else:
_UpperCamelCase = None
set_recursively(__snake_case, __snake_case, __snake_case, __snake_case, __snake_case )
continue
if not is_used:
unused_weights.append(__snake_case )
logger.warning(F'''Unused weights: {unused_weights}''' )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = full_name.split('''conv_layers.''' )[-1]
_UpperCamelCase = name.split('''.''' )
_UpperCamelCase = int(items[0] )
_UpperCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
_UpperCamelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
_UpperCamelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
_UpperCamelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
_UpperCamelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__snake_case )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> Dict:
"""simple docstring"""
_UpperCamelCase = full_name.split('''adaptor.''' )[-1]
_UpperCamelCase = name.split('''.''' )
if items[1].isdigit():
_UpperCamelCase = int(items[1] )
else:
_UpperCamelCase = None
if "adaptor" not in full_name:
if "proj_ln" in full_name:
# has to be layer norm
if "bias" in name:
assert (
value.shape == adapter.proj_layer_norm.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj_layer_norm.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
_UpperCamelCase = value
else:
# has to be projection layer
if "bias" in name:
assert (
value.shape == adapter.proj.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter proj layer bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter proj layer weight was initialized from {full_name}.''' )
elif isinstance(__snake_case, __snake_case ):
if "bias" in name:
assert (
value.shape == adapter.layers[layer_id].conv.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
elif "weight" in name:
assert (
value.shape == adapter.layers[layer_id].conv.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
else:
unused_weights.append(__snake_case )
def lowerCamelCase__ ( __snake_case ) -> str:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase = emb.weight.shape
_UpperCamelCase = nn.Linear(__snake_case, __snake_case, bias=__snake_case )
_UpperCamelCase = emb.weight.data
return lin_layer
@torch.no_grad()
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = WavaVecaConfig.from_pretrained(
__snake_case, add_adapter=__snake_case, adapter_stride=__snake_case, adapter_kernel_size=__snake_case, use_auth_token=__snake_case, output_hidden_size=__snake_case, )
_UpperCamelCase = MBartConfig.from_pretrained(__snake_case )
# load model
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path], arg_overrides={
'''config_yaml''': config_yaml_path,
'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ),
'''w2v_path''': checkpoint_path,
'''load_pretrained_decoder_from''': None,
}, )
_UpperCamelCase = model[0].eval()
# load feature extractor
_UpperCamelCase = WavaVecaFeatureExtractor.from_pretrained(__snake_case, use_auth_token=__snake_case )
# set weights for wav2vec2 encoder
_UpperCamelCase = WavaVecaModel(__snake_case )
recursively_load_weights_wavaveca(model.encoder, __snake_case )
# load decoder weights
_UpperCamelCase = MBartForCausalLM(__snake_case )
_UpperCamelCase , _UpperCamelCase = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(), strict=__snake_case )
logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}''' )
logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' )
_UpperCamelCase = SpeechEncoderDecoderModel(encoder=__snake_case, decoder=__snake_case )
_UpperCamelCase = False
_UpperCamelCase = MBartaaTokenizer(__snake_case )
tokenizer.save_pretrained(__snake_case )
_UpperCamelCase = hf_wavavec.config.to_dict()
_UpperCamelCase = tokenizer.pad_token_id
_UpperCamelCase = tokenizer.bos_token_id
_UpperCamelCase = tokenizer.eos_token_id
_UpperCamelCase = '''mbart50'''
_UpperCamelCase = '''wav2vec2'''
_UpperCamelCase = tokenizer.eos_token_id
_UpperCamelCase = 25_00_04
_UpperCamelCase = tokenizer.eos_token_id
_UpperCamelCase = SpeechEncoderDecoderConfig.from_dict(__snake_case )
hf_wavavec.save_pretrained(__snake_case )
feature_extractor.save_pretrained(__snake_case )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
parser.add_argument(
"""--encoder_config_path""",
default="""facebook/wav2vec2-xls-r-1b""",
type=str,
help="""Path to hf encoder wav2vec2 checkpoint config""",
)
parser.add_argument(
"""--decoder_config_path""",
default="""facebook/mbart-large-50-one-to-many-mmt""",
type=str,
help="""Path to hf decoder checkpoint config""",
)
parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""")
parser.add_argument("""--start_token_id""", default=25_0004, type=int, help="""`decoder_start_token_id` of model config""")
_a = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
args.config_yaml_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
add_adapter=args.add_adapter,
adapter_kernel_size=args.adapter_kernel_size,
adapter_stride=args.adapter_stride,
decoder_start_token_id=args.start_token_id,
encoder_output_dim=args.encoder_output_dim,
)
| 19 | 0 |
'''simple docstring'''
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
_snake_case : str = get_logger()
_snake_case : List[Any] = None
class A ( TensorFormatter[Mapping, 'jax.Array', Mapping] ):
def __init__( self : Any , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : Tuple=None , **lowerCAmelCase_ : Dict ) -> str:
"""simple docstring"""
super().__init__(features=__a )
import jax
from jaxlib.xla_client import Device
if isinstance(__a , __a ):
raise ValueError(
F'Expected {device} to be a `str` not {type(__a )}, as `jaxlib.xla_extension.Device` '
'''is not serializable neither with `pickle` nor with `dill`. Instead you can surround '''
'''the device with `str()` to get its string identifier that will be internally mapped '''
'''to the actual `jaxlib.xla_extension.Device`.''' )
_a = device if isinstance(__a , __a ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
_a = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
F'Device with string identifier {self.device} not listed among the available '
F'devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default '
F'device: {str(jax.devices()[0] )}.' )
_a = str(jax.devices()[0] )
_a = jnp_array_kwargs
@staticmethod
def __lowerCAmelCase ( ) -> Dict[str, "jaxlib.xla_extension.Device"]:
"""simple docstring"""
import jax
return {str(__a ): device for device in jax.devices()}
def __lowerCAmelCase ( self : int , lowerCAmelCase_ : str ) -> Any:
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(__a , __a ) and column:
if all(
isinstance(__a , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(__a , axis=0 )
return column
def __lowerCAmelCase ( self : Tuple , lowerCAmelCase_ : Any ) -> Dict:
"""simple docstring"""
import jax
import jax.numpy as jnp
if isinstance(__a , (str, bytes, type(__a )) ):
return value
elif isinstance(__a , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
_a = {}
if isinstance(__a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
_a = {'''dtype''': jnp.intaa}
else:
_a = {'''dtype''': jnp.intaa}
elif isinstance(__a , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
_a = {'''dtype''': jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(__a , PIL.Image.Image ):
_a = np.asarray(__a )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
_a = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(__a , **{**default_dtype, **self.jnp_array_kwargs} )
def __lowerCAmelCase ( self : Dict , lowerCAmelCase_ : List[str] ) -> str:
"""simple docstring"""
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(__a , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(__a , '''__array__''' ) and not isinstance(__a , jax.Array ):
_a = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(__a , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(__a ) for substruct in data_struct] )
elif isinstance(__a , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(__a ) for substruct in data_struct] )
return self._tensorize(__a )
def __lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Dict ) -> Optional[int]:
"""simple docstring"""
return map_nested(self._recursive_tensorize , __a , map_list=__a )
def __lowerCAmelCase ( self : List[Any] , lowerCAmelCase_ : Union[str, Any] ) -> Mapping:
"""simple docstring"""
_a = self.numpy_arrow_extractor().extract_row(__a )
_a = self.python_features_decoder.decode_row(__a )
return self.recursive_tensorize(__a )
def __lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Dict ) -> "jax.Array":
"""simple docstring"""
_a = self.numpy_arrow_extractor().extract_column(__a )
_a = self.python_features_decoder.decode_column(__a , pa_table.column_names[0] )
_a = self.recursive_tensorize(__a )
_a = self._consolidate(__a )
return column
def __lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : Union[str, Any] ) -> Mapping:
"""simple docstring"""
_a = self.numpy_arrow_extractor().extract_batch(__a )
_a = self.python_features_decoder.decode_batch(__a )
_a = self.recursive_tensorize(__a )
for column_name in batch:
_a = self._consolidate(batch[column_name] )
return batch
| 22 |
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case=None, **__snake_case ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()]
_UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()][: len(__snake_case )]
_UpperCamelCase = calculate_rouge(__snake_case, __snake_case, **__snake_case )
if save_path is not None:
save_json(__snake_case, __snake_case, indent=__snake_case )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 19 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class _lowerCAmelCase ( UpperCamelCase_ ):
"""simple docstring"""
lowerCAmelCase = 42
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 649 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = ['image_processor', 'tokenizer']
lowercase__ = 'ViTImageProcessor'
lowercase__ = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self , __a=None , __a=None , **__a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __a , )
_UpperCamelCase = kwargs.pop('''feature_extractor''')
_UpperCamelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''')
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''')
super().__init__(__a , __a)
def __call__( self , __a=None , __a=None , __a=None , __a=None , **__a) -> Tuple:
'''simple docstring'''
if text is None and visual_prompt is None and images is None:
raise ValueError('''You have to specify either text, visual prompt or images.''')
if text is not None and visual_prompt is not None:
raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''')
if text is not None:
_UpperCamelCase = self.tokenizer(__a , return_tensors=__a , **__a)
if visual_prompt is not None:
_UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a)
if images is not None:
_UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a)
if visual_prompt is not None and images is not None:
_UpperCamelCase = {
'''pixel_values''': image_features.pixel_values,
'''conditional_pixel_values''': prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
_UpperCamelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
_UpperCamelCase = {
'''conditional_pixel_values''': prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**__a) , tensor_type=__a)
def UpperCAmelCase ( self , *__a , **__a) -> Any:
'''simple docstring'''
return self.tokenizer.batch_decode(*__a , **__a)
def UpperCAmelCase ( self , *__a , **__a) -> List[str]:
'''simple docstring'''
return self.tokenizer.decode(*__a , **__a)
@property
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __a , )
return self.image_processor_class
@property
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __a , )
return self.image_processor
| 19 | 0 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
UpperCamelCase__ : int = datasets.logging.get_logger(__name__)
UpperCamelCase__ : int = '\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for Text Generation},\n author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n booktitle={ACL},\n year={2020},\n url={https://arxiv.org/abs/2004.04696}\n}\n'
UpperCamelCase__ : int = '\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'
UpperCamelCase__ : str = '\nBLEURT score.\n\nArgs:\n `predictions` (list of str): prediction/candidate sentences\n `references` (list of str): reference sentences\n `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n \'scores\': List of scores.\nExamples:\n\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> bleurt = datasets.load_metric(\"bleurt\")\n >>> results = bleurt.compute(predictions=predictions, references=references)\n >>> print([round(v, 2) for v in results[\"scores\"]])\n [1.03, 1.04]\n'
UpperCamelCase__ : Tuple = {
'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class _lowercase ( datasets.Metric ):
'''simple docstring'''
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,homepage='''https://github.com/google-research/bleurt''' ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' ,id='''sequence''' ),
'''references''': datasets.Value('''string''' ,id='''sequence''' ),
} ) ,codebase_urls=['''https://github.com/google-research/bleurt'''] ,reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696'''] ,)
def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> Union[str, Any]:
'''simple docstring'''
if self.config_name == "default":
logger.warning(
'''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
'''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').''' )
UpperCAmelCase__ : List[Any] = '''bleurt-base-128'''
if self.config_name.lower() in CHECKPOINT_URLS:
UpperCAmelCase__ : List[str] = self.config_name.lower()
elif self.config_name.upper() in CHECKPOINT_URLS:
UpperCAmelCase__ : Any = self.config_name.upper()
else:
raise KeyError(
f'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}''' )
# download the model checkpoint specified by self.config_name and set up the scorer
UpperCAmelCase__ : Tuple = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
UpperCAmelCase__ : Any = score.BleurtScorer(os.path.join(__a ,__a ) )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ) -> Dict:
'''simple docstring'''
UpperCAmelCase__ : List[str] = self.scorer.score(references=__a ,candidates=__a )
return {"scores": scores}
| 614 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import UperNetForSemanticSegmentation
from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCAmelCase:
def __init__( self , __a , __a=13 , __a=32 , __a=3 , __a=4 , __a=[10, 20, 30, 40] , __a=[2, 2, 3, 2] , __a=True , __a=True , __a=37 , __a="gelu" , __a=10 , __a=0.02 , __a=["stage2", "stage3", "stage4"] , __a=3 , __a=None , ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = image_size
_UpperCamelCase = num_channels
_UpperCamelCase = num_stages
_UpperCamelCase = hidden_sizes
_UpperCamelCase = depths
_UpperCamelCase = is_training
_UpperCamelCase = use_labels
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = out_features
_UpperCamelCase = num_labels
_UpperCamelCase = scope
_UpperCamelCase = num_stages
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_UpperCamelCase = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return ConvNextConfig(
num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , )
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return UperNetConfig(
backbone_config=self.get_backbone_config() , hidden_size=5_12 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=__a , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=2_56 , auxiliary_num_convs=1 , auxiliary_concat_input=__a , loss_ignore_index=2_55 , num_labels=self.num_labels , )
def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = UperNetForSemanticSegmentation(config=__a)
model.to(__a)
model.eval()
_UpperCamelCase = model(__a)
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size))
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase__ = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
lowercase__ = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = UperNetModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(__a)
_UpperCamelCase = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCamelCase = [*signature.parameters.keys()]
_UpperCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__a)
@unittest.skip(reason='''UperNet does not use inputs_embeds''')
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason='''UperNet does not support input and output embeddings''')
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''UperNet does not have a base model''')
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason='''UperNet does not have a base model''')
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''')
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
pass
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
def check_hidden_states_output(__a , __a , __a):
_UpperCamelCase = model_class(__a)
model.to(__a)
model.eval()
with torch.no_grad():
_UpperCamelCase = model(**self._prepare_for_class(__a , __a))
_UpperCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_UpperCamelCase = self.model_tester.num_stages
self.assertEqual(len(__a) , expected_num_stages + 1)
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = True
check_hidden_states_output(__a , __a , __a)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCamelCase = True
check_hidden_states_output(__a , __a , __a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCamelCase = _config_zero_init(__a)
_UpperCamelCase = _config_zero_init(configs_no_init.backbone_config)
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(config=__a)
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@unittest.skip(reason='''UperNet does not have tied weights''')
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
pass
@slow
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained(__a)
self.assertIsNotNone(__a)
def lowerCamelCase__ ( ) -> int:
"""simple docstring"""
_UpperCamelCase = hf_hub_download(
repo_id='''hf-internal-testing/fixtures_ade20k''', repo_type='''dataset''', filename='''ADE_val_00000001.jpg''' )
_UpperCamelCase = Image.open(__snake_case ).convert('''RGB''' )
return image
@require_torch
@require_vision
@slow
class _UpperCAmelCase( unittest.TestCase ):
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny''')
_UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny''').to(__a)
_UpperCamelCase = prepare_img()
_UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a)
with torch.no_grad():
_UpperCamelCase = model(**__a)
_UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12))
self.assertEqual(outputs.logits.shape , __a)
_UpperCamelCase = torch.tensor(
[[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]).to(__a)
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4))
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny''')
_UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny''').to(__a)
_UpperCamelCase = prepare_img()
_UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a)
with torch.no_grad():
_UpperCamelCase = model(**__a)
_UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12))
self.assertEqual(outputs.logits.shape , __a)
_UpperCamelCase = torch.tensor(
[[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]).to(__a)
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4))
| 19 | 0 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
lowercase =(
'This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '
'library. You can have a look at this example script for pointers: '
'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'
)
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] ):
'''simple docstring'''
warnings.warn(__snake_case , __snake_case )
requires_backends(__snake_case , 'sklearn' )
return (preds == labels).mean()
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : str ):
'''simple docstring'''
warnings.warn(__snake_case , __snake_case )
requires_backends(__snake_case , 'sklearn' )
_UpperCAmelCase : Tuple =simple_accuracy(__snake_case , __snake_case )
_UpperCAmelCase : Tuple =fa_score(y_true=__snake_case , y_pred=__snake_case )
return {
"acc": acc,
"f1": fa,
"acc_and_f1": (acc + fa) / 2,
}
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int ):
'''simple docstring'''
warnings.warn(__snake_case , __snake_case )
requires_backends(__snake_case , 'sklearn' )
_UpperCAmelCase : Any =pearsonr(__snake_case , __snake_case )[0]
_UpperCAmelCase : Union[str, Any] =spearmanr(__snake_case , __snake_case )[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : int ):
'''simple docstring'''
warnings.warn(__snake_case , __snake_case )
requires_backends(__snake_case , 'sklearn' )
assert len(__snake_case ) == len(__snake_case ), f"Predictions and labels have mismatched lengths {len(__snake_case )} and {len(__snake_case )}"
if task_name == "cola":
return {"mcc": matthews_corrcoef(__snake_case , __snake_case )}
elif task_name == "sst-2":
return {"acc": simple_accuracy(__snake_case , __snake_case )}
elif task_name == "mrpc":
return acc_and_fa(__snake_case , __snake_case )
elif task_name == "sts-b":
return pearson_and_spearman(__snake_case , __snake_case )
elif task_name == "qqp":
return acc_and_fa(__snake_case , __snake_case )
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(__snake_case , __snake_case )}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(__snake_case , __snake_case )}
elif task_name == "qnli":
return {"acc": simple_accuracy(__snake_case , __snake_case )}
elif task_name == "rte":
return {"acc": simple_accuracy(__snake_case , __snake_case )}
elif task_name == "wnli":
return {"acc": simple_accuracy(__snake_case , __snake_case )}
elif task_name == "hans":
return {"acc": simple_accuracy(__snake_case , __snake_case )}
else:
raise KeyError(__snake_case )
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : int , __lowerCamelCase : Optional[Any] ):
'''simple docstring'''
warnings.warn(__snake_case , __snake_case )
requires_backends(__snake_case , 'sklearn' )
if len(__snake_case ) != len(__snake_case ):
raise ValueError(f"Predictions and labels have mismatched lengths {len(__snake_case )} and {len(__snake_case )}" )
if task_name == "xnli":
return {"acc": simple_accuracy(__snake_case , __snake_case )}
else:
raise KeyError(__snake_case )
| 446 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = (DDPMScheduler,)
def UpperCAmelCase ( self , **__a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = {
'''num_train_timesteps''': 10_00,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''variance_type''': '''fixed_small''',
'''clip_sample''': True,
}
config.update(**__a)
return config
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
for timesteps in [1, 5, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__a)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2]):
self.check_over_configs(beta_start=__a , beta_end=__a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__a)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=__a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
self.check_over_configs(thresholding=__a)
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=__a , prediction_type=__a , sample_max_value=__a , )
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=__a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
for t in [0, 5_00, 9_99]:
self.check_over_forward(time_step=__a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0979)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1e-5
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = len(__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter
_UpperCamelCase = torch.manual_seed(0)
for t in reversed(range(__a)):
# 1. predict noise residual
_UpperCamelCase = model(__a , __a)
# 2. predict previous mean of sample x_t-1
_UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCamelCase = pred_prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
assert abs(result_sum.item() - 258.9606) < 1e-2
assert abs(result_mean.item() - 0.3372) < 1e-3
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''')
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = len(__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter
_UpperCamelCase = torch.manual_seed(0)
for t in reversed(range(__a)):
# 1. predict noise residual
_UpperCamelCase = model(__a , __a)
# 2. predict previous mean of sample x_t-1
_UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCamelCase = pred_prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
assert abs(result_sum.item() - 202.0296) < 1e-2
assert abs(result_mean.item() - 0.2631) < 1e-3
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [1_00, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=__a)
_UpperCamelCase = scheduler.timesteps
for i, timestep in enumerate(__a):
if i == len(__a) - 1:
_UpperCamelCase = -1
else:
_UpperCamelCase = timesteps[i + 1]
_UpperCamelCase = scheduler.previous_timestep(__a)
_UpperCamelCase = prev_t.item()
self.assertEqual(__a , __a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [1_00, 87, 50, 51, 0]
with self.assertRaises(__a , msg='''`custom_timesteps` must be in descending order.'''):
scheduler.set_timesteps(timesteps=__a)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [1_00, 87, 50, 1, 0]
_UpperCamelCase = len(__a)
with self.assertRaises(__a , msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
scheduler.set_timesteps(num_inference_steps=__a , timesteps=__a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
__a , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ):
scheduler.set_timesteps(timesteps=__a)
| 19 | 0 |
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Any, _UpperCAmelCase : int, _UpperCAmelCase : str=1_3, _UpperCAmelCase : Tuple=7, _UpperCAmelCase : int=True, _UpperCAmelCase : Any=True, _UpperCAmelCase : Optional[Any]=True, _UpperCAmelCase : List[str]=True, _UpperCAmelCase : Union[str, Any]=9_9, _UpperCAmelCase : Tuple=6_4, _UpperCAmelCase : Optional[Any]=3_2, _UpperCAmelCase : Dict=5, _UpperCAmelCase : Any=4, _UpperCAmelCase : int=3_7, _UpperCAmelCase : Union[str, Any]="gelu", _UpperCAmelCase : Optional[int]=0.1, _UpperCAmelCase : Optional[int]=0.1, _UpperCAmelCase : str=5_1_2, _UpperCAmelCase : Tuple=1_6, _UpperCAmelCase : List[str]=2, _UpperCAmelCase : int=0.02, _UpperCAmelCase : Optional[Any]=3, _UpperCAmelCase : int=4, _UpperCAmelCase : Optional[int]=None, ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = parent
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size
SCREAMING_SNAKE_CASE__ : List[str] = seq_length
SCREAMING_SNAKE_CASE__ : str = is_training
SCREAMING_SNAKE_CASE__ : Tuple = use_input_mask
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_token_type_ids
SCREAMING_SNAKE_CASE__ : Any = use_labels
SCREAMING_SNAKE_CASE__ : Tuple = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : int = embedding_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : List[str] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : str = hidden_act
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Any = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Tuple = max_position_embeddings
SCREAMING_SNAKE_CASE__ : str = type_vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : Any = initializer_range
SCREAMING_SNAKE_CASE__ : Tuple = num_labels
SCREAMING_SNAKE_CASE__ : str = num_choices
SCREAMING_SNAKE_CASE__ : Optional[Any] = scope
def A_ ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
SCREAMING_SNAKE_CASE__ : List[str] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = None
SCREAMING_SNAKE_CASE__ : Optional[int] = None
SCREAMING_SNAKE_CASE__ : str = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : int = ids_tensor([self.batch_size], self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
SCREAMING_SNAKE_CASE__ : str = ids_tensor([self.batch_size], self.num_choices )
SCREAMING_SNAKE_CASE__ : int = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A_ ( self : Dict ) -> List[str]:
"""simple docstring"""
return MobileBertConfig(
vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, embedding_size=self.embedding_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=__a, initializer_range=self.initializer_range, )
def A_ ( self : Optional[int], _UpperCAmelCase : str, _UpperCAmelCase : Tuple, _UpperCAmelCase : Any, _UpperCAmelCase : Tuple, _UpperCAmelCase : Dict, _UpperCAmelCase : Dict, _UpperCAmelCase : List[str] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = MobileBertModel(config=__a )
model.to(__a )
model.eval()
SCREAMING_SNAKE_CASE__ : int = model(__a, attention_mask=__a, token_type_ids=__a )
SCREAMING_SNAKE_CASE__ : List[str] = model(__a, token_type_ids=__a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(__a )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size) )
def A_ ( self : Optional[int], _UpperCAmelCase : Union[str, Any], _UpperCAmelCase : str, _UpperCAmelCase : Optional[int], _UpperCAmelCase : Any, _UpperCAmelCase : Optional[int], _UpperCAmelCase : Union[str, Any], _UpperCAmelCase : List[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = MobileBertForMaskedLM(config=__a )
model.to(__a )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] = model(__a, attention_mask=__a, token_type_ids=__a, labels=__a )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def A_ ( self : List[str], _UpperCAmelCase : str, _UpperCAmelCase : str, _UpperCAmelCase : Dict, _UpperCAmelCase : Tuple, _UpperCAmelCase : Dict, _UpperCAmelCase : Any, _UpperCAmelCase : str ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = MobileBertForNextSentencePrediction(config=__a )
model.to(__a )
model.eval()
SCREAMING_SNAKE_CASE__ : Any = model(
__a, attention_mask=__a, token_type_ids=__a, labels=__a, )
self.parent.assertEqual(result.logits.shape, (self.batch_size, 2) )
def A_ ( self : Tuple, _UpperCAmelCase : Optional[int], _UpperCAmelCase : Union[str, Any], _UpperCAmelCase : str, _UpperCAmelCase : str, _UpperCAmelCase : int, _UpperCAmelCase : List[str], _UpperCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = MobileBertForPreTraining(config=__a )
model.to(__a )
model.eval()
SCREAMING_SNAKE_CASE__ : Optional[int] = model(
__a, attention_mask=__a, token_type_ids=__a, labels=__a, next_sentence_label=__a, )
self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2) )
def A_ ( self : Optional[Any], _UpperCAmelCase : List[str], _UpperCAmelCase : str, _UpperCAmelCase : str, _UpperCAmelCase : List[Any], _UpperCAmelCase : Optional[Any], _UpperCAmelCase : str, _UpperCAmelCase : List[str] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = MobileBertForQuestionAnswering(config=__a )
model.to(__a )
model.eval()
SCREAMING_SNAKE_CASE__ : int = model(
__a, attention_mask=__a, token_type_ids=__a, start_positions=__a, end_positions=__a, )
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length) )
def A_ ( self : Optional[Any], _UpperCAmelCase : Union[str, Any], _UpperCAmelCase : Optional[Any], _UpperCAmelCase : List[str], _UpperCAmelCase : Dict, _UpperCAmelCase : int, _UpperCAmelCase : int, _UpperCAmelCase : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.num_labels
SCREAMING_SNAKE_CASE__ : Dict = MobileBertForSequenceClassification(__a )
model.to(__a )
model.eval()
SCREAMING_SNAKE_CASE__ : Any = model(__a, attention_mask=__a, token_type_ids=__a, labels=__a )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def A_ ( self : Optional[Any], _UpperCAmelCase : Optional[int], _UpperCAmelCase : List[str], _UpperCAmelCase : List[Any], _UpperCAmelCase : Union[str, Any], _UpperCAmelCase : Tuple, _UpperCAmelCase : str, _UpperCAmelCase : List[Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.num_labels
SCREAMING_SNAKE_CASE__ : Any = MobileBertForTokenClassification(config=__a )
model.to(__a )
model.eval()
SCREAMING_SNAKE_CASE__ : List[Any] = model(__a, attention_mask=__a, token_type_ids=__a, labels=__a )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def A_ ( self : Union[str, Any], _UpperCAmelCase : List[str], _UpperCAmelCase : Optional[int], _UpperCAmelCase : Any, _UpperCAmelCase : str, _UpperCAmelCase : Dict, _UpperCAmelCase : Optional[Any], _UpperCAmelCase : int ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.num_choices
SCREAMING_SNAKE_CASE__ : Any = MobileBertForMultipleChoice(config=__a )
model.to(__a )
model.eval()
SCREAMING_SNAKE_CASE__ : List[str] = input_ids.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = token_type_ids.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Optional[int] = input_mask.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Tuple = model(
__a, attention_mask=__a, token_type_ids=__a, labels=__a, )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices) )
def A_ ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) ,(
SCREAMING_SNAKE_CASE__
) ,(
SCREAMING_SNAKE_CASE__
) ,(
SCREAMING_SNAKE_CASE__
) ,(
SCREAMING_SNAKE_CASE__
) ,(
SCREAMING_SNAKE_CASE__
) ,(
SCREAMING_SNAKE_CASE__
) ,
) : Optional[int] = config_and_inputs
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase (__lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase_ = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
UpperCAmelCase_ = (
{
"feature-extraction": MobileBertModel,
"fill-mask": MobileBertForMaskedLM,
"question-answering": MobileBertForQuestionAnswering,
"text-classification": MobileBertForSequenceClassification,
"token-classification": MobileBertForTokenClassification,
"zero-shot": MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase_ = True
def A_ ( self : Optional[Any], _UpperCAmelCase : Dict, _UpperCAmelCase : Tuple, _UpperCAmelCase : List[str]=False ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = super()._prepare_for_class(__a, __a, return_labels=__a )
if return_labels:
if model_class in get_values(__a ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=__a )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=__a )
return inputs_dict
def A_ ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = MobileBertModelTester(self )
SCREAMING_SNAKE_CASE__ : Tuple = ConfigTester(self, config_class=__a, hidden_size=3_7 )
def A_ ( self : Tuple ) -> int:
"""simple docstring"""
self.config_tester.run_common_tests()
def A_ ( self : int ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*__a )
def A_ ( self : Tuple ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*__a )
def A_ ( self : str ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__a )
def A_ ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__a )
def A_ ( self : str ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*__a )
def A_ ( self : str ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*__a )
def A_ ( self : Dict ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__a )
def A_ ( self : int ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*__a )
def _a ( SCREAMING_SNAKE_CASE__ : int ) -> Tuple:
'''simple docstring'''
return torch.tensor(
__snake_case , dtype=torch.long , device=__snake_case , )
_lowerCamelCase : Union[str, Any] = 1e-3
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
@slow
def A_ ( self : Dict ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(__a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Optional[int] = model(__a )[0]
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.Size((1, 9, 5_1_2) )
self.assertEqual(output.shape, __a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor(
[
[
[-2.4736526E07, 8.2691656E04, 1.6521838E05],
[-5.7541704E-01, 3.9056022E00, 4.4011507E00],
[2.6047359E00, 1.5677652E00, -1.7324188E-01],
]
], device=__a, )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
SCREAMING_SNAKE_CASE__ : List[str] = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 663 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
_a = 100
_a = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_a = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=1_00 )
def lowerCamelCase__ ( __snake_case ) -> set[int]:
"""simple docstring"""
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
_UpperCamelCase = set()
_UpperCamelCase = 42
_UpperCamelCase = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def lowerCamelCase__ ( __snake_case = 50_00 ) -> int | None:
"""simple docstring"""
for number_to_partition in range(1, __snake_case ):
if len(partition(__snake_case ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 19 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : Union[str, Any] = {
"Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}
class __magic_name__ ( A__ ):
lowercase : int ='''instructblip_vision_model'''
def __init__( self : Union[str, Any] , UpperCamelCase__ : Tuple=14_08 , UpperCamelCase__ : List[str]=61_44 , UpperCamelCase__ : List[Any]=39 , UpperCamelCase__ : Tuple=16 , UpperCamelCase__ : Dict=2_24 , UpperCamelCase__ : str=14 , UpperCamelCase__ : Optional[int]="gelu" , UpperCamelCase__ : List[Any]=1e-6 , UpperCamelCase__ : Tuple=0.0 , UpperCamelCase__ : Tuple=1e-1_0 , UpperCamelCase__ : Any=True , **UpperCamelCase__ : List[Any] , ) -> int:
'''simple docstring'''
super().__init__(**__a )
UpperCAmelCase = hidden_size
UpperCAmelCase = intermediate_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = patch_size
UpperCAmelCase = image_size
UpperCAmelCase = initializer_range
UpperCAmelCase = attention_dropout
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = hidden_act
UpperCAmelCase = qkv_bias
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , UpperCamelCase__ : Any , **UpperCamelCase__ : Dict ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__a )
UpperCAmelCase , UpperCAmelCase = cls.get_config_dict(__a , **__a )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get("model_type" ) == "instructblip":
UpperCAmelCase = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(__a , **__a )
class __magic_name__ ( A__ ):
lowercase : List[str] ='''instructblip_qformer'''
def __init__( self : str , UpperCamelCase__ : List[str]=3_05_22 , UpperCamelCase__ : Dict=7_68 , UpperCamelCase__ : Optional[Any]=12 , UpperCamelCase__ : Dict=12 , UpperCamelCase__ : Tuple=30_72 , UpperCamelCase__ : Union[str, Any]="gelu" , UpperCamelCase__ : List[Any]=0.1 , UpperCamelCase__ : str=0.1 , UpperCamelCase__ : Optional[int]=5_12 , UpperCamelCase__ : Dict=0.02 , UpperCamelCase__ : int=1e-1_2 , UpperCamelCase__ : str=0 , UpperCamelCase__ : Any="absolute" , UpperCamelCase__ : Union[str, Any]=2 , UpperCamelCase__ : List[Any]=14_08 , **UpperCamelCase__ : Union[str, Any] , ) -> Dict:
'''simple docstring'''
super().__init__(pad_token_id=__a , **__a )
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = hidden_act
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = position_embedding_type
UpperCAmelCase = cross_attention_frequency
UpperCAmelCase = encoder_hidden_size
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Tuple , UpperCamelCase__ : Dict , **UpperCamelCase__ : Any ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__a )
UpperCAmelCase , UpperCAmelCase = cls.get_config_dict(__a , **__a )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get("model_type" ) == "instructblip":
UpperCAmelCase = config_dict["qformer_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(__a , **__a )
class __magic_name__ ( A__ ):
lowercase : Optional[int] ='''instructblip'''
lowercase : List[str] =True
def __init__( self : Union[str, Any] , UpperCamelCase__ : List[str]=None , UpperCamelCase__ : Dict=None , UpperCamelCase__ : Optional[int]=None , UpperCamelCase__ : Union[str, Any]=32 , **UpperCamelCase__ : Optional[int] ) -> List[str]:
'''simple docstring'''
super().__init__(**__a )
if vision_config is None:
UpperCAmelCase = {}
logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values." )
if qformer_config is None:
UpperCAmelCase = {}
logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values." )
if text_config is None:
UpperCAmelCase = {}
logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)." )
UpperCAmelCase = InstructBlipVisionConfig(**__a )
UpperCAmelCase = InstructBlipQFormerConfig(**__a )
UpperCAmelCase = text_config["model_type"] if "model_type" in text_config else "opt"
UpperCAmelCase = CONFIG_MAPPING[text_model_type](**__a )
UpperCAmelCase = self.text_config.tie_word_embeddings
UpperCAmelCase = self.text_config.is_encoder_decoder
UpperCAmelCase = num_query_tokens
UpperCAmelCase = self.vision_config.hidden_size
UpperCAmelCase = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
UpperCAmelCase = 1.0
UpperCAmelCase = 0.02
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[str] , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Union[str, Any] , **UpperCamelCase__ : Dict , ) -> int:
'''simple docstring'''
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **__a , )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase = copy.deepcopy(self.__dict__ )
UpperCAmelCase = self.vision_config.to_dict()
UpperCAmelCase = self.qformer_config.to_dict()
UpperCAmelCase = self.text_config.to_dict()
UpperCAmelCase = self.__class__.model_type
return output
| 323 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> np.array:
"""simple docstring"""
_UpperCamelCase = int(np.ceil((x_end - xa) / step_size ) )
_UpperCamelCase = np.zeros((n + 1,) )
_UpperCamelCase = ya
_UpperCamelCase = xa
for k in range(__snake_case ):
_UpperCamelCase = y[k] + step_size * ode_func(__snake_case, y[k] )
_UpperCamelCase = y[k] + (
(step_size / 2) * (ode_func(__snake_case, y[k] ) + ode_func(x + step_size, __snake_case ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 19 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ["image_processor", "tokenizer"]
SCREAMING_SNAKE_CASE = "AutoImageProcessor"
SCREAMING_SNAKE_CASE = "AutoTokenizer"
def __init__( self , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
super().__init__(__a , __a )
lowercase__ : Union[str, Any] = self.image_processor
def __call__( self , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , **__lowerCAmelCase ) -> Any:
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
lowercase__ : Optional[int] = self.tokenizer(__a , return_tensors=__a , **__a )
if images is not None:
lowercase__ : List[Any] = self.image_processor(__a , return_tensors=__a , **__a )
if text is not None and images is not None:
lowercase__ : Tuple = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__a ) , tensor_type=__a )
def _lowerCAmelCase( self , *__lowerCAmelCase , **__lowerCAmelCase ) -> Any:
return self.tokenizer.batch_decode(*__a , **__a )
def _lowerCAmelCase( self , *__lowerCAmelCase , **__lowerCAmelCase ) -> Any:
return self.tokenizer.decode(*__a , **__a )
@property
def _lowerCAmelCase( self ) -> int:
return ["input_ids", "attention_mask", "pixel_values"]
| 152 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_a = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
""" Distillation"""
)
)
parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
parser.add_argument("""--vocab_transform""", action="""store_true""")
_a = parser.parse_args()
if args.model_type == "bert":
_a = BertForMaskedLM.from_pretrained(args.model_name)
_a = """bert"""
else:
raise ValueError("""args.model_type should be \"bert\".""")
_a = model.state_dict()
_a = {}
for w in ["word_embeddings", "position_embeddings"]:
_a = state_dict[F"""{prefix}.embeddings.{w}.weight"""]
for w in ["weight", "bias"]:
_a = state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]
_a = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
]
std_idx += 1
_a = state_dict["""cls.predictions.decoder.weight"""]
_a = state_dict["""cls.predictions.bias"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
_a = state_dict[F"""cls.predictions.transform.dense.{w}"""]
_a = state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]
print(F"""N layers selected for distillation: {std_idx}""")
print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 19 | 0 |
_snake_case = {}
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
lowerCamelCase : Any = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
lowerCamelCase : int = _calculate(days - 1 , __snake_case , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
lowerCamelCase : Optional[int] = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
lowerCamelCase : Optional[int] = _calculate(days - 1 , __snake_case , 0 )
lowerCamelCase : List[Any] = state_late + state_absent + state_ontime
lowerCamelCase : Any = prizestrings
return prizestrings
def lowercase_( SCREAMING_SNAKE_CASE_ = 30 ):
'''simple docstring'''
return _calculate(__snake_case , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 340 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
_a = """platform"""
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class _UpperCAmelCase:
lowercase__ = PegasusConfig
lowercase__ = {}
lowercase__ = 'gelu'
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=False , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a=0.1 , __a=0.1 , __a=20 , __a=2 , __a=1 , __a=0 , ) -> int:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = eos_token_id
_UpperCamelCase = pad_token_id
_UpperCamelCase = bos_token_id
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size).clip(3 , self.vocab_size)
_UpperCamelCase = np.expand_dims(np.array([self.eos_token_id] * self.batch_size) , 1)
_UpperCamelCase = np.concatenate([input_ids, eos_tensor] , axis=1)
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCamelCase = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_UpperCamelCase = prepare_pegasus_inputs_dict(__a , __a , __a)
return config, inputs_dict
def UpperCAmelCase ( self , __a , __a , __a) -> Tuple:
'''simple docstring'''
_UpperCamelCase = 20
_UpperCamelCase = model_class_name(__a)
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''])
_UpperCamelCase , _UpperCamelCase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
_UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a)
_UpperCamelCase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''')
_UpperCamelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCamelCase = model.decode(
decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , )
_UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''')
_UpperCamelCase = model.decode(
decoder_input_ids[:, -1:] , __a , decoder_attention_mask=__a , past_key_values=outputs_cache.past_key_values , decoder_position_ids=__a , )
_UpperCamelCase = model.decode(__a , __a)
_UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''')
def UpperCAmelCase ( self , __a , __a , __a) -> Tuple:
'''simple docstring'''
_UpperCamelCase = 20
_UpperCamelCase = model_class_name(__a)
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''])
_UpperCamelCase , _UpperCamelCase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
_UpperCamelCase = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
_UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a)
_UpperCamelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCamelCase = model.decode(
decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , )
_UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''')
_UpperCamelCase = model.decode(
decoder_input_ids[:, -1:] , __a , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=__a , decoder_position_ids=__a , )
_UpperCamelCase = model.decode(__a , __a , decoder_attention_mask=__a)
_UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''')
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=None, __snake_case=None, ) -> Union[str, Any]:
"""simple docstring"""
if attention_mask is None:
_UpperCamelCase = np.not_equal(__snake_case, config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
_UpperCamelCase = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape, dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:], config.pad_token_id ).astype(np.inta ),
], axis=-1, )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class _UpperCAmelCase( lowerCamelCase , unittest.TestCase ):
lowercase__ = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
lowercase__ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
lowercase__ = True
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = FlaxPegasusModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(__a , __a , __a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(__a , __a , __a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
_UpperCamelCase = self._prepare_for_class(__a , __a)
_UpperCamelCase = model_class(__a)
@jax.jit
def encode_jitted(__a , __a=None , **__a):
return model.encode(input_ids=__a , attention_mask=__a)
with self.subTest('''JIT Enabled'''):
_UpperCamelCase = encode_jitted(**__a).to_tuple()
with self.subTest('''JIT Disabled'''):
with jax.disable_jit():
_UpperCamelCase = encode_jitted(**__a).to_tuple()
self.assertEqual(len(__a) , len(__a))
for jitted_output, output in zip(__a , __a):
self.assertEqual(jitted_output.shape , output.shape)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
_UpperCamelCase = model_class(__a)
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask'''])
_UpperCamelCase = {
'''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
'''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
'''encoder_outputs''': encoder_outputs,
}
@jax.jit
def decode_jitted(__a , __a , __a):
return model.decode(
decoder_input_ids=__a , decoder_attention_mask=__a , encoder_outputs=__a , )
with self.subTest('''JIT Enabled'''):
_UpperCamelCase = decode_jitted(**__a).to_tuple()
with self.subTest('''JIT Disabled'''):
with jax.disable_jit():
_UpperCamelCase = decode_jitted(**__a).to_tuple()
self.assertEqual(len(__a) , len(__a))
for jitted_output, output in zip(__a , __a):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
for model_class_name in self.all_model_classes:
_UpperCamelCase = model_class_name.from_pretrained('''google/pegasus-large''' , from_pt=__a)
_UpperCamelCase = np.ones((1, 1))
_UpperCamelCase = model(__a)
self.assertIsNotNone(__a)
@slow
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''')
_UpperCamelCase = PegasusTokenizer.from_pretrained('''google/pegasus-xsum''')
_UpperCamelCase = [
''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
]
_UpperCamelCase = [
'''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
'''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
]
_UpperCamelCase = tokenizer(__a , return_tensors='''np''' , truncation=__a , max_length=5_12 , padding=__a)
_UpperCamelCase = model.generate(**__a , num_beams=2).sequences
_UpperCamelCase = tokenizer.batch_decode(__a , skip_special_tokens=__a)
assert tgt_text == decoded
| 19 | 0 |
import math
import flax.linen as nn
import jax.numpy as jnp
def lowerCAmelCase__ ( lowerCamelCase_ : int ,lowerCamelCase_ : Dict ,lowerCamelCase_ : Union[str, Any] = 1 ,lowerCamelCase_ : Union[str, Any] = 1 ,lowerCamelCase_ : str = 1.0E4 ,lowerCamelCase_ : Union[str, Any] = False ,lowerCamelCase_ : Any = 1.0 ,):
'''simple docstring'''
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, f"""Embedding dimension {embedding_dim} should be even"""
lowerCAmelCase__ : Optional[int] = float(embedding_dim // 2)
lowerCAmelCase__ : Tuple = math.log(max_timescale / min_timescale) / (num_timescales - freq_shift)
lowerCAmelCase__ : List[str] = min_timescale * jnp.exp(jnp.arange(__snake_case ,dtype=jnp.floataa) * -log_timescale_increment)
lowerCAmelCase__ : List[Any] = jnp.expand_dims(__snake_case ,1) * jnp.expand_dims(__snake_case ,0)
# scale embeddings
lowerCAmelCase__ : List[Any] = scale * emb
if flip_sin_to_cos:
lowerCAmelCase__ : Dict = jnp.concatenate([jnp.cos(__snake_case), jnp.sin(__snake_case)] ,axis=1)
else:
lowerCAmelCase__ : int = jnp.concatenate([jnp.sin(__snake_case), jnp.cos(__snake_case)] ,axis=1)
lowerCAmelCase__ : int = jnp.reshape(__snake_case ,[jnp.shape(__snake_case)[0], embedding_dim])
return signal
class lowerCamelCase__ ( nn.Module):
'''simple docstring'''
snake_case_ =32
snake_case_ =jnp.floataa
@nn.compact
def __call__(self ,__lowerCamelCase ) -> Any:
"""simple docstring"""
lowerCAmelCase__ : str = nn.Dense(self.time_embed_dim ,dtype=self.dtype ,name='''linear_1''' )(__a )
lowerCAmelCase__ : Any = nn.silu(__a )
lowerCAmelCase__ : List[str] = nn.Dense(self.time_embed_dim ,dtype=self.dtype ,name='''linear_2''' )(__a )
return temb
class lowerCamelCase__ ( nn.Module):
'''simple docstring'''
snake_case_ =32
snake_case_ =False
snake_case_ =1
@nn.compact
def __call__(self ,__lowerCamelCase ) -> Optional[Any]:
"""simple docstring"""
return get_sinusoidal_embeddings(
__a ,embedding_dim=self.dim ,flip_sin_to_cos=self.flip_sin_to_cos ,freq_shift=self.freq_shift )
| 647 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
BertConfig,
DPRConfig,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
class _UpperCAmelCase:
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=2 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=5_12 , __a=16 , __a=2 , __a=0.02 , __a=3 , __a=4 , __a=None , __a=0 , ) -> Any:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_mask
_UpperCamelCase = use_token_type_ids
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = type_vocab_size
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = num_labels
_UpperCamelCase = num_choices
_UpperCamelCase = scope
_UpperCamelCase = projection_dim
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCamelCase = None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length])
_UpperCamelCase = None
if self.use_token_type_ids:
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
_UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices)
_UpperCamelCase = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__a , initializer_range=self.initializer_range , )
_UpperCamelCase = DPRConfig(projection_dim=self.projection_dim , **config.to_dict())
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = TFDPRContextEncoder(config=__a)
_UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a)
_UpperCamelCase = model(__a , token_type_ids=__a)
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size))
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = TFDPRQuestionEncoder(config=__a)
_UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a)
_UpperCamelCase = model(__a , token_type_ids=__a)
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size))
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Dict:
'''simple docstring'''
_UpperCamelCase = TFDPRReader(config=__a)
_UpperCamelCase = model(__a , attention_mask=__a)
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,))
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {'''input_ids''': input_ids}
return config, inputs_dict
@require_tf
class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase__ = (
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
lowercase__ = {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = TFDPRModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a , hidden_size=37)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*__a)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*__a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*__a)
@slow
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRQuestionEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRReader.from_pretrained(__a)
self.assertIsNotNone(__a)
@require_tf
class _UpperCAmelCase( unittest.TestCase ):
@slow
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base''')
_UpperCamelCase = tf.constant(
[[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]]) # [CLS] hello, is my dog cute? [SEP]
_UpperCamelCase = model(__a)[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
_UpperCamelCase = tf.constant(
[
[
0.0323_6253,
0.1275_3335,
0.1681_8509,
0.0027_9786,
0.389_6933,
0.2426_4945,
0.217_8971,
-0.0233_5227,
-0.0848_1959,
-0.1432_4117,
]
])
self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1e-4))
| 19 | 0 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {"""vocab_file""": """spiece.model"""}
__lowerCAmelCase = {
"""vocab_file""": {
"""TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
}
}
class UpperCAmelCase__ ( lowercase__ ):
"""simple docstring"""
def __init__( self : Dict ,_a : Any ,_a : Union[str, Any]=False ,_a : List[Any]=True ,_a : Any=False ,_a : List[str]="<s>" ,_a : Optional[Any]="</s>" ,_a : Optional[int]="<unk>" ,_a : Optional[int]="<sep>" ,_a : Optional[int]="<pad>" ,_a : Tuple="<cls>" ,_a : List[str]="<mask>" ,_a : str=["<eop>", "<eod>"] ,_a : Any = None ,**_a : Tuple ,):
'''simple docstring'''
_a : Any = AddedToken(__a ,lstrip=__a ,rstrip=__a ) if isinstance(__a ,__a ) else mask_token
_a : Tuple = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=__a ,remove_space=__a ,keep_accents=__a ,bos_token=__a ,eos_token=__a ,unk_token=__a ,sep_token=__a ,pad_token=__a ,cls_token=__a ,mask_token=__a ,additional_special_tokens=__a ,sp_model_kwargs=self.sp_model_kwargs ,**__a ,)
_a : Any = 3
_a : Any = do_lower_case
_a : Optional[int] = remove_space
_a : List[str] = keep_accents
_a : str = vocab_file
_a : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__a )
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__(
'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
'See https://pypi.org/project/jieba/ for installation.' )
_a : List[Any] = jieba
_a : Optional[int] = str.maketrans(' \n' ,'\u2582\u2583' )
@property
# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return len(self.sp_model )
def __lowercase ( self : Any ):
'''simple docstring'''
_a : Union[str, Any] = {self.convert_ids_to_tokens(__a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Union[str, Any] ):
'''simple docstring'''
_a : Optional[Any] = self.__dict__.copy()
_a : Any = None
return state
def __setstate__( self : str ,_a : Optional[Any] ):
'''simple docstring'''
_a : str = d
# for backward compatibility
if not hasattr(self ,'sp_model_kwargs' ):
_a : Tuple = {}
_a : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowercase ( self : Any ,_a : Any ):
'''simple docstring'''
if self.remove_space:
_a : Any = ' '.join(inputs.strip().split() )
else:
_a : int = inputs
_a : List[str] = outputs.replace('``' ,'"' ).replace('\'\'' ,'"' )
if not self.keep_accents:
_a : Optional[int] = unicodedata.normalize('NFKD' ,__a )
_a : Dict = ''.join([c for c in outputs if not unicodedata.combining(__a )] )
if self.do_lower_case:
_a : Union[str, Any] = outputs.lower()
return outputs
def __lowercase ( self : int ,_a : Tuple ):
'''simple docstring'''
_a : int = self.preprocess_text(__a )
_a : Any = self.sp_model.encode(__a ,out_type=__a )
_a : Dict = []
for piece in pieces:
if len(__a ) > 1 and piece[-1] == str(',' ) and piece[-2].isdigit():
_a : List[Any] = self.sp_model.EncodeAsPieces(piece[:-1].replace(__a ,'' ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
_a : Any = cur_pieces[1:]
else:
_a : List[str] = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(__a )
else:
new_pieces.append(__a )
return new_pieces
def __lowercase ( self : Dict ,_a : Union[str, Any] ):
'''simple docstring'''
return self.sp_model.PieceToId(__a )
def __lowercase ( self : Optional[int] ,_a : List[str] ):
'''simple docstring'''
return self.sp_model.IdToPiece(__a )
def __lowercase ( self : str ,_a : Dict ):
'''simple docstring'''
_a : Dict = ''.join(__a ).replace(__a ,' ' ).strip()
return out_string
def __lowercase ( self : Dict ,_a : str ,_a : str = None ):
'''simple docstring'''
_a : Union[str, Any] = [self.sep_token_id]
_a : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __lowercase ( self : str ,_a : List[str] ,_a : Optional[Any] = None ,_a : List[str] = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__a ,token_ids_a=__a ,already_has_special_tokens=__a )
if token_ids_a is not None:
return ([0] * len(__a )) + [1] + ([0] * len(__a )) + [1, 1]
return ([0] * len(__a )) + [1, 1]
def __lowercase ( self : Optional[Any] ,_a : int ,_a : Any = None ):
'''simple docstring'''
_a : Dict = [self.sep_token_id]
_a : Optional[int] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def __lowercase ( self : Optional[int] ,_a : Union[str, Any] ,_a : Any = None ):
'''simple docstring'''
if not os.path.isdir(__a ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_a : Dict = os.path.join(
__a ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,__a )
elif not os.path.isfile(self.vocab_file ):
with open(__a ,'wb' ) as fi:
_a : Optional[Any] = self.sp_model.serialized_model_proto()
fi.write(__a )
return (out_vocab_file,)
def __lowercase ( self : Tuple ,*_a : List[str] ,**_a : Any ):
'''simple docstring'''
_a : Tuple = super()._decode(*__a ,**__a )
_a : int = text.replace(' ' ,'' ).replace('\u2582' ,' ' ).replace('\u2583' ,'\n' )
return text
| 229 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowerCamelCase__ ( __snake_case ) -> Optional[Any]:
"""simple docstring"""
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
or (cp >= 0x3400 and cp <= 0x4DBF) #
or (cp >= 0x2_0000 and cp <= 0x2_A6DF) #
or (cp >= 0x2_A700 and cp <= 0x2_B73F) #
or (cp >= 0x2_B740 and cp <= 0x2_B81F) #
or (cp >= 0x2_B820 and cp <= 0x2_CEAF) #
or (cp >= 0xF900 and cp <= 0xFAFF)
or (cp >= 0x2_F800 and cp <= 0x2_FA1F) #
): #
return True
return False
def lowerCamelCase__ ( __snake_case ) -> Optional[Any]:
"""simple docstring"""
for char in word:
_UpperCamelCase = ord(__snake_case )
if not _is_chinese_char(__snake_case ):
return 0
return 1
def lowerCamelCase__ ( __snake_case ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = set()
for token in tokens:
_UpperCamelCase = len(__snake_case ) > 1 and is_chinese(__snake_case )
if chinese_word:
word_set.add(__snake_case )
_UpperCamelCase = list(__snake_case )
return word_list
def lowerCamelCase__ ( __snake_case, __snake_case ) -> int:
"""simple docstring"""
if not chinese_word_set:
return bert_tokens
_UpperCamelCase = max([len(__snake_case ) for w in chinese_word_set] )
_UpperCamelCase = bert_tokens
_UpperCamelCase , _UpperCamelCase = 0, len(__snake_case )
while start < end:
_UpperCamelCase = True
if is_chinese(bert_word[start] ):
_UpperCamelCase = min(end - start, __snake_case )
for i in range(__snake_case, 1, -1 ):
_UpperCamelCase = ''''''.join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1, start + i ):
_UpperCamelCase = '''##''' + bert_word[j]
_UpperCamelCase = start + i
_UpperCamelCase = False
break
if single_word:
start += 1
return bert_word
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = []
for i in range(0, len(__snake_case ), 1_00 ):
_UpperCamelCase = ltp_tokenizer.pipeline(lines[i : i + 1_00], tasks=['''cws'''] ).cws
_UpperCamelCase = [get_chinese_word(__snake_case ) for r in res]
ltp_res.extend(__snake_case )
assert len(__snake_case ) == len(__snake_case )
_UpperCamelCase = []
for i in range(0, len(__snake_case ), 1_00 ):
_UpperCamelCase = bert_tokenizer(lines[i : i + 1_00], add_special_tokens=__snake_case, truncation=__snake_case, max_length=5_12 )
bert_res.extend(res['''input_ids'''] )
assert len(__snake_case ) == len(__snake_case )
_UpperCamelCase = []
for input_ids, chinese_word in zip(__snake_case, __snake_case ):
_UpperCamelCase = []
for id in input_ids:
_UpperCamelCase = bert_tokenizer._convert_id_to_token(__snake_case )
input_tokens.append(__snake_case )
_UpperCamelCase = add_sub_symbol(__snake_case, __snake_case )
_UpperCamelCase = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__snake_case ):
if token[:2] == "##":
_UpperCamelCase = token[2:]
# save chinese tokens' pos
if len(__snake_case ) == 1 and _is_chinese_char(ord(__snake_case ) ):
ref_id.append(__snake_case )
ref_ids.append(__snake_case )
assert len(__snake_case ) == len(__snake_case )
return ref_ids
def lowerCamelCase__ ( __snake_case ) -> Optional[int]:
"""simple docstring"""
with open(args.file_name, '''r''', encoding='''utf-8''' ) as f:
_UpperCamelCase = f.readlines()
_UpperCamelCase = [line.strip() for line in data if len(__snake_case ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
_UpperCamelCase = LTP(args.ltp ) # faster in GPU device
_UpperCamelCase = BertTokenizer.from_pretrained(args.bert )
_UpperCamelCase = prepare_ref(__snake_case, __snake_case, __snake_case )
with open(args.save_path, '''w''', encoding='''utf-8''' ) as f:
_UpperCamelCase = [json.dumps(__snake_case ) + '''\n''' for ref in ref_ids]
f.writelines(__snake_case )
if __name__ == "__main__":
_a = argparse.ArgumentParser(description="""prepare_chinese_ref""")
parser.add_argument(
"""--file_name""",
required=False,
type=str,
default="""./resources/chinese-demo.txt""",
help="""file need process, same as training data in lm""",
)
parser.add_argument(
"""--ltp""",
required=False,
type=str,
default="""./resources/ltp""",
help="""resources for LTP tokenizer, usually a path""",
)
parser.add_argument(
"""--bert""",
required=False,
type=str,
default="""./resources/robert""",
help="""resources for Bert tokenizer""",
)
parser.add_argument(
"""--save_path""",
required=False,
type=str,
default="""./resources/ref.txt""",
help="""path to save res""",
)
_a = parser.parse_args()
main(args)
| 19 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[str] = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[Any] = [
'''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''PegasusXForConditionalGeneration''',
'''PegasusXModel''',
'''PegasusXPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
_lowerCamelCase : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 403 |
"""simple docstring"""
import heapq
def lowerCamelCase__ ( __snake_case ) -> set[int]:
"""simple docstring"""
_UpperCamelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(__snake_case, [-1 * len(__snake_case ), (key, value)] )
# chosen_vertices = set of chosen vertices
_UpperCamelCase = set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
_UpperCamelCase = heapq.heappop(__snake_case )[1][0]
chosen_vertices.add(__snake_case )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
_UpperCamelCase = elem[1][1].index(__snake_case )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(__snake_case )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
_a = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
| 19 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('0.12.2'):
raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
raise Exception('requires fairseq < v2')
logging.set_verbosity_info()
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : int = 'Hello, World!'
_snake_case : str = 'en_XX'
def snake_case_ (UpperCamelCase : List[Any] , UpperCamelCase : Dict , UpperCamelCase : List[Any] ):
'''simple docstring'''
_a = Path('''data_bin''' )
_a = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(__snake_case ).parent ) , checkpoint_file=Path(__snake_case ).name , _name='''xmod_base''' , arch='''xmod_base''' , task='''multilingual_masked_lm''' , data_name_or_path=str(__snake_case ) , bpe='''sentencepiece''' , sentencepiece_model=str(Path(__snake_case ).parent / '''sentencepiece.bpe.model''' ) , src_dict=str(data_dir / '''dict.txt''' ) , )
xmod.eval() # disable dropout
print(__snake_case )
_a = xmod.model.encoder.sentence_encoder
_a = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , '''bottleneck''' , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
_a = xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
print('''Our X-MOD config:''' , __snake_case )
_a = XmodForSequenceClassification(__snake_case ) if classification_head else XmodForMaskedLM(__snake_case )
model.eval()
# Now let's copy all the weights.
# Embeddings
_a = xmod_sent_encoder.embed_tokens.weight
_a = xmod_sent_encoder.embed_positions.weight
_a = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
_a = xmod_sent_encoder.layernorm_embedding.weight
_a = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
_a = model.roberta.encoder.layer[i]
_a = xmod_sent_encoder.layers[i]
# self attention
_a = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError('''Dimensions of self-attention weights do not match.''' )
_a = xmod_layer.self_attn.q_proj.weight
_a = xmod_layer.self_attn.q_proj.bias
_a = xmod_layer.self_attn.k_proj.weight
_a = xmod_layer.self_attn.k_proj.bias
_a = xmod_layer.self_attn.v_proj.weight
_a = xmod_layer.self_attn.v_proj.bias
# self-attention output
_a = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError('''Dimensions of self-attention output weights do not match.''' )
_a = xmod_layer.self_attn.out_proj.weight
_a = xmod_layer.self_attn.out_proj.bias
_a = xmod_layer.self_attn_layer_norm.weight
_a = xmod_layer.self_attn_layer_norm.bias
# intermediate
_a = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError('''Dimensions of intermediate weights do not match.''' )
_a = xmod_layer.fca.weight
_a = xmod_layer.fca.bias
# output
_a = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError('''Dimensions of feed-forward weights do not match.''' )
_a = xmod_layer.fca.weight
_a = xmod_layer.fca.bias
_a = xmod_layer.final_layer_norm.weight
_a = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
_a = xmod_layer.adapter_layer_norm.weight
_a = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError('''Lists of language adapters do not match.''' )
for lang_code, adapter in xmod_layer.adapter_modules.items():
_a = bert_output.adapter_modules[lang_code]
_a = xmod_layer.adapter_modules[lang_code]
_a = from_adapter.fca.weight
_a = from_adapter.fca.bias
_a = from_adapter.fca.weight
_a = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
_a = xmod_sent_encoder.layer_norm.weight
_a = xmod_sent_encoder.layer_norm.bias
if classification_head:
_a = xmod.model.classification_heads['''mnli'''].dense.weight
_a = xmod.model.classification_heads['''mnli'''].dense.bias
_a = xmod.model.classification_heads['''mnli'''].out_proj.weight
_a = xmod.model.classification_heads['''mnli'''].out_proj.bias
else:
# LM Head
_a = xmod.model.encoder.lm_head.dense.weight
_a = xmod.model.encoder.lm_head.dense.bias
_a = xmod.model.encoder.lm_head.layer_norm.weight
_a = xmod.model.encoder.lm_head.layer_norm.bias
_a = xmod.model.encoder.lm_head.weight
_a = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
_a = xmod.encode(__snake_case ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(__snake_case )
_a = model(__snake_case )[0]
if classification_head:
_a = xmod.model.classification_heads['''mnli'''](xmod.extract_features(__snake_case ) )
else:
_a = xmod.model(__snake_case , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
_a = torch.max(torch.abs(our_output - their_output ) ).item()
print(f'max_absolute_diff = {max_absolute_diff}' ) # ~ 1e-7
_a = torch.allclose(__snake_case , __snake_case , atol=1e-3 )
print('''Do both models output the same tensors?''' , '''🔥''' if success else '''💩''' )
if not success:
raise Exception('''Something went wRoNg''' )
Path(__snake_case ).mkdir(parents=__snake_case , exist_ok=__snake_case )
print(f'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(__snake_case )
if __name__ == "__main__":
_snake_case : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--classification_head', action='store_true', help='Whether to convert a final classification head.'
)
_snake_case : Optional[Any] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 22 |
"""simple docstring"""
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def lowerCamelCase__ ( ) -> List[str]:
"""simple docstring"""
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
_UpperCamelCase = '''__test_patch_submodule_mock__'''
with patch_submodule(_test_patching, '''os.path.join''', __snake_case ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os, _PatchedModuleObj )
assert isinstance(_test_patching.os.path, _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path, _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os, _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path, _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path, _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def lowerCamelCase__ ( ) -> List[str]:
"""simple docstring"""
assert _test_patching.open is open
_UpperCamelCase = '''__test_patch_submodule_builtin_mock__'''
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching, '''open''', __snake_case ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def lowerCamelCase__ ( ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_missing_mock__'''
with patch_submodule(_test_patching, '''pandas.read_csv''', __snake_case ):
pass
def lowerCamelCase__ ( ) -> Dict:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_missing_builtin_mock__'''
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching, '''len''', __snake_case ) is None
with patch_submodule(_test_patching, '''len''', __snake_case ):
assert _test_patching.len is mock
assert _test_patching.len is len
def lowerCamelCase__ ( ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_start_and_stop_mock__'''
_UpperCamelCase = patch_submodule(_test_patching, '''open''', __snake_case )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def lowerCamelCase__ ( ) -> Optional[int]:
"""simple docstring"""
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
_UpperCamelCase = '''__test_patch_submodule_successive_join__'''
_UpperCamelCase = '''__test_patch_submodule_successive_dirname__'''
_UpperCamelCase = '''__test_patch_submodule_successive_rename__'''
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching, '''os.path.join''', __snake_case ):
with patch_submodule(_test_patching, '''os.rename''', __snake_case ):
with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching, '''os.rename''', __snake_case ):
with patch_submodule(_test_patching, '''os.path.join''', __snake_case ):
with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def lowerCamelCase__ ( ) -> str:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_doesnt_exist_mock__'''
with patch_submodule(_test_patching, '''__module_that_doesn_exist__.__attribute_that_doesn_exist__''', __snake_case ):
pass
with patch_submodule(_test_patching, '''os.__attribute_that_doesn_exist__''', __snake_case ):
pass
| 19 | 0 |
'''simple docstring'''
def __a ( A__ = 6008_5147_5143 ) -> int:
try:
lowerCAmelCase = int(__snake_case )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
raise ValueError("Parameter n must be greater than or equal to one." )
lowerCAmelCase = 2
lowerCAmelCase = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
lowerCAmelCase = i
while n % i == 0:
lowerCAmelCase = n // i
i += 1
return int(__snake_case )
if __name__ == "__main__":
print(f"{solution() = }")
| 649 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = original_name.split('''.''' )[0]
_UpperCamelCase = key.split('''.''' )
_UpperCamelCase = int(key_list[key_list.index(__snake_case ) - 2] )
_UpperCamelCase = int(key_list[key_list.index(__snake_case ) - 1] )
_UpperCamelCase = orig_block_num - offset
_UpperCamelCase = key.replace(F'''{orig_block_num}.{layer_num}.{original_name}''', F'''block.{new_block_num}.{layer_num}.{new_name}''' )
return key
def lowerCamelCase__ ( __snake_case ) -> str:
"""simple docstring"""
_UpperCamelCase = OrderedDict()
_UpperCamelCase , _UpperCamelCase = 0, 0
for key, value in state_dict.items():
if key.startswith('''network''' ):
_UpperCamelCase = key.replace('''network''', '''poolformer.encoder''' )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith('''bias''' ) and "patch_embed" not in key:
patch_emb_offset += 1
_UpperCamelCase = key[: key.find('''proj''' )]
_UpperCamelCase = key.replace(__snake_case, F'''patch_embeddings.{total_embed_found}.''' )
_UpperCamelCase = key.replace('''proj''', '''projection''' )
if key.endswith('''bias''' ):
total_embed_found += 1
if "patch_embeddings" in key:
_UpperCamelCase = '''poolformer.encoder.''' + key
if "mlp.fc1" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''mlp.fc1''', '''output.conv1''' )
if "mlp.fc2" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''mlp.fc2''', '''output.conv2''' )
if "norm1" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''norm1''', '''before_norm''' )
if "norm2" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''norm2''', '''after_norm''' )
if "layer_scale_1" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''layer_scale_1''', '''layer_scale_1''' )
if "layer_scale_2" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''layer_scale_2''', '''layer_scale_2''' )
if "head" in key:
_UpperCamelCase = key.replace('''head''', '''classifier''' )
_UpperCamelCase = value
return new_state_dict
def lowerCamelCase__ ( ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_UpperCamelCase = Image.open(requests.get(__snake_case, stream=__snake_case ).raw )
return image
@torch.no_grad()
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> int:
"""simple docstring"""
_UpperCamelCase = PoolFormerConfig()
# set attributes based on model_name
_UpperCamelCase = '''huggingface/label-files'''
_UpperCamelCase = model_name[-3:]
_UpperCamelCase = 10_00
_UpperCamelCase = '''imagenet-1k-id2label.json'''
_UpperCamelCase = (1, 10_00)
# set config attributes
_UpperCamelCase = json.load(open(hf_hub_download(__snake_case, __snake_case, repo_type='''dataset''' ), '''r''' ) )
_UpperCamelCase = {int(__snake_case ): v for k, v in idalabel.items()}
_UpperCamelCase = idalabel
_UpperCamelCase = {v: k for k, v in idalabel.items()}
if size == "s12":
_UpperCamelCase = [2, 2, 6, 2]
_UpperCamelCase = [64, 1_28, 3_20, 5_12]
_UpperCamelCase = 4.0
_UpperCamelCase = 0.9
elif size == "s24":
_UpperCamelCase = [4, 4, 12, 4]
_UpperCamelCase = [64, 1_28, 3_20, 5_12]
_UpperCamelCase = 4.0
_UpperCamelCase = 0.9
elif size == "s36":
_UpperCamelCase = [6, 6, 18, 6]
_UpperCamelCase = [64, 1_28, 3_20, 5_12]
_UpperCamelCase = 4.0
_UpperCamelCase = 1e-6
_UpperCamelCase = 0.9
elif size == "m36":
_UpperCamelCase = [6, 6, 18, 6]
_UpperCamelCase = [96, 1_92, 3_84, 7_68]
_UpperCamelCase = 4.0
_UpperCamelCase = 1e-6
_UpperCamelCase = 0.95
elif size == "m48":
_UpperCamelCase = [8, 8, 24, 8]
_UpperCamelCase = [96, 1_92, 3_84, 7_68]
_UpperCamelCase = 4.0
_UpperCamelCase = 1e-6
_UpperCamelCase = 0.95
else:
raise ValueError(F'''Size {size} not supported''' )
# load image processor
_UpperCamelCase = PoolFormerImageProcessor(crop_pct=__snake_case )
# Prepare image
_UpperCamelCase = prepare_img()
_UpperCamelCase = image_processor(images=__snake_case, return_tensors='''pt''' ).pixel_values
logger.info(F'''Converting model {model_name}...''' )
# load original state dict
_UpperCamelCase = torch.load(__snake_case, map_location=torch.device('''cpu''' ) )
# rename keys
_UpperCamelCase = rename_keys(__snake_case )
# create HuggingFace model and load state dict
_UpperCamelCase = PoolFormerForImageClassification(__snake_case )
model.load_state_dict(__snake_case )
model.eval()
# Define image processor
_UpperCamelCase = PoolFormerImageProcessor(crop_pct=__snake_case )
_UpperCamelCase = image_processor(images=prepare_img(), return_tensors='''pt''' ).pixel_values
# forward pass
_UpperCamelCase = model(__snake_case )
_UpperCamelCase = outputs.logits
# define expected logit slices for different models
if size == "s12":
_UpperCamelCase = torch.tensor([-0.3045, -0.6758, -0.4869] )
elif size == "s24":
_UpperCamelCase = torch.tensor([0.4402, -0.1374, -0.8045] )
elif size == "s36":
_UpperCamelCase = torch.tensor([-0.6080, -0.5133, -0.5898] )
elif size == "m36":
_UpperCamelCase = torch.tensor([0.3952, 0.2263, -1.2668] )
elif size == "m48":
_UpperCamelCase = torch.tensor([0.1167, -0.0656, -0.3423] )
else:
raise ValueError(F'''Size {size} not supported''' )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3], __snake_case, atol=1e-2 )
# finally, save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(__snake_case ).mkdir(exist_ok=__snake_case )
model.save_pretrained(__snake_case )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__snake_case )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument(
"""--model_name""",
default="""poolformer_s12""",
type=str,
help="""Name of the model you'd like to convert.""",
)
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
_a = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 19 | 0 |
'''simple docstring'''
import math
def __UpperCamelCase( _A : Any ):
'''simple docstring'''
UpperCAmelCase__ : str = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__snake_case )
def __UpperCamelCase( _A : Union[str, Any] = 1 / 1_23_45 ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = 0
UpperCAmelCase__ : List[Any] = 0
UpperCAmelCase__ : Any = 3
while True:
UpperCAmelCase__ : Dict = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(__snake_case ):
UpperCAmelCase__ : Tuple = int(__snake_case )
total_partitions += 1
if check_partition_perfect(__snake_case ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(__snake_case )
integer += 1
if __name__ == "__main__":
print(f"""{solution() = }""")
| 614 |
"""simple docstring"""
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = (DPMSolverSDEScheduler,)
lowercase__ = 10
def UpperCAmelCase ( self , **__a) -> int:
'''simple docstring'''
_UpperCamelCase = {
'''num_train_timesteps''': 11_00,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''noise_sampler_seed''': 0,
}
config.update(**__a)
return config
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02]):
self.check_over_configs(beta_start=__a , beta_end=__a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__a)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__a)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
scheduler.set_timesteps(self.num_inference_steps)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCamelCase = sample.to(__a)
for i, t in enumerate(scheduler.timesteps):
_UpperCamelCase = scheduler.scale_model_input(__a , __a)
_UpperCamelCase = model(__a , __a)
_UpperCamelCase = scheduler.step(__a , __a , __a)
_UpperCamelCase = output.prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47_8210_4492_1875) < 1e-2
assert abs(result_mean.item() - 0.2178_7059_6456_5277) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3521_1181_6406) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9068_9229_9652) < 1e-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''')
_UpperCamelCase = scheduler_class(**__a)
scheduler.set_timesteps(self.num_inference_steps)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCamelCase = sample.to(__a)
for i, t in enumerate(scheduler.timesteps):
_UpperCamelCase = scheduler.scale_model_input(__a , __a)
_UpperCamelCase = model(__a , __a)
_UpperCamelCase = scheduler.step(__a , __a , __a)
_UpperCamelCase = output.prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77_1492_0043_9453) < 1e-2
assert abs(result_mean.item() - 0.1_6226_2890_1481_6284) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1_6633_6059_5703) < 1e-2
assert abs(result_mean.item() - 0.1_6688_3260_0116_7297) < 1e-3
else:
assert abs(result_sum.item() - 119.8_4875_4882_8125) < 1e-2
assert abs(result_mean.item() - 0.1560_5306_6253_6621) < 1e-3
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
scheduler.set_timesteps(self.num_inference_steps , device=__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
_UpperCamelCase = scheduler.scale_model_input(__a , __a)
_UpperCamelCase = model(__a , __a)
_UpperCamelCase = scheduler.step(__a , __a , __a)
_UpperCamelCase = output.prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46_9573_9746_0938) < 1e-2
assert abs(result_mean.item() - 0.2_1805_9346_0798_2635) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3536_3769_5312) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9083_8241_5771) < 1e-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a , use_karras_sigmas=__a)
scheduler.set_timesteps(self.num_inference_steps , device=__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma
_UpperCamelCase = sample.to(__a)
for t in scheduler.timesteps:
_UpperCamelCase = scheduler.scale_model_input(__a , __a)
_UpperCamelCase = model(__a , __a)
_UpperCamelCase = scheduler.step(__a , __a , __a)
_UpperCamelCase = output.prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66_9741_3574_2188) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63_6535_6445_3125) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
else:
assert abs(result_sum.item() - 170.3_1352_2338_8672) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
| 19 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase =logging.get_logger(__name__)
lowercase ={
'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
'microsoft/markuplm-large': 'https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json',
}
class __magic_name__ ( lowerCAmelCase ):
UpperCAmelCase ="markuplm"
def __init__( self , snake_case=3_0_5_2_2 , snake_case=7_6_8 , snake_case=1_2 , snake_case=1_2 , snake_case=3_0_7_2 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=5_1_2 , snake_case=2 , snake_case=0.02 , snake_case=1E-1_2 , snake_case=0 , snake_case=0 , snake_case=2 , snake_case=2_5_6 , snake_case=1_0_2_4 , snake_case=2_1_6 , snake_case=1_0_0_1 , snake_case=3_2 , snake_case=5_0 , snake_case="absolute" , snake_case=True , snake_case=None , **snake_case , ) -> List[Any]:
'''simple docstring'''
super().__init__(
pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a , )
_UpperCAmelCase : Any =vocab_size
_UpperCAmelCase : Union[str, Any] =hidden_size
_UpperCAmelCase : Dict =num_hidden_layers
_UpperCAmelCase : int =num_attention_heads
_UpperCAmelCase : List[str] =hidden_act
_UpperCAmelCase : Any =intermediate_size
_UpperCAmelCase : int =hidden_dropout_prob
_UpperCAmelCase : List[str] =attention_probs_dropout_prob
_UpperCAmelCase : Optional[Any] =max_position_embeddings
_UpperCAmelCase : Optional[int] =type_vocab_size
_UpperCAmelCase : List[Any] =initializer_range
_UpperCAmelCase : Tuple =layer_norm_eps
_UpperCAmelCase : str =position_embedding_type
_UpperCAmelCase : Optional[Any] =use_cache
_UpperCAmelCase : Optional[Any] =classifier_dropout
# additional properties
_UpperCAmelCase : Optional[int] =max_depth
_UpperCAmelCase : Optional[int] =max_xpath_tag_unit_embeddings
_UpperCAmelCase : Optional[int] =max_xpath_subs_unit_embeddings
_UpperCAmelCase : Dict =tag_pad_id
_UpperCAmelCase : List[str] =subs_pad_id
_UpperCAmelCase : int =xpath_unit_hidden_size
| 446 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_a = logging.get_logger(__name__)
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = ['pixel_values']
def __init__( self , __a = True , __a = None , __a = PILImageResampling.BICUBIC , __a = True , __a = True , __a = 1 / 2_55 , __a = None , __a = True , __a = None , __a = None , **__a , ) -> None:
'''simple docstring'''
super().__init__(**__a)
_UpperCamelCase = size if size is not None else {'''height''': 2_24, '''width''': 2_24}
_UpperCamelCase = get_size_dict(__a)
_UpperCamelCase = crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
_UpperCamelCase = get_size_dict(__a , default_to_square=__a , param_name='''crop_size''')
_UpperCamelCase = do_resize
_UpperCamelCase = do_rescale
_UpperCamelCase = do_normalize
_UpperCamelCase = do_center_crop
_UpperCamelCase = crop_size
_UpperCamelCase = size
_UpperCamelCase = resample
_UpperCamelCase = rescale_factor
_UpperCamelCase = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
_UpperCamelCase = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCAmelCase ( self , __a , __a , __a = PILImageResampling.BILINEAR , __a = None , **__a , ) -> np.ndarray:
'''simple docstring'''
_UpperCamelCase = get_size_dict(__a)
if "shortest_edge" in size:
_UpperCamelCase = get_resize_output_image_size(__a , size=size['''shortest_edge'''] , default_to_square=__a)
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
_UpperCamelCase = (size['''height'''], size['''width'''])
else:
raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''')
return resize(__a , size=__a , resample=__a , data_format=__a , **__a)
def UpperCAmelCase ( self , __a , __a , __a = None , **__a , ) -> np.ndarray:
'''simple docstring'''
_UpperCamelCase = get_size_dict(__a)
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''')
return center_crop(__a , size=(size['''height'''], size['''width''']) , data_format=__a , **__a)
def UpperCAmelCase ( self , __a , __a , __a = None , **__a) -> np.ndarray:
'''simple docstring'''
return rescale(__a , scale=__a , data_format=__a , **__a)
def UpperCAmelCase ( self , __a , __a , __a , __a = None , **__a , ) -> np.ndarray:
'''simple docstring'''
return normalize(__a , mean=__a , std=__a , data_format=__a , **__a)
def UpperCAmelCase ( self , __a , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = ChannelDimension.FIRST , **__a , ) -> BatchFeature:
'''simple docstring'''
_UpperCamelCase = do_resize if do_resize is not None else self.do_resize
_UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
_UpperCamelCase = crop_size if crop_size is not None else self.crop_size
_UpperCamelCase = get_size_dict(__a , param_name='''crop_size''' , default_to_square=__a)
_UpperCamelCase = resample if resample is not None else self.resample
_UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCamelCase = image_mean if image_mean is not None else self.image_mean
_UpperCamelCase = image_std if image_std is not None else self.image_std
_UpperCamelCase = size if size is not None else self.size
_UpperCamelCase = get_size_dict(__a)
if not is_batched(__a):
_UpperCamelCase = [images]
if not valid_images(__a):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''')
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''')
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''')
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''')
# All transformations expect numpy arrays.
_UpperCamelCase = [to_numpy_array(__a) for image in images]
if do_resize:
_UpperCamelCase = [self.resize(image=__a , size=__a , resample=__a) for image in images]
if do_center_crop:
_UpperCamelCase = [self.center_crop(image=__a , size=__a) for image in images]
if do_rescale:
_UpperCamelCase = [self.rescale(image=__a , scale=__a) for image in images]
if do_normalize:
_UpperCamelCase = [self.normalize(image=__a , mean=__a , std=__a) for image in images]
_UpperCamelCase = [to_channel_dimension_format(__a , __a) for image in images]
_UpperCamelCase = {'''pixel_values''': images}
return BatchFeature(data=__a , tensor_type=__a)
| 19 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCamelCase : Dict = {
'''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class lowerCamelCase (__lowerCamelCase , __lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase_ = "nat"
UpperCAmelCase_ = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self : str, _UpperCAmelCase : Union[str, Any]=4, _UpperCAmelCase : List[str]=3, _UpperCAmelCase : Dict=6_4, _UpperCAmelCase : Union[str, Any]=[3, 4, 6, 5], _UpperCAmelCase : List[str]=[2, 4, 8, 1_6], _UpperCAmelCase : Dict=7, _UpperCAmelCase : int=3.0, _UpperCAmelCase : str=True, _UpperCAmelCase : List[Any]=0.0, _UpperCAmelCase : Optional[int]=0.0, _UpperCAmelCase : Optional[int]=0.1, _UpperCAmelCase : Union[str, Any]="gelu", _UpperCAmelCase : str=0.02, _UpperCAmelCase : Any=1E-5, _UpperCAmelCase : int=0.0, _UpperCAmelCase : Optional[Any]=None, _UpperCAmelCase : Union[str, Any]=None, **_UpperCAmelCase : List[str], ) -> Tuple:
"""simple docstring"""
super().__init__(**__a )
SCREAMING_SNAKE_CASE__ : List[Any] = patch_size
SCREAMING_SNAKE_CASE__ : Dict = num_channels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = embed_dim
SCREAMING_SNAKE_CASE__ : List[Any] = depths
SCREAMING_SNAKE_CASE__ : Optional[int] = len(__a )
SCREAMING_SNAKE_CASE__ : Tuple = num_heads
SCREAMING_SNAKE_CASE__ : Any = kernel_size
SCREAMING_SNAKE_CASE__ : str = mlp_ratio
SCREAMING_SNAKE_CASE__ : List[str] = qkv_bias
SCREAMING_SNAKE_CASE__ : List[str] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] = drop_path_rate
SCREAMING_SNAKE_CASE__ : str = hidden_act
SCREAMING_SNAKE_CASE__ : List[str] = layer_norm_eps
SCREAMING_SNAKE_CASE__ : List[str] = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
SCREAMING_SNAKE_CASE__ : Any = int(embed_dim * 2 ** (len(__a ) - 1) )
SCREAMING_SNAKE_CASE__ : Dict = layer_scale_init_value
SCREAMING_SNAKE_CASE__ : Optional[Any] = ["stem"] + [F'''stage{idx}''' for idx in range(1, len(__a ) + 1 )]
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Optional[int] = get_aligned_output_features_output_indices(
out_features=__a, out_indices=__a, stage_names=self.stage_names )
| 663 |
"""simple docstring"""
# Imports
import numpy as np
class _UpperCAmelCase:
def __init__( self , __a=None , __a=None , __a=None , __a=None , __a=None) -> Dict:
'''simple docstring'''
self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a)
def UpperCAmelCase ( self , __a=None , __a=None , __a=None , __a=None , __a=None) -> Dict:
'''simple docstring'''
if red is not None:
_UpperCamelCase = red
if green is not None:
_UpperCamelCase = green
if blue is not None:
_UpperCamelCase = blue
if red_edge is not None:
_UpperCamelCase = red_edge
if nir is not None:
_UpperCamelCase = nir
return True
def UpperCAmelCase ( self , __a="" , __a=None , __a=None , __a=None , __a=None , __a=None) -> List[str]:
'''simple docstring'''
self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a)
_UpperCamelCase = {
'''ARVI2''': self.arvaa,
'''CCCI''': self.ccci,
'''CVI''': self.cvi,
'''GLI''': self.gli,
'''NDVI''': self.ndvi,
'''BNDVI''': self.bndvi,
'''redEdgeNDVI''': self.red_edge_ndvi,
'''GNDVI''': self.gndvi,
'''GBNDVI''': self.gbndvi,
'''GRNDVI''': self.grndvi,
'''RBNDVI''': self.rbndvi,
'''PNDVI''': self.pndvi,
'''ATSAVI''': self.atsavi,
'''BWDRVI''': self.bwdrvi,
'''CIgreen''': self.ci_green,
'''CIrededge''': self.ci_rededge,
'''CI''': self.ci,
'''CTVI''': self.ctvi,
'''GDVI''': self.gdvi,
'''EVI''': self.evi,
'''GEMI''': self.gemi,
'''GOSAVI''': self.gosavi,
'''GSAVI''': self.gsavi,
'''Hue''': self.hue,
'''IVI''': self.ivi,
'''IPVI''': self.ipvi,
'''I''': self.i,
'''RVI''': self.rvi,
'''MRVI''': self.mrvi,
'''MSAVI''': self.m_savi,
'''NormG''': self.norm_g,
'''NormNIR''': self.norm_nir,
'''NormR''': self.norm_r,
'''NGRDI''': self.ngrdi,
'''RI''': self.ri,
'''S''': self.s,
'''IF''': self._if,
'''DVI''': self.dvi,
'''TVI''': self.tvi,
'''NDRE''': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('''Index not in the list!''')
return False
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return self.nir * (self.red / (self.green**2))
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
return (self.nir - self.red) / (self.nir + self.red)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
return (self.nir - self.blue) / (self.nir + self.blue)
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return (self.redEdge - self.red) / (self.redEdge + self.red)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return (self.nir - self.green) / (self.nir + self.green)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def UpperCAmelCase ( self , __a=0.08 , __a=1.22 , __a=0.03) -> Optional[Any]:
'''simple docstring'''
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
return (self.nir / self.green) - 1
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return (self.nir / self.redEdge) - 1
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
return (self.red - self.blue) / self.red
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5))) * (abs(ndvi + 0.5) ** (1 / 2))
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return self.nir - self.green
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)
def UpperCAmelCase ( self , __a=0.16) -> Optional[Any]:
'''simple docstring'''
return (self.nir - self.green) / (self.nir + self.green + y)
def UpperCAmelCase ( self , __a=0.5) -> Dict:
'''simple docstring'''
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue))
def UpperCAmelCase ( self , __a=None , __a=None) -> Any:
'''simple docstring'''
return (self.nir - b) / (a * self.red)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
return (self.red + self.green + self.blue) / 30.5
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return self.nir / self.red
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return (self.rvi() - 1) / (self.rvi() + 1)
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return self.green / (self.nir + self.red + self.green)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
return self.nir / (self.nir + self.red + self.green)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return self.red / (self.nir + self.red + self.green)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return (self.green - self.red) / (self.green + self.red)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
return (self.red - self.green) / (self.red + self.green)
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = np.max([np.max(self.red), np.max(self.green), np.max(self.blue)])
_UpperCamelCase = np.min([np.min(self.red), np.min(self.green), np.min(self.blue)])
return (max_value - min_value) / max_value
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return self.nir / self.red
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return (self.ndvi() + 0.5) ** (1 / 2)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 19 | 0 |
import argparse
import os
import re
import packaging.version
__lowerCamelCase : Dict = "examples/"
__lowerCamelCase : int = {
"examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(r"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
"setup": (re.compile(r"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), r"\1version=\"VERSION\","),
"doc": (re.compile(r"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
__lowerCamelCase : Dict = {
"init": "src/diffusers/__init__.py",
"setup": "setup.py",
}
__lowerCamelCase : int = "README.md"
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Union[str, Any]:
with open(__snake_case , "r" , encoding="utf-8" , newline="\n" ) as f:
UpperCAmelCase = f.read()
UpperCAmelCase , UpperCAmelCase = REPLACE_PATTERNS[pattern]
UpperCAmelCase = replace.replace("VERSION" , __snake_case )
UpperCAmelCase = re_pattern.sub(__snake_case , __snake_case )
with open(__snake_case , "w" , encoding="utf-8" , newline="\n" ) as f:
f.write(__snake_case )
def lowerCamelCase_(lowerCamelCase_ ) -> str:
for folder, directories, fnames in os.walk(__snake_case ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("research_projects" )
if "legacy" in directories:
directories.remove("legacy" )
for fname in fnames:
if fname.endswith(".py" ):
update_version_in_file(os.path.join(__snake_case , __snake_case ) , __snake_case , pattern="examples" )
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_=False ) -> Dict:
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__snake_case , __snake_case , __snake_case )
if not patch:
update_version_in_examples(__snake_case )
def lowerCamelCase_() -> Union[str, Any]:
UpperCAmelCase = "🤗 Transformers currently provides the following architectures"
UpperCAmelCase = "1. Want to contribute a new model?"
with open(__snake_case , "r" , encoding="utf-8" , newline="\n" ) as f:
UpperCAmelCase = f.readlines()
# Find the start of the list.
UpperCAmelCase = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
UpperCAmelCase = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("1." ):
UpperCAmelCase = lines[index].replace(
"https://huggingface.co/docs/diffusers/main/model_doc" , "https://huggingface.co/docs/diffusers/model_doc" , )
index += 1
with open(__snake_case , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(__snake_case )
def lowerCamelCase_() -> str:
with open(REPLACE_FILES["init"] , "r" ) as f:
UpperCAmelCase = f.read()
UpperCAmelCase = REPLACE_PATTERNS["init"][0].search(__snake_case ).groups()[0]
return packaging.version.parse(__snake_case )
def lowerCamelCase_(lowerCamelCase_=False ) -> Optional[Any]:
UpperCAmelCase = get_version()
if patch and default_version.is_devrelease:
raise ValueError("Can\'t create a patch version from the dev branch, checkout a released version!" )
if default_version.is_devrelease:
UpperCAmelCase = default_version.base_version
elif patch:
UpperCAmelCase = F'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
else:
UpperCAmelCase = F'{default_version.major}.{default_version.minor + 1}.0'
# Now let's ask nicely if that's the right one.
UpperCAmelCase = input(F'Which version are you releasing? [{default_version}]' )
if len(__snake_case ) == 0:
UpperCAmelCase = default_version
print(F'Updating version to {version}.' )
global_version_update(__snake_case , patch=__snake_case )
def lowerCamelCase_() -> Any:
UpperCAmelCase = get_version()
UpperCAmelCase = F'{current_version.major}.{current_version.minor + 1}.0.dev0'
UpperCAmelCase = current_version.base_version
# Check with the user we got that right.
UpperCAmelCase = input(F'Which version are we developing now? [{dev_version}]' )
if len(__snake_case ) == 0:
UpperCAmelCase = dev_version
print(F'Updating version to {version}.' )
global_version_update(__snake_case )
# print("Cleaning main README, don't forget to run `make fix-copies`.")
# clean_main_ref_in_model_list()
if __name__ == "__main__":
__lowerCamelCase : str = argparse.ArgumentParser()
parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
__lowerCamelCase : int = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print("Nothing to do after a patch :-)")
else:
post_release_work()
| 323 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class _UpperCAmelCase:
def __init__( self , __a , __a=13 , __a=64 , __a=2 , __a=3 , __a=True , __a=True , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=10 , __a=0.02 , __a=[1, 16, 4, 4] , __a=None , ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = image_size
_UpperCamelCase = patch_size
_UpperCamelCase = num_channels
_UpperCamelCase = is_training
_UpperCamelCase = use_labels
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = scope
_UpperCamelCase = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
_UpperCamelCase = (self.image_size // 32) ** 2
_UpperCamelCase = num_patches + 1
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_UpperCamelCase = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [4, 8, 16, 32],
'''num_groups''': 2,
}
return ViTHybridConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__a , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=__a , )
def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = ViTHybridModel(config=__a)
model.to(__a)
model.eval()
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCAmelCase ( self , __a , __a , __a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = self.type_sequence_label_size
_UpperCamelCase = ViTHybridForImageClassification(__a)
model.to(__a)
model.eval()
_UpperCamelCase = model(__a , labels=__a)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = self.prepare_config_and_inputs()
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = config_and_inputs
_UpperCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase__ = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
lowercase__ = (
{'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
if is_torch_available()
else {}
)
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = ViTHybridModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37)
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''')
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
pass
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(__a)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
_UpperCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__a , nn.Linear))
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(__a)
_UpperCamelCase = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCamelCase = [*signature.parameters.keys()]
_UpperCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __a)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__a)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCamelCase = _config_zero_init(__a)
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(config=__a)
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
_UpperCamelCase = [F'''{name}.{key}''' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@slow
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = ViTHybridModel.from_pretrained(__a)
self.assertIsNotNone(__a)
def lowerCamelCase__ ( ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class _UpperCAmelCase( unittest.TestCase ):
@cached_property
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
if is_vision_available()
else None
)
@slow
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
__a)
_UpperCamelCase = self.default_image_processor
_UpperCamelCase = prepare_img()
_UpperCamelCase = image_processor(images=__a , return_tensors='''pt''').to(__a)
# forward pass
with torch.no_grad():
_UpperCamelCase = model(**__a)
# verify the logits
_UpperCamelCase = torch.Size((1, 10_00))
self.assertEqual(outputs.logits.shape , __a)
_UpperCamelCase = torch.tensor([-1.9090, -0.4993, -0.2389]).to(__a)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __a , atol=1e-4))
@slow
@require_accelerate
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''')
_UpperCamelCase = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''' , device_map='''auto''')
_UpperCamelCase = prepare_img()
_UpperCamelCase = image_processor(images=__a , return_tensors='''pt''')
_UpperCamelCase = model(**__a)
_UpperCamelCase = outputs.logits
# model predicts one of the 1000 ImageNet classes
_UpperCamelCase = logits.argmax(-1).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] , '''tabby, tabby cat''')
| 19 | 0 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def __UpperCamelCase ( UpperCAmelCase ):
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 152 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = ['vqvae']
def __init__( self , __a , __a , __a , __a , ) -> List[str]:
'''simple docstring'''
super().__init__()
self.register_modules(unet=__a , scheduler=__a , mel=__a , vqvae=__a)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return 50 if isinstance(self.scheduler , __a) else 10_00
@torch.no_grad()
def __call__( self , __a = 1 , __a = None , __a = None , __a = 0 , __a = 0 , __a = None , __a = None , __a = 0 , __a = 0 , __a = None , __a = 0 , __a = None , __a = None , __a=True , ) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
'''simple docstring'''
_UpperCamelCase = steps or self.get_default_steps()
self.scheduler.set_timesteps(__a)
_UpperCamelCase = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size) == int:
_UpperCamelCase = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
_UpperCamelCase = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=__a , device=self.device , )
_UpperCamelCase = noise
_UpperCamelCase = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(__a , __a)
_UpperCamelCase = self.mel.audio_slice_to_image(__a)
_UpperCamelCase = np.frombuffer(input_image.tobytes() , dtype='''uint8''').reshape(
(input_image.height, input_image.width))
_UpperCamelCase = (input_image / 2_55) * 2 - 1
_UpperCamelCase = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float).to(self.device)
if self.vqvae is not None:
_UpperCamelCase = self.vqvae.encode(torch.unsqueeze(__a , 0)).latent_dist.sample(
generator=__a)[0]
_UpperCamelCase = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
_UpperCamelCase = self.scheduler.add_noise(__a , __a , self.scheduler.timesteps[start_step - 1])
_UpperCamelCase = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
_UpperCamelCase = int(mask_start_secs * pixels_per_second)
_UpperCamelCase = int(mask_end_secs * pixels_per_second)
_UpperCamelCase = self.scheduler.add_noise(__a , __a , torch.tensor(self.scheduler.timesteps[start_step:]))
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
if isinstance(self.unet , __a):
_UpperCamelCase = self.unet(__a , __a , __a)['''sample''']
else:
_UpperCamelCase = self.unet(__a , __a)['''sample''']
if isinstance(self.scheduler , __a):
_UpperCamelCase = self.scheduler.step(
model_output=__a , timestep=__a , sample=__a , eta=__a , generator=__a , )['''prev_sample''']
else:
_UpperCamelCase = self.scheduler.step(
model_output=__a , timestep=__a , sample=__a , generator=__a , )['''prev_sample''']
if mask is not None:
if mask_start > 0:
_UpperCamelCase = mask[:, step, :, :mask_start]
if mask_end > 0:
_UpperCamelCase = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
_UpperCamelCase = 1 / self.vqvae.config.scaling_factor * images
_UpperCamelCase = self.vqvae.decode(__a)['''sample''']
_UpperCamelCase = (images / 2 + 0.5).clamp(0 , 1)
_UpperCamelCase = images.cpu().permute(0 , 2 , 3 , 1).numpy()
_UpperCamelCase = (images * 2_55).round().astype('''uint8''')
_UpperCamelCase = list(
(Image.fromarray(_[:, :, 0]) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(__a , mode='''RGB''').convert('''L''') for _ in images))
_UpperCamelCase = [self.mel.image_to_audio(__a) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(__a)[:, np.newaxis, :]) , **ImagePipelineOutput(__a))
@torch.no_grad()
def UpperCAmelCase ( self , __a , __a = 50) -> np.ndarray:
'''simple docstring'''
assert isinstance(self.scheduler , __a)
self.scheduler.set_timesteps(__a)
_UpperCamelCase = np.array(
[np.frombuffer(image.tobytes() , dtype='''uint8''').reshape((1, image.height, image.width)) for image in images])
_UpperCamelCase = (sample / 2_55) * 2 - 1
_UpperCamelCase = torch.Tensor(__a).to(self.device)
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,))):
_UpperCamelCase = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
_UpperCamelCase = self.scheduler.alphas_cumprod[t]
_UpperCamelCase = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
_UpperCamelCase = 1 - alpha_prod_t
_UpperCamelCase = self.unet(__a , __a)['''sample''']
_UpperCamelCase = (1 - alpha_prod_t_prev) ** 0.5 * model_output
_UpperCamelCase = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
_UpperCamelCase = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def UpperCAmelCase ( __a , __a , __a) -> torch.Tensor:
'''simple docstring'''
_UpperCamelCase = acos(torch.dot(torch.flatten(__a) , torch.flatten(__a)) / torch.norm(__a) / torch.norm(__a))
return sin((1 - alpha) * theta) * xa / sin(__a) + sin(alpha * theta) * xa / sin(__a)
| 19 | 0 |
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(__snake_case , __snake_case ):
raise TypeError("Input value must be a \'int\' type" )
return bin(__snake_case ).count("1" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 340 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_a = logging.get_logger(__name__)
_a = {
"""facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = 'detr'
lowercase__ = ['past_key_values']
lowercase__ = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , __a=True , __a=None , __a=3 , __a=1_00 , __a=6 , __a=20_48 , __a=8 , __a=6 , __a=20_48 , __a=8 , __a=0.0 , __a=0.0 , __a=True , __a="relu" , __a=2_56 , __a=0.1 , __a=0.0 , __a=0.0 , __a=0.02 , __a=1.0 , __a=False , __a="sine" , __a="resnet50" , __a=True , __a=False , __a=1 , __a=5 , __a=2 , __a=1 , __a=1 , __a=5 , __a=2 , __a=0.1 , **__a , ) -> int:
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
_UpperCamelCase = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
elif isinstance(__a , __a):
_UpperCamelCase = backbone_config.get('''model_type''')
_UpperCamelCase = CONFIG_MAPPING[backbone_model_type]
_UpperCamelCase = config_class.from_dict(__a)
# set timm attributes to None
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None, None, None
_UpperCamelCase = use_timm_backbone
_UpperCamelCase = backbone_config
_UpperCamelCase = num_channels
_UpperCamelCase = num_queries
_UpperCamelCase = d_model
_UpperCamelCase = encoder_ffn_dim
_UpperCamelCase = encoder_layers
_UpperCamelCase = encoder_attention_heads
_UpperCamelCase = decoder_ffn_dim
_UpperCamelCase = decoder_layers
_UpperCamelCase = decoder_attention_heads
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = activation_function
_UpperCamelCase = init_std
_UpperCamelCase = init_xavier_std
_UpperCamelCase = encoder_layerdrop
_UpperCamelCase = decoder_layerdrop
_UpperCamelCase = encoder_layers
_UpperCamelCase = auxiliary_loss
_UpperCamelCase = position_embedding_type
_UpperCamelCase = backbone
_UpperCamelCase = use_pretrained_backbone
_UpperCamelCase = dilation
# Hungarian matcher
_UpperCamelCase = class_cost
_UpperCamelCase = bbox_cost
_UpperCamelCase = giou_cost
# Loss coefficients
_UpperCamelCase = mask_loss_coefficient
_UpperCamelCase = dice_loss_coefficient
_UpperCamelCase = bbox_loss_coefficient
_UpperCamelCase = giou_loss_coefficient
_UpperCamelCase = eos_coefficient
super().__init__(is_encoder_decoder=__a , **__a)
@property
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return self.d_model
@classmethod
def UpperCAmelCase ( cls , __a , **__a) -> int:
'''simple docstring'''
return cls(backbone_config=__a , **__a)
def UpperCAmelCase ( self) -> Dict[str, any]:
'''simple docstring'''
_UpperCamelCase = copy.deepcopy(self.__dict__)
if output["backbone_config"] is not None:
_UpperCamelCase = self.backbone_config.to_dict()
_UpperCamelCase = self.__class__.model_type
return output
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = version.parse('1.11' )
@property
def UpperCAmelCase ( self) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
])
@property
def UpperCAmelCase ( self) -> float:
'''simple docstring'''
return 1e-5
@property
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return 12
| 19 | 0 |
from __future__ import annotations
class lowerCamelCase__ :
'''simple docstring'''
def __init__(self ,__lowerCamelCase ) -> None:
"""simple docstring"""
lowerCAmelCase__ : List[str] = data
lowerCAmelCase__ : int = None
lowerCAmelCase__ : Optional[Any] = None
def lowerCAmelCase__ ( lowerCamelCase_ : str): # In Order traversal of the tree
'''simple docstring'''
if tree:
display(tree.left)
print(tree.data)
display(tree.right)
def lowerCAmelCase__ ( lowerCamelCase_ : List[str]):
'''simple docstring'''
return 1 + max(depth_of_tree(tree.left) ,depth_of_tree(tree.right)) if tree else 0
def lowerCAmelCase__ ( lowerCamelCase_ : Optional[int]):
'''simple docstring'''
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left) and is_full_binary_tree(tree.right)
else:
return not tree.left and not tree.right
def lowerCAmelCase__ ( ): # Main function for testing.
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = Node(1)
lowerCAmelCase__ : List[Any] = Node(2)
lowerCAmelCase__ : Union[str, Any] = Node(3)
lowerCAmelCase__ : Tuple = Node(4)
lowerCAmelCase__ : Union[str, Any] = Node(5)
lowerCAmelCase__ : Optional[int] = Node(6)
lowerCAmelCase__ : Tuple = Node(7)
lowerCAmelCase__ : Optional[Any] = Node(8)
lowerCAmelCase__ : Optional[Any] = Node(9)
print(is_full_binary_tree(__snake_case))
print(depth_of_tree(__snake_case))
print('''Tree is: ''')
display(__snake_case)
if __name__ == "__main__":
main()
| 647 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = 'wavlm'
def __init__( self , __a=32 , __a=7_68 , __a=12 , __a=12 , __a=30_72 , __a="gelu" , __a=0.1 , __a=0.1 , __a=0.1 , __a=0.0 , __a=0.1 , __a=0.1 , __a=0.02 , __a=1e-5 , __a="group" , __a="gelu" , __a=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __a=(5, 2, 2, 2, 2, 2, 2) , __a=(10, 3, 3, 3, 3, 2, 2) , __a=False , __a=1_28 , __a=16 , __a=3_20 , __a=8_00 , __a=False , __a=True , __a=0.05 , __a=10 , __a=2 , __a=0.0 , __a=10 , __a=3_20 , __a=2 , __a=0.1 , __a=1_00 , __a=2_56 , __a=2_56 , __a=0.1 , __a="mean" , __a=False , __a=False , __a=2_56 , __a=(5_12, 5_12, 5_12, 5_12, 15_00) , __a=(5, 3, 3, 1, 1) , __a=(1, 2, 3, 1, 1) , __a=5_12 , __a=80 , __a=0 , __a=1 , __a=2 , __a=False , __a=3 , __a=2 , __a=3 , __a=None , **__a , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**__a , pad_token_id=__a , bos_token_id=__a , eos_token_id=__a)
_UpperCamelCase = hidden_size
_UpperCamelCase = feat_extract_norm
_UpperCamelCase = feat_extract_activation
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = conv_bias
_UpperCamelCase = num_buckets
_UpperCamelCase = max_bucket_distance
_UpperCamelCase = num_conv_pos_embeddings
_UpperCamelCase = num_conv_pos_embedding_groups
_UpperCamelCase = len(self.conv_dim)
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = num_attention_heads
_UpperCamelCase = hidden_dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = feat_proj_dropout
_UpperCamelCase = final_dropout
_UpperCamelCase = layerdrop
_UpperCamelCase = layer_norm_eps
_UpperCamelCase = initializer_range
_UpperCamelCase = num_ctc_classes
_UpperCamelCase = vocab_size
_UpperCamelCase = do_stable_layer_norm
_UpperCamelCase = use_weighted_layer_sum
_UpperCamelCase = classifier_proj_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F''' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,'''
F''' `len(config.conv_kernel) = {len(self.conv_kernel)}`.''')
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_UpperCamelCase = apply_spec_augment
_UpperCamelCase = mask_time_prob
_UpperCamelCase = mask_time_length
_UpperCamelCase = mask_time_min_masks
_UpperCamelCase = mask_feature_prob
_UpperCamelCase = mask_feature_length
# parameters for pretraining with codevector quantized representations
_UpperCamelCase = num_codevectors_per_group
_UpperCamelCase = num_codevector_groups
_UpperCamelCase = contrastive_logits_temperature
_UpperCamelCase = num_negatives
_UpperCamelCase = codevector_dim
_UpperCamelCase = proj_codevector_dim
_UpperCamelCase = diversity_loss_weight
# ctc loss
_UpperCamelCase = ctc_loss_reduction
_UpperCamelCase = ctc_zero_infinity
# adapter
_UpperCamelCase = add_adapter
_UpperCamelCase = adapter_kernel_size
_UpperCamelCase = adapter_stride
_UpperCamelCase = num_adapter_layers
_UpperCamelCase = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_UpperCamelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = xvector_output_dim
@property
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1)
| 19 | 0 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def UpperCAmelCase_ (__a : Any ):
"""simple docstring"""
if (
(cp >= 0x4E_00 and cp <= 0x9F_FF)
or (cp >= 0x34_00 and cp <= 0x4D_BF) #
or (cp >= 0x2_00_00 and cp <= 0x2_A6_DF) #
or (cp >= 0x2_A7_00 and cp <= 0x2_B7_3F) #
or (cp >= 0x2_B7_40 and cp <= 0x2_B8_1F) #
or (cp >= 0x2_B8_20 and cp <= 0x2_CE_AF) #
or (cp >= 0xF9_00 and cp <= 0xFA_FF)
or (cp >= 0x2_F8_00 and cp <= 0x2_FA_1F) #
): #
return True
return False
def UpperCAmelCase_ (__a : Union[str, Any] ):
"""simple docstring"""
for char in word:
_a : Optional[int] = ord(__snake_case )
if not _is_chinese_char(__snake_case ):
return 0
return 1
def UpperCAmelCase_ (__a : Dict ):
"""simple docstring"""
_a : Optional[int] = set()
for token in tokens:
_a : Any = len(__snake_case ) > 1 and is_chinese(__snake_case )
if chinese_word:
word_set.add(__snake_case )
_a : Dict = list(__snake_case )
return word_list
def UpperCAmelCase_ (__a : Union[str, Any] , __a : Any ):
"""simple docstring"""
if not chinese_word_set:
return bert_tokens
_a : str = max([len(__snake_case ) for w in chinese_word_set] )
_a : str = bert_tokens
_a, _a : Dict = 0, len(__snake_case )
while start < end:
_a : str = True
if is_chinese(bert_word[start] ):
_a : Optional[Any] = min(end - start , __snake_case )
for i in range(__snake_case , 1 , -1 ):
_a : List[Any] = ''.join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
_a : List[str] = '##' + bert_word[j]
_a : Dict = start + i
_a : Union[str, Any] = False
break
if single_word:
start += 1
return bert_word
def UpperCAmelCase_ (__a : Optional[int] , __a : Union[str, Any] , __a : str ):
"""simple docstring"""
_a : Any = []
for i in range(0 , len(__snake_case ) , 1_0_0 ):
_a : Optional[Any] = ltp_tokenizer.pipeline(lines[i : i + 1_0_0] , tasks=['cws'] ).cws
_a : str = [get_chinese_word(__snake_case ) for r in res]
ltp_res.extend(__snake_case )
assert len(__snake_case ) == len(__snake_case )
_a : Union[str, Any] = []
for i in range(0 , len(__snake_case ) , 1_0_0 ):
_a : Dict = bert_tokenizer(lines[i : i + 1_0_0] , add_special_tokens=__snake_case , truncation=__snake_case , max_length=5_1_2 )
bert_res.extend(res['input_ids'] )
assert len(__snake_case ) == len(__snake_case )
_a : Union[str, Any] = []
for input_ids, chinese_word in zip(__snake_case , __snake_case ):
_a : int = []
for id in input_ids:
_a : List[str] = bert_tokenizer._convert_id_to_token(__snake_case )
input_tokens.append(__snake_case )
_a : List[str] = add_sub_symbol(__snake_case , __snake_case )
_a : Union[str, Any] = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__snake_case ):
if token[:2] == "##":
_a : Optional[int] = token[2:]
# save chinese tokens' pos
if len(__snake_case ) == 1 and _is_chinese_char(ord(__snake_case ) ):
ref_id.append(__snake_case )
ref_ids.append(__snake_case )
assert len(__snake_case ) == len(__snake_case )
return ref_ids
def UpperCAmelCase_ (__a : int ):
"""simple docstring"""
with open(args.file_name , 'r' , encoding='utf-8' ) as f:
_a : int = f.readlines()
_a : Dict = [line.strip() for line in data if len(__snake_case ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
_a : Any = LTP(args.ltp ) # faster in GPU device
_a : str = BertTokenizer.from_pretrained(args.bert )
_a : Union[str, Any] = prepare_ref(__snake_case , __snake_case , __snake_case )
with open(args.save_path , 'w' , encoding='utf-8' ) as f:
_a : int = [json.dumps(__snake_case ) + '\n' for ref in ref_ids]
f.writelines(__snake_case )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser(description="""prepare_chinese_ref""")
parser.add_argument(
"""--file_name""",
required=False,
type=str,
default="""./resources/chinese-demo.txt""",
help="""file need process, same as training data in lm""",
)
parser.add_argument(
"""--ltp""",
required=False,
type=str,
default="""./resources/ltp""",
help="""resources for LTP tokenizer, usually a path""",
)
parser.add_argument(
"""--bert""",
required=False,
type=str,
default="""./resources/robert""",
help="""resources for Bert tokenizer""",
)
parser.add_argument(
"""--save_path""",
required=False,
type=str,
default="""./resources/ref.txt""",
help="""path to save res""",
)
__lowerCAmelCase = parser.parse_args()
main(args)
| 229 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
_a = """bart"""
_a = True
@st.cache(allow_output_mutation=__snake_case )
def lowerCamelCase__ ( ) -> Dict:
"""simple docstring"""
if LOAD_DENSE_INDEX:
_UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' )
_UpperCamelCase = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' )
_UpperCamelCase = qar_model.eval()
else:
_UpperCamelCase , _UpperCamelCase = (None, None)
if MODEL_TYPE == "bart":
_UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' )
_UpperCamelCase = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' )
_UpperCamelCase = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' )
sas_model.load_state_dict(save_dict['''model'''] )
_UpperCamelCase = sas_model.eval()
else:
_UpperCamelCase , _UpperCamelCase = make_qa_sas_model(
model_name='''t5-small''', from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''', device='''cuda:0''' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=__snake_case )
def lowerCamelCase__ ( ) -> Tuple:
"""simple docstring"""
if LOAD_DENSE_INDEX:
_UpperCamelCase = faiss.StandardGpuResources()
_UpperCamelCase = datasets.load_dataset(path='''wiki_snippets''', name='''wiki40b_en_100_0''' )['''train''']
_UpperCamelCase = np.memmap(
'''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''', dtype='''float32''', mode='''r''', shape=(wikiaab_passages.num_rows, 1_28), )
_UpperCamelCase = faiss.IndexFlatIP(1_28 )
_UpperCamelCase = faiss.index_cpu_to_gpu(__snake_case, 1, __snake_case )
wikiaab_gpu_index_flat.add(__snake_case ) # TODO fix for larger GPU
else:
_UpperCamelCase , _UpperCamelCase = (None, None)
_UpperCamelCase = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=__snake_case )
def lowerCamelCase__ ( ) -> int:
"""simple docstring"""
_UpperCamelCase = datasets.load_dataset('''eli5''', name='''LFQA_reddit''' )
_UpperCamelCase = elia['''train_eli5''']
_UpperCamelCase = np.memmap(
'''eli5_questions_reps.dat''', dtype='''float32''', mode='''r''', shape=(elia_train.num_rows, 1_28) )
_UpperCamelCase = faiss.IndexFlatIP(1_28 )
eli5_train_q_index.add(__snake_case )
return (elia_train, eli5_train_q_index)
_a , _a , _a = load_indexes()
_a , _a , _a , _a = load_models()
_a , _a = load_train_data()
def lowerCamelCase__ ( __snake_case, __snake_case=10 ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = embed_questions_for_retrieval([question], __snake_case, __snake_case )
_UpperCamelCase , _UpperCamelCase = eli5_train_q_index.search(__snake_case, __snake_case )
_UpperCamelCase = [elia_train[int(__snake_case )] for i in I[0]]
return nn_examples
def lowerCamelCase__ ( __snake_case, __snake_case="wiki40b", __snake_case="dense", __snake_case=10 ) -> List[str]:
"""simple docstring"""
if source == "none":
_UpperCamelCase , _UpperCamelCase = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
_UpperCamelCase , _UpperCamelCase = query_qa_dense_index(
__snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case )
else:
_UpperCamelCase , _UpperCamelCase = query_es_index(
__snake_case, __snake_case, index_name='''english_wiki40b_snippets_100w''', n_results=__snake_case, )
_UpperCamelCase = [
(res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
]
_UpperCamelCase = '''question: {} context: {}'''.format(__snake_case, __snake_case )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda __snake_case : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __snake_case : None),
} )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=64, __snake_case=2_56, __snake_case=False, __snake_case=2, __snake_case=0.95, __snake_case=0.8 ) -> Dict:
"""simple docstring"""
with torch.no_grad():
_UpperCamelCase = qa_sas_generate(
__snake_case, __snake_case, __snake_case, num_answers=1, num_beams=__snake_case, min_len=__snake_case, max_len=__snake_case, do_sample=__snake_case, temp=__snake_case, top_p=__snake_case, top_k=__snake_case, max_input_length=10_24, device='''cuda:0''', )[0]
return (answer, support_list)
st.title("""Long Form Question Answering with ELI5""")
# Start sidebar
_a = """<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
_a = """
<html>
<head>
<style>
.img-container {
padding-left: 90px;
padding-right: 90px;
padding-top: 50px;
padding-bottom: 50px;
background-color: #f0f3f9;
}
</style>
</head>
<body>
<span class=\"img-container\"> <!-- Inline parent element -->
%s
</span>
</body>
</html>
""" % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
_a = """
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
"""
st.sidebar.markdown(description, unsafe_allow_html=True)
_a = [
"""Answer the question""",
"""View the retrieved document only""",
"""View the most similar ELI5 question and answer""",
"""Show me everything, please!""",
]
_a = st.sidebar.checkbox("""Demo options""")
if demo_options:
_a = st.sidebar.selectbox(
"""""",
action_list,
index=3,
)
_a = action_list.index(action_st)
_a = st.sidebar.selectbox(
"""""",
["""Show full text of passages""", """Show passage section titles"""],
index=0,
)
_a = show_type == """Show full text of passages"""
else:
_a = 3
_a = True
_a = st.sidebar.checkbox("""Retrieval options""")
if retrieval_options:
_a = """
### Information retriever options
The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
"""
st.sidebar.markdown(retriever_info)
_a = st.sidebar.selectbox("""Which Wikipedia format should the model use?""", ["""wiki40b""", """none"""])
_a = st.sidebar.selectbox("""Which Wikipedia indexer should the model use?""", ["""dense""", """sparse""", """mixed"""])
else:
_a = """wiki40b"""
_a = """dense"""
_a = """beam"""
_a = 2
_a = 64
_a = 256
_a = None
_a = None
_a = st.sidebar.checkbox("""Generation options""")
if generate_options:
_a = """
### Answer generation options
The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
**beam** search, or **sample** from the decoder's output probabilities.
"""
st.sidebar.markdown(generate_info)
_a = st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
_a = st.sidebar.slider(
"""Minimum generation length""", min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
_a = st.sidebar.slider(
"""Maximum generation length""", min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
_a = st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
_a = st.sidebar.slider(
"""Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
_a = st.sidebar.slider(
"""Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
_a = None
# start main text
_a = [
"""<MY QUESTION>""",
"""How do people make chocolate?""",
"""Why do we get a fever when we are sick?""",
"""How can different animals perceive different colors?""",
"""What is natural language processing?""",
"""What's the best way to treat a sunburn?""",
"""What exactly are vitamins ?""",
"""How does nuclear energy provide electricity?""",
"""What's the difference between viruses and bacteria?""",
"""Why are flutes classified as woodwinds when most of them are made out of metal ?""",
"""Why do people like drinking coffee even though it tastes so bad?""",
"""What happens when wine ages? How does it make the wine taste better?""",
"""If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?""",
"""How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?""",
"""How does New Zealand have so many large bird predators?""",
]
_a = st.selectbox(
"""What would you like to ask? ---- select <MY QUESTION> to enter a new query""",
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
_a = st.text_input("""Enter your question here:""", """""")
else:
_a = question_s
if st.button("""Show me!"""):
if action in [0, 1, 3]:
if index_type == "mixed":
_a , _a = make_support(question, source=wiki_source, method="""dense""", n_results=10)
_a , _a = make_support(question, source=wiki_source, method="""sparse""", n_results=10)
_a = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
_a = support_list[:10]
_a = """<P> """ + """ <P> """.join([res[-1] for res in support_list])
else:
_a , _a = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
_a , _a = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == """sampled"""),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown("""### The model generated answer is:""")
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown("""--- \n ### The model is drawing information from the following Wikipedia passages:""")
for i, res in enumerate(support_list):
_a = """https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_"""))
_a = res[1].strip()
if sec_titles == "":
_a = """[{}]({})""".format(res[0], wiki_url)
else:
_a = sec_titles.split(""" & """)
_a = """ & """.join(
["""[{}]({}#{})""".format(sec.strip(), wiki_url, sec.strip().replace(""" """, """_""")) for sec in sec_list]
)
st.markdown(
"""{0:02d} - **Article**: {1:<18} <br> _Section_: {2}""".format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
"""> <span style=\"font-family:arial; font-size:10pt;\">""" + res[-1] + """</span>""", unsafe_allow_html=True
)
if action in [2, 3]:
_a = find_nearest_training(question)
_a = nn_train_list[0]
st.markdown(
"""--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""])
)
_a = [
"""{}. {}""".format(i + 1, """ \n""".join([line.strip() for line in ans.split("""\n""") if line.strip() != """"""]))
for i, (ans, sc) in enumerate(zip(train_exple["""answers"""]["""text"""], train_exple["""answers"""]["""score"""]))
if i == 0 or sc > 2
]
st.markdown("""##### Its answers were: \n\n {}""".format("""\n""".join(answers_st)))
_a = """
---
**Disclaimer**
*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
"""
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 19 | 0 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def __lowerCamelCase ():
SCREAMING_SNAKE_CASE = 1_0
SCREAMING_SNAKE_CASE = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string" ) ),
"labels": datasets.Sequence(datasets.ClassLabel(names=["negative", "positive"] ) ),
"answers": datasets.Sequence(
{
"text": datasets.Value("string" ),
"answer_start": datasets.Value("int32" ),
} ),
"id": datasets.Value("int64" ),
} )
SCREAMING_SNAKE_CASE = datasets.Dataset.from_dict(
{
"tokens": [["foo"] * 5] * n,
"labels": [[1] * 5] * n,
"answers": [{"answer_start": [9_7], "text": ["1976"]}] * 1_0,
"id": list(range(__snake_case ) ),
} , features=__snake_case , )
return dataset
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : str , UpperCAmelCase__ : List[str] ):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "file.arrow" )
dataset.map(cache_file_name=__snake_case )
return filename
# FILE_CONTENT + files
_lowerCamelCase : Any = '''\
Text data.
Second line of data.'''
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Union[str, Any] ):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.txt"
SCREAMING_SNAKE_CASE = FILE_CONTENT
with open(__snake_case , "w" ) as f:
f.write(__snake_case )
return filename
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Any ):
import bza
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.txt.bz2"
SCREAMING_SNAKE_CASE = bytes(__snake_case , "utf-8" )
with bza.open(__snake_case , "wb" ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Tuple ):
import gzip
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "file.txt.gz" )
SCREAMING_SNAKE_CASE = bytes(__snake_case , "utf-8" )
with gzip.open(__snake_case , "wb" ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Optional[Any] ):
if datasets.config.LZ4_AVAILABLE:
import lza.frame
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.txt.lz4"
SCREAMING_SNAKE_CASE = bytes(__snake_case , "utf-8" )
with lza.frame.open(__snake_case , "wb" ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : int , UpperCAmelCase__ : Dict ):
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.txt.7z"
with pyazr.SevenZipFile(__snake_case , "w" ) as archive:
archive.write(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Any , UpperCAmelCase__ : str ):
import tarfile
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.txt.tar"
with tarfile.TarFile(__snake_case , "w" ) as f:
f.add(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : int ):
import lzma
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.txt.xz"
SCREAMING_SNAKE_CASE = bytes(__snake_case , "utf-8" )
with lzma.open(__snake_case , "wb" ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[int] ):
import zipfile
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.txt.zip"
with zipfile.ZipFile(__snake_case , "w" ) as f:
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Tuple ):
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.txt.zst"
SCREAMING_SNAKE_CASE = bytes(__snake_case , "utf-8" )
with zstd.open(__snake_case , "wb" ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Any ):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "file.xml"
SCREAMING_SNAKE_CASE = textwrap.dedent(
"\\n <?xml version=\"1.0\" encoding=\"UTF-8\" ?>\n <tmx version=\"1.4\">\n <header segtype=\"sentence\" srclang=\"ca\" />\n <body>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>\n </tu>\n </body>\n </tmx>" )
with open(__snake_case , "w" ) as f:
f.write(__snake_case )
return filename
_lowerCamelCase : Dict = [
{'''col_1''': '''0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''3''', '''col_2''': 3, '''col_3''': 3.0},
]
_lowerCamelCase : Optional[Any] = [
{'''col_1''': '''4''', '''col_2''': 4, '''col_3''': 4.0},
{'''col_1''': '''5''', '''col_2''': 5, '''col_3''': 5.0},
]
_lowerCamelCase : Dict = {
'''col_1''': ['''0''', '''1''', '''2''', '''3'''],
'''col_2''': [0, 1, 2, 3],
'''col_3''': [0.0, 1.0, 2.0, 3.0],
}
_lowerCamelCase : str = [
{'''col_3''': 0.0, '''col_1''': '''0''', '''col_2''': 0},
{'''col_3''': 1.0, '''col_1''': '''1''', '''col_2''': 1},
]
_lowerCamelCase : str = [
{'''col_1''': '''s0''', '''col_2''': 0, '''col_3''': 0.0},
{'''col_1''': '''s1''', '''col_2''': 1, '''col_3''': 1.0},
{'''col_1''': '''s2''', '''col_2''': 2, '''col_3''': 2.0},
{'''col_1''': '''s3''', '''col_2''': 3, '''col_3''': 3.0},
]
@pytest.fixture(scope="session" )
def __lowerCamelCase ():
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Any ):
SCREAMING_SNAKE_CASE = datasets.Dataset.from_dict(__snake_case )
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.arrow" )
dataset.map(cache_file_name=__snake_case )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Dict ):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.sqlite" )
with contextlib.closing(sqlitea.connect(__snake_case ) ) as con:
SCREAMING_SNAKE_CASE = con.cursor()
cur.execute("CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)" )
for item in DATA:
cur.execute("INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)" , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : List[Any] ):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.csv" )
with open(__snake_case , "w" , newline="" ) as f:
SCREAMING_SNAKE_CASE = csv.DictWriter(__snake_case , fieldnames=["col_1", "col_2", "col_3"] )
writer.writeheader()
for item in DATA:
writer.writerow(__snake_case )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Union[str, Any] ):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset2.csv" )
with open(__snake_case , "w" , newline="" ) as f:
SCREAMING_SNAKE_CASE = csv.DictWriter(__snake_case , fieldnames=["col_1", "col_2", "col_3"] )
writer.writeheader()
for item in DATA:
writer.writerow(__snake_case )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : str , UpperCAmelCase__ : Any ):
import bza
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.csv.bz2"
with open(__snake_case , "rb" ) as f:
SCREAMING_SNAKE_CASE = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(__snake_case , "wb" ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Dict ):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.csv.zip"
with zipfile.ZipFile(__snake_case , "w" ) as f:
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] ):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.csv.zip"
with zipfile.ZipFile(__snake_case , "w" ) as f:
f.write(__snake_case , arcname=os.path.basename(csv_path.replace(".csv" , ".CSV" ) ) )
f.write(__snake_case , arcname=os.path.basename(csva_path.replace(".csv" , ".CSV" ) ) )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[int] ):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.csv.zip"
with zipfile.ZipFile(__snake_case , "w" ) as f:
f.write(__snake_case , arcname=os.path.join("main_dir" , os.path.basename(__snake_case ) ) )
f.write(__snake_case , arcname=os.path.join("main_dir" , os.path.basename(__snake_case ) ) )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Any ):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.parquet" )
SCREAMING_SNAKE_CASE = pa.schema(
{
"col_1": pa.string(),
"col_2": pa.intaa(),
"col_3": pa.floataa(),
} )
with open(__snake_case , "wb" ) as f:
SCREAMING_SNAKE_CASE = pq.ParquetWriter(__snake_case , schema=__snake_case )
SCREAMING_SNAKE_CASE = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(__snake_case ) )] for k in DATA[0]} , schema=__snake_case )
writer.write_table(__snake_case )
writer.close()
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : str ):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.json" )
SCREAMING_SNAKE_CASE = {"data": DATA}
with open(__snake_case , "w" ) as f:
json.dump(__snake_case , __snake_case )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Tuple ):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.json" )
SCREAMING_SNAKE_CASE = {"data": DATA_DICT_OF_LISTS}
with open(__snake_case , "w" ) as f:
json.dump(__snake_case , __snake_case )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : str ):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl" )
with open(__snake_case , "w" ) as f:
for item in DATA:
f.write(json.dumps(__snake_case ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Tuple ):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset2.jsonl" )
with open(__snake_case , "w" ) as f:
for item in DATA:
f.write(json.dumps(__snake_case ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : int ):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset_312.jsonl" )
with open(__snake_case , "w" ) as f:
for item in DATA_312:
f.write(json.dumps(__snake_case ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : str ):
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset-str.jsonl" )
with open(__snake_case , "w" ) as f:
for item in DATA_STR:
f.write(json.dumps(__snake_case ) + "\n" )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] ):
import gzip
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.txt.gz" )
with open(__snake_case , "rb" ) as orig_file:
with gzip.open(__snake_case , "wb" ) as zipped_file:
zipped_file.writelines(__snake_case )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[str] ):
import gzip
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.jsonl.gz" )
with open(__snake_case , "rb" ) as orig_file:
with gzip.open(__snake_case , "wb" ) as zipped_file:
zipped_file.writelines(__snake_case )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : int , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[Any] ):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.jsonl.zip"
with zipfile.ZipFile(__snake_case , "w" ) as f:
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Dict , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Any ):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset_nested.jsonl.zip"
with zipfile.ZipFile(__snake_case , "w" ) as f:
f.write(__snake_case , arcname=os.path.join("nested" , os.path.basename(__snake_case ) ) )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Any , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[str] ):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.jsonl.zip"
with zipfile.ZipFile(__snake_case , "w" ) as f:
f.write(__snake_case , arcname=os.path.join("main_dir" , os.path.basename(__snake_case ) ) )
f.write(__snake_case , arcname=os.path.join("main_dir" , os.path.basename(__snake_case ) ) )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : Dict ):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.jsonl.tar"
with tarfile.TarFile(__snake_case , "w" ) as f:
f.add(__snake_case , arcname=os.path.basename(__snake_case ) )
f.add(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : str , UpperCAmelCase__ : List[str] ):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset_nested.jsonl.tar"
with tarfile.TarFile(__snake_case , "w" ) as f:
f.add(__snake_case , arcname=os.path.join("nested" , os.path.basename(__snake_case ) ) )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : List[str] ):
SCREAMING_SNAKE_CASE = ["0", "1", "2", "3"]
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset.txt" )
with open(__snake_case , "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Union[str, Any] ):
SCREAMING_SNAKE_CASE = ["0", "1", "2", "3"]
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset2.txt" )
with open(__snake_case , "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Union[str, Any] ):
SCREAMING_SNAKE_CASE = ["0", "1", "2", "3"]
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.abc"
with open(__snake_case , "w" ) as f:
for item in data:
f.write(item + "\n" )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : str , UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str] ):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.text.zip"
with zipfile.ZipFile(__snake_case , "w" ) as f:
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : List[str] ):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset_with_dir.text.zip"
with zipfile.ZipFile(__snake_case , "w" ) as f:
f.write(__snake_case , arcname=os.path.join("main_dir" , os.path.basename(__snake_case ) ) )
f.write(__snake_case , arcname=os.path.join("main_dir" , os.path.basename(__snake_case ) ) )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Dict ):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.ext.zip"
with zipfile.ZipFile(__snake_case , "w" ) as f:
f.write(__snake_case , arcname=os.path.basename("unsupported.ext" ) )
f.write(__snake_case , arcname=os.path.basename("unsupported_2.ext" ) )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : Optional[int] ):
SCREAMING_SNAKE_CASE = "\n".join(["First", "Second\u2029with Unicode new line", "Third"] )
SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("data" ) / "dataset_with_unicode_new_lines.txt" )
with open(__snake_case , "w" , encoding="utf-8" ) as f:
f.write(__snake_case )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase ():
return os.path.join("tests" , "features" , "data" , "test_image_rgb.jpg" )
@pytest.fixture(scope="session" )
def __lowerCamelCase ():
return os.path.join("tests" , "features" , "data" , "test_audio_44100.wav" )
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : int , UpperCAmelCase__ : List[str] ):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data" ) / "dataset.img.zip"
with zipfile.ZipFile(__snake_case , "w" ) as f:
f.write(__snake_case , arcname=os.path.basename(__snake_case ) )
f.write(__snake_case , arcname=os.path.basename(__snake_case ).replace(".jpg" , "2.jpg" ) )
return path
@pytest.fixture(scope="session" )
def __lowerCamelCase (UpperCAmelCase__ : int ):
SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("data_dir" )
(data_dir / "subdir").mkdir()
with open(data_dir / "subdir" / "train.txt" , "w" ) as f:
f.write("foo\n" * 1_0 )
with open(data_dir / "subdir" / "test.txt" , "w" ) as f:
f.write("bar\n" * 1_0 )
# hidden file
with open(data_dir / "subdir" / ".test.txt" , "w" ) as f:
f.write("bar\n" * 1_0 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / ".subdir" / "train.txt" , "w" ) as f:
f.write("foo\n" * 1_0 )
with open(data_dir / ".subdir" / "test.txt" , "w" ) as f:
f.write("bar\n" * 1_0 )
return data_dir
| 403 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
_a = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
_a = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> Tuple:
"""simple docstring"""
for attribute in key.split('''.''' ):
_UpperCamelCase = getattr(__snake_case, __snake_case )
if weight_type is not None:
_UpperCamelCase = getattr(__snake_case, __snake_case ).shape
else:
_UpperCamelCase = hf_pointer.shape
assert hf_shape == value.shape, (
F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
_UpperCamelCase = value
elif weight_type == "weight_g":
_UpperCamelCase = value
elif weight_type == "weight_v":
_UpperCamelCase = value
elif weight_type == "bias":
_UpperCamelCase = value
else:
_UpperCamelCase = value
logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def lowerCamelCase__ ( __snake_case, __snake_case ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = []
_UpperCamelCase = fairseq_model.state_dict()
_UpperCamelCase = hf_model.feature_extractor
_UpperCamelCase = hf_model.adapter
for name, value in fairseq_dict.items():
_UpperCamelCase = False
if "conv_layers" in name:
load_conv_layer(
__snake_case, __snake_case, __snake_case, __snake_case, hf_model.config.feat_extract_norm == '''group''', )
_UpperCamelCase = True
elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.'''] ):
load_adapter(__snake_case, __snake_case, __snake_case, __snake_case )
_UpperCamelCase = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
_UpperCamelCase = True
if "*" in mapped_key:
_UpperCamelCase = name.split(__snake_case )[0].split('''.''' )[-2]
_UpperCamelCase = mapped_key.replace('''*''', __snake_case )
if "weight_g" in name:
_UpperCamelCase = '''weight_g'''
elif "weight_v" in name:
_UpperCamelCase = '''weight_v'''
elif "bias" in name:
_UpperCamelCase = '''bias'''
elif "weight" in name:
_UpperCamelCase = '''weight'''
else:
_UpperCamelCase = None
set_recursively(__snake_case, __snake_case, __snake_case, __snake_case, __snake_case )
continue
if not is_used:
unused_weights.append(__snake_case )
logger.warning(F'''Unused weights: {unused_weights}''' )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = full_name.split('''conv_layers.''' )[-1]
_UpperCamelCase = name.split('''.''' )
_UpperCamelCase = int(items[0] )
_UpperCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
_UpperCamelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
_UpperCamelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
_UpperCamelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
_UpperCamelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__snake_case )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> Dict:
"""simple docstring"""
_UpperCamelCase = full_name.split('''adaptor.''' )[-1]
_UpperCamelCase = name.split('''.''' )
if items[1].isdigit():
_UpperCamelCase = int(items[1] )
else:
_UpperCamelCase = None
if "adaptor" not in full_name:
if "proj_ln" in full_name:
# has to be layer norm
if "bias" in name:
assert (
value.shape == adapter.proj_layer_norm.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj_layer_norm.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
_UpperCamelCase = value
else:
# has to be projection layer
if "bias" in name:
assert (
value.shape == adapter.proj.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter proj layer bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter proj layer weight was initialized from {full_name}.''' )
elif isinstance(__snake_case, __snake_case ):
if "bias" in name:
assert (
value.shape == adapter.layers[layer_id].conv.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
elif "weight" in name:
assert (
value.shape == adapter.layers[layer_id].conv.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
else:
unused_weights.append(__snake_case )
def lowerCamelCase__ ( __snake_case ) -> str:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase = emb.weight.shape
_UpperCamelCase = nn.Linear(__snake_case, __snake_case, bias=__snake_case )
_UpperCamelCase = emb.weight.data
return lin_layer
@torch.no_grad()
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = WavaVecaConfig.from_pretrained(
__snake_case, add_adapter=__snake_case, adapter_stride=__snake_case, adapter_kernel_size=__snake_case, use_auth_token=__snake_case, output_hidden_size=__snake_case, )
_UpperCamelCase = MBartConfig.from_pretrained(__snake_case )
# load model
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path], arg_overrides={
'''config_yaml''': config_yaml_path,
'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ),
'''w2v_path''': checkpoint_path,
'''load_pretrained_decoder_from''': None,
}, )
_UpperCamelCase = model[0].eval()
# load feature extractor
_UpperCamelCase = WavaVecaFeatureExtractor.from_pretrained(__snake_case, use_auth_token=__snake_case )
# set weights for wav2vec2 encoder
_UpperCamelCase = WavaVecaModel(__snake_case )
recursively_load_weights_wavaveca(model.encoder, __snake_case )
# load decoder weights
_UpperCamelCase = MBartForCausalLM(__snake_case )
_UpperCamelCase , _UpperCamelCase = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(), strict=__snake_case )
logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}''' )
logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' )
_UpperCamelCase = SpeechEncoderDecoderModel(encoder=__snake_case, decoder=__snake_case )
_UpperCamelCase = False
_UpperCamelCase = MBartaaTokenizer(__snake_case )
tokenizer.save_pretrained(__snake_case )
_UpperCamelCase = hf_wavavec.config.to_dict()
_UpperCamelCase = tokenizer.pad_token_id
_UpperCamelCase = tokenizer.bos_token_id
_UpperCamelCase = tokenizer.eos_token_id
_UpperCamelCase = '''mbart50'''
_UpperCamelCase = '''wav2vec2'''
_UpperCamelCase = tokenizer.eos_token_id
_UpperCamelCase = 25_00_04
_UpperCamelCase = tokenizer.eos_token_id
_UpperCamelCase = SpeechEncoderDecoderConfig.from_dict(__snake_case )
hf_wavavec.save_pretrained(__snake_case )
feature_extractor.save_pretrained(__snake_case )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
parser.add_argument(
"""--encoder_config_path""",
default="""facebook/wav2vec2-xls-r-1b""",
type=str,
help="""Path to hf encoder wav2vec2 checkpoint config""",
)
parser.add_argument(
"""--decoder_config_path""",
default="""facebook/mbart-large-50-one-to-many-mmt""",
type=str,
help="""Path to hf decoder checkpoint config""",
)
parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""")
parser.add_argument("""--start_token_id""", default=25_0004, type=int, help="""`decoder_start_token_id` of model config""")
_a = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
args.config_yaml_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
add_adapter=args.add_adapter,
adapter_kernel_size=args.adapter_kernel_size,
adapter_stride=args.adapter_stride,
decoder_start_token_id=args.start_token_id,
encoder_output_dim=args.encoder_output_dim,
)
| 19 | 0 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_snake_case : Tuple = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n booktitle = {},\n year = {2002},\n pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n author = \"Lin, Chin-Yew and\n Och, Franz Josef\",\n booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n month = \"aug 23{--}aug 27\",\n year = \"2004\",\n address = \"Geneva, Switzerland\",\n publisher = \"COLING\",\n url = \"https://www.aclweb.org/anthology/C04-1072\",\n pages = \"501--507\",\n}\n'
_snake_case : Any = '\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n'
_snake_case : Dict = '\nComputes BLEU score of translated segments against one or more references.\nArgs:\n predictions: list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n max_order: Maximum n-gram order to use when computing BLEU score.\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n \'bleu\': bleu score,\n \'precisions\': geometric mean of n-gram precisions,\n \'brevity_penalty\': brevity penalty,\n \'length_ratio\': ratio of lengths,\n \'translation_length\': translation_length,\n \'reference_length\': reference_length\nExamples:\n\n >>> predictions = [\n ... [\"hello\", \"there\", \"general\", \"kenobi\"], # tokenized prediction of the first sample\n ... [\"foo\", \"bar\", \"foobar\"] # tokenized prediction of the second sample\n ... ]\n >>> references = [\n ... [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]], # tokenized references for the first sample (2 references)\n ... [[\"foo\", \"bar\", \"foobar\"]] # tokenized references for the second sample (1 reference)\n ... ]\n >>> bleu = datasets.load_metric(\"bleu\")\n >>> results = bleu.compute(predictions=predictions, references=references)\n >>> print(results[\"bleu\"])\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class A ( datasets.Metric ):
def __lowerCAmelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ),
'''references''': datasets.Sequence(
datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
] , )
def __lowerCAmelCase ( self : List[str] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Dict=4 , lowerCAmelCase_ : Union[str, Any]=False ) -> Dict:
"""simple docstring"""
_a = compute_bleu(
reference_corpus=__a , translation_corpus=__a , max_order=__a , smooth=__a )
((_a) , (_a) , (_a) , (_a) , (_a) , (_a)) = score
return {
"bleu": bleu,
"precisions": precisions,
"brevity_penalty": bp,
"length_ratio": ratio,
"translation_length": translation_length,
"reference_length": reference_length,
}
| 22 |
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case=None, **__snake_case ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()]
_UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()][: len(__snake_case )]
_UpperCamelCase = calculate_rouge(__snake_case, __snake_case, **__snake_case )
if save_path is not None:
save_json(__snake_case, __snake_case, indent=__snake_case )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 19 | 0 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def __a ( A__ = 150_0000 ) -> int:
lowerCAmelCase = defaultdict(__snake_case )
lowerCAmelCase = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , __snake_case , 2 ):
if gcd(__snake_case , __snake_case ) > 1:
continue
lowerCAmelCase = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(__snake_case , limit + 1 , __snake_case ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(f"{solution() = }")
| 649 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = ['image_processor', 'tokenizer']
lowercase__ = 'ViTImageProcessor'
lowercase__ = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self , __a=None , __a=None , **__a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __a , )
_UpperCamelCase = kwargs.pop('''feature_extractor''')
_UpperCamelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''')
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''')
super().__init__(__a , __a)
def __call__( self , __a=None , __a=None , __a=None , __a=None , **__a) -> Tuple:
'''simple docstring'''
if text is None and visual_prompt is None and images is None:
raise ValueError('''You have to specify either text, visual prompt or images.''')
if text is not None and visual_prompt is not None:
raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''')
if text is not None:
_UpperCamelCase = self.tokenizer(__a , return_tensors=__a , **__a)
if visual_prompt is not None:
_UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a)
if images is not None:
_UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a)
if visual_prompt is not None and images is not None:
_UpperCamelCase = {
'''pixel_values''': image_features.pixel_values,
'''conditional_pixel_values''': prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
_UpperCamelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
_UpperCamelCase = {
'''conditional_pixel_values''': prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**__a) , tensor_type=__a)
def UpperCAmelCase ( self , *__a , **__a) -> Any:
'''simple docstring'''
return self.tokenizer.batch_decode(*__a , **__a)
def UpperCAmelCase ( self , *__a , **__a) -> List[str]:
'''simple docstring'''
return self.tokenizer.decode(*__a , **__a)
@property
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __a , )
return self.image_processor_class
@property
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __a , )
return self.image_processor
| 19 | 0 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = (DDPMScheduler,)
def lowerCAmelCase__ ( self ,**lowerCamelCase_ ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = {
'''num_train_timesteps''': 1000,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''variance_type''': '''fixed_small''',
'''clip_sample''': True,
}
config.update(**__a )
return config
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=__a )
def lowerCAmelCase__ ( self ) -> int:
'''simple docstring'''
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,[0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=__a ,beta_end=__a )
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__a )
def lowerCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=__a )
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__a )
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
self.check_over_configs(thresholding=__a )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=__a ,prediction_type=__a ,sample_max_value=__a ,)
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=__a )
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
for t in [0, 500, 999]:
self.check_over_forward(time_step=__a )
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase__ : Tuple = self.scheduler_classes[0]
UpperCAmelCase__ : List[str] = self.get_scheduler_config()
UpperCAmelCase__ : Dict = scheduler_class(**__a )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0979 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1e-5
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.scheduler_classes[0]
UpperCAmelCase__ : Any = self.get_scheduler_config()
UpperCAmelCase__ : Tuple = scheduler_class(**__a )
UpperCAmelCase__ : str = len(__a )
UpperCAmelCase__ : List[Any] = self.dummy_model()
UpperCAmelCase__ : Optional[Any] = self.dummy_sample_deter
UpperCAmelCase__ : List[Any] = torch.manual_seed(0 )
for t in reversed(range(__a ) ):
# 1. predict noise residual
UpperCAmelCase__ : Dict = model(__a ,__a )
# 2. predict previous mean of sample x_t-1
UpperCAmelCase__ : Dict = scheduler.step(__a ,__a ,__a ,generator=__a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
UpperCAmelCase__ : List[str] = pred_prev_sample
UpperCAmelCase__ : Tuple = torch.sum(torch.abs(__a ) )
UpperCAmelCase__ : int = torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 258.9606 ) < 1e-2
assert abs(result_mean.item() - 0.3372 ) < 1e-3
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.scheduler_classes[0]
UpperCAmelCase__ : Any = self.get_scheduler_config(prediction_type='''v_prediction''' )
UpperCAmelCase__ : Optional[Any] = scheduler_class(**__a )
UpperCAmelCase__ : str = len(__a )
UpperCAmelCase__ : int = self.dummy_model()
UpperCAmelCase__ : Optional[int] = self.dummy_sample_deter
UpperCAmelCase__ : Union[str, Any] = torch.manual_seed(0 )
for t in reversed(range(__a ) ):
# 1. predict noise residual
UpperCAmelCase__ : Optional[Any] = model(__a ,__a )
# 2. predict previous mean of sample x_t-1
UpperCAmelCase__ : Optional[Any] = scheduler.step(__a ,__a ,__a ,generator=__a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
UpperCAmelCase__ : Optional[Any] = pred_prev_sample
UpperCAmelCase__ : List[Any] = torch.sum(torch.abs(__a ) )
UpperCAmelCase__ : Tuple = torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 202.0296 ) < 1e-2
assert abs(result_mean.item() - 0.2631 ) < 1e-3
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = self.scheduler_classes[0]
UpperCAmelCase__ : Dict = self.get_scheduler_config()
UpperCAmelCase__ : Optional[int] = scheduler_class(**__a )
UpperCAmelCase__ : Optional[Any] = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=__a )
UpperCAmelCase__ : Dict = scheduler.timesteps
for i, timestep in enumerate(__a ):
if i == len(__a ) - 1:
UpperCAmelCase__ : Tuple = -1
else:
UpperCAmelCase__ : List[str] = timesteps[i + 1]
UpperCAmelCase__ : Optional[Any] = scheduler.previous_timestep(__a )
UpperCAmelCase__ : Any = prev_t.item()
self.assertEqual(__a ,__a )
def lowerCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase__ : List[str] = self.scheduler_classes[0]
UpperCAmelCase__ : Optional[int] = self.get_scheduler_config()
UpperCAmelCase__ : Optional[Any] = scheduler_class(**__a )
UpperCAmelCase__ : Any = [100, 87, 50, 51, 0]
with self.assertRaises(__a ,msg='''`custom_timesteps` must be in descending order.''' ):
scheduler.set_timesteps(timesteps=__a )
def lowerCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase__ : Any = self.scheduler_classes[0]
UpperCAmelCase__ : Any = self.get_scheduler_config()
UpperCAmelCase__ : List[str] = scheduler_class(**__a )
UpperCAmelCase__ : str = [100, 87, 50, 1, 0]
UpperCAmelCase__ : List[Any] = len(__a )
with self.assertRaises(__a ,msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.''' ):
scheduler.set_timesteps(num_inference_steps=__a ,timesteps=__a )
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase__ : str = self.scheduler_classes[0]
UpperCAmelCase__ : Any = self.get_scheduler_config()
UpperCAmelCase__ : Dict = scheduler_class(**__a )
UpperCAmelCase__ : str = [scheduler.config.num_train_timesteps]
with self.assertRaises(
__a ,msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' ,):
scheduler.set_timesteps(timesteps=__a )
| 614 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import UperNetForSemanticSegmentation
from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCAmelCase:
def __init__( self , __a , __a=13 , __a=32 , __a=3 , __a=4 , __a=[10, 20, 30, 40] , __a=[2, 2, 3, 2] , __a=True , __a=True , __a=37 , __a="gelu" , __a=10 , __a=0.02 , __a=["stage2", "stage3", "stage4"] , __a=3 , __a=None , ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = image_size
_UpperCamelCase = num_channels
_UpperCamelCase = num_stages
_UpperCamelCase = hidden_sizes
_UpperCamelCase = depths
_UpperCamelCase = is_training
_UpperCamelCase = use_labels
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = out_features
_UpperCamelCase = num_labels
_UpperCamelCase = scope
_UpperCamelCase = num_stages
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_UpperCamelCase = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return ConvNextConfig(
num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , )
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return UperNetConfig(
backbone_config=self.get_backbone_config() , hidden_size=5_12 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=__a , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=2_56 , auxiliary_num_convs=1 , auxiliary_concat_input=__a , loss_ignore_index=2_55 , num_labels=self.num_labels , )
def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = UperNetForSemanticSegmentation(config=__a)
model.to(__a)
model.eval()
_UpperCamelCase = model(__a)
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size))
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase__ = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
lowercase__ = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = UperNetModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(__a)
_UpperCamelCase = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCamelCase = [*signature.parameters.keys()]
_UpperCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__a)
@unittest.skip(reason='''UperNet does not use inputs_embeds''')
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason='''UperNet does not support input and output embeddings''')
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''UperNet does not have a base model''')
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason='''UperNet does not have a base model''')
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''')
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
pass
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
def check_hidden_states_output(__a , __a , __a):
_UpperCamelCase = model_class(__a)
model.to(__a)
model.eval()
with torch.no_grad():
_UpperCamelCase = model(**self._prepare_for_class(__a , __a))
_UpperCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_UpperCamelCase = self.model_tester.num_stages
self.assertEqual(len(__a) , expected_num_stages + 1)
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = True
check_hidden_states_output(__a , __a , __a)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCamelCase = True
check_hidden_states_output(__a , __a , __a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCamelCase = _config_zero_init(__a)
_UpperCamelCase = _config_zero_init(configs_no_init.backbone_config)
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(config=__a)
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@unittest.skip(reason='''UperNet does not have tied weights''')
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
pass
@slow
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained(__a)
self.assertIsNotNone(__a)
def lowerCamelCase__ ( ) -> int:
"""simple docstring"""
_UpperCamelCase = hf_hub_download(
repo_id='''hf-internal-testing/fixtures_ade20k''', repo_type='''dataset''', filename='''ADE_val_00000001.jpg''' )
_UpperCamelCase = Image.open(__snake_case ).convert('''RGB''' )
return image
@require_torch
@require_vision
@slow
class _UpperCAmelCase( unittest.TestCase ):
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny''')
_UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny''').to(__a)
_UpperCamelCase = prepare_img()
_UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a)
with torch.no_grad():
_UpperCamelCase = model(**__a)
_UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12))
self.assertEqual(outputs.logits.shape , __a)
_UpperCamelCase = torch.tensor(
[[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]).to(__a)
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4))
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny''')
_UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny''').to(__a)
_UpperCamelCase = prepare_img()
_UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a)
with torch.no_grad():
_UpperCamelCase = model(**__a)
_UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12))
self.assertEqual(outputs.logits.shape , __a)
_UpperCamelCase = torch.tensor(
[[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]).to(__a)
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4))
| 19 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
BertConfig,
DPRConfig,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
class __magic_name__ :
def __init__( self , snake_case , snake_case=1_3 , snake_case=7 , snake_case=True , snake_case=True , snake_case=True , snake_case=True , snake_case=9_9 , snake_case=3_2 , snake_case=2 , snake_case=4 , snake_case=3_7 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=5_1_2 , snake_case=1_6 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , snake_case=0 , ) -> Any:
'''simple docstring'''
_UpperCAmelCase : str =parent
_UpperCAmelCase : Optional[Any] =batch_size
_UpperCAmelCase : Tuple =seq_length
_UpperCAmelCase : Any =is_training
_UpperCAmelCase : Optional[Any] =use_input_mask
_UpperCAmelCase : str =use_token_type_ids
_UpperCAmelCase : str =use_labels
_UpperCAmelCase : str =vocab_size
_UpperCAmelCase : Tuple =hidden_size
_UpperCAmelCase : List[Any] =num_hidden_layers
_UpperCAmelCase : int =num_attention_heads
_UpperCAmelCase : Union[str, Any] =intermediate_size
_UpperCAmelCase : Optional[Any] =hidden_act
_UpperCAmelCase : List[Any] =hidden_dropout_prob
_UpperCAmelCase : List[Any] =attention_probs_dropout_prob
_UpperCAmelCase : Optional[int] =max_position_embeddings
_UpperCAmelCase : Dict =type_vocab_size
_UpperCAmelCase : Optional[int] =type_sequence_label_size
_UpperCAmelCase : int =initializer_range
_UpperCAmelCase : str =num_labels
_UpperCAmelCase : Any =num_choices
_UpperCAmelCase : Any =scope
_UpperCAmelCase : Union[str, Any] =projection_dim
def lowerCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase : List[Any] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCAmelCase : List[str] =None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
_UpperCAmelCase : List[Any] =random_attention_mask([self.batch_size, self.seq_length])
_UpperCAmelCase : Optional[Any] =None
if self.use_token_type_ids:
_UpperCAmelCase : List[Any] =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
_UpperCAmelCase : int =None
_UpperCAmelCase : Optional[Any] =None
_UpperCAmelCase : List[str] =None
if self.use_labels:
_UpperCAmelCase : int =ids_tensor([self.batch_size] , self.type_sequence_label_size)
_UpperCAmelCase : Dict =ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
_UpperCAmelCase : Union[str, Any] =ids_tensor([self.batch_size] , self.num_choices)
_UpperCAmelCase : Union[str, Any] =BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__a , initializer_range=self.initializer_range , )
_UpperCAmelCase : Any =DPRConfig(projection_dim=self.projection_dim , **config.to_dict())
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : List[str] =TFDPRContextEncoder(config=__a)
_UpperCAmelCase : Union[str, Any] =model(__a , attention_mask=__a , token_type_ids=__a)
_UpperCAmelCase : List[str] =model(__a , token_type_ids=__a)
_UpperCAmelCase : Dict =model(__a)
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size))
def lowerCAmelCase ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : List[str] =TFDPRQuestionEncoder(config=__a)
_UpperCAmelCase : str =model(__a , attention_mask=__a , token_type_ids=__a)
_UpperCAmelCase : Union[str, Any] =model(__a , token_type_ids=__a)
_UpperCAmelCase : int =model(__a)
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size))
def lowerCAmelCase ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case) -> Dict:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] =TFDPRReader(config=__a)
_UpperCAmelCase : str =model(__a , attention_mask=__a)
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,))
def lowerCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCAmelCase : List[str] =self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : Optional[int] =config_and_inputs
_UpperCAmelCase : List[str] ={'input_ids': input_ids}
return config, inputs_dict
@require_tf
class __magic_name__ ( lowerCAmelCase ,lowerCAmelCase ,unittest.TestCase ):
UpperCAmelCase =(
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
UpperCAmelCase ={"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}
UpperCAmelCase =False
UpperCAmelCase =False
UpperCAmelCase =False
UpperCAmelCase =False
UpperCAmelCase =False
def lowerCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCAmelCase : Dict =TFDPRModelTester(self)
_UpperCAmelCase : Optional[int] =ConfigTester(self , config_class=__a , hidden_size=3_7)
def lowerCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*__a)
def lowerCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*__a)
def lowerCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCAmelCase : Dict =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*__a)
@slow
def lowerCAmelCase ( self) -> str:
'''simple docstring'''
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Any =TFDPRContextEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : int =TFDPRContextEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Union[str, Any] =TFDPRQuestionEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Tuple =TFDPRReader.from_pretrained(__a)
self.assertIsNotNone(__a)
@require_tf
class __magic_name__ ( unittest.TestCase ):
@slow
def lowerCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCAmelCase : int =TFDPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base')
_UpperCAmelCase : Optional[int] =tf.constant(
[[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]]) # [CLS] hello, is my dog cute? [SEP]
_UpperCAmelCase : Optional[int] =model(__a)[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
_UpperCAmelCase : Optional[Any] =tf.constant(
[
[
0.03_23_62_53,
0.12_75_33_35,
0.16_81_85_09,
0.00_27_97_86,
0.3_89_69_33,
0.24_26_49_45,
0.2_17_89_71,
-0.02_33_52_27,
-0.08_48_19_59,
-0.14_32_41_17,
]
])
self.assertTrue(numpy.allclose(output[:, :1_0].numpy() , expected_slice.numpy() , atol=1E-4))
| 446 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = (DDPMScheduler,)
def UpperCAmelCase ( self , **__a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = {
'''num_train_timesteps''': 10_00,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''variance_type''': '''fixed_small''',
'''clip_sample''': True,
}
config.update(**__a)
return config
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
for timesteps in [1, 5, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__a)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2]):
self.check_over_configs(beta_start=__a , beta_end=__a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__a)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=__a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
self.check_over_configs(thresholding=__a)
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=__a , prediction_type=__a , sample_max_value=__a , )
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=__a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
for t in [0, 5_00, 9_99]:
self.check_over_forward(time_step=__a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0979)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1e-5
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = len(__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter
_UpperCamelCase = torch.manual_seed(0)
for t in reversed(range(__a)):
# 1. predict noise residual
_UpperCamelCase = model(__a , __a)
# 2. predict previous mean of sample x_t-1
_UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCamelCase = pred_prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
assert abs(result_sum.item() - 258.9606) < 1e-2
assert abs(result_mean.item() - 0.3372) < 1e-3
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''')
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = len(__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter
_UpperCamelCase = torch.manual_seed(0)
for t in reversed(range(__a)):
# 1. predict noise residual
_UpperCamelCase = model(__a , __a)
# 2. predict previous mean of sample x_t-1
_UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCamelCase = pred_prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
assert abs(result_sum.item() - 202.0296) < 1e-2
assert abs(result_mean.item() - 0.2631) < 1e-3
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [1_00, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=__a)
_UpperCamelCase = scheduler.timesteps
for i, timestep in enumerate(__a):
if i == len(__a) - 1:
_UpperCamelCase = -1
else:
_UpperCamelCase = timesteps[i + 1]
_UpperCamelCase = scheduler.previous_timestep(__a)
_UpperCamelCase = prev_t.item()
self.assertEqual(__a , __a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [1_00, 87, 50, 51, 0]
with self.assertRaises(__a , msg='''`custom_timesteps` must be in descending order.'''):
scheduler.set_timesteps(timesteps=__a)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [1_00, 87, 50, 1, 0]
_UpperCamelCase = len(__a)
with self.assertRaises(__a , msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
scheduler.set_timesteps(num_inference_steps=__a , timesteps=__a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
__a , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ):
scheduler.set_timesteps(timesteps=__a)
| 19 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Optional[int] = {
'''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
'''tokenization_roformer''': ['''RoFormerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[int] = ['''RoFormerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Any = [
'''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RoFormerForCausalLM''',
'''RoFormerForMaskedLM''',
'''RoFormerForMultipleChoice''',
'''RoFormerForQuestionAnswering''',
'''RoFormerForSequenceClassification''',
'''RoFormerForTokenClassification''',
'''RoFormerLayer''',
'''RoFormerModel''',
'''RoFormerPreTrainedModel''',
'''load_tf_weights_in_roformer''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Union[str, Any] = [
'''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRoFormerForCausalLM''',
'''TFRoFormerForMaskedLM''',
'''TFRoFormerForMultipleChoice''',
'''TFRoFormerForQuestionAnswering''',
'''TFRoFormerForSequenceClassification''',
'''TFRoFormerForTokenClassification''',
'''TFRoFormerLayer''',
'''TFRoFormerModel''',
'''TFRoFormerPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Union[str, Any] = [
'''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FlaxRoFormerForMaskedLM''',
'''FlaxRoFormerForMultipleChoice''',
'''FlaxRoFormerForQuestionAnswering''',
'''FlaxRoFormerForSequenceClassification''',
'''FlaxRoFormerForTokenClassification''',
'''FlaxRoFormerModel''',
'''FlaxRoFormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
from .tokenization_roformer import RoFormerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roformer_fast import RoFormerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roformer import (
FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
FlaxRoFormerPreTrainedModel,
)
else:
import sys
_lowerCamelCase : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 663 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
_a = 100
_a = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_a = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=1_00 )
def lowerCamelCase__ ( __snake_case ) -> set[int]:
"""simple docstring"""
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
_UpperCamelCase = set()
_UpperCamelCase = 42
_UpperCamelCase = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def lowerCamelCase__ ( __snake_case = 50_00 ) -> int | None:
"""simple docstring"""
for number_to_partition in range(1, __snake_case ):
if len(partition(__snake_case ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 19 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import UperNetForSemanticSegmentation
from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __magic_name__ :
def __init__( self : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str=13 , UpperCamelCase__ : Optional[int]=32 , UpperCamelCase__ : Tuple=3 , UpperCamelCase__ : Optional[Any]=4 , UpperCamelCase__ : str=[10, 20, 30, 40] , UpperCamelCase__ : str=[2, 2, 3, 2] , UpperCamelCase__ : Union[str, Any]=True , UpperCamelCase__ : List[Any]=True , UpperCamelCase__ : Dict=37 , UpperCamelCase__ : str="gelu" , UpperCamelCase__ : List[str]=10 , UpperCamelCase__ : int=0.02 , UpperCamelCase__ : List[str]=["stage2", "stage3", "stage4"] , UpperCamelCase__ : List[Any]=3 , UpperCamelCase__ : Dict=None , ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = image_size
UpperCAmelCase = num_channels
UpperCAmelCase = num_stages
UpperCAmelCase = hidden_sizes
UpperCAmelCase = depths
UpperCAmelCase = is_training
UpperCAmelCase = use_labels
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = type_sequence_label_size
UpperCAmelCase = initializer_range
UpperCAmelCase = out_features
UpperCAmelCase = num_labels
UpperCAmelCase = scope
UpperCAmelCase = num_stages
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE_ ( self : int ) -> Any:
'''simple docstring'''
return ConvNextConfig(
num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
return UperNetConfig(
backbone_config=self.get_backbone_config() , hidden_size=5_12 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=__a , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=2_56 , auxiliary_num_convs=1 , auxiliary_concat_input=__a , loss_ignore_index=2_55 , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , UpperCamelCase__ : str , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase = UperNetForSemanticSegmentation(config=__a )
model.to(__a )
model.eval()
UpperCAmelCase = model(__a )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def SCREAMING_SNAKE_CASE_ ( self : str ) -> Dict:
'''simple docstring'''
UpperCAmelCase = self.prepare_config_and_inputs()
(
(
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) ,
) = config_and_inputs
UpperCAmelCase = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class __magic_name__ ( A__, A__, unittest.TestCase ):
lowercase : Optional[int] =(UperNetForSemanticSegmentation,) if is_torch_available() else ()
lowercase : List[str] ={'''image-segmentation''': UperNetForSemanticSegmentation} if is_torch_available() else {}
lowercase : List[Any] =False
lowercase : Dict =False
lowercase : Any =False
lowercase : int =False
lowercase : Optional[Any] =False
lowercase : Dict =False
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase = UperNetModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37 )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
return
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> str:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(__a )
UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase = [*signature.parameters.keys()]
UpperCAmelCase = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __a )
def SCREAMING_SNAKE_CASE_ ( self : Any ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__a )
@unittest.skip(reason="UperNet does not use inputs_embeds" )
def SCREAMING_SNAKE_CASE_ ( self : int ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason="UperNet does not support input and output embeddings" )
def SCREAMING_SNAKE_CASE_ ( self : str ) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="UperNet does not have a base model" )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason="UperNet does not have a base model" )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason="UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`" )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def SCREAMING_SNAKE_CASE_ ( self : int ) -> Any:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self : int ) -> List[Any]:
'''simple docstring'''
def check_hidden_states_output(UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : str , UpperCamelCase__ : List[str] ):
UpperCAmelCase = model_class(__a )
model.to(__a )
model.eval()
with torch.no_grad():
UpperCAmelCase = model(**self._prepare_for_class(__a , __a ) )
UpperCAmelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
UpperCAmelCase = self.model_tester.num_stages
self.assertEqual(len(__a ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = True
check_hidden_states_output(__a , __a , __a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase = True
check_hidden_states_output(__a , __a , __a )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase = _config_zero_init(__a )
UpperCAmelCase = _config_zero_init(configs_no_init.backbone_config )
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(config=__a )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'Parameter {name} of model {model_class} seems not properly initialized' , )
@unittest.skip(reason="UperNet does not have tied weights" )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
pass
@slow
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase = UperNetForSemanticSegmentation.from_pretrained(__a )
self.assertIsNotNone(__a )
def lowerCamelCase_() -> int:
UpperCAmelCase = hf_hub_download(
repo_id="hf-internal-testing/fixtures_ade20k" , repo_type="dataset" , filename="ADE_val_00000001.jpg" )
UpperCAmelCase = Image.open(__snake_case ).convert("RGB" )
return image
@require_torch
@require_vision
@slow
class __magic_name__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase = AutoImageProcessor.from_pretrained("openmmlab/upernet-swin-tiny" )
UpperCAmelCase = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-swin-tiny" ).to(__a )
UpperCAmelCase = prepare_img()
UpperCAmelCase = processor(images=__a , return_tensors="pt" ).to(__a )
with torch.no_grad():
UpperCAmelCase = model(**__a )
UpperCAmelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12) )
self.assertEqual(outputs.logits.shape , __a )
UpperCAmelCase = torch.tensor(
[[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]] ).to(__a )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4 ) )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-tiny" )
UpperCAmelCase = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-tiny" ).to(__a )
UpperCAmelCase = prepare_img()
UpperCAmelCase = processor(images=__a , return_tensors="pt" ).to(__a )
with torch.no_grad():
UpperCAmelCase = model(**__a )
UpperCAmelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12) )
self.assertEqual(outputs.logits.shape , __a )
UpperCAmelCase = torch.tensor(
[[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]] ).to(__a )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4 ) )
| 323 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> np.array:
"""simple docstring"""
_UpperCamelCase = int(np.ceil((x_end - xa) / step_size ) )
_UpperCamelCase = np.zeros((n + 1,) )
_UpperCamelCase = ya
_UpperCamelCase = xa
for k in range(__snake_case ):
_UpperCamelCase = y[k] + step_size * ode_func(__snake_case, y[k] )
_UpperCamelCase = y[k] + (
(step_size / 2) * (ode_func(__snake_case, y[k] ) + ode_func(x + step_size, __snake_case ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 19 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class UpperCAmelCase ( a__ , a__ , a__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = StableDiffusionInpaintPipeline
SCREAMING_SNAKE_CASE = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
SCREAMING_SNAKE_CASE = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
SCREAMING_SNAKE_CASE = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
SCREAMING_SNAKE_CASE = frozenset([] )
def _lowerCAmelCase( self ) -> str:
torch.manual_seed(0 )
lowercase__ : List[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=9 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=__a , )
lowercase__ : Union[str, Any] = PNDMScheduler(skip_prk_steps=__a )
torch.manual_seed(0 )
lowercase__ : Dict = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
lowercase__ : List[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='''gelu''' , projection_dim=512 , )
lowercase__ : Union[str, Any] = CLIPTextModel(__a )
lowercase__ : Dict = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
lowercase__ : Optional[int] = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Optional[int]:
lowercase__ : List[str] = floats_tensor((1, 3, 32, 32) , rng=random.Random(__a ) ).to(__a )
lowercase__ : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ : Optional[int] = Image.fromarray(np.uinta(__a ) ).convert('''RGB''' ).resize((64, 64) )
lowercase__ : int = Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
if str(__a ).startswith('''mps''' ):
lowercase__ : Optional[Any] = torch.manual_seed(__a )
else:
lowercase__ : Tuple = torch.Generator(device=__a ).manual_seed(__a )
lowercase__ : List[str] = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': init_image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def _lowerCAmelCase( self ) -> List[Any]:
lowercase__ : Union[str, Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
lowercase__ : Tuple = self.get_dummy_components()
lowercase__ : Optional[Any] = StableDiffusionInpaintPipeline(**__a )
lowercase__ : Optional[Any] = sd_pipe.to(__a )
sd_pipe.set_progress_bar_config(disable=__a )
lowercase__ : int = self.get_dummy_inputs(__a )
lowercase__ : Any = sd_pipe(**__a ).images
lowercase__ : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase__ : List[str] = np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowerCAmelCase( self ) -> Any:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase( self ) -> Any:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCAmelCase( self ) -> int:
lowercase__ : Any = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
lowercase__ : Union[str, Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
lowercase__ : str = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
'''/yellow_cat_sitting_on_a_park_bench.npy''' )
lowercase__ : int = '''stabilityai/stable-diffusion-2-inpainting'''
lowercase__ : Union[str, Any] = StableDiffusionInpaintPipeline.from_pretrained(__a , safety_checker=__a )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
pipe.enable_attention_slicing()
lowercase__ : Optional[int] = '''Face of a yellow cat, high resolution, sitting on a park bench'''
lowercase__ : Dict = torch.manual_seed(0 )
lowercase__ : Union[str, Any] = pipe(
prompt=__a , image=__a , mask_image=__a , generator=__a , output_type='''np''' , )
lowercase__ : Optional[int] = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 9E-3
def _lowerCAmelCase( self ) -> Any:
lowercase__ : Tuple = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
lowercase__ : List[str] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
lowercase__ : Optional[int] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
'''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' )
lowercase__ : Dict = '''stabilityai/stable-diffusion-2-inpainting'''
lowercase__ : Optional[int] = StableDiffusionInpaintPipeline.from_pretrained(
__a , torch_dtype=torch.floataa , safety_checker=__a , )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
pipe.enable_attention_slicing()
lowercase__ : Optional[int] = '''Face of a yellow cat, high resolution, sitting on a park bench'''
lowercase__ : Tuple = torch.manual_seed(0 )
lowercase__ : Optional[int] = pipe(
prompt=__a , image=__a , mask_image=__a , generator=__a , output_type='''np''' , )
lowercase__ : Optional[int] = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 5E-1
def _lowerCAmelCase( self ) -> str:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowercase__ : Tuple = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
lowercase__ : List[Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
lowercase__ : Optional[int] = '''stabilityai/stable-diffusion-2-inpainting'''
lowercase__ : Optional[Any] = PNDMScheduler.from_pretrained(__a , subfolder='''scheduler''' )
lowercase__ : List[Any] = StableDiffusionInpaintPipeline.from_pretrained(
__a , safety_checker=__a , scheduler=__a , torch_dtype=torch.floataa , )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowercase__ : Union[str, Any] = '''Face of a yellow cat, high resolution, sitting on a park bench'''
lowercase__ : List[str] = torch.manual_seed(0 )
lowercase__ : Optional[Any] = pipe(
prompt=__a , image=__a , mask_image=__a , generator=__a , num_inference_steps=2 , output_type='''np''' , )
lowercase__ : Optional[Any] = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.6_5 * 10**9
| 152 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_a = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
""" Distillation"""
)
)
parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
parser.add_argument("""--vocab_transform""", action="""store_true""")
_a = parser.parse_args()
if args.model_type == "bert":
_a = BertForMaskedLM.from_pretrained(args.model_name)
_a = """bert"""
else:
raise ValueError("""args.model_type should be \"bert\".""")
_a = model.state_dict()
_a = {}
for w in ["word_embeddings", "position_embeddings"]:
_a = state_dict[F"""{prefix}.embeddings.{w}.weight"""]
for w in ["weight", "bias"]:
_a = state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]
_a = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
]
std_idx += 1
_a = state_dict["""cls.predictions.decoder.weight"""]
_a = state_dict["""cls.predictions.bias"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
_a = state_dict[F"""cls.predictions.transform.dense.{w}"""]
_a = state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]
print(F"""N layers selected for distillation: {std_idx}""")
print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 19 | 0 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_snake_case = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
_snake_case = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
_snake_case = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : Dict = len([g for position, g in enumerate(__snake_case ) if g == main_target[position]] )
return (item, float(__snake_case ))
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : int = random.randint(0 , len(__snake_case ) - 1 )
lowerCamelCase : str = parent_a[:random_slice] + parent_a[random_slice:]
lowerCamelCase : int = parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : int = list(__snake_case )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
lowerCamelCase : List[Any] = random.choice(__snake_case )
return "".join(__snake_case )
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
'''simple docstring'''
lowerCamelCase : Any = []
# Generate more children proportionally to the fitness score.
lowerCamelCase : str = int(parent_a[1] * 100 ) + 1
lowerCamelCase : Optional[Any] = 10 if child_n >= 10 else child_n
for _ in range(__snake_case ):
lowerCamelCase : Tuple = population_score[random.randint(0 , __snake_case )][0]
lowerCamelCase , lowerCamelCase : List[Any] = crossover(parent_a[0] , __snake_case )
# Append new string to the population list.
pop.append(mutate(__snake_case , __snake_case ) )
pop.append(mutate(__snake_case , __snake_case ) )
return pop
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = True ):
'''simple docstring'''
if N_POPULATION < N_SELECTED:
lowerCamelCase : str = f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
raise ValueError(__snake_case )
# Verify that the target contains no genes besides the ones inside genes variable.
lowerCamelCase : Dict = sorted({c for c in target if c not in genes} )
if not_in_genes_list:
lowerCamelCase : Union[str, Any] = f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
raise ValueError(__snake_case )
# Generate random starting population.
lowerCamelCase : Tuple = []
for _ in range(__snake_case ):
population.append("".join([random.choice(__snake_case ) for i in range(len(__snake_case ) )] ) )
# Just some logs to know what the algorithms is doing.
lowerCamelCase , lowerCamelCase : Union[str, Any] = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(__snake_case )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
lowerCamelCase : Union[str, Any] = [evaluate(__snake_case , __snake_case ) for item in population]
# Check if there is a matching evolution.
lowerCamelCase : Dict = sorted(__snake_case , key=lambda SCREAMING_SNAKE_CASE_ : x[1] , reverse=__snake_case )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
f"""\nGeneration: {generation}"""
f"""\nTotal Population:{total_population}"""
f"""\nBest score: {population_score[0][1]}"""
f"""\nBest string: {population_score[0][0]}""" )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
lowerCamelCase : Tuple = population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(__snake_case )
# Normalize population score to be between 0 and 1.
lowerCamelCase : Tuple = [
(item, score / len(__snake_case )) for item, score in population_score
]
# This is selection
for i in range(__snake_case ):
population.extend(select(population_score[int(__snake_case )] , __snake_case , __snake_case ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(__snake_case ) > N_POPULATION:
break
if __name__ == "__main__":
_snake_case = (
'''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
)
_snake_case = list(
''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
'''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
)
_snake_case , _snake_case , _snake_case = basic(target_str, genes_list)
print(
f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
)
| 340 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
_a = """platform"""
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class _UpperCAmelCase:
lowercase__ = PegasusConfig
lowercase__ = {}
lowercase__ = 'gelu'
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=False , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a=0.1 , __a=0.1 , __a=20 , __a=2 , __a=1 , __a=0 , ) -> int:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = eos_token_id
_UpperCamelCase = pad_token_id
_UpperCamelCase = bos_token_id
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size).clip(3 , self.vocab_size)
_UpperCamelCase = np.expand_dims(np.array([self.eos_token_id] * self.batch_size) , 1)
_UpperCamelCase = np.concatenate([input_ids, eos_tensor] , axis=1)
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCamelCase = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_UpperCamelCase = prepare_pegasus_inputs_dict(__a , __a , __a)
return config, inputs_dict
def UpperCAmelCase ( self , __a , __a , __a) -> Tuple:
'''simple docstring'''
_UpperCamelCase = 20
_UpperCamelCase = model_class_name(__a)
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''])
_UpperCamelCase , _UpperCamelCase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
_UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a)
_UpperCamelCase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''')
_UpperCamelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCamelCase = model.decode(
decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , )
_UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''')
_UpperCamelCase = model.decode(
decoder_input_ids[:, -1:] , __a , decoder_attention_mask=__a , past_key_values=outputs_cache.past_key_values , decoder_position_ids=__a , )
_UpperCamelCase = model.decode(__a , __a)
_UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''')
def UpperCAmelCase ( self , __a , __a , __a) -> Tuple:
'''simple docstring'''
_UpperCamelCase = 20
_UpperCamelCase = model_class_name(__a)
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''])
_UpperCamelCase , _UpperCamelCase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
_UpperCamelCase = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
_UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a)
_UpperCamelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCamelCase = model.decode(
decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , )
_UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''')
_UpperCamelCase = model.decode(
decoder_input_ids[:, -1:] , __a , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=__a , decoder_position_ids=__a , )
_UpperCamelCase = model.decode(__a , __a , decoder_attention_mask=__a)
_UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''')
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=None, __snake_case=None, ) -> Union[str, Any]:
"""simple docstring"""
if attention_mask is None:
_UpperCamelCase = np.not_equal(__snake_case, config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
_UpperCamelCase = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape, dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:], config.pad_token_id ).astype(np.inta ),
], axis=-1, )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class _UpperCAmelCase( lowerCamelCase , unittest.TestCase ):
lowercase__ = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
lowercase__ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
lowercase__ = True
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = FlaxPegasusModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(__a , __a , __a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(__a , __a , __a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
_UpperCamelCase = self._prepare_for_class(__a , __a)
_UpperCamelCase = model_class(__a)
@jax.jit
def encode_jitted(__a , __a=None , **__a):
return model.encode(input_ids=__a , attention_mask=__a)
with self.subTest('''JIT Enabled'''):
_UpperCamelCase = encode_jitted(**__a).to_tuple()
with self.subTest('''JIT Disabled'''):
with jax.disable_jit():
_UpperCamelCase = encode_jitted(**__a).to_tuple()
self.assertEqual(len(__a) , len(__a))
for jitted_output, output in zip(__a , __a):
self.assertEqual(jitted_output.shape , output.shape)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
_UpperCamelCase = model_class(__a)
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask'''])
_UpperCamelCase = {
'''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
'''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
'''encoder_outputs''': encoder_outputs,
}
@jax.jit
def decode_jitted(__a , __a , __a):
return model.decode(
decoder_input_ids=__a , decoder_attention_mask=__a , encoder_outputs=__a , )
with self.subTest('''JIT Enabled'''):
_UpperCamelCase = decode_jitted(**__a).to_tuple()
with self.subTest('''JIT Disabled'''):
with jax.disable_jit():
_UpperCamelCase = decode_jitted(**__a).to_tuple()
self.assertEqual(len(__a) , len(__a))
for jitted_output, output in zip(__a , __a):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
for model_class_name in self.all_model_classes:
_UpperCamelCase = model_class_name.from_pretrained('''google/pegasus-large''' , from_pt=__a)
_UpperCamelCase = np.ones((1, 1))
_UpperCamelCase = model(__a)
self.assertIsNotNone(__a)
@slow
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''')
_UpperCamelCase = PegasusTokenizer.from_pretrained('''google/pegasus-xsum''')
_UpperCamelCase = [
''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
]
_UpperCamelCase = [
'''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
'''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
]
_UpperCamelCase = tokenizer(__a , return_tensors='''np''' , truncation=__a , max_length=5_12 , padding=__a)
_UpperCamelCase = model.generate(**__a , num_beams=2).sequences
_UpperCamelCase = tokenizer.batch_decode(__a , skip_special_tokens=__a)
assert tgt_text == decoded
| 19 | 0 |
def lowerCAmelCase__ ( lowerCamelCase_ : str):
'''simple docstring'''
lowerCAmelCase__ : int = 1
for i in range(1 ,num + 1):
fact *= i
return fact
def lowerCAmelCase__ ( lowerCamelCase_ : Tuple):
'''simple docstring'''
lowerCAmelCase__ : Dict = 0
while number > 0:
lowerCAmelCase__ : List[str] = number % 10
sum_of_digits += last_digit
lowerCAmelCase__ : Any = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def lowerCAmelCase__ ( lowerCamelCase_ : Optional[int] = 100):
'''simple docstring'''
lowerCAmelCase__ : Union[str, Any] = factorial(__snake_case)
lowerCAmelCase__ : int = split_and_add(__snake_case)
return result
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 647 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
BertConfig,
DPRConfig,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
class _UpperCAmelCase:
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=2 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=5_12 , __a=16 , __a=2 , __a=0.02 , __a=3 , __a=4 , __a=None , __a=0 , ) -> Any:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_mask
_UpperCamelCase = use_token_type_ids
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = type_vocab_size
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = num_labels
_UpperCamelCase = num_choices
_UpperCamelCase = scope
_UpperCamelCase = projection_dim
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCamelCase = None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length])
_UpperCamelCase = None
if self.use_token_type_ids:
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
_UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices)
_UpperCamelCase = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__a , initializer_range=self.initializer_range , )
_UpperCamelCase = DPRConfig(projection_dim=self.projection_dim , **config.to_dict())
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = TFDPRContextEncoder(config=__a)
_UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a)
_UpperCamelCase = model(__a , token_type_ids=__a)
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size))
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = TFDPRQuestionEncoder(config=__a)
_UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a)
_UpperCamelCase = model(__a , token_type_ids=__a)
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size))
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Dict:
'''simple docstring'''
_UpperCamelCase = TFDPRReader(config=__a)
_UpperCamelCase = model(__a , attention_mask=__a)
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,))
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {'''input_ids''': input_ids}
return config, inputs_dict
@require_tf
class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase__ = (
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
lowercase__ = {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = TFDPRModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a , hidden_size=37)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*__a)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*__a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*__a)
@slow
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRQuestionEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRReader.from_pretrained(__a)
self.assertIsNotNone(__a)
@require_tf
class _UpperCAmelCase( unittest.TestCase ):
@slow
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base''')
_UpperCamelCase = tf.constant(
[[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]]) # [CLS] hello, is my dog cute? [SEP]
_UpperCamelCase = model(__a)[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
_UpperCamelCase = tf.constant(
[
[
0.0323_6253,
0.1275_3335,
0.1681_8509,
0.0027_9786,
0.389_6933,
0.2426_4945,
0.217_8971,
-0.0233_5227,
-0.0848_1959,
-0.1432_4117,
]
])
self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1e-4))
| 19 | 0 |
'''simple docstring'''
def UpperCAmelCase_ (__a : List[str] , __a : Optional[Any] ):
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def UpperCAmelCase_ ():
"""simple docstring"""
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 229 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowerCamelCase__ ( __snake_case ) -> Optional[Any]:
"""simple docstring"""
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
or (cp >= 0x3400 and cp <= 0x4DBF) #
or (cp >= 0x2_0000 and cp <= 0x2_A6DF) #
or (cp >= 0x2_A700 and cp <= 0x2_B73F) #
or (cp >= 0x2_B740 and cp <= 0x2_B81F) #
or (cp >= 0x2_B820 and cp <= 0x2_CEAF) #
or (cp >= 0xF900 and cp <= 0xFAFF)
or (cp >= 0x2_F800 and cp <= 0x2_FA1F) #
): #
return True
return False
def lowerCamelCase__ ( __snake_case ) -> Optional[Any]:
"""simple docstring"""
for char in word:
_UpperCamelCase = ord(__snake_case )
if not _is_chinese_char(__snake_case ):
return 0
return 1
def lowerCamelCase__ ( __snake_case ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = set()
for token in tokens:
_UpperCamelCase = len(__snake_case ) > 1 and is_chinese(__snake_case )
if chinese_word:
word_set.add(__snake_case )
_UpperCamelCase = list(__snake_case )
return word_list
def lowerCamelCase__ ( __snake_case, __snake_case ) -> int:
"""simple docstring"""
if not chinese_word_set:
return bert_tokens
_UpperCamelCase = max([len(__snake_case ) for w in chinese_word_set] )
_UpperCamelCase = bert_tokens
_UpperCamelCase , _UpperCamelCase = 0, len(__snake_case )
while start < end:
_UpperCamelCase = True
if is_chinese(bert_word[start] ):
_UpperCamelCase = min(end - start, __snake_case )
for i in range(__snake_case, 1, -1 ):
_UpperCamelCase = ''''''.join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1, start + i ):
_UpperCamelCase = '''##''' + bert_word[j]
_UpperCamelCase = start + i
_UpperCamelCase = False
break
if single_word:
start += 1
return bert_word
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = []
for i in range(0, len(__snake_case ), 1_00 ):
_UpperCamelCase = ltp_tokenizer.pipeline(lines[i : i + 1_00], tasks=['''cws'''] ).cws
_UpperCamelCase = [get_chinese_word(__snake_case ) for r in res]
ltp_res.extend(__snake_case )
assert len(__snake_case ) == len(__snake_case )
_UpperCamelCase = []
for i in range(0, len(__snake_case ), 1_00 ):
_UpperCamelCase = bert_tokenizer(lines[i : i + 1_00], add_special_tokens=__snake_case, truncation=__snake_case, max_length=5_12 )
bert_res.extend(res['''input_ids'''] )
assert len(__snake_case ) == len(__snake_case )
_UpperCamelCase = []
for input_ids, chinese_word in zip(__snake_case, __snake_case ):
_UpperCamelCase = []
for id in input_ids:
_UpperCamelCase = bert_tokenizer._convert_id_to_token(__snake_case )
input_tokens.append(__snake_case )
_UpperCamelCase = add_sub_symbol(__snake_case, __snake_case )
_UpperCamelCase = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__snake_case ):
if token[:2] == "##":
_UpperCamelCase = token[2:]
# save chinese tokens' pos
if len(__snake_case ) == 1 and _is_chinese_char(ord(__snake_case ) ):
ref_id.append(__snake_case )
ref_ids.append(__snake_case )
assert len(__snake_case ) == len(__snake_case )
return ref_ids
def lowerCamelCase__ ( __snake_case ) -> Optional[int]:
"""simple docstring"""
with open(args.file_name, '''r''', encoding='''utf-8''' ) as f:
_UpperCamelCase = f.readlines()
_UpperCamelCase = [line.strip() for line in data if len(__snake_case ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
_UpperCamelCase = LTP(args.ltp ) # faster in GPU device
_UpperCamelCase = BertTokenizer.from_pretrained(args.bert )
_UpperCamelCase = prepare_ref(__snake_case, __snake_case, __snake_case )
with open(args.save_path, '''w''', encoding='''utf-8''' ) as f:
_UpperCamelCase = [json.dumps(__snake_case ) + '''\n''' for ref in ref_ids]
f.writelines(__snake_case )
if __name__ == "__main__":
_a = argparse.ArgumentParser(description="""prepare_chinese_ref""")
parser.add_argument(
"""--file_name""",
required=False,
type=str,
default="""./resources/chinese-demo.txt""",
help="""file need process, same as training data in lm""",
)
parser.add_argument(
"""--ltp""",
required=False,
type=str,
default="""./resources/ltp""",
help="""resources for LTP tokenizer, usually a path""",
)
parser.add_argument(
"""--bert""",
required=False,
type=str,
default="""./resources/robert""",
help="""resources for Bert tokenizer""",
)
parser.add_argument(
"""--save_path""",
required=False,
type=str,
default="""./resources/ref.txt""",
help="""path to save res""",
)
_a = parser.parse_args()
main(args)
| 19 | 0 |
import math
def __lowerCamelCase (UpperCAmelCase__ : Dict ):
assert isinstance(__snake_case , __snake_case ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
SCREAMING_SNAKE_CASE = range(3 , int(math.sqrt(__snake_case ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def __lowerCamelCase (UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Any=1 , **UpperCAmelCase__ : List[Any] ):
SCREAMING_SNAKE_CASE = factor * value
SCREAMING_SNAKE_CASE = value
while not is_prime(__snake_case ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **__snake_case )
return value
| 403 |
"""simple docstring"""
import heapq
def lowerCamelCase__ ( __snake_case ) -> set[int]:
"""simple docstring"""
_UpperCamelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(__snake_case, [-1 * len(__snake_case ), (key, value)] )
# chosen_vertices = set of chosen vertices
_UpperCamelCase = set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
_UpperCamelCase = heapq.heappop(__snake_case )[1][0]
chosen_vertices.add(__snake_case )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
_UpperCamelCase = elem[1][1].index(__snake_case )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(__snake_case )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
_a = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
| 19 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Dict = logging.get_logger(__name__)
_snake_case : str = {
'BridgeTower/bridgetower-base': 'https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json',
'BridgeTower/bridgetower-base-itm-mlm': (
'https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'
),
}
class A ( _a ):
lowercase_ = 'bridgetower_vision_model'
def __init__( self : Tuple , lowerCAmelCase_ : Tuple=7_68 , lowerCAmelCase_ : Dict=12 , lowerCAmelCase_ : Tuple=3 , lowerCAmelCase_ : Optional[Any]=16 , lowerCAmelCase_ : Any=2_88 , lowerCAmelCase_ : List[str]=1 , lowerCAmelCase_ : Optional[int]=1e-05 , lowerCAmelCase_ : str=False , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : str=False , **lowerCAmelCase_ : List[str] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**__a )
_a = hidden_size
_a = num_hidden_layers
_a = num_channels
_a = patch_size
_a = image_size
_a = initializer_factor
_a = layer_norm_eps
_a = stop_gradient
_a = share_layernorm
_a = remove_last_layer
@classmethod
def __lowerCAmelCase ( cls : str , lowerCAmelCase_ : List[str] , **lowerCAmelCase_ : Optional[Any] ) -> "PretrainedConfig":
"""simple docstring"""
_a , _a = cls.get_config_dict(__a , **__a )
if config_dict.get('''model_type''' ) == "bridgetower":
_a = config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(__a , **__a )
class A ( _a ):
lowercase_ = 'bridgetower_text_model'
def __init__( self : List[Any] , lowerCAmelCase_ : Any=5_02_65 , lowerCAmelCase_ : int=7_68 , lowerCAmelCase_ : str=12 , lowerCAmelCase_ : Tuple=12 , lowerCAmelCase_ : Union[str, Any]=1 , lowerCAmelCase_ : Union[str, Any]=30_72 , lowerCAmelCase_ : Union[str, Any]="gelu" , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : Optional[int]=0.1 , lowerCAmelCase_ : int=5_14 , lowerCAmelCase_ : Any=1 , lowerCAmelCase_ : Any=1e-05 , lowerCAmelCase_ : List[str]=1 , lowerCAmelCase_ : Optional[Any]=0 , lowerCAmelCase_ : List[str]=2 , lowerCAmelCase_ : int="absolute" , lowerCAmelCase_ : Dict=True , **lowerCAmelCase_ : str , ) -> Any:
"""simple docstring"""
super().__init__(**__a )
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = hidden_act
_a = initializer_factor
_a = intermediate_size
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = layer_norm_eps
_a = position_embedding_type
_a = use_cache
_a = pad_token_id
_a = bos_token_id
_a = eos_token_id
@classmethod
def __lowerCAmelCase ( cls : Tuple , lowerCAmelCase_ : List[str] , **lowerCAmelCase_ : Optional[int] ) -> "PretrainedConfig":
"""simple docstring"""
_a , _a = cls.get_config_dict(__a , **__a )
if config_dict.get('''model_type''' ) == "bridgetower":
_a = config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(__a , **__a )
class A ( _a ):
lowercase_ = 'bridgetower'
def __init__( self : int , lowerCAmelCase_ : Tuple=True , lowerCAmelCase_ : Any="gelu" , lowerCAmelCase_ : Union[str, Any]=7_68 , lowerCAmelCase_ : Optional[Any]=1 , lowerCAmelCase_ : int=1e-05 , lowerCAmelCase_ : List[Any]=False , lowerCAmelCase_ : Optional[Any]="add" , lowerCAmelCase_ : List[str]=12 , lowerCAmelCase_ : Any=6 , lowerCAmelCase_ : List[Any]=False , lowerCAmelCase_ : Tuple=False , lowerCAmelCase_ : Tuple=None , lowerCAmelCase_ : Dict=None , **lowerCAmelCase_ : Any , ) -> Optional[int]:
"""simple docstring"""
_a = kwargs.pop('''text_config_dict''' , __a )
_a = kwargs.pop('''vision_config_dict''' , __a )
super().__init__(**__a )
_a = share_cross_modal_transformer_layers
_a = hidden_act
_a = hidden_size
_a = initializer_factor
_a = layer_norm_eps
_a = share_link_tower_layers
_a = link_tower_type
_a = num_attention_heads
_a = num_hidden_layers
_a = tie_word_embeddings
_a = init_layernorm_from_vision_encoder
if text_config is None:
_a = {}
logger.info('''`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.''' )
if vision_config is None:
_a = {}
logger.info('''`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.''' )
_a = BridgeTowerTextConfig(**__a )
_a = BridgeTowerVisionConfig(**__a )
@classmethod
def __lowerCAmelCase ( cls : List[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Union[str, Any] , **lowerCAmelCase_ : str ) -> str:
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__a )
def __lowerCAmelCase ( self : List[str] ) -> Any:
"""simple docstring"""
_a = copy.deepcopy(self.__dict__ )
_a = self.text_config.to_dict()
_a = self.vision_config.to_dict()
_a = self.__class__.model_type
return output
| 22 |
"""simple docstring"""
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def lowerCamelCase__ ( ) -> List[str]:
"""simple docstring"""
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
_UpperCamelCase = '''__test_patch_submodule_mock__'''
with patch_submodule(_test_patching, '''os.path.join''', __snake_case ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os, _PatchedModuleObj )
assert isinstance(_test_patching.os.path, _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path, _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os, _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path, _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path, _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def lowerCamelCase__ ( ) -> List[str]:
"""simple docstring"""
assert _test_patching.open is open
_UpperCamelCase = '''__test_patch_submodule_builtin_mock__'''
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching, '''open''', __snake_case ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def lowerCamelCase__ ( ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_missing_mock__'''
with patch_submodule(_test_patching, '''pandas.read_csv''', __snake_case ):
pass
def lowerCamelCase__ ( ) -> Dict:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_missing_builtin_mock__'''
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching, '''len''', __snake_case ) is None
with patch_submodule(_test_patching, '''len''', __snake_case ):
assert _test_patching.len is mock
assert _test_patching.len is len
def lowerCamelCase__ ( ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_start_and_stop_mock__'''
_UpperCamelCase = patch_submodule(_test_patching, '''open''', __snake_case )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def lowerCamelCase__ ( ) -> Optional[int]:
"""simple docstring"""
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
_UpperCamelCase = '''__test_patch_submodule_successive_join__'''
_UpperCamelCase = '''__test_patch_submodule_successive_dirname__'''
_UpperCamelCase = '''__test_patch_submodule_successive_rename__'''
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching, '''os.path.join''', __snake_case ):
with patch_submodule(_test_patching, '''os.rename''', __snake_case ):
with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching, '''os.rename''', __snake_case ):
with patch_submodule(_test_patching, '''os.path.join''', __snake_case ):
with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def lowerCamelCase__ ( ) -> str:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_doesnt_exist_mock__'''
with patch_submodule(_test_patching, '''__module_that_doesn_exist__.__attribute_that_doesn_exist__''', __snake_case ):
pass
with patch_submodule(_test_patching, '''os.__attribute_that_doesn_exist__''', __snake_case ):
pass
| 19 | 0 |
'''simple docstring'''
import random
def __a ( A__ , A__ , A__ ) -> Dict:
lowerCAmelCase = a[left_index]
lowerCAmelCase = left_index + 1
for j in range(left_index + 1 , __snake_case ):
if a[j] < pivot:
lowerCAmelCase , lowerCAmelCase = a[i], a[j]
i += 1
lowerCAmelCase , lowerCAmelCase = a[i - 1], a[left_index]
return i - 1
def __a ( A__ , A__ , A__ ) -> Tuple:
if left < right:
lowerCAmelCase = random.randint(__snake_case , right - 1 )
lowerCAmelCase , lowerCAmelCase = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
lowerCAmelCase = partition(__snake_case , __snake_case , __snake_case )
quick_sort_random(
__snake_case , __snake_case , __snake_case ) # recursive quicksort to the left of the pivot point
quick_sort_random(
__snake_case , pivot_index + 1 , __snake_case ) # recursive quicksort to the right of the pivot point
def __a ( ) -> str:
lowerCAmelCase = input("Enter numbers separated by a comma:\n" ).strip()
lowerCAmelCase = [int(__snake_case ) for item in user_input.split("," )]
quick_sort_random(__snake_case , 0 , len(__snake_case ) )
print(__snake_case )
if __name__ == "__main__":
main()
| 649 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = original_name.split('''.''' )[0]
_UpperCamelCase = key.split('''.''' )
_UpperCamelCase = int(key_list[key_list.index(__snake_case ) - 2] )
_UpperCamelCase = int(key_list[key_list.index(__snake_case ) - 1] )
_UpperCamelCase = orig_block_num - offset
_UpperCamelCase = key.replace(F'''{orig_block_num}.{layer_num}.{original_name}''', F'''block.{new_block_num}.{layer_num}.{new_name}''' )
return key
def lowerCamelCase__ ( __snake_case ) -> str:
"""simple docstring"""
_UpperCamelCase = OrderedDict()
_UpperCamelCase , _UpperCamelCase = 0, 0
for key, value in state_dict.items():
if key.startswith('''network''' ):
_UpperCamelCase = key.replace('''network''', '''poolformer.encoder''' )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith('''bias''' ) and "patch_embed" not in key:
patch_emb_offset += 1
_UpperCamelCase = key[: key.find('''proj''' )]
_UpperCamelCase = key.replace(__snake_case, F'''patch_embeddings.{total_embed_found}.''' )
_UpperCamelCase = key.replace('''proj''', '''projection''' )
if key.endswith('''bias''' ):
total_embed_found += 1
if "patch_embeddings" in key:
_UpperCamelCase = '''poolformer.encoder.''' + key
if "mlp.fc1" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''mlp.fc1''', '''output.conv1''' )
if "mlp.fc2" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''mlp.fc2''', '''output.conv2''' )
if "norm1" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''norm1''', '''before_norm''' )
if "norm2" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''norm2''', '''after_norm''' )
if "layer_scale_1" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''layer_scale_1''', '''layer_scale_1''' )
if "layer_scale_2" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''layer_scale_2''', '''layer_scale_2''' )
if "head" in key:
_UpperCamelCase = key.replace('''head''', '''classifier''' )
_UpperCamelCase = value
return new_state_dict
def lowerCamelCase__ ( ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_UpperCamelCase = Image.open(requests.get(__snake_case, stream=__snake_case ).raw )
return image
@torch.no_grad()
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> int:
"""simple docstring"""
_UpperCamelCase = PoolFormerConfig()
# set attributes based on model_name
_UpperCamelCase = '''huggingface/label-files'''
_UpperCamelCase = model_name[-3:]
_UpperCamelCase = 10_00
_UpperCamelCase = '''imagenet-1k-id2label.json'''
_UpperCamelCase = (1, 10_00)
# set config attributes
_UpperCamelCase = json.load(open(hf_hub_download(__snake_case, __snake_case, repo_type='''dataset''' ), '''r''' ) )
_UpperCamelCase = {int(__snake_case ): v for k, v in idalabel.items()}
_UpperCamelCase = idalabel
_UpperCamelCase = {v: k for k, v in idalabel.items()}
if size == "s12":
_UpperCamelCase = [2, 2, 6, 2]
_UpperCamelCase = [64, 1_28, 3_20, 5_12]
_UpperCamelCase = 4.0
_UpperCamelCase = 0.9
elif size == "s24":
_UpperCamelCase = [4, 4, 12, 4]
_UpperCamelCase = [64, 1_28, 3_20, 5_12]
_UpperCamelCase = 4.0
_UpperCamelCase = 0.9
elif size == "s36":
_UpperCamelCase = [6, 6, 18, 6]
_UpperCamelCase = [64, 1_28, 3_20, 5_12]
_UpperCamelCase = 4.0
_UpperCamelCase = 1e-6
_UpperCamelCase = 0.9
elif size == "m36":
_UpperCamelCase = [6, 6, 18, 6]
_UpperCamelCase = [96, 1_92, 3_84, 7_68]
_UpperCamelCase = 4.0
_UpperCamelCase = 1e-6
_UpperCamelCase = 0.95
elif size == "m48":
_UpperCamelCase = [8, 8, 24, 8]
_UpperCamelCase = [96, 1_92, 3_84, 7_68]
_UpperCamelCase = 4.0
_UpperCamelCase = 1e-6
_UpperCamelCase = 0.95
else:
raise ValueError(F'''Size {size} not supported''' )
# load image processor
_UpperCamelCase = PoolFormerImageProcessor(crop_pct=__snake_case )
# Prepare image
_UpperCamelCase = prepare_img()
_UpperCamelCase = image_processor(images=__snake_case, return_tensors='''pt''' ).pixel_values
logger.info(F'''Converting model {model_name}...''' )
# load original state dict
_UpperCamelCase = torch.load(__snake_case, map_location=torch.device('''cpu''' ) )
# rename keys
_UpperCamelCase = rename_keys(__snake_case )
# create HuggingFace model and load state dict
_UpperCamelCase = PoolFormerForImageClassification(__snake_case )
model.load_state_dict(__snake_case )
model.eval()
# Define image processor
_UpperCamelCase = PoolFormerImageProcessor(crop_pct=__snake_case )
_UpperCamelCase = image_processor(images=prepare_img(), return_tensors='''pt''' ).pixel_values
# forward pass
_UpperCamelCase = model(__snake_case )
_UpperCamelCase = outputs.logits
# define expected logit slices for different models
if size == "s12":
_UpperCamelCase = torch.tensor([-0.3045, -0.6758, -0.4869] )
elif size == "s24":
_UpperCamelCase = torch.tensor([0.4402, -0.1374, -0.8045] )
elif size == "s36":
_UpperCamelCase = torch.tensor([-0.6080, -0.5133, -0.5898] )
elif size == "m36":
_UpperCamelCase = torch.tensor([0.3952, 0.2263, -1.2668] )
elif size == "m48":
_UpperCamelCase = torch.tensor([0.1167, -0.0656, -0.3423] )
else:
raise ValueError(F'''Size {size} not supported''' )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3], __snake_case, atol=1e-2 )
# finally, save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(__snake_case ).mkdir(exist_ok=__snake_case )
model.save_pretrained(__snake_case )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__snake_case )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument(
"""--model_name""",
default="""poolformer_s12""",
type=str,
help="""Name of the model you'd like to convert.""",
)
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
_a = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 19 | 0 |
'''simple docstring'''
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class _lowercase ( lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = None
UpperCAmelCase_ : Tuple = None
@property
def lowerCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
return self.feat_extract_tester.prepare_feat_extract_dict()
def lowerCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(__a ,'''feature_size''' ) )
self.assertTrue(hasattr(__a ,'''sampling_rate''' ) )
self.assertTrue(hasattr(__a ,'''padding_value''' ) )
def lowerCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase__ : int = self.feat_extract_tester.prepare_inputs_for_common()
UpperCAmelCase__ : Any = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase__ : Optional[Any] = feat_extract.model_input_names[0]
UpperCAmelCase__ : List[str] = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(__a ) == len(__a ) for x, y in zip(__a ,processed_features[input_name] ) ) )
UpperCAmelCase__ : int = self.feat_extract_tester.prepare_inputs_for_common(equal_length=__a )
UpperCAmelCase__ : int = BatchFeature({input_name: speech_inputs} ,tensor_type='''np''' )
UpperCAmelCase__ : Optional[int] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
UpperCAmelCase__ : Tuple = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def lowerCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.feat_extract_tester.prepare_inputs_for_common(equal_length=__a )
UpperCAmelCase__ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase__ : Any = feat_extract.model_input_names[0]
UpperCAmelCase__ : List[str] = BatchFeature({input_name: speech_inputs} ,tensor_type='''pt''' )
UpperCAmelCase__ : Optional[Any] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
UpperCAmelCase__ : List[Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def lowerCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.feat_extract_tester.prepare_inputs_for_common(equal_length=__a )
UpperCAmelCase__ : str = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase__ : Optional[int] = feat_extract.model_input_names[0]
UpperCAmelCase__ : List[Any] = BatchFeature({input_name: speech_inputs} ,tensor_type='''tf''' )
UpperCAmelCase__ : Optional[int] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
UpperCAmelCase__ : Tuple = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def lowerCAmelCase__ ( self ,lowerCamelCase_=False ) -> Union[str, Any]:
'''simple docstring'''
def _inputs_have_equal_length(lowerCamelCase_ ):
UpperCAmelCase__ : Optional[int] = len(input[0] )
for input_slice in input[1:]:
if len(__a ) != length:
return False
return True
def _inputs_are_equal(lowerCamelCase_ ,lowerCamelCase_ ):
if len(__a ) != len(__a ):
return False
for input_slice_a, input_slice_a in zip(__a ,__a ):
if not np.allclose(np.asarray(__a ) ,np.asarray(__a ) ,atol=1e-3 ):
return False
return True
UpperCAmelCase__ : List[str] = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase__ : List[str] = self.feat_extract_tester.prepare_inputs_for_common(numpify=__a )
UpperCAmelCase__ : Dict = feat_extract.model_input_names[0]
UpperCAmelCase__ : Dict = BatchFeature({input_name: speech_inputs} )
UpperCAmelCase__ : List[Any] = self.feat_extract_tester.seq_length_diff
UpperCAmelCase__ : Tuple = self.feat_extract_tester.max_seq_length + pad_diff
UpperCAmelCase__ : List[str] = self.feat_extract_tester.min_seq_length
UpperCAmelCase__ : Dict = self.feat_extract_tester.batch_size
UpperCAmelCase__ : List[Any] = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
UpperCAmelCase__ : Tuple = feat_extract.pad(__a ,padding=__a )
UpperCAmelCase__ : List[Any] = input_a[input_name]
UpperCAmelCase__ : Dict = feat_extract.pad(__a ,padding='''longest''' )
UpperCAmelCase__ : List[str] = input_a[input_name]
UpperCAmelCase__ : str = feat_extract.pad(__a ,padding='''max_length''' ,max_length=len(speech_inputs[-1] ) )
UpperCAmelCase__ : Optional[Any] = input_a[input_name]
UpperCAmelCase__ : Union[str, Any] = feat_extract.pad(__a ,padding='''longest''' ,return_tensors='''np''' )
UpperCAmelCase__ : List[str] = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(__a ):
feat_extract.pad(__a ,padding='''max_length''' )[input_name]
UpperCAmelCase__ : List[str] = feat_extract.pad(
__a ,padding='''max_length''' ,max_length=__a ,return_tensors='''np''' )
UpperCAmelCase__ : Optional[int] = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(__a ) )
self.assertTrue(_inputs_have_equal_length(__a ) )
self.assertTrue(_inputs_have_equal_length(__a ) )
self.assertTrue(_inputs_are_equal(__a ,__a ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
UpperCAmelCase__ : List[Any] = feat_extract.pad(__a ,pad_to_multiple_of=10 )
UpperCAmelCase__ : List[str] = input_a[input_name]
UpperCAmelCase__ : Optional[int] = feat_extract.pad(__a ,padding='''longest''' ,pad_to_multiple_of=10 )
UpperCAmelCase__ : Tuple = input_a[input_name]
UpperCAmelCase__ : Optional[Any] = feat_extract.pad(
__a ,padding='''max_length''' ,pad_to_multiple_of=10 ,max_length=__a )
UpperCAmelCase__ : List[str] = input_a[input_name]
UpperCAmelCase__ : int = feat_extract.pad(
__a ,padding='''max_length''' ,pad_to_multiple_of=10 ,max_length=__a ,return_tensors='''np''' ,)
UpperCAmelCase__ : Union[str, Any] = input_a[input_name]
self.assertTrue(all(len(__a ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(__a ,__a ) )
UpperCAmelCase__ : Union[str, Any] = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(__a ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] ,(batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
UpperCAmelCase__ : Union[str, Any] = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def lowerCAmelCase__ ( self ,lowerCamelCase_=False ) -> List[Any]:
'''simple docstring'''
def _inputs_have_equal_length(lowerCamelCase_ ):
UpperCAmelCase__ : Dict = len(input[0] )
for input_slice in input[1:]:
if len(__a ) != length:
return False
return True
def _inputs_are_equal(lowerCamelCase_ ,lowerCamelCase_ ):
if len(__a ) != len(__a ):
return False
for input_slice_a, input_slice_a in zip(__a ,__a ):
if not np.allclose(np.asarray(__a ) ,np.asarray(__a ) ,atol=1e-3 ):
return False
return True
UpperCAmelCase__ : Any = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase__ : Union[str, Any] = self.feat_extract_tester.prepare_inputs_for_common(numpify=__a )
UpperCAmelCase__ : int = feat_extract.model_input_names[0]
UpperCAmelCase__ : Optional[Any] = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
UpperCAmelCase__ : List[str] = feat_extract.pad(
__a ,padding='''max_length''' ,max_length=len(speech_inputs[0] ) ,truncation=__a )
UpperCAmelCase__ : List[Any] = input_a[input_name]
UpperCAmelCase__ : str = feat_extract.pad(__a ,padding='''max_length''' ,max_length=len(speech_inputs[0] ) )
UpperCAmelCase__ : Tuple = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(__a ) )
self.assertFalse(_inputs_have_equal_length(__a ) )
# truncate to smallest with np
UpperCAmelCase__ : Dict = feat_extract.pad(
__a ,padding='''max_length''' ,max_length=len(speech_inputs[0] ) ,return_tensors='''np''' ,truncation=__a ,)
UpperCAmelCase__ : Tuple = input_a[input_name]
UpperCAmelCase__ : Union[str, Any] = feat_extract.pad(
__a ,padding='''max_length''' ,max_length=len(speech_inputs[0] ) ,return_tensors='''np''' )
UpperCAmelCase__ : Optional[Any] = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(__a ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(__a ) )
# truncate to middle
UpperCAmelCase__ : int = feat_extract.pad(
__a ,padding='''max_length''' ,max_length=len(speech_inputs[1] ) ,truncation=__a ,return_tensors='''np''' ,)
UpperCAmelCase__ : Dict = input_a[input_name]
UpperCAmelCase__ : Optional[int] = feat_extract.pad(
__a ,padding='''max_length''' ,max_length=len(speech_inputs[1] ) ,truncation=__a )
UpperCAmelCase__ : str = input_a[input_name]
UpperCAmelCase__ : List[str] = feat_extract.pad(
__a ,padding='''max_length''' ,max_length=len(speech_inputs[1] ) ,return_tensors='''np''' )
UpperCAmelCase__ : Union[str, Any] = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(__a ) )
self.assertTrue(_inputs_have_equal_length(__a ) )
self.assertTrue(_inputs_are_equal(__a ,__a ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(__a ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(__a ):
feat_extract.pad(__a ,truncation=__a )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(__a ):
feat_extract.pad(__a ,padding='''longest''' ,truncation=__a )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(__a ):
feat_extract.pad(__a ,padding='''longest''' ,truncation=__a )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(__a ):
feat_extract.pad(__a ,padding='''max_length''' ,truncation=__a )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
UpperCAmelCase__ : str = 12
UpperCAmelCase__ : Any = feat_extract.pad(
__a ,padding='''max_length''' ,max_length=len(speech_inputs[0] ) ,pad_to_multiple_of=__a ,truncation=__a ,)
UpperCAmelCase__ : Tuple = input_a[input_name]
UpperCAmelCase__ : str = feat_extract.pad(
__a ,padding='''max_length''' ,max_length=len(speech_inputs[0] ) ,pad_to_multiple_of=__a ,)
UpperCAmelCase__ : Optional[Any] = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
UpperCAmelCase__ : str = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
UpperCAmelCase__ : Any = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(__a ) )
self.assertFalse(_inputs_have_equal_length(__a ) )
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
self._check_padding(numpify=__a )
def lowerCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
self._check_padding(numpify=__a )
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
self._check_truncation(numpify=__a )
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
self._check_truncation(numpify=__a )
@require_torch
def lowerCAmelCase__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase__ : int = self.feat_extract_tester.prepare_inputs_for_common()
UpperCAmelCase__ : Any = feat_extract.model_input_names[0]
UpperCAmelCase__ : List[Any] = BatchFeature({input_name: speech_inputs} )
UpperCAmelCase__ : int = feat_extract.pad(__a ,padding='''longest''' ,return_tensors='''np''' )[input_name]
UpperCAmelCase__ : str = feat_extract.pad(__a ,padding='''longest''' ,return_tensors='''pt''' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def lowerCAmelCase__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ : str = self.feature_extraction_class(**self.feat_extract_dict )
UpperCAmelCase__ : Dict = self.feat_extract_tester.prepare_inputs_for_common()
UpperCAmelCase__ : Tuple = feat_extract.model_input_names[0]
UpperCAmelCase__ : Union[str, Any] = BatchFeature({input_name: speech_inputs} )
UpperCAmelCase__ : Any = feat_extract.pad(__a ,padding='''longest''' ,return_tensors='''np''' )[input_name]
UpperCAmelCase__ : Union[str, Any] = feat_extract.pad(__a ,padding='''longest''' ,return_tensors='''tf''' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def lowerCAmelCase__ ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = self.feat_extract_dict
UpperCAmelCase__ : str = True
UpperCAmelCase__ : Union[str, Any] = self.feature_extraction_class(**__a )
UpperCAmelCase__ : Union[str, Any] = self.feat_extract_tester.prepare_inputs_for_common()
UpperCAmelCase__ : Dict = [len(__a ) for x in speech_inputs]
UpperCAmelCase__ : Optional[Any] = feat_extract.model_input_names[0]
UpperCAmelCase__ : int = BatchFeature({input_name: speech_inputs} )
UpperCAmelCase__ : Optional[Any] = feat_extract.pad(__a ,padding='''longest''' ,return_tensors='''np''' )
self.assertIn('''attention_mask''' ,__a )
self.assertListEqual(list(processed.attention_mask.shape ) ,list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() ,__a )
def lowerCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = self.feat_extract_dict
UpperCAmelCase__ : Union[str, Any] = True
UpperCAmelCase__ : int = self.feature_extraction_class(**__a )
UpperCAmelCase__ : Union[str, Any] = self.feat_extract_tester.prepare_inputs_for_common()
UpperCAmelCase__ : Optional[int] = [len(__a ) for x in speech_inputs]
UpperCAmelCase__ : Union[str, Any] = feat_extract.model_input_names[0]
UpperCAmelCase__ : Optional[Any] = BatchFeature({input_name: speech_inputs} )
UpperCAmelCase__ : List[str] = min(__a )
UpperCAmelCase__ : Optional[Any] = feat_extract.pad(
__a ,padding='''max_length''' ,max_length=__a ,truncation=__a ,return_tensors='''np''' )
self.assertIn('''attention_mask''' ,__a )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) ,[processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() ,[max_length for x in speech_inputs] )
| 614 |
"""simple docstring"""
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = (DPMSolverSDEScheduler,)
lowercase__ = 10
def UpperCAmelCase ( self , **__a) -> int:
'''simple docstring'''
_UpperCamelCase = {
'''num_train_timesteps''': 11_00,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''noise_sampler_seed''': 0,
}
config.update(**__a)
return config
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02]):
self.check_over_configs(beta_start=__a , beta_end=__a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__a)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__a)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
scheduler.set_timesteps(self.num_inference_steps)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCamelCase = sample.to(__a)
for i, t in enumerate(scheduler.timesteps):
_UpperCamelCase = scheduler.scale_model_input(__a , __a)
_UpperCamelCase = model(__a , __a)
_UpperCamelCase = scheduler.step(__a , __a , __a)
_UpperCamelCase = output.prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47_8210_4492_1875) < 1e-2
assert abs(result_mean.item() - 0.2178_7059_6456_5277) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3521_1181_6406) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9068_9229_9652) < 1e-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''')
_UpperCamelCase = scheduler_class(**__a)
scheduler.set_timesteps(self.num_inference_steps)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCamelCase = sample.to(__a)
for i, t in enumerate(scheduler.timesteps):
_UpperCamelCase = scheduler.scale_model_input(__a , __a)
_UpperCamelCase = model(__a , __a)
_UpperCamelCase = scheduler.step(__a , __a , __a)
_UpperCamelCase = output.prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77_1492_0043_9453) < 1e-2
assert abs(result_mean.item() - 0.1_6226_2890_1481_6284) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1_6633_6059_5703) < 1e-2
assert abs(result_mean.item() - 0.1_6688_3260_0116_7297) < 1e-3
else:
assert abs(result_sum.item() - 119.8_4875_4882_8125) < 1e-2
assert abs(result_mean.item() - 0.1560_5306_6253_6621) < 1e-3
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
scheduler.set_timesteps(self.num_inference_steps , device=__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
_UpperCamelCase = scheduler.scale_model_input(__a , __a)
_UpperCamelCase = model(__a , __a)
_UpperCamelCase = scheduler.step(__a , __a , __a)
_UpperCamelCase = output.prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46_9573_9746_0938) < 1e-2
assert abs(result_mean.item() - 0.2_1805_9346_0798_2635) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3536_3769_5312) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9083_8241_5771) < 1e-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a , use_karras_sigmas=__a)
scheduler.set_timesteps(self.num_inference_steps , device=__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma
_UpperCamelCase = sample.to(__a)
for t in scheduler.timesteps:
_UpperCamelCase = scheduler.scale_model_input(__a , __a)
_UpperCamelCase = model(__a , __a)
_UpperCamelCase = scheduler.step(__a , __a , __a)
_UpperCamelCase = output.prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66_9741_3574_2188) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63_6535_6445_3125) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
else:
assert abs(result_sum.item() - 170.3_1352_2338_8672) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
| 19 | 0 |
'''simple docstring'''
lowercase =[0, 2, 4, 6, 8]
lowercase =[1, 3, 5, 7, 9]
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : Optional[int] , __lowerCamelCase : List[Any] , __lowerCamelCase : int ):
'''simple docstring'''
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 1_0
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
_UpperCAmelCase : Any =0
for digit in range(1_0 ):
_UpperCAmelCase : List[str] =digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 1_0 , __snake_case , __snake_case )
return result
_UpperCAmelCase : str =0
for digita in range(1_0 ):
_UpperCAmelCase : List[Any] =digita
if (remainder + digita) % 2 == 0:
_UpperCAmelCase : str =ODD_DIGITS
else:
_UpperCAmelCase : Tuple =EVEN_DIGITS
for digita in other_parity_digits:
_UpperCAmelCase : Any =digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 1_0 , __snake_case , __snake_case , )
return result
def lowerCamelCase__ ( __lowerCamelCase : Union[str, Any] = 9 ):
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] =0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(__snake_case , 0 , [0] * length , __snake_case )
return result
if __name__ == "__main__":
print(F"""{solution() = }""")
| 446 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_a = logging.get_logger(__name__)
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = ['pixel_values']
def __init__( self , __a = True , __a = None , __a = PILImageResampling.BICUBIC , __a = True , __a = True , __a = 1 / 2_55 , __a = None , __a = True , __a = None , __a = None , **__a , ) -> None:
'''simple docstring'''
super().__init__(**__a)
_UpperCamelCase = size if size is not None else {'''height''': 2_24, '''width''': 2_24}
_UpperCamelCase = get_size_dict(__a)
_UpperCamelCase = crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
_UpperCamelCase = get_size_dict(__a , default_to_square=__a , param_name='''crop_size''')
_UpperCamelCase = do_resize
_UpperCamelCase = do_rescale
_UpperCamelCase = do_normalize
_UpperCamelCase = do_center_crop
_UpperCamelCase = crop_size
_UpperCamelCase = size
_UpperCamelCase = resample
_UpperCamelCase = rescale_factor
_UpperCamelCase = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
_UpperCamelCase = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCAmelCase ( self , __a , __a , __a = PILImageResampling.BILINEAR , __a = None , **__a , ) -> np.ndarray:
'''simple docstring'''
_UpperCamelCase = get_size_dict(__a)
if "shortest_edge" in size:
_UpperCamelCase = get_resize_output_image_size(__a , size=size['''shortest_edge'''] , default_to_square=__a)
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
_UpperCamelCase = (size['''height'''], size['''width'''])
else:
raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''')
return resize(__a , size=__a , resample=__a , data_format=__a , **__a)
def UpperCAmelCase ( self , __a , __a , __a = None , **__a , ) -> np.ndarray:
'''simple docstring'''
_UpperCamelCase = get_size_dict(__a)
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''')
return center_crop(__a , size=(size['''height'''], size['''width''']) , data_format=__a , **__a)
def UpperCAmelCase ( self , __a , __a , __a = None , **__a) -> np.ndarray:
'''simple docstring'''
return rescale(__a , scale=__a , data_format=__a , **__a)
def UpperCAmelCase ( self , __a , __a , __a , __a = None , **__a , ) -> np.ndarray:
'''simple docstring'''
return normalize(__a , mean=__a , std=__a , data_format=__a , **__a)
def UpperCAmelCase ( self , __a , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = ChannelDimension.FIRST , **__a , ) -> BatchFeature:
'''simple docstring'''
_UpperCamelCase = do_resize if do_resize is not None else self.do_resize
_UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
_UpperCamelCase = crop_size if crop_size is not None else self.crop_size
_UpperCamelCase = get_size_dict(__a , param_name='''crop_size''' , default_to_square=__a)
_UpperCamelCase = resample if resample is not None else self.resample
_UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCamelCase = image_mean if image_mean is not None else self.image_mean
_UpperCamelCase = image_std if image_std is not None else self.image_std
_UpperCamelCase = size if size is not None else self.size
_UpperCamelCase = get_size_dict(__a)
if not is_batched(__a):
_UpperCamelCase = [images]
if not valid_images(__a):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''')
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''')
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''')
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''')
# All transformations expect numpy arrays.
_UpperCamelCase = [to_numpy_array(__a) for image in images]
if do_resize:
_UpperCamelCase = [self.resize(image=__a , size=__a , resample=__a) for image in images]
if do_center_crop:
_UpperCamelCase = [self.center_crop(image=__a , size=__a) for image in images]
if do_rescale:
_UpperCamelCase = [self.rescale(image=__a , scale=__a) for image in images]
if do_normalize:
_UpperCamelCase = [self.normalize(image=__a , mean=__a , std=__a) for image in images]
_UpperCamelCase = [to_channel_dimension_format(__a , __a) for image in images]
_UpperCamelCase = {'''pixel_values''': images}
return BatchFeature(data=__a , tensor_type=__a)
| 19 | 0 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _a ( SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> bool:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = int(number**0.5 )
return number == sq * sq
def _a ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> tuple[int, int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
SCREAMING_SNAKE_CASE__ : Optional[int] = x_den * y_den * z_den
SCREAMING_SNAKE_CASE__ : Dict = gcd(__snake_case , __snake_case )
top //= hcf
bottom //= hcf
return top, bottom
def _a ( SCREAMING_SNAKE_CASE__ : str = 35 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = set()
SCREAMING_SNAKE_CASE__ : Tuple = 42
SCREAMING_SNAKE_CASE__ : Optional[int] = Fraction(0 )
SCREAMING_SNAKE_CASE__ : str = 42
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
SCREAMING_SNAKE_CASE__ : Tuple = x_num * y_den + x_den * y_num
SCREAMING_SNAKE_CASE__ : Optional[int] = x_den * y_den
SCREAMING_SNAKE_CASE__ : Tuple = gcd(__snake_case , __snake_case )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE__ : Optional[int] = add_three(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case )
unique_s.add(__snake_case )
# n=2
SCREAMING_SNAKE_CASE__ : str = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = x_den * x_den * y_den * y_den
if is_sq(__snake_case ) and is_sq(__snake_case ):
SCREAMING_SNAKE_CASE__ : List[str] = int(sqrt(__snake_case ) )
SCREAMING_SNAKE_CASE__ : Tuple = int(sqrt(__snake_case ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = gcd(__snake_case , __snake_case )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE__ : Optional[int] = add_three(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case )
unique_s.add(__snake_case )
# n=-1
SCREAMING_SNAKE_CASE__ : int = x_num * y_num
SCREAMING_SNAKE_CASE__ : List[str] = x_den * y_num + x_num * y_den
SCREAMING_SNAKE_CASE__ : int = gcd(__snake_case , __snake_case )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE__ : Any = add_three(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case )
unique_s.add(__snake_case )
# n=2
SCREAMING_SNAKE_CASE__ : Dict = x_num * x_num * y_num * y_num
SCREAMING_SNAKE_CASE__ : Dict = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(__snake_case ) and is_sq(__snake_case ):
SCREAMING_SNAKE_CASE__ : int = int(sqrt(__snake_case ) )
SCREAMING_SNAKE_CASE__ : int = int(sqrt(__snake_case ) )
SCREAMING_SNAKE_CASE__ : str = gcd(__snake_case , __snake_case )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
SCREAMING_SNAKE_CASE__ : str = add_three(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case )
unique_s.add(__snake_case )
for num, den in unique_s:
total += Fraction(__snake_case , __snake_case )
return total.denominator + total.numerator
if __name__ == "__main__":
print(f"{solution() = }")
| 663 |
"""simple docstring"""
# Imports
import numpy as np
class _UpperCAmelCase:
def __init__( self , __a=None , __a=None , __a=None , __a=None , __a=None) -> Dict:
'''simple docstring'''
self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a)
def UpperCAmelCase ( self , __a=None , __a=None , __a=None , __a=None , __a=None) -> Dict:
'''simple docstring'''
if red is not None:
_UpperCamelCase = red
if green is not None:
_UpperCamelCase = green
if blue is not None:
_UpperCamelCase = blue
if red_edge is not None:
_UpperCamelCase = red_edge
if nir is not None:
_UpperCamelCase = nir
return True
def UpperCAmelCase ( self , __a="" , __a=None , __a=None , __a=None , __a=None , __a=None) -> List[str]:
'''simple docstring'''
self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a)
_UpperCamelCase = {
'''ARVI2''': self.arvaa,
'''CCCI''': self.ccci,
'''CVI''': self.cvi,
'''GLI''': self.gli,
'''NDVI''': self.ndvi,
'''BNDVI''': self.bndvi,
'''redEdgeNDVI''': self.red_edge_ndvi,
'''GNDVI''': self.gndvi,
'''GBNDVI''': self.gbndvi,
'''GRNDVI''': self.grndvi,
'''RBNDVI''': self.rbndvi,
'''PNDVI''': self.pndvi,
'''ATSAVI''': self.atsavi,
'''BWDRVI''': self.bwdrvi,
'''CIgreen''': self.ci_green,
'''CIrededge''': self.ci_rededge,
'''CI''': self.ci,
'''CTVI''': self.ctvi,
'''GDVI''': self.gdvi,
'''EVI''': self.evi,
'''GEMI''': self.gemi,
'''GOSAVI''': self.gosavi,
'''GSAVI''': self.gsavi,
'''Hue''': self.hue,
'''IVI''': self.ivi,
'''IPVI''': self.ipvi,
'''I''': self.i,
'''RVI''': self.rvi,
'''MRVI''': self.mrvi,
'''MSAVI''': self.m_savi,
'''NormG''': self.norm_g,
'''NormNIR''': self.norm_nir,
'''NormR''': self.norm_r,
'''NGRDI''': self.ngrdi,
'''RI''': self.ri,
'''S''': self.s,
'''IF''': self._if,
'''DVI''': self.dvi,
'''TVI''': self.tvi,
'''NDRE''': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('''Index not in the list!''')
return False
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return self.nir * (self.red / (self.green**2))
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
return (self.nir - self.red) / (self.nir + self.red)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
return (self.nir - self.blue) / (self.nir + self.blue)
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return (self.redEdge - self.red) / (self.redEdge + self.red)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return (self.nir - self.green) / (self.nir + self.green)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def UpperCAmelCase ( self , __a=0.08 , __a=1.22 , __a=0.03) -> Optional[Any]:
'''simple docstring'''
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
return (self.nir / self.green) - 1
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return (self.nir / self.redEdge) - 1
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
return (self.red - self.blue) / self.red
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5))) * (abs(ndvi + 0.5) ** (1 / 2))
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return self.nir - self.green
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)
def UpperCAmelCase ( self , __a=0.16) -> Optional[Any]:
'''simple docstring'''
return (self.nir - self.green) / (self.nir + self.green + y)
def UpperCAmelCase ( self , __a=0.5) -> Dict:
'''simple docstring'''
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue))
def UpperCAmelCase ( self , __a=None , __a=None) -> Any:
'''simple docstring'''
return (self.nir - b) / (a * self.red)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
return (self.red + self.green + self.blue) / 30.5
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return self.nir / self.red
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return (self.rvi() - 1) / (self.rvi() + 1)
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return self.green / (self.nir + self.red + self.green)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
return self.nir / (self.nir + self.red + self.green)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return self.red / (self.nir + self.red + self.green)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return (self.green - self.red) / (self.green + self.red)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
return (self.red - self.green) / (self.red + self.green)
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = np.max([np.max(self.red), np.max(self.green), np.max(self.blue)])
_UpperCamelCase = np.min([np.min(self.red), np.min(self.green), np.min(self.blue)])
return (max_value - min_value) / max_value
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return self.nir / self.red
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return (self.ndvi() + 0.5) ** (1 / 2)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 19 | 0 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class __magic_name__ ( unittest.TestCase ):
def __init__( self : int , UpperCamelCase__ : Dict , UpperCamelCase__ : int=7 , UpperCamelCase__ : Optional[int]=3 , UpperCamelCase__ : Optional[int]=18 , UpperCamelCase__ : List[Any]=30 , UpperCamelCase__ : Dict=4_00 , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : Any=None , UpperCamelCase__ : Optional[Any]=True , ) -> Any:
'''simple docstring'''
UpperCAmelCase = size if size is not None else {"height": 18, "width": 18}
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = num_channels
UpperCAmelCase = image_size
UpperCAmelCase = min_resolution
UpperCAmelCase = max_resolution
UpperCAmelCase = do_resize
UpperCAmelCase = size
UpperCAmelCase = do_normalize
def SCREAMING_SNAKE_CASE_ ( self : int ) -> int:
'''simple docstring'''
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
[-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
] ),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class __magic_name__ ( A__, unittest.TestCase ):
lowercase : str =ImageGPTImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase = ImageGPTImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Dict:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__a , "clusters" ) )
self.assertTrue(hasattr(__a , "do_resize" ) )
self.assertTrue(hasattr(__a , "size" ) )
self.assertTrue(hasattr(__a , "do_normalize" ) )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Dict:
'''simple docstring'''
UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"height": 18, "width": 18} )
UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"height": 42, "width": 42} )
def SCREAMING_SNAKE_CASE_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
UpperCAmelCase = json.loads(image_processor.to_json_string() )
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(__a , obj[key] ) )
else:
self.assertEqual(obj[key] , __a )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase = os.path.join(__a , "image_processor.json" )
image_processor_first.to_json_file(__a )
UpperCAmelCase = self.image_processing_class.from_json_file(__a ).to_dict()
UpperCAmelCase = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(__a , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , __a )
def SCREAMING_SNAKE_CASE_ ( self : str ) -> Tuple:
'''simple docstring'''
UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(__a )
UpperCAmelCase = self.image_processing_class.from_pretrained(__a ).to_dict()
UpperCAmelCase = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(__a , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , __a )
@unittest.skip("ImageGPT requires clusters at initialization" )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
pass
def lowerCamelCase_() -> Any:
UpperCAmelCase = load_dataset("hf-internal-testing/fixtures_image_utils" , split="test" )
UpperCAmelCase = Image.open(dataset[4]["file"] )
UpperCAmelCase = Image.open(dataset[5]["file"] )
UpperCAmelCase = [imagea, imagea]
return images
@require_vision
@require_torch
class __magic_name__ ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Tuple:
'''simple docstring'''
UpperCAmelCase = ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small" )
UpperCAmelCase = prepare_images()
# test non-batched
UpperCAmelCase = image_processing(images[0] , return_tensors="pt" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (1, 10_24) )
UpperCAmelCase = [3_06, 1_91, 1_91]
self.assertEqual(encoding.input_ids[0, :3].tolist() , __a )
# test batched
UpperCAmelCase = image_processing(__a , return_tensors="pt" )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (2, 10_24) )
UpperCAmelCase = [3_03, 13, 13]
self.assertEqual(encoding.input_ids[1, -3:].tolist() , __a )
| 323 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class _UpperCAmelCase:
def __init__( self , __a , __a=13 , __a=64 , __a=2 , __a=3 , __a=True , __a=True , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=10 , __a=0.02 , __a=[1, 16, 4, 4] , __a=None , ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = image_size
_UpperCamelCase = patch_size
_UpperCamelCase = num_channels
_UpperCamelCase = is_training
_UpperCamelCase = use_labels
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = scope
_UpperCamelCase = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
_UpperCamelCase = (self.image_size // 32) ** 2
_UpperCamelCase = num_patches + 1
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_UpperCamelCase = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [4, 8, 16, 32],
'''num_groups''': 2,
}
return ViTHybridConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__a , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=__a , )
def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = ViTHybridModel(config=__a)
model.to(__a)
model.eval()
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCAmelCase ( self , __a , __a , __a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = self.type_sequence_label_size
_UpperCamelCase = ViTHybridForImageClassification(__a)
model.to(__a)
model.eval()
_UpperCamelCase = model(__a , labels=__a)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = self.prepare_config_and_inputs()
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = config_and_inputs
_UpperCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase__ = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
lowercase__ = (
{'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
if is_torch_available()
else {}
)
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = ViTHybridModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37)
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''')
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
pass
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(__a)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
_UpperCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__a , nn.Linear))
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(__a)
_UpperCamelCase = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCamelCase = [*signature.parameters.keys()]
_UpperCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __a)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__a)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCamelCase = _config_zero_init(__a)
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(config=__a)
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
_UpperCamelCase = [F'''{name}.{key}''' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@slow
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = ViTHybridModel.from_pretrained(__a)
self.assertIsNotNone(__a)
def lowerCamelCase__ ( ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class _UpperCAmelCase( unittest.TestCase ):
@cached_property
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
if is_vision_available()
else None
)
@slow
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
__a)
_UpperCamelCase = self.default_image_processor
_UpperCamelCase = prepare_img()
_UpperCamelCase = image_processor(images=__a , return_tensors='''pt''').to(__a)
# forward pass
with torch.no_grad():
_UpperCamelCase = model(**__a)
# verify the logits
_UpperCamelCase = torch.Size((1, 10_00))
self.assertEqual(outputs.logits.shape , __a)
_UpperCamelCase = torch.tensor([-1.9090, -0.4993, -0.2389]).to(__a)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __a , atol=1e-4))
@slow
@require_accelerate
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''')
_UpperCamelCase = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''' , device_map='''auto''')
_UpperCamelCase = prepare_img()
_UpperCamelCase = image_processor(images=__a , return_tensors='''pt''')
_UpperCamelCase = model(**__a)
_UpperCamelCase = outputs.logits
# model predicts one of the 1000 ImageNet classes
_UpperCamelCase = logits.argmax(-1).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] , '''tabby, tabby cat''')
| 19 | 0 |
'''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
if curr_ind == len(__snake_case ):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next_ver in range(0 , len(__snake_case ) ):
if valid_connection(__snake_case , __snake_case , __snake_case , __snake_case ):
# Insert current vertex into path as next transition
lowercase__ : Any = next_ver
# Validate created path
if util_hamilton_cycle(__snake_case , __snake_case , curr_ind + 1 ):
return True
# Backtrack
lowercase__ : List[str] = -1
return False
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase = 0 ):
lowercase__ : List[Any] = [-1] * (len(__snake_case ) + 1)
# initialize start and end of path with starting index
lowercase__ : List[Any] = start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(__snake_case , __snake_case , 1 ) else []
| 152 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = ['vqvae']
def __init__( self , __a , __a , __a , __a , ) -> List[str]:
'''simple docstring'''
super().__init__()
self.register_modules(unet=__a , scheduler=__a , mel=__a , vqvae=__a)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return 50 if isinstance(self.scheduler , __a) else 10_00
@torch.no_grad()
def __call__( self , __a = 1 , __a = None , __a = None , __a = 0 , __a = 0 , __a = None , __a = None , __a = 0 , __a = 0 , __a = None , __a = 0 , __a = None , __a = None , __a=True , ) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
'''simple docstring'''
_UpperCamelCase = steps or self.get_default_steps()
self.scheduler.set_timesteps(__a)
_UpperCamelCase = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size) == int:
_UpperCamelCase = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
_UpperCamelCase = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=__a , device=self.device , )
_UpperCamelCase = noise
_UpperCamelCase = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(__a , __a)
_UpperCamelCase = self.mel.audio_slice_to_image(__a)
_UpperCamelCase = np.frombuffer(input_image.tobytes() , dtype='''uint8''').reshape(
(input_image.height, input_image.width))
_UpperCamelCase = (input_image / 2_55) * 2 - 1
_UpperCamelCase = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float).to(self.device)
if self.vqvae is not None:
_UpperCamelCase = self.vqvae.encode(torch.unsqueeze(__a , 0)).latent_dist.sample(
generator=__a)[0]
_UpperCamelCase = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
_UpperCamelCase = self.scheduler.add_noise(__a , __a , self.scheduler.timesteps[start_step - 1])
_UpperCamelCase = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
_UpperCamelCase = int(mask_start_secs * pixels_per_second)
_UpperCamelCase = int(mask_end_secs * pixels_per_second)
_UpperCamelCase = self.scheduler.add_noise(__a , __a , torch.tensor(self.scheduler.timesteps[start_step:]))
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
if isinstance(self.unet , __a):
_UpperCamelCase = self.unet(__a , __a , __a)['''sample''']
else:
_UpperCamelCase = self.unet(__a , __a)['''sample''']
if isinstance(self.scheduler , __a):
_UpperCamelCase = self.scheduler.step(
model_output=__a , timestep=__a , sample=__a , eta=__a , generator=__a , )['''prev_sample''']
else:
_UpperCamelCase = self.scheduler.step(
model_output=__a , timestep=__a , sample=__a , generator=__a , )['''prev_sample''']
if mask is not None:
if mask_start > 0:
_UpperCamelCase = mask[:, step, :, :mask_start]
if mask_end > 0:
_UpperCamelCase = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
_UpperCamelCase = 1 / self.vqvae.config.scaling_factor * images
_UpperCamelCase = self.vqvae.decode(__a)['''sample''']
_UpperCamelCase = (images / 2 + 0.5).clamp(0 , 1)
_UpperCamelCase = images.cpu().permute(0 , 2 , 3 , 1).numpy()
_UpperCamelCase = (images * 2_55).round().astype('''uint8''')
_UpperCamelCase = list(
(Image.fromarray(_[:, :, 0]) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(__a , mode='''RGB''').convert('''L''') for _ in images))
_UpperCamelCase = [self.mel.image_to_audio(__a) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(__a)[:, np.newaxis, :]) , **ImagePipelineOutput(__a))
@torch.no_grad()
def UpperCAmelCase ( self , __a , __a = 50) -> np.ndarray:
'''simple docstring'''
assert isinstance(self.scheduler , __a)
self.scheduler.set_timesteps(__a)
_UpperCamelCase = np.array(
[np.frombuffer(image.tobytes() , dtype='''uint8''').reshape((1, image.height, image.width)) for image in images])
_UpperCamelCase = (sample / 2_55) * 2 - 1
_UpperCamelCase = torch.Tensor(__a).to(self.device)
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,))):
_UpperCamelCase = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
_UpperCamelCase = self.scheduler.alphas_cumprod[t]
_UpperCamelCase = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
_UpperCamelCase = 1 - alpha_prod_t
_UpperCamelCase = self.unet(__a , __a)['''sample''']
_UpperCamelCase = (1 - alpha_prod_t_prev) ** 0.5 * model_output
_UpperCamelCase = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
_UpperCamelCase = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def UpperCAmelCase ( __a , __a , __a) -> torch.Tensor:
'''simple docstring'''
_UpperCamelCase = acos(torch.dot(torch.flatten(__a) , torch.flatten(__a)) / torch.norm(__a) / torch.norm(__a))
return sin((1 - alpha) * theta) * xa / sin(__a) + sin(alpha * theta) * xa / sin(__a)
| 19 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Any = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
lowerCamelCase : Tuple = get_activation("gelu" )
self.assertTrue(torch.allclose(gelu_python(__a ) , torch_builtin(__a ) ) )
self.assertFalse(torch.allclose(gelu_python(__a ) , gelu_new(__a ) ) )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : int = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
lowerCamelCase : Optional[int] = get_activation("gelu" )
lowerCamelCase : Optional[Any] = get_activation("gelu_10" )
lowerCamelCase : str = torch_builtin(__a )
lowerCamelCase : List[Any] = geluaa(__a )
lowerCamelCase : Optional[Any] = torch.where(y_gelu_aa < 10.0 , 1 , 0 )
self.assertTrue(torch.max(__a ).item() == 10.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask ) )
def _snake_case ( self ):
"""simple docstring"""
get_activation("gelu" )
get_activation("gelu_10" )
get_activation("gelu_fast" )
get_activation("gelu_new" )
get_activation("gelu_python" )
get_activation("gelu_pytorch_tanh" )
get_activation("linear" )
get_activation("mish" )
get_activation("quick_gelu" )
get_activation("relu" )
get_activation("sigmoid" )
get_activation("silu" )
get_activation("swish" )
get_activation("tanh" )
with self.assertRaises(__a ):
get_activation("bogus" )
with self.assertRaises(__a ):
get_activation(__a )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Tuple = get_activation("gelu" )
lowerCamelCase : Optional[int] = 1
lowerCamelCase : List[str] = get_activation("gelu" )
self.assertEqual(acta.a , 1 )
with self.assertRaises(__a ):
lowerCamelCase : List[str] = acta.a
| 340 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_a = logging.get_logger(__name__)
_a = {
"""facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = 'detr'
lowercase__ = ['past_key_values']
lowercase__ = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , __a=True , __a=None , __a=3 , __a=1_00 , __a=6 , __a=20_48 , __a=8 , __a=6 , __a=20_48 , __a=8 , __a=0.0 , __a=0.0 , __a=True , __a="relu" , __a=2_56 , __a=0.1 , __a=0.0 , __a=0.0 , __a=0.02 , __a=1.0 , __a=False , __a="sine" , __a="resnet50" , __a=True , __a=False , __a=1 , __a=5 , __a=2 , __a=1 , __a=1 , __a=5 , __a=2 , __a=0.1 , **__a , ) -> int:
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
_UpperCamelCase = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
elif isinstance(__a , __a):
_UpperCamelCase = backbone_config.get('''model_type''')
_UpperCamelCase = CONFIG_MAPPING[backbone_model_type]
_UpperCamelCase = config_class.from_dict(__a)
# set timm attributes to None
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None, None, None
_UpperCamelCase = use_timm_backbone
_UpperCamelCase = backbone_config
_UpperCamelCase = num_channels
_UpperCamelCase = num_queries
_UpperCamelCase = d_model
_UpperCamelCase = encoder_ffn_dim
_UpperCamelCase = encoder_layers
_UpperCamelCase = encoder_attention_heads
_UpperCamelCase = decoder_ffn_dim
_UpperCamelCase = decoder_layers
_UpperCamelCase = decoder_attention_heads
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = activation_function
_UpperCamelCase = init_std
_UpperCamelCase = init_xavier_std
_UpperCamelCase = encoder_layerdrop
_UpperCamelCase = decoder_layerdrop
_UpperCamelCase = encoder_layers
_UpperCamelCase = auxiliary_loss
_UpperCamelCase = position_embedding_type
_UpperCamelCase = backbone
_UpperCamelCase = use_pretrained_backbone
_UpperCamelCase = dilation
# Hungarian matcher
_UpperCamelCase = class_cost
_UpperCamelCase = bbox_cost
_UpperCamelCase = giou_cost
# Loss coefficients
_UpperCamelCase = mask_loss_coefficient
_UpperCamelCase = dice_loss_coefficient
_UpperCamelCase = bbox_loss_coefficient
_UpperCamelCase = giou_loss_coefficient
_UpperCamelCase = eos_coefficient
super().__init__(is_encoder_decoder=__a , **__a)
@property
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return self.d_model
@classmethod
def UpperCAmelCase ( cls , __a , **__a) -> int:
'''simple docstring'''
return cls(backbone_config=__a , **__a)
def UpperCAmelCase ( self) -> Dict[str, any]:
'''simple docstring'''
_UpperCamelCase = copy.deepcopy(self.__dict__)
if output["backbone_config"] is not None:
_UpperCamelCase = self.backbone_config.to_dict()
_UpperCamelCase = self.__class__.model_type
return output
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = version.parse('1.11' )
@property
def UpperCAmelCase ( self) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
])
@property
def UpperCAmelCase ( self) -> float:
'''simple docstring'''
return 1e-5
@property
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return 12
| 19 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase__)
class lowerCamelCase__ ( lowerCamelCase__):
'''simple docstring'''
snake_case_ =field(default="""automatic-speech-recognition""" , metadata={"""include_in_asdict_even_if_is_default""": True})
snake_case_ =Features({"""audio""": Audio()})
snake_case_ =Features({"""transcription""": Value("""string""")})
snake_case_ ="""audio"""
snake_case_ ="""transcription"""
def lowerCAmelCase__ (self ,__lowerCamelCase ) -> int:
"""simple docstring"""
if self.audio_column not in features:
raise ValueError(f"""Column {self.audio_column} is not present in features.""" )
if not isinstance(features[self.audio_column] ,__a ):
raise ValueError(f"""Column {self.audio_column} is not an Audio type.""" )
lowerCAmelCase__ : Optional[int] = copy.deepcopy(self )
lowerCAmelCase__ : Optional[int] = self.input_schema.copy()
lowerCAmelCase__ : Optional[Any] = features[self.audio_column]
lowerCAmelCase__ : Optional[int] = input_schema
return task_template
@property
def lowerCAmelCase__ (self ) -> Dict[str, str]:
"""simple docstring"""
return {self.audio_column: "audio", self.transcription_column: "transcription"}
| 647 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = 'wavlm'
def __init__( self , __a=32 , __a=7_68 , __a=12 , __a=12 , __a=30_72 , __a="gelu" , __a=0.1 , __a=0.1 , __a=0.1 , __a=0.0 , __a=0.1 , __a=0.1 , __a=0.02 , __a=1e-5 , __a="group" , __a="gelu" , __a=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __a=(5, 2, 2, 2, 2, 2, 2) , __a=(10, 3, 3, 3, 3, 2, 2) , __a=False , __a=1_28 , __a=16 , __a=3_20 , __a=8_00 , __a=False , __a=True , __a=0.05 , __a=10 , __a=2 , __a=0.0 , __a=10 , __a=3_20 , __a=2 , __a=0.1 , __a=1_00 , __a=2_56 , __a=2_56 , __a=0.1 , __a="mean" , __a=False , __a=False , __a=2_56 , __a=(5_12, 5_12, 5_12, 5_12, 15_00) , __a=(5, 3, 3, 1, 1) , __a=(1, 2, 3, 1, 1) , __a=5_12 , __a=80 , __a=0 , __a=1 , __a=2 , __a=False , __a=3 , __a=2 , __a=3 , __a=None , **__a , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**__a , pad_token_id=__a , bos_token_id=__a , eos_token_id=__a)
_UpperCamelCase = hidden_size
_UpperCamelCase = feat_extract_norm
_UpperCamelCase = feat_extract_activation
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = conv_bias
_UpperCamelCase = num_buckets
_UpperCamelCase = max_bucket_distance
_UpperCamelCase = num_conv_pos_embeddings
_UpperCamelCase = num_conv_pos_embedding_groups
_UpperCamelCase = len(self.conv_dim)
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = num_attention_heads
_UpperCamelCase = hidden_dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = feat_proj_dropout
_UpperCamelCase = final_dropout
_UpperCamelCase = layerdrop
_UpperCamelCase = layer_norm_eps
_UpperCamelCase = initializer_range
_UpperCamelCase = num_ctc_classes
_UpperCamelCase = vocab_size
_UpperCamelCase = do_stable_layer_norm
_UpperCamelCase = use_weighted_layer_sum
_UpperCamelCase = classifier_proj_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F''' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,'''
F''' `len(config.conv_kernel) = {len(self.conv_kernel)}`.''')
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_UpperCamelCase = apply_spec_augment
_UpperCamelCase = mask_time_prob
_UpperCamelCase = mask_time_length
_UpperCamelCase = mask_time_min_masks
_UpperCamelCase = mask_feature_prob
_UpperCamelCase = mask_feature_length
# parameters for pretraining with codevector quantized representations
_UpperCamelCase = num_codevectors_per_group
_UpperCamelCase = num_codevector_groups
_UpperCamelCase = contrastive_logits_temperature
_UpperCamelCase = num_negatives
_UpperCamelCase = codevector_dim
_UpperCamelCase = proj_codevector_dim
_UpperCamelCase = diversity_loss_weight
# ctc loss
_UpperCamelCase = ctc_loss_reduction
_UpperCamelCase = ctc_zero_infinity
# adapter
_UpperCamelCase = add_adapter
_UpperCamelCase = adapter_kernel_size
_UpperCamelCase = adapter_stride
_UpperCamelCase = num_adapter_layers
_UpperCamelCase = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_UpperCamelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = xvector_output_dim
@property
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1)
| 19 | 0 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def UpperCAmelCase_ ():
"""simple docstring"""
_a, _a : int = 9, 1_4 # noqa: F841
_a : Optional[int] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 1_4],
[3, 4, 9],
[5, 4, 1_0],
[1, 7, 1_1],
]
_a : List[Any] = defaultdict(__snake_case )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
_a : Optional[Any] = mst(__snake_case )
_a : Optional[int] = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
_a : Any = tuple(answer[:2] )
_a : int = tuple(edge[::-1] )
assert edge in result or reverse in result
| 229 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
_a = """bart"""
_a = True
@st.cache(allow_output_mutation=__snake_case )
def lowerCamelCase__ ( ) -> Dict:
"""simple docstring"""
if LOAD_DENSE_INDEX:
_UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' )
_UpperCamelCase = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' )
_UpperCamelCase = qar_model.eval()
else:
_UpperCamelCase , _UpperCamelCase = (None, None)
if MODEL_TYPE == "bart":
_UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' )
_UpperCamelCase = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' )
_UpperCamelCase = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' )
sas_model.load_state_dict(save_dict['''model'''] )
_UpperCamelCase = sas_model.eval()
else:
_UpperCamelCase , _UpperCamelCase = make_qa_sas_model(
model_name='''t5-small''', from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''', device='''cuda:0''' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=__snake_case )
def lowerCamelCase__ ( ) -> Tuple:
"""simple docstring"""
if LOAD_DENSE_INDEX:
_UpperCamelCase = faiss.StandardGpuResources()
_UpperCamelCase = datasets.load_dataset(path='''wiki_snippets''', name='''wiki40b_en_100_0''' )['''train''']
_UpperCamelCase = np.memmap(
'''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''', dtype='''float32''', mode='''r''', shape=(wikiaab_passages.num_rows, 1_28), )
_UpperCamelCase = faiss.IndexFlatIP(1_28 )
_UpperCamelCase = faiss.index_cpu_to_gpu(__snake_case, 1, __snake_case )
wikiaab_gpu_index_flat.add(__snake_case ) # TODO fix for larger GPU
else:
_UpperCamelCase , _UpperCamelCase = (None, None)
_UpperCamelCase = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=__snake_case )
def lowerCamelCase__ ( ) -> int:
"""simple docstring"""
_UpperCamelCase = datasets.load_dataset('''eli5''', name='''LFQA_reddit''' )
_UpperCamelCase = elia['''train_eli5''']
_UpperCamelCase = np.memmap(
'''eli5_questions_reps.dat''', dtype='''float32''', mode='''r''', shape=(elia_train.num_rows, 1_28) )
_UpperCamelCase = faiss.IndexFlatIP(1_28 )
eli5_train_q_index.add(__snake_case )
return (elia_train, eli5_train_q_index)
_a , _a , _a = load_indexes()
_a , _a , _a , _a = load_models()
_a , _a = load_train_data()
def lowerCamelCase__ ( __snake_case, __snake_case=10 ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = embed_questions_for_retrieval([question], __snake_case, __snake_case )
_UpperCamelCase , _UpperCamelCase = eli5_train_q_index.search(__snake_case, __snake_case )
_UpperCamelCase = [elia_train[int(__snake_case )] for i in I[0]]
return nn_examples
def lowerCamelCase__ ( __snake_case, __snake_case="wiki40b", __snake_case="dense", __snake_case=10 ) -> List[str]:
"""simple docstring"""
if source == "none":
_UpperCamelCase , _UpperCamelCase = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
_UpperCamelCase , _UpperCamelCase = query_qa_dense_index(
__snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case )
else:
_UpperCamelCase , _UpperCamelCase = query_es_index(
__snake_case, __snake_case, index_name='''english_wiki40b_snippets_100w''', n_results=__snake_case, )
_UpperCamelCase = [
(res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
]
_UpperCamelCase = '''question: {} context: {}'''.format(__snake_case, __snake_case )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda __snake_case : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __snake_case : None),
} )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=64, __snake_case=2_56, __snake_case=False, __snake_case=2, __snake_case=0.95, __snake_case=0.8 ) -> Dict:
"""simple docstring"""
with torch.no_grad():
_UpperCamelCase = qa_sas_generate(
__snake_case, __snake_case, __snake_case, num_answers=1, num_beams=__snake_case, min_len=__snake_case, max_len=__snake_case, do_sample=__snake_case, temp=__snake_case, top_p=__snake_case, top_k=__snake_case, max_input_length=10_24, device='''cuda:0''', )[0]
return (answer, support_list)
st.title("""Long Form Question Answering with ELI5""")
# Start sidebar
_a = """<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
_a = """
<html>
<head>
<style>
.img-container {
padding-left: 90px;
padding-right: 90px;
padding-top: 50px;
padding-bottom: 50px;
background-color: #f0f3f9;
}
</style>
</head>
<body>
<span class=\"img-container\"> <!-- Inline parent element -->
%s
</span>
</body>
</html>
""" % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
_a = """
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
"""
st.sidebar.markdown(description, unsafe_allow_html=True)
_a = [
"""Answer the question""",
"""View the retrieved document only""",
"""View the most similar ELI5 question and answer""",
"""Show me everything, please!""",
]
_a = st.sidebar.checkbox("""Demo options""")
if demo_options:
_a = st.sidebar.selectbox(
"""""",
action_list,
index=3,
)
_a = action_list.index(action_st)
_a = st.sidebar.selectbox(
"""""",
["""Show full text of passages""", """Show passage section titles"""],
index=0,
)
_a = show_type == """Show full text of passages"""
else:
_a = 3
_a = True
_a = st.sidebar.checkbox("""Retrieval options""")
if retrieval_options:
_a = """
### Information retriever options
The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
"""
st.sidebar.markdown(retriever_info)
_a = st.sidebar.selectbox("""Which Wikipedia format should the model use?""", ["""wiki40b""", """none"""])
_a = st.sidebar.selectbox("""Which Wikipedia indexer should the model use?""", ["""dense""", """sparse""", """mixed"""])
else:
_a = """wiki40b"""
_a = """dense"""
_a = """beam"""
_a = 2
_a = 64
_a = 256
_a = None
_a = None
_a = st.sidebar.checkbox("""Generation options""")
if generate_options:
_a = """
### Answer generation options
The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
**beam** search, or **sample** from the decoder's output probabilities.
"""
st.sidebar.markdown(generate_info)
_a = st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
_a = st.sidebar.slider(
"""Minimum generation length""", min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
_a = st.sidebar.slider(
"""Maximum generation length""", min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
_a = st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
_a = st.sidebar.slider(
"""Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
_a = st.sidebar.slider(
"""Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
_a = None
# start main text
_a = [
"""<MY QUESTION>""",
"""How do people make chocolate?""",
"""Why do we get a fever when we are sick?""",
"""How can different animals perceive different colors?""",
"""What is natural language processing?""",
"""What's the best way to treat a sunburn?""",
"""What exactly are vitamins ?""",
"""How does nuclear energy provide electricity?""",
"""What's the difference between viruses and bacteria?""",
"""Why are flutes classified as woodwinds when most of them are made out of metal ?""",
"""Why do people like drinking coffee even though it tastes so bad?""",
"""What happens when wine ages? How does it make the wine taste better?""",
"""If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?""",
"""How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?""",
"""How does New Zealand have so many large bird predators?""",
]
_a = st.selectbox(
"""What would you like to ask? ---- select <MY QUESTION> to enter a new query""",
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
_a = st.text_input("""Enter your question here:""", """""")
else:
_a = question_s
if st.button("""Show me!"""):
if action in [0, 1, 3]:
if index_type == "mixed":
_a , _a = make_support(question, source=wiki_source, method="""dense""", n_results=10)
_a , _a = make_support(question, source=wiki_source, method="""sparse""", n_results=10)
_a = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
_a = support_list[:10]
_a = """<P> """ + """ <P> """.join([res[-1] for res in support_list])
else:
_a , _a = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
_a , _a = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == """sampled"""),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown("""### The model generated answer is:""")
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown("""--- \n ### The model is drawing information from the following Wikipedia passages:""")
for i, res in enumerate(support_list):
_a = """https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_"""))
_a = res[1].strip()
if sec_titles == "":
_a = """[{}]({})""".format(res[0], wiki_url)
else:
_a = sec_titles.split(""" & """)
_a = """ & """.join(
["""[{}]({}#{})""".format(sec.strip(), wiki_url, sec.strip().replace(""" """, """_""")) for sec in sec_list]
)
st.markdown(
"""{0:02d} - **Article**: {1:<18} <br> _Section_: {2}""".format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
"""> <span style=\"font-family:arial; font-size:10pt;\">""" + res[-1] + """</span>""", unsafe_allow_html=True
)
if action in [2, 3]:
_a = find_nearest_training(question)
_a = nn_train_list[0]
st.markdown(
"""--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""])
)
_a = [
"""{}. {}""".format(i + 1, """ \n""".join([line.strip() for line in ans.split("""\n""") if line.strip() != """"""]))
for i, (ans, sc) in enumerate(zip(train_exple["""answers"""]["""text"""], train_exple["""answers"""]["""score"""]))
if i == 0 or sc > 2
]
st.markdown("""##### Its answers were: \n\n {}""".format("""\n""".join(answers_st)))
_a = """
---
**Disclaimer**
*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
"""
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 19 | 0 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowercase ( a ):
lowercase__ : Tuple = ["""image_processor""", """tokenizer"""]
lowercase__ : Dict = """AutoImageProcessor"""
lowercase__ : Union[str, Any] = """AutoTokenizer"""
def __init__( self : int , _UpperCamelCase : Any=None , _UpperCamelCase : List[Any]=None , **_UpperCamelCase : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , __a , )
SCREAMING_SNAKE_CASE = kwargs.pop("feature_extractor" )
SCREAMING_SNAKE_CASE = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(__a , __a )
SCREAMING_SNAKE_CASE = self.image_processor
SCREAMING_SNAKE_CASE = False
def __call__( self : Union[str, Any] , *_UpperCamelCase : int , **_UpperCamelCase : List[str] ) -> Optional[int]:
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*__a , **__a )
SCREAMING_SNAKE_CASE = kwargs.pop("images" , __a )
SCREAMING_SNAKE_CASE = kwargs.pop("text" , __a )
if len(__a ) > 0:
SCREAMING_SNAKE_CASE = args[0]
SCREAMING_SNAKE_CASE = args[1:]
if images is None and text is None:
raise ValueError("You need to specify either an `images` or `text` input to process." )
if images is not None:
SCREAMING_SNAKE_CASE = self.image_processor(__a , *__a , **__a )
if text is not None:
SCREAMING_SNAKE_CASE = self.tokenizer(__a , **__a )
if text is None:
return inputs
elif images is None:
return encodings
else:
SCREAMING_SNAKE_CASE = encodings["input_ids"]
return inputs
def __snake_case( self : Dict , *_UpperCamelCase : Union[str, Any] , **_UpperCamelCase : Dict ) -> List[Any]:
'''simple docstring'''
return self.tokenizer.batch_decode(*__a , **__a )
def __snake_case( self : List[str] , *_UpperCamelCase : Dict , **_UpperCamelCase : Any ) -> List[str]:
'''simple docstring'''
return self.tokenizer.decode(*__a , **__a )
@contextmanager
def __snake_case( self : List[str] ) -> Any:
'''simple docstring'''
warnings.warn(
"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
"labels by using the argument `text` of the regular `__call__` method (either in the same call as "
"your images inputs, or in a separate call." )
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = self.tokenizer
yield
SCREAMING_SNAKE_CASE = self.image_processor
SCREAMING_SNAKE_CASE = False
def __snake_case( self : Optional[int] , _UpperCamelCase : Any , _UpperCamelCase : int=False , _UpperCamelCase : int=None ) -> Any:
'''simple docstring'''
if added_vocab is None:
SCREAMING_SNAKE_CASE = self.tokenizer.get_added_vocab()
SCREAMING_SNAKE_CASE = {}
while tokens:
SCREAMING_SNAKE_CASE = re.search(R"<s_(.*?)>" , __a , re.IGNORECASE )
if start_token is None:
break
SCREAMING_SNAKE_CASE = start_token.group(1 )
SCREAMING_SNAKE_CASE = re.search(RF"</s_{key}>" , __a , re.IGNORECASE )
SCREAMING_SNAKE_CASE = start_token.group()
if end_token is None:
SCREAMING_SNAKE_CASE = tokens.replace(__a , "" )
else:
SCREAMING_SNAKE_CASE = end_token.group()
SCREAMING_SNAKE_CASE = re.escape(__a )
SCREAMING_SNAKE_CASE = re.escape(__a )
SCREAMING_SNAKE_CASE = re.search(F"{start_token_escaped}(.*?){end_token_escaped}" , __a , re.IGNORECASE )
if content is not None:
SCREAMING_SNAKE_CASE = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
SCREAMING_SNAKE_CASE = self.tokenajson(__a , is_inner_value=__a , added_vocab=__a )
if value:
if len(__a ) == 1:
SCREAMING_SNAKE_CASE = value[0]
SCREAMING_SNAKE_CASE = value
else: # leaf nodes
SCREAMING_SNAKE_CASE = []
for leaf in content.split(R"<sep/>" ):
SCREAMING_SNAKE_CASE = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
SCREAMING_SNAKE_CASE = leaf[1:-2] # for categorical special tokens
output[key].append(__a )
if len(output[key] ) == 1:
SCREAMING_SNAKE_CASE = output[key][0]
SCREAMING_SNAKE_CASE = tokens[tokens.find(__a ) + len(__a ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:] , is_inner_value=__a , added_vocab=__a )
if len(__a ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def __snake_case( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , __a , )
return self.image_processor_class
@property
def __snake_case( self : Tuple ) -> Tuple:
'''simple docstring'''
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , __a , )
return self.image_processor
| 403 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
_a = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
_a = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> Tuple:
"""simple docstring"""
for attribute in key.split('''.''' ):
_UpperCamelCase = getattr(__snake_case, __snake_case )
if weight_type is not None:
_UpperCamelCase = getattr(__snake_case, __snake_case ).shape
else:
_UpperCamelCase = hf_pointer.shape
assert hf_shape == value.shape, (
F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
_UpperCamelCase = value
elif weight_type == "weight_g":
_UpperCamelCase = value
elif weight_type == "weight_v":
_UpperCamelCase = value
elif weight_type == "bias":
_UpperCamelCase = value
else:
_UpperCamelCase = value
logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def lowerCamelCase__ ( __snake_case, __snake_case ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = []
_UpperCamelCase = fairseq_model.state_dict()
_UpperCamelCase = hf_model.feature_extractor
_UpperCamelCase = hf_model.adapter
for name, value in fairseq_dict.items():
_UpperCamelCase = False
if "conv_layers" in name:
load_conv_layer(
__snake_case, __snake_case, __snake_case, __snake_case, hf_model.config.feat_extract_norm == '''group''', )
_UpperCamelCase = True
elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.'''] ):
load_adapter(__snake_case, __snake_case, __snake_case, __snake_case )
_UpperCamelCase = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
_UpperCamelCase = True
if "*" in mapped_key:
_UpperCamelCase = name.split(__snake_case )[0].split('''.''' )[-2]
_UpperCamelCase = mapped_key.replace('''*''', __snake_case )
if "weight_g" in name:
_UpperCamelCase = '''weight_g'''
elif "weight_v" in name:
_UpperCamelCase = '''weight_v'''
elif "bias" in name:
_UpperCamelCase = '''bias'''
elif "weight" in name:
_UpperCamelCase = '''weight'''
else:
_UpperCamelCase = None
set_recursively(__snake_case, __snake_case, __snake_case, __snake_case, __snake_case )
continue
if not is_used:
unused_weights.append(__snake_case )
logger.warning(F'''Unused weights: {unused_weights}''' )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = full_name.split('''conv_layers.''' )[-1]
_UpperCamelCase = name.split('''.''' )
_UpperCamelCase = int(items[0] )
_UpperCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
_UpperCamelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
_UpperCamelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
_UpperCamelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
_UpperCamelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__snake_case )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> Dict:
"""simple docstring"""
_UpperCamelCase = full_name.split('''adaptor.''' )[-1]
_UpperCamelCase = name.split('''.''' )
if items[1].isdigit():
_UpperCamelCase = int(items[1] )
else:
_UpperCamelCase = None
if "adaptor" not in full_name:
if "proj_ln" in full_name:
# has to be layer norm
if "bias" in name:
assert (
value.shape == adapter.proj_layer_norm.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj_layer_norm.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
_UpperCamelCase = value
else:
# has to be projection layer
if "bias" in name:
assert (
value.shape == adapter.proj.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter proj layer bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter proj layer weight was initialized from {full_name}.''' )
elif isinstance(__snake_case, __snake_case ):
if "bias" in name:
assert (
value.shape == adapter.layers[layer_id].conv.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
elif "weight" in name:
assert (
value.shape == adapter.layers[layer_id].conv.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
else:
unused_weights.append(__snake_case )
def lowerCamelCase__ ( __snake_case ) -> str:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase = emb.weight.shape
_UpperCamelCase = nn.Linear(__snake_case, __snake_case, bias=__snake_case )
_UpperCamelCase = emb.weight.data
return lin_layer
@torch.no_grad()
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = WavaVecaConfig.from_pretrained(
__snake_case, add_adapter=__snake_case, adapter_stride=__snake_case, adapter_kernel_size=__snake_case, use_auth_token=__snake_case, output_hidden_size=__snake_case, )
_UpperCamelCase = MBartConfig.from_pretrained(__snake_case )
# load model
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path], arg_overrides={
'''config_yaml''': config_yaml_path,
'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ),
'''w2v_path''': checkpoint_path,
'''load_pretrained_decoder_from''': None,
}, )
_UpperCamelCase = model[0].eval()
# load feature extractor
_UpperCamelCase = WavaVecaFeatureExtractor.from_pretrained(__snake_case, use_auth_token=__snake_case )
# set weights for wav2vec2 encoder
_UpperCamelCase = WavaVecaModel(__snake_case )
recursively_load_weights_wavaveca(model.encoder, __snake_case )
# load decoder weights
_UpperCamelCase = MBartForCausalLM(__snake_case )
_UpperCamelCase , _UpperCamelCase = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(), strict=__snake_case )
logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}''' )
logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' )
_UpperCamelCase = SpeechEncoderDecoderModel(encoder=__snake_case, decoder=__snake_case )
_UpperCamelCase = False
_UpperCamelCase = MBartaaTokenizer(__snake_case )
tokenizer.save_pretrained(__snake_case )
_UpperCamelCase = hf_wavavec.config.to_dict()
_UpperCamelCase = tokenizer.pad_token_id
_UpperCamelCase = tokenizer.bos_token_id
_UpperCamelCase = tokenizer.eos_token_id
_UpperCamelCase = '''mbart50'''
_UpperCamelCase = '''wav2vec2'''
_UpperCamelCase = tokenizer.eos_token_id
_UpperCamelCase = 25_00_04
_UpperCamelCase = tokenizer.eos_token_id
_UpperCamelCase = SpeechEncoderDecoderConfig.from_dict(__snake_case )
hf_wavavec.save_pretrained(__snake_case )
feature_extractor.save_pretrained(__snake_case )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
parser.add_argument(
"""--encoder_config_path""",
default="""facebook/wav2vec2-xls-r-1b""",
type=str,
help="""Path to hf encoder wav2vec2 checkpoint config""",
)
parser.add_argument(
"""--decoder_config_path""",
default="""facebook/mbart-large-50-one-to-many-mmt""",
type=str,
help="""Path to hf decoder checkpoint config""",
)
parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""")
parser.add_argument("""--start_token_id""", default=25_0004, type=int, help="""`decoder_start_token_id` of model config""")
_a = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
args.config_yaml_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
add_adapter=args.add_adapter,
adapter_kernel_size=args.adapter_kernel_size,
adapter_stride=args.adapter_stride,
decoder_start_token_id=args.start_token_id,
encoder_output_dim=args.encoder_output_dim,
)
| 19 | 0 |
'''simple docstring'''
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
_snake_case : int = logging.getLogger(__name__)
def snake_case_ ():
'''simple docstring'''
_a = argparse.ArgumentParser(
description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.''' )
parser.add_argument(
'''--dataset_name''' , type=__snake_case , default='''wikitext''' , help='''Name of the training. Explore datasets at: hf.co/datasets.''' , )
parser.add_argument(
'''--dataset_config''' , type=__snake_case , default='''wikitext-103-raw-v1''' , help='''Configuration name of the dataset.''' )
parser.add_argument(
'''--tokenizer_name_or_path''' , type=__snake_case , default='''sayakpaul/unigram-tokenizer-wikitext''' , help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.''' , )
parser.add_argument(
'''--shard_size''' , type=__snake_case , default=1000 , help='''Number of entries to go in a single shard.''' , )
parser.add_argument('''--split''' , type=__snake_case , default='''train''' , choices=['''train''', '''test''', '''validation'''] )
parser.add_argument(
'''--limit''' , default=__snake_case , type=__snake_case , help='''Limit the number of shards (used for debugging).''' , )
parser.add_argument(
'''--max_length''' , type=__snake_case , default=512 , help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
''' sequence length that is a multiple of 8.''' , )
parser.add_argument(
'''--output_dir''' , default='''tf-tpu''' , type=__snake_case , help='''Output directory where the TFRecord shards will be saved. If the'''
''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
''' shards will be directly saved to a Google Cloud Storage bucket.''' , )
_a = parser.parse_args()
return args
def snake_case_ (UpperCamelCase : List[Any] ):
'''simple docstring'''
def fn(UpperCamelCase : List[str] ):
return tokenizer(examples['''text'''] )
return fn
def snake_case_ (UpperCamelCase : Dict ):
'''simple docstring'''
_a = []
for i in range(len(tokenized_data['''input_ids'''] ) ):
_a = {
'''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i] ) ),
'''attention_mask''': tf.train.Feature(
intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i] ) ),
}
_a = tf.train.Features(feature=__snake_case )
_a = tf.train.Example(features=__snake_case )
_a = example.SerializeToString()
records.append(__snake_case )
return records
def snake_case_ (UpperCamelCase : str ):
'''simple docstring'''
_a = datasets.load_dataset(args.dataset_name , args.dataset_config , split=args.split )
if args.limit is not None:
_a = min(len(__snake_case ) , args.limit )
_a = dataset.select(range(__snake_case ) )
print(f'Limiting the dataset to {args.limit} entries.' )
_a = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path )
# Handle output directory creation.
# For serializing into a Google Cloud Storage Bucket, one needs to first
# create a bucket.
if "gs" not in args.output_dir:
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
_a = os.path.join(args.output_dir , args.split )
if not os.path.exists(__snake_case ):
os.makedirs(__snake_case )
else:
_a = os.path.join(args.output_dir , args.split )
# Tokenize the whole dataset at once.
_a = tokenize_function(__snake_case )
_a = dataset.map(__snake_case , batched=__snake_case , num_proc=4 , remove_columns=['''text'''] )
# We need to concatenate all our texts together, and then split the result
# into chunks of a fixed size, which we will call block_size. To do this, we
# will use the map method again, with the option batched=True. When we use batched=True,
# the function we pass to map() will be passed multiple inputs at once, allowing us
# to group them into more or fewer examples than we had in the input.
# This allows us to create our new fixed-length samples. The advantage of this
# method is that we don't lose a whole lot of content from the dataset compared to the
# case where we simply tokenize with a pre-defined max_length.
def group_texts(UpperCamelCase : Optional[Any] ):
# Concatenate all texts.
_a = {k: sum(examples[k] , [] ) for k in examples.keys()}
_a = len(concatenated_examples[list(examples.keys() )[0]] )
# We drop the small remainder, though you could add padding instead if the model supports it
# In this, as in all things, we advise you to follow your heart 🫀
_a = (total_length // args.max_length) * args.max_length
# Split by chunks of max_len.
_a = {
k: [t[i : i + args.max_length] for i in range(0 , __snake_case , args.max_length )]
for k, t in concatenated_examples.items()
}
return result
_a = dataset_tokenized.map(__snake_case , batched=__snake_case , batch_size=1000 , num_proc=4 )
_a = 0
_a = 0
for shard in range(0 , len(__snake_case ) , args.shard_size ):
_a = grouped_dataset[shard : shard + args.shard_size]
_a = len(dataset_snapshot['''input_ids'''] )
_a = os.path.join(__snake_case , f'dataset-{shard_count}-{records_containing}.tfrecord' )
_a = get_serialized_examples(__snake_case )
with tf.io.TFRecordWriter(__snake_case ) as out_file:
for i in range(len(__snake_case ) ):
_a = serialized_examples[i]
out_file.write(__snake_case )
print('''Wrote file {} containing {} records'''.format(__snake_case , __snake_case ) )
shard_count += 1
total_records += records_containing
with open(f'split-{args.split}-records-count.txt' , '''w''' ) as f:
print(f'Total {args.split} records: {total_records}' , file=__snake_case )
if __name__ == "__main__":
_snake_case : Optional[int] = parse_args()
main(args)
| 22 |
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case=None, **__snake_case ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()]
_UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()][: len(__snake_case )]
_UpperCamelCase = calculate_rouge(__snake_case, __snake_case, **__snake_case )
if save_path is not None:
save_json(__snake_case, __snake_case, indent=__snake_case )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 19 | 0 |
'''simple docstring'''
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
'stable diffusion controlnet',
'0.22.0',
'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.',
standard_warn=False,
stacklevel=3,
)
| 649 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = ['image_processor', 'tokenizer']
lowercase__ = 'ViTImageProcessor'
lowercase__ = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self , __a=None , __a=None , **__a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __a , )
_UpperCamelCase = kwargs.pop('''feature_extractor''')
_UpperCamelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''')
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''')
super().__init__(__a , __a)
def __call__( self , __a=None , __a=None , __a=None , __a=None , **__a) -> Tuple:
'''simple docstring'''
if text is None and visual_prompt is None and images is None:
raise ValueError('''You have to specify either text, visual prompt or images.''')
if text is not None and visual_prompt is not None:
raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''')
if text is not None:
_UpperCamelCase = self.tokenizer(__a , return_tensors=__a , **__a)
if visual_prompt is not None:
_UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a)
if images is not None:
_UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a)
if visual_prompt is not None and images is not None:
_UpperCamelCase = {
'''pixel_values''': image_features.pixel_values,
'''conditional_pixel_values''': prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
_UpperCamelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
_UpperCamelCase = {
'''conditional_pixel_values''': prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**__a) , tensor_type=__a)
def UpperCAmelCase ( self , *__a , **__a) -> Any:
'''simple docstring'''
return self.tokenizer.batch_decode(*__a , **__a)
def UpperCAmelCase ( self , *__a , **__a) -> List[str]:
'''simple docstring'''
return self.tokenizer.decode(*__a , **__a)
@property
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __a , )
return self.image_processor_class
@property
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __a , )
return self.image_processor
| 19 | 0 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCamelCase__ : Tuple = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n'
UpperCamelCase__ : List[Any] = '\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'
UpperCamelCase__ : Dict = '\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n \'google_bleu\': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.4\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class _lowercase ( datasets.Metric ):
'''simple docstring'''
def lowerCAmelCase__ ( self ) -> MetricInfo:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' ,id='''token''' ) ,id='''sequence''' ),
'''references''': datasets.Sequence(
datasets.Sequence(datasets.Value('''string''' ,id='''token''' ) ,id='''sequence''' ) ,id='''references''' ),
} ) ,)
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = 1 ,lowerCamelCase_ = 4 ,) -> Dict[str, float]:
'''simple docstring'''
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=__a ,hypotheses=__a ,min_len=__a ,max_len=__a )
}
| 614 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import UperNetForSemanticSegmentation
from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCAmelCase:
def __init__( self , __a , __a=13 , __a=32 , __a=3 , __a=4 , __a=[10, 20, 30, 40] , __a=[2, 2, 3, 2] , __a=True , __a=True , __a=37 , __a="gelu" , __a=10 , __a=0.02 , __a=["stage2", "stage3", "stage4"] , __a=3 , __a=None , ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = image_size
_UpperCamelCase = num_channels
_UpperCamelCase = num_stages
_UpperCamelCase = hidden_sizes
_UpperCamelCase = depths
_UpperCamelCase = is_training
_UpperCamelCase = use_labels
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = out_features
_UpperCamelCase = num_labels
_UpperCamelCase = scope
_UpperCamelCase = num_stages
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_UpperCamelCase = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return ConvNextConfig(
num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , )
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return UperNetConfig(
backbone_config=self.get_backbone_config() , hidden_size=5_12 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=__a , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=2_56 , auxiliary_num_convs=1 , auxiliary_concat_input=__a , loss_ignore_index=2_55 , num_labels=self.num_labels , )
def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = UperNetForSemanticSegmentation(config=__a)
model.to(__a)
model.eval()
_UpperCamelCase = model(__a)
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size))
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase__ = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
lowercase__ = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = UperNetModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(__a)
_UpperCamelCase = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCamelCase = [*signature.parameters.keys()]
_UpperCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__a)
@unittest.skip(reason='''UperNet does not use inputs_embeds''')
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason='''UperNet does not support input and output embeddings''')
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''UperNet does not have a base model''')
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason='''UperNet does not have a base model''')
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''')
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
pass
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
def check_hidden_states_output(__a , __a , __a):
_UpperCamelCase = model_class(__a)
model.to(__a)
model.eval()
with torch.no_grad():
_UpperCamelCase = model(**self._prepare_for_class(__a , __a))
_UpperCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_UpperCamelCase = self.model_tester.num_stages
self.assertEqual(len(__a) , expected_num_stages + 1)
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = True
check_hidden_states_output(__a , __a , __a)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCamelCase = True
check_hidden_states_output(__a , __a , __a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCamelCase = _config_zero_init(__a)
_UpperCamelCase = _config_zero_init(configs_no_init.backbone_config)
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(config=__a)
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@unittest.skip(reason='''UperNet does not have tied weights''')
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
pass
@slow
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained(__a)
self.assertIsNotNone(__a)
def lowerCamelCase__ ( ) -> int:
"""simple docstring"""
_UpperCamelCase = hf_hub_download(
repo_id='''hf-internal-testing/fixtures_ade20k''', repo_type='''dataset''', filename='''ADE_val_00000001.jpg''' )
_UpperCamelCase = Image.open(__snake_case ).convert('''RGB''' )
return image
@require_torch
@require_vision
@slow
class _UpperCAmelCase( unittest.TestCase ):
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny''')
_UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny''').to(__a)
_UpperCamelCase = prepare_img()
_UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a)
with torch.no_grad():
_UpperCamelCase = model(**__a)
_UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12))
self.assertEqual(outputs.logits.shape , __a)
_UpperCamelCase = torch.tensor(
[[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]).to(__a)
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4))
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny''')
_UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny''').to(__a)
_UpperCamelCase = prepare_img()
_UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a)
with torch.no_grad():
_UpperCamelCase = model(**__a)
_UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12))
self.assertEqual(outputs.logits.shape , __a)
_UpperCamelCase = torch.tensor(
[[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]).to(__a)
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4))
| 19 | 0 |
'''simple docstring'''
import random
class __magic_name__ :
@staticmethod
def lowerCAmelCase ( snake_case) -> tuple[list[int], list[int]]:
'''simple docstring'''
_UpperCAmelCase : str =[ord(__a) for i in text]
_UpperCAmelCase : Any =[]
_UpperCAmelCase : str =[]
for i in plain:
_UpperCAmelCase : Optional[int] =random.randint(1 , 3_0_0)
_UpperCAmelCase : int =(i + k) * k
cipher.append(__a)
key.append(__a)
return cipher, key
@staticmethod
def lowerCAmelCase ( snake_case , snake_case) -> str:
'''simple docstring'''
_UpperCAmelCase : str =[]
for i in range(len(__a)):
_UpperCAmelCase : List[str] =int((cipher[i] - (key[i]) ** 2) / key[i])
plain.append(chr(__a))
return "".join(__a)
if __name__ == "__main__":
lowercase, lowercase =Onepad().encrypt('Hello')
print(c, k)
print(Onepad().decrypt(c, k))
| 446 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = (DDPMScheduler,)
def UpperCAmelCase ( self , **__a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = {
'''num_train_timesteps''': 10_00,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''variance_type''': '''fixed_small''',
'''clip_sample''': True,
}
config.update(**__a)
return config
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
for timesteps in [1, 5, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__a)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2]):
self.check_over_configs(beta_start=__a , beta_end=__a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__a)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=__a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
self.check_over_configs(thresholding=__a)
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=__a , prediction_type=__a , sample_max_value=__a , )
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=__a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
for t in [0, 5_00, 9_99]:
self.check_over_forward(time_step=__a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0979)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1e-5
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = len(__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter
_UpperCamelCase = torch.manual_seed(0)
for t in reversed(range(__a)):
# 1. predict noise residual
_UpperCamelCase = model(__a , __a)
# 2. predict previous mean of sample x_t-1
_UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCamelCase = pred_prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
assert abs(result_sum.item() - 258.9606) < 1e-2
assert abs(result_mean.item() - 0.3372) < 1e-3
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''')
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = len(__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter
_UpperCamelCase = torch.manual_seed(0)
for t in reversed(range(__a)):
# 1. predict noise residual
_UpperCamelCase = model(__a , __a)
# 2. predict previous mean of sample x_t-1
_UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCamelCase = pred_prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
assert abs(result_sum.item() - 202.0296) < 1e-2
assert abs(result_mean.item() - 0.2631) < 1e-3
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [1_00, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=__a)
_UpperCamelCase = scheduler.timesteps
for i, timestep in enumerate(__a):
if i == len(__a) - 1:
_UpperCamelCase = -1
else:
_UpperCamelCase = timesteps[i + 1]
_UpperCamelCase = scheduler.previous_timestep(__a)
_UpperCamelCase = prev_t.item()
self.assertEqual(__a , __a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [1_00, 87, 50, 51, 0]
with self.assertRaises(__a , msg='''`custom_timesteps` must be in descending order.'''):
scheduler.set_timesteps(timesteps=__a)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [1_00, 87, 50, 1, 0]
_UpperCamelCase = len(__a)
with self.assertRaises(__a , msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
scheduler.set_timesteps(num_inference_steps=__a , timesteps=__a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
__a , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ):
scheduler.set_timesteps(timesteps=__a)
| 19 | 0 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def _a ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str] ) -> np.ndarray:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = cva.getAffineTransform(__snake_case , __snake_case )
return cva.warpAffine(__snake_case , __snake_case , (rows, cols) )
if __name__ == "__main__":
# read original image
_lowerCamelCase : Optional[Any] = cva.imread(
str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
)
# turn image in gray scale value
_lowerCamelCase : str = cva.cvtColor(image, cva.COLOR_BGR2GRAY)
# get image shape
_lowerCamelCase , _lowerCamelCase : Union[str, Any] = gray_img.shape
# set different points to rotate image
_lowerCamelCase : Optional[int] = np.array([[5_0, 5_0], [2_0_0, 5_0], [5_0, 2_0_0]], np.floataa)
_lowerCamelCase : List[str] = np.array([[1_0, 1_0_0], [2_0_0, 5_0], [1_0_0, 2_5_0]], np.floataa)
_lowerCamelCase : Optional[int] = np.array([[5_0, 5_0], [1_5_0, 5_0], [1_2_0, 2_0_0]], np.floataa)
_lowerCamelCase : List[str] = np.array([[1_0, 1_0_0], [8_0, 5_0], [1_8_0, 2_5_0]], np.floataa)
# add all rotated images in a list
_lowerCamelCase : Dict = [
gray_img,
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
]
# plot different image rotations
_lowerCamelCase : str = plt.figure(1)
_lowerCamelCase : Any = ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
for i, image in enumerate(images):
plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
plt.title(titles[i])
plt.axis('''off''')
plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
plt.show()
| 663 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
_a = 100
_a = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_a = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=1_00 )
def lowerCamelCase__ ( __snake_case ) -> set[int]:
"""simple docstring"""
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
_UpperCamelCase = set()
_UpperCamelCase = 42
_UpperCamelCase = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def lowerCamelCase__ ( __snake_case = 50_00 ) -> int | None:
"""simple docstring"""
for number_to_partition in range(1, __snake_case ):
if len(partition(__snake_case ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 19 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCamelCase_(lowerCamelCase_ ) -> List[str]:
UpperCAmelCase = filter(lambda lowerCamelCase_ : p.requires_grad , model.parameters() )
UpperCAmelCase = sum([np.prod(p.size() ) for p in model_parameters] )
return params
__lowerCamelCase : int = logging.getLogger(__name__)
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ ) -> Union[str, Any]:
if metric == "rouge2":
UpperCAmelCase = "{val_avg_rouge2:.4f}-{step_count}"
elif metric == "bleu":
UpperCAmelCase = "{val_avg_bleu:.4f}-{step_count}"
elif metric == "em":
UpperCAmelCase = "{val_avg_em:.4f}-{step_count}"
elif metric == "loss":
UpperCAmelCase = "{val_avg_loss:.4f}-{step_count}"
else:
raise NotImplementedError(
F'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
" function." )
UpperCAmelCase = ModelCheckpoint(
dirpath=__snake_case , filename=__snake_case , monitor=F'val_{metric}' , mode="max" , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ ) -> Any:
return EarlyStopping(
monitor=F'val_{metric}' , mode="min" if "loss" in metric else "max" , patience=__snake_case , verbose=__snake_case , )
class __magic_name__ ( pl.Callback ):
def SCREAMING_SNAKE_CASE_ ( self : str , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int] ) -> Any:
'''simple docstring'''
UpperCAmelCase = {F'lr_group_{i}': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(__a )
@rank_zero_only
def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : Dict , UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple=True ) -> None:
'''simple docstring'''
logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****' )
UpperCAmelCase = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} )
# Log results
UpperCAmelCase = Path(pl_module.hparams.output_dir )
if type_path == "test":
UpperCAmelCase = od / "test_results.txt"
UpperCAmelCase = od / "test_generations.txt"
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
UpperCAmelCase = od / F'{type_path}_results/{trainer.global_step:05d}.txt'
UpperCAmelCase = od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
results_file.parent.mkdir(exist_ok=__a )
generations_file.parent.mkdir(exist_ok=__a )
with open(__a , "a+" ) as writer:
for key in sorted(__a ):
if key in ["log", "progress_bar", "preds"]:
continue
UpperCAmelCase = metrics[key]
if isinstance(__a , torch.Tensor ):
UpperCAmelCase = val.item()
UpperCAmelCase = F'{key}: {val:.6f}\n'
writer.write(__a )
if not save_generations:
return
if "preds" in metrics:
UpperCAmelCase = "\n".join(metrics["preds"] )
generations_file.open("w+" ).write(__a )
@rank_zero_only
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : int ) -> str:
'''simple docstring'''
try:
UpperCAmelCase = pl_module.model.model.num_parameters()
except AttributeError:
UpperCAmelCase = pl_module.model.num_parameters()
UpperCAmelCase = count_trainable_parameters(__a )
# mp stands for million parameters
trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6} )
@rank_zero_only
def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Any ) -> Tuple:
'''simple docstring'''
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(__a , __a , "test" )
@rank_zero_only
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , UpperCamelCase__ : int , UpperCamelCase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 323 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> np.array:
"""simple docstring"""
_UpperCamelCase = int(np.ceil((x_end - xa) / step_size ) )
_UpperCamelCase = np.zeros((n + 1,) )
_UpperCamelCase = ya
_UpperCamelCase = xa
for k in range(__snake_case ):
_UpperCamelCase = y[k] + step_size * ode_func(__snake_case, y[k] )
_UpperCamelCase = y[k] + (
(step_size / 2) * (ode_func(__snake_case, y[k] ) + ode_func(x + step_size, __snake_case ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 19 | 0 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a: Any = logging.get_logger(__name__)
__a: List[str] = {
"""facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
"""facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}
class UpperCAmelCase ( a__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = "encodec"
def __init__( self , __lowerCAmelCase=[1.5, 3.0, 6.0, 1_2.0, 2_4.0] , __lowerCAmelCase=24000 , __lowerCAmelCase=1 , __lowerCAmelCase=False , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=128 , __lowerCAmelCase=32 , __lowerCAmelCase=1 , __lowerCAmelCase=[8, 5, 4, 2] , __lowerCAmelCase="weight_norm" , __lowerCAmelCase=7 , __lowerCAmelCase=7 , __lowerCAmelCase=3 , __lowerCAmelCase=2 , __lowerCAmelCase=True , __lowerCAmelCase="reflect" , __lowerCAmelCase=2 , __lowerCAmelCase=2 , __lowerCAmelCase=1.0 , __lowerCAmelCase=1024 , __lowerCAmelCase=None , __lowerCAmelCase=True , **__lowerCAmelCase , ) -> int:
lowercase__ : Tuple = target_bandwidths
lowercase__ : Dict = sampling_rate
lowercase__ : List[Any] = audio_channels
lowercase__ : Optional[Any] = normalize
lowercase__ : int = chunk_length_s
lowercase__ : Any = overlap
lowercase__ : Optional[Any] = hidden_size
lowercase__ : Union[str, Any] = num_filters
lowercase__ : Optional[int] = num_residual_layers
lowercase__ : Tuple = upsampling_ratios
lowercase__ : Any = norm_type
lowercase__ : Union[str, Any] = kernel_size
lowercase__ : Union[str, Any] = last_kernel_size
lowercase__ : Any = residual_kernel_size
lowercase__ : Tuple = dilation_growth_rate
lowercase__ : List[str] = use_causal_conv
lowercase__ : Optional[Any] = pad_mode
lowercase__ : List[str] = compress
lowercase__ : List[str] = num_lstm_layers
lowercase__ : Tuple = trim_right_ratio
lowercase__ : Optional[Any] = codebook_size
lowercase__ : List[Any] = codebook_dim if codebook_dim is not None else hidden_size
lowercase__ : Optional[int] = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}""" )
super().__init__(**__a )
@property
def _lowerCAmelCase( self ) -> Optional[int]:
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _lowerCAmelCase( self ) -> Optional[int]:
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def _lowerCAmelCase( self ) -> int:
lowercase__ : Tuple = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def _lowerCAmelCase( self ) -> int:
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 152 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_a = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
""" Distillation"""
)
)
parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
parser.add_argument("""--vocab_transform""", action="""store_true""")
_a = parser.parse_args()
if args.model_type == "bert":
_a = BertForMaskedLM.from_pretrained(args.model_name)
_a = """bert"""
else:
raise ValueError("""args.model_type should be \"bert\".""")
_a = model.state_dict()
_a = {}
for w in ["word_embeddings", "position_embeddings"]:
_a = state_dict[F"""{prefix}.embeddings.{w}.weight"""]
for w in ["weight", "bias"]:
_a = state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]
_a = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
]
std_idx += 1
_a = state_dict["""cls.predictions.decoder.weight"""]
_a = state_dict["""cls.predictions.bias"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
_a = state_dict[F"""cls.predictions.transform.dense.{w}"""]
_a = state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]
print(F"""N layers selected for distillation: {std_idx}""")
print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 19 | 0 |
from typing import List
from .keymap import KEYMAP, get_character
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def decorator(SCREAMING_SNAKE_CASE_ ):
lowerCamelCase : Tuple = getattr(__snake_case , "handle_key" , [] )
handle += [key]
setattr(__snake_case , "handle_key" , __snake_case )
return func
return decorator
def lowercase_( *SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def decorator(SCREAMING_SNAKE_CASE_ ):
lowerCamelCase : int = getattr(__snake_case , "handle_key" , [] )
handle += keys
setattr(__snake_case , "handle_key" , __snake_case )
return func
return decorator
class UpperCAmelCase_ ( UpperCamelCase ):
'''simple docstring'''
def __new__( cls , __A , __A , __A ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = super().__new__(cls , __a , __a , __a )
if not hasattr(__a , "key_handler" ):
setattr(__a , "key_handler" , {} )
setattr(__a , "handle_input" , KeyHandler.handle_input )
for value in attrs.values():
lowerCamelCase : Tuple = getattr(__a , "handle_key" , [] )
for key in handled_keys:
lowerCamelCase : Optional[Any] = value
return new_cls
@staticmethod
def _snake_case ( cls ):
"""simple docstring"""
lowerCamelCase : List[str] = get_character()
if char != KEYMAP["undefined"]:
lowerCamelCase : Optional[Any] = ord(__a )
lowerCamelCase : str = cls.key_handler.get(__a )
if handler:
lowerCamelCase : Any = char
return handler(cls )
else:
return None
def lowercase_( cls ):
'''simple docstring'''
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 340 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
_a = """platform"""
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class _UpperCAmelCase:
lowercase__ = PegasusConfig
lowercase__ = {}
lowercase__ = 'gelu'
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=False , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a=0.1 , __a=0.1 , __a=20 , __a=2 , __a=1 , __a=0 , ) -> int:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = eos_token_id
_UpperCamelCase = pad_token_id
_UpperCamelCase = bos_token_id
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size).clip(3 , self.vocab_size)
_UpperCamelCase = np.expand_dims(np.array([self.eos_token_id] * self.batch_size) , 1)
_UpperCamelCase = np.concatenate([input_ids, eos_tensor] , axis=1)
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCamelCase = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_UpperCamelCase = prepare_pegasus_inputs_dict(__a , __a , __a)
return config, inputs_dict
def UpperCAmelCase ( self , __a , __a , __a) -> Tuple:
'''simple docstring'''
_UpperCamelCase = 20
_UpperCamelCase = model_class_name(__a)
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''])
_UpperCamelCase , _UpperCamelCase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
_UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a)
_UpperCamelCase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''')
_UpperCamelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCamelCase = model.decode(
decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , )
_UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''')
_UpperCamelCase = model.decode(
decoder_input_ids[:, -1:] , __a , decoder_attention_mask=__a , past_key_values=outputs_cache.past_key_values , decoder_position_ids=__a , )
_UpperCamelCase = model.decode(__a , __a)
_UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''')
def UpperCAmelCase ( self , __a , __a , __a) -> Tuple:
'''simple docstring'''
_UpperCamelCase = 20
_UpperCamelCase = model_class_name(__a)
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''])
_UpperCamelCase , _UpperCamelCase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
_UpperCamelCase = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
_UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a)
_UpperCamelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCamelCase = model.decode(
decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , )
_UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''')
_UpperCamelCase = model.decode(
decoder_input_ids[:, -1:] , __a , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=__a , decoder_position_ids=__a , )
_UpperCamelCase = model.decode(__a , __a , decoder_attention_mask=__a)
_UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''')
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=None, __snake_case=None, ) -> Union[str, Any]:
"""simple docstring"""
if attention_mask is None:
_UpperCamelCase = np.not_equal(__snake_case, config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
_UpperCamelCase = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape, dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:], config.pad_token_id ).astype(np.inta ),
], axis=-1, )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class _UpperCAmelCase( lowerCamelCase , unittest.TestCase ):
lowercase__ = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
lowercase__ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
lowercase__ = True
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = FlaxPegasusModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(__a , __a , __a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(__a , __a , __a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
_UpperCamelCase = self._prepare_for_class(__a , __a)
_UpperCamelCase = model_class(__a)
@jax.jit
def encode_jitted(__a , __a=None , **__a):
return model.encode(input_ids=__a , attention_mask=__a)
with self.subTest('''JIT Enabled'''):
_UpperCamelCase = encode_jitted(**__a).to_tuple()
with self.subTest('''JIT Disabled'''):
with jax.disable_jit():
_UpperCamelCase = encode_jitted(**__a).to_tuple()
self.assertEqual(len(__a) , len(__a))
for jitted_output, output in zip(__a , __a):
self.assertEqual(jitted_output.shape , output.shape)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
_UpperCamelCase = model_class(__a)
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask'''])
_UpperCamelCase = {
'''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
'''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
'''encoder_outputs''': encoder_outputs,
}
@jax.jit
def decode_jitted(__a , __a , __a):
return model.decode(
decoder_input_ids=__a , decoder_attention_mask=__a , encoder_outputs=__a , )
with self.subTest('''JIT Enabled'''):
_UpperCamelCase = decode_jitted(**__a).to_tuple()
with self.subTest('''JIT Disabled'''):
with jax.disable_jit():
_UpperCamelCase = decode_jitted(**__a).to_tuple()
self.assertEqual(len(__a) , len(__a))
for jitted_output, output in zip(__a , __a):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
for model_class_name in self.all_model_classes:
_UpperCamelCase = model_class_name.from_pretrained('''google/pegasus-large''' , from_pt=__a)
_UpperCamelCase = np.ones((1, 1))
_UpperCamelCase = model(__a)
self.assertIsNotNone(__a)
@slow
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''')
_UpperCamelCase = PegasusTokenizer.from_pretrained('''google/pegasus-xsum''')
_UpperCamelCase = [
''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
]
_UpperCamelCase = [
'''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
'''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
]
_UpperCamelCase = tokenizer(__a , return_tensors='''np''' , truncation=__a , max_length=5_12 , padding=__a)
_UpperCamelCase = model.generate(**__a , num_beams=2).sequences
_UpperCamelCase = tokenizer.batch_decode(__a , skip_special_tokens=__a)
assert tgt_text == decoded
| 19 | 0 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class lowerCamelCase__ ( unittest.TestCase , lowerCamelCase__):
'''simple docstring'''
def lowerCAmelCase__ (self ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase__ : int = load_tool('''text-classification''' )
self.tool.setup()
lowerCAmelCase__ : List[str] = load_tool('''text-classification''' ,remote=__a )
def lowerCAmelCase__ (self ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase__ : Any = self.tool('''That\'s quite cool''' ,['''positive''', '''negative'''] )
self.assertEqual(__a ,'''positive''' )
def lowerCAmelCase__ (self ) -> int:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = self.remote_tool('''That\'s quite cool''' ,['''positive''', '''negative'''] )
self.assertEqual(__a ,'''positive''' )
def lowerCAmelCase__ (self ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ : List[Any] = self.tool(text='''That\'s quite cool''' ,labels=['''positive''', '''negative'''] )
self.assertEqual(__a ,'''positive''' )
def lowerCAmelCase__ (self ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = self.remote_tool(text='''That\'s quite cool''' ,labels=['''positive''', '''negative'''] )
self.assertEqual(__a ,'''positive''' )
| 647 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
BertConfig,
DPRConfig,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
class _UpperCAmelCase:
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=2 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=5_12 , __a=16 , __a=2 , __a=0.02 , __a=3 , __a=4 , __a=None , __a=0 , ) -> Any:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_mask
_UpperCamelCase = use_token_type_ids
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = type_vocab_size
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = num_labels
_UpperCamelCase = num_choices
_UpperCamelCase = scope
_UpperCamelCase = projection_dim
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCamelCase = None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length])
_UpperCamelCase = None
if self.use_token_type_ids:
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
_UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices)
_UpperCamelCase = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__a , initializer_range=self.initializer_range , )
_UpperCamelCase = DPRConfig(projection_dim=self.projection_dim , **config.to_dict())
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = TFDPRContextEncoder(config=__a)
_UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a)
_UpperCamelCase = model(__a , token_type_ids=__a)
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size))
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = TFDPRQuestionEncoder(config=__a)
_UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a)
_UpperCamelCase = model(__a , token_type_ids=__a)
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size))
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Dict:
'''simple docstring'''
_UpperCamelCase = TFDPRReader(config=__a)
_UpperCamelCase = model(__a , attention_mask=__a)
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,))
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {'''input_ids''': input_ids}
return config, inputs_dict
@require_tf
class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase__ = (
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
lowercase__ = {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = TFDPRModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a , hidden_size=37)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*__a)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*__a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*__a)
@slow
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRQuestionEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRReader.from_pretrained(__a)
self.assertIsNotNone(__a)
@require_tf
class _UpperCAmelCase( unittest.TestCase ):
@slow
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base''')
_UpperCamelCase = tf.constant(
[[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]]) # [CLS] hello, is my dog cute? [SEP]
_UpperCamelCase = model(__a)[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
_UpperCamelCase = tf.constant(
[
[
0.0323_6253,
0.1275_3335,
0.1681_8509,
0.0027_9786,
0.389_6933,
0.2426_4945,
0.217_8971,
-0.0233_5227,
-0.0848_1959,
-0.1432_4117,
]
])
self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1e-4))
| 19 | 0 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def UpperCAmelCase_ (__a : Tuple , __a : List[str] , __a : Tuple , __a : Tuple , __a : List[str] ):
"""simple docstring"""
_a : str = int(np.ceil((x_end - xa) / step_size ) )
_a : Optional[int] = np.zeros((n + 1,) )
_a : str = ya
_a : Dict = xa
for k in range(__snake_case ):
_a : List[Any] = y[k] + step_size * ode_func(__snake_case , y[k] )
_a : List[Any] = y[k] + (
(step_size / 2) * (ode_func(__snake_case , y[k] ) + ode_func(x + step_size , __snake_case ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 229 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowerCamelCase__ ( __snake_case ) -> Optional[Any]:
"""simple docstring"""
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
or (cp >= 0x3400 and cp <= 0x4DBF) #
or (cp >= 0x2_0000 and cp <= 0x2_A6DF) #
or (cp >= 0x2_A700 and cp <= 0x2_B73F) #
or (cp >= 0x2_B740 and cp <= 0x2_B81F) #
or (cp >= 0x2_B820 and cp <= 0x2_CEAF) #
or (cp >= 0xF900 and cp <= 0xFAFF)
or (cp >= 0x2_F800 and cp <= 0x2_FA1F) #
): #
return True
return False
def lowerCamelCase__ ( __snake_case ) -> Optional[Any]:
"""simple docstring"""
for char in word:
_UpperCamelCase = ord(__snake_case )
if not _is_chinese_char(__snake_case ):
return 0
return 1
def lowerCamelCase__ ( __snake_case ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = set()
for token in tokens:
_UpperCamelCase = len(__snake_case ) > 1 and is_chinese(__snake_case )
if chinese_word:
word_set.add(__snake_case )
_UpperCamelCase = list(__snake_case )
return word_list
def lowerCamelCase__ ( __snake_case, __snake_case ) -> int:
"""simple docstring"""
if not chinese_word_set:
return bert_tokens
_UpperCamelCase = max([len(__snake_case ) for w in chinese_word_set] )
_UpperCamelCase = bert_tokens
_UpperCamelCase , _UpperCamelCase = 0, len(__snake_case )
while start < end:
_UpperCamelCase = True
if is_chinese(bert_word[start] ):
_UpperCamelCase = min(end - start, __snake_case )
for i in range(__snake_case, 1, -1 ):
_UpperCamelCase = ''''''.join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1, start + i ):
_UpperCamelCase = '''##''' + bert_word[j]
_UpperCamelCase = start + i
_UpperCamelCase = False
break
if single_word:
start += 1
return bert_word
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = []
for i in range(0, len(__snake_case ), 1_00 ):
_UpperCamelCase = ltp_tokenizer.pipeline(lines[i : i + 1_00], tasks=['''cws'''] ).cws
_UpperCamelCase = [get_chinese_word(__snake_case ) for r in res]
ltp_res.extend(__snake_case )
assert len(__snake_case ) == len(__snake_case )
_UpperCamelCase = []
for i in range(0, len(__snake_case ), 1_00 ):
_UpperCamelCase = bert_tokenizer(lines[i : i + 1_00], add_special_tokens=__snake_case, truncation=__snake_case, max_length=5_12 )
bert_res.extend(res['''input_ids'''] )
assert len(__snake_case ) == len(__snake_case )
_UpperCamelCase = []
for input_ids, chinese_word in zip(__snake_case, __snake_case ):
_UpperCamelCase = []
for id in input_ids:
_UpperCamelCase = bert_tokenizer._convert_id_to_token(__snake_case )
input_tokens.append(__snake_case )
_UpperCamelCase = add_sub_symbol(__snake_case, __snake_case )
_UpperCamelCase = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__snake_case ):
if token[:2] == "##":
_UpperCamelCase = token[2:]
# save chinese tokens' pos
if len(__snake_case ) == 1 and _is_chinese_char(ord(__snake_case ) ):
ref_id.append(__snake_case )
ref_ids.append(__snake_case )
assert len(__snake_case ) == len(__snake_case )
return ref_ids
def lowerCamelCase__ ( __snake_case ) -> Optional[int]:
"""simple docstring"""
with open(args.file_name, '''r''', encoding='''utf-8''' ) as f:
_UpperCamelCase = f.readlines()
_UpperCamelCase = [line.strip() for line in data if len(__snake_case ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
_UpperCamelCase = LTP(args.ltp ) # faster in GPU device
_UpperCamelCase = BertTokenizer.from_pretrained(args.bert )
_UpperCamelCase = prepare_ref(__snake_case, __snake_case, __snake_case )
with open(args.save_path, '''w''', encoding='''utf-8''' ) as f:
_UpperCamelCase = [json.dumps(__snake_case ) + '''\n''' for ref in ref_ids]
f.writelines(__snake_case )
if __name__ == "__main__":
_a = argparse.ArgumentParser(description="""prepare_chinese_ref""")
parser.add_argument(
"""--file_name""",
required=False,
type=str,
default="""./resources/chinese-demo.txt""",
help="""file need process, same as training data in lm""",
)
parser.add_argument(
"""--ltp""",
required=False,
type=str,
default="""./resources/ltp""",
help="""resources for LTP tokenizer, usually a path""",
)
parser.add_argument(
"""--bert""",
required=False,
type=str,
default="""./resources/robert""",
help="""resources for Bert tokenizer""",
)
parser.add_argument(
"""--save_path""",
required=False,
type=str,
default="""./resources/ref.txt""",
help="""path to save res""",
)
_a = parser.parse_args()
main(args)
| 19 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_lowerCamelCase : Tuple = logging.get_logger(__name__)
def __lowerCamelCase (UpperCAmelCase__ : Union[str, Any] ):
if isinstance(__snake_case , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(__snake_case , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(__snake_case ):
return [[videos]]
raise ValueError(F"Could not make batched video from {videos}" )
class lowercase ( a ):
lowercase__ : List[str] = ["""pixel_values"""]
def __init__( self : Optional[Any] , _UpperCamelCase : Dict = True , _UpperCamelCase : Optional[int] = None , _UpperCamelCase : List[Any] = PILImageResampling.BILINEAR , _UpperCamelCase : Optional[Any] = True , _UpperCamelCase : Tuple = None , _UpperCamelCase : str = True , _UpperCamelCase : int = 1 / 255 , _UpperCamelCase : Any = True , _UpperCamelCase : str = None , _UpperCamelCase : List[Any] = None , **_UpperCamelCase : Dict , ) -> None:
'''simple docstring'''
super().__init__(**__a )
SCREAMING_SNAKE_CASE = size if size is not None else {"shortest_edge": 224}
SCREAMING_SNAKE_CASE = get_size_dict(__a , default_to_square=__a )
SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else {"height": 224, "width": 224}
SCREAMING_SNAKE_CASE = get_size_dict(__a , param_name="crop_size" )
SCREAMING_SNAKE_CASE = do_resize
SCREAMING_SNAKE_CASE = size
SCREAMING_SNAKE_CASE = do_center_crop
SCREAMING_SNAKE_CASE = crop_size
SCREAMING_SNAKE_CASE = resample
SCREAMING_SNAKE_CASE = do_rescale
SCREAMING_SNAKE_CASE = rescale_factor
SCREAMING_SNAKE_CASE = do_normalize
SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
SCREAMING_SNAKE_CASE = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __snake_case( self : Union[str, Any] , _UpperCamelCase : Optional[int] , _UpperCamelCase : int , _UpperCamelCase : Tuple = PILImageResampling.BILINEAR , _UpperCamelCase : Optional[int] = None , **_UpperCamelCase : str , ) -> np.ndarray:
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_size_dict(__a , default_to_square=__a )
if "shortest_edge" in size:
SCREAMING_SNAKE_CASE = get_resize_output_image_size(__a , size["shortest_edge"] , default_to_square=__a )
elif "height" in size and "width" in size:
SCREAMING_SNAKE_CASE = (size["height"], size["width"])
else:
raise ValueError(F"Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}" )
return resize(__a , size=__a , resample=__a , data_format=__a , **__a )
def __snake_case( self : List[str] , _UpperCamelCase : Any , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Tuple = None , **_UpperCamelCase : Optional[int] , ) -> np.ndarray:
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_size_dict(__a )
if "height" not in size or "width" not in size:
raise ValueError(F"Size must have \'height\' and \'width\' as keys. Got {size.keys()}" )
return center_crop(__a , size=(size["height"], size["width"]) , data_format=__a , **__a )
def __snake_case( self : Union[str, Any] , _UpperCamelCase : Dict , _UpperCamelCase : Dict , _UpperCamelCase : Tuple = None , **_UpperCamelCase : Any , ) -> Dict:
'''simple docstring'''
return rescale(__a , scale=__a , data_format=__a , **__a )
def __snake_case( self : Dict , _UpperCamelCase : int , _UpperCamelCase : Tuple , _UpperCamelCase : Dict , _UpperCamelCase : Any = None , **_UpperCamelCase : Any , ) -> np.ndarray:
'''simple docstring'''
return normalize(__a , mean=__a , std=__a , data_format=__a , **__a )
def __snake_case( self : str , _UpperCamelCase : int , _UpperCamelCase : Tuple = None , _UpperCamelCase : Optional[int] = None , _UpperCamelCase : Dict = None , _UpperCamelCase : Dict = None , _UpperCamelCase : List[str] = None , _UpperCamelCase : Tuple = None , _UpperCamelCase : Dict = None , _UpperCamelCase : Optional[int] = None , _UpperCamelCase : int = None , _UpperCamelCase : List[str] = None , _UpperCamelCase : Optional[Any] = ChannelDimension.FIRST , ) -> np.ndarray:
'''simple docstring'''
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE = to_numpy_array(__a )
if do_resize:
SCREAMING_SNAKE_CASE = self.resize(image=__a , size=__a , resample=__a )
if do_center_crop:
SCREAMING_SNAKE_CASE = self.center_crop(__a , size=__a )
if do_rescale:
SCREAMING_SNAKE_CASE = self.rescale(image=__a , scale=__a )
if do_normalize:
SCREAMING_SNAKE_CASE = self.normalize(image=__a , mean=__a , std=__a )
SCREAMING_SNAKE_CASE = to_channel_dimension_format(__a , __a )
return image
def __snake_case( self : List[str] , _UpperCamelCase : List[Any] , _UpperCamelCase : Tuple = None , _UpperCamelCase : Tuple = None , _UpperCamelCase : Any = None , _UpperCamelCase : List[str] = None , _UpperCamelCase : Any = None , _UpperCamelCase : Any = None , _UpperCamelCase : Tuple = None , _UpperCamelCase : Tuple = None , _UpperCamelCase : Tuple = None , _UpperCamelCase : List[Any] = None , _UpperCamelCase : Dict = None , _UpperCamelCase : Dict = ChannelDimension.FIRST , **_UpperCamelCase : str , ) -> PIL.Image.Image:
'''simple docstring'''
SCREAMING_SNAKE_CASE = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE = do_center_crop if do_center_crop is not None else self.do_center_crop
SCREAMING_SNAKE_CASE = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE = size if size is not None else self.size
SCREAMING_SNAKE_CASE = get_size_dict(__a , default_to_square=__a )
SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE = get_size_dict(__a , param_name="crop_size" )
if not valid_images(__a ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
SCREAMING_SNAKE_CASE = make_batched(__a )
SCREAMING_SNAKE_CASE = [
[
self._preprocess_image(
image=__a , do_resize=__a , size=__a , resample=__a , do_center_crop=__a , crop_size=__a , do_rescale=__a , rescale_factor=__a , do_normalize=__a , image_mean=__a , image_std=__a , data_format=__a , )
for img in video
]
for video in videos
]
SCREAMING_SNAKE_CASE = {"pixel_values": videos}
return BatchFeature(data=__a , tensor_type=__a )
| 403 |
"""simple docstring"""
import heapq
def lowerCamelCase__ ( __snake_case ) -> set[int]:
"""simple docstring"""
_UpperCamelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(__snake_case, [-1 * len(__snake_case ), (key, value)] )
# chosen_vertices = set of chosen vertices
_UpperCamelCase = set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
_UpperCamelCase = heapq.heappop(__snake_case )[1][0]
chosen_vertices.add(__snake_case )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
_UpperCamelCase = elem[1][1].index(__snake_case )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(__snake_case )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
_a = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
| 19 | 0 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import BaseTransformersCLICommand
def snake_case_ (UpperCamelCase : Dict ):
'''simple docstring'''
return EnvironmentCommand()
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
return EnvironmentCommand(args.accelerate_config_file )
class A ( _a ):
@staticmethod
def __lowerCAmelCase ( lowerCAmelCase_ : Any ) -> Union[str, Any]:
"""simple docstring"""
_a = parser.add_parser('''env''' )
download_parser.set_defaults(func=__a )
download_parser.add_argument(
'''--accelerate-config_file''' , default=__a , help='''The accelerate config file to use for the default values in the launching script.''' , )
download_parser.set_defaults(func=__a )
def __init__( self : Tuple , lowerCAmelCase_ : List[Any] , *lowerCAmelCase_ : List[Any] ) -> None:
"""simple docstring"""
_a = accelerate_config_file
def __lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
_a = '''not installed'''
if is_safetensors_available():
import safetensors
_a = safetensors.__version__
elif importlib.util.find_spec('''safetensors''' ) is not None:
import safetensors
_a = F'{safetensors.__version__} but is ignored because of PyTorch version too old.'
_a = '''not installed'''
_a = _a = '''not found'''
if is_accelerate_available():
import accelerate
from accelerate.commands.config import default_config_file, load_config_from_file
_a = accelerate.__version__
# Get the default from the config file.
if self._accelerate_config_file is not None or os.path.isfile(__a ):
_a = load_config_from_file(self._accelerate_config_file ).to_dict()
_a = (
'''\n'''.join([F'\t- {prop}: {val}' for prop, val in accelerate_config.items()] )
if isinstance(__a , __a )
else F'\t{accelerate_config}'
)
_a = '''not installed'''
_a = '''NA'''
if is_torch_available():
import torch
_a = torch.__version__
_a = torch.cuda.is_available()
_a = '''not installed'''
_a = '''NA'''
if is_tf_available():
import tensorflow as tf
_a = tf.__version__
try:
# deprecated in v2.1
_a = tf.test.is_gpu_available()
except AttributeError:
# returns list of devices, convert to bool
_a = bool(tf.config.list_physical_devices('''GPU''' ) )
_a = '''not installed'''
_a = '''not installed'''
_a = '''not installed'''
_a = '''NA'''
if is_flax_available():
import flax
import jax
import jaxlib
_a = flax.__version__
_a = jax.__version__
_a = jaxlib.__version__
_a = jax.lib.xla_bridge.get_backend().platform
_a = {
'''`transformers` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''Huggingface_hub version''': huggingface_hub.__version__,
'''Safetensors version''': F'{safetensors_version}',
'''Accelerate version''': F'{accelerate_version}',
'''Accelerate config''': F'{accelerate_config_str}',
'''PyTorch version (GPU?)''': F'{pt_version} ({pt_cuda_available})',
'''Tensorflow version (GPU?)''': F'{tf_version} ({tf_cuda_available})',
'''Flax version (CPU?/GPU?/TPU?)''': F'{flax_version} ({jax_backend})',
'''Jax version''': F'{jax_version}',
'''JaxLib version''': F'{jaxlib_version}',
'''Using GPU in script?''': '''<fill in>''',
'''Using distributed or parallel set-up in script?''': '''<fill in>''',
}
print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n''' )
print(self.format_dict(__a ) )
return info
@staticmethod
def __lowerCAmelCase ( lowerCAmelCase_ : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return "\n".join([F'- {prop}: {val}' for prop, val in d.items()] ) + "\n"
| 22 |
"""simple docstring"""
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def lowerCamelCase__ ( ) -> List[str]:
"""simple docstring"""
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
_UpperCamelCase = '''__test_patch_submodule_mock__'''
with patch_submodule(_test_patching, '''os.path.join''', __snake_case ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os, _PatchedModuleObj )
assert isinstance(_test_patching.os.path, _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path, _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os, _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path, _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path, _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def lowerCamelCase__ ( ) -> List[str]:
"""simple docstring"""
assert _test_patching.open is open
_UpperCamelCase = '''__test_patch_submodule_builtin_mock__'''
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching, '''open''', __snake_case ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def lowerCamelCase__ ( ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_missing_mock__'''
with patch_submodule(_test_patching, '''pandas.read_csv''', __snake_case ):
pass
def lowerCamelCase__ ( ) -> Dict:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_missing_builtin_mock__'''
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching, '''len''', __snake_case ) is None
with patch_submodule(_test_patching, '''len''', __snake_case ):
assert _test_patching.len is mock
assert _test_patching.len is len
def lowerCamelCase__ ( ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_start_and_stop_mock__'''
_UpperCamelCase = patch_submodule(_test_patching, '''open''', __snake_case )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def lowerCamelCase__ ( ) -> Optional[int]:
"""simple docstring"""
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
_UpperCamelCase = '''__test_patch_submodule_successive_join__'''
_UpperCamelCase = '''__test_patch_submodule_successive_dirname__'''
_UpperCamelCase = '''__test_patch_submodule_successive_rename__'''
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching, '''os.path.join''', __snake_case ):
with patch_submodule(_test_patching, '''os.rename''', __snake_case ):
with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching, '''os.rename''', __snake_case ):
with patch_submodule(_test_patching, '''os.path.join''', __snake_case ):
with patch_submodule(_test_patching, '''os.path.dirname''', __snake_case ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def lowerCamelCase__ ( ) -> str:
"""simple docstring"""
_UpperCamelCase = '''__test_patch_submodule_doesnt_exist_mock__'''
with patch_submodule(_test_patching, '''__module_that_doesn_exist__.__attribute_that_doesn_exist__''', __snake_case ):
pass
with patch_submodule(_test_patching, '''os.__attribute_that_doesn_exist__''', __snake_case ):
pass
| 19 | 0 |
'''simple docstring'''
from __future__ import annotations
def __a ( A__ ) -> None:
create_state_space_tree(__snake_case , [] , 0 , [0 for i in range(len(__snake_case ) )] )
def __a ( A__ , A__ , A__ , A__ , ) -> None:
if index == len(__snake_case ):
print(__snake_case )
return
for i in range(len(__snake_case ) ):
if not index_used[i]:
current_sequence.append(sequence[i] )
lowerCAmelCase = True
create_state_space_tree(__snake_case , __snake_case , index + 1 , __snake_case )
current_sequence.pop()
lowerCAmelCase = False
lowercase : Union[str, Any] = [3, 1, 2, 4]
generate_all_permutations(sequence)
lowercase : List[str] = ['A', 'B', 'C']
generate_all_permutations(sequence_a)
| 649 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = original_name.split('''.''' )[0]
_UpperCamelCase = key.split('''.''' )
_UpperCamelCase = int(key_list[key_list.index(__snake_case ) - 2] )
_UpperCamelCase = int(key_list[key_list.index(__snake_case ) - 1] )
_UpperCamelCase = orig_block_num - offset
_UpperCamelCase = key.replace(F'''{orig_block_num}.{layer_num}.{original_name}''', F'''block.{new_block_num}.{layer_num}.{new_name}''' )
return key
def lowerCamelCase__ ( __snake_case ) -> str:
"""simple docstring"""
_UpperCamelCase = OrderedDict()
_UpperCamelCase , _UpperCamelCase = 0, 0
for key, value in state_dict.items():
if key.startswith('''network''' ):
_UpperCamelCase = key.replace('''network''', '''poolformer.encoder''' )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith('''bias''' ) and "patch_embed" not in key:
patch_emb_offset += 1
_UpperCamelCase = key[: key.find('''proj''' )]
_UpperCamelCase = key.replace(__snake_case, F'''patch_embeddings.{total_embed_found}.''' )
_UpperCamelCase = key.replace('''proj''', '''projection''' )
if key.endswith('''bias''' ):
total_embed_found += 1
if "patch_embeddings" in key:
_UpperCamelCase = '''poolformer.encoder.''' + key
if "mlp.fc1" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''mlp.fc1''', '''output.conv1''' )
if "mlp.fc2" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''mlp.fc2''', '''output.conv2''' )
if "norm1" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''norm1''', '''before_norm''' )
if "norm2" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''norm2''', '''after_norm''' )
if "layer_scale_1" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''layer_scale_1''', '''layer_scale_1''' )
if "layer_scale_2" in key:
_UpperCamelCase = replace_key_with_offset(__snake_case, __snake_case, '''layer_scale_2''', '''layer_scale_2''' )
if "head" in key:
_UpperCamelCase = key.replace('''head''', '''classifier''' )
_UpperCamelCase = value
return new_state_dict
def lowerCamelCase__ ( ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_UpperCamelCase = Image.open(requests.get(__snake_case, stream=__snake_case ).raw )
return image
@torch.no_grad()
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> int:
"""simple docstring"""
_UpperCamelCase = PoolFormerConfig()
# set attributes based on model_name
_UpperCamelCase = '''huggingface/label-files'''
_UpperCamelCase = model_name[-3:]
_UpperCamelCase = 10_00
_UpperCamelCase = '''imagenet-1k-id2label.json'''
_UpperCamelCase = (1, 10_00)
# set config attributes
_UpperCamelCase = json.load(open(hf_hub_download(__snake_case, __snake_case, repo_type='''dataset''' ), '''r''' ) )
_UpperCamelCase = {int(__snake_case ): v for k, v in idalabel.items()}
_UpperCamelCase = idalabel
_UpperCamelCase = {v: k for k, v in idalabel.items()}
if size == "s12":
_UpperCamelCase = [2, 2, 6, 2]
_UpperCamelCase = [64, 1_28, 3_20, 5_12]
_UpperCamelCase = 4.0
_UpperCamelCase = 0.9
elif size == "s24":
_UpperCamelCase = [4, 4, 12, 4]
_UpperCamelCase = [64, 1_28, 3_20, 5_12]
_UpperCamelCase = 4.0
_UpperCamelCase = 0.9
elif size == "s36":
_UpperCamelCase = [6, 6, 18, 6]
_UpperCamelCase = [64, 1_28, 3_20, 5_12]
_UpperCamelCase = 4.0
_UpperCamelCase = 1e-6
_UpperCamelCase = 0.9
elif size == "m36":
_UpperCamelCase = [6, 6, 18, 6]
_UpperCamelCase = [96, 1_92, 3_84, 7_68]
_UpperCamelCase = 4.0
_UpperCamelCase = 1e-6
_UpperCamelCase = 0.95
elif size == "m48":
_UpperCamelCase = [8, 8, 24, 8]
_UpperCamelCase = [96, 1_92, 3_84, 7_68]
_UpperCamelCase = 4.0
_UpperCamelCase = 1e-6
_UpperCamelCase = 0.95
else:
raise ValueError(F'''Size {size} not supported''' )
# load image processor
_UpperCamelCase = PoolFormerImageProcessor(crop_pct=__snake_case )
# Prepare image
_UpperCamelCase = prepare_img()
_UpperCamelCase = image_processor(images=__snake_case, return_tensors='''pt''' ).pixel_values
logger.info(F'''Converting model {model_name}...''' )
# load original state dict
_UpperCamelCase = torch.load(__snake_case, map_location=torch.device('''cpu''' ) )
# rename keys
_UpperCamelCase = rename_keys(__snake_case )
# create HuggingFace model and load state dict
_UpperCamelCase = PoolFormerForImageClassification(__snake_case )
model.load_state_dict(__snake_case )
model.eval()
# Define image processor
_UpperCamelCase = PoolFormerImageProcessor(crop_pct=__snake_case )
_UpperCamelCase = image_processor(images=prepare_img(), return_tensors='''pt''' ).pixel_values
# forward pass
_UpperCamelCase = model(__snake_case )
_UpperCamelCase = outputs.logits
# define expected logit slices for different models
if size == "s12":
_UpperCamelCase = torch.tensor([-0.3045, -0.6758, -0.4869] )
elif size == "s24":
_UpperCamelCase = torch.tensor([0.4402, -0.1374, -0.8045] )
elif size == "s36":
_UpperCamelCase = torch.tensor([-0.6080, -0.5133, -0.5898] )
elif size == "m36":
_UpperCamelCase = torch.tensor([0.3952, 0.2263, -1.2668] )
elif size == "m48":
_UpperCamelCase = torch.tensor([0.1167, -0.0656, -0.3423] )
else:
raise ValueError(F'''Size {size} not supported''' )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3], __snake_case, atol=1e-2 )
# finally, save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(__snake_case ).mkdir(exist_ok=__snake_case )
model.save_pretrained(__snake_case )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__snake_case )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument(
"""--model_name""",
default="""poolformer_s12""",
type=str,
help="""Name of the model you'd like to convert.""",
)
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
_a = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 19 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : Dict = logging.get_logger(__name__)
UpperCamelCase__ : Tuple = '▁'
UpperCamelCase__ : Dict = {'vocab_file': 'sentencepiece.bpe.model'}
UpperCamelCase__ : Any = {
'vocab_file': {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
}
}
UpperCamelCase__ : Any = {
'facebook/xglm-564M': 2_048,
}
class _lowercase ( lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase_ : Dict = VOCAB_FILES_NAMES
UpperCAmelCase_ : List[str] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase_ : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase_ : Optional[Any] = ['''input_ids''', '''attention_mask''']
def __init__( self ,lowerCamelCase_ ,lowerCamelCase_="<s>" ,lowerCamelCase_="</s>" ,lowerCamelCase_="</s>" ,lowerCamelCase_="<s>" ,lowerCamelCase_="<unk>" ,lowerCamelCase_="<pad>" ,lowerCamelCase_ = None ,**lowerCamelCase_ ,) -> None:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
UpperCAmelCase__ : int = 7
UpperCAmelCase__ : Any = [f'''<madeupword{i}>''' for i in range(self.num_madeup_words )]
UpperCAmelCase__ : Any = kwargs.get('''additional_special_tokens''' ,[] )
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
super().__init__(
bos_token=__a ,eos_token=__a ,unk_token=__a ,sep_token=__a ,cls_token=__a ,pad_token=__a ,sp_model_kwargs=self.sp_model_kwargs ,**__a ,)
UpperCAmelCase__ : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__a ) )
UpperCAmelCase__ : List[Any] = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
UpperCAmelCase__ : Optional[Any] = 1
# Mimic fairseq token-to-id alignment for the first 4 token
UpperCAmelCase__ : Any = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
UpperCAmelCase__ : Dict = len(self.sp_model )
UpperCAmelCase__ : List[str] = {f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(__a )
UpperCAmelCase__ : int = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.__dict__.copy()
UpperCAmelCase__ : Dict = None
UpperCAmelCase__ : Union[str, Any] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self ,lowerCamelCase_ ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ : Tuple = d
# for backward compatibility
if not hasattr(self ,'''sp_model_kwargs''' ):
UpperCAmelCase__ : Dict = {}
UpperCAmelCase__ : Union[str, Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
UpperCAmelCase__ : int = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = None ,lowerCamelCase_ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__a ,token_ids_a=__a ,already_has_special_tokens=__a )
if token_ids_a is None:
return [1] + ([0] * len(__a ))
return [1] + ([0] * len(__a )) + [1, 1] + ([0] * len(__a ))
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = None ) -> List[int]:
'''simple docstring'''
UpperCAmelCase__ : str = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a ) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
@property
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def lowerCAmelCase__ ( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase__ : Dict = {self.convert_ids_to_tokens(__a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(__a ,out_type=__a )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> Union[str, Any]:
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCAmelCase__ : Any = self.sp_model.PieceToId(__a )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> Tuple:
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def lowerCAmelCase__ ( self ,lowerCamelCase_ ) -> int:
'''simple docstring'''
UpperCAmelCase__ : Tuple = ''''''.join(__a ).replace(__a ,''' ''' ).strip()
return out_string
def lowerCAmelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(__a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase__ : List[Any] = os.path.join(
__a ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,__a )
elif not os.path.isfile(self.vocab_file ):
with open(__a ,'''wb''' ) as fi:
UpperCAmelCase__ : Tuple = self.sp_model.serialized_model_proto()
fi.write(__a )
return (out_vocab_file,)
| 614 |
"""simple docstring"""
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = (DPMSolverSDEScheduler,)
lowercase__ = 10
def UpperCAmelCase ( self , **__a) -> int:
'''simple docstring'''
_UpperCamelCase = {
'''num_train_timesteps''': 11_00,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''noise_sampler_seed''': 0,
}
config.update(**__a)
return config
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02]):
self.check_over_configs(beta_start=__a , beta_end=__a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__a)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__a)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
scheduler.set_timesteps(self.num_inference_steps)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCamelCase = sample.to(__a)
for i, t in enumerate(scheduler.timesteps):
_UpperCamelCase = scheduler.scale_model_input(__a , __a)
_UpperCamelCase = model(__a , __a)
_UpperCamelCase = scheduler.step(__a , __a , __a)
_UpperCamelCase = output.prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47_8210_4492_1875) < 1e-2
assert abs(result_mean.item() - 0.2178_7059_6456_5277) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3521_1181_6406) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9068_9229_9652) < 1e-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''')
_UpperCamelCase = scheduler_class(**__a)
scheduler.set_timesteps(self.num_inference_steps)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCamelCase = sample.to(__a)
for i, t in enumerate(scheduler.timesteps):
_UpperCamelCase = scheduler.scale_model_input(__a , __a)
_UpperCamelCase = model(__a , __a)
_UpperCamelCase = scheduler.step(__a , __a , __a)
_UpperCamelCase = output.prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77_1492_0043_9453) < 1e-2
assert abs(result_mean.item() - 0.1_6226_2890_1481_6284) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1_6633_6059_5703) < 1e-2
assert abs(result_mean.item() - 0.1_6688_3260_0116_7297) < 1e-3
else:
assert abs(result_sum.item() - 119.8_4875_4882_8125) < 1e-2
assert abs(result_mean.item() - 0.1560_5306_6253_6621) < 1e-3
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
scheduler.set_timesteps(self.num_inference_steps , device=__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
_UpperCamelCase = scheduler.scale_model_input(__a , __a)
_UpperCamelCase = model(__a , __a)
_UpperCamelCase = scheduler.step(__a , __a , __a)
_UpperCamelCase = output.prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46_9573_9746_0938) < 1e-2
assert abs(result_mean.item() - 0.2_1805_9346_0798_2635) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3536_3769_5312) < 1e-2
assert abs(result_mean.item() - 0.2_2342_9083_8241_5771) < 1e-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a , use_karras_sigmas=__a)
scheduler.set_timesteps(self.num_inference_steps , device=__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma
_UpperCamelCase = sample.to(__a)
for t in scheduler.timesteps:
_UpperCamelCase = scheduler.scale_model_input(__a , __a)
_UpperCamelCase = model(__a , __a)
_UpperCamelCase = scheduler.step(__a , __a , __a)
_UpperCamelCase = output.prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66_9741_3574_2188) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63_6535_6445_3125) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
else:
assert abs(result_sum.item() - 170.3_1352_2338_8672) < 1e-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
| 19 | 0 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase =logging.get_logger(__name__)
lowercase ={
'microsoft/xprophetnet-large-wiki100-cased': (
'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'
),
}
class __magic_name__ ( lowerCAmelCase ):
UpperCAmelCase ="xlm-prophetnet"
UpperCAmelCase =["past_key_values"]
UpperCAmelCase ={
"num_attention_heads": "num_encoder_attention_heads",
}
def __init__( self , snake_case = 0.1 , snake_case = "gelu" , snake_case = 3_0_5_2_2 , snake_case = 1_0_2_4 , snake_case = 4_0_9_6 , snake_case = 1_2 , snake_case = 1_6 , snake_case = 4_0_9_6 , snake_case = 1_2 , snake_case = 1_6 , snake_case = 0.1 , snake_case = 0.1 , snake_case = 5_1_2 , snake_case = 0.02 , snake_case = True , snake_case = True , snake_case = 0 , snake_case = 2 , snake_case = 3_2 , snake_case = 1_2_8 , snake_case = False , snake_case = 0.0 , snake_case = True , snake_case = 0 , snake_case = 1 , snake_case = 2 , **snake_case , ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase : List[Any] =vocab_size
_UpperCAmelCase : int =hidden_size
_UpperCAmelCase : Dict =encoder_ffn_dim
_UpperCAmelCase : Tuple =num_encoder_layers
_UpperCAmelCase : Optional[int] =num_encoder_attention_heads
_UpperCAmelCase : str =decoder_ffn_dim
_UpperCAmelCase : Any =num_decoder_layers
_UpperCAmelCase : str =num_decoder_attention_heads
_UpperCAmelCase : List[str] =max_position_embeddings
_UpperCAmelCase : Dict =init_std # Normal(0, this parameter)
_UpperCAmelCase : Optional[Any] =activation_function
# parameters for xlmprophetnet
_UpperCAmelCase : Dict =ngram
_UpperCAmelCase : Dict =num_buckets
_UpperCAmelCase : List[str] =relative_max_distance
_UpperCAmelCase : Any =disable_ngram_loss
_UpperCAmelCase : int =eps
# 3 Types of Dropout
_UpperCAmelCase : List[str] =attention_dropout
_UpperCAmelCase : str =activation_dropout
_UpperCAmelCase : Dict =dropout
_UpperCAmelCase : List[str] =use_cache
super().__init__(
pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , is_encoder_decoder=__a , add_cross_attention=__a , decoder_start_token_id=__a , **__a , )
@property
def lowerCAmelCase ( self) -> int:
'''simple docstring'''
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def lowerCAmelCase ( self , snake_case) -> str:
'''simple docstring'''
raise NotImplementedError(
'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
' `num_decoder_layers`.')
| 446 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_batched,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_a = logging.get_logger(__name__)
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = ['pixel_values']
def __init__( self , __a = True , __a = None , __a = PILImageResampling.BICUBIC , __a = True , __a = True , __a = 1 / 2_55 , __a = None , __a = True , __a = None , __a = None , **__a , ) -> None:
'''simple docstring'''
super().__init__(**__a)
_UpperCamelCase = size if size is not None else {'''height''': 2_24, '''width''': 2_24}
_UpperCamelCase = get_size_dict(__a)
_UpperCamelCase = crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
_UpperCamelCase = get_size_dict(__a , default_to_square=__a , param_name='''crop_size''')
_UpperCamelCase = do_resize
_UpperCamelCase = do_rescale
_UpperCamelCase = do_normalize
_UpperCamelCase = do_center_crop
_UpperCamelCase = crop_size
_UpperCamelCase = size
_UpperCamelCase = resample
_UpperCamelCase = rescale_factor
_UpperCamelCase = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
_UpperCamelCase = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCAmelCase ( self , __a , __a , __a = PILImageResampling.BILINEAR , __a = None , **__a , ) -> np.ndarray:
'''simple docstring'''
_UpperCamelCase = get_size_dict(__a)
if "shortest_edge" in size:
_UpperCamelCase = get_resize_output_image_size(__a , size=size['''shortest_edge'''] , default_to_square=__a)
# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
elif "height" in size and "width" in size:
_UpperCamelCase = (size['''height'''], size['''width'''])
else:
raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''')
return resize(__a , size=__a , resample=__a , data_format=__a , **__a)
def UpperCAmelCase ( self , __a , __a , __a = None , **__a , ) -> np.ndarray:
'''simple docstring'''
_UpperCamelCase = get_size_dict(__a)
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''')
return center_crop(__a , size=(size['''height'''], size['''width''']) , data_format=__a , **__a)
def UpperCAmelCase ( self , __a , __a , __a = None , **__a) -> np.ndarray:
'''simple docstring'''
return rescale(__a , scale=__a , data_format=__a , **__a)
def UpperCAmelCase ( self , __a , __a , __a , __a = None , **__a , ) -> np.ndarray:
'''simple docstring'''
return normalize(__a , mean=__a , std=__a , data_format=__a , **__a)
def UpperCAmelCase ( self , __a , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = ChannelDimension.FIRST , **__a , ) -> BatchFeature:
'''simple docstring'''
_UpperCamelCase = do_resize if do_resize is not None else self.do_resize
_UpperCamelCase = do_rescale if do_rescale is not None else self.do_rescale
_UpperCamelCase = do_normalize if do_normalize is not None else self.do_normalize
_UpperCamelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
_UpperCamelCase = crop_size if crop_size is not None else self.crop_size
_UpperCamelCase = get_size_dict(__a , param_name='''crop_size''' , default_to_square=__a)
_UpperCamelCase = resample if resample is not None else self.resample
_UpperCamelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCamelCase = image_mean if image_mean is not None else self.image_mean
_UpperCamelCase = image_std if image_std is not None else self.image_std
_UpperCamelCase = size if size is not None else self.size
_UpperCamelCase = get_size_dict(__a)
if not is_batched(__a):
_UpperCamelCase = [images]
if not valid_images(__a):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''')
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''')
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''')
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''')
# All transformations expect numpy arrays.
_UpperCamelCase = [to_numpy_array(__a) for image in images]
if do_resize:
_UpperCamelCase = [self.resize(image=__a , size=__a , resample=__a) for image in images]
if do_center_crop:
_UpperCamelCase = [self.center_crop(image=__a , size=__a) for image in images]
if do_rescale:
_UpperCamelCase = [self.rescale(image=__a , scale=__a) for image in images]
if do_normalize:
_UpperCamelCase = [self.normalize(image=__a , mean=__a , std=__a) for image in images]
_UpperCamelCase = [to_channel_dimension_format(__a , __a) for image in images]
_UpperCamelCase = {'''pixel_values''': images}
return BatchFeature(data=__a , tensor_type=__a)
| 19 | 0 |
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowerCamelCase : Tuple = abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def _a ( SCREAMING_SNAKE_CASE__ : List[str] ) -> List[str]:
'''simple docstring'''
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__snake_case )
def _a ( SCREAMING_SNAKE_CASE__ : Tuple ) -> Union[str, Any]:
'''simple docstring'''
from diffusers.utils.testing_utils import pytest_terminal_summary_main
SCREAMING_SNAKE_CASE__ : str = terminalreporter.config.getoption("--make-reports" )
if make_reports:
pytest_terminal_summary_main(__snake_case , id=__snake_case )
| 663 |
"""simple docstring"""
# Imports
import numpy as np
class _UpperCAmelCase:
def __init__( self , __a=None , __a=None , __a=None , __a=None , __a=None) -> Dict:
'''simple docstring'''
self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a)
def UpperCAmelCase ( self , __a=None , __a=None , __a=None , __a=None , __a=None) -> Dict:
'''simple docstring'''
if red is not None:
_UpperCamelCase = red
if green is not None:
_UpperCamelCase = green
if blue is not None:
_UpperCamelCase = blue
if red_edge is not None:
_UpperCamelCase = red_edge
if nir is not None:
_UpperCamelCase = nir
return True
def UpperCAmelCase ( self , __a="" , __a=None , __a=None , __a=None , __a=None , __a=None) -> List[str]:
'''simple docstring'''
self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a)
_UpperCamelCase = {
'''ARVI2''': self.arvaa,
'''CCCI''': self.ccci,
'''CVI''': self.cvi,
'''GLI''': self.gli,
'''NDVI''': self.ndvi,
'''BNDVI''': self.bndvi,
'''redEdgeNDVI''': self.red_edge_ndvi,
'''GNDVI''': self.gndvi,
'''GBNDVI''': self.gbndvi,
'''GRNDVI''': self.grndvi,
'''RBNDVI''': self.rbndvi,
'''PNDVI''': self.pndvi,
'''ATSAVI''': self.atsavi,
'''BWDRVI''': self.bwdrvi,
'''CIgreen''': self.ci_green,
'''CIrededge''': self.ci_rededge,
'''CI''': self.ci,
'''CTVI''': self.ctvi,
'''GDVI''': self.gdvi,
'''EVI''': self.evi,
'''GEMI''': self.gemi,
'''GOSAVI''': self.gosavi,
'''GSAVI''': self.gsavi,
'''Hue''': self.hue,
'''IVI''': self.ivi,
'''IPVI''': self.ipvi,
'''I''': self.i,
'''RVI''': self.rvi,
'''MRVI''': self.mrvi,
'''MSAVI''': self.m_savi,
'''NormG''': self.norm_g,
'''NormNIR''': self.norm_nir,
'''NormR''': self.norm_r,
'''NGRDI''': self.ngrdi,
'''RI''': self.ri,
'''S''': self.s,
'''IF''': self._if,
'''DVI''': self.dvi,
'''TVI''': self.tvi,
'''NDRE''': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('''Index not in the list!''')
return False
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return self.nir * (self.red / (self.green**2))
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
return (self.nir - self.red) / (self.nir + self.red)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
return (self.nir - self.blue) / (self.nir + self.blue)
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return (self.redEdge - self.red) / (self.redEdge + self.red)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return (self.nir - self.green) / (self.nir + self.green)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def UpperCAmelCase ( self , __a=0.08 , __a=1.22 , __a=0.03) -> Optional[Any]:
'''simple docstring'''
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
return (self.nir / self.green) - 1
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return (self.nir / self.redEdge) - 1
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
return (self.red - self.blue) / self.red
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5))) * (abs(ndvi + 0.5) ** (1 / 2))
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return self.nir - self.green
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)
def UpperCAmelCase ( self , __a=0.16) -> Optional[Any]:
'''simple docstring'''
return (self.nir - self.green) / (self.nir + self.green + y)
def UpperCAmelCase ( self , __a=0.5) -> Dict:
'''simple docstring'''
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue))
def UpperCAmelCase ( self , __a=None , __a=None) -> Any:
'''simple docstring'''
return (self.nir - b) / (a * self.red)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
return (self.red + self.green + self.blue) / 30.5
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return self.nir / self.red
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return (self.rvi() - 1) / (self.rvi() + 1)
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return self.green / (self.nir + self.red + self.green)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
return self.nir / (self.nir + self.red + self.green)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return self.red / (self.nir + self.red + self.green)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return (self.green - self.red) / (self.green + self.red)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
return (self.red - self.green) / (self.red + self.green)
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = np.max([np.max(self.red), np.max(self.green), np.max(self.blue)])
_UpperCamelCase = np.min([np.min(self.red), np.min(self.green), np.min(self.blue)])
return (max_value - min_value) / max_value
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return self.nir / self.red
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return (self.ndvi() + 0.5) ** (1 / 2)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 19 | 0 |
from math import pi, sqrt
def lowerCamelCase_(lowerCamelCase_ ) -> float:
if num <= 0:
raise ValueError("math domain error" )
if num > 171.5:
raise OverflowError("math range error" )
elif num - int(__snake_case ) not in (0, 0.5):
raise NotImplementedError("num must be an integer or a half-integer" )
elif num == 0.5:
return sqrt(__snake_case )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def lowerCamelCase_() -> None:
assert gamma(0.5 ) == sqrt(__snake_case )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
__lowerCamelCase : int = 1.0
while num:
__lowerCamelCase : List[Any] = float(input("Gamma of: "))
print(F'''gamma({num}) = {gamma(num)}''')
print("\nEnter 0 to exit...")
| 323 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class _UpperCAmelCase:
def __init__( self , __a , __a=13 , __a=64 , __a=2 , __a=3 , __a=True , __a=True , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=10 , __a=0.02 , __a=[1, 16, 4, 4] , __a=None , ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = image_size
_UpperCamelCase = patch_size
_UpperCamelCase = num_channels
_UpperCamelCase = is_training
_UpperCamelCase = use_labels
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = scope
_UpperCamelCase = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
_UpperCamelCase = (self.image_size // 32) ** 2
_UpperCamelCase = num_patches + 1
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_UpperCamelCase = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [4, 8, 16, 32],
'''num_groups''': 2,
}
return ViTHybridConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__a , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=__a , )
def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = ViTHybridModel(config=__a)
model.to(__a)
model.eval()
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCAmelCase ( self , __a , __a , __a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = self.type_sequence_label_size
_UpperCamelCase = ViTHybridForImageClassification(__a)
model.to(__a)
model.eval()
_UpperCamelCase = model(__a , labels=__a)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size))
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = self.prepare_config_and_inputs()
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = config_and_inputs
_UpperCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase__ = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
lowercase__ = (
{'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
if is_torch_available()
else {}
)
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = ViTHybridModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37)
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''')
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
pass
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(__a)
self.assertIsInstance(model.get_input_embeddings() , (nn.Module))
_UpperCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__a , nn.Linear))
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(__a)
_UpperCamelCase = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCamelCase = [*signature.parameters.keys()]
_UpperCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __a)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__a)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCamelCase = _config_zero_init(__a)
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(config=__a)
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
_UpperCamelCase = [F'''{name}.{key}''' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@slow
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = ViTHybridModel.from_pretrained(__a)
self.assertIsNotNone(__a)
def lowerCamelCase__ ( ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class _UpperCAmelCase( unittest.TestCase ):
@cached_property
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
if is_vision_available()
else None
)
@slow
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
__a)
_UpperCamelCase = self.default_image_processor
_UpperCamelCase = prepare_img()
_UpperCamelCase = image_processor(images=__a , return_tensors='''pt''').to(__a)
# forward pass
with torch.no_grad():
_UpperCamelCase = model(**__a)
# verify the logits
_UpperCamelCase = torch.Size((1, 10_00))
self.assertEqual(outputs.logits.shape , __a)
_UpperCamelCase = torch.tensor([-1.9090, -0.4993, -0.2389]).to(__a)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __a , atol=1e-4))
@slow
@require_accelerate
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''')
_UpperCamelCase = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''' , device_map='''auto''')
_UpperCamelCase = prepare_img()
_UpperCamelCase = image_processor(images=__a , return_tensors='''pt''')
_UpperCamelCase = model(**__a)
_UpperCamelCase = outputs.logits
# model predicts one of the 1000 ImageNet classes
_UpperCamelCase = logits.argmax(-1).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] , '''tabby, tabby cat''')
| 19 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
__a: Optional[Any] = """0.12""" # assumed parallelism: 8
if is_torch_available():
import torch
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None ):
if rng is None:
lowercase__ : int = random.Random()
lowercase__ : Union[str, Any] = 1
for dim in shape:
total_dims *= dim
lowercase__ : str = []
for _ in range(__snake_case ):
values.append(rng.randint(0 , vocab_size - 1 ) )
lowercase__ : Optional[int] = np.array(__snake_case , dtype=jnp.intaa ).reshape(__snake_case )
return output
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase=None ):
lowercase__ : Optional[Any] = ids_tensor(__snake_case , vocab_size=2 , rng=__snake_case )
# make sure that at least one token is attended to for each batch
lowercase__ : Dict = 1
return attn_mask
@require_flax
class UpperCAmelCase :
'''simple docstring'''
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = ()
def _lowerCAmelCase( self ) -> str:
lowercase__ , lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
# cut to half length & take max batch_size 3
lowercase__ : Union[str, Any] = 2
lowercase__ : List[str] = inputs['''input_ids'''].shape[-1] // 2
lowercase__ : Tuple = inputs['''input_ids'''][:max_batch_size, :sequence_length]
lowercase__ : Any = jnp.ones_like(__a )
lowercase__ : Any = attention_mask[:max_batch_size, :sequence_length]
# generate max 5 tokens
lowercase__ : Tuple = input_ids.shape[-1] + 5
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
lowercase__ : Optional[Any] = config.eos_token_id
return config, input_ids, attention_mask, max_length
@is_pt_flax_cross_test
def _lowerCAmelCase( self ) -> Union[str, Any]:
lowercase__ , lowercase__ , lowercase__ , lowercase__ : List[Any] = self._get_input_ids_and_config()
lowercase__ : int = False
lowercase__ : Optional[int] = max_length
lowercase__ : List[str] = 0
for model_class in self.all_generative_model_classes:
lowercase__ : Any = model_class(__a )
lowercase__ : int = model_class.__name__[4:] # Skip the "Flax" at the beginning
lowercase__ : Optional[Any] = getattr(__a , __a )
lowercase__ : Optional[Any] = pt_model_class(__a ).eval()
lowercase__ : List[str] = load_flax_weights_in_pytorch_model(__a , flax_model.params )
lowercase__ : str = flax_model.generate(__a ).sequences
lowercase__ : Tuple = pt_model.generate(torch.tensor(__a , dtype=torch.long ) )
if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
lowercase__ : Dict = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]
self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist() )
def _lowerCAmelCase( self ) -> Dict:
lowercase__ , lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = self._get_input_ids_and_config()
lowercase__ : Dict = False
lowercase__ : Optional[int] = max_length
for model_class in self.all_generative_model_classes:
lowercase__ : Optional[int] = model_class(__a )
lowercase__ : Optional[Any] = model.generate(__a ).sequences
self.assertEqual(generation_outputs.shape[-1] , __a )
lowercase__ : str = jit(model.generate )
lowercase__ : Tuple = jit_generate(__a ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _lowerCAmelCase( self ) -> Optional[Any]:
lowercase__ , lowercase__ , lowercase__ , lowercase__ : Any = self._get_input_ids_and_config()
lowercase__ : str = True
lowercase__ : Optional[int] = max_length
for model_class in self.all_generative_model_classes:
lowercase__ : int = model_class(__a )
lowercase__ : Optional[int] = model.generate(__a ).sequences
self.assertEqual(generation_outputs.shape[-1] , __a )
lowercase__ : int = jit(model.generate )
lowercase__ : Optional[int] = jit_generate(__a ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _lowerCAmelCase( self ) -> List[str]:
lowercase__ , lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = self._get_input_ids_and_config()
lowercase__ : Optional[int] = False
lowercase__ : int = max_length
lowercase__ : Tuple = 2
for model_class in self.all_generative_model_classes:
lowercase__ : int = model_class(__a )
lowercase__ : Optional[int] = model.generate(__a ).sequences
self.assertEqual(generation_outputs.shape[-1] , __a )
lowercase__ : int = jit(model.generate )
lowercase__ : str = jit_generate(__a ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _lowerCAmelCase( self ) -> Optional[Any]:
lowercase__ , lowercase__ , lowercase__ , lowercase__ : Optional[Any] = self._get_input_ids_and_config()
lowercase__ : Optional[Any] = False
lowercase__ : Dict = max_length
lowercase__ : List[Any] = 2
lowercase__ : Optional[Any] = 2
for model_class in self.all_generative_model_classes:
lowercase__ : Optional[Any] = model_class(__a )
lowercase__ : Union[str, Any] = model.generate(__a ).sequences
self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences )
def _lowerCAmelCase( self ) -> Optional[Any]:
lowercase__ , lowercase__ , lowercase__ , lowercase__ : int = self._get_input_ids_and_config()
lowercase__ : int = True
lowercase__ : Optional[int] = max_length
lowercase__ : Optional[int] = 0.8
lowercase__ : Optional[int] = 10
lowercase__ : Optional[int] = 0.3
lowercase__ : Tuple = 1
lowercase__ : List[str] = 8
lowercase__ : int = 9
for model_class in self.all_generative_model_classes:
lowercase__ : str = model_class(__a )
lowercase__ : str = model.generate(__a ).sequences
self.assertEqual(generation_outputs.shape[-1] , __a )
lowercase__ : Tuple = jit(model.generate )
lowercase__ : List[str] = jit_generate(__a ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _lowerCAmelCase( self ) -> Optional[Any]:
lowercase__ , lowercase__ , lowercase__ , lowercase__ : List[str] = self._get_input_ids_and_config()
lowercase__ : Optional[Any] = max_length
lowercase__ : Optional[int] = 1
lowercase__ : List[Any] = 8
lowercase__ : str = 9
for model_class in self.all_generative_model_classes:
lowercase__ : Optional[Any] = model_class(__a )
lowercase__ : str = model.generate(__a ).sequences
self.assertEqual(generation_outputs.shape[-1] , __a )
lowercase__ : Tuple = jit(model.generate )
lowercase__ : List[Any] = jit_generate(__a ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _lowerCAmelCase( self ) -> Optional[int]:
lowercase__ , lowercase__ , lowercase__ , lowercase__ : Optional[int] = self._get_input_ids_and_config()
lowercase__ : List[Any] = max_length
lowercase__ : Dict = 2
lowercase__ : int = 1
lowercase__ : Optional[int] = 8
lowercase__ : int = 9
for model_class in self.all_generative_model_classes:
lowercase__ : List[str] = model_class(__a )
lowercase__ : Dict = model.generate(__a ).sequences
self.assertEqual(generation_outputs.shape[-1] , __a )
lowercase__ : Union[str, Any] = jit(model.generate )
lowercase__ : Tuple = jit_generate(__a ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _lowerCAmelCase( self ) -> List[Any]:
lowercase__ , lowercase__ , lowercase__ , lowercase__ : Tuple = self._get_input_ids_and_config()
# pad attention mask on the left
lowercase__ : int = attention_mask.at[(0, 0)].set(0 )
lowercase__ : str = False
lowercase__ : Union[str, Any] = max_length
for model_class in self.all_generative_model_classes:
lowercase__ : Optional[int] = model_class(__a )
lowercase__ : int = model.generate(__a , attention_mask=__a ).sequences
self.assertEqual(generation_outputs.shape[-1] , __a )
lowercase__ : Any = jit(model.generate )
lowercase__ : int = jit_generate(__a , attention_mask=__a ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _lowerCAmelCase( self ) -> Optional[Any]:
lowercase__ , lowercase__ , lowercase__ , lowercase__ : str = self._get_input_ids_and_config()
# pad attention mask on the left
lowercase__ : List[Any] = attention_mask.at[(0, 0)].set(0 )
lowercase__ : Optional[Any] = True
lowercase__ : List[str] = max_length
for model_class in self.all_generative_model_classes:
lowercase__ : List[str] = model_class(__a )
lowercase__ : Union[str, Any] = model.generate(__a , attention_mask=__a ).sequences
self.assertEqual(generation_outputs.shape[-1] , __a )
lowercase__ : Optional[int] = jit(model.generate )
lowercase__ : Optional[Any] = jit_generate(__a , attention_mask=__a ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def _lowerCAmelCase( self ) -> int:
lowercase__ , lowercase__ , lowercase__ , lowercase__ : Tuple = self._get_input_ids_and_config()
# pad attention mask on the left
lowercase__ : str = attention_mask.at[(0, 0)].set(0 )
lowercase__ : Optional[Any] = 2
lowercase__ : Union[str, Any] = max_length
for model_class in self.all_generative_model_classes:
lowercase__ : List[Any] = model_class(__a )
lowercase__ : str = model.generate(__a , attention_mask=__a ).sequences
self.assertEqual(generation_outputs.shape[-1] , __a )
lowercase__ : List[Any] = jit(model.generate )
lowercase__ : Tuple = jit_generate(__a , attention_mask=__a ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
@require_flax
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase( self ) -> str:
lowercase__ : List[str] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert''' )
lowercase__ : str = FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
lowercase__ : List[str] = '''Hello world'''
lowercase__ : Optional[Any] = tokenizer(__a , return_tensors='''np''' ).input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(__a , '''do_samples''' ):
model.generate(__a , do_samples=__a )
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(__a , '''foo''' ):
lowercase__ : str = {'''foo''': '''bar'''}
model.generate(__a , **__a )
| 152 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = ['vqvae']
def __init__( self , __a , __a , __a , __a , ) -> List[str]:
'''simple docstring'''
super().__init__()
self.register_modules(unet=__a , scheduler=__a , mel=__a , vqvae=__a)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return 50 if isinstance(self.scheduler , __a) else 10_00
@torch.no_grad()
def __call__( self , __a = 1 , __a = None , __a = None , __a = 0 , __a = 0 , __a = None , __a = None , __a = 0 , __a = 0 , __a = None , __a = 0 , __a = None , __a = None , __a=True , ) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
'''simple docstring'''
_UpperCamelCase = steps or self.get_default_steps()
self.scheduler.set_timesteps(__a)
_UpperCamelCase = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size) == int:
_UpperCamelCase = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
_UpperCamelCase = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=__a , device=self.device , )
_UpperCamelCase = noise
_UpperCamelCase = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(__a , __a)
_UpperCamelCase = self.mel.audio_slice_to_image(__a)
_UpperCamelCase = np.frombuffer(input_image.tobytes() , dtype='''uint8''').reshape(
(input_image.height, input_image.width))
_UpperCamelCase = (input_image / 2_55) * 2 - 1
_UpperCamelCase = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float).to(self.device)
if self.vqvae is not None:
_UpperCamelCase = self.vqvae.encode(torch.unsqueeze(__a , 0)).latent_dist.sample(
generator=__a)[0]
_UpperCamelCase = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
_UpperCamelCase = self.scheduler.add_noise(__a , __a , self.scheduler.timesteps[start_step - 1])
_UpperCamelCase = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
_UpperCamelCase = int(mask_start_secs * pixels_per_second)
_UpperCamelCase = int(mask_end_secs * pixels_per_second)
_UpperCamelCase = self.scheduler.add_noise(__a , __a , torch.tensor(self.scheduler.timesteps[start_step:]))
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
if isinstance(self.unet , __a):
_UpperCamelCase = self.unet(__a , __a , __a)['''sample''']
else:
_UpperCamelCase = self.unet(__a , __a)['''sample''']
if isinstance(self.scheduler , __a):
_UpperCamelCase = self.scheduler.step(
model_output=__a , timestep=__a , sample=__a , eta=__a , generator=__a , )['''prev_sample''']
else:
_UpperCamelCase = self.scheduler.step(
model_output=__a , timestep=__a , sample=__a , generator=__a , )['''prev_sample''']
if mask is not None:
if mask_start > 0:
_UpperCamelCase = mask[:, step, :, :mask_start]
if mask_end > 0:
_UpperCamelCase = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
_UpperCamelCase = 1 / self.vqvae.config.scaling_factor * images
_UpperCamelCase = self.vqvae.decode(__a)['''sample''']
_UpperCamelCase = (images / 2 + 0.5).clamp(0 , 1)
_UpperCamelCase = images.cpu().permute(0 , 2 , 3 , 1).numpy()
_UpperCamelCase = (images * 2_55).round().astype('''uint8''')
_UpperCamelCase = list(
(Image.fromarray(_[:, :, 0]) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(__a , mode='''RGB''').convert('''L''') for _ in images))
_UpperCamelCase = [self.mel.image_to_audio(__a) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(__a)[:, np.newaxis, :]) , **ImagePipelineOutput(__a))
@torch.no_grad()
def UpperCAmelCase ( self , __a , __a = 50) -> np.ndarray:
'''simple docstring'''
assert isinstance(self.scheduler , __a)
self.scheduler.set_timesteps(__a)
_UpperCamelCase = np.array(
[np.frombuffer(image.tobytes() , dtype='''uint8''').reshape((1, image.height, image.width)) for image in images])
_UpperCamelCase = (sample / 2_55) * 2 - 1
_UpperCamelCase = torch.Tensor(__a).to(self.device)
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,))):
_UpperCamelCase = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
_UpperCamelCase = self.scheduler.alphas_cumprod[t]
_UpperCamelCase = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
_UpperCamelCase = 1 - alpha_prod_t
_UpperCamelCase = self.unet(__a , __a)['''sample''']
_UpperCamelCase = (1 - alpha_prod_t_prev) ** 0.5 * model_output
_UpperCamelCase = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
_UpperCamelCase = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def UpperCAmelCase ( __a , __a , __a) -> torch.Tensor:
'''simple docstring'''
_UpperCamelCase = acos(torch.dot(torch.flatten(__a) , torch.flatten(__a)) / torch.norm(__a) / torch.norm(__a))
return sin((1 - alpha) * theta) * xa / sin(__a) + sin(alpha * theta) * xa / sin(__a)
| 19 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''',
'''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''',
'''microsoft/deberta-v2-xlarge-mnli''': (
'''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'''
),
'''microsoft/deberta-v2-xxlarge-mnli''': (
'''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'''
),
}
class UpperCAmelCase_ ( UpperCamelCase ):
'''simple docstring'''
__A : List[str] = "deberta-v2"
def __init__( self , __A=12_8100 , __A=1536 , __A=24 , __A=24 , __A=6144 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=0 , __A=0.02 , __A=1e-7 , __A=False , __A=-1 , __A=0 , __A=True , __A=None , __A=0 , __A="gelu" , **__A , ):
"""simple docstring"""
super().__init__(**__a )
lowerCamelCase : Tuple = hidden_size
lowerCamelCase : Optional[Any] = num_hidden_layers
lowerCamelCase : Optional[int] = num_attention_heads
lowerCamelCase : str = intermediate_size
lowerCamelCase : List[Any] = hidden_act
lowerCamelCase : Optional[Any] = hidden_dropout_prob
lowerCamelCase : Dict = attention_probs_dropout_prob
lowerCamelCase : List[str] = max_position_embeddings
lowerCamelCase : Any = type_vocab_size
lowerCamelCase : Tuple = initializer_range
lowerCamelCase : Optional[Any] = relative_attention
lowerCamelCase : List[Any] = max_relative_positions
lowerCamelCase : int = pad_token_id
lowerCamelCase : Optional[int] = position_biased_input
# Backwards compatibility
if type(__a ) == str:
lowerCamelCase : Optional[Any] = [x.strip() for x in pos_att_type.lower().split("|" )]
lowerCamelCase : Any = pos_att_type
lowerCamelCase : Tuple = vocab_size
lowerCamelCase : int = layer_norm_eps
lowerCamelCase : int = kwargs.get("pooler_hidden_size" , __a )
lowerCamelCase : Optional[Any] = pooler_dropout
lowerCamelCase : Tuple = pooler_hidden_act
class UpperCAmelCase_ ( UpperCamelCase ):
'''simple docstring'''
@property
def _snake_case ( self ):
"""simple docstring"""
if self.task == "multiple-choice":
lowerCamelCase : Any = {0: "batch", 1: "choice", 2: "sequence"}
else:
lowerCamelCase : Tuple = {0: "batch", 1: "sequence"}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)] )
else:
return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)] )
@property
def _snake_case ( self ):
"""simple docstring"""
return 12
def _snake_case ( self , __A , __A = -1 , __A = -1 , __A = -1 , __A = False , __A = None , __A = 3 , __A = 40 , __A = 40 , __A = None , ):
"""simple docstring"""
lowerCamelCase : Tuple = super().generate_dummy_inputs(preprocessor=__a , framework=__a )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 340 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_a = logging.get_logger(__name__)
_a = {
"""facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
# See all DETR models at https://huggingface.co/models?filter=detr
}
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = 'detr'
lowercase__ = ['past_key_values']
lowercase__ = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , __a=True , __a=None , __a=3 , __a=1_00 , __a=6 , __a=20_48 , __a=8 , __a=6 , __a=20_48 , __a=8 , __a=0.0 , __a=0.0 , __a=True , __a="relu" , __a=2_56 , __a=0.1 , __a=0.0 , __a=0.0 , __a=0.02 , __a=1.0 , __a=False , __a="sine" , __a="resnet50" , __a=True , __a=False , __a=1 , __a=5 , __a=2 , __a=1 , __a=1 , __a=5 , __a=2 , __a=0.1 , **__a , ) -> int:
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
_UpperCamelCase = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
elif isinstance(__a , __a):
_UpperCamelCase = backbone_config.get('''model_type''')
_UpperCamelCase = CONFIG_MAPPING[backbone_model_type]
_UpperCamelCase = config_class.from_dict(__a)
# set timm attributes to None
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None, None, None
_UpperCamelCase = use_timm_backbone
_UpperCamelCase = backbone_config
_UpperCamelCase = num_channels
_UpperCamelCase = num_queries
_UpperCamelCase = d_model
_UpperCamelCase = encoder_ffn_dim
_UpperCamelCase = encoder_layers
_UpperCamelCase = encoder_attention_heads
_UpperCamelCase = decoder_ffn_dim
_UpperCamelCase = decoder_layers
_UpperCamelCase = decoder_attention_heads
_UpperCamelCase = dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = activation_function
_UpperCamelCase = init_std
_UpperCamelCase = init_xavier_std
_UpperCamelCase = encoder_layerdrop
_UpperCamelCase = decoder_layerdrop
_UpperCamelCase = encoder_layers
_UpperCamelCase = auxiliary_loss
_UpperCamelCase = position_embedding_type
_UpperCamelCase = backbone
_UpperCamelCase = use_pretrained_backbone
_UpperCamelCase = dilation
# Hungarian matcher
_UpperCamelCase = class_cost
_UpperCamelCase = bbox_cost
_UpperCamelCase = giou_cost
# Loss coefficients
_UpperCamelCase = mask_loss_coefficient
_UpperCamelCase = dice_loss_coefficient
_UpperCamelCase = bbox_loss_coefficient
_UpperCamelCase = giou_loss_coefficient
_UpperCamelCase = eos_coefficient
super().__init__(is_encoder_decoder=__a , **__a)
@property
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return self.d_model
@classmethod
def UpperCAmelCase ( cls , __a , **__a) -> int:
'''simple docstring'''
return cls(backbone_config=__a , **__a)
def UpperCAmelCase ( self) -> Dict[str, any]:
'''simple docstring'''
_UpperCamelCase = copy.deepcopy(self.__dict__)
if output["backbone_config"] is not None:
_UpperCamelCase = self.backbone_config.to_dict()
_UpperCamelCase = self.__class__.model_type
return output
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = version.parse('1.11' )
@property
def UpperCAmelCase ( self) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
])
@property
def UpperCAmelCase ( self) -> float:
'''simple docstring'''
return 1e-5
@property
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
return 12
| 19 | 0 |
import math
def lowerCAmelCase__ ( lowerCamelCase_ : Dict):
'''simple docstring'''
lowerCAmelCase__ : Dict = 0
lowerCAmelCase__ : Union[str, Any] = 0
while num > 0:
lowerCAmelCase__ : List[Any] = num % 8
lowerCAmelCase__ : str = octal + (remainder * math.floor(math.pow(10 ,__snake_case)))
counter += 1
lowerCAmelCase__ : Any = math.floor(num / 8) # basically /= 8 without remainder if any
# This formatting removes trailing '.0' from `octal`.
return f"""0o{int(__snake_case)}"""
def lowerCAmelCase__ ( ):
'''simple docstring'''
print('''\n2 in octal is:''')
print(decimal_to_octal(2)) # = 2
print('''\n8 in octal is:''')
print(decimal_to_octal(8)) # = 10
print('''\n65 in octal is:''')
print(decimal_to_octal(65)) # = 101
print('''\n216 in octal is:''')
print(decimal_to_octal(216)) # = 330
print('''\n512 in octal is:''')
print(decimal_to_octal(512)) # = 1000
print('''\n''')
if __name__ == "__main__":
main()
| 647 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = 'wavlm'
def __init__( self , __a=32 , __a=7_68 , __a=12 , __a=12 , __a=30_72 , __a="gelu" , __a=0.1 , __a=0.1 , __a=0.1 , __a=0.0 , __a=0.1 , __a=0.1 , __a=0.02 , __a=1e-5 , __a="group" , __a="gelu" , __a=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __a=(5, 2, 2, 2, 2, 2, 2) , __a=(10, 3, 3, 3, 3, 2, 2) , __a=False , __a=1_28 , __a=16 , __a=3_20 , __a=8_00 , __a=False , __a=True , __a=0.05 , __a=10 , __a=2 , __a=0.0 , __a=10 , __a=3_20 , __a=2 , __a=0.1 , __a=1_00 , __a=2_56 , __a=2_56 , __a=0.1 , __a="mean" , __a=False , __a=False , __a=2_56 , __a=(5_12, 5_12, 5_12, 5_12, 15_00) , __a=(5, 3, 3, 1, 1) , __a=(1, 2, 3, 1, 1) , __a=5_12 , __a=80 , __a=0 , __a=1 , __a=2 , __a=False , __a=3 , __a=2 , __a=3 , __a=None , **__a , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(**__a , pad_token_id=__a , bos_token_id=__a , eos_token_id=__a)
_UpperCamelCase = hidden_size
_UpperCamelCase = feat_extract_norm
_UpperCamelCase = feat_extract_activation
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = conv_bias
_UpperCamelCase = num_buckets
_UpperCamelCase = max_bucket_distance
_UpperCamelCase = num_conv_pos_embeddings
_UpperCamelCase = num_conv_pos_embedding_groups
_UpperCamelCase = len(self.conv_dim)
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = num_attention_heads
_UpperCamelCase = hidden_dropout
_UpperCamelCase = attention_dropout
_UpperCamelCase = activation_dropout
_UpperCamelCase = feat_proj_dropout
_UpperCamelCase = final_dropout
_UpperCamelCase = layerdrop
_UpperCamelCase = layer_norm_eps
_UpperCamelCase = initializer_range
_UpperCamelCase = num_ctc_classes
_UpperCamelCase = vocab_size
_UpperCamelCase = do_stable_layer_norm
_UpperCamelCase = use_weighted_layer_sum
_UpperCamelCase = classifier_proj_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F''' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,'''
F''' `len(config.conv_kernel) = {len(self.conv_kernel)}`.''')
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_UpperCamelCase = apply_spec_augment
_UpperCamelCase = mask_time_prob
_UpperCamelCase = mask_time_length
_UpperCamelCase = mask_time_min_masks
_UpperCamelCase = mask_feature_prob
_UpperCamelCase = mask_feature_length
# parameters for pretraining with codevector quantized representations
_UpperCamelCase = num_codevectors_per_group
_UpperCamelCase = num_codevector_groups
_UpperCamelCase = contrastive_logits_temperature
_UpperCamelCase = num_negatives
_UpperCamelCase = codevector_dim
_UpperCamelCase = proj_codevector_dim
_UpperCamelCase = diversity_loss_weight
# ctc loss
_UpperCamelCase = ctc_loss_reduction
_UpperCamelCase = ctc_zero_infinity
# adapter
_UpperCamelCase = add_adapter
_UpperCamelCase = adapter_kernel_size
_UpperCamelCase = adapter_stride
_UpperCamelCase = num_adapter_layers
_UpperCamelCase = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_UpperCamelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = list(__a)
_UpperCamelCase = xvector_output_dim
@property
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1)
| 19 | 0 |
'''simple docstring'''
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenizer
logging.basicConfig(
format="""%(asctime)s | %(levelname)s | %(name)s | [%(filename)s:%(lineno)d] %(message)s""",
datefmt="""%Y-%m-%d %H:%M:%S""",
level=os.environ.get("""LOGLEVEL""", """INFO""").upper(),
stream=sys.stdout,
)
__lowerCAmelCase = logging.getLogger(__name__)
__lowerCAmelCase = {"""facebook/bart-base""": BartForConditionalGeneration}
__lowerCAmelCase = {"""facebook/bart-base""": BartTokenizer}
def UpperCAmelCase_ ():
"""simple docstring"""
_a : List[str] = argparse.ArgumentParser(description='Export Bart model + Beam Search to ONNX graph.' )
parser.add_argument(
'--validation_file' , type=__snake_case , default=__snake_case , help='A csv or a json file containing the validation data.' )
parser.add_argument(
'--max_length' , type=__snake_case , default=5 , help='The maximum total input sequence length after tokenization.' , )
parser.add_argument(
'--num_beams' , type=__snake_case , default=__snake_case , help=(
'Number of beams to use for evaluation. This argument will be '
'passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'
) , )
parser.add_argument(
'--model_name_or_path' , type=__snake_case , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=__snake_case , )
parser.add_argument(
'--config_name' , type=__snake_case , default=__snake_case , help='Pretrained config name or path if not the same as model_name' , )
parser.add_argument(
'--device' , type=__snake_case , default='cpu' , help='Device where the model will be run' , )
parser.add_argument('--output_file_path' , type=__snake_case , default=__snake_case , help='Where to store the final ONNX file.' )
_a : List[str] = parser.parse_args()
return args
def UpperCAmelCase_ (__a : List[str] , __a : Any="cpu" ):
"""simple docstring"""
_a : Union[str, Any] = model_dict[model_name].from_pretrained(__snake_case ).to(__snake_case )
_a : Tuple = tokenizer_dict[model_name].from_pretrained(__snake_case )
if model_name in ["facebook/bart-base"]:
_a : List[Any] = 0
_a : Any = None
_a : List[Any] = 0
return huggingface_model, tokenizer
def UpperCAmelCase_ (__a : List[Any] , __a : Any , __a : Optional[int] , __a : int , __a : Any ):
"""simple docstring"""
model.eval()
_a : List[Any] = None
_a : int = torch.jit.script(BARTBeamSearchGenerator(__snake_case ) )
with torch.no_grad():
_a : Union[str, Any] = 'My friends are cool but they eat too many carbs.'
_a : List[str] = tokenizer([ARTICLE_TO_SUMMARIZE] , max_length=1_0_2_4 , return_tensors='pt' ).to(model.device )
_a : List[str] = model.generate(
inputs['input_ids'] , attention_mask=inputs['attention_mask'] , num_beams=__snake_case , max_length=__snake_case , early_stopping=__snake_case , decoder_start_token_id=model.config.decoder_start_token_id , )
torch.onnx.export(
__snake_case , (
inputs['input_ids'],
inputs['attention_mask'],
num_beams,
max_length,
model.config.decoder_start_token_id,
) , __snake_case , opset_version=1_4 , input_names=['input_ids', 'attention_mask', 'num_beams', 'max_length', 'decoder_start_token_id'] , output_names=['output_ids'] , dynamic_axes={
'input_ids': {0: 'batch', 1: 'seq'},
'output_ids': {0: 'batch', 1: 'seq_out'},
} , example_outputs=__snake_case , )
logger.info('Model exported to {}'.format(__snake_case ) )
_a : Union[str, Any] = remove_dup_initializers(os.path.abspath(__snake_case ) )
logger.info('Deduplicated and optimized model written to {}'.format(__snake_case ) )
_a : List[Any] = onnxruntime.InferenceSession(__snake_case )
_a : Optional[Any] = ort_sess.run(
__snake_case , {
'input_ids': inputs['input_ids'].cpu().numpy(),
'attention_mask': inputs['attention_mask'].cpu().numpy(),
'num_beams': np.array(__snake_case ),
'max_length': np.array(__snake_case ),
'decoder_start_token_id': np.array(model.config.decoder_start_token_id ),
} , )
np.testing.assert_allclose(summary_ids.cpu().numpy() , ort_out[0] , rtol=1e-3 , atol=1e-3 )
logger.info('Model outputs from torch and ONNX Runtime are similar.' )
logger.info('Success.' )
def UpperCAmelCase_ ():
"""simple docstring"""
_a : str = parse_args()
_a : Tuple = 5
_a : List[Any] = 4
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO , )
logger.setLevel(logging.INFO )
transformers.utils.logging.set_verbosity_error()
_a : Any = torch.device(args.device )
_a, _a : Any = load_model_tokenizer(args.model_name_or_path , __snake_case )
if model.config.decoder_start_token_id is None:
raise ValueError('Make sure that `config.decoder_start_token_id` is correctly defined' )
model.to(__snake_case )
if args.max_length:
_a : Dict = args.max_length
if args.num_beams:
_a : Optional[int] = args.num_beams
if args.output_file_path:
_a : Dict = args.output_file_path
else:
_a : Union[str, Any] = 'BART.onnx'
logger.info('Exporting model to ONNX' )
export_and_validate_model(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case )
if __name__ == "__main__":
main()
| 229 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
_a = """bart"""
_a = True
@st.cache(allow_output_mutation=__snake_case )
def lowerCamelCase__ ( ) -> Dict:
"""simple docstring"""
if LOAD_DENSE_INDEX:
_UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''' )
_UpperCamelCase = AutoModel.from_pretrained('''yjernite/retribert-base-uncased''' ).to('''cuda:0''' )
_UpperCamelCase = qar_model.eval()
else:
_UpperCamelCase , _UpperCamelCase = (None, None)
if MODEL_TYPE == "bart":
_UpperCamelCase = AutoTokenizer.from_pretrained('''yjernite/bart_eli5''' )
_UpperCamelCase = AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''' ).to('''cuda:0''' )
_UpperCamelCase = torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''' )
sas_model.load_state_dict(save_dict['''model'''] )
_UpperCamelCase = sas_model.eval()
else:
_UpperCamelCase , _UpperCamelCase = make_qa_sas_model(
model_name='''t5-small''', from_file='''seq2seq_models/eli5_t5_model_1024_4.pth''', device='''cuda:0''' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=__snake_case )
def lowerCamelCase__ ( ) -> Tuple:
"""simple docstring"""
if LOAD_DENSE_INDEX:
_UpperCamelCase = faiss.StandardGpuResources()
_UpperCamelCase = datasets.load_dataset(path='''wiki_snippets''', name='''wiki40b_en_100_0''' )['''train''']
_UpperCamelCase = np.memmap(
'''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''', dtype='''float32''', mode='''r''', shape=(wikiaab_passages.num_rows, 1_28), )
_UpperCamelCase = faiss.IndexFlatIP(1_28 )
_UpperCamelCase = faiss.index_cpu_to_gpu(__snake_case, 1, __snake_case )
wikiaab_gpu_index_flat.add(__snake_case ) # TODO fix for larger GPU
else:
_UpperCamelCase , _UpperCamelCase = (None, None)
_UpperCamelCase = Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=__snake_case )
def lowerCamelCase__ ( ) -> int:
"""simple docstring"""
_UpperCamelCase = datasets.load_dataset('''eli5''', name='''LFQA_reddit''' )
_UpperCamelCase = elia['''train_eli5''']
_UpperCamelCase = np.memmap(
'''eli5_questions_reps.dat''', dtype='''float32''', mode='''r''', shape=(elia_train.num_rows, 1_28) )
_UpperCamelCase = faiss.IndexFlatIP(1_28 )
eli5_train_q_index.add(__snake_case )
return (elia_train, eli5_train_q_index)
_a , _a , _a = load_indexes()
_a , _a , _a , _a = load_models()
_a , _a = load_train_data()
def lowerCamelCase__ ( __snake_case, __snake_case=10 ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = embed_questions_for_retrieval([question], __snake_case, __snake_case )
_UpperCamelCase , _UpperCamelCase = eli5_train_q_index.search(__snake_case, __snake_case )
_UpperCamelCase = [elia_train[int(__snake_case )] for i in I[0]]
return nn_examples
def lowerCamelCase__ ( __snake_case, __snake_case="wiki40b", __snake_case="dense", __snake_case=10 ) -> List[str]:
"""simple docstring"""
if source == "none":
_UpperCamelCase , _UpperCamelCase = (''' <P> '''.join(['''''' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
_UpperCamelCase , _UpperCamelCase = query_qa_dense_index(
__snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case )
else:
_UpperCamelCase , _UpperCamelCase = query_es_index(
__snake_case, __snake_case, index_name='''english_wiki40b_snippets_100w''', n_results=__snake_case, )
_UpperCamelCase = [
(res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
]
_UpperCamelCase = '''question: {} context: {}'''.format(__snake_case, __snake_case )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda __snake_case : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __snake_case : None),
} )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=64, __snake_case=2_56, __snake_case=False, __snake_case=2, __snake_case=0.95, __snake_case=0.8 ) -> Dict:
"""simple docstring"""
with torch.no_grad():
_UpperCamelCase = qa_sas_generate(
__snake_case, __snake_case, __snake_case, num_answers=1, num_beams=__snake_case, min_len=__snake_case, max_len=__snake_case, do_sample=__snake_case, temp=__snake_case, top_p=__snake_case, top_k=__snake_case, max_input_length=10_24, device='''cuda:0''', )[0]
return (answer, support_list)
st.title("""Long Form Question Answering with ELI5""")
# Start sidebar
_a = """<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
_a = """
<html>
<head>
<style>
.img-container {
padding-left: 90px;
padding-right: 90px;
padding-top: 50px;
padding-bottom: 50px;
background-color: #f0f3f9;
}
</style>
</head>
<body>
<span class=\"img-container\"> <!-- Inline parent element -->
%s
</span>
</body>
</html>
""" % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
_a = """
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
"""
st.sidebar.markdown(description, unsafe_allow_html=True)
_a = [
"""Answer the question""",
"""View the retrieved document only""",
"""View the most similar ELI5 question and answer""",
"""Show me everything, please!""",
]
_a = st.sidebar.checkbox("""Demo options""")
if demo_options:
_a = st.sidebar.selectbox(
"""""",
action_list,
index=3,
)
_a = action_list.index(action_st)
_a = st.sidebar.selectbox(
"""""",
["""Show full text of passages""", """Show passage section titles"""],
index=0,
)
_a = show_type == """Show full text of passages"""
else:
_a = 3
_a = True
_a = st.sidebar.checkbox("""Retrieval options""")
if retrieval_options:
_a = """
### Information retriever options
The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
"""
st.sidebar.markdown(retriever_info)
_a = st.sidebar.selectbox("""Which Wikipedia format should the model use?""", ["""wiki40b""", """none"""])
_a = st.sidebar.selectbox("""Which Wikipedia indexer should the model use?""", ["""dense""", """sparse""", """mixed"""])
else:
_a = """wiki40b"""
_a = """dense"""
_a = """beam"""
_a = 2
_a = 64
_a = 256
_a = None
_a = None
_a = st.sidebar.checkbox("""Generation options""")
if generate_options:
_a = """
### Answer generation options
The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
**beam** search, or **sample** from the decoder's output probabilities.
"""
st.sidebar.markdown(generate_info)
_a = st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
_a = st.sidebar.slider(
"""Minimum generation length""", min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
_a = st.sidebar.slider(
"""Maximum generation length""", min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
_a = st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
_a = st.sidebar.slider(
"""Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
_a = st.sidebar.slider(
"""Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
_a = None
# start main text
_a = [
"""<MY QUESTION>""",
"""How do people make chocolate?""",
"""Why do we get a fever when we are sick?""",
"""How can different animals perceive different colors?""",
"""What is natural language processing?""",
"""What's the best way to treat a sunburn?""",
"""What exactly are vitamins ?""",
"""How does nuclear energy provide electricity?""",
"""What's the difference between viruses and bacteria?""",
"""Why are flutes classified as woodwinds when most of them are made out of metal ?""",
"""Why do people like drinking coffee even though it tastes so bad?""",
"""What happens when wine ages? How does it make the wine taste better?""",
"""If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?""",
"""How can we set a date to the beginning or end of an artistic period? Doesn't the change happen gradually?""",
"""How does New Zealand have so many large bird predators?""",
]
_a = st.selectbox(
"""What would you like to ask? ---- select <MY QUESTION> to enter a new query""",
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
_a = st.text_input("""Enter your question here:""", """""")
else:
_a = question_s
if st.button("""Show me!"""):
if action in [0, 1, 3]:
if index_type == "mixed":
_a , _a = make_support(question, source=wiki_source, method="""dense""", n_results=10)
_a , _a = make_support(question, source=wiki_source, method="""sparse""", n_results=10)
_a = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
_a = support_list[:10]
_a = """<P> """ + """ <P> """.join([res[-1] for res in support_list])
else:
_a , _a = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
_a , _a = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == """sampled"""),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown("""### The model generated answer is:""")
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown("""--- \n ### The model is drawing information from the following Wikipedia passages:""")
for i, res in enumerate(support_list):
_a = """https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_"""))
_a = res[1].strip()
if sec_titles == "":
_a = """[{}]({})""".format(res[0], wiki_url)
else:
_a = sec_titles.split(""" & """)
_a = """ & """.join(
["""[{}]({}#{})""".format(sec.strip(), wiki_url, sec.strip().replace(""" """, """_""")) for sec in sec_list]
)
st.markdown(
"""{0:02d} - **Article**: {1:<18} <br> _Section_: {2}""".format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
"""> <span style=\"font-family:arial; font-size:10pt;\">""" + res[-1] + """</span>""", unsafe_allow_html=True
)
if action in [2, 3]:
_a = find_nearest_training(question)
_a = nn_train_list[0]
st.markdown(
"""--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""])
)
_a = [
"""{}. {}""".format(i + 1, """ \n""".join([line.strip() for line in ans.split("""\n""") if line.strip() != """"""]))
for i, (ans, sc) in enumerate(zip(train_exple["""answers"""]["""text"""], train_exple["""answers"""]["""score"""]))
if i == 0 or sc > 2
]
st.markdown("""##### Its answers were: \n\n {}""".format("""\n""".join(answers_st)))
_a = """
---
**Disclaimer**
*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
"""
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 19 | 0 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_lowerCamelCase : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCamelCase : Union[str, Any] = ''' \"\"\"
Output class for the scheduler\'s step function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample (x_{0}) based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
\"\"\"
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
'''
class lowercase ( unittest.TestCase ):
def __snake_case( self : Any ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , "schedulers/" ) )
SCREAMING_SNAKE_CASE = self.diffusers_dir
shutil.copy(
os.path.join(__a , "src/diffusers/schedulers/scheduling_ddpm.py" ) , os.path.join(self.diffusers_dir , "schedulers/scheduling_ddpm.py" ) , )
def __snake_case( self : Optional[int] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = "src/diffusers"
shutil.rmtree(self.diffusers_dir )
def __snake_case( self : Any , _UpperCamelCase : Dict , _UpperCamelCase : str , _UpperCamelCase : Any , _UpperCamelCase : Optional[int]=None ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = comment + F"\nclass {class_name}(nn.Module):\n" + class_code
if overwrite_result is not None:
SCREAMING_SNAKE_CASE = comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
SCREAMING_SNAKE_CASE = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
SCREAMING_SNAKE_CASE = black.format_str(__a , mode=__a )
SCREAMING_SNAKE_CASE = os.path.join(self.diffusers_dir , "new_code.py" )
with open(__a , "w" , newline="\n" ) as f:
f.write(__a )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(__a ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=__a )
with open(__a , "r" ) as f:
self.assertTrue(f.read() , __a )
def __snake_case( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput" )
self.assertEqual(__a , __a )
def __snake_case( self : Tuple ) -> Dict:
'''simple docstring'''
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , REFERENCE_CODE + "\n" , )
# With no empty line at the end
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , __a , )
# Copy consistency with rename
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , re.sub("DDPM" , "Test" , __a ) , )
# Copy consistency with a really long name
SCREAMING_SNAKE_CASE = "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
self.check_copy_consistency(
F"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}" , F"{long_class_name}SchedulerOutput" , re.sub("Bert" , __a , __a ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , __a , overwrite_result=re.sub("DDPM" , "Test" , __a ) , )
| 403 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
_a = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
_a = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> Tuple:
"""simple docstring"""
for attribute in key.split('''.''' ):
_UpperCamelCase = getattr(__snake_case, __snake_case )
if weight_type is not None:
_UpperCamelCase = getattr(__snake_case, __snake_case ).shape
else:
_UpperCamelCase = hf_pointer.shape
assert hf_shape == value.shape, (
F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
_UpperCamelCase = value
elif weight_type == "weight_g":
_UpperCamelCase = value
elif weight_type == "weight_v":
_UpperCamelCase = value
elif weight_type == "bias":
_UpperCamelCase = value
else:
_UpperCamelCase = value
logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def lowerCamelCase__ ( __snake_case, __snake_case ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = []
_UpperCamelCase = fairseq_model.state_dict()
_UpperCamelCase = hf_model.feature_extractor
_UpperCamelCase = hf_model.adapter
for name, value in fairseq_dict.items():
_UpperCamelCase = False
if "conv_layers" in name:
load_conv_layer(
__snake_case, __snake_case, __snake_case, __snake_case, hf_model.config.feat_extract_norm == '''group''', )
_UpperCamelCase = True
elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.'''] ):
load_adapter(__snake_case, __snake_case, __snake_case, __snake_case )
_UpperCamelCase = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
_UpperCamelCase = True
if "*" in mapped_key:
_UpperCamelCase = name.split(__snake_case )[0].split('''.''' )[-2]
_UpperCamelCase = mapped_key.replace('''*''', __snake_case )
if "weight_g" in name:
_UpperCamelCase = '''weight_g'''
elif "weight_v" in name:
_UpperCamelCase = '''weight_v'''
elif "bias" in name:
_UpperCamelCase = '''bias'''
elif "weight" in name:
_UpperCamelCase = '''weight'''
else:
_UpperCamelCase = None
set_recursively(__snake_case, __snake_case, __snake_case, __snake_case, __snake_case )
continue
if not is_used:
unused_weights.append(__snake_case )
logger.warning(F'''Unused weights: {unused_weights}''' )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = full_name.split('''conv_layers.''' )[-1]
_UpperCamelCase = name.split('''.''' )
_UpperCamelCase = int(items[0] )
_UpperCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
_UpperCamelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
_UpperCamelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
_UpperCamelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
_UpperCamelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__snake_case )
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case ) -> Dict:
"""simple docstring"""
_UpperCamelCase = full_name.split('''adaptor.''' )[-1]
_UpperCamelCase = name.split('''.''' )
if items[1].isdigit():
_UpperCamelCase = int(items[1] )
else:
_UpperCamelCase = None
if "adaptor" not in full_name:
if "proj_ln" in full_name:
# has to be layer norm
if "bias" in name:
assert (
value.shape == adapter.proj_layer_norm.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj_layer_norm.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
_UpperCamelCase = value
else:
# has to be projection layer
if "bias" in name:
assert (
value.shape == adapter.proj.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter proj layer bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter proj layer weight was initialized from {full_name}.''' )
elif isinstance(__snake_case, __snake_case ):
if "bias" in name:
assert (
value.shape == adapter.layers[layer_id].conv.bias.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
elif "weight" in name:
assert (
value.shape == adapter.layers[layer_id].conv.weight.data.shape
), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
_UpperCamelCase = value
logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
else:
unused_weights.append(__snake_case )
def lowerCamelCase__ ( __snake_case ) -> str:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase = emb.weight.shape
_UpperCamelCase = nn.Linear(__snake_case, __snake_case, bias=__snake_case )
_UpperCamelCase = emb.weight.data
return lin_layer
@torch.no_grad()
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = WavaVecaConfig.from_pretrained(
__snake_case, add_adapter=__snake_case, adapter_stride=__snake_case, adapter_kernel_size=__snake_case, use_auth_token=__snake_case, output_hidden_size=__snake_case, )
_UpperCamelCase = MBartConfig.from_pretrained(__snake_case )
# load model
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path], arg_overrides={
'''config_yaml''': config_yaml_path,
'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ),
'''w2v_path''': checkpoint_path,
'''load_pretrained_decoder_from''': None,
}, )
_UpperCamelCase = model[0].eval()
# load feature extractor
_UpperCamelCase = WavaVecaFeatureExtractor.from_pretrained(__snake_case, use_auth_token=__snake_case )
# set weights for wav2vec2 encoder
_UpperCamelCase = WavaVecaModel(__snake_case )
recursively_load_weights_wavaveca(model.encoder, __snake_case )
# load decoder weights
_UpperCamelCase = MBartForCausalLM(__snake_case )
_UpperCamelCase , _UpperCamelCase = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(), strict=__snake_case )
logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}''' )
logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' )
_UpperCamelCase = SpeechEncoderDecoderModel(encoder=__snake_case, decoder=__snake_case )
_UpperCamelCase = False
_UpperCamelCase = MBartaaTokenizer(__snake_case )
tokenizer.save_pretrained(__snake_case )
_UpperCamelCase = hf_wavavec.config.to_dict()
_UpperCamelCase = tokenizer.pad_token_id
_UpperCamelCase = tokenizer.bos_token_id
_UpperCamelCase = tokenizer.eos_token_id
_UpperCamelCase = '''mbart50'''
_UpperCamelCase = '''wav2vec2'''
_UpperCamelCase = tokenizer.eos_token_id
_UpperCamelCase = 25_00_04
_UpperCamelCase = tokenizer.eos_token_id
_UpperCamelCase = SpeechEncoderDecoderConfig.from_dict(__snake_case )
hf_wavavec.save_pretrained(__snake_case )
feature_extractor.save_pretrained(__snake_case )
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
parser.add_argument(
"""--encoder_config_path""",
default="""facebook/wav2vec2-xls-r-1b""",
type=str,
help="""Path to hf encoder wav2vec2 checkpoint config""",
)
parser.add_argument(
"""--decoder_config_path""",
default="""facebook/mbart-large-50-one-to-many-mmt""",
type=str,
help="""Path to hf decoder checkpoint config""",
)
parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
parser.add_argument("""--encoder_output_dim""", default=1024, type=int, help="""encoder output dim""")
parser.add_argument("""--start_token_id""", default=25_0004, type=int, help="""`decoder_start_token_id` of model config""")
_a = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
args.config_yaml_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
add_adapter=args.add_adapter,
adapter_kernel_size=args.adapter_kernel_size,
adapter_stride=args.adapter_stride,
decoder_start_token_id=args.start_token_id,
encoder_output_dim=args.encoder_output_dim,
)
| 19 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class A ( _a ,unittest.TestCase ):
lowercase_ = MobileBertTokenizer
lowercase_ = MobileBertTokenizerFast
lowercase_ = True
lowercase_ = True
lowercase_ = filter_non_english
lowercase_ = 'google/mobilebert-uncased'
def __lowerCAmelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
super().setUp()
_a = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
_a = [
(tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped
for tokenizer_def in self.tokenizers_list
]
def __lowerCAmelCase ( self : Tuple , lowerCAmelCase_ : str ) -> List[str]:
"""simple docstring"""
_a = '''UNwant\u00E9d,running'''
_a = '''unwanted, running'''
return input_text, output_text
def __lowerCAmelCase ( self : Dict ) -> Any:
"""simple docstring"""
_a = self.tokenizer_class(self.vocab_file )
_a = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(__a , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , [9, 6, 7, 12, 10, 11] )
def __lowerCAmelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
_a = self.get_tokenizer()
_a = self.get_rust_tokenizer()
_a = '''UNwant\u00E9d,running'''
_a = tokenizer.tokenize(__a )
_a = rust_tokenizer.tokenize(__a )
self.assertListEqual(__a , __a )
_a = tokenizer.encode(__a , add_special_tokens=__a )
_a = rust_tokenizer.encode(__a , add_special_tokens=__a )
self.assertListEqual(__a , __a )
_a = self.get_rust_tokenizer()
_a = tokenizer.encode(__a )
_a = rust_tokenizer.encode(__a )
self.assertListEqual(__a , __a )
# With lower casing
_a = self.get_tokenizer(do_lower_case=__a )
_a = self.get_rust_tokenizer(do_lower_case=__a )
_a = '''UNwant\u00E9d,running'''
_a = tokenizer.tokenize(__a )
_a = rust_tokenizer.tokenize(__a )
self.assertListEqual(__a , __a )
_a = tokenizer.encode(__a , add_special_tokens=__a )
_a = rust_tokenizer.encode(__a , add_special_tokens=__a )
self.assertListEqual(__a , __a )
_a = self.get_rust_tokenizer()
_a = tokenizer.encode(__a )
_a = rust_tokenizer.encode(__a )
self.assertListEqual(__a , __a )
def __lowerCAmelCase ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
_a = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz''' ) , ['''ah''', '''\u535A''', '''\u63A8''', '''zz'''] )
def __lowerCAmelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
_a = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? ''' ) , ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] )
def __lowerCAmelCase ( self : int ) -> Union[str, Any]:
"""simple docstring"""
_a = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''h\u00E9llo'''] )
def __lowerCAmelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
_a = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] )
def __lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
_a = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] )
def __lowerCAmelCase ( self : List[Any] ) -> Dict:
"""simple docstring"""
_a = BasicTokenizer(do_lower_case=__a )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? ''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def __lowerCAmelCase ( self : str ) -> List[Any]:
"""simple docstring"""
_a = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def __lowerCAmelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
_a = BasicTokenizer(do_lower_case=__a , strip_accents=__a )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def __lowerCAmelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
_a = BasicTokenizer(do_lower_case=__a , never_split=['''[UNK]'''] )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? [UNK]''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]'''] )
def __lowerCAmelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
_a = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']
_a = {}
for i, token in enumerate(__a ):
_a = i
_a = WordpieceTokenizer(vocab=__a , unk_token='''[UNK]''' )
self.assertListEqual(tokenizer.tokenize('''''' ) , [] )
self.assertListEqual(tokenizer.tokenize('''unwanted running''' ) , ['''un''', '''##want''', '''##ed''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.tokenize('''unwantedX running''' ) , ['''[UNK]''', '''runn''', '''##ing'''] )
def __lowerCAmelCase ( self : Any ) -> List[str]:
"""simple docstring"""
self.assertTrue(_is_whitespace(''' ''' ) )
self.assertTrue(_is_whitespace('''\t''' ) )
self.assertTrue(_is_whitespace('''\r''' ) )
self.assertTrue(_is_whitespace('''\n''' ) )
self.assertTrue(_is_whitespace('''\u00A0''' ) )
self.assertFalse(_is_whitespace('''A''' ) )
self.assertFalse(_is_whitespace('''-''' ) )
def __lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
self.assertTrue(_is_control('''\u0005''' ) )
self.assertFalse(_is_control('''A''' ) )
self.assertFalse(_is_control(''' ''' ) )
self.assertFalse(_is_control('''\t''' ) )
self.assertFalse(_is_control('''\r''' ) )
def __lowerCAmelCase ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
self.assertTrue(_is_punctuation('''-''' ) )
self.assertTrue(_is_punctuation('''$''' ) )
self.assertTrue(_is_punctuation('''`''' ) )
self.assertTrue(_is_punctuation('''.''' ) )
self.assertFalse(_is_punctuation('''A''' ) )
self.assertFalse(_is_punctuation(''' ''' ) )
def __lowerCAmelCase ( self : str ) -> List[Any]:
"""simple docstring"""
_a = self.get_tokenizer()
_a = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(__a ) for t in ['''Test''', '''\xad''', '''test''']] , [['''[UNK]'''], [], ['''[UNK]''']] )
self.assertListEqual(
[rust_tokenizer.tokenize(__a ) for t in ['''Test''', '''\xad''', '''test''']] , [['''[UNK]'''], [], ['''[UNK]''']] )
@slow
def __lowerCAmelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
_a = self.tokenizer_class.from_pretrained('''google/mobilebert-uncased''' )
_a = tokenizer.encode('''sequence builders''' , add_special_tokens=__a )
_a = tokenizer.encode('''multi-sequence build''' , add_special_tokens=__a )
_a = tokenizer.build_inputs_with_special_tokens(__a )
_a = tokenizer.build_inputs_with_special_tokens(__a , __a )
assert encoded_sentence == [1_01] + text + [1_02]
assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]
def __lowerCAmelCase ( self : int ) -> List[str]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_a = self.rust_tokenizer_class.from_pretrained(__a , **__a )
_a = F'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
_a = tokenizer_r.encode_plus(
__a , return_attention_mask=__a , return_token_type_ids=__a , return_offsets_mapping=__a , add_special_tokens=__a , )
_a = tokenizer_r.do_lower_case if hasattr(__a , '''do_lower_case''' ) else False
_a = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), '''A'''),
((1, 2), ''','''),
((3, 5), '''na'''),
((5, 6), '''##ï'''),
((6, 8), '''##ve'''),
((9, 15), tokenizer_r.mask_token),
((16, 21), '''Allen'''),
((21, 23), '''##NL'''),
((23, 24), '''##P'''),
((25, 33), '''sentence'''),
((33, 34), '''.'''),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), '''a'''),
((1, 2), ''','''),
((3, 8), '''naive'''),
((9, 15), tokenizer_r.mask_token),
((16, 21), '''allen'''),
((21, 23), '''##nl'''),
((23, 24), '''##p'''),
((25, 33), '''sentence'''),
((33, 34), '''.'''),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['''input_ids'''] ) )
self.assertEqual([e[0] for e in expected_results] , tokens['''offset_mapping'''] )
def __lowerCAmelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
_a = ['''的''', '''人''', '''有''']
_a = ''''''.join(__a )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_a = True
_a = self.tokenizer_class.from_pretrained(__a , **__a )
_a = self.rust_tokenizer_class.from_pretrained(__a , **__a )
_a = tokenizer_p.encode(__a , add_special_tokens=__a )
_a = tokenizer_r.encode(__a , add_special_tokens=__a )
_a = tokenizer_r.convert_ids_to_tokens(__a )
_a = tokenizer_p.convert_ids_to_tokens(__a )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(__a , __a )
self.assertListEqual(__a , __a )
_a = False
_a = self.rust_tokenizer_class.from_pretrained(__a , **__a )
_a = self.tokenizer_class.from_pretrained(__a , **__a )
_a = tokenizer_r.encode(__a , add_special_tokens=__a )
_a = tokenizer_p.encode(__a , add_special_tokens=__a )
_a = tokenizer_r.convert_ids_to_tokens(__a )
_a = tokenizer_p.convert_ids_to_tokens(__a )
# it is expected that only the first Chinese character is not preceded by "##".
_a = [
F'##{token}' if idx != 0 else token for idx, token in enumerate(__a )
]
self.assertListEqual(__a , __a )
self.assertListEqual(__a , __a )
| 22 |
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case=None, **__snake_case ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()]
_UpperCamelCase = [x.strip() for x in open(__snake_case ).readlines()][: len(__snake_case )]
_UpperCamelCase = calculate_rouge(__snake_case, __snake_case, **__snake_case )
if save_path is not None:
save_json(__snake_case, __snake_case, indent=__snake_case )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 19 | 0 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowercase : Optional[Any] = logging.get_logger(__name__)
lowercase : int = {'vocab_file': 'vocab.txt', 'emoji_file': 'emoji.json'}
lowercase : Dict = {
'vocab_file': {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt',
},
'emoji_file': {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json',
},
}
lowercase : Tuple = {
'abeja/gpt-neox-japanese-2.7b': 2_0_4_8,
}
def __a ( A__ , A__ ) -> Union[str, Any]:
with open(__snake_case , "r" , encoding="utf-8" ) as f:
lowerCAmelCase = json.loads(f.read() )
lowerCAmelCase = collections.OrderedDict()
lowerCAmelCase = collections.OrderedDict()
lowerCAmelCase = collections.OrderedDict()
with open(__snake_case , "r" , encoding="utf-8" ) as f:
lowerCAmelCase = f.readlines()
lowerCAmelCase = [[t.rstrip("\n" )] if (t == "," or "," not in t) else t.rstrip("\n" ).split("," ) for t in token]
for idx, b in enumerate(__snake_case ):
lowerCAmelCase = b
lowerCAmelCase = idx
for wd in b:
lowerCAmelCase = idx
return vocab, raw_vocab, ids_to_tokens, emoji
class _lowerCAmelCase ( UpperCamelCase_ ):
"""simple docstring"""
lowerCAmelCase = VOCAB_FILES_NAMES
lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase = ['input_ids', 'attention_mask']
def __init__( self : List[str] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[Any]="<|endoftext|>" , SCREAMING_SNAKE_CASE : str="<|endoftext|>" , SCREAMING_SNAKE_CASE : Any="<|startoftext|>" , SCREAMING_SNAKE_CASE : Dict="<|endoftext|>" , SCREAMING_SNAKE_CASE : Any=False , **SCREAMING_SNAKE_CASE : Dict , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(
unk_token=__a , pad_token=__a , bos_token=__a , eos_token=__a , do_clean_text=__a , **__a , )
if not os.path.isfile(__a ):
raise ValueError(
f"Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained"
" model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
if not os.path.isfile(__a ):
raise ValueError(
f"Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google"
" pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
lowerCAmelCase = do_clean_text
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = load_vocab_and_emoji(__a , __a )
lowerCAmelCase = SubWordJapaneseTokenizer(
vocab=self.vocab , ids_to_tokens=self.ids_to_tokens , emoji=self.emoji )
@property
def __A ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
return len(self.raw_vocab )
def __A ( self : Dict ) -> Optional[int]:
"""simple docstring"""
return dict(self.raw_vocab , **self.added_tokens_encoder )
def __A ( self : Optional[int] , SCREAMING_SNAKE_CASE : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return self.subword_tokenizer.tokenize(__a , clean=self.do_clean_text )
def __A ( self : Tuple , SCREAMING_SNAKE_CASE : Dict ) -> Optional[Any]:
"""simple docstring"""
return self.vocab.get(__a , self.vocab.get(self.unk_token ) )
def __A ( self : str , SCREAMING_SNAKE_CASE : List[str] ) -> str:
"""simple docstring"""
return self.subword_tokenizer.convert_id_to_token(__a )
def __A ( self : Optional[int] , SCREAMING_SNAKE_CASE : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase = "".join(__a ).strip()
return out_string
def __A ( self : Optional[int] , SCREAMING_SNAKE_CASE : Tuple ) -> List[int]:
"""simple docstring"""
lowerCAmelCase = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__a , add_special_tokens=__a ) + [self.eos_token_id] )
if len(__a ) > self.model_max_length:
lowerCAmelCase = input_ids[-self.model_max_length :]
return input_ids
def __A ( self : List[Any] , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Any = None ) -> Tuple[str]:
"""simple docstring"""
lowerCAmelCase = 0
if os.path.isdir(__a ):
lowerCAmelCase = os.path.join(
__a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase = os.path.join(
__a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"] )
else:
lowerCAmelCase = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
)
lowerCAmelCase = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
)
with open(__a , "w" , encoding="utf-8" ) as writer:
for token_index, token in self.ids_to_tokens.items():
if index != token_index:
logger.warning(
f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!" )
lowerCAmelCase = token_index
writer.write(",".join(__a ) + "\n" )
index += 1
with open(__a , "w" , encoding="utf-8" ) as writer:
json.dump(self.emoji , __a )
return vocab_file, emoji_file
class _lowerCAmelCase ( UpperCamelCase_ ):
"""simple docstring"""
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Union[str, Any] ) -> int:
"""simple docstring"""
lowerCAmelCase = vocab # same as swe
lowerCAmelCase = ids_to_tokens # same as bpe
lowerCAmelCase = emoji
lowerCAmelCase = np.max([len(__a ) for w in self.vocab.keys()] )
lowerCAmelCase = re.compile(R"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)" )
lowerCAmelCase = re.compile(R"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*" )
lowerCAmelCase = re.compile(R"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}" )
lowerCAmelCase = re.compile(
R"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
lowerCAmelCase = re.compile(
R"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
lowerCAmelCase = re.compile(
R"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*" )
lowerCAmelCase = "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
lowerCAmelCase = "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
lowerCAmelCase = str.maketrans({k: "<BLOCK>" for k in keisen + blocks} )
def __len__( self : Any ) -> Optional[Any]:
"""simple docstring"""
return len(self.ids_to_tokens )
def __A ( self : Any , SCREAMING_SNAKE_CASE : List[str] ) -> Dict:
"""simple docstring"""
lowerCAmelCase = self.content_repattera.sub("<URL>" , __a )
lowerCAmelCase = self.content_repattera.sub("<EMAIL>" , __a )
lowerCAmelCase = self.content_repattera.sub("<TEL>" , __a )
lowerCAmelCase = self.content_repattera.sub("<DATE>" , __a )
lowerCAmelCase = self.content_repattera.sub("<DATE>" , __a )
lowerCAmelCase = self.content_repattera.sub("<PRICE>" , __a )
lowerCAmelCase = content.translate(self.content_transa )
while "<BLOCK><BLOCK>" in content:
lowerCAmelCase = content.replace("<BLOCK><BLOCK>" , "<BLOCK>" )
return content
def __A ( self : Tuple , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : List[Any]=False ) -> Any:
"""simple docstring"""
lowerCAmelCase = text.replace(" " , "<SP>" )
lowerCAmelCase = text.replace(" " , "<SP>" )
lowerCAmelCase = text.replace("\r\n" , "<BR>" )
lowerCAmelCase = text.replace("\n" , "<BR>" )
lowerCAmelCase = text.replace("\r" , "<BR>" )
lowerCAmelCase = text.replace("\t" , "<TAB>" )
lowerCAmelCase = text.replace("—" , "ー" )
lowerCAmelCase = text.replace("−" , "ー" )
for k, v in self.emoji["emoji"].items():
if k in text:
lowerCAmelCase = text.replace(__a , __a )
if clean:
lowerCAmelCase = self.clean_text(__a )
def check_simbol(SCREAMING_SNAKE_CASE : Union[str, Any] ):
lowerCAmelCase = x.encode()
if len(__a ) == 1 and len(__a ) == 2:
lowerCAmelCase = (int(e[0] ) << 8) + int(e[1] )
if (
(c >= 0xC2A1 and c <= 0xC2BF)
or (c >= 0xC780 and c <= 0xC783)
or (c >= 0xCAB9 and c <= 0xCBBF)
or (c >= 0xCC80 and c <= 0xCDA2)
):
return True
return False
def checkuae(SCREAMING_SNAKE_CASE : List[Any] ):
lowerCAmelCase = x.encode()
if len(__a ) == 1 and len(__a ) == 3:
lowerCAmelCase = (int(e[0] ) << 1_6) + (int(e[1] ) << 8) + int(e[2] )
if c >= 0xE2_8080 and c <= 0xE2_B07F:
return True
return False
lowerCAmelCase = 0
lowerCAmelCase = []
while pos < len(__a ):
lowerCAmelCase = min(len(__a ) , pos + self.maxlen + 1 ) if text[pos] == "<" else pos + 3
lowerCAmelCase = [] # (token_id, token, pos)
for e in range(__a , __a , -1 ):
lowerCAmelCase = text[pos:e]
if wd in self.vocab:
if wd[0] == "<" and len(__a ) > 2:
lowerCAmelCase = [(self.vocab[wd], wd, e)]
break
else:
candidates.append((self.vocab[wd], wd, e) )
if len(__a ) > 0:
# the smallest token_id is adopted
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = sorted(__a , key=lambda SCREAMING_SNAKE_CASE : x[0] )[0]
result.append(__a )
lowerCAmelCase = e
else:
lowerCAmelCase = pos + 1
lowerCAmelCase = text[pos:end]
if check_simbol(__a ):
result.append("<KIGOU>" )
elif checkuae(__a ):
result.append("<U2000U2BFF>" )
else:
for i in wd.encode("utf-8" ):
result.append("<|byte%d|>" % i )
lowerCAmelCase = end
return result
def __A ( self : List[Any] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : List[Any]="\n" ) -> int:
"""simple docstring"""
lowerCAmelCase = []
lowerCAmelCase = []
lowerCAmelCase = self.ids_to_tokens[index][0]
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2] ) )
else:
if len(__a ) > 0:
words.append(bytearray(__a ).decode("utf-8" , errors="replace" ) )
lowerCAmelCase = []
if word[:7] == "<|emoji" and word[-2:] == "|>":
words.append(self.emoji["emoji_inv"][word] )
elif word == "<SP>":
words.append(" " )
elif word == "<BR>":
words.append(__a )
elif word == "<TAB>":
words.append("\t" )
elif word == "<BLOCK>":
words.append("▀" )
elif word == "<KIGOU>":
words.append("ǀ" )
elif word == "<U2000U2BFF>":
words.append("‖" )
else:
words.append(__a )
if len(__a ) > 0:
words.append(bytearray(__a ).decode("utf-8" , errors="replace" ) )
lowerCAmelCase = "".join(__a )
return text
| 649 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = ['image_processor', 'tokenizer']
lowercase__ = 'ViTImageProcessor'
lowercase__ = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self , __a=None , __a=None , **__a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __a , )
_UpperCamelCase = kwargs.pop('''feature_extractor''')
_UpperCamelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''')
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''')
super().__init__(__a , __a)
def __call__( self , __a=None , __a=None , __a=None , __a=None , **__a) -> Tuple:
'''simple docstring'''
if text is None and visual_prompt is None and images is None:
raise ValueError('''You have to specify either text, visual prompt or images.''')
if text is not None and visual_prompt is not None:
raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''')
if text is not None:
_UpperCamelCase = self.tokenizer(__a , return_tensors=__a , **__a)
if visual_prompt is not None:
_UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a)
if images is not None:
_UpperCamelCase = self.image_processor(__a , return_tensors=__a , **__a)
if visual_prompt is not None and images is not None:
_UpperCamelCase = {
'''pixel_values''': image_features.pixel_values,
'''conditional_pixel_values''': prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
_UpperCamelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
_UpperCamelCase = {
'''conditional_pixel_values''': prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**__a) , tensor_type=__a)
def UpperCAmelCase ( self , *__a , **__a) -> Any:
'''simple docstring'''
return self.tokenizer.batch_decode(*__a , **__a)
def UpperCAmelCase ( self , *__a , **__a) -> List[str]:
'''simple docstring'''
return self.tokenizer.decode(*__a , **__a)
@property
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __a , )
return self.image_processor_class
@property
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __a , )
return self.image_processor
| 19 | 0 |
'''simple docstring'''
import argparse
import datetime
def __UpperCamelCase( _A : str ):
'''simple docstring'''
UpperCAmelCase__ : Dict = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday''',
'''3''': '''Wednesday''',
'''4''': '''Thursday''',
'''5''': '''Friday''',
'''6''': '''Saturday''',
}
UpperCAmelCase__ : Tuple = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(__snake_case ) < 11:
raise ValueError('''Must be 10 characters long''' )
# Get month
UpperCAmelCase__ : Union[str, Any] = int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 13:
raise ValueError('''Month must be between 1 - 12''' )
UpperCAmelCase__ : Any = date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('''Date separator must be \'-\' or \'/\'''' )
# Get day
UpperCAmelCase__ : List[str] = int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 32:
raise ValueError('''Date must be between 1 - 31''' )
# Get second separator
UpperCAmelCase__ : Any = date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('''Date separator must be \'-\' or \'/\'''' )
# Get year
UpperCAmelCase__ : int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 45 < y < 85_00:
raise ValueError(
'''Year out of range. There has to be some sort of limit...right?''' )
# Get datetime obj for validation
UpperCAmelCase__ : int = datetime.date(int(__snake_case ) , int(__snake_case ) , int(__snake_case ) )
# Start math
if m <= 2:
UpperCAmelCase__ : Union[str, Any] = y - 1
UpperCAmelCase__ : Dict = m + 12
# maths var
UpperCAmelCase__ : Tuple = int(str(__snake_case )[:2] )
UpperCAmelCase__ : Optional[int] = int(str(__snake_case )[2:] )
UpperCAmelCase__ : int = int(2.6 * m - 5.3_9 )
UpperCAmelCase__ : Optional[Any] = int(c / 4 )
UpperCAmelCase__ : Tuple = int(k / 4 )
UpperCAmelCase__ : List[str] = int(d + k )
UpperCAmelCase__ : int = int(t + u + v + x )
UpperCAmelCase__ : Optional[int] = int(z - (2 * c) )
UpperCAmelCase__ : Dict = round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError('''The date was evaluated incorrectly. Contact developer.''' )
# Response
UpperCAmelCase__ : List[str] = F'''Your date {date_input}, is a {days[str(__snake_case )]}!'''
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase__ : str = argparse.ArgumentParser(
description=(
'Find out what day of the week nearly any date is or was. Enter '
'date as a string in the mm-dd-yyyy or mm/dd/yyyy format'
)
)
parser.add_argument(
'date_input', type=str, help='Date as a string (mm-dd-yyyy or mm/dd/yyyy)'
)
UpperCamelCase__ : Dict = parser.parse_args()
zeller(args.date_input)
| 614 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import UperNetForSemanticSegmentation
from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCAmelCase:
def __init__( self , __a , __a=13 , __a=32 , __a=3 , __a=4 , __a=[10, 20, 30, 40] , __a=[2, 2, 3, 2] , __a=True , __a=True , __a=37 , __a="gelu" , __a=10 , __a=0.02 , __a=["stage2", "stage3", "stage4"] , __a=3 , __a=None , ) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = image_size
_UpperCamelCase = num_channels
_UpperCamelCase = num_stages
_UpperCamelCase = hidden_sizes
_UpperCamelCase = depths
_UpperCamelCase = is_training
_UpperCamelCase = use_labels
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = out_features
_UpperCamelCase = num_labels
_UpperCamelCase = scope
_UpperCamelCase = num_stages
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_UpperCamelCase = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
return ConvNextConfig(
num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , )
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
return UperNetConfig(
backbone_config=self.get_backbone_config() , hidden_size=5_12 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=__a , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=2_56 , auxiliary_num_convs=1 , auxiliary_concat_input=__a , loss_ignore_index=2_55 , num_labels=self.num_labels , )
def UpperCAmelCase ( self , __a , __a , __a) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = UperNetForSemanticSegmentation(config=__a)
model.to(__a)
model.eval()
_UpperCamelCase = model(__a)
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size))
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase__ = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
lowercase__ = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = UperNetModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=37)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
return
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(__a)
_UpperCamelCase = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCamelCase = [*signature.parameters.keys()]
_UpperCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__a)
@unittest.skip(reason='''UperNet does not use inputs_embeds''')
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason='''UperNet does not support input and output embeddings''')
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''UperNet does not have a base model''')
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason='''UperNet does not have a base model''')
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''')
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
pass
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
def check_hidden_states_output(__a , __a , __a):
_UpperCamelCase = model_class(__a)
model.to(__a)
model.eval()
with torch.no_grad():
_UpperCamelCase = model(**self._prepare_for_class(__a , __a))
_UpperCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_UpperCamelCase = self.model_tester.num_stages
self.assertEqual(len(__a) , expected_num_stages + 1)
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCamelCase = True
check_hidden_states_output(__a , __a , __a)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCamelCase = True
check_hidden_states_output(__a , __a , __a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCamelCase = _config_zero_init(__a)
_UpperCamelCase = _config_zero_init(configs_no_init.backbone_config)
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(config=__a)
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@unittest.skip(reason='''UperNet does not have tied weights''')
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
pass
@slow
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained(__a)
self.assertIsNotNone(__a)
def lowerCamelCase__ ( ) -> int:
"""simple docstring"""
_UpperCamelCase = hf_hub_download(
repo_id='''hf-internal-testing/fixtures_ade20k''', repo_type='''dataset''', filename='''ADE_val_00000001.jpg''' )
_UpperCamelCase = Image.open(__snake_case ).convert('''RGB''' )
return image
@require_torch
@require_vision
@slow
class _UpperCAmelCase( unittest.TestCase ):
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny''')
_UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny''').to(__a)
_UpperCamelCase = prepare_img()
_UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a)
with torch.no_grad():
_UpperCamelCase = model(**__a)
_UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12))
self.assertEqual(outputs.logits.shape , __a)
_UpperCamelCase = torch.tensor(
[[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]).to(__a)
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4))
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny''')
_UpperCamelCase = UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny''').to(__a)
_UpperCamelCase = prepare_img()
_UpperCamelCase = processor(images=__a , return_tensors='''pt''').to(__a)
with torch.no_grad():
_UpperCamelCase = model(**__a)
_UpperCamelCase = torch.Size((1, model.config.num_labels, 5_12, 5_12))
self.assertEqual(outputs.logits.shape , __a)
_UpperCamelCase = torch.tensor(
[[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]]).to(__a)
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , __a , atol=1e-4))
| 19 | 0 |
'''simple docstring'''
import requests
lowercase ='YOUR API KEY'
def lowerCamelCase__ ( __lowerCamelCase : Any , __lowerCamelCase : str = giphy_api_key ):
'''simple docstring'''
_UpperCAmelCase : Any ='+'.join(query.split() )
_UpperCAmelCase : Any =f"https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"
_UpperCAmelCase : Optional[Any] =requests.get(__snake_case ).json()['data']
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print('\n'.join(get_gifs('space ship')))
| 446 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = (DDPMScheduler,)
def UpperCAmelCase ( self , **__a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = {
'''num_train_timesteps''': 10_00,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''variance_type''': '''fixed_small''',
'''clip_sample''': True,
}
config.update(**__a)
return config
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
for timesteps in [1, 5, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=__a)
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2]):
self.check_over_configs(beta_start=__a , beta_end=__a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__a)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=__a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
self.check_over_configs(thresholding=__a)
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=__a , prediction_type=__a , sample_max_value=__a , )
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=__a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
for t in [0, 5_00, 9_99]:
self.check_over_forward(time_step=__a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0979)) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1e-5
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = len(__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter
_UpperCamelCase = torch.manual_seed(0)
for t in reversed(range(__a)):
# 1. predict noise residual
_UpperCamelCase = model(__a , __a)
# 2. predict previous mean of sample x_t-1
_UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCamelCase = pred_prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
assert abs(result_sum.item() - 258.9606) < 1e-2
assert abs(result_mean.item() - 0.3372) < 1e-3
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config(prediction_type='''v_prediction''')
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = len(__a)
_UpperCamelCase = self.dummy_model()
_UpperCamelCase = self.dummy_sample_deter
_UpperCamelCase = torch.manual_seed(0)
for t in reversed(range(__a)):
# 1. predict noise residual
_UpperCamelCase = model(__a , __a)
# 2. predict previous mean of sample x_t-1
_UpperCamelCase = scheduler.step(__a , __a , __a , generator=__a).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_UpperCamelCase = pred_prev_sample
_UpperCamelCase = torch.sum(torch.abs(__a))
_UpperCamelCase = torch.mean(torch.abs(__a))
assert abs(result_sum.item() - 202.0296) < 1e-2
assert abs(result_mean.item() - 0.2631) < 1e-3
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [1_00, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=__a)
_UpperCamelCase = scheduler.timesteps
for i, timestep in enumerate(__a):
if i == len(__a) - 1:
_UpperCamelCase = -1
else:
_UpperCamelCase = timesteps[i + 1]
_UpperCamelCase = scheduler.previous_timestep(__a)
_UpperCamelCase = prev_t.item()
self.assertEqual(__a , __a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [1_00, 87, 50, 51, 0]
with self.assertRaises(__a , msg='''`custom_timesteps` must be in descending order.'''):
scheduler.set_timesteps(timesteps=__a)
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [1_00, 87, 50, 1, 0]
_UpperCamelCase = len(__a)
with self.assertRaises(__a , msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
scheduler.set_timesteps(num_inference_steps=__a , timesteps=__a)
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.scheduler_classes[0]
_UpperCamelCase = self.get_scheduler_config()
_UpperCamelCase = scheduler_class(**__a)
_UpperCamelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
__a , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ):
scheduler.set_timesteps(timesteps=__a)
| 19 | 0 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase (__lowerCamelCase ):
"""simple docstring"""
def __init__( self : List[Any], _UpperCAmelCase : Tuple, _UpperCAmelCase : List[str], _UpperCAmelCase : List[Any] ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
self.register_modules(vqvae=__a, unet=__a, scheduler=__a )
@torch.no_grad()
def __call__( self : List[Any], _UpperCAmelCase : List[Any] = 1, _UpperCAmelCase : int = None, _UpperCAmelCase : Any = 0.0, _UpperCAmelCase : List[str] = 5_0, _UpperCAmelCase : List[Any] = "pil", _UpperCAmelCase : Any = True, **_UpperCAmelCase : List[Any], ) -> Union[Tuple, ImagePipelineOutput]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size), generator=__a, )
SCREAMING_SNAKE_CASE__ : Optional[int] = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
SCREAMING_SNAKE_CASE__ : List[str] = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(__a )
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
SCREAMING_SNAKE_CASE__ : Any = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
SCREAMING_SNAKE_CASE__ : Dict = {}
if accepts_eta:
SCREAMING_SNAKE_CASE__ : int = eta
for t in self.progress_bar(self.scheduler.timesteps ):
SCREAMING_SNAKE_CASE__ : int = self.scheduler.scale_model_input(__a, __a )
# predict the noise residual
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.unet(__a, __a ).sample
# compute the previous noisy sample x_t -> x_t-1
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler.step(__a, __a, __a, **__a ).prev_sample
# decode the image latents with the VAE
SCREAMING_SNAKE_CASE__ : int = self.vqvae.decode(__a ).sample
SCREAMING_SNAKE_CASE__ : Optional[Any] = (image / 2 + 0.5).clamp(0, 1 )
SCREAMING_SNAKE_CASE__ : str = image.cpu().permute(0, 2, 3, 1 ).numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.numpy_to_pil(__a )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__a )
| 663 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
_a = 100
_a = set(range(3, NUM_PRIMES, 2))
primes.add(2)
_a = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=1_00 )
def lowerCamelCase__ ( __snake_case ) -> set[int]:
"""simple docstring"""
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
_UpperCamelCase = set()
_UpperCamelCase = 42
_UpperCamelCase = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def lowerCamelCase__ ( __snake_case = 50_00 ) -> int | None:
"""simple docstring"""
for number_to_partition in range(1, __snake_case ):
if len(partition(__snake_case ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 19 | 0 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class __magic_name__ :
def __init__( self : List[str] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Any=13 , UpperCamelCase__ : Union[str, Any]=7 , UpperCamelCase__ : int=True , UpperCamelCase__ : Union[str, Any]=True , UpperCamelCase__ : Dict=True , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : Optional[Any]=99 , UpperCamelCase__ : List[str]=32 , UpperCamelCase__ : int=2 , UpperCamelCase__ : List[str]=4 , UpperCamelCase__ : List[str]=37 , UpperCamelCase__ : Optional[int]="gelu" , UpperCamelCase__ : Tuple=0.1 , UpperCamelCase__ : int=0.1 , UpperCamelCase__ : int=5_12 , UpperCamelCase__ : Optional[int]=16 , UpperCamelCase__ : List[Any]=2 , UpperCamelCase__ : int=0.02 , UpperCamelCase__ : Dict=3 , UpperCamelCase__ : List[str]=4 , UpperCamelCase__ : Optional[Any]=None , ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase = parent
UpperCAmelCase = 13
UpperCAmelCase = 7
UpperCAmelCase = True
UpperCAmelCase = True
UpperCAmelCase = True
UpperCAmelCase = True
UpperCAmelCase = 99
UpperCAmelCase = 32
UpperCAmelCase = 2
UpperCAmelCase = 4
UpperCAmelCase = 37
UpperCAmelCase = "gelu"
UpperCAmelCase = 0.1
UpperCAmelCase = 0.1
UpperCAmelCase = 5_12
UpperCAmelCase = 16
UpperCAmelCase = 2
UpperCAmelCase = 0.02
UpperCAmelCase = 3
UpperCAmelCase = 4
UpperCAmelCase = None
def SCREAMING_SNAKE_CASE_ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase = None
if self.use_input_mask:
UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase = None
if self.use_token_type_ids:
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=__a , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , UpperCamelCase__ : int , UpperCamelCase__ : Tuple , UpperCamelCase__ : Tuple , UpperCamelCase__ : str , UpperCamelCase__ : List[Any] , UpperCamelCase__ : str , UpperCamelCase__ : Tuple ) -> List[str]:
'''simple docstring'''
UpperCAmelCase = TFRoFormerModel(config=__a )
UpperCAmelCase = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
UpperCAmelCase = [input_ids, input_mask]
UpperCAmelCase = model(__a )
UpperCAmelCase = model(__a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE_ ( self : str , UpperCamelCase__ : Dict , UpperCamelCase__ : str , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[int] ) -> List[str]:
'''simple docstring'''
UpperCAmelCase = True
UpperCAmelCase = TFRoFormerForCausalLM(config=__a )
UpperCAmelCase = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase = model(__a )["logits"]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Dict , UpperCamelCase__ : Dict , UpperCamelCase__ : str , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[str] ) -> int:
'''simple docstring'''
UpperCAmelCase = TFRoFormerForMaskedLM(config=__a )
UpperCAmelCase = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase = model(__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str , UpperCamelCase__ : Optional[int] ) -> Tuple:
'''simple docstring'''
UpperCAmelCase = self.num_labels
UpperCAmelCase = TFRoFormerForSequenceClassification(config=__a )
UpperCAmelCase = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase = model(__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self : str , UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int , UpperCamelCase__ : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase = self.num_choices
UpperCAmelCase = TFRoFormerForMultipleChoice(config=__a )
UpperCAmelCase = tf.tile(tf.expand_dims(__a , 1 ) , (1, self.num_choices, 1) )
UpperCAmelCase = tf.tile(tf.expand_dims(__a , 1 ) , (1, self.num_choices, 1) )
UpperCAmelCase = tf.tile(tf.expand_dims(__a , 1 ) , (1, self.num_choices, 1) )
UpperCAmelCase = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
UpperCAmelCase = model(__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCamelCase__ : Dict , UpperCamelCase__ : Dict , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Any , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any] ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase = self.num_labels
UpperCAmelCase = TFRoFormerForTokenClassification(config=__a )
UpperCAmelCase = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase = model(__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self : Dict , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Dict , UpperCamelCase__ : Any , UpperCamelCase__ : int , UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Tuple ) -> str:
'''simple docstring'''
UpperCAmelCase = TFRoFormerForQuestionAnswering(config=__a )
UpperCAmelCase = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase = model(__a )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
UpperCAmelCase = self.prepare_config_and_inputs()
(
(
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) ,
) = config_and_inputs
UpperCAmelCase = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class __magic_name__ ( A__, A__, unittest.TestCase ):
lowercase : List[Any] =(
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
lowercase : str =(
{
'''feature-extraction''': TFRoFormerModel,
'''fill-mask''': TFRoFormerForMaskedLM,
'''question-answering''': TFRoFormerForQuestionAnswering,
'''text-classification''': TFRoFormerForSequenceClassification,
'''text-generation''': TFRoFormerForCausalLM,
'''token-classification''': TFRoFormerForTokenClassification,
'''zero-shot''': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
lowercase : Optional[Any] =False
lowercase : str =False
def SCREAMING_SNAKE_CASE_ ( self : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Union[str, Any] ) -> List[str]:
'''simple docstring'''
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def SCREAMING_SNAKE_CASE_ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase = TFRoFormerModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=__a , hidden_size=37 )
def SCREAMING_SNAKE_CASE_ ( self : Any ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
def SCREAMING_SNAKE_CASE_ ( self : Dict ) -> Any:
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__a )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*__a )
def SCREAMING_SNAKE_CASE_ ( self : str ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__a )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__a )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Tuple:
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__a )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__a )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase = TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base" )
self.assertIsNotNone(__a )
@require_tf
class __magic_name__ ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
UpperCAmelCase = TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base" )
UpperCAmelCase = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCAmelCase = model(__a )[0]
# TODO Replace vocab size
UpperCAmelCase = 5_00_00
UpperCAmelCase = [1, 6, vocab_size]
self.assertEqual(output.shape , __a )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
UpperCAmelCase = tf.constant(
[
[
[-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46],
[-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07],
[-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , __a , atol=1e-4 )
@require_tf
class __magic_name__ ( unittest.TestCase ):
lowercase : Any =1E-4
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
UpperCAmelCase = tf.constant([[4, 10]] )
UpperCAmelCase = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
UpperCAmelCase = emba(input_ids.shape )
UpperCAmelCase = tf.constant(
[[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]] )
tf.debugging.assert_near(__a , __a , atol=self.tolerance )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
UpperCAmelCase = tf.constant(
[
[0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00],
[0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17],
[0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70],
] )
UpperCAmelCase = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12 , embedding_dim=5_12 )
emba([2, 16, 5_12] )
UpperCAmelCase = emba.weight[:3, :5]
tf.debugging.assert_near(__a , __a , atol=self.tolerance )
@require_tf
class __magic_name__ ( unittest.TestCase ):
lowercase : int =1E-4
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
UpperCAmelCase = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
UpperCAmelCase = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 )
UpperCAmelCase = embed_positions([2, 16, 7_68] )[None, None, :, :]
UpperCAmelCase , UpperCAmelCase = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
__a , __a , __a )
UpperCAmelCase = tf.constant(
[
[0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00],
[-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43],
[-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85],
[-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71],
[0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80],
[3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53],
] )
UpperCAmelCase = tf.constant(
[
[0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00],
[0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43],
[1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85],
[2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71],
[-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80],
[-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , __a , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , __a , atol=self.tolerance )
| 323 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> np.array:
"""simple docstring"""
_UpperCamelCase = int(np.ceil((x_end - xa) / step_size ) )
_UpperCamelCase = np.zeros((n + 1,) )
_UpperCamelCase = ya
_UpperCamelCase = xa
for k in range(__snake_case ):
_UpperCamelCase = y[k] + step_size * ode_func(__snake_case, y[k] )
_UpperCamelCase = y[k] + (
(step_size / 2) * (ode_func(__snake_case, y[k] ) + ode_func(x + step_size, __snake_case ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 19 | 0 |
'''simple docstring'''
import numpy as np
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None ) -> Dict:
self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a )
def _lowerCAmelCase( self , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None ) -> Dict:
if red is not None:
lowercase__ : Any = red
if green is not None:
lowercase__ : List[Any] = green
if blue is not None:
lowercase__ : Union[str, Any] = blue
if red_edge is not None:
lowercase__ : Optional[int] = red_edge
if nir is not None:
lowercase__ : List[str] = nir
return True
def _lowerCAmelCase( self , __lowerCAmelCase="" , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None ) -> List[str]:
self.set_matricies(red=__a , green=__a , blue=__a , red_edge=__a , nir=__a )
lowercase__ : Union[str, Any] = {
'''ARVI2''': self.arvaa,
'''CCCI''': self.ccci,
'''CVI''': self.cvi,
'''GLI''': self.gli,
'''NDVI''': self.ndvi,
'''BNDVI''': self.bndvi,
'''redEdgeNDVI''': self.red_edge_ndvi,
'''GNDVI''': self.gndvi,
'''GBNDVI''': self.gbndvi,
'''GRNDVI''': self.grndvi,
'''RBNDVI''': self.rbndvi,
'''PNDVI''': self.pndvi,
'''ATSAVI''': self.atsavi,
'''BWDRVI''': self.bwdrvi,
'''CIgreen''': self.ci_green,
'''CIrededge''': self.ci_rededge,
'''CI''': self.ci,
'''CTVI''': self.ctvi,
'''GDVI''': self.gdvi,
'''EVI''': self.evi,
'''GEMI''': self.gemi,
'''GOSAVI''': self.gosavi,
'''GSAVI''': self.gsavi,
'''Hue''': self.hue,
'''IVI''': self.ivi,
'''IPVI''': self.ipvi,
'''I''': self.i,
'''RVI''': self.rvi,
'''MRVI''': self.mrvi,
'''MSAVI''': self.m_savi,
'''NormG''': self.norm_g,
'''NormNIR''': self.norm_nir,
'''NormR''': self.norm_r,
'''NGRDI''': self.ngrdi,
'''RI''': self.ri,
'''S''': self.s,
'''IF''': self._if,
'''DVI''': self.dvi,
'''TVI''': self.tvi,
'''NDRE''': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('''Index not in the list!''' )
return False
def _lowerCAmelCase( self ) -> List[Any]:
return -0.1_8 + (1.1_7 * ((self.nir - self.red) / (self.nir + self.red)))
def _lowerCAmelCase( self ) -> Any:
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def _lowerCAmelCase( self ) -> Optional[int]:
return self.nir * (self.red / (self.green**2))
def _lowerCAmelCase( self ) -> str:
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def _lowerCAmelCase( self ) -> List[str]:
return (self.nir - self.red) / (self.nir + self.red)
def _lowerCAmelCase( self ) -> str:
return (self.nir - self.blue) / (self.nir + self.blue)
def _lowerCAmelCase( self ) -> List[Any]:
return (self.redEdge - self.red) / (self.redEdge + self.red)
def _lowerCAmelCase( self ) -> Optional[int]:
return (self.nir - self.green) / (self.nir + self.green)
def _lowerCAmelCase( self ) -> Optional[Any]:
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def _lowerCAmelCase( self ) -> Tuple:
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def _lowerCAmelCase( self ) -> List[Any]:
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def _lowerCAmelCase( self ) -> List[str]:
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def _lowerCAmelCase( self , __lowerCAmelCase=0.0_8 , __lowerCAmelCase=1.2_2 , __lowerCAmelCase=0.0_3 ) -> Optional[Any]:
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def _lowerCAmelCase( self ) -> Dict:
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def _lowerCAmelCase( self ) -> List[str]:
return (self.nir / self.green) - 1
def _lowerCAmelCase( self ) -> List[Any]:
return (self.nir / self.redEdge) - 1
def _lowerCAmelCase( self ) -> Union[str, Any]:
return (self.red - self.blue) / self.red
def _lowerCAmelCase( self ) -> Optional[int]:
lowercase__ : int = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def _lowerCAmelCase( self ) -> Optional[int]:
return self.nir - self.green
def _lowerCAmelCase( self ) -> List[str]:
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def _lowerCAmelCase( self ) -> Tuple:
lowercase__ : Optional[int] = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.2_5 * n) - (self.red - 0.1_2_5) / (1 - self.red)
def _lowerCAmelCase( self , __lowerCAmelCase=0.1_6 ) -> Optional[Any]:
return (self.nir - self.green) / (self.nir + self.green + y)
def _lowerCAmelCase( self , __lowerCAmelCase=0.5 ) -> Dict:
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def _lowerCAmelCase( self ) -> Dict:
return np.arctan(
((2 * self.red - self.green - self.blue) / 3_0.5) * (self.green - self.blue) )
def _lowerCAmelCase( self , __lowerCAmelCase=None , __lowerCAmelCase=None ) -> Any:
return (self.nir - b) / (a * self.red)
def _lowerCAmelCase( self ) -> Optional[Any]:
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def _lowerCAmelCase( self ) -> Optional[Any]:
return (self.red + self.green + self.blue) / 3_0.5
def _lowerCAmelCase( self ) -> Any:
return self.nir / self.red
def _lowerCAmelCase( self ) -> Tuple:
return (self.rvi() - 1) / (self.rvi() + 1)
def _lowerCAmelCase( self ) -> List[Any]:
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def _lowerCAmelCase( self ) -> Optional[int]:
return self.green / (self.nir + self.red + self.green)
def _lowerCAmelCase( self ) -> str:
return self.nir / (self.nir + self.red + self.green)
def _lowerCAmelCase( self ) -> Optional[int]:
return self.red / (self.nir + self.red + self.green)
def _lowerCAmelCase( self ) -> Tuple:
return (self.green - self.red) / (self.green + self.red)
def _lowerCAmelCase( self ) -> Dict:
return (self.red - self.green) / (self.red + self.green)
def _lowerCAmelCase( self ) -> Any:
lowercase__ : Optional[int] = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
lowercase__ : str = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def _lowerCAmelCase( self ) -> str:
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def _lowerCAmelCase( self ) -> int:
return self.nir / self.red
def _lowerCAmelCase( self ) -> Any:
return (self.ndvi() + 0.5) ** (1 / 2)
def _lowerCAmelCase( self ) -> Union[str, Any]:
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 152 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_a = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
""" Distillation"""
)
)
parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
parser.add_argument("""--vocab_transform""", action="""store_true""")
_a = parser.parse_args()
if args.model_type == "bert":
_a = BertForMaskedLM.from_pretrained(args.model_name)
_a = """bert"""
else:
raise ValueError("""args.model_type should be \"bert\".""")
_a = model.state_dict()
_a = {}
for w in ["word_embeddings", "position_embeddings"]:
_a = state_dict[F"""{prefix}.embeddings.{w}.weight"""]
for w in ["weight", "bias"]:
_a = state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]
_a = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
]
_a = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
]
std_idx += 1
_a = state_dict["""cls.predictions.decoder.weight"""]
_a = state_dict["""cls.predictions.bias"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
_a = state_dict[F"""cls.predictions.transform.dense.{w}"""]
_a = state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]
print(F"""N layers selected for distillation: {std_idx}""")
print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 19 | 0 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small" )
lowerCamelCase : int = AutoTokenizer.from_pretrained("google/mt5-small" )
lowerCamelCase : Optional[int] = tokenizer("Hello there" , return_tensors="np" ).input_ids
lowerCamelCase : str = tokenizer("Hi I am" , return_tensors="np" ).input_ids
lowerCamelCase : Optional[Any] = shift_tokens_right(__a , model.config.pad_token_id , model.config.decoder_start_token_id )
lowerCamelCase : Dict = model(__a , decoder_input_ids=__a ).logits
lowerCamelCase : Any = optax.softmax_cross_entropy(__a , onehot(__a , logits.shape[-1] ) ).mean()
lowerCamelCase : str = -(labels.shape[-1] * loss.item())
lowerCamelCase : Optional[int] = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 340 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
_a = """platform"""
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class _UpperCAmelCase:
lowercase__ = PegasusConfig
lowercase__ = {}
lowercase__ = 'gelu'
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=False , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a=0.1 , __a=0.1 , __a=20 , __a=2 , __a=1 , __a=0 , ) -> int:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = eos_token_id
_UpperCamelCase = pad_token_id
_UpperCamelCase = bos_token_id
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size).clip(3 , self.vocab_size)
_UpperCamelCase = np.expand_dims(np.array([self.eos_token_id] * self.batch_size) , 1)
_UpperCamelCase = np.concatenate([input_ids, eos_tensor] , axis=1)
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCamelCase = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_UpperCamelCase = prepare_pegasus_inputs_dict(__a , __a , __a)
return config, inputs_dict
def UpperCAmelCase ( self , __a , __a , __a) -> Tuple:
'''simple docstring'''
_UpperCamelCase = 20
_UpperCamelCase = model_class_name(__a)
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''])
_UpperCamelCase , _UpperCamelCase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
_UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a)
_UpperCamelCase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''')
_UpperCamelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCamelCase = model.decode(
decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , )
_UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''')
_UpperCamelCase = model.decode(
decoder_input_ids[:, -1:] , __a , decoder_attention_mask=__a , past_key_values=outputs_cache.past_key_values , decoder_position_ids=__a , )
_UpperCamelCase = model.decode(__a , __a)
_UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''')
def UpperCAmelCase ( self , __a , __a , __a) -> Tuple:
'''simple docstring'''
_UpperCamelCase = 20
_UpperCamelCase = model_class_name(__a)
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''])
_UpperCamelCase , _UpperCamelCase = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
_UpperCamelCase = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
] , axis=-1 , )
_UpperCamelCase = model.init_cache(decoder_input_ids.shape[0] , __a , __a)
_UpperCamelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_UpperCamelCase = model.decode(
decoder_input_ids[:, :-1] , __a , decoder_attention_mask=__a , past_key_values=__a , decoder_position_ids=__a , )
_UpperCamelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''')
_UpperCamelCase = model.decode(
decoder_input_ids[:, -1:] , __a , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=__a , decoder_position_ids=__a , )
_UpperCamelCase = model.decode(__a , __a , decoder_attention_mask=__a)
_UpperCamelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}''')
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=None, __snake_case=None, ) -> Union[str, Any]:
"""simple docstring"""
if attention_mask is None:
_UpperCamelCase = np.not_equal(__snake_case, config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
_UpperCamelCase = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape, dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:], config.pad_token_id ).astype(np.inta ),
], axis=-1, )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class _UpperCAmelCase( lowerCamelCase , unittest.TestCase ):
lowercase__ = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
lowercase__ = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
lowercase__ = True
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = FlaxPegasusModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(__a , __a , __a)
def UpperCAmelCase ( self) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(__a , __a , __a)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
_UpperCamelCase = self._prepare_for_class(__a , __a)
_UpperCamelCase = model_class(__a)
@jax.jit
def encode_jitted(__a , __a=None , **__a):
return model.encode(input_ids=__a , attention_mask=__a)
with self.subTest('''JIT Enabled'''):
_UpperCamelCase = encode_jitted(**__a).to_tuple()
with self.subTest('''JIT Disabled'''):
with jax.disable_jit():
_UpperCamelCase = encode_jitted(**__a).to_tuple()
self.assertEqual(len(__a) , len(__a))
for jitted_output, output in zip(__a , __a):
self.assertEqual(jitted_output.shape , output.shape)
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
_UpperCamelCase = model_class(__a)
_UpperCamelCase = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask'''])
_UpperCamelCase = {
'''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
'''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
'''encoder_outputs''': encoder_outputs,
}
@jax.jit
def decode_jitted(__a , __a , __a):
return model.decode(
decoder_input_ids=__a , decoder_attention_mask=__a , encoder_outputs=__a , )
with self.subTest('''JIT Enabled'''):
_UpperCamelCase = decode_jitted(**__a).to_tuple()
with self.subTest('''JIT Disabled'''):
with jax.disable_jit():
_UpperCamelCase = decode_jitted(**__a).to_tuple()
self.assertEqual(len(__a) , len(__a))
for jitted_output, output in zip(__a , __a):
self.assertEqual(jitted_output.shape , output.shape)
@slow
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
for model_class_name in self.all_model_classes:
_UpperCamelCase = model_class_name.from_pretrained('''google/pegasus-large''' , from_pt=__a)
_UpperCamelCase = np.ones((1, 1))
_UpperCamelCase = model(__a)
self.assertIsNotNone(__a)
@slow
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''')
_UpperCamelCase = PegasusTokenizer.from_pretrained('''google/pegasus-xsum''')
_UpperCamelCase = [
''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
]
_UpperCamelCase = [
'''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
'''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
]
_UpperCamelCase = tokenizer(__a , return_tensors='''np''' , truncation=__a , max_length=5_12 , padding=__a)
_UpperCamelCase = model.generate(**__a , num_beams=2).sequences
_UpperCamelCase = tokenizer.batch_decode(__a , skip_special_tokens=__a)
assert tgt_text == decoded
| 19 | 0 |
def lowerCAmelCase__ ( lowerCamelCase_ : Tuple ,lowerCamelCase_ : Dict ,lowerCamelCase_ : Optional[Any]):
'''simple docstring'''
lowerCAmelCase__ : str = len(__snake_case)
lowerCAmelCase__ : Tuple = [[0] * n for i in range(__snake_case)]
for i in range(__snake_case):
lowerCAmelCase__ : int = y_points[i]
for i in range(2 ,__snake_case):
for j in range(__snake_case ,__snake_case):
lowerCAmelCase__ : List[Any] = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 647 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
BertConfig,
DPRConfig,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
class _UpperCAmelCase:
def __init__( self , __a , __a=13 , __a=7 , __a=True , __a=True , __a=True , __a=True , __a=99 , __a=32 , __a=2 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=5_12 , __a=16 , __a=2 , __a=0.02 , __a=3 , __a=4 , __a=None , __a=0 , ) -> Any:
'''simple docstring'''
_UpperCamelCase = parent
_UpperCamelCase = batch_size
_UpperCamelCase = seq_length
_UpperCamelCase = is_training
_UpperCamelCase = use_input_mask
_UpperCamelCase = use_token_type_ids
_UpperCamelCase = use_labels
_UpperCamelCase = vocab_size
_UpperCamelCase = hidden_size
_UpperCamelCase = num_hidden_layers
_UpperCamelCase = num_attention_heads
_UpperCamelCase = intermediate_size
_UpperCamelCase = hidden_act
_UpperCamelCase = hidden_dropout_prob
_UpperCamelCase = attention_probs_dropout_prob
_UpperCamelCase = max_position_embeddings
_UpperCamelCase = type_vocab_size
_UpperCamelCase = type_sequence_label_size
_UpperCamelCase = initializer_range
_UpperCamelCase = num_labels
_UpperCamelCase = num_choices
_UpperCamelCase = scope
_UpperCamelCase = projection_dim
def UpperCAmelCase ( self) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCamelCase = None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
_UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length])
_UpperCamelCase = None
if self.use_token_type_ids:
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
_UpperCamelCase = None
_UpperCamelCase = None
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
_UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices)
_UpperCamelCase = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__a , initializer_range=self.initializer_range , )
_UpperCamelCase = DPRConfig(projection_dim=self.projection_dim , **config.to_dict())
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase = TFDPRContextEncoder(config=__a)
_UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a)
_UpperCamelCase = model(__a , token_type_ids=__a)
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size))
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = TFDPRQuestionEncoder(config=__a)
_UpperCamelCase = model(__a , attention_mask=__a , token_type_ids=__a)
_UpperCamelCase = model(__a , token_type_ids=__a)
_UpperCamelCase = model(__a)
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size))
def UpperCAmelCase ( self , __a , __a , __a , __a , __a , __a , __a) -> Dict:
'''simple docstring'''
_UpperCamelCase = TFDPRReader(config=__a)
_UpperCamelCase = model(__a , attention_mask=__a)
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,))
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {'''input_ids''': input_ids}
return config, inputs_dict
@require_tf
class _UpperCAmelCase( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
lowercase__ = (
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
lowercase__ = {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = TFDPRModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=__a , hidden_size=37)
def UpperCAmelCase ( self) -> Optional[int]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*__a)
def UpperCAmelCase ( self) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*__a)
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*__a)
@slow
def UpperCAmelCase ( self) -> str:
'''simple docstring'''
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRContextEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRQuestionEncoder.from_pretrained(__a)
self.assertIsNotNone(__a)
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFDPRReader.from_pretrained(__a)
self.assertIsNotNone(__a)
@require_tf
class _UpperCAmelCase( unittest.TestCase ):
@slow
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
_UpperCamelCase = TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base''')
_UpperCamelCase = tf.constant(
[[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]]) # [CLS] hello, is my dog cute? [SEP]
_UpperCamelCase = model(__a)[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
_UpperCamelCase = tf.constant(
[
[
0.0323_6253,
0.1275_3335,
0.1681_8509,
0.0027_9786,
0.389_6933,
0.2426_4945,
0.217_8971,
-0.0233_5227,
-0.0848_1959,
-0.1432_4117,
]
])
self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1e-4))
| 19 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.