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|---|---|---|---|---|
'''simple docstring'''
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def A (__lowerCamelCase :Optional[int] ):
return x + 2
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """x = 3"""
_lowerCAmelCase = {}
_lowerCAmelCase = evaluate(_lowercase , {} , state=_lowercase )
assert result == 3
self.assertDictEqual(_lowercase , {"""x""": 3} )
_lowerCAmelCase = """x = y"""
_lowerCAmelCase = {"""y""": 5}
_lowerCAmelCase = evaluate(_lowercase , {} , state=_lowercase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_lowercase , {"""x""": 5, """y""": 5} )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """y = add_two(x)"""
_lowerCAmelCase = {"""x""": 3}
_lowerCAmelCase = evaluate(_lowercase , {"""add_two""": add_two} , state=_lowercase )
assert result == 5
self.assertDictEqual(_lowercase , {"""x""": 3, """y""": 5} )
# Won't work without the tool
with CaptureStdout() as out:
_lowerCAmelCase = evaluate(_lowercase , {} , state=_lowercase )
assert result is None
assert "tried to execute add_two" in out.out
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """x = 3"""
_lowerCAmelCase = {}
_lowerCAmelCase = evaluate(_lowercase , {} , state=_lowercase )
assert result == 3
self.assertDictEqual(_lowercase , {"""x""": 3} )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """test_dict = {'x': x, 'y': add_two(x)}"""
_lowerCAmelCase = {"""x""": 3}
_lowerCAmelCase = evaluate(_lowercase , {"""add_two""": add_two} , state=_lowercase )
self.assertDictEqual(_lowercase , {"""x""": 3, """y""": 5} )
self.assertDictEqual(_lowercase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """x = 3\ny = 5"""
_lowerCAmelCase = {}
_lowerCAmelCase = evaluate(_lowercase , {} , state=_lowercase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_lowercase , {"""x""": 3, """y""": 5} )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """text = f'This is x: {x}.'"""
_lowerCAmelCase = {"""x""": 3}
_lowerCAmelCase = evaluate(_lowercase , {} , state=_lowercase )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(_lowercase , {"""x""": 3, """text""": """This is x: 3."""} )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """if x <= 3:\n y = 2\nelse:\n y = 5"""
_lowerCAmelCase = {"""x""": 3}
_lowerCAmelCase = evaluate(_lowercase , {} , state=_lowercase )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(_lowercase , {"""x""": 3, """y""": 2} )
_lowerCAmelCase = {"""x""": 8}
_lowerCAmelCase = evaluate(_lowercase , {} , state=_lowercase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_lowercase , {"""x""": 8, """y""": 5} )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """test_list = [x, add_two(x)]"""
_lowerCAmelCase = {"""x""": 3}
_lowerCAmelCase = evaluate(_lowercase , {"""add_two""": add_two} , state=_lowercase )
self.assertListEqual(_lowercase , [3, 5] )
self.assertDictEqual(_lowercase , {"""x""": 3, """test_list""": [3, 5]} )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """y = x"""
_lowerCAmelCase = {"""x""": 3}
_lowerCAmelCase = evaluate(_lowercase , {} , state=_lowercase )
assert result == 3
self.assertDictEqual(_lowercase , {"""x""": 3, """y""": 3} )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """test_list = [x, add_two(x)]\ntest_list[1]"""
_lowerCAmelCase = {"""x""": 3}
_lowerCAmelCase = evaluate(_lowercase , {"""add_two""": add_two} , state=_lowercase )
assert result == 5
self.assertDictEqual(_lowercase , {"""x""": 3, """test_list""": [3, 5]} )
_lowerCAmelCase = """test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"""
_lowerCAmelCase = {"""x""": 3}
_lowerCAmelCase = evaluate(_lowercase , {"""add_two""": add_two} , state=_lowercase )
assert result == 5
self.assertDictEqual(_lowercase , {"""x""": 3, """test_dict""": {"""x""": 3, """y""": 5}} )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """x = 0\nfor i in range(3):\n x = i"""
_lowerCAmelCase = {}
_lowerCAmelCase = evaluate(_lowercase , {"""range""": range} , state=_lowercase )
assert result == 2
self.assertDictEqual(_lowercase , {"""x""": 2, """i""": 2} )
| 5 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_lowercase = logging.get_logger(__name__)
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[str] = ['''input_values''', '''padding_mask''']
def __init__( self , _lowercase = 1 , _lowercase = 24_000 , _lowercase = 0.0 , _lowercase = None , _lowercase = None , **_lowercase , ):
"""simple docstring"""
super().__init__(feature_size=_lowercase , sampling_rate=_lowercase , padding_value=_lowercase , **_lowercase )
_lowerCAmelCase = chunk_length_s
_lowerCAmelCase = overlap
@property
def _lowercase ( self ):
"""simple docstring"""
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _lowercase ( self ):
"""simple docstring"""
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 ) )
def __call__( self , _lowercase , _lowercase = None , _lowercase = False , _lowercase = None , _lowercase = None , _lowercase = None , ):
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
F' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
if padding and truncation:
raise ValueError("""Both padding and truncation were set. Make sure you only set one.""" )
elif padding is None:
# by default let's pad the inputs
_lowerCAmelCase = True
_lowerCAmelCase = bool(
isinstance(_lowercase , (list, tuple) ) and (isinstance(raw_audio[0] , (np.ndarray, tuple, list) )) )
if is_batched:
_lowerCAmelCase = [np.asarray(_lowercase , dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(_lowercase , np.ndarray ):
_lowerCAmelCase = np.asarray(_lowercase , dtype=np.floataa )
elif isinstance(_lowercase , np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
_lowerCAmelCase = raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
_lowerCAmelCase = [np.asarray(_lowercase ).T]
# verify inputs are valid
for idx, example in enumerate(_lowercase ):
if example.ndim > 2:
raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}' )
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels' )
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels' )
_lowerCAmelCase = None
_lowerCAmelCase = BatchFeature({"""input_values""": raw_audio} )
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
_lowerCAmelCase = min(array.shape[0] for array in raw_audio )
_lowerCAmelCase = int(np.floor(max_length / self.chunk_stride ) )
_lowerCAmelCase = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
_lowerCAmelCase = max(array.shape[0] for array in raw_audio )
_lowerCAmelCase = int(np.ceil(max_length / self.chunk_stride ) )
_lowerCAmelCase = (nb_step - 1) * self.chunk_stride + self.chunk_length
_lowerCAmelCase = """max_length"""
else:
_lowerCAmelCase = input_values
# normal padding on batch
if padded_inputs is None:
_lowerCAmelCase = self.pad(
_lowercase , max_length=_lowercase , truncation=_lowercase , padding=_lowercase , return_attention_mask=_lowercase , )
if padding:
_lowerCAmelCase = padded_inputs.pop("""attention_mask""" )
_lowerCAmelCase = []
for example in padded_inputs.pop("""input_values""" ):
if self.feature_size == 1:
_lowerCAmelCase = example[..., None]
input_values.append(example.T )
_lowerCAmelCase = input_values
if return_tensors is not None:
_lowerCAmelCase = padded_inputs.convert_to_tensors(_lowercase )
return padded_inputs
| 5 | 1 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
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 (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase , _lowercase=14 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=99 , _lowercase=32 , _lowercase=5 , _lowercase=4 , _lowercase=37 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=16 , _lowercase=2 , _lowercase=0.02 , _lowercase=3 , _lowercase=4 , _lowercase=None , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = seq_length
_lowerCAmelCase = is_training
_lowerCAmelCase = use_token_type_ids
_lowerCAmelCase = use_input_mask
_lowerCAmelCase = use_labels
_lowerCAmelCase = use_mc_token_ids
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_vocab_size
_lowerCAmelCase = type_sequence_label_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = num_labels
_lowerCAmelCase = num_choices
_lowerCAmelCase = scope
_lowerCAmelCase = self.vocab_size - 1
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = None
if self.use_input_mask:
_lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCAmelCase = None
if self.use_token_type_ids:
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_lowerCAmelCase = None
if self.use_mc_token_ids:
_lowerCAmelCase = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowerCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
_lowerCAmelCase = self.get_config()
_lowerCAmelCase = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def _lowercase ( self ):
"""simple docstring"""
return CTRLConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , *_lowercase ):
"""simple docstring"""
_lowerCAmelCase = CTRLModel(config=_lowercase )
model.to(_lowercase )
model.eval()
model(_lowercase , token_type_ids=_lowercase , head_mask=_lowercase )
model(_lowercase , token_type_ids=_lowercase )
_lowerCAmelCase = model(_lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , *_lowercase ):
"""simple docstring"""
_lowerCAmelCase = CTRLLMHeadModel(_lowercase )
model.to(_lowercase )
model.eval()
_lowerCAmelCase = model(_lowercase , token_type_ids=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = config_and_inputs
_lowerCAmelCase = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """head_mask""": head_mask}
return config, inputs_dict
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , *_lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = CTRLForSequenceClassification(_lowercase )
model.to(_lowercase )
model.eval()
_lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase = model(_lowercase , token_type_ids=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
_lowercase : List[str] = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
_lowercase : List[str] = (CTRLLMHeadModel,) if is_torch_available() else ()
_lowercase : Dict = (
{
'''feature-extraction''': CTRLModel,
'''text-classification''': CTRLForSequenceClassification,
'''text-generation''': CTRLLMHeadModel,
'''zero-shot''': CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
_lowercase : Any = True
_lowercase : Dict = False
_lowercase : Dict = False
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
# config could not be created.
return True
return False
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = CTRLModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=_lowercase , n_embd=37 )
def _lowercase ( self ):
"""simple docstring"""
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*_lowercase )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _lowercase ( self ):
"""simple docstring"""
pass
@slow
def _lowercase ( self ):
"""simple docstring"""
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase = CTRLModel.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
@unittest.skip("""The model doesn't support left padding""" ) # and it's not used enough to be worth fixing :)
def _lowercase ( self ):
"""simple docstring"""
pass
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = CTRLLMHeadModel.from_pretrained("""ctrl""" )
model.to(_lowercase )
_lowerCAmelCase = torch.tensor(
[[11_859, 0, 1_611, 8]] , dtype=torch.long , device=_lowercase ) # Legal the president is
_lowerCAmelCase = [
11_859,
0,
1_611,
8,
5,
150,
26_449,
2,
19,
348,
469,
3,
2_595,
48,
20_740,
246_533,
246_533,
19,
30,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
_lowerCAmelCase = model.generate(_lowercase , do_sample=_lowercase )
self.assertListEqual(output_ids[0].tolist() , _lowercase )
| 5 |
'''simple docstring'''
_lowercase = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_lowercase = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
_lowercase = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 5 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase = {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ["""MBartTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ["""MBartTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MBartForCausalLM""",
"""MBartForConditionalGeneration""",
"""MBartForQuestionAnswering""",
"""MBartForSequenceClassification""",
"""MBartModel""",
"""MBartPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""TFMBartForConditionalGeneration""",
"""TFMBartModel""",
"""TFMBartPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""FlaxMBartForConditionalGeneration""",
"""FlaxMBartForQuestionAnswering""",
"""FlaxMBartForSequenceClassification""",
"""FlaxMBartModel""",
"""FlaxMBartPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 5 |
'''simple docstring'''
import functools
def A (__lowerCamelCase :list[int] , __lowerCamelCase :list[int] ):
# Validation
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not all(isinstance(__lowerCamelCase , __lowerCamelCase ) for day in days ):
raise ValueError("""The parameter days should be a list of integers""" )
if len(__lowerCamelCase ) != 3 or not all(isinstance(__lowerCamelCase , __lowerCamelCase ) for cost in costs ):
raise ValueError("""The parameter costs should be a list of three integers""" )
if len(__lowerCamelCase ) == 0:
return 0
if min(__lowerCamelCase ) <= 0:
raise ValueError("""All days elements should be greater than 0""" )
if max(__lowerCamelCase ) >= 366:
raise ValueError("""All days elements should be less than 366""" )
_lowerCAmelCase = set(__lowerCamelCase )
@functools.cache
def dynamic_programming(__lowerCamelCase :int ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
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
_lowercase = """platform"""
import jax
import jax.numpy as jnp
from transformers import BlenderbotTokenizer
from transformers.models.blenderbot.modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
shift_tokens_right,
)
def A (__lowerCamelCase :int , __lowerCamelCase :Any , __lowerCamelCase :Optional[int]=None , __lowerCamelCase :int=None , __lowerCamelCase :str=None , __lowerCamelCase :Union[str, Any]=None , __lowerCamelCase :Any=None , __lowerCamelCase :Any=None , ):
if attention_mask is None:
_lowerCAmelCase = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
_lowerCAmelCase = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
_lowerCAmelCase = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_lowerCAmelCase = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
_lowerCAmelCase = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase , _lowercase=13 , _lowercase=7 , _lowercase=True , _lowercase=False , _lowercase=99 , _lowercase=16 , _lowercase=2 , _lowercase=4 , _lowercase=4 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=32 , _lowercase=2 , _lowercase=1 , _lowercase=0 , _lowercase=0.02 , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = seq_length
_lowerCAmelCase = is_training
_lowerCAmelCase = use_labels
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = eos_token_id
_lowerCAmelCase = pad_token_id
_lowerCAmelCase = bos_token_id
_lowerCAmelCase = initializer_range
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
_lowerCAmelCase = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
_lowerCAmelCase = shift_tokens_right(_lowercase , 1 , 2 )
_lowerCAmelCase = BlenderbotConfig(
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_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=_lowercase , )
_lowerCAmelCase = prepare_blenderbot_inputs_dict(_lowercase , _lowercase , _lowercase )
return config, inputs_dict
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def _lowercase ( self , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = 20
_lowerCAmelCase = model_class_name(_lowercase )
_lowerCAmelCase = model.encode(inputs_dict["""input_ids"""] )
_lowerCAmelCase , _lowerCAmelCase = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
_lowerCAmelCase = model.init_cache(decoder_input_ids.shape[0] , _lowercase , _lowercase )
_lowerCAmelCase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""" )
_lowerCAmelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_lowerCAmelCase = model.decode(
decoder_input_ids[:, :-1] , _lowercase , decoder_attention_mask=_lowercase , past_key_values=_lowercase , decoder_position_ids=_lowercase , )
_lowerCAmelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" )
_lowerCAmelCase = model.decode(
decoder_input_ids[:, -1:] , _lowercase , decoder_attention_mask=_lowercase , past_key_values=outputs_cache.past_key_values , decoder_position_ids=_lowercase , )
_lowerCAmelCase = model.decode(_lowercase , _lowercase )
_lowerCAmelCase = 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 _lowercase ( self , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = 20
_lowerCAmelCase = model_class_name(_lowercase )
_lowerCAmelCase = model.encode(inputs_dict["""input_ids"""] )
_lowerCAmelCase , _lowerCAmelCase = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
_lowerCAmelCase = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
_lowerCAmelCase = model.init_cache(decoder_input_ids.shape[0] , _lowercase , _lowercase )
_lowerCAmelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_lowerCAmelCase = model.decode(
decoder_input_ids[:, :-1] , _lowercase , decoder_attention_mask=_lowercase , past_key_values=_lowercase , decoder_position_ids=_lowercase , )
_lowerCAmelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" )
_lowerCAmelCase = model.decode(
decoder_input_ids[:, -1:] , _lowercase , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=_lowercase , decoder_position_ids=_lowercase , )
_lowerCAmelCase = model.decode(_lowercase , _lowercase , decoder_attention_mask=_lowercase )
_lowerCAmelCase = 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}' )
@require_flax
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
_lowercase : str = 9_9
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
_lowerCAmelCase = input_ids.shape[0]
_lowerCAmelCase = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self._get_config_and_data()
_lowerCAmelCase = FlaxBlenderbotForConditionalGeneration(_lowercase )
_lowerCAmelCase = lm_model(input_ids=_lowercase )
_lowerCAmelCase = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , _lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
_lowerCAmelCase = FlaxBlenderbotForConditionalGeneration(_lowercase )
_lowerCAmelCase = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
_lowerCAmelCase = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
_lowerCAmelCase = lm_model(input_ids=_lowercase , decoder_input_ids=_lowercase )
_lowerCAmelCase = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , _lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
_lowerCAmelCase = shift_tokens_right(_lowercase , 1 , 2 )
_lowerCAmelCase = np.equal(_lowercase , 1 ).astype(np.floataa ).sum()
_lowerCAmelCase = np.equal(_lowercase , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(_lowercase , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase , _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[str] = True
_lowercase : Tuple = (
(
FlaxBlenderbotModel,
FlaxBlenderbotForConditionalGeneration,
)
if is_flax_available()
else ()
)
_lowercase : Tuple = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = FlaxBlenderbotModelTester(self )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(_lowercase , _lowercase , _lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(_lowercase , _lowercase , _lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_lowerCAmelCase = self._prepare_for_class(_lowercase , _lowercase )
_lowerCAmelCase = model_class(_lowercase )
@jax.jit
def encode_jitted(_lowercase , _lowercase=None , **_lowercase ):
return model.encode(input_ids=_lowercase , attention_mask=_lowercase )
with self.subTest("""JIT Enabled""" ):
_lowerCAmelCase = encode_jitted(**_lowercase ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
_lowerCAmelCase = encode_jitted(**_lowercase ).to_tuple()
self.assertEqual(len(_lowercase ) , len(_lowercase ) )
for jitted_output, output in zip(_lowercase , _lowercase ):
self.assertEqual(jitted_output.shape , output.shape )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_lowerCAmelCase = model_class(_lowercase )
_lowerCAmelCase = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""] )
_lowerCAmelCase = {
"""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(_lowercase , _lowercase , _lowercase ):
return model.decode(
decoder_input_ids=_lowercase , decoder_attention_mask=_lowercase , encoder_outputs=_lowercase , )
with self.subTest("""JIT Enabled""" ):
_lowerCAmelCase = decode_jitted(**_lowercase ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
_lowerCAmelCase = decode_jitted(**_lowercase ).to_tuple()
self.assertEqual(len(_lowercase ) , len(_lowercase ) )
for jitted_output, output in zip(_lowercase , _lowercase ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def _lowercase ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
_lowerCAmelCase = model_class_name.from_pretrained("""facebook/blenderbot-400M-distill""" )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
_lowerCAmelCase = np.ones((1, 1) ) * model.config.eos_token_id
_lowerCAmelCase = model(_lowercase )
self.assertIsNotNone(_lowercase )
@unittest.skipUnless(jax_device != """cpu""" , """3B test too slow on CPU.""" )
@slow
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = {"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
_lowerCAmelCase = {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}
_lowerCAmelCase = FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B""" , from_pt=_lowercase )
_lowerCAmelCase = BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B""" )
_lowerCAmelCase = ["""Sam"""]
_lowerCAmelCase = tokenizer(_lowercase , return_tensors="""jax""" )
_lowerCAmelCase = model.generate(**_lowercase , **_lowercase )
_lowerCAmelCase = """Sam is a great name. It means \"sun\" in Gaelic."""
_lowerCAmelCase = tokenizer.batch_decode(_lowercase , **_lowercase )
assert generated_txt[0].strip() == tgt_text
| 5 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def A (__lowerCamelCase :List[Any] ):
_lowerCAmelCase = 384
if "tiny" in model_name:
_lowerCAmelCase = [3, 3, 9, 3]
_lowerCAmelCase = [96, 192, 384, 768]
if "small" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [96, 192, 384, 768]
if "base" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [128, 256, 512, 1024]
_lowerCAmelCase = 512
if "large" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [192, 384, 768, 1536]
_lowerCAmelCase = 768
if "xlarge" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [256, 512, 1024, 2048]
_lowerCAmelCase = 1024
# set label information
_lowerCAmelCase = 150
_lowerCAmelCase = """huggingface/label-files"""
_lowerCAmelCase = """ade20k-id2label.json"""
_lowerCAmelCase = json.load(open(hf_hub_download(__lowerCamelCase , __lowerCamelCase , repo_type="""dataset""" ) , """r""" ) )
_lowerCAmelCase = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
_lowerCAmelCase = {v: k for k, v in idalabel.items()}
_lowerCAmelCase = ConvNextConfig(
depths=__lowerCamelCase , hidden_sizes=__lowerCamelCase , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
_lowerCAmelCase = UperNetConfig(
backbone_config=__lowerCamelCase , auxiliary_in_channels=__lowerCamelCase , num_labels=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid=__lowerCamelCase , )
return config
def A (__lowerCamelCase :Optional[Any] ):
_lowerCAmelCase = []
# fmt: off
# stem
rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""") )
rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""") )
rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""") )
rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f'backbone.stages.{i}.{j}.gamma', f'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter') )
rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.weight', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.bias', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias') )
rename_keys.append((f'backbone.stages.{i}.{j}.norm.weight', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.norm.bias', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias') )
if i > 0:
rename_keys.append((f'backbone.downsample_layers.{i}.0.weight', f'backbone.encoder.stages.{i}.downsampling_layer.0.weight') )
rename_keys.append((f'backbone.downsample_layers.{i}.0.bias', f'backbone.encoder.stages.{i}.downsampling_layer.0.bias') )
rename_keys.append((f'backbone.downsample_layers.{i}.1.weight', f'backbone.encoder.stages.{i}.downsampling_layer.1.weight') )
rename_keys.append((f'backbone.downsample_layers.{i}.1.bias', f'backbone.encoder.stages.{i}.downsampling_layer.1.bias') )
rename_keys.append((f'backbone.norm{i}.weight', f'backbone.hidden_states_norms.stage{i+1}.weight') )
rename_keys.append((f'backbone.norm{i}.bias', f'backbone.hidden_states_norms.stage{i+1}.bias') )
# decode head
rename_keys.extend(
[
("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
] )
# fmt: on
return rename_keys
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :Dict , __lowerCamelCase :Tuple ):
_lowerCAmelCase = dct.pop(__lowerCamelCase )
_lowerCAmelCase = val
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Optional[Any] , __lowerCamelCase :Any ):
_lowerCAmelCase = {
"""upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
"""upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
"""upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
"""upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
"""upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
}
_lowerCAmelCase = model_name_to_url[model_name]
_lowerCAmelCase = torch.hub.load_state_dict_from_url(__lowerCamelCase , map_location="""cpu""" )["""state_dict"""]
_lowerCAmelCase = get_upernet_config(__lowerCamelCase )
_lowerCAmelCase = UperNetForSemanticSegmentation(__lowerCamelCase )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
_lowerCAmelCase = state_dict.pop(__lowerCamelCase )
if "bn" in key:
_lowerCAmelCase = key.replace("""bn""" , """batch_norm""" )
_lowerCAmelCase = val
# rename keys
_lowerCAmelCase = create_rename_keys(__lowerCamelCase )
for src, dest in rename_keys:
rename_key(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
model.load_state_dict(__lowerCamelCase )
# verify on image
_lowerCAmelCase = """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
_lowerCAmelCase = Image.open(requests.get(__lowerCamelCase , stream=__lowerCamelCase ).raw ).convert("""RGB""" )
_lowerCAmelCase = SegformerImageProcessor()
_lowerCAmelCase = processor(__lowerCamelCase , return_tensors="""pt""" ).pixel_values
with torch.no_grad():
_lowerCAmelCase = model(__lowerCamelCase )
if model_name == "upernet-convnext-tiny":
_lowerCAmelCase = torch.tensor(
[[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]] )
elif model_name == "upernet-convnext-small":
_lowerCAmelCase = torch.tensor(
[[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]] )
elif model_name == "upernet-convnext-base":
_lowerCAmelCase = torch.tensor(
[[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]] )
elif model_name == "upernet-convnext-large":
_lowerCAmelCase = torch.tensor(
[[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]] )
elif model_name == "upernet-convnext-xlarge":
_lowerCAmelCase = torch.tensor(
[[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]] )
print("""Logits:""" , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , __lowerCamelCase , atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(f'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(__lowerCamelCase )
print(f'Saving processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(__lowerCamelCase )
if push_to_hub:
print(f'Pushing model and processor for {model_name} to hub' )
model.push_to_hub(f'openmmlab/{model_name}' )
processor.push_to_hub(f'openmmlab/{model_name}' )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""upernet-convnext-tiny""",
type=str,
choices=[F"""upernet-convnext-{size}""" for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
help="""Name of the ConvNext UperNet model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_lowercase = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 5 | 1 |
'''simple docstring'''
def A (__lowerCamelCase :int , __lowerCamelCase :int ):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(__lowerCamelCase , int(b / 2 ) ) * actual_power(__lowerCamelCase , int(b / 2 ) )
else:
return a * actual_power(__lowerCamelCase , int(b / 2 ) ) * actual_power(__lowerCamelCase , int(b / 2 ) )
def A (__lowerCamelCase :int , __lowerCamelCase :int ):
if b < 0:
return 1 / actual_power(__lowerCamelCase , __lowerCamelCase )
return actual_power(__lowerCamelCase , __lowerCamelCase )
if __name__ == "__main__":
print(power(-2, -3))
| 5 |
'''simple docstring'''
from itertools import product
def A (__lowerCamelCase :int , __lowerCamelCase :int ):
_lowerCAmelCase = sides_number
_lowerCAmelCase = max_face_number * dice_number
_lowerCAmelCase = [0] * (max_total + 1)
_lowerCAmelCase = 1
_lowerCAmelCase = range(__lowerCamelCase , max_face_number + 1 )
for dice_numbers in product(__lowerCamelCase , repeat=__lowerCamelCase ):
_lowerCAmelCase = sum(__lowerCamelCase )
totals_frequencies[total] += 1
return totals_frequencies
def A ():
_lowerCAmelCase = total_frequency_distribution(
sides_number=4 , dice_number=9 )
_lowerCAmelCase = total_frequency_distribution(
sides_number=6 , dice_number=6 )
_lowerCAmelCase = 0
_lowerCAmelCase = 9
_lowerCAmelCase = 4 * 9
_lowerCAmelCase = 6
for peter_total in range(__lowerCamelCase , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
_lowerCAmelCase = (4**9) * (6**6)
_lowerCAmelCase = peter_wins_count / total_games_number
_lowerCAmelCase = round(__lowerCamelCase , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(F"""{solution() = }""")
| 5 | 1 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json""",
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[str] = '''mvp'''
_lowercase : Any = ['''past_key_values''']
_lowercase : Optional[Any] = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self , _lowercase=50_267 , _lowercase=1_024 , _lowercase=12 , _lowercase=4_096 , _lowercase=16 , _lowercase=12 , _lowercase=4_096 , _lowercase=16 , _lowercase=0.0 , _lowercase=0.0 , _lowercase="gelu" , _lowercase=1_024 , _lowercase=0.1 , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.02 , _lowercase=0.0 , _lowercase=False , _lowercase=True , _lowercase=1 , _lowercase=0 , _lowercase=2 , _lowercase=True , _lowercase=2 , _lowercase=2 , _lowercase=False , _lowercase=100 , _lowercase=800 , **_lowercase , ):
"""simple docstring"""
_lowerCAmelCase = vocab_size
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = d_model
_lowerCAmelCase = encoder_ffn_dim
_lowerCAmelCase = encoder_layers
_lowerCAmelCase = encoder_attention_heads
_lowerCAmelCase = decoder_ffn_dim
_lowerCAmelCase = decoder_layers
_lowerCAmelCase = decoder_attention_heads
_lowerCAmelCase = dropout
_lowerCAmelCase = attention_dropout
_lowerCAmelCase = activation_dropout
_lowerCAmelCase = activation_function
_lowerCAmelCase = init_std
_lowerCAmelCase = encoder_layerdrop
_lowerCAmelCase = decoder_layerdrop
_lowerCAmelCase = classifier_dropout
_lowerCAmelCase = use_cache
_lowerCAmelCase = encoder_layers
_lowerCAmelCase = scale_embedding # scale factor will be sqrt(d_model) if True
_lowerCAmelCase = use_prompt
_lowerCAmelCase = prompt_length
_lowerCAmelCase = prompt_mid_dim
super().__init__(
pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase , is_encoder_decoder=_lowercase , decoder_start_token_id=_lowercase , forced_eos_token_id=_lowercase , **_lowercase , )
if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""" , _lowercase ):
_lowerCAmelCase = self.bos_token_id
warnings.warn(
F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
"""The config can simply be saved and uploaded again to be fixed.""" )
| 5 |
'''simple docstring'''
from manim import *
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = Rectangle(height=0.5 , width=0.5 )
_lowerCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = VGroup(_lowercase , _lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""CPU""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_lowercase )
_lowerCAmelCase = [mem.copy() for i in range(1 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""GPU""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
gpu.align_to(_lowercase , _lowercase )
gpu.set_x(gpu.get_x() - 1 )
self.add(_lowercase )
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""Model""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
model.move_to([3, -1.0, 0] )
self.play(
Create(_lowercase , run_time=1 ) , Create(_lowercase , run_time=1 ) , Create(_lowercase , run_time=1 ) , )
_lowerCAmelCase = MarkupText(
F'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.' , font_size=24 , )
_lowerCAmelCase = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_lowerCAmelCase = MarkupText(
F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(_lowercase , run_time=2.5 ) , Write(_lowercase ) , Write(_lowercase ) )
self.add(_lowercase )
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = []
for i, rect in enumerate(_lowercase ):
_lowerCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(_lowercase , opacity=0.7 )
cpu_target.move_to(_lowercase )
cpu_target.generate_target()
_lowerCAmelCase = 0.46 / 4
_lowerCAmelCase = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=_lowercase )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=_lowercase , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=_lowercase , buff=0.0 )
cpu_targs.append(_lowercase )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(_lowercase ) )
second_animations.append(MoveToTarget(_lowercase , run_time=1.5 ) )
self.play(*_lowercase )
self.play(*_lowercase )
self.wait()
| 5 | 1 |
'''simple docstring'''
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@require_tf
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
self.assertEqual(len(_lowercase ) , len(_lowercase ) )
for a, b in zip(_lowercase , _lowercase ):
self.assertAlmostEqual(_lowercase , _lowercase , delta=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0] )] )
accumulator([tf.constant([-2.0, 1.0] )] )
accumulator([tf.constant([-1.0, 2.0] )] )
with self.assertRaises(_lowercase ):
accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
self.assertEqual(accumulator.step , 3 )
self.assertEqual(len(accumulator.gradients ) , 1 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1e-2 )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = None
ops.enable_eager_execution_internal()
_lowerCAmelCase = tf.config.list_physical_devices("""CPU""" )
if len(_lowercase ) == 1:
tf.config.set_logical_device_configuration(
physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
_lowerCAmelCase = tf.config.list_logical_devices(device_type="""CPU""" )
_lowerCAmelCase = tf.distribute.MirroredStrategy(devices=devices[:2] )
with strategy.scope():
_lowerCAmelCase = GradientAccumulator()
_lowerCAmelCase = tf.Variable([4.0, 3.0] )
_lowerCAmelCase , _lowerCAmelCase = create_optimizer(5e-5 , 10 , 5 )
_lowerCAmelCase = tf.Variable([0.0, 0.0] , trainable=_lowercase )
def accumulate_on_replica(_lowercase ):
accumulator([gradient] )
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) )
@tf.function
def accumulate(_lowercase , _lowercase ):
with strategy.scope():
_lowerCAmelCase = strategy.experimental_local_results(_lowercase )
local_variables[0].assign(_lowercase )
local_variables[1].assign(_lowercase )
strategy.run(_lowercase , args=(gradient_placeholder,) )
@tf.function
def apply_grad():
with strategy.scope():
strategy.run(_lowercase )
def _check_local_values(_lowercase , _lowercase ):
_lowerCAmelCase = strategy.experimental_local_results(accumulator._gradients[0] )
self.assertListAlmostEqual(values[0].value() , _lowercase , tol=1e-2 )
self.assertListAlmostEqual(values[1].value() , _lowercase , tol=1e-2 )
accumulate([1.0, 2.0] , [-1.0, 1.0] )
accumulate([3.0, -1.0] , [-1.0, -1.0] )
accumulate([-2.0, 2.0] , [3.0, -2.0] )
self.assertEqual(accumulator.step , 3 )
_check_local_values([2.0, 3.0] , [1.0, -2.0] )
apply_grad()
self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
_check_local_values([0.0, 0.0] , [0.0, 0.0] )
| 5 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_lowercase = False
try:
_lowercase = _is_package_available("""google.colab""")
except ModuleNotFoundError:
pass
@input.register
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase = None , _lowercase = [] ):
"""simple docstring"""
_lowerCAmelCase = 0
_lowerCAmelCase = choices
_lowerCAmelCase = prompt
if sys.platform == "win32":
_lowerCAmelCase = """*"""
else:
_lowerCAmelCase = """➔ """
def _lowercase ( self , _lowercase , _lowercase = "" ):
"""simple docstring"""
if sys.platform != "win32":
writeColor(self.choices[index] , 32 , _lowercase )
else:
forceWrite(self.choices[index] , _lowercase )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
if index == self.position:
forceWrite(F' {self.arrow_char} ' )
self.write_choice(_lowercase )
else:
forceWrite(F' {self.choices[index]}' )
reset_cursor()
def _lowercase ( self , _lowercase , _lowercase = 1 ):
"""simple docstring"""
_lowerCAmelCase = self.position
if direction == Direction.DOWN:
if self.position + 1 >= len(self.choices ):
return
self.position += num_spaces
else:
if self.position - 1 < 0:
return
self.position -= num_spaces
clear_line()
self.print_choice(_lowercase )
move_cursor(_lowercase , direction.name )
self.print_choice(self.position )
@input.mark(KEYMAP["""up"""] )
def _lowercase ( self ):
"""simple docstring"""
self.move_direction(Direction.UP )
@input.mark(KEYMAP["""down"""] )
def _lowercase ( self ):
"""simple docstring"""
self.move_direction(Direction.DOWN )
@input.mark(KEYMAP["""newline"""] )
def _lowercase ( self ):
"""simple docstring"""
move_cursor(len(self.choices ) - self.position , """DOWN""" )
return self.position
@input.mark(KEYMAP["""interrupt"""] )
def _lowercase ( self ):
"""simple docstring"""
move_cursor(len(self.choices ) - self.position , """DOWN""" )
raise KeyboardInterrupt
@input.mark_multiple(*[KEYMAP[str(_lowercase )] for number in range(10 )] )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = int(chr(self.current_selection ) )
_lowerCAmelCase = index - self.position
if index == self.position:
return
if index < len(self.choices ):
if self.position > index:
self.move_direction(Direction.UP , -movement )
elif self.position < index:
self.move_direction(Direction.DOWN , _lowercase )
else:
return
else:
return
def _lowercase ( self , _lowercase = 0 ):
"""simple docstring"""
if self.prompt:
linebreak()
forceWrite(self.prompt , """\n""" )
if in_colab:
forceWrite("""Please input a choice index (starting from 0), and press enter""" , """\n""" )
else:
forceWrite("""Please select a choice using the arrow or number keys, and selecting with enter""" , """\n""" )
_lowerCAmelCase = default_choice
for i in range(len(self.choices ) ):
self.print_choice(_lowercase )
forceWrite("""\n""" )
move_cursor(len(self.choices ) - self.position , """UP""" )
with cursor.hide():
while True:
if in_colab:
try:
_lowerCAmelCase = int(builtins.input() )
except ValueError:
_lowerCAmelCase = default_choice
else:
_lowerCAmelCase = self.handle_input()
if choice is not None:
reset_cursor()
for _ in range(len(self.choices ) + 1 ):
move_cursor(1 , """UP""" )
clear_line()
self.write_choice(_lowercase , """\n""" )
return choice
| 5 | 1 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def A (__lowerCamelCase :int ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0X4e00 and cp <= 0X9fff)
or (cp >= 0X3400 and cp <= 0X4dbf) #
or (cp >= 0X20000 and cp <= 0X2a6df) #
or (cp >= 0X2a700 and cp <= 0X2b73f) #
or (cp >= 0X2b740 and cp <= 0X2b81f) #
or (cp >= 0X2b820 and cp <= 0X2ceaf) #
or (cp >= 0Xf900 and cp <= 0Xfaff)
or (cp >= 0X2f800 and cp <= 0X2fa1f) #
): #
return True
return False
def A (__lowerCamelCase :str ):
# word like '180' or '身高' or '神'
for char in word:
_lowerCAmelCase = ord(__lowerCamelCase )
if not _is_chinese_char(__lowerCamelCase ):
return 0
return 1
def A (__lowerCamelCase :List[str] ):
_lowerCAmelCase = set()
for token in tokens:
_lowerCAmelCase = len(__lowerCamelCase ) > 1 and is_chinese(__lowerCamelCase )
if chinese_word:
word_set.add(__lowerCamelCase )
_lowerCAmelCase = list(__lowerCamelCase )
return word_list
def A (__lowerCamelCase :List[str] , __lowerCamelCase :set() ):
if not chinese_word_set:
return bert_tokens
_lowerCAmelCase = max([len(__lowerCamelCase ) for w in chinese_word_set] )
_lowerCAmelCase = bert_tokens
_lowerCAmelCase , _lowerCAmelCase = 0, len(__lowerCamelCase )
while start < end:
_lowerCAmelCase = True
if is_chinese(bert_word[start] ):
_lowerCAmelCase = min(end - start , __lowerCamelCase )
for i in range(__lowerCamelCase , 1 , -1 ):
_lowerCAmelCase = """""".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
_lowerCAmelCase = """##""" + bert_word[j]
_lowerCAmelCase = start + i
_lowerCAmelCase = False
break
if single_word:
start += 1
return bert_word
def A (__lowerCamelCase :List[str] , __lowerCamelCase :LTP , __lowerCamelCase :BertTokenizer ):
_lowerCAmelCase = []
for i in range(0 , len(__lowerCamelCase ) , 100 ):
_lowerCAmelCase = ltp_tokenizer.seg(lines[i : i + 100] )[0]
_lowerCAmelCase = [get_chinese_word(__lowerCamelCase ) for r in res]
ltp_res.extend(__lowerCamelCase )
assert len(__lowerCamelCase ) == len(__lowerCamelCase )
_lowerCAmelCase = []
for i in range(0 , len(__lowerCamelCase ) , 100 ):
_lowerCAmelCase = bert_tokenizer(lines[i : i + 100] , add_special_tokens=__lowerCamelCase , truncation=__lowerCamelCase , max_length=512 )
bert_res.extend(res["""input_ids"""] )
assert len(__lowerCamelCase ) == len(__lowerCamelCase )
_lowerCAmelCase = []
for input_ids, chinese_word in zip(__lowerCamelCase , __lowerCamelCase ):
_lowerCAmelCase = []
for id in input_ids:
_lowerCAmelCase = bert_tokenizer._convert_id_to_token(__lowerCamelCase )
input_tokens.append(__lowerCamelCase )
_lowerCAmelCase = add_sub_symbol(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__lowerCamelCase ):
if token[:2] == "##":
_lowerCAmelCase = token[2:]
# save chinese tokens' pos
if len(__lowerCamelCase ) == 1 and _is_chinese_char(ord(__lowerCamelCase ) ):
ref_id.append(__lowerCamelCase )
ref_ids.append(__lowerCamelCase )
assert len(__lowerCamelCase ) == len(__lowerCamelCase )
return ref_ids
def A (__lowerCamelCase :List[Any] ):
# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
with open(args.file_name , """r""" , encoding="""utf-8""" ) as f:
_lowerCAmelCase = f.readlines()
_lowerCAmelCase = [line.strip() for line in data if len(__lowerCamelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
_lowerCAmelCase = LTP(args.ltp ) # faster in GPU device
_lowerCAmelCase = BertTokenizer.from_pretrained(args.bert )
_lowerCAmelCase = prepare_ref(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
with open(args.save_path , """w""" , encoding="""utf-8""" ) as f:
_lowerCAmelCase = [json.dumps(__lowerCamelCase ) + """\n""" for ref in ref_ids]
f.writelines(__lowerCamelCase )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser(description="""prepare_chinese_ref""")
parser.add_argument(
"""--file_name""",
type=str,
default="""./resources/chinese-demo.txt""",
help="""file need process, same as training data in lm""",
)
parser.add_argument(
"""--ltp""", type=str, default="""./resources/ltp""", help="""resources for LTP tokenizer, usually a path"""
)
parser.add_argument("""--bert""", type=str, default="""./resources/robert""", help="""resources for Bert tokenizer""")
parser.add_argument("""--save_path""", type=str, default="""./resources/ref.txt""", help="""path to save res""")
_lowercase = parser.parse_args()
main(args)
| 5 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""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""",
"""adapter_layer""": """encoder.layers.*.adapter_layer""",
"""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""",
"""pooling_layer.linear""": """projector""",
"""pooling_layer.projection""": """classifier""",
}
_lowercase = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""projector""",
"""classifier""",
]
def A (__lowerCamelCase :Optional[int] ):
_lowerCAmelCase = {}
with open(__lowerCamelCase , """r""" ) as file:
for line_number, line in enumerate(__lowerCamelCase ):
_lowerCAmelCase = line.strip()
if line:
_lowerCAmelCase = line.split()
_lowerCAmelCase = line_number
_lowerCAmelCase = words[0]
_lowerCAmelCase = value
return result
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :Any , __lowerCamelCase :Tuple , __lowerCamelCase :List[Any] , __lowerCamelCase :List[str] ):
for attribute in key.split(""".""" ):
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(__lowerCamelCase ):
_lowerCAmelCase = PARAM_MAPPING[full_name.split(""".""" )[-1]]
_lowerCAmelCase = """param"""
if weight_type is not None and weight_type != "param":
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase ).shape
elif weight_type is not None and weight_type == "param":
_lowerCAmelCase = hf_pointer
for attribute in hf_param_name.split(""".""" ):
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = shape_pointer.shape
# let's reduce dimension
_lowerCAmelCase = value[0]
else:
_lowerCAmelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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":
_lowerCAmelCase = value
elif weight_type == "weight_g":
_lowerCAmelCase = value
elif weight_type == "weight_v":
_lowerCAmelCase = value
elif weight_type == "bias":
_lowerCAmelCase = value
elif weight_type == "param":
for attribute in hf_param_name.split(""".""" ):
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = value
else:
_lowerCAmelCase = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Tuple , __lowerCamelCase :Dict , __lowerCamelCase :List[Any] , __lowerCamelCase :int ):
_lowerCAmelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(__lowerCamelCase ):
_lowerCAmelCase = PARAM_MAPPING[full_name.split(""".""" )[-1]]
_lowerCAmelCase = """param"""
if weight_type is not None and weight_type != "param":
_lowerCAmelCase = """.""".join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
_lowerCAmelCase = """.""".join([key, hf_param_name] )
else:
_lowerCAmelCase = key
_lowerCAmelCase = value if """lm_head""" in full_key else value[0]
_lowercase = {
"""W_a""": """linear_1.weight""",
"""W_b""": """linear_2.weight""",
"""b_a""": """linear_1.bias""",
"""b_b""": """linear_2.bias""",
"""ln_W""": """norm.weight""",
"""ln_b""": """norm.bias""",
}
def A (__lowerCamelCase :Any , __lowerCamelCase :int , __lowerCamelCase :List[str]=None , __lowerCamelCase :List[Any]=None ):
_lowerCAmelCase = False
for key, mapped_key in MAPPING.items():
_lowerCAmelCase = """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
_lowerCAmelCase = True
if "*" in mapped_key:
_lowerCAmelCase = name.split(__lowerCamelCase )[0].split(""".""" )[-2]
_lowerCAmelCase = mapped_key.replace("""*""" , __lowerCamelCase )
if "weight_g" in name:
_lowerCAmelCase = """weight_g"""
elif "weight_v" in name:
_lowerCAmelCase = """weight_v"""
elif "bias" in name:
_lowerCAmelCase = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_lowerCAmelCase = """weight"""
else:
_lowerCAmelCase = None
if hf_dict is not None:
rename_dict(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
else:
set_recursively(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return is_used
return is_used
def A (__lowerCamelCase :Any , __lowerCamelCase :Dict , __lowerCamelCase :Dict ):
_lowerCAmelCase = []
_lowerCAmelCase = fairseq_model.state_dict()
_lowerCAmelCase = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
_lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , hf_model.config.feat_extract_norm == """group""" , )
_lowerCAmelCase = True
else:
_lowerCAmelCase = load_wavaveca_layer(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
if not is_used:
unused_weights.append(__lowerCamelCase )
logger.warning(f'Unused weights: {unused_weights}' )
def A (__lowerCamelCase :Tuple , __lowerCamelCase :Optional[int] , __lowerCamelCase :Any , __lowerCamelCase :List[Any] , __lowerCamelCase :List[Any] ):
_lowerCAmelCase = full_name.split("""conv_layers.""" )[-1]
_lowerCAmelCase = name.split(""".""" )
_lowerCAmelCase = int(items[0] )
_lowerCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
_lowerCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
_lowerCAmelCase = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.' )
_lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.' )
_lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(__lowerCamelCase )
@torch.no_grad()
def A (__lowerCamelCase :List[str] , __lowerCamelCase :Tuple , __lowerCamelCase :List[Any]=None , __lowerCamelCase :Union[str, Any]=None , __lowerCamelCase :str=True , __lowerCamelCase :str=False ):
if config_path is not None:
_lowerCAmelCase = WavaVecaConfig.from_pretrained(__lowerCamelCase )
else:
_lowerCAmelCase = WavaVecaConfig()
if is_seq_class:
_lowerCAmelCase = read_txt_into_dict(__lowerCamelCase )
_lowerCAmelCase = idalabel
_lowerCAmelCase = WavaVecaForSequenceClassification(__lowerCamelCase )
_lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__lowerCamelCase , return_attention_mask=__lowerCamelCase , )
feature_extractor.save_pretrained(__lowerCamelCase )
elif is_finetuned:
if dict_path:
_lowerCAmelCase = Dictionary.load(__lowerCamelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_lowerCAmelCase = target_dict.pad_index
_lowerCAmelCase = target_dict.bos_index
_lowerCAmelCase = target_dict.eos_index
_lowerCAmelCase = len(target_dict.symbols )
_lowerCAmelCase = os.path.join(__lowerCamelCase , """vocab.json""" )
if not os.path.isdir(__lowerCamelCase ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__lowerCamelCase ) )
return
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
_lowerCAmelCase = target_dict.indices
# fairseq has the <pad> and <s> switched
_lowerCAmelCase = 0
_lowerCAmelCase = 1
with open(__lowerCamelCase , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = WavaVecaCTCTokenizer(
__lowerCamelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=__lowerCamelCase , )
_lowerCAmelCase = True if config.feat_extract_norm == """layer""" else False
_lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__lowerCamelCase , return_attention_mask=__lowerCamelCase , )
_lowerCAmelCase = WavaVecaProcessor(feature_extractor=__lowerCamelCase , tokenizer=__lowerCamelCase )
processor.save_pretrained(__lowerCamelCase )
_lowerCAmelCase = WavaVecaForCTC(__lowerCamelCase )
else:
_lowerCAmelCase = WavaVecaForPreTraining(__lowerCamelCase )
if is_finetuned or is_seq_class:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
_lowerCAmelCase = argparse.Namespace(task="""audio_pretraining""" )
_lowerCAmelCase = fairseq.tasks.setup_task(__lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=__lowerCamelCase )
_lowerCAmelCase = model[0].eval()
recursively_load_weights(__lowerCamelCase , __lowerCamelCase , not is_finetuned )
hf_wavavec.save_pretrained(__lowerCamelCase )
if __name__ == "__main__":
_lowercase = 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_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
parser.add_argument(
"""--is_seq_class""",
action="""store_true""",
help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
)
_lowercase = parser.parse_args()
_lowercase = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 5 | 1 |
'''simple docstring'''
import os
import sys
import unittest
_lowercase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
_lowercase = os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
_lowercase = os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = get_test_to_tester_mapping(_lowercase )
_lowerCAmelCase = get_test_to_tester_mapping(_lowercase )
_lowerCAmelCase = {"""BertModelTest""": """BertModelTester"""}
_lowerCAmelCase = {
"""BlipModelTest""": """BlipModelTester""",
"""BlipTextImageModelTest""": """BlipTextImageModelsModelTester""",
"""BlipTextModelTest""": """BlipTextModelTester""",
"""BlipTextRetrievalModelTest""": """BlipTextRetrievalModelTester""",
"""BlipVQAModelTest""": """BlipVQAModelTester""",
"""BlipVisionModelTest""": """BlipVisionModelTester""",
}
self.assertEqual(get_test_info.to_json(_lowercase ) , _lowercase )
self.assertEqual(get_test_info.to_json(_lowercase ) , _lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = get_model_to_test_mapping(_lowercase )
_lowerCAmelCase = get_model_to_test_mapping(_lowercase )
_lowerCAmelCase = {
"""BertForMaskedLM""": ["""BertModelTest"""],
"""BertForMultipleChoice""": ["""BertModelTest"""],
"""BertForNextSentencePrediction""": ["""BertModelTest"""],
"""BertForPreTraining""": ["""BertModelTest"""],
"""BertForQuestionAnswering""": ["""BertModelTest"""],
"""BertForSequenceClassification""": ["""BertModelTest"""],
"""BertForTokenClassification""": ["""BertModelTest"""],
"""BertLMHeadModel""": ["""BertModelTest"""],
"""BertModel""": ["""BertModelTest"""],
}
_lowerCAmelCase = {
"""BlipForConditionalGeneration""": ["""BlipTextImageModelTest"""],
"""BlipForImageTextRetrieval""": ["""BlipTextRetrievalModelTest"""],
"""BlipForQuestionAnswering""": ["""BlipVQAModelTest"""],
"""BlipModel""": ["""BlipModelTest"""],
"""BlipTextModel""": ["""BlipTextModelTest"""],
"""BlipVisionModel""": ["""BlipVisionModelTest"""],
}
self.assertEqual(get_test_info.to_json(_lowercase ) , _lowercase )
self.assertEqual(get_test_info.to_json(_lowercase ) , _lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = get_model_to_tester_mapping(_lowercase )
_lowerCAmelCase = get_model_to_tester_mapping(_lowercase )
_lowerCAmelCase = {
"""BertForMaskedLM""": ["""BertModelTester"""],
"""BertForMultipleChoice""": ["""BertModelTester"""],
"""BertForNextSentencePrediction""": ["""BertModelTester"""],
"""BertForPreTraining""": ["""BertModelTester"""],
"""BertForQuestionAnswering""": ["""BertModelTester"""],
"""BertForSequenceClassification""": ["""BertModelTester"""],
"""BertForTokenClassification""": ["""BertModelTester"""],
"""BertLMHeadModel""": ["""BertModelTester"""],
"""BertModel""": ["""BertModelTester"""],
}
_lowerCAmelCase = {
"""BlipForConditionalGeneration""": ["""BlipTextImageModelsModelTester"""],
"""BlipForImageTextRetrieval""": ["""BlipTextRetrievalModelTester"""],
"""BlipForQuestionAnswering""": ["""BlipVQAModelTester"""],
"""BlipModel""": ["""BlipModelTester"""],
"""BlipTextModel""": ["""BlipTextModelTester"""],
"""BlipVisionModel""": ["""BlipVisionModelTester"""],
}
self.assertEqual(get_test_info.to_json(_lowercase ) , _lowercase )
self.assertEqual(get_test_info.to_json(_lowercase ) , _lowercase )
| 5 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[str] = '''decision_transformer'''
_lowercase : Optional[Any] = ['''past_key_values''']
_lowercase : str = {
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , _lowercase=17 , _lowercase=4 , _lowercase=128 , _lowercase=4_096 , _lowercase=True , _lowercase=1 , _lowercase=1_024 , _lowercase=3 , _lowercase=1 , _lowercase=None , _lowercase="relu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=0.1 , _lowercase=1e-5 , _lowercase=0.02 , _lowercase=True , _lowercase=True , _lowercase=50_256 , _lowercase=50_256 , _lowercase=False , _lowercase=False , **_lowercase , ):
"""simple docstring"""
_lowerCAmelCase = state_dim
_lowerCAmelCase = act_dim
_lowerCAmelCase = hidden_size
_lowerCAmelCase = max_ep_len
_lowerCAmelCase = action_tanh
_lowerCAmelCase = vocab_size
_lowerCAmelCase = n_positions
_lowerCAmelCase = n_layer
_lowerCAmelCase = n_head
_lowerCAmelCase = n_inner
_lowerCAmelCase = activation_function
_lowerCAmelCase = resid_pdrop
_lowerCAmelCase = embd_pdrop
_lowerCAmelCase = attn_pdrop
_lowerCAmelCase = layer_norm_epsilon
_lowerCAmelCase = initializer_range
_lowerCAmelCase = scale_attn_weights
_lowerCAmelCase = use_cache
_lowerCAmelCase = scale_attn_by_inverse_layer_idx
_lowerCAmelCase = reorder_and_upcast_attn
_lowerCAmelCase = bos_token_id
_lowerCAmelCase = eos_token_id
super().__init__(bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase )
| 5 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Any = '''gpt_neo'''
_lowercase : Dict = ['''past_key_values''']
_lowercase : str = {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self , _lowercase=50_257 , _lowercase=2_048 , _lowercase=2_048 , _lowercase=24 , _lowercase=[[["global", "local"], 12]] , _lowercase=16 , _lowercase=None , _lowercase=256 , _lowercase="gelu_new" , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.1 , _lowercase=1e-5 , _lowercase=0.02 , _lowercase=True , _lowercase=50_256 , _lowercase=50_256 , **_lowercase , ):
"""simple docstring"""
_lowerCAmelCase = vocab_size
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_layers
_lowerCAmelCase = num_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = window_size
_lowerCAmelCase = activation_function
_lowerCAmelCase = resid_dropout
_lowerCAmelCase = embed_dropout
_lowerCAmelCase = attention_dropout
_lowerCAmelCase = classifier_dropout
_lowerCAmelCase = layer_norm_epsilon
_lowerCAmelCase = initializer_range
_lowerCAmelCase = use_cache
_lowerCAmelCase = bos_token_id
_lowerCAmelCase = eos_token_id
_lowerCAmelCase = attention_types
_lowerCAmelCase = self.expand_attention_types_params(_lowercase )
if len(self.attention_layers ) != self.num_layers:
raise ValueError(
"""Configuration for convolutional module is incorrect. """
"""It is required that `len(config.attention_layers)` == `config.num_layers` """
F'but is `len(config.attention_layers) = {len(self.attention_layers )}`, '
F'`config.num_layers = {self.num_layers}`. '
"""`config.attention_layers` is prepared using `config.attention_types`. """
"""Please verify the value of `config.attention_types` argument.""" )
super().__init__(bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase )
@staticmethod
def _lowercase ( _lowercase ):
"""simple docstring"""
_lowerCAmelCase = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def A (__lowerCamelCase :Tuple , __lowerCamelCase :Optional[Any] , __lowerCamelCase :List[str] , __lowerCamelCase :int ):
import torch
_lowerCAmelCase = input.size()
_lowerCAmelCase = len(__lowerCamelCase )
_lowerCAmelCase = shape[dimension]
_lowerCAmelCase = torch.arange(0 , __lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = torch.div(sizedim - size , __lowerCamelCase , rounding_mode="""floor""" ) + 1
_lowerCAmelCase = torch.arange(__lowerCamelCase ) + low_indices[:min_length][:, None]
_lowerCAmelCase = [slice(__lowerCamelCase )] * rank
_lowerCAmelCase = indices
_lowerCAmelCase = input[s]
_lowerCAmelCase = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(__lowerCamelCase )
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :List[Any] ):
import torch
_lowerCAmelCase = torch.arange(1 , __lowerCamelCase )
_lowerCAmelCase = torch.remainder(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = remainders == 0
_lowerCAmelCase = candidates[divisor_indices]
_lowerCAmelCase = torch.max(__lowerCamelCase )
return largest_divisor, torch.div(__lowerCamelCase , __lowerCamelCase , rounding_mode="""floor""" )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(_lowercase , direction="""inputs""" )
_lowerCAmelCase = {0: """batch""", 1: """past_sequence + sequence"""}
else:
_lowerCAmelCase = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def _lowercase ( self ):
"""simple docstring"""
return self._config.num_heads
def _lowercase ( self , _lowercase , _lowercase = -1 , _lowercase = -1 , _lowercase = False , _lowercase = None , ):
"""simple docstring"""
_lowerCAmelCase = super(_lowercase , self ).generate_dummy_inputs(
_lowercase , batch_size=_lowercase , seq_length=_lowercase , is_pair=_lowercase , framework=_lowercase )
# We need to order the input in the way they appears in the forward()
_lowerCAmelCase = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
_lowerCAmelCase , _lowerCAmelCase = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
_lowerCAmelCase = seqlen + 2
_lowerCAmelCase = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_lowerCAmelCase = [
(torch.zeros(_lowercase ), torch.zeros(_lowercase )) for _ in range(self.num_layers )
]
_lowerCAmelCase = common_inputs["""attention_mask"""]
if self.use_past:
_lowerCAmelCase = ordered_inputs["""attention_mask"""].dtype
_lowerCAmelCase = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(_lowercase , _lowercase , dtype=_lowercase )] , dim=1 )
return ordered_inputs
@property
def _lowercase ( self ):
"""simple docstring"""
return 13
| 5 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
"""The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"""
)
_lowercase = None
_lowercase = {
"""7B""": 11008,
"""13B""": 13824,
"""30B""": 17920,
"""65B""": 22016,
"""70B""": 28672,
}
_lowercase = {
"""7B""": 1,
"""7Bf""": 1,
"""13B""": 2,
"""13Bf""": 2,
"""30B""": 4,
"""65B""": 8,
"""70B""": 8,
"""70Bf""": 8,
}
def A (__lowerCamelCase :int , __lowerCamelCase :Optional[Any]=1 , __lowerCamelCase :List[Any]=256 ):
return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of)
def A (__lowerCamelCase :Any ):
with open(__lowerCamelCase , """r""" ) as f:
return json.load(__lowerCamelCase )
def A (__lowerCamelCase :List[Any] , __lowerCamelCase :int ):
with open(__lowerCamelCase , """w""" ) as f:
json.dump(__lowerCamelCase , __lowerCamelCase )
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :Tuple , __lowerCamelCase :Optional[Any] , __lowerCamelCase :Tuple=True ):
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
_lowerCAmelCase = os.path.join(__lowerCamelCase , """tmp""" )
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
_lowerCAmelCase = read_json(os.path.join(__lowerCamelCase , """params.json""" ) )
_lowerCAmelCase = NUM_SHARDS[model_size]
_lowerCAmelCase = params["""n_layers"""]
_lowerCAmelCase = params["""n_heads"""]
_lowerCAmelCase = n_heads // num_shards
_lowerCAmelCase = params["""dim"""]
_lowerCAmelCase = dim // n_heads
_lowerCAmelCase = 10_000.0
_lowerCAmelCase = 1.0 / (base ** (torch.arange(0 , __lowerCamelCase , 2 ).float() / dims_per_head))
if "n_kv_heads" in params:
_lowerCAmelCase = params["""n_kv_heads"""] # for GQA / MQA
_lowerCAmelCase = n_heads_per_shard // num_key_value_heads
_lowerCAmelCase = dim // num_key_value_heads
else: # compatibility with other checkpoints
_lowerCAmelCase = n_heads
_lowerCAmelCase = n_heads_per_shard
_lowerCAmelCase = dim
# permute for sliced rotary
def permute(__lowerCamelCase :Optional[int] , __lowerCamelCase :str=n_heads , __lowerCamelCase :str=dim , __lowerCamelCase :List[Any]=dim ):
return w.view(__lowerCamelCase , dima // n_heads // 2 , 2 , __lowerCamelCase ).transpose(1 , 2 ).reshape(__lowerCamelCase , __lowerCamelCase )
print(f'Fetching all parameters from the checkpoint at {input_base_path}.' )
# Load weights
if model_size == "7B":
# Not sharded
# (The sharded implementation would also work, but this is simpler.)
_lowerCAmelCase = torch.load(os.path.join(__lowerCamelCase , """consolidated.00.pth""" ) , map_location="""cpu""" )
else:
# Sharded
_lowerCAmelCase = [
torch.load(os.path.join(__lowerCamelCase , f'consolidated.{i:02d}.pth' ) , map_location="""cpu""" )
for i in range(__lowerCamelCase )
]
_lowerCAmelCase = 0
_lowerCAmelCase = {"""weight_map""": {}}
for layer_i in range(__lowerCamelCase ):
_lowerCAmelCase = f'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
if model_size == "7B":
# Unsharded
_lowerCAmelCase = {
f'model.layers.{layer_i}.self_attn.q_proj.weight': permute(
loaded[f'layers.{layer_i}.attention.wq.weight'] ),
f'model.layers.{layer_i}.self_attn.k_proj.weight': permute(
loaded[f'layers.{layer_i}.attention.wk.weight'] ),
f'model.layers.{layer_i}.self_attn.v_proj.weight': loaded[f'layers.{layer_i}.attention.wv.weight'],
f'model.layers.{layer_i}.self_attn.o_proj.weight': loaded[f'layers.{layer_i}.attention.wo.weight'],
f'model.layers.{layer_i}.mlp.gate_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w1.weight'],
f'model.layers.{layer_i}.mlp.down_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w2.weight'],
f'model.layers.{layer_i}.mlp.up_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w3.weight'],
f'model.layers.{layer_i}.input_layernorm.weight': loaded[f'layers.{layer_i}.attention_norm.weight'],
f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[f'layers.{layer_i}.ffn_norm.weight'],
}
else:
# Sharded
# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
_lowerCAmelCase = {
f'model.layers.{layer_i}.input_layernorm.weight': loaded[0][
f'layers.{layer_i}.attention_norm.weight'
].clone(),
f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[0][
f'layers.{layer_i}.ffn_norm.weight'
].clone(),
}
_lowerCAmelCase = permute(
torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wq.weight'].view(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for i in range(__lowerCamelCase )
] , dim=0 , ).reshape(__lowerCamelCase , __lowerCamelCase ) )
_lowerCAmelCase = permute(
torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wk.weight'].view(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for i in range(__lowerCamelCase )
] , dim=0 , ).reshape(__lowerCamelCase , __lowerCamelCase ) , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , )
_lowerCAmelCase = torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wv.weight'].view(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for i in range(__lowerCamelCase )
] , dim=0 , ).reshape(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.attention.wo.weight'] for i in range(__lowerCamelCase )] , dim=1 )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w1.weight'] for i in range(__lowerCamelCase )] , dim=0 )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w2.weight'] for i in range(__lowerCamelCase )] , dim=1 )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w3.weight'] for i in range(__lowerCamelCase )] , dim=0 )
_lowerCAmelCase = inv_freq
for k, v in state_dict.items():
_lowerCAmelCase = filename
param_count += v.numel()
torch.save(__lowerCamelCase , os.path.join(__lowerCamelCase , __lowerCamelCase ) )
_lowerCAmelCase = f'pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'
if model_size == "7B":
# Unsharded
_lowerCAmelCase = {
"""model.embed_tokens.weight""": loaded["""tok_embeddings.weight"""],
"""model.norm.weight""": loaded["""norm.weight"""],
"""lm_head.weight""": loaded["""output.weight"""],
}
else:
_lowerCAmelCase = {
"""model.norm.weight""": loaded[0]["""norm.weight"""],
"""model.embed_tokens.weight""": torch.cat(
[loaded[i]["""tok_embeddings.weight"""] for i in range(__lowerCamelCase )] , dim=1 ),
"""lm_head.weight""": torch.cat([loaded[i]["""output.weight"""] for i in range(__lowerCamelCase )] , dim=0 ),
}
for k, v in state_dict.items():
_lowerCAmelCase = filename
param_count += v.numel()
torch.save(__lowerCamelCase , os.path.join(__lowerCamelCase , __lowerCamelCase ) )
# Write configs
_lowerCAmelCase = {"""total_size""": param_count * 2}
write_json(__lowerCamelCase , os.path.join(__lowerCamelCase , """pytorch_model.bin.index.json""" ) )
_lowerCAmelCase = params["""ffn_dim_multiplier"""] if """ffn_dim_multiplier""" in params else 1
_lowerCAmelCase = params["""multiple_of"""] if """multiple_of""" in params else 256
_lowerCAmelCase = LlamaConfig(
hidden_size=__lowerCamelCase , intermediate_size=compute_intermediate_size(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) , num_attention_heads=params["""n_heads"""] , num_hidden_layers=params["""n_layers"""] , rms_norm_eps=params["""norm_eps"""] , num_key_value_heads=__lowerCamelCase , )
config.save_pretrained(__lowerCamelCase )
# Make space so we can load the model properly now.
del state_dict
del loaded
gc.collect()
print("""Loading the checkpoint in a Llama model.""" )
_lowerCAmelCase = LlamaForCausalLM.from_pretrained(__lowerCamelCase , torch_dtype=torch.floataa , low_cpu_mem_usage=__lowerCamelCase )
# Avoid saving this as part of the config.
del model.config._name_or_path
print("""Saving in the Transformers format.""" )
model.save_pretrained(__lowerCamelCase , safe_serialization=__lowerCamelCase )
shutil.rmtree(__lowerCamelCase )
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Union[str, Any] ):
# Initialize the tokenizer based on the `spm` model
_lowerCAmelCase = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
print(f'Saving a {tokenizer_class.__name__} to {tokenizer_path}.' )
_lowerCAmelCase = tokenizer_class(__lowerCamelCase )
tokenizer.save_pretrained(__lowerCamelCase )
def A ():
_lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--input_dir""" , help="""Location of LLaMA weights, which contains tokenizer.model and model folders""" , )
parser.add_argument(
"""--model_size""" , choices=["""7B""", """7Bf""", """13B""", """13Bf""", """30B""", """65B""", """70B""", """70Bf""", """tokenizer_only"""] , )
parser.add_argument(
"""--output_dir""" , help="""Location to write HF model and tokenizer""" , )
parser.add_argument("""--safe_serialization""" , type=__lowerCamelCase , help="""Whether or not to save using `safetensors`.""" )
_lowerCAmelCase = parser.parse_args()
if args.model_size != "tokenizer_only":
write_model(
model_path=args.output_dir , input_base_path=os.path.join(args.input_dir , args.model_size ) , model_size=args.model_size , safe_serialization=args.safe_serialization , )
_lowerCAmelCase = os.path.join(args.input_dir , """tokenizer.model""" )
write_tokenizer(args.output_dir , __lowerCamelCase )
if __name__ == "__main__":
main()
| 5 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
def A (__lowerCamelCase :List[Any] ):
_lowerCAmelCase = DPTConfig(embedding_type="""hybrid""" )
if "large" in checkpoint_url:
_lowerCAmelCase = 1024
_lowerCAmelCase = 4096
_lowerCAmelCase = 24
_lowerCAmelCase = 16
_lowerCAmelCase = [5, 11, 17, 23]
_lowerCAmelCase = [256, 512, 1024, 1024]
_lowerCAmelCase = (1, 384, 384)
if "nyu" or "midas" in checkpoint_url:
_lowerCAmelCase = 768
_lowerCAmelCase = [1, 1, 1, 0.5]
_lowerCAmelCase = [256, 512, 768, 768]
_lowerCAmelCase = 150
_lowerCAmelCase = 16
_lowerCAmelCase = (1, 384, 384)
_lowerCAmelCase = False
_lowerCAmelCase = """project"""
if "ade" in checkpoint_url:
_lowerCAmelCase = True
_lowerCAmelCase = 768
_lowerCAmelCase = [1, 1, 1, 0.5]
_lowerCAmelCase = 150
_lowerCAmelCase = 16
_lowerCAmelCase = """huggingface/label-files"""
_lowerCAmelCase = """ade20k-id2label.json"""
_lowerCAmelCase = json.load(open(cached_download(hf_hub_url(__lowerCamelCase , __lowerCamelCase , repo_type="""dataset""" ) ) , """r""" ) )
_lowerCAmelCase = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
_lowerCAmelCase = idalabel
_lowerCAmelCase = {v: k for k, v in idalabel.items()}
_lowerCAmelCase = [1, 150, 480, 480]
return config, expected_shape
def A (__lowerCamelCase :str ):
_lowerCAmelCase = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
for k in ignore_keys:
state_dict.pop(__lowerCamelCase , __lowerCamelCase )
def A (__lowerCamelCase :Union[str, Any] ):
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
_lowerCAmelCase = name.replace("""pretrained.model""" , """dpt.encoder""" )
if "pretrained.model" in name:
_lowerCAmelCase = name.replace("""pretrained.model""" , """dpt.embeddings""" )
if "patch_embed" in name:
_lowerCAmelCase = name.replace("""patch_embed""" , """""" )
if "pos_embed" in name:
_lowerCAmelCase = name.replace("""pos_embed""" , """position_embeddings""" )
if "attn.proj" in name:
_lowerCAmelCase = name.replace("""attn.proj""" , """attention.output.dense""" )
if "proj" in name and "project" not in name:
_lowerCAmelCase = name.replace("""proj""" , """projection""" )
if "blocks" in name:
_lowerCAmelCase = name.replace("""blocks""" , """layer""" )
if "mlp.fc1" in name:
_lowerCAmelCase = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
_lowerCAmelCase = name.replace("""mlp.fc2""" , """output.dense""" )
if "norm1" in name and "backbone" not in name:
_lowerCAmelCase = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name and "backbone" not in name:
_lowerCAmelCase = name.replace("""norm2""" , """layernorm_after""" )
if "scratch.output_conv" in name:
_lowerCAmelCase = name.replace("""scratch.output_conv""" , """head""" )
if "scratch" in name:
_lowerCAmelCase = name.replace("""scratch""" , """neck""" )
if "layer1_rn" in name:
_lowerCAmelCase = name.replace("""layer1_rn""" , """convs.0""" )
if "layer2_rn" in name:
_lowerCAmelCase = name.replace("""layer2_rn""" , """convs.1""" )
if "layer3_rn" in name:
_lowerCAmelCase = name.replace("""layer3_rn""" , """convs.2""" )
if "layer4_rn" in name:
_lowerCAmelCase = name.replace("""layer4_rn""" , """convs.3""" )
if "refinenet" in name:
_lowerCAmelCase = int(name[len("""neck.refinenet""" ) : len("""neck.refinenet""" ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
_lowerCAmelCase = name.replace(f'refinenet{layer_idx}' , f'fusion_stage.layers.{abs(layer_idx-4 )}' )
if "out_conv" in name:
_lowerCAmelCase = name.replace("""out_conv""" , """projection""" )
if "resConfUnit1" in name:
_lowerCAmelCase = name.replace("""resConfUnit1""" , """residual_layer1""" )
if "resConfUnit2" in name:
_lowerCAmelCase = name.replace("""resConfUnit2""" , """residual_layer2""" )
if "conv1" in name:
_lowerCAmelCase = name.replace("""conv1""" , """convolution1""" )
if "conv2" in name:
_lowerCAmelCase = name.replace("""conv2""" , """convolution2""" )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess1.0.project.0""" , """neck.reassemble_stage.readout_projects.0.0""" )
if "pretrained.act_postprocess2.0.project.0" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess2.0.project.0""" , """neck.reassemble_stage.readout_projects.1.0""" )
if "pretrained.act_postprocess3.0.project.0" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess3.0.project.0""" , """neck.reassemble_stage.readout_projects.2.0""" )
if "pretrained.act_postprocess4.0.project.0" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess4.0.project.0""" , """neck.reassemble_stage.readout_projects.3.0""" )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess1.3""" , """neck.reassemble_stage.layers.0.projection""" )
if "pretrained.act_postprocess1.4" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess1.4""" , """neck.reassemble_stage.layers.0.resize""" )
if "pretrained.act_postprocess2.3" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess2.3""" , """neck.reassemble_stage.layers.1.projection""" )
if "pretrained.act_postprocess2.4" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess2.4""" , """neck.reassemble_stage.layers.1.resize""" )
if "pretrained.act_postprocess3.3" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess3.3""" , """neck.reassemble_stage.layers.2.projection""" )
if "pretrained.act_postprocess4.3" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess4.3""" , """neck.reassemble_stage.layers.3.projection""" )
if "pretrained.act_postprocess4.4" in name:
_lowerCAmelCase = name.replace("""pretrained.act_postprocess4.4""" , """neck.reassemble_stage.layers.3.resize""" )
if "pretrained" in name:
_lowerCAmelCase = name.replace("""pretrained""" , """dpt""" )
if "bn" in name:
_lowerCAmelCase = name.replace("""bn""" , """batch_norm""" )
if "head" in name:
_lowerCAmelCase = name.replace("""head""" , """head.head""" )
if "encoder.norm" in name:
_lowerCAmelCase = name.replace("""encoder.norm""" , """layernorm""" )
if "auxlayer" in name:
_lowerCAmelCase = name.replace("""auxlayer""" , """auxiliary_head.head""" )
if "backbone" in name:
_lowerCAmelCase = name.replace("""backbone""" , """backbone.bit.encoder""" )
if ".." in name:
_lowerCAmelCase = name.replace("""..""" , """.""" )
if "stem.conv" in name:
_lowerCAmelCase = name.replace("""stem.conv""" , """bit.embedder.convolution""" )
if "blocks" in name:
_lowerCAmelCase = name.replace("""blocks""" , """layers""" )
if "convolution" in name and "backbone" in name:
_lowerCAmelCase = name.replace("""convolution""" , """conv""" )
if "layer" in name and "backbone" in name:
_lowerCAmelCase = name.replace("""layer""" , """layers""" )
if "backbone.bit.encoder.bit" in name:
_lowerCAmelCase = name.replace("""backbone.bit.encoder.bit""" , """backbone.bit""" )
if "embedder.conv" in name:
_lowerCAmelCase = name.replace("""embedder.conv""" , """embedder.convolution""" )
if "backbone.bit.encoder.stem.norm" in name:
_lowerCAmelCase = name.replace("""backbone.bit.encoder.stem.norm""" , """backbone.bit.embedder.norm""" )
return name
def A (__lowerCamelCase :str , __lowerCamelCase :Any ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_lowerCAmelCase = state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight' )
_lowerCAmelCase = state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCAmelCase = in_proj_weight[: config.hidden_size, :]
_lowerCAmelCase = in_proj_bias[: config.hidden_size]
_lowerCAmelCase = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_lowerCAmelCase = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_lowerCAmelCase = in_proj_weight[
-config.hidden_size :, :
]
_lowerCAmelCase = in_proj_bias[-config.hidden_size :]
def A ():
_lowerCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_lowerCAmelCase = Image.open(requests.get(__lowerCamelCase , stream=__lowerCamelCase ).raw )
return im
@torch.no_grad()
def A (__lowerCamelCase :List[str] , __lowerCamelCase :int , __lowerCamelCase :Any , __lowerCamelCase :str , __lowerCamelCase :List[str] ):
_lowerCAmelCase , _lowerCAmelCase = get_dpt_config(__lowerCamelCase )
# load original state_dict from URL
# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
_lowerCAmelCase = torch.load(__lowerCamelCase , map_location="""cpu""" )
# remove certain keys
remove_ignore_keys_(__lowerCamelCase )
# rename keys
for key in state_dict.copy().keys():
_lowerCAmelCase = state_dict.pop(__lowerCamelCase )
_lowerCAmelCase = val
# read in qkv matrices
read_in_q_k_v(__lowerCamelCase , __lowerCamelCase )
# load HuggingFace model
_lowerCAmelCase = DPTForSemanticSegmentation(__lowerCamelCase ) if """ade""" in checkpoint_url else DPTForDepthEstimation(__lowerCamelCase )
model.load_state_dict(__lowerCamelCase )
model.eval()
# Check outputs on an image
_lowerCAmelCase = 480 if """ade""" in checkpoint_url else 384
_lowerCAmelCase = DPTImageProcessor(size=__lowerCamelCase )
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(__lowerCamelCase , return_tensors="""pt""" )
# forward pass
_lowerCAmelCase = model(**__lowerCamelCase ).logits if """ade""" in checkpoint_url else model(**__lowerCamelCase ).predicted_depth
if show_prediction:
_lowerCAmelCase = (
torch.nn.functional.interpolate(
outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode="""bicubic""" , align_corners=__lowerCamelCase , )
.squeeze()
.cpu()
.numpy()
)
Image.fromarray((prediction / prediction.max()) * 255 ).show()
if pytorch_dump_folder_path is not None:
Path(__lowerCamelCase ).mkdir(exist_ok=__lowerCamelCase )
print(f'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(__lowerCamelCase )
print(f'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(__lowerCamelCase )
if push_to_hub:
model.push_to_hub("""ybelkada/dpt-hybrid-midas""" )
image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas""" )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
type=str,
help="""URL of the original DPT checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=False,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
)
parser.add_argument(
"""--model_name""",
default="""dpt-large""",
type=str,
help="""Name of the model, in case you're pushing to the hub.""",
)
parser.add_argument(
"""--show_prediction""",
action="""store_true""",
)
_lowercase = parser.parse_args()
convert_dpt_checkpoint(
args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
)
| 5 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Tuple = (DDPMScheduler,)
def _lowercase ( self , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_lowercase )
return config
def _lowercase ( self ):
"""simple docstring"""
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_lowercase )
def _lowercase ( self ):
"""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=_lowercase , beta_end=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
self.check_over_configs(thresholding=_lowercase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_lowercase , prediction_type=_lowercase , sample_max_value=_lowercase , )
def _lowercase ( self ):
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
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 _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = len(_lowercase )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter
_lowerCAmelCase = torch.manual_seed(0 )
for t in reversed(range(_lowercase ) ):
# 1. predict noise residual
_lowerCAmelCase = model(_lowercase , _lowercase )
# 2. predict previous mean of sample x_t-1
_lowerCAmelCase = scheduler.step(_lowercase , _lowercase , _lowercase , generator=_lowercase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_lowerCAmelCase = pred_prev_sample
_lowerCAmelCase = torch.sum(torch.abs(_lowercase ) )
_lowerCAmelCase = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 258.9606 ) < 1e-2
assert abs(result_mean.item() - 0.3372 ) < 1e-3
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config(prediction_type="""v_prediction""" )
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = len(_lowercase )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter
_lowerCAmelCase = torch.manual_seed(0 )
for t in reversed(range(_lowercase ) ):
# 1. predict noise residual
_lowerCAmelCase = model(_lowercase , _lowercase )
# 2. predict previous mean of sample x_t-1
_lowerCAmelCase = scheduler.step(_lowercase , _lowercase , _lowercase , generator=_lowercase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_lowerCAmelCase = pred_prev_sample
_lowerCAmelCase = torch.sum(torch.abs(_lowercase ) )
_lowerCAmelCase = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 202.0296 ) < 1e-2
assert abs(result_mean.item() - 0.2631 ) < 1e-3
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_lowercase )
_lowerCAmelCase = scheduler.timesteps
for i, timestep in enumerate(_lowercase ):
if i == len(_lowercase ) - 1:
_lowerCAmelCase = -1
else:
_lowerCAmelCase = timesteps[i + 1]
_lowerCAmelCase = scheduler.previous_timestep(_lowercase )
_lowerCAmelCase = prev_t.item()
self.assertEqual(_lowercase , _lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 51, 0]
with self.assertRaises(_lowercase , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 1, 0]
_lowerCAmelCase = len(_lowercase )
with self.assertRaises(_lowercase , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_lowercase , timesteps=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_lowercase , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_lowercase )
| 5 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowercase = {
"""configuration_clip""": [
"""CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPConfig""",
"""CLIPOnnxConfig""",
"""CLIPTextConfig""",
"""CLIPVisionConfig""",
],
"""processing_clip""": ["""CLIPProcessor"""],
"""tokenization_clip""": ["""CLIPTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ["""CLIPTokenizerFast"""]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ["""CLIPFeatureExtractor"""]
_lowercase = ["""CLIPImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CLIPModel""",
"""CLIPPreTrainedModel""",
"""CLIPTextModel""",
"""CLIPTextModelWithProjection""",
"""CLIPVisionModel""",
"""CLIPVisionModelWithProjection""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFCLIPModel""",
"""TFCLIPPreTrainedModel""",
"""TFCLIPTextModel""",
"""TFCLIPVisionModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""FlaxCLIPModel""",
"""FlaxCLIPPreTrainedModel""",
"""FlaxCLIPTextModel""",
"""FlaxCLIPTextPreTrainedModel""",
"""FlaxCLIPVisionModel""",
"""FlaxCLIPVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_clip import (
CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPConfig,
CLIPOnnxConfig,
CLIPTextConfig,
CLIPVisionConfig,
)
from .processing_clip import CLIPProcessor
from .tokenization_clip import CLIPTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_clip_fast import CLIPTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clip import CLIPFeatureExtractor
from .image_processing_clip import CLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clip import (
CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPModel,
CLIPPreTrainedModel,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPVisionModel,
CLIPVisionModelWithProjection,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_clip import (
TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCLIPModel,
TFCLIPPreTrainedModel,
TFCLIPTextModel,
TFCLIPVisionModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_clip import (
FlaxCLIPModel,
FlaxCLIPPreTrainedModel,
FlaxCLIPTextModel,
FlaxCLIPTextPreTrainedModel,
FlaxCLIPVisionModel,
FlaxCLIPVisionPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 5 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_lowercase = logging.get_logger(__name__)
_lowercase = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_lowercase = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class UpperCAmelCase_ :
'''simple docstring'''
_lowercase : str = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(_SCREAMING_SNAKE_CASE )} )
_lowercase : str = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''} )
_lowercase : int = field(
default=1_2_8 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_lowercase : int = field(
default=1_2_8 , metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''} , )
_lowercase : int = field(
default=6_4 , metadata={
'''help''': (
'''The maximum number of tokens for the question. Questions longer than this will '''
'''be truncated to this length.'''
)
} , )
_lowercase : int = field(
default=3_0 , metadata={
'''help''': (
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
)
} , )
_lowercase : bool = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
_lowercase : bool = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''} )
_lowercase : float = field(
default=0.0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
_lowercase : int = field(
default=2_0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
_lowercase : int = field(
default=0 , metadata={
'''help''': (
'''language id of input for language-specific xlm models (see'''
''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
)
} , )
_lowercase : int = field(default=1 , metadata={'''help''': '''multiple threads for converting example to features'''} )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : str = '''train'''
_lowercase : Union[str, Any] = '''dev'''
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : SquadDataTrainingArguments
_lowercase : List[SquadFeatures]
_lowercase : Split
_lowercase : bool
def __init__( self , _lowercase , _lowercase , _lowercase = None , _lowercase = Split.train , _lowercase = False , _lowercase = None , _lowercase = "pt" , ):
"""simple docstring"""
_lowerCAmelCase = args
_lowerCAmelCase = is_language_sensitive
_lowerCAmelCase = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(_lowercase , _lowercase ):
try:
_lowerCAmelCase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
_lowerCAmelCase = mode
# Load data features from cache or dataset file
_lowerCAmelCase = """v2""" if args.version_2_with_negative else """v1"""
_lowerCAmelCase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
_lowerCAmelCase = cached_features_file + """.lock"""
with FileLock(_lowercase ):
if os.path.exists(_lowercase ) and not args.overwrite_cache:
_lowerCAmelCase = time.time()
_lowerCAmelCase = torch.load(_lowercase )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
_lowerCAmelCase = self.old_features["""features"""]
_lowerCAmelCase = self.old_features.get("""dataset""" , _lowercase )
_lowerCAmelCase = self.old_features.get("""examples""" , _lowercase )
logger.info(
F'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
F'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
""" future run""" )
else:
if mode == Split.dev:
_lowerCAmelCase = self.processor.get_dev_examples(args.data_dir )
else:
_lowerCAmelCase = self.processor.get_train_examples(args.data_dir )
_lowerCAmelCase , _lowerCAmelCase = squad_convert_examples_to_features(
examples=self.examples , tokenizer=_lowercase , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=_lowercase , )
_lowerCAmelCase = time.time()
torch.save(
{"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples} , _lowercase , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self ):
"""simple docstring"""
return len(self.features )
def __getitem__( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.features[i]
_lowerCAmelCase = torch.tensor(feature.input_ids , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.attention_mask , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.token_type_ids , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.cls_index , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.p_mask , dtype=torch.float )
_lowerCAmelCase = torch.tensor(feature.is_impossible , dtype=torch.float )
_lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": attention_mask,
"""token_type_ids""": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"""is_impossible""": is_impossible} )
if self.is_language_sensitive:
inputs.update({"""langs""": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
_lowerCAmelCase = torch.tensor(feature.start_position , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions} )
return inputs
| 5 | 1 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def A (__lowerCamelCase :int = 1000000 , __lowerCamelCase :int = 10 ):
_lowerCAmelCase = defaultdict(__lowerCamelCase )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
_lowerCAmelCase = max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
_lowerCAmelCase = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(__lowerCamelCase , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 5 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-question_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-reader-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-ctx_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
),
"""facebook/dpr-question_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
),
"""facebook/dpr-reader-multiset-base""": (
"""https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
),
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : str = '''dpr'''
def __init__( self , _lowercase=30_522 , _lowercase=768 , _lowercase=12 , _lowercase=12 , _lowercase=3_072 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=2 , _lowercase=0.02 , _lowercase=1e-12 , _lowercase=0 , _lowercase="absolute" , _lowercase = 0 , **_lowercase , ):
"""simple docstring"""
super().__init__(pad_token_id=_lowercase , **_lowercase )
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = hidden_act
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_vocab_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = projection_dim
_lowerCAmelCase = position_embedding_type
| 5 | 1 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_lowercase = logging.get_logger(__name__)
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , _lowercase ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = nn.ModuleList(_lowercase )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = False , _lowercase = True , ):
"""simple docstring"""
for i, (image, scale, controlnet) in enumerate(zip(_lowercase , _lowercase , self.nets ) ):
_lowerCAmelCase , _lowerCAmelCase = controlnet(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , )
# merge samples
if i == 0:
_lowerCAmelCase , _lowerCAmelCase = down_samples, mid_sample
else:
_lowerCAmelCase = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(_lowercase , _lowercase )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def _lowercase ( self , _lowercase , _lowercase = True , _lowercase = None , _lowercase = False , _lowercase = None , ):
"""simple docstring"""
_lowerCAmelCase = 0
_lowerCAmelCase = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
_lowercase , is_main_process=_lowercase , save_function=_lowercase , safe_serialization=_lowercase , variant=_lowercase , )
idx += 1
_lowerCAmelCase = model_path_to_save + F'_{idx}'
@classmethod
def _lowercase ( cls , _lowercase , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase = 0
_lowerCAmelCase = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
_lowerCAmelCase = pretrained_model_path
while os.path.isdir(_lowercase ):
_lowerCAmelCase = ControlNetModel.from_pretrained(_lowercase , **_lowercase )
controlnets.append(_lowercase )
idx += 1
_lowerCAmelCase = pretrained_model_path + F'_{idx}'
logger.info(F'{len(_lowercase )} controlnets loaded from {pretrained_model_path}.' )
if len(_lowercase ) == 0:
raise ValueError(
F'No ControlNets found under {os.path.dirname(_lowercase )}. Expected at least {pretrained_model_path + "_0"}.' )
return cls(_lowercase )
| 5 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
_lowercase = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
_lowercase = """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""
_lowercase = """
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
\"raw_values\" : Returns a full set of errors in case of multioutput input.
\"uniform_average\" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric(\"mse\")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{'mse': 0.6123724356957945}
If you're using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{'mse': array([0.41666667, 1. ])}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"""
] , )
def _lowercase ( self ):
"""simple docstring"""
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value("""float""" ) ),
"references": datasets.Sequence(datasets.Value("""float""" ) ),
}
else:
return {
"predictions": datasets.Value("""float""" ),
"references": datasets.Value("""float""" ),
}
def _lowercase ( self , _lowercase , _lowercase , _lowercase=None , _lowercase="uniform_average" , _lowercase=True ):
"""simple docstring"""
_lowerCAmelCase = mean_squared_error(
_lowercase , _lowercase , sample_weight=_lowercase , multioutput=_lowercase , squared=_lowercase )
return {"mse": mse}
| 5 | 1 |
'''simple docstring'''
import functools
def A (__lowerCamelCase :list[int] , __lowerCamelCase :list[int] ):
# Validation
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not all(isinstance(__lowerCamelCase , __lowerCamelCase ) for day in days ):
raise ValueError("""The parameter days should be a list of integers""" )
if len(__lowerCamelCase ) != 3 or not all(isinstance(__lowerCamelCase , __lowerCamelCase ) for cost in costs ):
raise ValueError("""The parameter costs should be a list of three integers""" )
if len(__lowerCamelCase ) == 0:
return 0
if min(__lowerCamelCase ) <= 0:
raise ValueError("""All days elements should be greater than 0""" )
if max(__lowerCamelCase ) >= 366:
raise ValueError("""All days elements should be less than 366""" )
_lowerCAmelCase = set(__lowerCamelCase )
@functools.cache
def dynamic_programming(__lowerCamelCase :int ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 |
'''simple docstring'''
def A ():
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def A (__lowerCamelCase :List[Any] ):
_lowerCAmelCase = 1
_lowerCAmelCase = 2
while i * i <= n:
_lowerCAmelCase = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def A ():
return next(i for i in triangle_number_generator() if count_divisors(__lowerCamelCase ) > 500 )
if __name__ == "__main__":
print(solution())
| 5 | 1 |
'''simple docstring'''
def A (__lowerCamelCase :str ):
_lowerCAmelCase = len(__lowerCamelCase )
while cur > 1:
# Find the maximum number in arr
_lowerCAmelCase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
_lowerCAmelCase = arr[mi::-1] + arr[mi + 1 : len(__lowerCamelCase )]
# Reverse whole list
_lowerCAmelCase = arr[cur - 1 :: -1] + arr[cur : len(__lowerCamelCase )]
cur -= 1
return arr
if __name__ == "__main__":
_lowercase = input("""Enter numbers separated by a comma:\n""").strip()
_lowercase = [int(item) for item in user_input.split(""",""")]
print(pancake_sort(unsorted))
| 5 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
_lowercase = logging.get_logger(__name__)
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , *_lowercase , **_lowercase ):
"""simple docstring"""
warnings.warn(
"""The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use DonutImageProcessor instead.""" , _lowercase , )
super().__init__(*_lowercase , **_lowercase )
| 5 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowercase = {
"""configuration_wav2vec2""": ["""WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Wav2Vec2Config"""],
"""feature_extraction_wav2vec2""": ["""Wav2Vec2FeatureExtractor"""],
"""processing_wav2vec2""": ["""Wav2Vec2Processor"""],
"""tokenization_wav2vec2""": ["""Wav2Vec2CTCTokenizer""", """Wav2Vec2Tokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Wav2Vec2ForAudioFrameClassification""",
"""Wav2Vec2ForCTC""",
"""Wav2Vec2ForMaskedLM""",
"""Wav2Vec2ForPreTraining""",
"""Wav2Vec2ForSequenceClassification""",
"""Wav2Vec2ForXVector""",
"""Wav2Vec2Model""",
"""Wav2Vec2PreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFWav2Vec2ForCTC""",
"""TFWav2Vec2Model""",
"""TFWav2Vec2PreTrainedModel""",
"""TFWav2Vec2ForSequenceClassification""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""FlaxWav2Vec2ForCTC""",
"""FlaxWav2Vec2ForPreTraining""",
"""FlaxWav2Vec2Model""",
"""FlaxWav2Vec2PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 5 |
'''simple docstring'''
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_min_version,
require_tpu,
require_xpu,
skip,
slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU
from .scripts import test_script, test_sync, test_ops # isort: skip
| 5 | 1 |
'''simple docstring'''
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
_lowercase = logging.get_logger(__name__)
def A (__lowerCamelCase :str , __lowerCamelCase :List[str] , __lowerCamelCase :str , __lowerCamelCase :Optional[Any]=False ):
try:
import torch # noqa: F401
except ImportError:
logger.error(
"""Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"""
""" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
""" instructions.""" )
raise
if not is_sharded:
_lowerCAmelCase = os.path.abspath(__lowerCamelCase )
logger.info(f'Loading PyTorch weights from {pt_path}' )
_lowerCAmelCase = torch.load(__lowerCamelCase , map_location="""cpu""" )
logger.info(f'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.' )
_lowerCAmelCase = convert_pytorch_state_dict_to_flax(__lowerCamelCase , __lowerCamelCase )
else:
# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
_lowerCAmelCase = convert_pytorch_sharded_state_dict_to_flax(__lowerCamelCase , __lowerCamelCase )
return flax_state_dict
def A (__lowerCamelCase :Tuple[str] , __lowerCamelCase :np.ndarray , __lowerCamelCase :Dict[str, jnp.ndarray] , __lowerCamelCase :str , ):
def is_key_or_prefix_key_in_dict(__lowerCamelCase :Tuple[str] ) -> bool:
return len(set(__lowerCamelCase ) & {key, (model_prefix,) + key} ) > 0
# layer norm
_lowerCAmelCase = pt_tuple_key[:-1] + ("""scale""",)
if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer mean
_lowerCAmelCase = pt_tuple_key[:-1] + ("""mean""",)
if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer var
_lowerCAmelCase = pt_tuple_key[:-1] + ("""var""",)
if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# embedding
_lowerCAmelCase = pt_tuple_key[:-1] + ("""embedding""",)
if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# conv layer
_lowerCAmelCase = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
_lowerCAmelCase = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
_lowerCAmelCase = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__lowerCamelCase ):
_lowerCAmelCase = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
_lowerCAmelCase = pt_tuple_key[:-1] + ("""weight""",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
_lowerCAmelCase = pt_tuple_key[:-1] + ("""bias""",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
_lowerCAmelCase = None
if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
_lowerCAmelCase = pt_tuple_key[-2] + """_g"""
elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
_lowerCAmelCase = pt_tuple_key[-2] + """_v"""
if name is not None:
_lowerCAmelCase = pt_tuple_key[:-3] + (name,)
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Dict ):
# convert pytorch tensor to numpy
_lowerCAmelCase = {k: v.numpy() for k, v in pt_state_dict.items()}
_lowerCAmelCase = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers
if "params" in flax_model.params:
_lowerCAmelCase = flax_model.params["""params"""]
else:
_lowerCAmelCase = flax_model.params
_lowerCAmelCase = flatten_dict(__lowerCamelCase )
# add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
_lowerCAmelCase = flatten_dict(flax_model.params["""batch_stats"""] )
random_flax_state_dict.update(__lowerCamelCase )
_lowerCAmelCase = {}
_lowerCAmelCase = (model_prefix not in flax_model_params) and (
model_prefix in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
_lowerCAmelCase = (model_prefix in flax_model_params) and (
model_prefix not in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
_lowerCAmelCase = tuple(pt_key.split(""".""" ) )
# remove base model prefix if necessary
_lowerCAmelCase = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
_lowerCAmelCase = pt_tuple_key[1:]
# Correctly rename weight parameters
_lowerCAmelCase , _lowerCAmelCase = rename_key_and_reshape_tensor(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# add model prefix if necessary
_lowerCAmelCase = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
_lowerCAmelCase = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1] or "var" in flax_key[-1]:
_lowerCAmelCase = jnp.asarray(__lowerCamelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(__lowerCamelCase , __lowerCamelCase )
continue
# also add unexpected weight so that warning is thrown
_lowerCAmelCase = jnp.asarray(__lowerCamelCase )
else:
# also add unexpected weight so that warning is thrown
_lowerCAmelCase = jnp.asarray(__lowerCamelCase )
return unflatten_dict(__lowerCamelCase )
def A (__lowerCamelCase :List[str] , __lowerCamelCase :List[str] ):
import torch
# Load the index
_lowerCAmelCase = {}
for shard_file in shard_filenames:
# load using msgpack utils
_lowerCAmelCase = torch.load(__lowerCamelCase )
_lowerCAmelCase = {k: v.numpy() for k, v in pt_state_dict.items()}
_lowerCAmelCase = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
_lowerCAmelCase = flax_model.params["""params"""]
_lowerCAmelCase = flatten_dict(__lowerCamelCase )
random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""] ) )
else:
_lowerCAmelCase = flax_model.params
_lowerCAmelCase = flatten_dict(__lowerCamelCase )
_lowerCAmelCase = (model_prefix not in flax_model_params) and (
model_prefix in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
_lowerCAmelCase = (model_prefix in flax_model_params) and (
model_prefix not in {k.split(""".""" )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
_lowerCAmelCase = tuple(pt_key.split(""".""" ) )
# remove base model prefix if necessary
_lowerCAmelCase = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
_lowerCAmelCase = pt_tuple_key[1:]
# Correctly rename weight parameters
_lowerCAmelCase , _lowerCAmelCase = rename_key_and_reshape_tensor(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# add model prefix if necessary
_lowerCAmelCase = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
_lowerCAmelCase = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1]:
_lowerCAmelCase = jnp.asarray(__lowerCamelCase )
continue
if "var" in flax_key[-1]:
_lowerCAmelCase = jnp.asarray(__lowerCamelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(__lowerCamelCase , __lowerCamelCase )
continue
# also add unexpected weight so that warning is thrown
_lowerCAmelCase = jnp.asarray(__lowerCamelCase )
else:
# also add unexpected weight so that warning is thrown
_lowerCAmelCase = jnp.asarray(__lowerCamelCase )
return unflatten_dict(__lowerCamelCase )
def A (__lowerCamelCase :Tuple , __lowerCamelCase :List[str] ):
_lowerCAmelCase = os.path.abspath(__lowerCamelCase )
logger.info(f'Loading Flax weights from {flax_checkpoint_path}' )
# import correct flax class
_lowerCAmelCase = getattr(__lowerCamelCase , """Flax""" + model.__class__.__name__ )
# load flax weight dict
with open(__lowerCamelCase , """rb""" ) as state_f:
try:
_lowerCAmelCase = from_bytes(__lowerCamelCase , state_f.read() )
except UnpicklingError:
raise EnvironmentError(f'Unable to convert {flax_checkpoint_path} to Flax deserializable object. ' )
return load_flax_weights_in_pytorch_model(__lowerCamelCase , __lowerCamelCase )
def A (__lowerCamelCase :Optional[int] , __lowerCamelCase :Optional[int] ):
try:
import torch # noqa: F401
except ImportError:
logger.error(
"""Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"""
""" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
""" instructions.""" )
raise
# check if we have bf16 weights
_lowerCAmelCase = flatten_dict(jax.tree_util.tree_map(lambda __lowerCamelCase : x.dtype == jnp.bfloataa , __lowerCamelCase ) ).values()
if any(__lowerCamelCase ):
# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
"""Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
"""before loading those in PyTorch model.""" )
_lowerCAmelCase = jax.tree_util.tree_map(
lambda __lowerCamelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , __lowerCamelCase )
_lowerCAmelCase = flatten_dict(__lowerCamelCase )
_lowerCAmelCase = pt_model.state_dict()
_lowerCAmelCase = (pt_model.base_model_prefix in flax_state) and (
pt_model.base_model_prefix not in {k.split(""".""" )[0] for k in pt_model_dict.keys()}
)
_lowerCAmelCase = (pt_model.base_model_prefix not in flax_state) and (
pt_model.base_model_prefix in {k.split(""".""" )[0] for k in pt_model_dict.keys()}
)
# keep track of unexpected & missing keys
_lowerCAmelCase = []
_lowerCAmelCase = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
_lowerCAmelCase = flax_key_tuple[0] == pt_model.base_model_prefix
_lowerCAmelCase = """.""".join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict
# adapt flax_key to prepare for loading from/to base model only
if load_model_with_head_into_base_model and has_base_model_prefix:
_lowerCAmelCase = flax_key_tuple[1:]
elif load_base_model_into_model_with_head and require_base_model_prefix:
_lowerCAmelCase = (pt_model.base_model_prefix,) + flax_key_tuple
# rename flax weights to PyTorch format
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__lowerCamelCase ) not in pt_model_dict:
# conv layer
_lowerCAmelCase = flax_key_tuple[:-1] + ("""weight""",)
_lowerCAmelCase = jnp.transpose(__lowerCamelCase , (3, 2, 0, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCamelCase ) not in pt_model_dict:
# linear layer
_lowerCAmelCase = flax_key_tuple[:-1] + ("""weight""",)
_lowerCAmelCase = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
_lowerCAmelCase = flax_key_tuple[:-1] + ("""weight""",)
# adding batch stats from flax batch norm to pt
elif "mean" in flax_key_tuple[-1]:
_lowerCAmelCase = flax_key_tuple[:-1] + ("""running_mean""",)
elif "var" in flax_key_tuple[-1]:
_lowerCAmelCase = flax_key_tuple[:-1] + ("""running_var""",)
if "batch_stats" in flax_state:
_lowerCAmelCase = """.""".join(flax_key_tuple[1:] ) # Remove the params/batch_stats header
else:
_lowerCAmelCase = """.""".join(__lowerCamelCase )
# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
_lowerCAmelCase = {}
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
for key in pt_model_dict:
_lowerCAmelCase = key.split(""".""" )
_lowerCAmelCase = None
if key_components[-3::2] == ["parametrizations", "original0"]:
_lowerCAmelCase = key_components[-2] + """_g"""
elif key_components[-3::2] == ["parametrizations", "original1"]:
_lowerCAmelCase = key_components[-2] + """_v"""
if name is not None:
_lowerCAmelCase = key_components[:-3] + [name]
_lowerCAmelCase = """.""".join(__lowerCamelCase )
_lowerCAmelCase = key
if flax_key in special_pt_names:
_lowerCAmelCase = special_pt_names[flax_key]
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
else:
# add weight to pytorch dict
_lowerCAmelCase = np.asarray(__lowerCamelCase ) if not isinstance(__lowerCamelCase , np.ndarray ) else flax_tensor
_lowerCAmelCase = torch.from_numpy(__lowerCamelCase )
# remove from missing keys
missing_keys.remove(__lowerCamelCase )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(__lowerCamelCase )
pt_model.load_state_dict(__lowerCamelCase )
# re-transform missing_keys to list
_lowerCAmelCase = list(__lowerCamelCase )
if len(__lowerCamelCase ) > 0:
logger.warning(
"""Some weights of the Flax model were not used when initializing the PyTorch model"""
f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
""" (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
""" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
""" FlaxBertForSequenceClassification model).""" )
else:
logger.warning(f'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n' )
if len(__lowerCamelCase ) > 0:
logger.warning(
f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
""" use it for predictions and inference.""" )
else:
logger.warning(
f'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
"""If your task is similar to the task the model of the checkpoint was trained on, """
f'you can already use {pt_model.__class__.__name__} for predictions without further training.' )
return pt_model
| 5 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("""Googling.....""")
_lowercase = """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
_lowercase = requests.get(url, headers={"""UserAgent""": UserAgent().random})
# res.raise_for_status()
with open("""project1a.html""", """wb""") as out_file: # only for knowing the class
for data in res.iter_content(10000):
out_file.write(data)
_lowercase = BeautifulSoup(res.text, """html.parser""")
_lowercase = list(soup.select(""".eZt8xd"""))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get("""href"""))
else:
webbrowser.open(F"""https://google.com{link.get('href')}""")
| 5 | 1 |
'''simple docstring'''
def A ():
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def A (__lowerCamelCase :List[Any] ):
_lowerCAmelCase = 1
_lowerCAmelCase = 2
while i * i <= n:
_lowerCAmelCase = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def A ():
return next(i for i in triangle_number_generator() if count_divisors(__lowerCamelCase ) > 500 )
if __name__ == "__main__":
print(solution())
| 5 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_lowercase = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
def A ():
_lowerCAmelCase = Github(os.environ["""GITHUB_TOKEN"""] )
_lowerCAmelCase = g.get_repo("""huggingface/diffusers""" )
_lowerCAmelCase = repo.get_issues(state="""open""" )
for issue in open_issues:
_lowerCAmelCase = sorted(issue.get_comments() , key=lambda __lowerCamelCase : i.created_at , reverse=__lowerCamelCase )
_lowerCAmelCase = comments[0] if len(__lowerCamelCase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state="""closed""" )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state="""open""" )
issue.remove_from_labels("""stale""" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
"""This issue has been automatically marked as stale because it has not had """
"""recent activity. If you think this still needs to be addressed """
"""please comment on this thread.\n\nPlease note that issues that do not follow the """
"""[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) """
"""are likely to be ignored.""" )
issue.add_to_labels("""stale""" )
if __name__ == "__main__":
main()
| 5 | 1 |
'''simple docstring'''
def A (__lowerCamelCase :Any , __lowerCamelCase :str , __lowerCamelCase :List[Any] , __lowerCamelCase :int , __lowerCamelCase :Tuple , __lowerCamelCase :Any ):
if index == r:
for j in range(__lowerCamelCase ):
print(data[j] , end=""" """ )
print(""" """ )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
_lowerCAmelCase = arr[i]
combination_util(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , index + 1 , __lowerCamelCase , i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :str , __lowerCamelCase :int ):
# A temporary array to store all combination one by one
_lowerCAmelCase = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , 0 , __lowerCamelCase , 0 )
if __name__ == "__main__":
# Driver code to check the function above
_lowercase = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 5 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
ImageTextPipelineOutput,
UniDiffuserPipeline,
)
else:
from .modeling_text_decoder import UniDiffuserTextDecoder
from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
| 5 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : str = '''blenderbot-small'''
_lowercase : List[Any] = ['''past_key_values''']
_lowercase : List[Any] = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self , _lowercase=50_265 , _lowercase=512 , _lowercase=8 , _lowercase=2_048 , _lowercase=16 , _lowercase=8 , _lowercase=2_048 , _lowercase=16 , _lowercase=0.0 , _lowercase=0.0 , _lowercase=True , _lowercase=True , _lowercase="gelu" , _lowercase=512 , _lowercase=0.1 , _lowercase=0.0 , _lowercase=0.0 , _lowercase=0.02 , _lowercase=1 , _lowercase=False , _lowercase=0 , _lowercase=1 , _lowercase=2 , _lowercase=2 , **_lowercase , ):
"""simple docstring"""
_lowerCAmelCase = vocab_size
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = d_model
_lowerCAmelCase = encoder_ffn_dim
_lowerCAmelCase = encoder_layers
_lowerCAmelCase = encoder_attention_heads
_lowerCAmelCase = decoder_ffn_dim
_lowerCAmelCase = decoder_layers
_lowerCAmelCase = decoder_attention_heads
_lowerCAmelCase = dropout
_lowerCAmelCase = attention_dropout
_lowerCAmelCase = activation_dropout
_lowerCAmelCase = activation_function
_lowerCAmelCase = init_std
_lowerCAmelCase = encoder_layerdrop
_lowerCAmelCase = decoder_layerdrop
_lowerCAmelCase = use_cache
_lowerCAmelCase = encoder_layers
_lowerCAmelCase = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase , is_encoder_decoder=_lowercase , decoder_start_token_id=_lowercase , forced_eos_token_id=_lowercase , **_lowercase , )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _lowercase ( self ):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
_lowerCAmelCase = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
_lowerCAmelCase = {0: """batch"""}
_lowerCAmelCase = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
_lowerCAmelCase = {0: """batch""", 1: """decoder_sequence"""}
_lowerCAmelCase = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_lowercase , direction="""inputs""" )
elif self.task == "causal-lm":
# TODO: figure this case out.
_lowerCAmelCase = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
_lowerCAmelCase , _lowerCAmelCase = self.num_layers
for i in range(_lowercase ):
_lowerCAmelCase = {0: """batch""", 2: """past_sequence + sequence"""}
_lowerCAmelCase = {0: """batch""", 2: """past_sequence + sequence"""}
else:
_lowerCAmelCase = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
] )
return common_inputs
@property
def _lowercase ( self ):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
_lowerCAmelCase = super().outputs
else:
_lowerCAmelCase = super(_lowercase , self ).outputs
if self.use_past:
_lowerCAmelCase , _lowerCAmelCase = self.num_layers
for i in range(_lowercase ):
_lowerCAmelCase = {0: """batch""", 2: """past_sequence + sequence"""}
_lowerCAmelCase = {0: """batch""", 2: """past_sequence + sequence"""}
return common_outputs
def _lowercase ( self , _lowercase , _lowercase = -1 , _lowercase = -1 , _lowercase = False , _lowercase = None , ):
"""simple docstring"""
_lowerCAmelCase = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
# Generate decoder inputs
_lowerCAmelCase = seq_length if not self.use_past else 1
_lowerCAmelCase = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
_lowerCAmelCase = {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
_lowerCAmelCase = dict(**_lowercase , **_lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
_lowerCAmelCase , _lowerCAmelCase = common_inputs["""input_ids"""].shape
_lowerCAmelCase = common_inputs["""decoder_input_ids"""].shape[1]
_lowerCAmelCase , _lowerCAmelCase = self.num_attention_heads
_lowerCAmelCase = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_lowerCAmelCase = decoder_seq_length + 3
_lowerCAmelCase = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
_lowerCAmelCase = torch.cat(
[common_inputs["""decoder_attention_mask"""], torch.ones(_lowercase , _lowercase )] , dim=1 )
_lowerCAmelCase = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
_lowerCAmelCase , _lowerCAmelCase = self.num_layers
_lowerCAmelCase = min(_lowercase , _lowercase )
_lowerCAmelCase = max(_lowercase , _lowercase ) - min_num_layers
_lowerCAmelCase = """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
for _ in range(_lowercase ):
common_inputs["past_key_values"].append(
(
torch.zeros(_lowercase ),
torch.zeros(_lowercase ),
torch.zeros(_lowercase ),
torch.zeros(_lowercase ),
) )
# TODO: test this.
_lowerCAmelCase = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
for _ in range(_lowercase , _lowercase ):
common_inputs["past_key_values"].append((torch.zeros(_lowercase ), torch.zeros(_lowercase )) )
return common_inputs
def _lowercase ( self , _lowercase , _lowercase = -1 , _lowercase = -1 , _lowercase = False , _lowercase = None , ):
"""simple docstring"""
_lowerCAmelCase = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
_lowerCAmelCase , _lowerCAmelCase = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
_lowerCAmelCase = seqlen + 2
_lowerCAmelCase , _lowerCAmelCase = self.num_layers
_lowerCAmelCase , _lowerCAmelCase = self.num_attention_heads
_lowerCAmelCase = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_lowerCAmelCase = common_inputs["""attention_mask"""].dtype
_lowerCAmelCase = torch.cat(
[common_inputs["""attention_mask"""], torch.ones(_lowercase , _lowercase , dtype=_lowercase )] , dim=1 )
_lowerCAmelCase = [
(torch.zeros(_lowercase ), torch.zeros(_lowercase )) for _ in range(_lowercase )
]
return common_inputs
def _lowercase ( self , _lowercase , _lowercase = -1 , _lowercase = -1 , _lowercase = False , _lowercase = None , ):
"""simple docstring"""
_lowerCAmelCase = compute_effective_axis_dimension(
_lowercase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
_lowerCAmelCase = tokenizer.num_special_tokens_to_add(_lowercase )
_lowerCAmelCase = compute_effective_axis_dimension(
_lowercase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=_lowercase )
# Generate dummy inputs according to compute batch and sequence
_lowerCAmelCase = [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
_lowerCAmelCase = dict(tokenizer(_lowercase , return_tensors=_lowercase ) )
return common_inputs
def _lowercase ( self , _lowercase , _lowercase = -1 , _lowercase = -1 , _lowercase = False , _lowercase = None , ):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
_lowerCAmelCase = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
_lowercase , batch_size=_lowercase , seq_length=_lowercase , is_pair=_lowercase , framework=_lowercase )
elif self.task == "causal-lm":
_lowerCAmelCase = self._generate_dummy_inputs_for_causal_lm(
_lowercase , batch_size=_lowercase , seq_length=_lowercase , is_pair=_lowercase , framework=_lowercase )
else:
_lowerCAmelCase = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
_lowercase , batch_size=_lowercase , seq_length=_lowercase , is_pair=_lowercase , framework=_lowercase )
return common_inputs
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
_lowerCAmelCase = super()._flatten_past_key_values_(_lowercase , _lowercase , _lowercase , _lowercase )
else:
_lowerCAmelCase = super(_lowercase , self )._flatten_past_key_values_(
_lowercase , _lowercase , _lowercase , _lowercase )
| 5 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowercase = {"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ViTMAEForPreTraining""",
"""ViTMAELayer""",
"""ViTMAEModel""",
"""ViTMAEPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""TFViTMAEForPreTraining""",
"""TFViTMAEModel""",
"""TFViTMAEPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 5 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_lowercase = logging.get_logger(__name__)
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[str] = ['''input_values''', '''padding_mask''']
def __init__( self , _lowercase = 1 , _lowercase = 24_000 , _lowercase = 0.0 , _lowercase = None , _lowercase = None , **_lowercase , ):
"""simple docstring"""
super().__init__(feature_size=_lowercase , sampling_rate=_lowercase , padding_value=_lowercase , **_lowercase )
_lowerCAmelCase = chunk_length_s
_lowerCAmelCase = overlap
@property
def _lowercase ( self ):
"""simple docstring"""
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _lowercase ( self ):
"""simple docstring"""
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 ) )
def __call__( self , _lowercase , _lowercase = None , _lowercase = False , _lowercase = None , _lowercase = None , _lowercase = None , ):
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
F' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
if padding and truncation:
raise ValueError("""Both padding and truncation were set. Make sure you only set one.""" )
elif padding is None:
# by default let's pad the inputs
_lowerCAmelCase = True
_lowerCAmelCase = bool(
isinstance(_lowercase , (list, tuple) ) and (isinstance(raw_audio[0] , (np.ndarray, tuple, list) )) )
if is_batched:
_lowerCAmelCase = [np.asarray(_lowercase , dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(_lowercase , np.ndarray ):
_lowerCAmelCase = np.asarray(_lowercase , dtype=np.floataa )
elif isinstance(_lowercase , np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
_lowerCAmelCase = raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
_lowerCAmelCase = [np.asarray(_lowercase ).T]
# verify inputs are valid
for idx, example in enumerate(_lowercase ):
if example.ndim > 2:
raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}' )
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels' )
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels' )
_lowerCAmelCase = None
_lowerCAmelCase = BatchFeature({"""input_values""": raw_audio} )
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
_lowerCAmelCase = min(array.shape[0] for array in raw_audio )
_lowerCAmelCase = int(np.floor(max_length / self.chunk_stride ) )
_lowerCAmelCase = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
_lowerCAmelCase = max(array.shape[0] for array in raw_audio )
_lowerCAmelCase = int(np.ceil(max_length / self.chunk_stride ) )
_lowerCAmelCase = (nb_step - 1) * self.chunk_stride + self.chunk_length
_lowerCAmelCase = """max_length"""
else:
_lowerCAmelCase = input_values
# normal padding on batch
if padded_inputs is None:
_lowerCAmelCase = self.pad(
_lowercase , max_length=_lowercase , truncation=_lowercase , padding=_lowercase , return_attention_mask=_lowercase , )
if padding:
_lowerCAmelCase = padded_inputs.pop("""attention_mask""" )
_lowerCAmelCase = []
for example in padded_inputs.pop("""input_values""" ):
if self.feature_size == 1:
_lowerCAmelCase = example[..., None]
input_values.append(example.T )
_lowerCAmelCase = input_values
if return_tensors is not None:
_lowerCAmelCase = padded_inputs.convert_to_tensors(_lowercase )
return padded_inputs
| 5 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
_lowercase = logging.get_logger(__name__)
@add_end_docstrings(_SCREAMING_SNAKE_CASE )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , **_lowercase ):
"""simple docstring"""
super().__init__(**_lowercase )
if self.framework != "pt":
raise ValueError(F'The {self.__class__} is only available in PyTorch.' )
# No specific FOR_XXX available yet
def __call__( self , _lowercase , **_lowercase ):
"""simple docstring"""
return super().__call__(_lowercase , **_lowercase )
def _lowercase ( self , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase = {}
if "candidate_labels" in kwargs:
_lowerCAmelCase = kwargs["""candidate_labels"""]
if "hypothesis_template" in kwargs:
_lowerCAmelCase = kwargs["""hypothesis_template"""]
return preprocess_params, {}, {}
def _lowercase ( self , _lowercase , _lowercase=None , _lowercase="This is a sound of {}." ):
"""simple docstring"""
if isinstance(_lowercase , _lowercase ):
if audio.startswith("""http://""" ) or audio.startswith("""https://""" ):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
_lowerCAmelCase = requests.get(_lowercase ).content
else:
with open(_lowercase , """rb""" ) as f:
_lowerCAmelCase = f.read()
if isinstance(_lowercase , _lowercase ):
_lowerCAmelCase = ffmpeg_read(_lowercase , self.feature_extractor.sampling_rate )
if not isinstance(_lowercase , np.ndarray ):
raise ValueError("""We expect a numpy ndarray as input""" )
if len(audio.shape ) != 1:
raise ValueError("""We expect a single channel audio input for ZeroShotAudioClassificationPipeline""" )
_lowerCAmelCase = self.feature_extractor(
[audio] , sampling_rate=self.feature_extractor.sampling_rate , return_tensors="""pt""" )
_lowerCAmelCase = candidate_labels
_lowerCAmelCase = [hypothesis_template.format(_lowercase ) for x in candidate_labels]
_lowerCAmelCase = self.tokenizer(_lowercase , return_tensors=self.framework , padding=_lowercase )
_lowerCAmelCase = [text_inputs]
return inputs
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = model_inputs.pop("""candidate_labels""" )
_lowerCAmelCase = model_inputs.pop("""text_inputs""" )
if isinstance(text_inputs[0] , _lowercase ):
_lowerCAmelCase = text_inputs[0]
else:
# Batching case.
_lowerCAmelCase = text_inputs[0][0]
_lowerCAmelCase = self.model(**_lowercase , **_lowercase )
_lowerCAmelCase = {
"""candidate_labels""": candidate_labels,
"""logits""": outputs.logits_per_audio,
}
return model_outputs
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = model_outputs.pop("""candidate_labels""" )
_lowerCAmelCase = model_outputs["""logits"""][0]
if self.framework == "pt":
_lowerCAmelCase = logits.softmax(dim=0 )
_lowerCAmelCase = probs.tolist()
else:
raise ValueError("""`tf` framework not supported.""" )
_lowerCAmelCase = [
{"""score""": score, """label""": candidate_label}
for score, candidate_label in sorted(zip(_lowercase , _lowercase ) , key=lambda _lowercase : -x[0] )
]
return result
| 5 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowercase = logging.get_logger(__name__)
# TODO: upload to AWS
_lowercase = {
"""yjernite/retribert-base-uncased""": (
"""https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"""
),
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : int = '''retribert'''
def __init__( self , _lowercase=30_522 , _lowercase=768 , _lowercase=8 , _lowercase=12 , _lowercase=3_072 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=2 , _lowercase=0.02 , _lowercase=1e-12 , _lowercase=True , _lowercase=128 , _lowercase=0 , **_lowercase , ):
"""simple docstring"""
super().__init__(pad_token_id=_lowercase , **_lowercase )
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = hidden_act
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_vocab_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = share_encoders
_lowerCAmelCase = projection_dim
| 5 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_lowercase = logging.get_logger(__name__)
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[str] = ['''input_values''', '''padding_mask''']
def __init__( self , _lowercase = 1 , _lowercase = 24_000 , _lowercase = 0.0 , _lowercase = None , _lowercase = None , **_lowercase , ):
"""simple docstring"""
super().__init__(feature_size=_lowercase , sampling_rate=_lowercase , padding_value=_lowercase , **_lowercase )
_lowerCAmelCase = chunk_length_s
_lowerCAmelCase = overlap
@property
def _lowercase ( self ):
"""simple docstring"""
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _lowercase ( self ):
"""simple docstring"""
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 ) )
def __call__( self , _lowercase , _lowercase = None , _lowercase = False , _lowercase = None , _lowercase = None , _lowercase = None , ):
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
F' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
if padding and truncation:
raise ValueError("""Both padding and truncation were set. Make sure you only set one.""" )
elif padding is None:
# by default let's pad the inputs
_lowerCAmelCase = True
_lowerCAmelCase = bool(
isinstance(_lowercase , (list, tuple) ) and (isinstance(raw_audio[0] , (np.ndarray, tuple, list) )) )
if is_batched:
_lowerCAmelCase = [np.asarray(_lowercase , dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(_lowercase , np.ndarray ):
_lowerCAmelCase = np.asarray(_lowercase , dtype=np.floataa )
elif isinstance(_lowercase , np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
_lowerCAmelCase = raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
_lowerCAmelCase = [np.asarray(_lowercase ).T]
# verify inputs are valid
for idx, example in enumerate(_lowercase ):
if example.ndim > 2:
raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}' )
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels' )
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels' )
_lowerCAmelCase = None
_lowerCAmelCase = BatchFeature({"""input_values""": raw_audio} )
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
_lowerCAmelCase = min(array.shape[0] for array in raw_audio )
_lowerCAmelCase = int(np.floor(max_length / self.chunk_stride ) )
_lowerCAmelCase = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
_lowerCAmelCase = max(array.shape[0] for array in raw_audio )
_lowerCAmelCase = int(np.ceil(max_length / self.chunk_stride ) )
_lowerCAmelCase = (nb_step - 1) * self.chunk_stride + self.chunk_length
_lowerCAmelCase = """max_length"""
else:
_lowerCAmelCase = input_values
# normal padding on batch
if padded_inputs is None:
_lowerCAmelCase = self.pad(
_lowercase , max_length=_lowercase , truncation=_lowercase , padding=_lowercase , return_attention_mask=_lowercase , )
if padding:
_lowerCAmelCase = padded_inputs.pop("""attention_mask""" )
_lowerCAmelCase = []
for example in padded_inputs.pop("""input_values""" ):
if self.feature_size == 1:
_lowerCAmelCase = example[..., None]
input_values.append(example.T )
_lowerCAmelCase = input_values
if return_tensors is not None:
_lowerCAmelCase = padded_inputs.convert_to_tensors(_lowercase )
return padded_inputs
| 5 | 1 |
'''simple docstring'''
from manim import *
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = Rectangle(height=0.5 , width=0.5 )
_lowerCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = VGroup(_lowercase , _lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""CPU""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_lowercase )
_lowerCAmelCase = [mem.copy() for i in range(4 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""GPU""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
gpu.move_to([-1, -1, 0] )
self.add(_lowercase )
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""Model""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
model.move_to([3, -1.0, 0] )
self.add(_lowercase )
_lowerCAmelCase = []
for i, rect in enumerate(_lowercase ):
rect.set_stroke(_lowercase )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
_lowerCAmelCase = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(_lowercase , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=_lowercase )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=_lowercase , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=_lowercase , buff=0.0 )
self.add(_lowercase )
cpu_targs.append(_lowercase )
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""Loaded Checkpoint""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , aligned_edge=_lowercase , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
_lowerCAmelCase = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_lowerCAmelCase = MarkupText(
F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(_lowercase , _lowercase )
_lowerCAmelCase = MarkupText(
F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint' , font_size=18 , )
blue_text.next_to(_lowercase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
_lowerCAmelCase = MarkupText(
F'Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(_lowercase ) , Write(_lowercase ) )
self.play(Write(_lowercase , run_time=1 ) , Create(_lowercase , run_time=1 ) )
_lowerCAmelCase = []
_lowerCAmelCase = []
for i, rect in enumerate(_lowercase ):
_lowerCAmelCase = fill.copy().set_fill(_lowercase , opacity=0.7 )
target.move_to(_lowercase )
first_animations.append(GrowFromCenter(_lowercase , run_time=1 ) )
_lowerCAmelCase = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(_lowercase , run_time=1.5 ) )
self.play(*_lowercase )
self.play(*_lowercase )
self.wait()
| 5 |
'''simple docstring'''
_lowercase = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_lowercase = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
_lowercase = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 5 | 1 |
'''simple docstring'''
def A (__lowerCamelCase :str ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 |
'''simple docstring'''
import functools
def A (__lowerCamelCase :list[int] , __lowerCamelCase :list[int] ):
# Validation
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not all(isinstance(__lowerCamelCase , __lowerCamelCase ) for day in days ):
raise ValueError("""The parameter days should be a list of integers""" )
if len(__lowerCamelCase ) != 3 or not all(isinstance(__lowerCamelCase , __lowerCamelCase ) for cost in costs ):
raise ValueError("""The parameter costs should be a list of three integers""" )
if len(__lowerCamelCase ) == 0:
return 0
if min(__lowerCamelCase ) <= 0:
raise ValueError("""All days elements should be greater than 0""" )
if max(__lowerCamelCase ) >= 366:
raise ValueError("""All days elements should be less than 366""" )
_lowerCAmelCase = set(__lowerCamelCase )
@functools.cache
def dynamic_programming(__lowerCamelCase :int ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 | 1 |
'''simple docstring'''
def A (__lowerCamelCase :int , __lowerCamelCase :int ):
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
_lowerCAmelCase = str(bin(__lowerCamelCase ) )[2:] # remove the leading "0b"
_lowerCAmelCase = str(bin(__lowerCamelCase ) )[2:] # remove the leading "0b"
_lowerCAmelCase = max(len(__lowerCamelCase ) , len(__lowerCamelCase ) )
return "0b" + "".join(
str(int(char_a != char_b ) )
for char_a, char_b in zip(a_binary.zfill(__lowerCamelCase ) , b_binary.zfill(__lowerCamelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def A (__lowerCamelCase :List[Any] ):
_lowerCAmelCase = 384
if "tiny" in model_name:
_lowerCAmelCase = [3, 3, 9, 3]
_lowerCAmelCase = [96, 192, 384, 768]
if "small" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [96, 192, 384, 768]
if "base" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [128, 256, 512, 1024]
_lowerCAmelCase = 512
if "large" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [192, 384, 768, 1536]
_lowerCAmelCase = 768
if "xlarge" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [256, 512, 1024, 2048]
_lowerCAmelCase = 1024
# set label information
_lowerCAmelCase = 150
_lowerCAmelCase = """huggingface/label-files"""
_lowerCAmelCase = """ade20k-id2label.json"""
_lowerCAmelCase = json.load(open(hf_hub_download(__lowerCamelCase , __lowerCamelCase , repo_type="""dataset""" ) , """r""" ) )
_lowerCAmelCase = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
_lowerCAmelCase = {v: k for k, v in idalabel.items()}
_lowerCAmelCase = ConvNextConfig(
depths=__lowerCamelCase , hidden_sizes=__lowerCamelCase , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
_lowerCAmelCase = UperNetConfig(
backbone_config=__lowerCamelCase , auxiliary_in_channels=__lowerCamelCase , num_labels=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid=__lowerCamelCase , )
return config
def A (__lowerCamelCase :Optional[Any] ):
_lowerCAmelCase = []
# fmt: off
# stem
rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""") )
rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""") )
rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""") )
rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f'backbone.stages.{i}.{j}.gamma', f'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter') )
rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.weight', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.bias', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias') )
rename_keys.append((f'backbone.stages.{i}.{j}.norm.weight', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.norm.bias', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias') )
if i > 0:
rename_keys.append((f'backbone.downsample_layers.{i}.0.weight', f'backbone.encoder.stages.{i}.downsampling_layer.0.weight') )
rename_keys.append((f'backbone.downsample_layers.{i}.0.bias', f'backbone.encoder.stages.{i}.downsampling_layer.0.bias') )
rename_keys.append((f'backbone.downsample_layers.{i}.1.weight', f'backbone.encoder.stages.{i}.downsampling_layer.1.weight') )
rename_keys.append((f'backbone.downsample_layers.{i}.1.bias', f'backbone.encoder.stages.{i}.downsampling_layer.1.bias') )
rename_keys.append((f'backbone.norm{i}.weight', f'backbone.hidden_states_norms.stage{i+1}.weight') )
rename_keys.append((f'backbone.norm{i}.bias', f'backbone.hidden_states_norms.stage{i+1}.bias') )
# decode head
rename_keys.extend(
[
("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
] )
# fmt: on
return rename_keys
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :Dict , __lowerCamelCase :Tuple ):
_lowerCAmelCase = dct.pop(__lowerCamelCase )
_lowerCAmelCase = val
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Optional[Any] , __lowerCamelCase :Any ):
_lowerCAmelCase = {
"""upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
"""upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
"""upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
"""upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
"""upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
}
_lowerCAmelCase = model_name_to_url[model_name]
_lowerCAmelCase = torch.hub.load_state_dict_from_url(__lowerCamelCase , map_location="""cpu""" )["""state_dict"""]
_lowerCAmelCase = get_upernet_config(__lowerCamelCase )
_lowerCAmelCase = UperNetForSemanticSegmentation(__lowerCamelCase )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
_lowerCAmelCase = state_dict.pop(__lowerCamelCase )
if "bn" in key:
_lowerCAmelCase = key.replace("""bn""" , """batch_norm""" )
_lowerCAmelCase = val
# rename keys
_lowerCAmelCase = create_rename_keys(__lowerCamelCase )
for src, dest in rename_keys:
rename_key(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
model.load_state_dict(__lowerCamelCase )
# verify on image
_lowerCAmelCase = """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
_lowerCAmelCase = Image.open(requests.get(__lowerCamelCase , stream=__lowerCamelCase ).raw ).convert("""RGB""" )
_lowerCAmelCase = SegformerImageProcessor()
_lowerCAmelCase = processor(__lowerCamelCase , return_tensors="""pt""" ).pixel_values
with torch.no_grad():
_lowerCAmelCase = model(__lowerCamelCase )
if model_name == "upernet-convnext-tiny":
_lowerCAmelCase = torch.tensor(
[[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]] )
elif model_name == "upernet-convnext-small":
_lowerCAmelCase = torch.tensor(
[[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]] )
elif model_name == "upernet-convnext-base":
_lowerCAmelCase = torch.tensor(
[[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]] )
elif model_name == "upernet-convnext-large":
_lowerCAmelCase = torch.tensor(
[[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]] )
elif model_name == "upernet-convnext-xlarge":
_lowerCAmelCase = torch.tensor(
[[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]] )
print("""Logits:""" , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , __lowerCamelCase , atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(f'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(__lowerCamelCase )
print(f'Saving processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(__lowerCamelCase )
if push_to_hub:
print(f'Pushing model and processor for {model_name} to hub' )
model.push_to_hub(f'openmmlab/{model_name}' )
processor.push_to_hub(f'openmmlab/{model_name}' )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""upernet-convnext-tiny""",
type=str,
choices=[F"""upernet-convnext-{size}""" for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
help="""Name of the ConvNext UperNet model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_lowercase = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 5 | 1 |
'''simple docstring'''
import os
def A ():
with open(os.path.dirname(__lowerCamelCase ) + """/p022_names.txt""" ) as file:
_lowerCAmelCase = str(file.readlines()[0] )
_lowerCAmelCase = names.replace("""\"""" , """""" ).split(""",""" )
names.sort()
_lowerCAmelCase = 0
_lowerCAmelCase = 0
for i, name in enumerate(__lowerCamelCase ):
for letter in name:
name_score += ord(__lowerCamelCase ) - 64
total_score += (i + 1) * name_score
_lowerCAmelCase = 0
return total_score
if __name__ == "__main__":
print(solution())
| 5 |
'''simple docstring'''
from itertools import product
def A (__lowerCamelCase :int , __lowerCamelCase :int ):
_lowerCAmelCase = sides_number
_lowerCAmelCase = max_face_number * dice_number
_lowerCAmelCase = [0] * (max_total + 1)
_lowerCAmelCase = 1
_lowerCAmelCase = range(__lowerCamelCase , max_face_number + 1 )
for dice_numbers in product(__lowerCamelCase , repeat=__lowerCamelCase ):
_lowerCAmelCase = sum(__lowerCamelCase )
totals_frequencies[total] += 1
return totals_frequencies
def A ():
_lowerCAmelCase = total_frequency_distribution(
sides_number=4 , dice_number=9 )
_lowerCAmelCase = total_frequency_distribution(
sides_number=6 , dice_number=6 )
_lowerCAmelCase = 0
_lowerCAmelCase = 9
_lowerCAmelCase = 4 * 9
_lowerCAmelCase = 6
for peter_total in range(__lowerCamelCase , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
_lowerCAmelCase = (4**9) * (6**6)
_lowerCAmelCase = peter_wins_count / total_games_number
_lowerCAmelCase = round(__lowerCamelCase , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(F"""{solution() = }""")
| 5 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self ):
"""simple docstring"""
self.test()
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = 0
_lowerCAmelCase = False
while not completed:
if counter == 1:
self.reset()
_lowerCAmelCase = self.advance()
if not self.does_advance(_lowercase ):
raise Exception(
"""Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.""" )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.update(_lowercase )
counter += 1
if counter > 10_000:
raise Exception("""update() does not fulfill the constraint.""" )
if self.remaining() != 0:
raise Exception("""Custom Constraint is not defined correctly.""" )
@abstractmethod
def _lowercase ( self ):
"""simple docstring"""
raise NotImplementedError(
F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def _lowercase ( self , _lowercase ):
"""simple docstring"""
raise NotImplementedError(
F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def _lowercase ( self , _lowercase ):
"""simple docstring"""
raise NotImplementedError(
F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def _lowercase ( self ):
"""simple docstring"""
raise NotImplementedError(
F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def _lowercase ( self ):
"""simple docstring"""
raise NotImplementedError(
F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def _lowercase ( self , _lowercase=False ):
"""simple docstring"""
raise NotImplementedError(
F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , _lowercase ):
"""simple docstring"""
super(_lowercase , self ).__init__()
if not isinstance(_lowercase , _lowercase ) or len(_lowercase ) == 0:
raise ValueError(F'`token_ids` has to be a non-empty list, but is {token_ids}.' )
if any((not isinstance(_lowercase , _lowercase ) or token_id < 0) for token_id in token_ids ):
raise ValueError(F'Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.' )
_lowerCAmelCase = token_ids
_lowerCAmelCase = len(self.token_ids )
_lowerCAmelCase = -1 # the index of the currently fulfilled step
_lowerCAmelCase = False
def _lowercase ( self ):
"""simple docstring"""
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def _lowercase ( self , _lowercase ):
"""simple docstring"""
if not isinstance(_lowercase , _lowercase ):
raise ValueError(F'`token_id` has to be an `int`, but is {token_id} of type {type(_lowercase )}' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def _lowercase ( self , _lowercase ):
"""simple docstring"""
if not isinstance(_lowercase , _lowercase ):
raise ValueError(F'`token_id` has to be an `int`, but is {token_id} of type {type(_lowercase )}' )
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
if self.does_advance(_lowercase ):
self.fulfilled_idx += 1
_lowerCAmelCase = True
if self.fulfilled_idx == (self.seqlen - 1):
_lowerCAmelCase = True
_lowerCAmelCase = completed
else:
# failed to make progress.
_lowerCAmelCase = True
self.reset()
return stepped, completed, reset
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = False
_lowerCAmelCase = 0
def _lowercase ( self ):
"""simple docstring"""
return self.seqlen - (self.fulfilled_idx + 1)
def _lowercase ( self , _lowercase=False ):
"""simple docstring"""
_lowerCAmelCase = PhrasalConstraint(self.token_ids )
if stateful:
_lowerCAmelCase = self.seqlen
_lowerCAmelCase = self.fulfilled_idx
_lowerCAmelCase = self.completed
return new_constraint
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase , _lowercase=True ):
"""simple docstring"""
_lowerCAmelCase = max([len(_lowercase ) for one in nested_token_ids] )
_lowerCAmelCase = {}
for token_ids in nested_token_ids:
_lowerCAmelCase = root
for tidx, token_id in enumerate(_lowercase ):
if token_id not in level:
_lowerCAmelCase = {}
_lowerCAmelCase = level[token_id]
if no_subsets and self.has_subsets(_lowercase , _lowercase ):
raise ValueError(
"""Each list in `nested_token_ids` can't be a complete subset of another list, but is"""
F' {nested_token_ids}.' )
_lowerCAmelCase = root
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.trie
for current_token in current_seq:
_lowerCAmelCase = start[current_token]
_lowerCAmelCase = list(start.keys() )
return next_tokens
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.next_tokens(_lowercase )
return len(_lowercase ) == 0
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = list(root.values() )
if len(_lowercase ) == 0:
return 1
else:
return sum([self.count_leaves(_lowercase ) for nn in next_nodes] )
def _lowercase ( self , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.count_leaves(_lowercase )
return len(_lowercase ) != leaf_count
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , _lowercase ):
"""simple docstring"""
super(_lowercase , self ).__init__()
if not isinstance(_lowercase , _lowercase ) or len(_lowercase ) == 0:
raise ValueError(F'`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.' )
if any(not isinstance(_lowercase , _lowercase ) for token_ids in nested_token_ids ):
raise ValueError(F'`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.' )
if any(
any((not isinstance(_lowercase , _lowercase ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
F'Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.' )
_lowerCAmelCase = DisjunctiveTrie(_lowercase )
_lowerCAmelCase = nested_token_ids
_lowerCAmelCase = self.trie.max_height
_lowerCAmelCase = []
_lowerCAmelCase = False
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.trie.next_tokens(self.current_seq )
if len(_lowercase ) == 0:
return None
else:
return token_list
def _lowercase ( self , _lowercase ):
"""simple docstring"""
if not isinstance(_lowercase , _lowercase ):
raise ValueError(F'`token_id` is supposed to be type `int`, but is {token_id} of type {type(_lowercase )}' )
_lowerCAmelCase = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def _lowercase ( self , _lowercase ):
"""simple docstring"""
if not isinstance(_lowercase , _lowercase ):
raise ValueError(F'`token_id` is supposed to be type `int`, but is {token_id} of type {type(_lowercase )}' )
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
if self.does_advance(_lowercase ):
self.current_seq.append(_lowercase )
_lowerCAmelCase = True
else:
_lowerCAmelCase = True
self.reset()
_lowerCAmelCase = self.trie.reached_leaf(self.current_seq )
_lowerCAmelCase = completed
return stepped, completed, reset
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = False
_lowerCAmelCase = []
def _lowercase ( self ):
"""simple docstring"""
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def _lowercase ( self , _lowercase=False ):
"""simple docstring"""
_lowerCAmelCase = DisjunctiveConstraint(self.token_ids )
if stateful:
_lowerCAmelCase = self.seqlen
_lowerCAmelCase = self.current_seq
_lowerCAmelCase = self.completed
return new_constraint
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = constraints
# max # of steps required to fulfill a given constraint
_lowerCAmelCase = max([c.seqlen for c in constraints] )
_lowerCAmelCase = len(_lowercase )
_lowerCAmelCase = False
self.init_state()
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = None
_lowerCAmelCase = [constraint.copy(stateful=_lowercase ) for constraint in self.constraints]
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
_lowerCAmelCase = constraint.advance()
if isinstance(_lowercase , _lowercase ):
token_list.append(_lowercase )
elif isinstance(_lowercase , _lowercase ):
token_list.extend(_lowercase )
else:
_lowerCAmelCase = self.inprogress_constraint.advance()
if isinstance(_lowercase , _lowercase ):
token_list.append(_lowercase )
elif isinstance(_lowercase , _lowercase ):
token_list.extend(_lowercase )
if len(_lowercase ) == 0:
return None
else:
return token_list
def _lowercase ( self , _lowercase ):
"""simple docstring"""
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
_lowerCAmelCase , _lowerCAmelCase = self.add(_lowercase )
# the entire list of constraints are fulfilled
if self.completed:
break
def _lowercase ( self , _lowercase ):
"""simple docstring"""
if not isinstance(_lowercase , _lowercase ):
raise ValueError(F'`token_id` should be an `int`, but is `{token_id}`.' )
_lowerCAmelCase , _lowerCAmelCase = False, False
if self.completed:
_lowerCAmelCase = True
_lowerCAmelCase = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.inprogress_constraint.update(_lowercase )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_lowercase ) )
_lowerCAmelCase = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
_lowerCAmelCase = None
if len(self.pending_constraints ) == 0:
# we're done!
_lowerCAmelCase = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(_lowercase ):
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = pending_constraint.update(_lowercase )
if not stepped:
raise Exception(
"""`constraint.update(token_id)` is not yielding incremental progress, """
"""even though `constraint.does_advance(token_id)` is true.""" )
if complete:
self.complete_constraints.append(_lowercase )
_lowerCAmelCase = None
if not complete and stepped:
_lowerCAmelCase = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
_lowerCAmelCase = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
_lowerCAmelCase = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def _lowercase ( self , _lowercase=True ):
"""simple docstring"""
_lowerCAmelCase = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
_lowerCAmelCase = [
constraint.copy(stateful=_lowercase ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
_lowerCAmelCase = self.inprogress_constraint.copy(stateful=_lowercase )
_lowerCAmelCase = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 5 |
'''simple docstring'''
from manim import *
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = Rectangle(height=0.5 , width=0.5 )
_lowerCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = VGroup(_lowercase , _lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""CPU""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_lowercase )
_lowerCAmelCase = [mem.copy() for i in range(1 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""GPU""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
gpu.align_to(_lowercase , _lowercase )
gpu.set_x(gpu.get_x() - 1 )
self.add(_lowercase )
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""Model""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
model.move_to([3, -1.0, 0] )
self.play(
Create(_lowercase , run_time=1 ) , Create(_lowercase , run_time=1 ) , Create(_lowercase , run_time=1 ) , )
_lowerCAmelCase = MarkupText(
F'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.' , font_size=24 , )
_lowerCAmelCase = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_lowerCAmelCase = MarkupText(
F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(_lowercase , run_time=2.5 ) , Write(_lowercase ) , Write(_lowercase ) )
self.add(_lowercase )
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = []
for i, rect in enumerate(_lowercase ):
_lowerCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(_lowercase , opacity=0.7 )
cpu_target.move_to(_lowercase )
cpu_target.generate_target()
_lowerCAmelCase = 0.46 / 4
_lowerCAmelCase = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=_lowercase )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=_lowercase , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=_lowercase , buff=0.0 )
cpu_targs.append(_lowercase )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(_lowercase ) )
second_animations.append(MoveToTarget(_lowercase , run_time=1.5 ) )
self.play(*_lowercase )
self.play(*_lowercase )
self.wait()
| 5 | 1 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCAmelCase_ ( unittest.TestCase , _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = load_tool("""text-to-speech""" )
self.tool.setup()
def _lowercase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
_lowerCAmelCase = self.tool("""hey""" )
_lowerCAmelCase = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
def _lowercase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
_lowerCAmelCase = self.tool("""hey""" )
_lowerCAmelCase = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.000_5966_6688_3211_5829, -0.000_3657_6401_9079_5064, -0.0001_3439_5027_9988_3485] ) , ) )
| 5 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_lowercase = False
try:
_lowercase = _is_package_available("""google.colab""")
except ModuleNotFoundError:
pass
@input.register
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase = None , _lowercase = [] ):
"""simple docstring"""
_lowerCAmelCase = 0
_lowerCAmelCase = choices
_lowerCAmelCase = prompt
if sys.platform == "win32":
_lowerCAmelCase = """*"""
else:
_lowerCAmelCase = """➔ """
def _lowercase ( self , _lowercase , _lowercase = "" ):
"""simple docstring"""
if sys.platform != "win32":
writeColor(self.choices[index] , 32 , _lowercase )
else:
forceWrite(self.choices[index] , _lowercase )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
if index == self.position:
forceWrite(F' {self.arrow_char} ' )
self.write_choice(_lowercase )
else:
forceWrite(F' {self.choices[index]}' )
reset_cursor()
def _lowercase ( self , _lowercase , _lowercase = 1 ):
"""simple docstring"""
_lowerCAmelCase = self.position
if direction == Direction.DOWN:
if self.position + 1 >= len(self.choices ):
return
self.position += num_spaces
else:
if self.position - 1 < 0:
return
self.position -= num_spaces
clear_line()
self.print_choice(_lowercase )
move_cursor(_lowercase , direction.name )
self.print_choice(self.position )
@input.mark(KEYMAP["""up"""] )
def _lowercase ( self ):
"""simple docstring"""
self.move_direction(Direction.UP )
@input.mark(KEYMAP["""down"""] )
def _lowercase ( self ):
"""simple docstring"""
self.move_direction(Direction.DOWN )
@input.mark(KEYMAP["""newline"""] )
def _lowercase ( self ):
"""simple docstring"""
move_cursor(len(self.choices ) - self.position , """DOWN""" )
return self.position
@input.mark(KEYMAP["""interrupt"""] )
def _lowercase ( self ):
"""simple docstring"""
move_cursor(len(self.choices ) - self.position , """DOWN""" )
raise KeyboardInterrupt
@input.mark_multiple(*[KEYMAP[str(_lowercase )] for number in range(10 )] )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = int(chr(self.current_selection ) )
_lowerCAmelCase = index - self.position
if index == self.position:
return
if index < len(self.choices ):
if self.position > index:
self.move_direction(Direction.UP , -movement )
elif self.position < index:
self.move_direction(Direction.DOWN , _lowercase )
else:
return
else:
return
def _lowercase ( self , _lowercase = 0 ):
"""simple docstring"""
if self.prompt:
linebreak()
forceWrite(self.prompt , """\n""" )
if in_colab:
forceWrite("""Please input a choice index (starting from 0), and press enter""" , """\n""" )
else:
forceWrite("""Please select a choice using the arrow or number keys, and selecting with enter""" , """\n""" )
_lowerCAmelCase = default_choice
for i in range(len(self.choices ) ):
self.print_choice(_lowercase )
forceWrite("""\n""" )
move_cursor(len(self.choices ) - self.position , """UP""" )
with cursor.hide():
while True:
if in_colab:
try:
_lowerCAmelCase = int(builtins.input() )
except ValueError:
_lowerCAmelCase = default_choice
else:
_lowerCAmelCase = self.handle_input()
if choice is not None:
reset_cursor()
for _ in range(len(self.choices ) + 1 ):
move_cursor(1 , """UP""" )
clear_line()
self.write_choice(_lowercase , """\n""" )
return choice
| 5 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
_lowercase : str = VideoToVideoSDPipeline
_lowercase : Tuple = TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({'''video'''} ) - {'''image''', '''width''', '''height'''}
_lowercase : List[str] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''video'''} ) - {'''image'''}
_lowercase : Tuple = PipelineTesterMixin.required_optional_params - {'''latents'''}
_lowercase : Any = False
# No `output_type`.
_lowercase : Tuple = frozenset(
[
'''num_inference_steps''',
'''generator''',
'''latents''',
'''return_dict''',
'''callback''',
'''callback_steps''',
] )
def _lowercase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
_lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""") , up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""") , cross_attention_dim=32 , attention_head_dim=4 , )
_lowerCAmelCase = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_lowercase , set_alpha_to_one=_lowercase , )
torch.manual_seed(0 )
_lowerCAmelCase = 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 )
_lowerCAmelCase = 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=1_000 , hidden_act="""gelu""" , projection_dim=512 , )
_lowerCAmelCase = CLIPTextModel(_lowercase )
_lowerCAmelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
_lowerCAmelCase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def _lowercase ( self , _lowercase , _lowercase=0 ):
"""simple docstring"""
_lowerCAmelCase = floats_tensor((1, 3, 3, 32, 32) , rng=random.Random(_lowercase ) ).to(_lowercase )
if str(_lowercase ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(_lowercase )
else:
_lowerCAmelCase = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
_lowerCAmelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""video""": video,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """pt""",
}
return inputs
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = VideoToVideoSDPipeline(**_lowercase )
_lowerCAmelCase = sd_pipe.to(_lowercase )
sd_pipe.set_progress_bar_config(disable=_lowercase )
_lowerCAmelCase = self.get_dummy_inputs(_lowercase )
_lowerCAmelCase = """np"""
_lowerCAmelCase = sd_pipe(**_lowercase ).frames
_lowerCAmelCase = frames[0][-3:, -3:, -1]
assert frames[0].shape == (32, 32, 3)
_lowerCAmelCase = np.array([106, 117, 113, 174, 137, 112, 148, 151, 131] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def _lowercase ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_lowercase , expected_max_diff=5e-3 )
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def _lowercase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def _lowercase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline.""" )
def _lowercase ( self ):
"""simple docstring"""
pass
def _lowercase ( self ):
"""simple docstring"""
return super().test_progress_bar()
@slow
@skip_mps
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = VideoToVideoSDPipeline.from_pretrained("""cerspense/zeroscope_v2_XL""" , torch_dtype=torch.floataa )
pipe.enable_model_cpu_offload()
# 10 frames
_lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
_lowerCAmelCase = torch.randn((1, 10, 3, 1_024, 576) , generator=_lowercase )
_lowerCAmelCase = video.to("""cuda""" )
_lowerCAmelCase = """Spiderman is surfing"""
_lowerCAmelCase = pipe(_lowercase , video=_lowercase , generator=_lowercase , num_inference_steps=3 , output_type="""pt""" ).frames
_lowerCAmelCase = np.array([-1.045_8984, -1.127_9297, -0.966_3086, -0.9150_3906, -0.7509_7656] )
assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array ).sum() < 1e-2
| 5 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""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""",
"""adapter_layer""": """encoder.layers.*.adapter_layer""",
"""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""",
"""pooling_layer.linear""": """projector""",
"""pooling_layer.projection""": """classifier""",
}
_lowercase = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""projector""",
"""classifier""",
]
def A (__lowerCamelCase :Optional[int] ):
_lowerCAmelCase = {}
with open(__lowerCamelCase , """r""" ) as file:
for line_number, line in enumerate(__lowerCamelCase ):
_lowerCAmelCase = line.strip()
if line:
_lowerCAmelCase = line.split()
_lowerCAmelCase = line_number
_lowerCAmelCase = words[0]
_lowerCAmelCase = value
return result
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :Any , __lowerCamelCase :Tuple , __lowerCamelCase :List[Any] , __lowerCamelCase :List[str] ):
for attribute in key.split(""".""" ):
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(__lowerCamelCase ):
_lowerCAmelCase = PARAM_MAPPING[full_name.split(""".""" )[-1]]
_lowerCAmelCase = """param"""
if weight_type is not None and weight_type != "param":
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase ).shape
elif weight_type is not None and weight_type == "param":
_lowerCAmelCase = hf_pointer
for attribute in hf_param_name.split(""".""" ):
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = shape_pointer.shape
# let's reduce dimension
_lowerCAmelCase = value[0]
else:
_lowerCAmelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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":
_lowerCAmelCase = value
elif weight_type == "weight_g":
_lowerCAmelCase = value
elif weight_type == "weight_v":
_lowerCAmelCase = value
elif weight_type == "bias":
_lowerCAmelCase = value
elif weight_type == "param":
for attribute in hf_param_name.split(""".""" ):
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = value
else:
_lowerCAmelCase = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Tuple , __lowerCamelCase :Dict , __lowerCamelCase :List[Any] , __lowerCamelCase :int ):
_lowerCAmelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(__lowerCamelCase ):
_lowerCAmelCase = PARAM_MAPPING[full_name.split(""".""" )[-1]]
_lowerCAmelCase = """param"""
if weight_type is not None and weight_type != "param":
_lowerCAmelCase = """.""".join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
_lowerCAmelCase = """.""".join([key, hf_param_name] )
else:
_lowerCAmelCase = key
_lowerCAmelCase = value if """lm_head""" in full_key else value[0]
_lowercase = {
"""W_a""": """linear_1.weight""",
"""W_b""": """linear_2.weight""",
"""b_a""": """linear_1.bias""",
"""b_b""": """linear_2.bias""",
"""ln_W""": """norm.weight""",
"""ln_b""": """norm.bias""",
}
def A (__lowerCamelCase :Any , __lowerCamelCase :int , __lowerCamelCase :List[str]=None , __lowerCamelCase :List[Any]=None ):
_lowerCAmelCase = False
for key, mapped_key in MAPPING.items():
_lowerCAmelCase = """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
_lowerCAmelCase = True
if "*" in mapped_key:
_lowerCAmelCase = name.split(__lowerCamelCase )[0].split(""".""" )[-2]
_lowerCAmelCase = mapped_key.replace("""*""" , __lowerCamelCase )
if "weight_g" in name:
_lowerCAmelCase = """weight_g"""
elif "weight_v" in name:
_lowerCAmelCase = """weight_v"""
elif "bias" in name:
_lowerCAmelCase = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_lowerCAmelCase = """weight"""
else:
_lowerCAmelCase = None
if hf_dict is not None:
rename_dict(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
else:
set_recursively(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return is_used
return is_used
def A (__lowerCamelCase :Any , __lowerCamelCase :Dict , __lowerCamelCase :Dict ):
_lowerCAmelCase = []
_lowerCAmelCase = fairseq_model.state_dict()
_lowerCAmelCase = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
_lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , hf_model.config.feat_extract_norm == """group""" , )
_lowerCAmelCase = True
else:
_lowerCAmelCase = load_wavaveca_layer(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
if not is_used:
unused_weights.append(__lowerCamelCase )
logger.warning(f'Unused weights: {unused_weights}' )
def A (__lowerCamelCase :Tuple , __lowerCamelCase :Optional[int] , __lowerCamelCase :Any , __lowerCamelCase :List[Any] , __lowerCamelCase :List[Any] ):
_lowerCAmelCase = full_name.split("""conv_layers.""" )[-1]
_lowerCAmelCase = name.split(""".""" )
_lowerCAmelCase = int(items[0] )
_lowerCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
_lowerCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
_lowerCAmelCase = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.' )
_lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.' )
_lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(__lowerCamelCase )
@torch.no_grad()
def A (__lowerCamelCase :List[str] , __lowerCamelCase :Tuple , __lowerCamelCase :List[Any]=None , __lowerCamelCase :Union[str, Any]=None , __lowerCamelCase :str=True , __lowerCamelCase :str=False ):
if config_path is not None:
_lowerCAmelCase = WavaVecaConfig.from_pretrained(__lowerCamelCase )
else:
_lowerCAmelCase = WavaVecaConfig()
if is_seq_class:
_lowerCAmelCase = read_txt_into_dict(__lowerCamelCase )
_lowerCAmelCase = idalabel
_lowerCAmelCase = WavaVecaForSequenceClassification(__lowerCamelCase )
_lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__lowerCamelCase , return_attention_mask=__lowerCamelCase , )
feature_extractor.save_pretrained(__lowerCamelCase )
elif is_finetuned:
if dict_path:
_lowerCAmelCase = Dictionary.load(__lowerCamelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_lowerCAmelCase = target_dict.pad_index
_lowerCAmelCase = target_dict.bos_index
_lowerCAmelCase = target_dict.eos_index
_lowerCAmelCase = len(target_dict.symbols )
_lowerCAmelCase = os.path.join(__lowerCamelCase , """vocab.json""" )
if not os.path.isdir(__lowerCamelCase ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__lowerCamelCase ) )
return
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
_lowerCAmelCase = target_dict.indices
# fairseq has the <pad> and <s> switched
_lowerCAmelCase = 0
_lowerCAmelCase = 1
with open(__lowerCamelCase , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = WavaVecaCTCTokenizer(
__lowerCamelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=__lowerCamelCase , )
_lowerCAmelCase = True if config.feat_extract_norm == """layer""" else False
_lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__lowerCamelCase , return_attention_mask=__lowerCamelCase , )
_lowerCAmelCase = WavaVecaProcessor(feature_extractor=__lowerCamelCase , tokenizer=__lowerCamelCase )
processor.save_pretrained(__lowerCamelCase )
_lowerCAmelCase = WavaVecaForCTC(__lowerCamelCase )
else:
_lowerCAmelCase = WavaVecaForPreTraining(__lowerCamelCase )
if is_finetuned or is_seq_class:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
_lowerCAmelCase = argparse.Namespace(task="""audio_pretraining""" )
_lowerCAmelCase = fairseq.tasks.setup_task(__lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=__lowerCamelCase )
_lowerCAmelCase = model[0].eval()
recursively_load_weights(__lowerCamelCase , __lowerCamelCase , not is_finetuned )
hf_wavavec.save_pretrained(__lowerCamelCase )
if __name__ == "__main__":
_lowercase = 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_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
parser.add_argument(
"""--is_seq_class""",
action="""store_true""",
help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
)
_lowercase = parser.parse_args()
_lowercase = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 5 | 1 |
'''simple docstring'''
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""kakaobrain/align-base""": """https://huggingface.co/kakaobrain/align-base/resolve/main/config.json""",
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Any = '''align_text_model'''
def __init__( self , _lowercase=30_522 , _lowercase=768 , _lowercase=12 , _lowercase=12 , _lowercase=3_072 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=2 , _lowercase=0.02 , _lowercase=1e-12 , _lowercase=0 , _lowercase="absolute" , _lowercase=True , **_lowercase , ):
"""simple docstring"""
super().__init__(**_lowercase )
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = hidden_act
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_vocab_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = position_embedding_type
_lowerCAmelCase = use_cache
_lowerCAmelCase = pad_token_id
@classmethod
def _lowercase ( cls , _lowercase , **_lowercase ):
"""simple docstring"""
cls._set_token_in_kwargs(_lowercase )
_lowerCAmelCase , _lowerCAmelCase = cls.get_config_dict(_lowercase , **_lowercase )
# get the text config dict if we are loading from AlignConfig
if config_dict.get("""model_type""" ) == "align":
_lowerCAmelCase = 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(_lowercase , **_lowercase )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Optional[Any] = '''align_vision_model'''
def __init__( self , _lowercase = 3 , _lowercase = 600 , _lowercase = 2.0 , _lowercase = 3.1 , _lowercase = 8 , _lowercase = [3, 3, 5, 3, 5, 5, 3] , _lowercase = [32, 16, 24, 40, 80, 112, 192] , _lowercase = [16, 24, 40, 80, 112, 192, 320] , _lowercase = [] , _lowercase = [1, 2, 2, 2, 1, 2, 1] , _lowercase = [1, 2, 2, 3, 3, 4, 1] , _lowercase = [1, 6, 6, 6, 6, 6, 6] , _lowercase = 0.25 , _lowercase = "swish" , _lowercase = 2_560 , _lowercase = "mean" , _lowercase = 0.02 , _lowercase = 0.001 , _lowercase = 0.99 , _lowercase = 0.2 , **_lowercase , ):
"""simple docstring"""
super().__init__(**_lowercase )
_lowerCAmelCase = num_channels
_lowerCAmelCase = image_size
_lowerCAmelCase = width_coefficient
_lowerCAmelCase = depth_coefficient
_lowerCAmelCase = depth_divisor
_lowerCAmelCase = kernel_sizes
_lowerCAmelCase = in_channels
_lowerCAmelCase = out_channels
_lowerCAmelCase = depthwise_padding
_lowerCAmelCase = strides
_lowerCAmelCase = num_block_repeats
_lowerCAmelCase = expand_ratios
_lowerCAmelCase = squeeze_expansion_ratio
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dim
_lowerCAmelCase = pooling_type
_lowerCAmelCase = initializer_range
_lowerCAmelCase = batch_norm_eps
_lowerCAmelCase = batch_norm_momentum
_lowerCAmelCase = drop_connect_rate
_lowerCAmelCase = sum(_lowercase ) * 4
@classmethod
def _lowercase ( cls , _lowercase , **_lowercase ):
"""simple docstring"""
cls._set_token_in_kwargs(_lowercase )
_lowerCAmelCase , _lowerCAmelCase = cls.get_config_dict(_lowercase , **_lowercase )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get("""model_type""" ) == "align":
_lowerCAmelCase = 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(_lowercase , **_lowercase )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Tuple = '''align'''
_lowercase : Optional[int] = True
def __init__( self , _lowercase=None , _lowercase=None , _lowercase=640 , _lowercase=1.0 , _lowercase=0.02 , **_lowercase , ):
"""simple docstring"""
super().__init__(**_lowercase )
if text_config is None:
_lowerCAmelCase = {}
logger.info("""text_config is None. Initializing the AlignTextConfig with default values.""" )
if vision_config is None:
_lowerCAmelCase = {}
logger.info("""vision_config is None. Initializing the AlignVisionConfig with default values.""" )
_lowerCAmelCase = AlignTextConfig(**_lowercase )
_lowerCAmelCase = AlignVisionConfig(**_lowercase )
_lowerCAmelCase = projection_dim
_lowerCAmelCase = temperature_init_value
_lowerCAmelCase = initializer_range
@classmethod
def _lowercase ( cls , _lowercase , _lowercase , **_lowercase ):
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = copy.deepcopy(self.__dict__ )
_lowerCAmelCase = self.text_config.to_dict()
_lowerCAmelCase = self.vision_config.to_dict()
_lowerCAmelCase = self.__class__.model_type
return output
| 5 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[str] = '''decision_transformer'''
_lowercase : Optional[Any] = ['''past_key_values''']
_lowercase : str = {
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , _lowercase=17 , _lowercase=4 , _lowercase=128 , _lowercase=4_096 , _lowercase=True , _lowercase=1 , _lowercase=1_024 , _lowercase=3 , _lowercase=1 , _lowercase=None , _lowercase="relu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=0.1 , _lowercase=1e-5 , _lowercase=0.02 , _lowercase=True , _lowercase=True , _lowercase=50_256 , _lowercase=50_256 , _lowercase=False , _lowercase=False , **_lowercase , ):
"""simple docstring"""
_lowerCAmelCase = state_dim
_lowerCAmelCase = act_dim
_lowerCAmelCase = hidden_size
_lowerCAmelCase = max_ep_len
_lowerCAmelCase = action_tanh
_lowerCAmelCase = vocab_size
_lowerCAmelCase = n_positions
_lowerCAmelCase = n_layer
_lowerCAmelCase = n_head
_lowerCAmelCase = n_inner
_lowerCAmelCase = activation_function
_lowerCAmelCase = resid_pdrop
_lowerCAmelCase = embd_pdrop
_lowerCAmelCase = attn_pdrop
_lowerCAmelCase = layer_norm_epsilon
_lowerCAmelCase = initializer_range
_lowerCAmelCase = scale_attn_weights
_lowerCAmelCase = use_cache
_lowerCAmelCase = scale_attn_by_inverse_layer_idx
_lowerCAmelCase = reorder_and_upcast_attn
_lowerCAmelCase = bos_token_id
_lowerCAmelCase = eos_token_id
super().__init__(bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase )
| 5 | 1 |
'''simple docstring'''
from itertools import product
def A (__lowerCamelCase :int , __lowerCamelCase :int ):
_lowerCAmelCase = sides_number
_lowerCAmelCase = max_face_number * dice_number
_lowerCAmelCase = [0] * (max_total + 1)
_lowerCAmelCase = 1
_lowerCAmelCase = range(__lowerCamelCase , max_face_number + 1 )
for dice_numbers in product(__lowerCamelCase , repeat=__lowerCamelCase ):
_lowerCAmelCase = sum(__lowerCamelCase )
totals_frequencies[total] += 1
return totals_frequencies
def A ():
_lowerCAmelCase = total_frequency_distribution(
sides_number=4 , dice_number=9 )
_lowerCAmelCase = total_frequency_distribution(
sides_number=6 , dice_number=6 )
_lowerCAmelCase = 0
_lowerCAmelCase = 9
_lowerCAmelCase = 4 * 9
_lowerCAmelCase = 6
for peter_total in range(__lowerCamelCase , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
_lowerCAmelCase = (4**9) * (6**6)
_lowerCAmelCase = peter_wins_count / total_games_number
_lowerCAmelCase = round(__lowerCamelCase , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(F"""{solution() = }""")
| 5 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
"""The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"""
)
_lowercase = None
_lowercase = {
"""7B""": 11008,
"""13B""": 13824,
"""30B""": 17920,
"""65B""": 22016,
"""70B""": 28672,
}
_lowercase = {
"""7B""": 1,
"""7Bf""": 1,
"""13B""": 2,
"""13Bf""": 2,
"""30B""": 4,
"""65B""": 8,
"""70B""": 8,
"""70Bf""": 8,
}
def A (__lowerCamelCase :int , __lowerCamelCase :Optional[Any]=1 , __lowerCamelCase :List[Any]=256 ):
return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of)
def A (__lowerCamelCase :Any ):
with open(__lowerCamelCase , """r""" ) as f:
return json.load(__lowerCamelCase )
def A (__lowerCamelCase :List[Any] , __lowerCamelCase :int ):
with open(__lowerCamelCase , """w""" ) as f:
json.dump(__lowerCamelCase , __lowerCamelCase )
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :Tuple , __lowerCamelCase :Optional[Any] , __lowerCamelCase :Tuple=True ):
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
_lowerCAmelCase = os.path.join(__lowerCamelCase , """tmp""" )
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
_lowerCAmelCase = read_json(os.path.join(__lowerCamelCase , """params.json""" ) )
_lowerCAmelCase = NUM_SHARDS[model_size]
_lowerCAmelCase = params["""n_layers"""]
_lowerCAmelCase = params["""n_heads"""]
_lowerCAmelCase = n_heads // num_shards
_lowerCAmelCase = params["""dim"""]
_lowerCAmelCase = dim // n_heads
_lowerCAmelCase = 10_000.0
_lowerCAmelCase = 1.0 / (base ** (torch.arange(0 , __lowerCamelCase , 2 ).float() / dims_per_head))
if "n_kv_heads" in params:
_lowerCAmelCase = params["""n_kv_heads"""] # for GQA / MQA
_lowerCAmelCase = n_heads_per_shard // num_key_value_heads
_lowerCAmelCase = dim // num_key_value_heads
else: # compatibility with other checkpoints
_lowerCAmelCase = n_heads
_lowerCAmelCase = n_heads_per_shard
_lowerCAmelCase = dim
# permute for sliced rotary
def permute(__lowerCamelCase :Optional[int] , __lowerCamelCase :str=n_heads , __lowerCamelCase :str=dim , __lowerCamelCase :List[Any]=dim ):
return w.view(__lowerCamelCase , dima // n_heads // 2 , 2 , __lowerCamelCase ).transpose(1 , 2 ).reshape(__lowerCamelCase , __lowerCamelCase )
print(f'Fetching all parameters from the checkpoint at {input_base_path}.' )
# Load weights
if model_size == "7B":
# Not sharded
# (The sharded implementation would also work, but this is simpler.)
_lowerCAmelCase = torch.load(os.path.join(__lowerCamelCase , """consolidated.00.pth""" ) , map_location="""cpu""" )
else:
# Sharded
_lowerCAmelCase = [
torch.load(os.path.join(__lowerCamelCase , f'consolidated.{i:02d}.pth' ) , map_location="""cpu""" )
for i in range(__lowerCamelCase )
]
_lowerCAmelCase = 0
_lowerCAmelCase = {"""weight_map""": {}}
for layer_i in range(__lowerCamelCase ):
_lowerCAmelCase = f'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
if model_size == "7B":
# Unsharded
_lowerCAmelCase = {
f'model.layers.{layer_i}.self_attn.q_proj.weight': permute(
loaded[f'layers.{layer_i}.attention.wq.weight'] ),
f'model.layers.{layer_i}.self_attn.k_proj.weight': permute(
loaded[f'layers.{layer_i}.attention.wk.weight'] ),
f'model.layers.{layer_i}.self_attn.v_proj.weight': loaded[f'layers.{layer_i}.attention.wv.weight'],
f'model.layers.{layer_i}.self_attn.o_proj.weight': loaded[f'layers.{layer_i}.attention.wo.weight'],
f'model.layers.{layer_i}.mlp.gate_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w1.weight'],
f'model.layers.{layer_i}.mlp.down_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w2.weight'],
f'model.layers.{layer_i}.mlp.up_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w3.weight'],
f'model.layers.{layer_i}.input_layernorm.weight': loaded[f'layers.{layer_i}.attention_norm.weight'],
f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[f'layers.{layer_i}.ffn_norm.weight'],
}
else:
# Sharded
# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
_lowerCAmelCase = {
f'model.layers.{layer_i}.input_layernorm.weight': loaded[0][
f'layers.{layer_i}.attention_norm.weight'
].clone(),
f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[0][
f'layers.{layer_i}.ffn_norm.weight'
].clone(),
}
_lowerCAmelCase = permute(
torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wq.weight'].view(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for i in range(__lowerCamelCase )
] , dim=0 , ).reshape(__lowerCamelCase , __lowerCamelCase ) )
_lowerCAmelCase = permute(
torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wk.weight'].view(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for i in range(__lowerCamelCase )
] , dim=0 , ).reshape(__lowerCamelCase , __lowerCamelCase ) , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , )
_lowerCAmelCase = torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wv.weight'].view(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for i in range(__lowerCamelCase )
] , dim=0 , ).reshape(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.attention.wo.weight'] for i in range(__lowerCamelCase )] , dim=1 )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w1.weight'] for i in range(__lowerCamelCase )] , dim=0 )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w2.weight'] for i in range(__lowerCamelCase )] , dim=1 )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w3.weight'] for i in range(__lowerCamelCase )] , dim=0 )
_lowerCAmelCase = inv_freq
for k, v in state_dict.items():
_lowerCAmelCase = filename
param_count += v.numel()
torch.save(__lowerCamelCase , os.path.join(__lowerCamelCase , __lowerCamelCase ) )
_lowerCAmelCase = f'pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'
if model_size == "7B":
# Unsharded
_lowerCAmelCase = {
"""model.embed_tokens.weight""": loaded["""tok_embeddings.weight"""],
"""model.norm.weight""": loaded["""norm.weight"""],
"""lm_head.weight""": loaded["""output.weight"""],
}
else:
_lowerCAmelCase = {
"""model.norm.weight""": loaded[0]["""norm.weight"""],
"""model.embed_tokens.weight""": torch.cat(
[loaded[i]["""tok_embeddings.weight"""] for i in range(__lowerCamelCase )] , dim=1 ),
"""lm_head.weight""": torch.cat([loaded[i]["""output.weight"""] for i in range(__lowerCamelCase )] , dim=0 ),
}
for k, v in state_dict.items():
_lowerCAmelCase = filename
param_count += v.numel()
torch.save(__lowerCamelCase , os.path.join(__lowerCamelCase , __lowerCamelCase ) )
# Write configs
_lowerCAmelCase = {"""total_size""": param_count * 2}
write_json(__lowerCamelCase , os.path.join(__lowerCamelCase , """pytorch_model.bin.index.json""" ) )
_lowerCAmelCase = params["""ffn_dim_multiplier"""] if """ffn_dim_multiplier""" in params else 1
_lowerCAmelCase = params["""multiple_of"""] if """multiple_of""" in params else 256
_lowerCAmelCase = LlamaConfig(
hidden_size=__lowerCamelCase , intermediate_size=compute_intermediate_size(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) , num_attention_heads=params["""n_heads"""] , num_hidden_layers=params["""n_layers"""] , rms_norm_eps=params["""norm_eps"""] , num_key_value_heads=__lowerCamelCase , )
config.save_pretrained(__lowerCamelCase )
# Make space so we can load the model properly now.
del state_dict
del loaded
gc.collect()
print("""Loading the checkpoint in a Llama model.""" )
_lowerCAmelCase = LlamaForCausalLM.from_pretrained(__lowerCamelCase , torch_dtype=torch.floataa , low_cpu_mem_usage=__lowerCamelCase )
# Avoid saving this as part of the config.
del model.config._name_or_path
print("""Saving in the Transformers format.""" )
model.save_pretrained(__lowerCamelCase , safe_serialization=__lowerCamelCase )
shutil.rmtree(__lowerCamelCase )
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Union[str, Any] ):
# Initialize the tokenizer based on the `spm` model
_lowerCAmelCase = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
print(f'Saving a {tokenizer_class.__name__} to {tokenizer_path}.' )
_lowerCAmelCase = tokenizer_class(__lowerCamelCase )
tokenizer.save_pretrained(__lowerCamelCase )
def A ():
_lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--input_dir""" , help="""Location of LLaMA weights, which contains tokenizer.model and model folders""" , )
parser.add_argument(
"""--model_size""" , choices=["""7B""", """7Bf""", """13B""", """13Bf""", """30B""", """65B""", """70B""", """70Bf""", """tokenizer_only"""] , )
parser.add_argument(
"""--output_dir""" , help="""Location to write HF model and tokenizer""" , )
parser.add_argument("""--safe_serialization""" , type=__lowerCamelCase , help="""Whether or not to save using `safetensors`.""" )
_lowerCAmelCase = parser.parse_args()
if args.model_size != "tokenizer_only":
write_model(
model_path=args.output_dir , input_base_path=os.path.join(args.input_dir , args.model_size ) , model_size=args.model_size , safe_serialization=args.safe_serialization , )
_lowerCAmelCase = os.path.join(args.input_dir , """tokenizer.model""" )
write_tokenizer(args.output_dir , __lowerCamelCase )
if __name__ == "__main__":
main()
| 5 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
"""configuration_time_series_transformer""": [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TimeSeriesTransformerConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TimeSeriesTransformerForPrediction""",
"""TimeSeriesTransformerModel""",
"""TimeSeriesTransformerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TimeSeriesTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimeSeriesTransformerForPrediction,
TimeSeriesTransformerModel,
TimeSeriesTransformerPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 5 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Tuple = (DDPMScheduler,)
def _lowercase ( self , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_lowercase )
return config
def _lowercase ( self ):
"""simple docstring"""
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_lowercase )
def _lowercase ( self ):
"""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=_lowercase , beta_end=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
self.check_over_configs(thresholding=_lowercase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_lowercase , prediction_type=_lowercase , sample_max_value=_lowercase , )
def _lowercase ( self ):
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
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 _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = len(_lowercase )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter
_lowerCAmelCase = torch.manual_seed(0 )
for t in reversed(range(_lowercase ) ):
# 1. predict noise residual
_lowerCAmelCase = model(_lowercase , _lowercase )
# 2. predict previous mean of sample x_t-1
_lowerCAmelCase = scheduler.step(_lowercase , _lowercase , _lowercase , generator=_lowercase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_lowerCAmelCase = pred_prev_sample
_lowerCAmelCase = torch.sum(torch.abs(_lowercase ) )
_lowerCAmelCase = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 258.9606 ) < 1e-2
assert abs(result_mean.item() - 0.3372 ) < 1e-3
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config(prediction_type="""v_prediction""" )
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = len(_lowercase )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter
_lowerCAmelCase = torch.manual_seed(0 )
for t in reversed(range(_lowercase ) ):
# 1. predict noise residual
_lowerCAmelCase = model(_lowercase , _lowercase )
# 2. predict previous mean of sample x_t-1
_lowerCAmelCase = scheduler.step(_lowercase , _lowercase , _lowercase , generator=_lowercase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_lowerCAmelCase = pred_prev_sample
_lowerCAmelCase = torch.sum(torch.abs(_lowercase ) )
_lowerCAmelCase = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 202.0296 ) < 1e-2
assert abs(result_mean.item() - 0.2631 ) < 1e-3
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_lowercase )
_lowerCAmelCase = scheduler.timesteps
for i, timestep in enumerate(_lowercase ):
if i == len(_lowercase ) - 1:
_lowerCAmelCase = -1
else:
_lowerCAmelCase = timesteps[i + 1]
_lowerCAmelCase = scheduler.previous_timestep(_lowercase )
_lowerCAmelCase = prev_t.item()
self.assertEqual(_lowercase , _lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 51, 0]
with self.assertRaises(_lowercase , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 1, 0]
_lowerCAmelCase = len(_lowercase )
with self.assertRaises(_lowercase , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_lowercase , timesteps=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_lowercase , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_lowercase )
| 5 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self , _lowercase ):
"""simple docstring"""
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["""bs"""] , model_result["""ss"""] ):
_lowerCAmelCase = model_result["""result"""][batch_size][sequence_length]
self.assertIsNotNone(_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """sshleifer/tiny-gpt2"""
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
_lowerCAmelCase = PyTorchBenchmark(_lowercase )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """sgugger/tiny-distilbert-classification"""
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , only_pretrain_model=_lowercase , )
_lowerCAmelCase = PyTorchBenchmark(_lowercase )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """sshleifer/tiny-gpt2"""
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , torchscript=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
_lowerCAmelCase = PyTorchBenchmark(_lowercase )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """sshleifer/tiny-gpt2"""
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , fpaa=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
_lowerCAmelCase = PyTorchBenchmark(_lowercase )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """sshleifer/tiny-gpt2"""
_lowerCAmelCase = AutoConfig.from_pretrained(_lowercase )
# set architectures equal to `None`
_lowerCAmelCase = None
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
_lowerCAmelCase = PyTorchBenchmark(_lowercase , configs=[config] )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """sshleifer/tiny-gpt2"""
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
_lowerCAmelCase = PyTorchBenchmark(_lowercase )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
@unittest.skipIf(torch_device == """cpu""" , """Can't do half precision""" )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """sshleifer/tiny-gpt2"""
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , fpaa=_lowercase , multi_process=_lowercase , )
_lowerCAmelCase = PyTorchBenchmark(_lowercase )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """sshleifer/tiny-gpt2"""
_lowerCAmelCase = AutoConfig.from_pretrained(_lowercase )
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
_lowerCAmelCase = PyTorchBenchmark(_lowercase , configs=[config] )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """sshleifer/tinier_bart"""
_lowerCAmelCase = AutoConfig.from_pretrained(_lowercase )
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
_lowerCAmelCase = PyTorchBenchmark(_lowercase , configs=[config] )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """sshleifer/tiny-gpt2"""
_lowerCAmelCase = AutoConfig.from_pretrained(_lowercase )
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
_lowerCAmelCase = PyTorchBenchmark(_lowercase , configs=[config] )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """sshleifer/tinier_bart"""
_lowerCAmelCase = AutoConfig.from_pretrained(_lowercase )
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=_lowercase , )
_lowerCAmelCase = PyTorchBenchmark(_lowercase , configs=[config] )
_lowerCAmelCase = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """sshleifer/tiny-gpt2"""
with tempfile.TemporaryDirectory() as tmp_dir:
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , save_to_csv=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(_lowercase , """inf_time.csv""" ) , train_memory_csv_file=os.path.join(_lowercase , """train_mem.csv""" ) , inference_memory_csv_file=os.path.join(_lowercase , """inf_mem.csv""" ) , train_time_csv_file=os.path.join(_lowercase , """train_time.csv""" ) , env_info_csv_file=os.path.join(_lowercase , """env.csv""" ) , multi_process=_lowercase , )
_lowerCAmelCase = PyTorchBenchmark(_lowercase )
benchmark.run()
self.assertTrue(Path(os.path.join(_lowercase , """inf_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(_lowercase , """train_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(_lowercase , """inf_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(_lowercase , """train_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(_lowercase , """env.csv""" ) ).exists() )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """sshleifer/tiny-gpt2"""
def _check_summary_is_not_empty(_lowercase ):
self.assertTrue(hasattr(_lowercase , """sequential""" ) )
self.assertTrue(hasattr(_lowercase , """cumulative""" ) )
self.assertTrue(hasattr(_lowercase , """current""" ) )
self.assertTrue(hasattr(_lowercase , """total""" ) )
with tempfile.TemporaryDirectory() as tmp_dir:
_lowerCAmelCase = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=_lowercase , inference=_lowercase , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(_lowercase , """log.txt""" ) , log_print=_lowercase , trace_memory_line_by_line=_lowercase , multi_process=_lowercase , )
_lowerCAmelCase = PyTorchBenchmark(_lowercase )
_lowerCAmelCase = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(_lowercase , """log.txt""" ) ).exists() )
| 5 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_lowercase = logging.get_logger(__name__)
_lowercase = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_lowercase = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class UpperCAmelCase_ :
'''simple docstring'''
_lowercase : str = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(_SCREAMING_SNAKE_CASE )} )
_lowercase : str = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''} )
_lowercase : int = field(
default=1_2_8 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_lowercase : int = field(
default=1_2_8 , metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''} , )
_lowercase : int = field(
default=6_4 , metadata={
'''help''': (
'''The maximum number of tokens for the question. Questions longer than this will '''
'''be truncated to this length.'''
)
} , )
_lowercase : int = field(
default=3_0 , metadata={
'''help''': (
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
)
} , )
_lowercase : bool = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
_lowercase : bool = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''} )
_lowercase : float = field(
default=0.0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
_lowercase : int = field(
default=2_0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
_lowercase : int = field(
default=0 , metadata={
'''help''': (
'''language id of input for language-specific xlm models (see'''
''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
)
} , )
_lowercase : int = field(default=1 , metadata={'''help''': '''multiple threads for converting example to features'''} )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : str = '''train'''
_lowercase : Union[str, Any] = '''dev'''
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : SquadDataTrainingArguments
_lowercase : List[SquadFeatures]
_lowercase : Split
_lowercase : bool
def __init__( self , _lowercase , _lowercase , _lowercase = None , _lowercase = Split.train , _lowercase = False , _lowercase = None , _lowercase = "pt" , ):
"""simple docstring"""
_lowerCAmelCase = args
_lowerCAmelCase = is_language_sensitive
_lowerCAmelCase = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(_lowercase , _lowercase ):
try:
_lowerCAmelCase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
_lowerCAmelCase = mode
# Load data features from cache or dataset file
_lowerCAmelCase = """v2""" if args.version_2_with_negative else """v1"""
_lowerCAmelCase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
_lowerCAmelCase = cached_features_file + """.lock"""
with FileLock(_lowercase ):
if os.path.exists(_lowercase ) and not args.overwrite_cache:
_lowerCAmelCase = time.time()
_lowerCAmelCase = torch.load(_lowercase )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
_lowerCAmelCase = self.old_features["""features"""]
_lowerCAmelCase = self.old_features.get("""dataset""" , _lowercase )
_lowerCAmelCase = self.old_features.get("""examples""" , _lowercase )
logger.info(
F'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
F'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
""" future run""" )
else:
if mode == Split.dev:
_lowerCAmelCase = self.processor.get_dev_examples(args.data_dir )
else:
_lowerCAmelCase = self.processor.get_train_examples(args.data_dir )
_lowerCAmelCase , _lowerCAmelCase = squad_convert_examples_to_features(
examples=self.examples , tokenizer=_lowercase , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=_lowercase , )
_lowerCAmelCase = time.time()
torch.save(
{"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples} , _lowercase , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self ):
"""simple docstring"""
return len(self.features )
def __getitem__( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.features[i]
_lowerCAmelCase = torch.tensor(feature.input_ids , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.attention_mask , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.token_type_ids , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.cls_index , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.p_mask , dtype=torch.float )
_lowerCAmelCase = torch.tensor(feature.is_impossible , dtype=torch.float )
_lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": attention_mask,
"""token_type_ids""": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"""is_impossible""": is_impossible} )
if self.is_language_sensitive:
inputs.update({"""langs""": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
_lowerCAmelCase = torch.tensor(feature.start_position , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions} )
return inputs
| 5 | 1 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
_lowercase = logging.get_logger(__name__)
_lowercase = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
# See all LED models at https://huggingface.co/models?filter=LED
_lowercase = {
"""vocab_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
},
"""merges_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
},
}
_lowercase = {
"""allenai/led-base-16384""": 16384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def A ():
_lowerCAmelCase = (
list(range(ord("""!""" ) , ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" ) , ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" ) , ord("""ÿ""" ) + 1 ) )
)
_lowerCAmelCase = bs[:]
_lowerCAmelCase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(__lowerCamelCase )
cs.append(2**8 + n )
n += 1
_lowerCAmelCase = [chr(__lowerCamelCase ) for n in cs]
return dict(zip(__lowerCamelCase , __lowerCamelCase ) )
def A (__lowerCamelCase :List[str] ):
_lowerCAmelCase = set()
_lowerCAmelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_lowerCAmelCase = char
return pairs
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Optional[int] = VOCAB_FILES_NAMES
_lowercase : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_lowercase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowercase : List[str] = ['''input_ids''', '''attention_mask''']
def __init__( self , _lowercase , _lowercase , _lowercase="replace" , _lowercase="<s>" , _lowercase="</s>" , _lowercase="</s>" , _lowercase="<s>" , _lowercase="<unk>" , _lowercase="<pad>" , _lowercase="<mask>" , _lowercase=False , **_lowercase , ):
"""simple docstring"""
_lowerCAmelCase = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else bos_token
_lowerCAmelCase = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else eos_token
_lowerCAmelCase = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else sep_token
_lowerCAmelCase = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else cls_token
_lowerCAmelCase = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else unk_token
_lowerCAmelCase = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_lowerCAmelCase = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else mask_token
super().__init__(
errors=_lowercase , bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , sep_token=_lowercase , cls_token=_lowercase , pad_token=_lowercase , mask_token=_lowercase , add_prefix_space=_lowercase , **_lowercase , )
with open(_lowercase , encoding="""utf-8""" ) as vocab_handle:
_lowerCAmelCase = json.load(_lowercase )
_lowerCAmelCase = {v: k for k, v in self.encoder.items()}
_lowerCAmelCase = errors # how to handle errors in decoding
_lowerCAmelCase = bytes_to_unicode()
_lowerCAmelCase = {v: k for k, v in self.byte_encoder.items()}
with open(_lowercase , encoding="""utf-8""" ) as merges_handle:
_lowerCAmelCase = merges_handle.read().split("""\n""" )[1:-1]
_lowerCAmelCase = [tuple(merge.split() ) for merge in bpe_merges]
_lowerCAmelCase = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
_lowerCAmelCase = {}
_lowerCAmelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_lowerCAmelCase = re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def _lowercase ( self ):
"""simple docstring"""
return len(self.encoder )
def _lowercase ( self ):
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
if token in self.cache:
return self.cache[token]
_lowerCAmelCase = tuple(_lowercase )
_lowerCAmelCase = get_pairs(_lowercase )
if not pairs:
return token
while True:
_lowerCAmelCase = min(_lowercase , key=lambda _lowercase : self.bpe_ranks.get(_lowercase , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
_lowerCAmelCase , _lowerCAmelCase = bigram
_lowerCAmelCase = []
_lowerCAmelCase = 0
while i < len(_lowercase ):
try:
_lowerCAmelCase = word.index(_lowercase , _lowercase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_lowerCAmelCase = j
if word[i] == first and i < len(_lowercase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_lowerCAmelCase = tuple(_lowercase )
_lowerCAmelCase = new_word
if len(_lowercase ) == 1:
break
else:
_lowerCAmelCase = get_pairs(_lowercase )
_lowerCAmelCase = """ """.join(_lowercase )
_lowerCAmelCase = word
return word
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = []
for token in re.findall(self.pat , _lowercase ):
_lowerCAmelCase = """""".join(
self.byte_encoder[b] for b in token.encode("""utf-8""" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowercase ).split(""" """ ) )
return bpe_tokens
def _lowercase ( self , _lowercase ):
"""simple docstring"""
return self.encoder.get(_lowercase , self.encoder.get(self.unk_token ) )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
return self.decoder.get(_lowercase )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = """""".join(_lowercase )
_lowerCAmelCase = bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8""" , errors=self.errors )
return text
def _lowercase ( self , _lowercase , _lowercase = None ):
"""simple docstring"""
if not os.path.isdir(_lowercase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
_lowerCAmelCase = os.path.join(
_lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
_lowerCAmelCase = os.path.join(
_lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(_lowercase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_lowercase , ensure_ascii=_lowercase ) + """\n""" )
_lowerCAmelCase = 0
with open(_lowercase , """w""" , encoding="""utf-8""" ) as writer:
writer.write("""#version: 0.2\n""" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _lowercase : kv[1] ):
if index != token_index:
logger.warning(
F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
""" Please check that the tokenizer is not corrupted!""" )
_lowerCAmelCase = token_index
writer.write(""" """.join(_lowercase ) + """\n""" )
index += 1
return vocab_file, merge_file
def _lowercase ( self , _lowercase , _lowercase = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_lowerCAmelCase = [self.cls_token_id]
_lowerCAmelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _lowercase ( self , _lowercase , _lowercase = None , _lowercase = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowercase , token_ids_a=_lowercase , already_has_special_tokens=_lowercase )
if token_ids_a is None:
return [1] + ([0] * len(_lowercase )) + [1]
return [1] + ([0] * len(_lowercase )) + [1, 1] + ([0] * len(_lowercase )) + [1]
def _lowercase ( self , _lowercase , _lowercase = None ):
"""simple docstring"""
_lowerCAmelCase = [self.sep_token_id]
_lowerCAmelCase = [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 _lowercase ( self , _lowercase , _lowercase=False , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase = kwargs.pop("""add_prefix_space""" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(_lowercase ) > 0 and not text[0].isspace()):
_lowerCAmelCase = """ """ + text
return (text, kwargs)
def _lowercase ( self , _lowercase , _lowercase = None , _lowercase = PaddingStrategy.DO_NOT_PAD , _lowercase = None , _lowercase = None , ):
"""simple docstring"""
_lowerCAmelCase = super()._pad(
encoded_inputs=_lowercase , max_length=_lowercase , padding_strategy=_lowercase , pad_to_multiple_of=_lowercase , return_attention_mask=_lowercase , )
# Load from model defaults
if return_attention_mask is None:
_lowerCAmelCase = """attention_mask""" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
_lowerCAmelCase = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
_lowerCAmelCase = len(encoded_inputs["""global_attention_mask"""] ) != len(_lowercase )
if needs_to_be_padded:
_lowerCAmelCase = len(_lowercase ) - len(encoded_inputs["""global_attention_mask"""] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
_lowerCAmelCase = (
encoded_inputs["""global_attention_mask"""] + [-1] * difference
)
elif self.padding_side == "left":
_lowerCAmelCase = [-1] * difference + encoded_inputs[
"""global_attention_mask"""
]
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )
return encoded_inputs
| 5 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-question_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-reader-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-ctx_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
),
"""facebook/dpr-question_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
),
"""facebook/dpr-reader-multiset-base""": (
"""https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
),
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : str = '''dpr'''
def __init__( self , _lowercase=30_522 , _lowercase=768 , _lowercase=12 , _lowercase=12 , _lowercase=3_072 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=2 , _lowercase=0.02 , _lowercase=1e-12 , _lowercase=0 , _lowercase="absolute" , _lowercase = 0 , **_lowercase , ):
"""simple docstring"""
super().__init__(pad_token_id=_lowercase , **_lowercase )
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = hidden_act
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_vocab_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = projection_dim
_lowerCAmelCase = position_embedding_type
| 5 | 1 |
'''simple docstring'''
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
_lowercase = logging.get_logger(__name__)
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Optional[int] = ['''audio_values''', '''audio_mask''']
def __init__( self , _lowercase=2_048 , _lowercase=1 , _lowercase=[16, 16] , _lowercase=128 , _lowercase=44_100 , _lowercase=86 , _lowercase=2_048 , _lowercase=0.0 , **_lowercase , ):
"""simple docstring"""
super().__init__(
feature_size=_lowercase , sampling_rate=_lowercase , padding_value=_lowercase , **_lowercase , )
_lowerCAmelCase = spectrogram_length
_lowerCAmelCase = num_channels
_lowerCAmelCase = patch_size
_lowerCAmelCase = feature_size // self.patch_size[1]
_lowerCAmelCase = n_fft
_lowerCAmelCase = sampling_rate // hop_length_to_sampling_rate
_lowerCAmelCase = sampling_rate
_lowerCAmelCase = padding_value
_lowerCAmelCase = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=_lowercase , min_frequency=0.0 , max_frequency=2_2050.0 , sampling_rate=_lowercase , norm="""slaney""" , mel_scale="""slaney""" , ).T
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = spectrogram(
_lowercase , window_function(self.n_fft , """hann""" ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel="""dB""" , db_range=80.0 , )
_lowerCAmelCase = log_spec[:, :-1]
_lowerCAmelCase = log_spec - 20.0
_lowerCAmelCase = np.clip(log_spec / 40.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self , _lowercase , _lowercase = None , _lowercase = True , _lowercase = None , _lowercase = False , _lowercase = False , **_lowercase , ):
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
"""This feature extractor is set to support sampling rate"""
F' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
F' with {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
_lowerCAmelCase = isinstance(_lowercase , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F'Only mono-channel audio is supported for input to {self}' )
_lowerCAmelCase = is_batched_numpy or (
isinstance(_lowercase , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_lowerCAmelCase = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(_lowercase , np.ndarray ):
_lowerCAmelCase = np.asarray(_lowercase , dtype=np.floataa )
elif isinstance(_lowercase , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_lowerCAmelCase = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_lowerCAmelCase = [np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
_lowerCAmelCase = [
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , _lowercase ):
_lowerCAmelCase = [np.asarray(_lowercase , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
_lowerCAmelCase = max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
_lowerCAmelCase = [
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
_lowerCAmelCase = np.array(_lowercase ).astype(np.floataa )
# convert into correct format for padding
_lowerCAmelCase = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
_lowerCAmelCase = np.ones([len(_lowercase ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
_lowerCAmelCase = padded_audio_features * self.padding_value
for i in range(len(_lowercase ) ):
_lowerCAmelCase = audio_features[i]
_lowerCAmelCase = feature
# return as BatchFeature
if return_attention_mask:
_lowerCAmelCase = {"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
else:
_lowerCAmelCase = {"""audio_values""": padded_audio_features}
_lowerCAmelCase = BatchFeature(data=_lowercase , tensor_type=_lowercase )
return encoded_inputs
| 5 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
_lowercase = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
_lowercase = """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""
_lowercase = """
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
\"raw_values\" : Returns a full set of errors in case of multioutput input.
\"uniform_average\" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric(\"mse\")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{'mse': 0.6123724356957945}
If you're using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{'mse': array([0.41666667, 1. ])}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"""
] , )
def _lowercase ( self ):
"""simple docstring"""
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value("""float""" ) ),
"references": datasets.Sequence(datasets.Value("""float""" ) ),
}
else:
return {
"predictions": datasets.Value("""float""" ),
"references": datasets.Value("""float""" ),
}
def _lowercase ( self , _lowercase , _lowercase , _lowercase=None , _lowercase="uniform_average" , _lowercase=True ):
"""simple docstring"""
_lowerCAmelCase = mean_squared_error(
_lowercase , _lowercase , sample_weight=_lowercase , multioutput=_lowercase , squared=_lowercase )
return {"mse": mse}
| 5 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GPTBigCodeForSequenceClassification""",
"""GPTBigCodeForTokenClassification""",
"""GPTBigCodeForCausalLM""",
"""GPTBigCodeModel""",
"""GPTBigCodePreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 5 |
'''simple docstring'''
def A ():
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def A (__lowerCamelCase :List[Any] ):
_lowerCAmelCase = 1
_lowerCAmelCase = 2
while i * i <= n:
_lowerCAmelCase = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def A ():
return next(i for i in triangle_number_generator() if count_divisors(__lowerCamelCase ) > 500 )
if __name__ == "__main__":
print(solution())
| 5 | 1 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def A (__lowerCamelCase :str , __lowerCamelCase :Any , __lowerCamelCase :str=None ):
# set parameter of one layer
assert torch_layer.weight.shape == weight.shape, f'{torch_layer} layer.weight does not match'
_lowerCAmelCase = nn.Parameter(__lowerCamelCase )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, f'{torch_layer} layer.bias does not match'
_lowerCAmelCase = nn.Parameter(__lowerCamelCase )
def A (__lowerCamelCase :Dict , __lowerCamelCase :Optional[int] , __lowerCamelCase :Tuple ):
# set torch weights for 1-to-1 comparison
_lowerCAmelCase = np.asarray(weights[0] )
_lowerCAmelCase = np.asarray(weights[1] )
_lowerCAmelCase = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(__lowerCamelCase ).transpose(1 , 2 ).contiguous().view(-1 , __lowerCamelCase ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(__lowerCamelCase ).transpose(1 , 2 ).contiguous().view(-1 , __lowerCamelCase ) , )
set_param(
torch_layer.output.dense , torch.tensor(__lowerCamelCase ).view(-1 , __lowerCamelCase ).contiguous().transpose(0 , 1 ) , )
def A (__lowerCamelCase :Dict , __lowerCamelCase :List[str] , __lowerCamelCase :str ):
# set torch weights for 1-to-1 comparison
_lowerCAmelCase = np.asarray(weights[0] )
_lowerCAmelCase = np.asarray(weights[1] )
_lowerCAmelCase = np.asarray(weights[2] )
_lowerCAmelCase = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(__lowerCamelCase ).transpose(1 , 2 ).contiguous().view(-1 , __lowerCamelCase ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(__lowerCamelCase ).transpose(1 , 2 ).contiguous().view(-1 , __lowerCamelCase ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(__lowerCamelCase ).transpose(1 , 2 ).contiguous().view(-1 , __lowerCamelCase ) , )
set_param(
torch_layer.output.dense , torch.tensor(__lowerCamelCase ).view(-1 , __lowerCamelCase ).contiguous().transpose(0 , 1 ) , )
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Dict , __lowerCamelCase :Any ):
# layernorm 1
_lowerCAmelCase = weights[0][0][0]
_lowerCAmelCase = np.asarray(layer_norm_a[0] )
_lowerCAmelCase = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(__lowerCamelCase ) , torch.tensor(__lowerCamelCase ) , )
# lsh weights + output
_lowerCAmelCase = weights[0][1]
if len(__lowerCamelCase ) < 4:
set_layer_weights_in_torch_lsh(__lowerCamelCase , torch_block.attention , __lowerCamelCase )
else:
set_layer_weights_in_torch_local(__lowerCamelCase , torch_block.attention , __lowerCamelCase )
# intermediate weighs
_lowerCAmelCase = weights[2][0][1][2]
# Chunked Feed Forward
if len(__lowerCamelCase ) == 4:
_lowerCAmelCase = intermediate_weights[2]
# layernorm 2
_lowerCAmelCase = np.asarray(intermediate_weights[0][0] )
_lowerCAmelCase = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(__lowerCamelCase ) , torch.tensor(__lowerCamelCase ) , )
# intermediate dense
_lowerCAmelCase = np.asarray(intermediate_weights[1][0] )
_lowerCAmelCase = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(__lowerCamelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(__lowerCamelCase ) , )
# intermediate out
_lowerCAmelCase = np.asarray(intermediate_weights[4][0] )
_lowerCAmelCase = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(__lowerCamelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(__lowerCamelCase ) , )
def A (__lowerCamelCase :List[str] , __lowerCamelCase :Optional[Any] , __lowerCamelCase :Dict ):
# reformer model
_lowerCAmelCase = torch_model.reformer
# word embeds
_lowerCAmelCase = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(__lowerCamelCase ) , )
if isinstance(weights[3] , __lowerCamelCase ):
_lowerCAmelCase = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
_lowerCAmelCase = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), f'{position_embeddings[emb_idx]} emb does not match'
_lowerCAmelCase = nn.Parameter(torch.tensor(__lowerCamelCase ) )
_lowerCAmelCase = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
__lowerCamelCase ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
_lowerCAmelCase = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# output layer norm
_lowerCAmelCase = np.asarray(weights[7][0] )
_lowerCAmelCase = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(__lowerCamelCase ) , torch.tensor(__lowerCamelCase ) , )
# output embeddings
_lowerCAmelCase = np.asarray(weights[9][0] )
_lowerCAmelCase = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(__lowerCamelCase ).transpose(0 , 1 ).contiguous() , torch.tensor(__lowerCamelCase ) , )
def A (__lowerCamelCase :List[str] , __lowerCamelCase :List[Any] , __lowerCamelCase :Tuple ):
# Initialise PyTorch model
_lowerCAmelCase = ReformerConfig.from_json_file(__lowerCamelCase )
print(f'Building PyTorch model from configuration: {config}' )
_lowerCAmelCase = ReformerModelWithLMHead(__lowerCamelCase )
with open(__lowerCamelCase , """rb""" ) as f:
_lowerCAmelCase = pickle.load(__lowerCamelCase )["""weights"""]
set_model_weights_in_torch(__lowerCamelCase , __lowerCamelCase , config.hidden_size )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , __lowerCamelCase )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained Reformer model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
_lowercase = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 5 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
_lowercase = logging.get_logger(__name__)
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , *_lowercase , **_lowercase ):
"""simple docstring"""
warnings.warn(
"""The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use DonutImageProcessor instead.""" , _lowercase , )
super().__init__(*_lowercase , **_lowercase )
| 5 | 1 |
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
),
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Tuple = '''efficientformer'''
def __init__( self , _lowercase = [3, 2, 6, 4] , _lowercase = [48, 96, 224, 448] , _lowercase = [True, True, True, True] , _lowercase = 448 , _lowercase = 32 , _lowercase = 4 , _lowercase = 7 , _lowercase = 5 , _lowercase = 8 , _lowercase = 4 , _lowercase = 0.0 , _lowercase = 16 , _lowercase = 3 , _lowercase = 3 , _lowercase = 3 , _lowercase = 2 , _lowercase = 1 , _lowercase = 0.0 , _lowercase = 1 , _lowercase = True , _lowercase = True , _lowercase = 1e-5 , _lowercase = "gelu" , _lowercase = 0.02 , _lowercase = 1e-12 , _lowercase = 224 , _lowercase = 1e-05 , **_lowercase , ):
"""simple docstring"""
super().__init__(**_lowercase )
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = hidden_sizes
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = patch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = depths
_lowerCAmelCase = mlp_expansion_ratio
_lowerCAmelCase = downsamples
_lowerCAmelCase = dim
_lowerCAmelCase = key_dim
_lowerCAmelCase = attention_ratio
_lowerCAmelCase = resolution
_lowerCAmelCase = pool_size
_lowerCAmelCase = downsample_patch_size
_lowerCAmelCase = downsample_stride
_lowerCAmelCase = downsample_pad
_lowerCAmelCase = drop_path_rate
_lowerCAmelCase = num_metaad_blocks
_lowerCAmelCase = distillation
_lowerCAmelCase = use_layer_scale
_lowerCAmelCase = layer_scale_init_value
_lowerCAmelCase = image_size
_lowerCAmelCase = batch_norm_eps
| 5 |
'''simple docstring'''
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_min_version,
require_tpu,
require_xpu,
skip,
slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU
from .scripts import test_script, test_sync, test_ops # isort: skip
| 5 | 1 |
'''simple docstring'''
from __future__ import annotations
def A (__lowerCamelCase :str ):
return [ord(__lowerCamelCase ) - 96 for elem in plain]
def A (__lowerCamelCase :list[int] ):
return "".join(chr(elem + 96 ) for elem in encoded )
def A ():
_lowerCAmelCase = encode(input("""-> """ ).strip().lower() )
print("""Encoded: """ , __lowerCamelCase )
print("""Decoded:""" , decode(__lowerCamelCase ) )
if __name__ == "__main__":
main()
| 5 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("""Googling.....""")
_lowercase = """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
_lowercase = requests.get(url, headers={"""UserAgent""": UserAgent().random})
# res.raise_for_status()
with open("""project1a.html""", """wb""") as out_file: # only for knowing the class
for data in res.iter_content(10000):
out_file.write(data)
_lowercase = BeautifulSoup(res.text, """html.parser""")
_lowercase = list(soup.select(""".eZt8xd"""))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get("""href"""))
else:
webbrowser.open(F"""https://google.com{link.get('href')}""")
| 5 | 1 |
'''simple docstring'''
def A (__lowerCamelCase :int ):
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("""Program to check whether a number is a Perfect number or not...""")
_lowercase = int(input("""Enter number: """).strip())
print(F"""{number} is {'' if perfect(number) else 'not '}a Perfect Number.""")
| 5 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_lowercase = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
def A ():
_lowerCAmelCase = Github(os.environ["""GITHUB_TOKEN"""] )
_lowerCAmelCase = g.get_repo("""huggingface/diffusers""" )
_lowerCAmelCase = repo.get_issues(state="""open""" )
for issue in open_issues:
_lowerCAmelCase = sorted(issue.get_comments() , key=lambda __lowerCamelCase : i.created_at , reverse=__lowerCamelCase )
_lowerCAmelCase = comments[0] if len(__lowerCamelCase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state="""closed""" )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state="""open""" )
issue.remove_from_labels("""stale""" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
"""This issue has been automatically marked as stale because it has not had """
"""recent activity. If you think this still needs to be addressed """
"""please comment on this thread.\n\nPlease note that issues that do not follow the """
"""[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) """
"""are likely to be ignored.""" )
issue.add_to_labels("""stale""" )
if __name__ == "__main__":
main()
| 5 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester
if is_tf_available():
from transformers import (
TFBertModel,
TFCLIPVisionModel,
TFDeiTModel,
TFRobertaModel,
TFVisionTextDualEncoderModel,
TFViTModel,
VisionTextDualEncoderConfig,
)
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor
def A (__lowerCamelCase :int ):
if isinstance(__lowerCamelCase , collections.abc.Iterable ):
return x
return (x, x)
@require_tf
class UpperCAmelCase_ :
'''simple docstring'''
def _lowercase ( self , _lowercase , _lowercase ):
"""simple docstring"""
pass
def _lowercase ( self ):
"""simple docstring"""
pass
def _lowercase ( self ):
"""simple docstring"""
pass
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase=None , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(_lowercase , _lowercase )
_lowerCAmelCase = TFVisionTextDualEncoderModel(_lowercase )
_lowerCAmelCase = model(input_ids=_lowercase , pixel_values=_lowercase , attention_mask=_lowercase )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase=None , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_lowercase , _lowercase )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_lowercase , text_model=_lowercase )
_lowerCAmelCase = model(input_ids=_lowercase , pixel_values=_lowercase , attention_mask=_lowercase )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase=None , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_lowercase , _lowercase )
_lowerCAmelCase = {"""vision_model""": vision_model, """text_model""": text_model}
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowercase )
_lowerCAmelCase = model(input_ids=_lowercase , pixel_values=_lowercase , attention_mask=_lowercase )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase=None , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_lowercase , _lowercase )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_lowercase , text_model=_lowercase )
_lowerCAmelCase = model(input_ids=_lowercase , pixel_values=_lowercase , attention_mask=_lowercase )
_lowerCAmelCase = output[0].numpy()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowercase )
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_lowercase )
_lowerCAmelCase = model(input_ids=_lowercase , pixel_values=_lowercase , attention_mask=_lowercase )
_lowerCAmelCase = after_output[0].numpy()
_lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowercase , 1e-5 )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase=None , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_lowercase , _lowercase )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_lowercase , text_model=_lowercase )
_lowerCAmelCase = model(
input_ids=_lowercase , pixel_values=_lowercase , attention_mask=_lowercase , output_attentions=_lowercase )
_lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_lowercase ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
_lowerCAmelCase = to_atuple(vision_model.config.image_size )
_lowerCAmelCase = to_atuple(vision_model.config.patch_size )
_lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_lowerCAmelCase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_lowercase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _lowercase ( self , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = np.abs((a - b) ).max()
self.assertLessEqual(_lowercase , _lowercase , F'Difference between torch and flax is {diff} (>= {tol}).' )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_model(**_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_save_load(**_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_lowercase )
@slow
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_pretrained_model_and_inputs()
_lowerCAmelCase = model_a(**_lowercase )
_lowerCAmelCase = outputs[0].numpy()
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_lowercase )
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(_lowercase )
_lowerCAmelCase = model_a(**_lowercase )
_lowerCAmelCase = after_outputs[0].numpy()
_lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowercase , 1e-5 )
@require_tf
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-random-bert""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _lowercase ( self , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = TFViTModel(_lowercase , name="""vision_model""" )
_lowerCAmelCase = TFBertModel(_lowercase , name="""text_model""" )
return vision_model, text_model
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFViTModelTester(self )
_lowerCAmelCase = TFBertModelTester(self )
_lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-deit-tf""" , """hf-internal-testing/tiny-random-roberta""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase=None , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.get_vision_text_model(_lowercase , _lowercase )
_lowerCAmelCase = TFVisionTextDualEncoderModel(vision_model=_lowercase , text_model=_lowercase )
_lowerCAmelCase = model(
input_ids=_lowercase , pixel_values=_lowercase , attention_mask=_lowercase , output_attentions=_lowercase )
_lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(_lowercase ) , vision_config.num_hidden_layers )
# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
_lowerCAmelCase = to_atuple(vision_model.config.image_size )
_lowerCAmelCase = to_atuple(vision_model.config.patch_size )
_lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
_lowerCAmelCase = num_patches + 2
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
_lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(_lowercase ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def _lowercase ( self , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = TFDeiTModel(_lowercase , name="""vision_model""" )
_lowerCAmelCase = TFRobertaModel(_lowercase , name="""text_model""" )
return vision_model, text_model
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFDeiTModelTester(self )
_lowerCAmelCase = TFRobertaModelTester(self )
_lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_tf
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_vision_text_pretrained(
"""Rocketknight1/tiny-random-clip-tf""" , """hf-internal-testing/tiny-random-bert""" )
_lowerCAmelCase = 13
_lowerCAmelCase = floats_tensor(
[
batch_size,
model.vision_model.config.num_channels,
model.vision_model.config.image_size,
model.vision_model.config.image_size,
] )
_lowerCAmelCase = ids_tensor([batch_size, 4] , model.text_model.config.vocab_size )
_lowerCAmelCase = random_attention_mask([batch_size, 4] )
_lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def _lowercase ( self , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = TFCLIPVisionModel(_lowercase , name="""vision_model""" )
_lowerCAmelCase = TFBertModel(_lowercase , name="""text_model""" )
return vision_model, text_model
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFCLIPVisionModelTester(self )
_lowerCAmelCase = TFBertModelTester(self )
_lowerCAmelCase = clip_model_tester.prepare_config_and_inputs()
_lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase = vision_config_and_inputs
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = text_config_and_inputs
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": input_mask,
"input_ids": input_ids,
"text_token_type_ids": token_type_ids,
"text_sequence_labels": sequence_labels,
"text_token_labels": token_labels,
"text_choice_labels": choice_labels,
}
@require_vision
@require_tf
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFVisionTextDualEncoderModel.from_pretrained(
"""clip-italian/clip-italian""" , logit_scale_init_value=1.0 , from_pt=_lowercase )
_lowerCAmelCase = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
_lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
_lowerCAmelCase = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=_lowercase , padding=_lowercase , return_tensors="""np""" )
_lowerCAmelCase = model(**_lowercase )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
_lowerCAmelCase = np.array([[1.228_4727, 0.310_4122]] )
self.assertTrue(np.allclose(outputs.logits_per_image.numpy() , _lowercase , atol=1e-3 ) )
| 5 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
ImageTextPipelineOutput,
UniDiffuserPipeline,
)
else:
from .modeling_text_decoder import UniDiffuserTextDecoder
from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
| 5 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[str] = '''decision_transformer'''
_lowercase : Optional[Any] = ['''past_key_values''']
_lowercase : str = {
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , _lowercase=17 , _lowercase=4 , _lowercase=128 , _lowercase=4_096 , _lowercase=True , _lowercase=1 , _lowercase=1_024 , _lowercase=3 , _lowercase=1 , _lowercase=None , _lowercase="relu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=0.1 , _lowercase=1e-5 , _lowercase=0.02 , _lowercase=True , _lowercase=True , _lowercase=50_256 , _lowercase=50_256 , _lowercase=False , _lowercase=False , **_lowercase , ):
"""simple docstring"""
_lowerCAmelCase = state_dim
_lowerCAmelCase = act_dim
_lowerCAmelCase = hidden_size
_lowerCAmelCase = max_ep_len
_lowerCAmelCase = action_tanh
_lowerCAmelCase = vocab_size
_lowerCAmelCase = n_positions
_lowerCAmelCase = n_layer
_lowerCAmelCase = n_head
_lowerCAmelCase = n_inner
_lowerCAmelCase = activation_function
_lowerCAmelCase = resid_pdrop
_lowerCAmelCase = embd_pdrop
_lowerCAmelCase = attn_pdrop
_lowerCAmelCase = layer_norm_epsilon
_lowerCAmelCase = initializer_range
_lowerCAmelCase = scale_attn_weights
_lowerCAmelCase = use_cache
_lowerCAmelCase = scale_attn_by_inverse_layer_idx
_lowerCAmelCase = reorder_and_upcast_attn
_lowerCAmelCase = bos_token_id
_lowerCAmelCase = eos_token_id
super().__init__(bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase )
| 5 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowercase = {"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ViTMAEForPreTraining""",
"""ViTMAELayer""",
"""ViTMAEModel""",
"""ViTMAEPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""TFViTMAEForPreTraining""",
"""TFViTMAEModel""",
"""TFViTMAEPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 5 | 1 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Tuple = (DDPMScheduler,)
def _lowercase ( self , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_lowercase )
return config
def _lowercase ( self ):
"""simple docstring"""
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_lowercase )
def _lowercase ( self ):
"""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=_lowercase , beta_end=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
self.check_over_configs(thresholding=_lowercase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_lowercase , prediction_type=_lowercase , sample_max_value=_lowercase , )
def _lowercase ( self ):
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
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 _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = len(_lowercase )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter
_lowerCAmelCase = torch.manual_seed(0 )
for t in reversed(range(_lowercase ) ):
# 1. predict noise residual
_lowerCAmelCase = model(_lowercase , _lowercase )
# 2. predict previous mean of sample x_t-1
_lowerCAmelCase = scheduler.step(_lowercase , _lowercase , _lowercase , generator=_lowercase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_lowerCAmelCase = pred_prev_sample
_lowerCAmelCase = torch.sum(torch.abs(_lowercase ) )
_lowerCAmelCase = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 258.9606 ) < 1e-2
assert abs(result_mean.item() - 0.3372 ) < 1e-3
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config(prediction_type="""v_prediction""" )
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = len(_lowercase )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter
_lowerCAmelCase = torch.manual_seed(0 )
for t in reversed(range(_lowercase ) ):
# 1. predict noise residual
_lowerCAmelCase = model(_lowercase , _lowercase )
# 2. predict previous mean of sample x_t-1
_lowerCAmelCase = scheduler.step(_lowercase , _lowercase , _lowercase , generator=_lowercase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_lowerCAmelCase = pred_prev_sample
_lowerCAmelCase = torch.sum(torch.abs(_lowercase ) )
_lowerCAmelCase = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 202.0296 ) < 1e-2
assert abs(result_mean.item() - 0.2631 ) < 1e-3
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_lowercase )
_lowerCAmelCase = scheduler.timesteps
for i, timestep in enumerate(_lowercase ):
if i == len(_lowercase ) - 1:
_lowerCAmelCase = -1
else:
_lowerCAmelCase = timesteps[i + 1]
_lowerCAmelCase = scheduler.previous_timestep(_lowercase )
_lowerCAmelCase = prev_t.item()
self.assertEqual(_lowercase , _lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 51, 0]
with self.assertRaises(_lowercase , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 1, 0]
_lowerCAmelCase = len(_lowercase )
with self.assertRaises(_lowercase , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_lowercase , timesteps=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_lowercase , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_lowercase )
| 5 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
_lowercase = logging.get_logger(__name__)
@add_end_docstrings(_SCREAMING_SNAKE_CASE )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , **_lowercase ):
"""simple docstring"""
super().__init__(**_lowercase )
if self.framework != "pt":
raise ValueError(F'The {self.__class__} is only available in PyTorch.' )
# No specific FOR_XXX available yet
def __call__( self , _lowercase , **_lowercase ):
"""simple docstring"""
return super().__call__(_lowercase , **_lowercase )
def _lowercase ( self , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase = {}
if "candidate_labels" in kwargs:
_lowerCAmelCase = kwargs["""candidate_labels"""]
if "hypothesis_template" in kwargs:
_lowerCAmelCase = kwargs["""hypothesis_template"""]
return preprocess_params, {}, {}
def _lowercase ( self , _lowercase , _lowercase=None , _lowercase="This is a sound of {}." ):
"""simple docstring"""
if isinstance(_lowercase , _lowercase ):
if audio.startswith("""http://""" ) or audio.startswith("""https://""" ):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
_lowerCAmelCase = requests.get(_lowercase ).content
else:
with open(_lowercase , """rb""" ) as f:
_lowerCAmelCase = f.read()
if isinstance(_lowercase , _lowercase ):
_lowerCAmelCase = ffmpeg_read(_lowercase , self.feature_extractor.sampling_rate )
if not isinstance(_lowercase , np.ndarray ):
raise ValueError("""We expect a numpy ndarray as input""" )
if len(audio.shape ) != 1:
raise ValueError("""We expect a single channel audio input for ZeroShotAudioClassificationPipeline""" )
_lowerCAmelCase = self.feature_extractor(
[audio] , sampling_rate=self.feature_extractor.sampling_rate , return_tensors="""pt""" )
_lowerCAmelCase = candidate_labels
_lowerCAmelCase = [hypothesis_template.format(_lowercase ) for x in candidate_labels]
_lowerCAmelCase = self.tokenizer(_lowercase , return_tensors=self.framework , padding=_lowercase )
_lowerCAmelCase = [text_inputs]
return inputs
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = model_inputs.pop("""candidate_labels""" )
_lowerCAmelCase = model_inputs.pop("""text_inputs""" )
if isinstance(text_inputs[0] , _lowercase ):
_lowerCAmelCase = text_inputs[0]
else:
# Batching case.
_lowerCAmelCase = text_inputs[0][0]
_lowerCAmelCase = self.model(**_lowercase , **_lowercase )
_lowerCAmelCase = {
"""candidate_labels""": candidate_labels,
"""logits""": outputs.logits_per_audio,
}
return model_outputs
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = model_outputs.pop("""candidate_labels""" )
_lowerCAmelCase = model_outputs["""logits"""][0]
if self.framework == "pt":
_lowerCAmelCase = logits.softmax(dim=0 )
_lowerCAmelCase = probs.tolist()
else:
raise ValueError("""`tf` framework not supported.""" )
_lowerCAmelCase = [
{"""score""": score, """label""": candidate_label}
for score, candidate_label in sorted(zip(_lowercase , _lowercase ) , key=lambda _lowercase : -x[0] )
]
return result
| 5 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
"""configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MegatronBertForCausalLM""",
"""MegatronBertForMaskedLM""",
"""MegatronBertForMultipleChoice""",
"""MegatronBertForNextSentencePrediction""",
"""MegatronBertForPreTraining""",
"""MegatronBertForQuestionAnswering""",
"""MegatronBertForSequenceClassification""",
"""MegatronBertForTokenClassification""",
"""MegatronBertModel""",
"""MegatronBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 5 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_lowercase = logging.get_logger(__name__)
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[str] = ['''input_values''', '''padding_mask''']
def __init__( self , _lowercase = 1 , _lowercase = 24_000 , _lowercase = 0.0 , _lowercase = None , _lowercase = None , **_lowercase , ):
"""simple docstring"""
super().__init__(feature_size=_lowercase , sampling_rate=_lowercase , padding_value=_lowercase , **_lowercase )
_lowerCAmelCase = chunk_length_s
_lowerCAmelCase = overlap
@property
def _lowercase ( self ):
"""simple docstring"""
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _lowercase ( self ):
"""simple docstring"""
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 ) )
def __call__( self , _lowercase , _lowercase = None , _lowercase = False , _lowercase = None , _lowercase = None , _lowercase = None , ):
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
F' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
if padding and truncation:
raise ValueError("""Both padding and truncation were set. Make sure you only set one.""" )
elif padding is None:
# by default let's pad the inputs
_lowerCAmelCase = True
_lowerCAmelCase = bool(
isinstance(_lowercase , (list, tuple) ) and (isinstance(raw_audio[0] , (np.ndarray, tuple, list) )) )
if is_batched:
_lowerCAmelCase = [np.asarray(_lowercase , dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(_lowercase , np.ndarray ):
_lowerCAmelCase = np.asarray(_lowercase , dtype=np.floataa )
elif isinstance(_lowercase , np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
_lowerCAmelCase = raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
_lowerCAmelCase = [np.asarray(_lowercase ).T]
# verify inputs are valid
for idx, example in enumerate(_lowercase ):
if example.ndim > 2:
raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}' )
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels' )
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels' )
_lowerCAmelCase = None
_lowerCAmelCase = BatchFeature({"""input_values""": raw_audio} )
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
_lowerCAmelCase = min(array.shape[0] for array in raw_audio )
_lowerCAmelCase = int(np.floor(max_length / self.chunk_stride ) )
_lowerCAmelCase = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
_lowerCAmelCase = max(array.shape[0] for array in raw_audio )
_lowerCAmelCase = int(np.ceil(max_length / self.chunk_stride ) )
_lowerCAmelCase = (nb_step - 1) * self.chunk_stride + self.chunk_length
_lowerCAmelCase = """max_length"""
else:
_lowerCAmelCase = input_values
# normal padding on batch
if padded_inputs is None:
_lowerCAmelCase = self.pad(
_lowercase , max_length=_lowercase , truncation=_lowercase , padding=_lowercase , return_attention_mask=_lowercase , )
if padding:
_lowerCAmelCase = padded_inputs.pop("""attention_mask""" )
_lowerCAmelCase = []
for example in padded_inputs.pop("""input_values""" ):
if self.feature_size == 1:
_lowerCAmelCase = example[..., None]
input_values.append(example.T )
_lowerCAmelCase = input_values
if return_tensors is not None:
_lowerCAmelCase = padded_inputs.convert_to_tensors(_lowercase )
return padded_inputs
| 5 | 1 |
'''simple docstring'''
import os
def A ():
_lowerCAmelCase = os.path.join(os.path.dirname(__lowerCamelCase ) , """num.txt""" )
with open(__lowerCamelCase ) as file_hand:
return str(sum(int(__lowerCamelCase ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 5 |
'''simple docstring'''
_lowercase = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_lowercase = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
_lowercase = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 5 | 1 |
'''simple docstring'''
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowercase = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
_lowercase : List[str] = XLMProphetNetTokenizer
_lowercase : str = False
_lowercase : List[Any] = True
def _lowercase ( self ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCAmelCase = XLMProphetNetTokenizer(_lowercase , keep_accents=_lowercase )
tokenizer.save_pretrained(self.tmpdirname )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """[PAD]"""
_lowerCAmelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase ) , _lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase ) , _lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """[PAD]""" )
self.assertEqual(vocab_keys[1] , """[CLS]""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(_lowercase ) , 1_012 )
def _lowercase ( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1_012 )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = XLMProphetNetTokenizer(_lowercase , keep_accents=_lowercase )
_lowerCAmelCase = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(_lowercase , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_lowercase ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
_lowerCAmelCase = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_lowercase , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
_lowerCAmelCase = tokenizer.convert_tokens_to_ids(_lowercase )
self.assertListEqual(
_lowercase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
] , )
_lowerCAmelCase = tokenizer.convert_ids_to_tokens(_lowercase )
self.assertListEqual(
_lowercase , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""[UNK]""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""[UNK]""",
""".""",
] , )
@cached_property
def _lowercase ( self ):
"""simple docstring"""
return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased""" )
@slow
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """Hello World!"""
_lowerCAmelCase = [35_389, 6_672, 49, 2]
self.assertListEqual(_lowercase , self.big_tokenizer.encode(_lowercase ) )
@slow
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = {"""input_ids""": [[11_073, 82_783, 18, 26, 82_783, 549, 51_540, 248, 17_209, 1_301, 217, 20, 215_186, 1_325, 147, 17_209, 1_301, 217, 20, 56_370, 53, 122_020, 20, 16_477, 27, 87_355, 4_548, 20, 4_728, 78_392, 17, 159_969, 18, 26, 24_491, 629, 15, 538, 22_704, 5_439, 15, 2_788, 24_491, 9_885, 15, 43_534, 605, 15, 814, 18_403, 33_200, 29, 15, 43_534, 24_458, 12_410, 111, 24_966, 83_669, 9_637, 144_068, 26, 850, 22_346, 27, 147, 24_966, 83_669, 83_490, 26, 39_113, 735, 27, 689, 656, 2_800, 1_339, 4_600, 53, 122_020, 115_785, 34, 816, 1_339, 46_887, 18, 147, 53_905, 1_951, 42_238, 41_170, 17_732, 834, 436, 15, 27_523, 98_733, 217, 147, 5_542, 4_981, 930, 17_347, 16, 2], [20_091, 629, 94, 82_786, 58, 490, 20, 1_528, 84, 53_905, 344, 80_592, 110_128, 18_822, 5_267, 1_306, 62, 152_537, 308, 7_997, 401, 124_427, 549, 35_442, 225, 109, 15_055, 25_748, 147, 7_119, 43_712, 34, 767, 135_366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63_784, 119_466, 17, 147_808, 88_214, 18, 656, 81, 32, 3_296, 10_280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_lowercase , model_name="""microsoft/xprophetnet-large-wiki100-cased""" , revision="""1acad1643ddd54a44df6a1b797ada8373685d90e""" , )
| 5 |
'''simple docstring'''
import functools
def A (__lowerCamelCase :list[int] , __lowerCamelCase :list[int] ):
# Validation
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not all(isinstance(__lowerCamelCase , __lowerCamelCase ) for day in days ):
raise ValueError("""The parameter days should be a list of integers""" )
if len(__lowerCamelCase ) != 3 or not all(isinstance(__lowerCamelCase , __lowerCamelCase ) for cost in costs ):
raise ValueError("""The parameter costs should be a list of three integers""" )
if len(__lowerCamelCase ) == 0:
return 0
if min(__lowerCamelCase ) <= 0:
raise ValueError("""All days elements should be greater than 0""" )
if max(__lowerCamelCase ) >= 366:
raise ValueError("""All days elements should be less than 366""" )
_lowerCAmelCase = set(__lowerCamelCase )
@functools.cache
def dynamic_programming(__lowerCamelCase :int ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :str , __lowerCamelCase :str , __lowerCamelCase :Path , __lowerCamelCase :str = None , __lowerCamelCase :str = None , __lowerCamelCase :str = None , ):
if config_name_or_path is None:
_lowerCAmelCase = """facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""
if generator_tokenizer_name_or_path is None:
_lowerCAmelCase = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
_lowerCAmelCase = question_encoder_name_or_path
_lowerCAmelCase = RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration
# Save model.
_lowerCAmelCase = RagConfig.from_pretrained(__lowerCamelCase )
_lowerCAmelCase = AutoConfig.from_pretrained(__lowerCamelCase )
_lowerCAmelCase = AutoConfig.from_pretrained(__lowerCamelCase )
_lowerCAmelCase = gen_config
_lowerCAmelCase = question_encoder_config
_lowerCAmelCase = model_class.from_pretrained_question_encoder_generator(
__lowerCamelCase , __lowerCamelCase , config=__lowerCamelCase )
rag_model.save_pretrained(__lowerCamelCase )
# Sanity check.
model_class.from_pretrained(__lowerCamelCase )
# Save tokenizers.
_lowerCAmelCase = AutoTokenizer.from_pretrained(__lowerCamelCase )
gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/""" )
_lowerCAmelCase = AutoTokenizer.from_pretrained(__lowerCamelCase )
question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/""" )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument(
"""--model_type""",
choices=["""rag_sequence""", """rag_token"""],
required=True,
type=str,
help="""RAG model type: rag_sequence, rag_token""",
)
parser.add_argument("""--dest""", type=str, required=True, help="""Path to the output checkpoint directory.""")
parser.add_argument("""--generator_name_or_path""", type=str, required=True, help="""Generator model identifier""")
parser.add_argument(
"""--question_encoder_name_or_path""", type=str, required=True, help="""Question encoder model identifier"""
)
parser.add_argument(
"""--generator_tokenizer_name_or_path""",
type=str,
help="""Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``""",
)
parser.add_argument(
"""--question_encoder_tokenizer_name_or_path""",
type=str,
help="""Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``""",
)
parser.add_argument(
"""--config_name_or_path""",
type=str,
help=(
"""Identifier of the model config to use, if not provided, resolves to a base config for a given"""
""" ``model_type``"""
),
)
_lowercase = parser.parse_args()
_lowercase = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 5 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def A (__lowerCamelCase :List[Any] ):
_lowerCAmelCase = 384
if "tiny" in model_name:
_lowerCAmelCase = [3, 3, 9, 3]
_lowerCAmelCase = [96, 192, 384, 768]
if "small" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [96, 192, 384, 768]
if "base" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [128, 256, 512, 1024]
_lowerCAmelCase = 512
if "large" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [192, 384, 768, 1536]
_lowerCAmelCase = 768
if "xlarge" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [256, 512, 1024, 2048]
_lowerCAmelCase = 1024
# set label information
_lowerCAmelCase = 150
_lowerCAmelCase = """huggingface/label-files"""
_lowerCAmelCase = """ade20k-id2label.json"""
_lowerCAmelCase = json.load(open(hf_hub_download(__lowerCamelCase , __lowerCamelCase , repo_type="""dataset""" ) , """r""" ) )
_lowerCAmelCase = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
_lowerCAmelCase = {v: k for k, v in idalabel.items()}
_lowerCAmelCase = ConvNextConfig(
depths=__lowerCamelCase , hidden_sizes=__lowerCamelCase , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
_lowerCAmelCase = UperNetConfig(
backbone_config=__lowerCamelCase , auxiliary_in_channels=__lowerCamelCase , num_labels=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid=__lowerCamelCase , )
return config
def A (__lowerCamelCase :Optional[Any] ):
_lowerCAmelCase = []
# fmt: off
# stem
rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""") )
rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""") )
rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""") )
rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f'backbone.stages.{i}.{j}.gamma', f'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter') )
rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.weight', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.bias', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias') )
rename_keys.append((f'backbone.stages.{i}.{j}.norm.weight', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.norm.bias', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias') )
if i > 0:
rename_keys.append((f'backbone.downsample_layers.{i}.0.weight', f'backbone.encoder.stages.{i}.downsampling_layer.0.weight') )
rename_keys.append((f'backbone.downsample_layers.{i}.0.bias', f'backbone.encoder.stages.{i}.downsampling_layer.0.bias') )
rename_keys.append((f'backbone.downsample_layers.{i}.1.weight', f'backbone.encoder.stages.{i}.downsampling_layer.1.weight') )
rename_keys.append((f'backbone.downsample_layers.{i}.1.bias', f'backbone.encoder.stages.{i}.downsampling_layer.1.bias') )
rename_keys.append((f'backbone.norm{i}.weight', f'backbone.hidden_states_norms.stage{i+1}.weight') )
rename_keys.append((f'backbone.norm{i}.bias', f'backbone.hidden_states_norms.stage{i+1}.bias') )
# decode head
rename_keys.extend(
[
("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
] )
# fmt: on
return rename_keys
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :Dict , __lowerCamelCase :Tuple ):
_lowerCAmelCase = dct.pop(__lowerCamelCase )
_lowerCAmelCase = val
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Optional[Any] , __lowerCamelCase :Any ):
_lowerCAmelCase = {
"""upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
"""upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
"""upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
"""upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
"""upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
}
_lowerCAmelCase = model_name_to_url[model_name]
_lowerCAmelCase = torch.hub.load_state_dict_from_url(__lowerCamelCase , map_location="""cpu""" )["""state_dict"""]
_lowerCAmelCase = get_upernet_config(__lowerCamelCase )
_lowerCAmelCase = UperNetForSemanticSegmentation(__lowerCamelCase )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
_lowerCAmelCase = state_dict.pop(__lowerCamelCase )
if "bn" in key:
_lowerCAmelCase = key.replace("""bn""" , """batch_norm""" )
_lowerCAmelCase = val
# rename keys
_lowerCAmelCase = create_rename_keys(__lowerCamelCase )
for src, dest in rename_keys:
rename_key(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
model.load_state_dict(__lowerCamelCase )
# verify on image
_lowerCAmelCase = """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
_lowerCAmelCase = Image.open(requests.get(__lowerCamelCase , stream=__lowerCamelCase ).raw ).convert("""RGB""" )
_lowerCAmelCase = SegformerImageProcessor()
_lowerCAmelCase = processor(__lowerCamelCase , return_tensors="""pt""" ).pixel_values
with torch.no_grad():
_lowerCAmelCase = model(__lowerCamelCase )
if model_name == "upernet-convnext-tiny":
_lowerCAmelCase = torch.tensor(
[[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]] )
elif model_name == "upernet-convnext-small":
_lowerCAmelCase = torch.tensor(
[[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]] )
elif model_name == "upernet-convnext-base":
_lowerCAmelCase = torch.tensor(
[[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]] )
elif model_name == "upernet-convnext-large":
_lowerCAmelCase = torch.tensor(
[[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]] )
elif model_name == "upernet-convnext-xlarge":
_lowerCAmelCase = torch.tensor(
[[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]] )
print("""Logits:""" , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , __lowerCamelCase , atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(f'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(__lowerCamelCase )
print(f'Saving processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(__lowerCamelCase )
if push_to_hub:
print(f'Pushing model and processor for {model_name} to hub' )
model.push_to_hub(f'openmmlab/{model_name}' )
processor.push_to_hub(f'openmmlab/{model_name}' )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""upernet-convnext-tiny""",
type=str,
choices=[F"""upernet-convnext-{size}""" for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
help="""Name of the ConvNext UperNet model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_lowercase = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 5 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class UpperCAmelCase_ :
'''simple docstring'''
_lowercase : List[str] = MBartConfig
_lowercase : Any = {}
_lowercase : Union[str, Any] = '''gelu'''
def __init__( self , _lowercase , _lowercase=13 , _lowercase=7 , _lowercase=True , _lowercase=False , _lowercase=99 , _lowercase=32 , _lowercase=2 , _lowercase=4 , _lowercase=37 , _lowercase=0.1 , _lowercase=0.1 , _lowercase=20 , _lowercase=2 , _lowercase=1 , _lowercase=0 , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = seq_length
_lowerCAmelCase = is_training
_lowerCAmelCase = use_labels
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = eos_token_id
_lowerCAmelCase = pad_token_id
_lowerCAmelCase = bos_token_id
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
_lowerCAmelCase = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
_lowerCAmelCase = tf.concat([input_ids, eos_tensor] , axis=1 )
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = 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 , )
_lowerCAmelCase = prepare_mbart_inputs_dict(_lowercase , _lowercase , _lowercase )
return config, inputs_dict
def _lowercase ( self , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = TFMBartModel(config=_lowercase ).get_decoder()
_lowerCAmelCase = inputs_dict["""input_ids"""]
_lowerCAmelCase = input_ids[:1, :]
_lowerCAmelCase = inputs_dict["""attention_mask"""][:1, :]
_lowerCAmelCase = inputs_dict["""head_mask"""]
_lowerCAmelCase = 1
# first forward pass
_lowerCAmelCase = model(_lowercase , attention_mask=_lowercase , head_mask=_lowercase , use_cache=_lowercase )
_lowerCAmelCase , _lowerCAmelCase = outputs.to_tuple()
_lowerCAmelCase = past_key_values[1]
def A (__lowerCamelCase :List[str] , __lowerCamelCase :Union[str, Any] , __lowerCamelCase :Union[str, Any] , __lowerCamelCase :Union[str, Any]=None , __lowerCamelCase :Optional[int]=None , __lowerCamelCase :Optional[int]=None , __lowerCamelCase :List[Any]=None , __lowerCamelCase :Optional[Any]=None , ):
if attention_mask is None:
_lowerCAmelCase = tf.cast(tf.math.not_equal(__lowerCamelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
_lowerCAmelCase = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
_lowerCAmelCase = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_lowerCAmelCase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
_lowerCAmelCase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
_lowercase : str = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
_lowercase : Optional[int] = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
_lowercase : Tuple = (
{
'''conversational''': TFMBartForConditionalGeneration,
'''feature-extraction''': TFMBartModel,
'''summarization''': TFMBartForConditionalGeneration,
'''text2text-generation''': TFMBartForConditionalGeneration,
'''translation''': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
_lowercase : Any = True
_lowercase : List[Any] = False
_lowercase : str = False
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFMBartModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_lowercase )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
_lowercase : Optional[Any] = [
''' UN Chief Says There Is No Military Solution in Syria''',
]
_lowercase : List[Any] = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
]
_lowercase : int = '''facebook/mbart-large-en-ro'''
@cached_property
def _lowercase ( self ):
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _lowercase ( self , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.translate_src_text(**_lowercase )
self.assertListEqual(self.expected_text , _lowercase )
def _lowercase ( self , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer(self.src_text , **_lowercase , return_tensors="""tf""" )
_lowerCAmelCase = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
_lowerCAmelCase = self.tokenizer.batch_decode(_lowercase , skip_special_tokens=_lowercase )
return generated_words
@slow
def _lowercase ( self ):
"""simple docstring"""
self._assert_generated_batch_equal_expected()
| 5 |
'''simple docstring'''
from itertools import product
def A (__lowerCamelCase :int , __lowerCamelCase :int ):
_lowerCAmelCase = sides_number
_lowerCAmelCase = max_face_number * dice_number
_lowerCAmelCase = [0] * (max_total + 1)
_lowerCAmelCase = 1
_lowerCAmelCase = range(__lowerCamelCase , max_face_number + 1 )
for dice_numbers in product(__lowerCamelCase , repeat=__lowerCamelCase ):
_lowerCAmelCase = sum(__lowerCamelCase )
totals_frequencies[total] += 1
return totals_frequencies
def A ():
_lowerCAmelCase = total_frequency_distribution(
sides_number=4 , dice_number=9 )
_lowerCAmelCase = total_frequency_distribution(
sides_number=6 , dice_number=6 )
_lowerCAmelCase = 0
_lowerCAmelCase = 9
_lowerCAmelCase = 4 * 9
_lowerCAmelCase = 6
for peter_total in range(__lowerCamelCase , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
_lowerCAmelCase = (4**9) * (6**6)
_lowerCAmelCase = peter_wins_count / total_games_number
_lowerCAmelCase = round(__lowerCamelCase , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(F"""{solution() = }""")
| 5 | 1 |
'''simple docstring'''
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
_lowercase = logging.get_logger(__name__)
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Optional[int] = '''vision-encoder-decoder'''
_lowercase : Optional[Any] = True
def __init__( self , **_lowercase ):
"""simple docstring"""
super().__init__(**_lowercase )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
F'A configuraton of type {self.model_type} cannot be instantiated because '
F'not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}' )
_lowerCAmelCase = kwargs.pop("""encoder""" )
_lowerCAmelCase = encoder_config.pop("""model_type""" )
_lowerCAmelCase = kwargs.pop("""decoder""" )
_lowerCAmelCase = decoder_config.pop("""model_type""" )
_lowerCAmelCase = AutoConfig.for_model(_lowercase , **_lowercase )
_lowerCAmelCase = AutoConfig.for_model(_lowercase , **_lowercase )
_lowerCAmelCase = True
@classmethod
def _lowercase ( cls , _lowercase , _lowercase , **_lowercase ):
"""simple docstring"""
logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""" )
_lowerCAmelCase = True
_lowerCAmelCase = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = copy.deepcopy(self.__dict__ )
_lowerCAmelCase = self.encoder.to_dict()
_lowerCAmelCase = self.decoder.to_dict()
_lowerCAmelCase = self.__class__.model_type
return output
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Optional[Any] = version.parse('''1.11''' )
@property
def _lowercase ( self ):
"""simple docstring"""
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def _lowercase ( self ):
"""simple docstring"""
return 1e-4
@property
def _lowercase ( self ):
"""simple docstring"""
return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}} )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = OrderedDict()
_lowerCAmelCase = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
_lowerCAmelCase = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
_lowerCAmelCase = {0: """batch""", 1: """encoder_sequence"""}
return common_inputs
def _lowercase ( self , _lowercase , _lowercase = -1 , _lowercase = -1 , _lowercase = False , _lowercase = None , ):
"""simple docstring"""
import torch
_lowerCAmelCase = OrderedDict()
_lowerCAmelCase = super().generate_dummy_inputs(
_lowercase , batch_size=_lowercase , seq_length=_lowercase , is_pair=_lowercase , framework=_lowercase )
_lowerCAmelCase , _lowerCAmelCase = dummy_input["""input_ids"""].shape
_lowerCAmelCase = (batch, encoder_sequence, self._config.encoder_hidden_size)
_lowerCAmelCase = dummy_input.pop("""input_ids""" )
_lowerCAmelCase = dummy_input.pop("""attention_mask""" )
_lowerCAmelCase = torch.zeros(_lowercase )
return common_inputs
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _lowercase ( self ):
"""simple docstring"""
pass
def _lowercase ( self , _lowercase ):
"""simple docstring"""
return VisionEncoderDecoderEncoderOnnxConfig(_lowercase )
def _lowercase ( self , _lowercase , _lowercase , _lowercase = "default" ):
"""simple docstring"""
_lowerCAmelCase = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(_lowercase , _lowercase )
| 5 |
'''simple docstring'''
from manim import *
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = Rectangle(height=0.5 , width=0.5 )
_lowerCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = VGroup(_lowercase , _lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""CPU""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_lowercase )
_lowerCAmelCase = [mem.copy() for i in range(1 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""GPU""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
gpu.align_to(_lowercase , _lowercase )
gpu.set_x(gpu.get_x() - 1 )
self.add(_lowercase )
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""Model""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
model.move_to([3, -1.0, 0] )
self.play(
Create(_lowercase , run_time=1 ) , Create(_lowercase , run_time=1 ) , Create(_lowercase , run_time=1 ) , )
_lowerCAmelCase = MarkupText(
F'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.' , font_size=24 , )
_lowerCAmelCase = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_lowerCAmelCase = MarkupText(
F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(_lowercase , run_time=2.5 ) , Write(_lowercase ) , Write(_lowercase ) )
self.add(_lowercase )
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = []
for i, rect in enumerate(_lowercase ):
_lowerCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(_lowercase , opacity=0.7 )
cpu_target.move_to(_lowercase )
cpu_target.generate_target()
_lowerCAmelCase = 0.46 / 4
_lowerCAmelCase = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=_lowercase )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=_lowercase , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=_lowercase , buff=0.0 )
cpu_targs.append(_lowercase )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(_lowercase ) )
second_animations.append(MoveToTarget(_lowercase , run_time=1.5 ) )
self.play(*_lowercase )
self.play(*_lowercase )
self.wait()
| 5 | 1 |
'''simple docstring'''
def A (__lowerCamelCase :int ):
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
_lowerCAmelCase = f'Input value of [number={number}] must be an integer'
raise TypeError(__lowerCamelCase )
if number < 0:
return False
_lowerCAmelCase = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_lowercase = False
try:
_lowercase = _is_package_available("""google.colab""")
except ModuleNotFoundError:
pass
@input.register
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase = None , _lowercase = [] ):
"""simple docstring"""
_lowerCAmelCase = 0
_lowerCAmelCase = choices
_lowerCAmelCase = prompt
if sys.platform == "win32":
_lowerCAmelCase = """*"""
else:
_lowerCAmelCase = """➔ """
def _lowercase ( self , _lowercase , _lowercase = "" ):
"""simple docstring"""
if sys.platform != "win32":
writeColor(self.choices[index] , 32 , _lowercase )
else:
forceWrite(self.choices[index] , _lowercase )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
if index == self.position:
forceWrite(F' {self.arrow_char} ' )
self.write_choice(_lowercase )
else:
forceWrite(F' {self.choices[index]}' )
reset_cursor()
def _lowercase ( self , _lowercase , _lowercase = 1 ):
"""simple docstring"""
_lowerCAmelCase = self.position
if direction == Direction.DOWN:
if self.position + 1 >= len(self.choices ):
return
self.position += num_spaces
else:
if self.position - 1 < 0:
return
self.position -= num_spaces
clear_line()
self.print_choice(_lowercase )
move_cursor(_lowercase , direction.name )
self.print_choice(self.position )
@input.mark(KEYMAP["""up"""] )
def _lowercase ( self ):
"""simple docstring"""
self.move_direction(Direction.UP )
@input.mark(KEYMAP["""down"""] )
def _lowercase ( self ):
"""simple docstring"""
self.move_direction(Direction.DOWN )
@input.mark(KEYMAP["""newline"""] )
def _lowercase ( self ):
"""simple docstring"""
move_cursor(len(self.choices ) - self.position , """DOWN""" )
return self.position
@input.mark(KEYMAP["""interrupt"""] )
def _lowercase ( self ):
"""simple docstring"""
move_cursor(len(self.choices ) - self.position , """DOWN""" )
raise KeyboardInterrupt
@input.mark_multiple(*[KEYMAP[str(_lowercase )] for number in range(10 )] )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = int(chr(self.current_selection ) )
_lowerCAmelCase = index - self.position
if index == self.position:
return
if index < len(self.choices ):
if self.position > index:
self.move_direction(Direction.UP , -movement )
elif self.position < index:
self.move_direction(Direction.DOWN , _lowercase )
else:
return
else:
return
def _lowercase ( self , _lowercase = 0 ):
"""simple docstring"""
if self.prompt:
linebreak()
forceWrite(self.prompt , """\n""" )
if in_colab:
forceWrite("""Please input a choice index (starting from 0), and press enter""" , """\n""" )
else:
forceWrite("""Please select a choice using the arrow or number keys, and selecting with enter""" , """\n""" )
_lowerCAmelCase = default_choice
for i in range(len(self.choices ) ):
self.print_choice(_lowercase )
forceWrite("""\n""" )
move_cursor(len(self.choices ) - self.position , """UP""" )
with cursor.hide():
while True:
if in_colab:
try:
_lowerCAmelCase = int(builtins.input() )
except ValueError:
_lowerCAmelCase = default_choice
else:
_lowerCAmelCase = self.handle_input()
if choice is not None:
reset_cursor()
for _ in range(len(self.choices ) + 1 ):
move_cursor(1 , """UP""" )
clear_line()
self.write_choice(_lowercase , """\n""" )
return choice
| 5 | 1 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __get__( self , _lowercase , _lowercase=None ):
"""simple docstring"""
if obj is None:
return self
if self.fget is None:
raise AttributeError("""unreadable attribute""" )
_lowerCAmelCase = """__cached_""" + self.fget.__name__
_lowerCAmelCase = getattr(_lowercase , _lowercase , _lowercase )
if cached is None:
_lowerCAmelCase = self.fget(_lowercase )
setattr(_lowercase , _lowercase , _lowercase )
return cached
def A (__lowerCamelCase :int ):
_lowerCAmelCase = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(f'invalid truth value {val!r}' )
def A (__lowerCamelCase :Union[str, Any] ):
if is_torch_fx_proxy(__lowerCamelCase ):
return True
if is_torch_available():
import torch
if isinstance(__lowerCamelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(__lowerCamelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(__lowerCamelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(__lowerCamelCase , np.ndarray )
def A (__lowerCamelCase :List[Any] ):
return isinstance(__lowerCamelCase , np.ndarray )
def A (__lowerCamelCase :str ):
return _is_numpy(__lowerCamelCase )
def A (__lowerCamelCase :Dict ):
import torch
return isinstance(__lowerCamelCase , torch.Tensor )
def A (__lowerCamelCase :Union[str, Any] ):
return False if not is_torch_available() else _is_torch(__lowerCamelCase )
def A (__lowerCamelCase :Union[str, Any] ):
import torch
return isinstance(__lowerCamelCase , torch.device )
def A (__lowerCamelCase :List[str] ):
return False if not is_torch_available() else _is_torch_device(__lowerCamelCase )
def A (__lowerCamelCase :Dict ):
import torch
if isinstance(__lowerCamelCase , __lowerCamelCase ):
if hasattr(__lowerCamelCase , __lowerCamelCase ):
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase )
else:
return False
return isinstance(__lowerCamelCase , torch.dtype )
def A (__lowerCamelCase :Dict ):
return False if not is_torch_available() else _is_torch_dtype(__lowerCamelCase )
def A (__lowerCamelCase :str ):
import tensorflow as tf
return isinstance(__lowerCamelCase , tf.Tensor )
def A (__lowerCamelCase :Optional[int] ):
return False if not is_tf_available() else _is_tensorflow(__lowerCamelCase )
def A (__lowerCamelCase :Tuple ):
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(__lowerCamelCase , """is_symbolic_tensor""" ):
return tf.is_symbolic_tensor(__lowerCamelCase )
return type(__lowerCamelCase ) == tf.Tensor
def A (__lowerCamelCase :int ):
return False if not is_tf_available() else _is_tf_symbolic_tensor(__lowerCamelCase )
def A (__lowerCamelCase :Optional[int] ):
import jax.numpy as jnp # noqa: F811
return isinstance(__lowerCamelCase , jnp.ndarray )
def A (__lowerCamelCase :List[str] ):
return False if not is_flax_available() else _is_jax(__lowerCamelCase )
def A (__lowerCamelCase :Dict ):
if isinstance(__lowerCamelCase , (dict, UserDict) ):
return {k: to_py_obj(__lowerCamelCase ) for k, v in obj.items()}
elif isinstance(__lowerCamelCase , (list, tuple) ):
return [to_py_obj(__lowerCamelCase ) for o in obj]
elif is_tf_tensor(__lowerCamelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(__lowerCamelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(__lowerCamelCase ):
return np.asarray(__lowerCamelCase ).tolist()
elif isinstance(__lowerCamelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def A (__lowerCamelCase :Optional[int] ):
if isinstance(__lowerCamelCase , (dict, UserDict) ):
return {k: to_numpy(__lowerCamelCase ) for k, v in obj.items()}
elif isinstance(__lowerCamelCase , (list, tuple) ):
return np.array(__lowerCamelCase )
elif is_tf_tensor(__lowerCamelCase ):
return obj.numpy()
elif is_torch_tensor(__lowerCamelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(__lowerCamelCase ):
return np.asarray(__lowerCamelCase )
else:
return obj
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = fields(self )
# Safety and consistency checks
if not len(_lowercase ):
raise ValueError(F'{self.__class__.__name__} has no fields.' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(F'{self.__class__.__name__} should not have more than one required field.' )
_lowerCAmelCase = getattr(self , class_fields[0].name )
_lowerCAmelCase = all(getattr(self , field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(_lowercase ):
if isinstance(_lowercase , _lowercase ):
_lowerCAmelCase = first_field.items()
_lowerCAmelCase = True
else:
try:
_lowerCAmelCase = iter(_lowercase )
_lowerCAmelCase = True
except TypeError:
_lowerCAmelCase = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(_lowercase ):
if (
not isinstance(_lowercase , (list, tuple) )
or not len(_lowercase ) == 2
or not isinstance(element[0] , _lowercase )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
_lowerCAmelCase = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
F'Cannot set key/value for {element}. It needs to be a tuple (key, value).' )
break
setattr(self , element[0] , element[1] )
if element[1] is not None:
_lowerCAmelCase = element[1]
elif first_field is not None:
_lowerCAmelCase = first_field
else:
for field in class_fields:
_lowerCAmelCase = getattr(self , field.name )
if v is not None:
_lowerCAmelCase = v
def __delitem__( self , *_lowercase , **_lowercase ):
"""simple docstring"""
raise Exception(F'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.' )
def _lowercase ( self , *_lowercase , **_lowercase ):
"""simple docstring"""
raise Exception(F'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.' )
def _lowercase ( self , *_lowercase , **_lowercase ):
"""simple docstring"""
raise Exception(F'You cannot use ``pop`` on a {self.__class__.__name__} instance.' )
def _lowercase ( self , *_lowercase , **_lowercase ):
"""simple docstring"""
raise Exception(F'You cannot use ``update`` on a {self.__class__.__name__} instance.' )
def __getitem__( self , _lowercase ):
"""simple docstring"""
if isinstance(_lowercase , _lowercase ):
_lowerCAmelCase = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self , _lowercase , _lowercase ):
"""simple docstring"""
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(_lowercase , _lowercase )
super().__setattr__(_lowercase , _lowercase )
def __setitem__( self , _lowercase , _lowercase ):
"""simple docstring"""
super().__setitem__(_lowercase , _lowercase )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(_lowercase , _lowercase )
def _lowercase ( self ):
"""simple docstring"""
return tuple(self[k] for k in self.keys() )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@classmethod
def _lowercase ( cls , _lowercase ):
"""simple docstring"""
raise ValueError(
F'{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}' )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : int = '''longest'''
_lowercase : str = '''max_length'''
_lowercase : List[Any] = '''do_not_pad'''
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[Any] = '''pt'''
_lowercase : Optional[Any] = '''tf'''
_lowercase : List[str] = '''np'''
_lowercase : int = '''jax'''
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = context_managers
_lowerCAmelCase = ExitStack()
def __enter__( self ):
"""simple docstring"""
for context_manager in self.context_managers:
self.stack.enter_context(_lowercase )
def __exit__( self , *_lowercase , **_lowercase ):
"""simple docstring"""
self.stack.__exit__(*_lowercase , **_lowercase )
def A (__lowerCamelCase :str ):
_lowerCAmelCase = infer_framework(__lowerCamelCase )
if framework == "tf":
_lowerCAmelCase = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_lowerCAmelCase = inspect.signature(model_class.forward ) # PyTorch models
else:
_lowerCAmelCase = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def A (__lowerCamelCase :List[str] ):
_lowerCAmelCase = model_class.__name__
_lowerCAmelCase = infer_framework(__lowerCamelCase )
if framework == "tf":
_lowerCAmelCase = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_lowerCAmelCase = inspect.signature(model_class.forward ) # PyTorch models
else:
_lowerCAmelCase = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def A (__lowerCamelCase :MutableMapping , __lowerCamelCase :str = "" , __lowerCamelCase :str = "." ):
def _flatten_dict(__lowerCamelCase :Optional[Any] , __lowerCamelCase :Optional[int]="" , __lowerCamelCase :Dict="." ):
for k, v in d.items():
_lowerCAmelCase = str(__lowerCamelCase ) + delimiter + str(__lowerCamelCase ) if parent_key else k
if v and isinstance(__lowerCamelCase , __lowerCamelCase ):
yield from flatten_dict(__lowerCamelCase , __lowerCamelCase , delimiter=__lowerCamelCase ).items()
else:
yield key, v
return dict(_flatten_dict(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) )
@contextmanager
def A (__lowerCamelCase :Any , __lowerCamelCase :bool = False ):
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def A (__lowerCamelCase :Dict , __lowerCamelCase :List[str]=None ):
if is_numpy_array(__lowerCamelCase ):
return np.transpose(__lowerCamelCase , axes=__lowerCamelCase )
elif is_torch_tensor(__lowerCamelCase ):
return array.T if axes is None else array.permute(*__lowerCamelCase )
elif is_tf_tensor(__lowerCamelCase ):
import tensorflow as tf
return tf.transpose(__lowerCamelCase , perm=__lowerCamelCase )
elif is_jax_tensor(__lowerCamelCase ):
return jnp.transpose(__lowerCamelCase , axes=__lowerCamelCase )
else:
raise ValueError(f'Type not supported for transpose: {type(__lowerCamelCase )}.' )
def A (__lowerCamelCase :Tuple , __lowerCamelCase :List[Any] ):
if is_numpy_array(__lowerCamelCase ):
return np.reshape(__lowerCamelCase , __lowerCamelCase )
elif is_torch_tensor(__lowerCamelCase ):
return array.reshape(*__lowerCamelCase )
elif is_tf_tensor(__lowerCamelCase ):
import tensorflow as tf
return tf.reshape(__lowerCamelCase , __lowerCamelCase )
elif is_jax_tensor(__lowerCamelCase ):
return jnp.reshape(__lowerCamelCase , __lowerCamelCase )
else:
raise ValueError(f'Type not supported for reshape: {type(__lowerCamelCase )}.' )
def A (__lowerCamelCase :str , __lowerCamelCase :Any=None ):
if is_numpy_array(__lowerCamelCase ):
return np.squeeze(__lowerCamelCase , axis=__lowerCamelCase )
elif is_torch_tensor(__lowerCamelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=__lowerCamelCase )
elif is_tf_tensor(__lowerCamelCase ):
import tensorflow as tf
return tf.squeeze(__lowerCamelCase , axis=__lowerCamelCase )
elif is_jax_tensor(__lowerCamelCase ):
return jnp.squeeze(__lowerCamelCase , axis=__lowerCamelCase )
else:
raise ValueError(f'Type not supported for squeeze: {type(__lowerCamelCase )}.' )
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Dict ):
if is_numpy_array(__lowerCamelCase ):
return np.expand_dims(__lowerCamelCase , __lowerCamelCase )
elif is_torch_tensor(__lowerCamelCase ):
return array.unsqueeze(dim=__lowerCamelCase )
elif is_tf_tensor(__lowerCamelCase ):
import tensorflow as tf
return tf.expand_dims(__lowerCamelCase , axis=__lowerCamelCase )
elif is_jax_tensor(__lowerCamelCase ):
return jnp.expand_dims(__lowerCamelCase , axis=__lowerCamelCase )
else:
raise ValueError(f'Type not supported for expand_dims: {type(__lowerCamelCase )}.' )
def A (__lowerCamelCase :Optional[Any] ):
if is_numpy_array(__lowerCamelCase ):
return np.size(__lowerCamelCase )
elif is_torch_tensor(__lowerCamelCase ):
return array.numel()
elif is_tf_tensor(__lowerCamelCase ):
import tensorflow as tf
return tf.size(__lowerCamelCase )
elif is_jax_tensor(__lowerCamelCase ):
return array.size
else:
raise ValueError(f'Type not supported for expand_dims: {type(__lowerCamelCase )}.' )
def A (__lowerCamelCase :List[Any] , __lowerCamelCase :Optional[int] ):
for key, value in auto_map.items():
if isinstance(__lowerCamelCase , (tuple, list) ):
_lowerCAmelCase = [f'{repo_id}--{v}' if (v is not None and """--""" not in v) else v for v in value]
elif value is not None and "--" not in value:
_lowerCAmelCase = f'{repo_id}--{value}'
return auto_map
def A (__lowerCamelCase :Tuple ):
for base_class in inspect.getmro(__lowerCamelCase ):
_lowerCAmelCase = base_class.__module__
_lowerCAmelCase = base_class.__name__
if module.startswith("""tensorflow""" ) or module.startswith("""keras""" ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith("""torch""" ) or name == "PreTrainedModel":
return "pt"
elif module.startswith("""flax""" ) or module.startswith("""jax""" ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(f'Could not infer framework from class {model_class}.' )
| 5 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""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""",
"""adapter_layer""": """encoder.layers.*.adapter_layer""",
"""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""",
"""pooling_layer.linear""": """projector""",
"""pooling_layer.projection""": """classifier""",
}
_lowercase = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""projector""",
"""classifier""",
]
def A (__lowerCamelCase :Optional[int] ):
_lowerCAmelCase = {}
with open(__lowerCamelCase , """r""" ) as file:
for line_number, line in enumerate(__lowerCamelCase ):
_lowerCAmelCase = line.strip()
if line:
_lowerCAmelCase = line.split()
_lowerCAmelCase = line_number
_lowerCAmelCase = words[0]
_lowerCAmelCase = value
return result
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :Any , __lowerCamelCase :Tuple , __lowerCamelCase :List[Any] , __lowerCamelCase :List[str] ):
for attribute in key.split(""".""" ):
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(__lowerCamelCase ):
_lowerCAmelCase = PARAM_MAPPING[full_name.split(""".""" )[-1]]
_lowerCAmelCase = """param"""
if weight_type is not None and weight_type != "param":
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase ).shape
elif weight_type is not None and weight_type == "param":
_lowerCAmelCase = hf_pointer
for attribute in hf_param_name.split(""".""" ):
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = shape_pointer.shape
# let's reduce dimension
_lowerCAmelCase = value[0]
else:
_lowerCAmelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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":
_lowerCAmelCase = value
elif weight_type == "weight_g":
_lowerCAmelCase = value
elif weight_type == "weight_v":
_lowerCAmelCase = value
elif weight_type == "bias":
_lowerCAmelCase = value
elif weight_type == "param":
for attribute in hf_param_name.split(""".""" ):
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = value
else:
_lowerCAmelCase = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Tuple , __lowerCamelCase :Dict , __lowerCamelCase :List[Any] , __lowerCamelCase :int ):
_lowerCAmelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(__lowerCamelCase ):
_lowerCAmelCase = PARAM_MAPPING[full_name.split(""".""" )[-1]]
_lowerCAmelCase = """param"""
if weight_type is not None and weight_type != "param":
_lowerCAmelCase = """.""".join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
_lowerCAmelCase = """.""".join([key, hf_param_name] )
else:
_lowerCAmelCase = key
_lowerCAmelCase = value if """lm_head""" in full_key else value[0]
_lowercase = {
"""W_a""": """linear_1.weight""",
"""W_b""": """linear_2.weight""",
"""b_a""": """linear_1.bias""",
"""b_b""": """linear_2.bias""",
"""ln_W""": """norm.weight""",
"""ln_b""": """norm.bias""",
}
def A (__lowerCamelCase :Any , __lowerCamelCase :int , __lowerCamelCase :List[str]=None , __lowerCamelCase :List[Any]=None ):
_lowerCAmelCase = False
for key, mapped_key in MAPPING.items():
_lowerCAmelCase = """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
_lowerCAmelCase = True
if "*" in mapped_key:
_lowerCAmelCase = name.split(__lowerCamelCase )[0].split(""".""" )[-2]
_lowerCAmelCase = mapped_key.replace("""*""" , __lowerCamelCase )
if "weight_g" in name:
_lowerCAmelCase = """weight_g"""
elif "weight_v" in name:
_lowerCAmelCase = """weight_v"""
elif "bias" in name:
_lowerCAmelCase = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_lowerCAmelCase = """weight"""
else:
_lowerCAmelCase = None
if hf_dict is not None:
rename_dict(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
else:
set_recursively(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return is_used
return is_used
def A (__lowerCamelCase :Any , __lowerCamelCase :Dict , __lowerCamelCase :Dict ):
_lowerCAmelCase = []
_lowerCAmelCase = fairseq_model.state_dict()
_lowerCAmelCase = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
_lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , hf_model.config.feat_extract_norm == """group""" , )
_lowerCAmelCase = True
else:
_lowerCAmelCase = load_wavaveca_layer(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
if not is_used:
unused_weights.append(__lowerCamelCase )
logger.warning(f'Unused weights: {unused_weights}' )
def A (__lowerCamelCase :Tuple , __lowerCamelCase :Optional[int] , __lowerCamelCase :Any , __lowerCamelCase :List[Any] , __lowerCamelCase :List[Any] ):
_lowerCAmelCase = full_name.split("""conv_layers.""" )[-1]
_lowerCAmelCase = name.split(""".""" )
_lowerCAmelCase = int(items[0] )
_lowerCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
_lowerCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
_lowerCAmelCase = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.' )
_lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.' )
_lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(__lowerCamelCase )
@torch.no_grad()
def A (__lowerCamelCase :List[str] , __lowerCamelCase :Tuple , __lowerCamelCase :List[Any]=None , __lowerCamelCase :Union[str, Any]=None , __lowerCamelCase :str=True , __lowerCamelCase :str=False ):
if config_path is not None:
_lowerCAmelCase = WavaVecaConfig.from_pretrained(__lowerCamelCase )
else:
_lowerCAmelCase = WavaVecaConfig()
if is_seq_class:
_lowerCAmelCase = read_txt_into_dict(__lowerCamelCase )
_lowerCAmelCase = idalabel
_lowerCAmelCase = WavaVecaForSequenceClassification(__lowerCamelCase )
_lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__lowerCamelCase , return_attention_mask=__lowerCamelCase , )
feature_extractor.save_pretrained(__lowerCamelCase )
elif is_finetuned:
if dict_path:
_lowerCAmelCase = Dictionary.load(__lowerCamelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_lowerCAmelCase = target_dict.pad_index
_lowerCAmelCase = target_dict.bos_index
_lowerCAmelCase = target_dict.eos_index
_lowerCAmelCase = len(target_dict.symbols )
_lowerCAmelCase = os.path.join(__lowerCamelCase , """vocab.json""" )
if not os.path.isdir(__lowerCamelCase ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__lowerCamelCase ) )
return
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
_lowerCAmelCase = target_dict.indices
# fairseq has the <pad> and <s> switched
_lowerCAmelCase = 0
_lowerCAmelCase = 1
with open(__lowerCamelCase , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = WavaVecaCTCTokenizer(
__lowerCamelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=__lowerCamelCase , )
_lowerCAmelCase = True if config.feat_extract_norm == """layer""" else False
_lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__lowerCamelCase , return_attention_mask=__lowerCamelCase , )
_lowerCAmelCase = WavaVecaProcessor(feature_extractor=__lowerCamelCase , tokenizer=__lowerCamelCase )
processor.save_pretrained(__lowerCamelCase )
_lowerCAmelCase = WavaVecaForCTC(__lowerCamelCase )
else:
_lowerCAmelCase = WavaVecaForPreTraining(__lowerCamelCase )
if is_finetuned or is_seq_class:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
_lowerCAmelCase = argparse.Namespace(task="""audio_pretraining""" )
_lowerCAmelCase = fairseq.tasks.setup_task(__lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=__lowerCamelCase )
_lowerCAmelCase = model[0].eval()
recursively_load_weights(__lowerCamelCase , __lowerCamelCase , not is_finetuned )
hf_wavavec.save_pretrained(__lowerCamelCase )
if __name__ == "__main__":
_lowercase = 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_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
parser.add_argument(
"""--is_seq_class""",
action="""store_true""",
help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
)
_lowercase = parser.parse_args()
_lowercase = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 5 | 1 |
'''simple docstring'''
def A (__lowerCamelCase :float , __lowerCamelCase :list[float] ):
if discount_rate < 0:
raise ValueError("""Discount rate cannot be negative""" )
if not cash_flows:
raise ValueError("""Cash flows list cannot be empty""" )
_lowerCAmelCase = sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(__lowerCamelCase ) )
return round(__lowerCamelCase , ndigits=2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[str] = '''decision_transformer'''
_lowercase : Optional[Any] = ['''past_key_values''']
_lowercase : str = {
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , _lowercase=17 , _lowercase=4 , _lowercase=128 , _lowercase=4_096 , _lowercase=True , _lowercase=1 , _lowercase=1_024 , _lowercase=3 , _lowercase=1 , _lowercase=None , _lowercase="relu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=0.1 , _lowercase=1e-5 , _lowercase=0.02 , _lowercase=True , _lowercase=True , _lowercase=50_256 , _lowercase=50_256 , _lowercase=False , _lowercase=False , **_lowercase , ):
"""simple docstring"""
_lowerCAmelCase = state_dim
_lowerCAmelCase = act_dim
_lowerCAmelCase = hidden_size
_lowerCAmelCase = max_ep_len
_lowerCAmelCase = action_tanh
_lowerCAmelCase = vocab_size
_lowerCAmelCase = n_positions
_lowerCAmelCase = n_layer
_lowerCAmelCase = n_head
_lowerCAmelCase = n_inner
_lowerCAmelCase = activation_function
_lowerCAmelCase = resid_pdrop
_lowerCAmelCase = embd_pdrop
_lowerCAmelCase = attn_pdrop
_lowerCAmelCase = layer_norm_epsilon
_lowerCAmelCase = initializer_range
_lowerCAmelCase = scale_attn_weights
_lowerCAmelCase = use_cache
_lowerCAmelCase = scale_attn_by_inverse_layer_idx
_lowerCAmelCase = reorder_and_upcast_attn
_lowerCAmelCase = bos_token_id
_lowerCAmelCase = eos_token_id
super().__init__(bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase )
| 5 | 1 |
'''simple docstring'''
import argparse
import struct
import unittest
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = data
# Initialize hash values
_lowerCAmelCase = [
0x6_A_0_9_E_6_6_7,
0xB_B_6_7_A_E_8_5,
0x3_C_6_E_F_3_7_2,
0xA_5_4_F_F_5_3_A,
0x5_1_0_E_5_2_7_F,
0x9_B_0_5_6_8_8_C,
0x1_F_8_3_D_9_A_B,
0x5_B_E_0_C_D_1_9,
]
# Initialize round constants
_lowerCAmelCase = [
0x4_2_8_A_2_F_9_8,
0x7_1_3_7_4_4_9_1,
0xB_5_C_0_F_B_C_F,
0xE_9_B_5_D_B_A_5,
0x3_9_5_6_C_2_5_B,
0x5_9_F_1_1_1_F_1,
0x9_2_3_F_8_2_A_4,
0xA_B_1_C_5_E_D_5,
0xD_8_0_7_A_A_9_8,
0x1_2_8_3_5_B_0_1,
0x2_4_3_1_8_5_B_E,
0x5_5_0_C_7_D_C_3,
0x7_2_B_E_5_D_7_4,
0x8_0_D_E_B_1_F_E,
0x9_B_D_C_0_6_A_7,
0xC_1_9_B_F_1_7_4,
0xE_4_9_B_6_9_C_1,
0xE_F_B_E_4_7_8_6,
0x0_F_C_1_9_D_C_6,
0x2_4_0_C_A_1_C_C,
0x2_D_E_9_2_C_6_F,
0x4_A_7_4_8_4_A_A,
0x5_C_B_0_A_9_D_C,
0x7_6_F_9_8_8_D_A,
0x9_8_3_E_5_1_5_2,
0xA_8_3_1_C_6_6_D,
0xB_0_0_3_2_7_C_8,
0xB_F_5_9_7_F_C_7,
0xC_6_E_0_0_B_F_3,
0xD_5_A_7_9_1_4_7,
0x0_6_C_A_6_3_5_1,
0x1_4_2_9_2_9_6_7,
0x2_7_B_7_0_A_8_5,
0x2_E_1_B_2_1_3_8,
0x4_D_2_C_6_D_F_C,
0x5_3_3_8_0_D_1_3,
0x6_5_0_A_7_3_5_4,
0x7_6_6_A_0_A_B_B,
0x8_1_C_2_C_9_2_E,
0x9_2_7_2_2_C_8_5,
0xA_2_B_F_E_8_A_1,
0xA_8_1_A_6_6_4_B,
0xC_2_4_B_8_B_7_0,
0xC_7_6_C_5_1_A_3,
0xD_1_9_2_E_8_1_9,
0xD_6_9_9_0_6_2_4,
0xF_4_0_E_3_5_8_5,
0x1_0_6_A_A_0_7_0,
0x1_9_A_4_C_1_1_6,
0x1_E_3_7_6_C_0_8,
0x2_7_4_8_7_7_4_C,
0x3_4_B_0_B_C_B_5,
0x3_9_1_C_0_C_B_3,
0x4_E_D_8_A_A_4_A,
0x5_B_9_C_C_A_4_F,
0x6_8_2_E_6_F_F_3,
0x7_4_8_F_8_2_E_E,
0x7_8_A_5_6_3_6_F,
0x8_4_C_8_7_8_1_4,
0x8_C_C_7_0_2_0_8,
0x9_0_B_E_F_F_F_A,
0xA_4_5_0_6_C_E_B,
0xB_E_F_9_A_3_F_7,
0xC_6_7_1_7_8_F_2,
]
_lowerCAmelCase = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def _lowercase ( _lowercase ):
"""simple docstring"""
_lowerCAmelCase = B"""\x80""" + (B"""\x00""" * (63 - (len(_lowercase ) + 8) % 64))
_lowerCAmelCase = struct.pack(""">Q""" , (len(_lowercase ) * 8) )
return data + padding + big_endian_integer
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = [
self.preprocessed_data[x : x + 64]
for x in range(0 , len(self.preprocessed_data ) , 64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
_lowerCAmelCase = list(struct.unpack(""">16L""" , _lowercase ) )
# add 48 0-ed integers
words += [0] * 48
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
_lowerCAmelCase = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
_lowerCAmelCase = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
_lowerCAmelCase = (
words[index - 16] + sa + words[index - 7] + sa
) % 0x1_0_0_0_0_0_0_0_0
# Compression
_lowerCAmelCase = self.ror(_lowercase , 6 ) ^ self.ror(_lowercase , 11 ) ^ self.ror(_lowercase , 25 )
_lowerCAmelCase = (e & f) ^ ((~e & 0xF_F_F_F_F_F_F_F) & g)
_lowerCAmelCase = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0x1_0_0_0_0_0_0_0_0
_lowerCAmelCase = self.ror(_lowercase , 2 ) ^ self.ror(_lowercase , 13 ) ^ self.ror(_lowercase , 22 )
_lowerCAmelCase = (a & b) ^ (a & c) ^ (b & c)
_lowerCAmelCase = (sa + maj) % 0x1_0_0_0_0_0_0_0_0
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = (
g,
f,
e,
((d + tempa) % 0x1_0_0_0_0_0_0_0_0),
c,
b,
a,
((tempa + tempa) % 0x1_0_0_0_0_0_0_0_0),
)
_lowerCAmelCase = [a, b, c, d, e, f, g, h]
# Modify final values
_lowerCAmelCase = [
((element + mutated_hash_values[index]) % 0x1_0_0_0_0_0_0_0_0)
for index, element in enumerate(self.hashes )
]
_lowerCAmelCase = """""".join([hex(_lowercase )[2:].zfill(8 ) for value in self.hashes] )
def _lowercase ( self , _lowercase , _lowercase ):
"""simple docstring"""
return 0xF_F_F_F_F_F_F_F & (value << (32 - rotations)) | (value >> rotations)
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
import hashlib
_lowerCAmelCase = bytes("""Test String""" , """utf-8""" )
self.assertEqual(SHAaaa(_lowercase ).hash , hashlib.shaaaa(_lowercase ).hexdigest() )
def A ():
import doctest
doctest.testmod()
_lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""-s""" , """--string""" , dest="""input_string""" , default="""Hello World!! Welcome to Cryptography""" , help="""Hash the string""" , )
parser.add_argument(
"""-f""" , """--file""" , dest="""input_file""" , help="""Hash contents of a file""" )
_lowerCAmelCase = parser.parse_args()
_lowerCAmelCase = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , """rb""" ) as f:
_lowerCAmelCase = f.read()
else:
_lowerCAmelCase = bytes(__lowerCamelCase , """utf-8""" )
print(SHAaaa(__lowerCamelCase ).hash )
if __name__ == "__main__":
main()
| 5 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
"""The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"""
)
_lowercase = None
_lowercase = {
"""7B""": 11008,
"""13B""": 13824,
"""30B""": 17920,
"""65B""": 22016,
"""70B""": 28672,
}
_lowercase = {
"""7B""": 1,
"""7Bf""": 1,
"""13B""": 2,
"""13Bf""": 2,
"""30B""": 4,
"""65B""": 8,
"""70B""": 8,
"""70Bf""": 8,
}
def A (__lowerCamelCase :int , __lowerCamelCase :Optional[Any]=1 , __lowerCamelCase :List[Any]=256 ):
return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of)
def A (__lowerCamelCase :Any ):
with open(__lowerCamelCase , """r""" ) as f:
return json.load(__lowerCamelCase )
def A (__lowerCamelCase :List[Any] , __lowerCamelCase :int ):
with open(__lowerCamelCase , """w""" ) as f:
json.dump(__lowerCamelCase , __lowerCamelCase )
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :Tuple , __lowerCamelCase :Optional[Any] , __lowerCamelCase :Tuple=True ):
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
_lowerCAmelCase = os.path.join(__lowerCamelCase , """tmp""" )
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
_lowerCAmelCase = read_json(os.path.join(__lowerCamelCase , """params.json""" ) )
_lowerCAmelCase = NUM_SHARDS[model_size]
_lowerCAmelCase = params["""n_layers"""]
_lowerCAmelCase = params["""n_heads"""]
_lowerCAmelCase = n_heads // num_shards
_lowerCAmelCase = params["""dim"""]
_lowerCAmelCase = dim // n_heads
_lowerCAmelCase = 10_000.0
_lowerCAmelCase = 1.0 / (base ** (torch.arange(0 , __lowerCamelCase , 2 ).float() / dims_per_head))
if "n_kv_heads" in params:
_lowerCAmelCase = params["""n_kv_heads"""] # for GQA / MQA
_lowerCAmelCase = n_heads_per_shard // num_key_value_heads
_lowerCAmelCase = dim // num_key_value_heads
else: # compatibility with other checkpoints
_lowerCAmelCase = n_heads
_lowerCAmelCase = n_heads_per_shard
_lowerCAmelCase = dim
# permute for sliced rotary
def permute(__lowerCamelCase :Optional[int] , __lowerCamelCase :str=n_heads , __lowerCamelCase :str=dim , __lowerCamelCase :List[Any]=dim ):
return w.view(__lowerCamelCase , dima // n_heads // 2 , 2 , __lowerCamelCase ).transpose(1 , 2 ).reshape(__lowerCamelCase , __lowerCamelCase )
print(f'Fetching all parameters from the checkpoint at {input_base_path}.' )
# Load weights
if model_size == "7B":
# Not sharded
# (The sharded implementation would also work, but this is simpler.)
_lowerCAmelCase = torch.load(os.path.join(__lowerCamelCase , """consolidated.00.pth""" ) , map_location="""cpu""" )
else:
# Sharded
_lowerCAmelCase = [
torch.load(os.path.join(__lowerCamelCase , f'consolidated.{i:02d}.pth' ) , map_location="""cpu""" )
for i in range(__lowerCamelCase )
]
_lowerCAmelCase = 0
_lowerCAmelCase = {"""weight_map""": {}}
for layer_i in range(__lowerCamelCase ):
_lowerCAmelCase = f'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
if model_size == "7B":
# Unsharded
_lowerCAmelCase = {
f'model.layers.{layer_i}.self_attn.q_proj.weight': permute(
loaded[f'layers.{layer_i}.attention.wq.weight'] ),
f'model.layers.{layer_i}.self_attn.k_proj.weight': permute(
loaded[f'layers.{layer_i}.attention.wk.weight'] ),
f'model.layers.{layer_i}.self_attn.v_proj.weight': loaded[f'layers.{layer_i}.attention.wv.weight'],
f'model.layers.{layer_i}.self_attn.o_proj.weight': loaded[f'layers.{layer_i}.attention.wo.weight'],
f'model.layers.{layer_i}.mlp.gate_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w1.weight'],
f'model.layers.{layer_i}.mlp.down_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w2.weight'],
f'model.layers.{layer_i}.mlp.up_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w3.weight'],
f'model.layers.{layer_i}.input_layernorm.weight': loaded[f'layers.{layer_i}.attention_norm.weight'],
f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[f'layers.{layer_i}.ffn_norm.weight'],
}
else:
# Sharded
# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
_lowerCAmelCase = {
f'model.layers.{layer_i}.input_layernorm.weight': loaded[0][
f'layers.{layer_i}.attention_norm.weight'
].clone(),
f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[0][
f'layers.{layer_i}.ffn_norm.weight'
].clone(),
}
_lowerCAmelCase = permute(
torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wq.weight'].view(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for i in range(__lowerCamelCase )
] , dim=0 , ).reshape(__lowerCamelCase , __lowerCamelCase ) )
_lowerCAmelCase = permute(
torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wk.weight'].view(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for i in range(__lowerCamelCase )
] , dim=0 , ).reshape(__lowerCamelCase , __lowerCamelCase ) , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , )
_lowerCAmelCase = torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wv.weight'].view(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for i in range(__lowerCamelCase )
] , dim=0 , ).reshape(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.attention.wo.weight'] for i in range(__lowerCamelCase )] , dim=1 )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w1.weight'] for i in range(__lowerCamelCase )] , dim=0 )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w2.weight'] for i in range(__lowerCamelCase )] , dim=1 )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w3.weight'] for i in range(__lowerCamelCase )] , dim=0 )
_lowerCAmelCase = inv_freq
for k, v in state_dict.items():
_lowerCAmelCase = filename
param_count += v.numel()
torch.save(__lowerCamelCase , os.path.join(__lowerCamelCase , __lowerCamelCase ) )
_lowerCAmelCase = f'pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'
if model_size == "7B":
# Unsharded
_lowerCAmelCase = {
"""model.embed_tokens.weight""": loaded["""tok_embeddings.weight"""],
"""model.norm.weight""": loaded["""norm.weight"""],
"""lm_head.weight""": loaded["""output.weight"""],
}
else:
_lowerCAmelCase = {
"""model.norm.weight""": loaded[0]["""norm.weight"""],
"""model.embed_tokens.weight""": torch.cat(
[loaded[i]["""tok_embeddings.weight"""] for i in range(__lowerCamelCase )] , dim=1 ),
"""lm_head.weight""": torch.cat([loaded[i]["""output.weight"""] for i in range(__lowerCamelCase )] , dim=0 ),
}
for k, v in state_dict.items():
_lowerCAmelCase = filename
param_count += v.numel()
torch.save(__lowerCamelCase , os.path.join(__lowerCamelCase , __lowerCamelCase ) )
# Write configs
_lowerCAmelCase = {"""total_size""": param_count * 2}
write_json(__lowerCamelCase , os.path.join(__lowerCamelCase , """pytorch_model.bin.index.json""" ) )
_lowerCAmelCase = params["""ffn_dim_multiplier"""] if """ffn_dim_multiplier""" in params else 1
_lowerCAmelCase = params["""multiple_of"""] if """multiple_of""" in params else 256
_lowerCAmelCase = LlamaConfig(
hidden_size=__lowerCamelCase , intermediate_size=compute_intermediate_size(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) , num_attention_heads=params["""n_heads"""] , num_hidden_layers=params["""n_layers"""] , rms_norm_eps=params["""norm_eps"""] , num_key_value_heads=__lowerCamelCase , )
config.save_pretrained(__lowerCamelCase )
# Make space so we can load the model properly now.
del state_dict
del loaded
gc.collect()
print("""Loading the checkpoint in a Llama model.""" )
_lowerCAmelCase = LlamaForCausalLM.from_pretrained(__lowerCamelCase , torch_dtype=torch.floataa , low_cpu_mem_usage=__lowerCamelCase )
# Avoid saving this as part of the config.
del model.config._name_or_path
print("""Saving in the Transformers format.""" )
model.save_pretrained(__lowerCamelCase , safe_serialization=__lowerCamelCase )
shutil.rmtree(__lowerCamelCase )
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Union[str, Any] ):
# Initialize the tokenizer based on the `spm` model
_lowerCAmelCase = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
print(f'Saving a {tokenizer_class.__name__} to {tokenizer_path}.' )
_lowerCAmelCase = tokenizer_class(__lowerCamelCase )
tokenizer.save_pretrained(__lowerCamelCase )
def A ():
_lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--input_dir""" , help="""Location of LLaMA weights, which contains tokenizer.model and model folders""" , )
parser.add_argument(
"""--model_size""" , choices=["""7B""", """7Bf""", """13B""", """13Bf""", """30B""", """65B""", """70B""", """70Bf""", """tokenizer_only"""] , )
parser.add_argument(
"""--output_dir""" , help="""Location to write HF model and tokenizer""" , )
parser.add_argument("""--safe_serialization""" , type=__lowerCamelCase , help="""Whether or not to save using `safetensors`.""" )
_lowerCAmelCase = parser.parse_args()
if args.model_size != "tokenizer_only":
write_model(
model_path=args.output_dir , input_base_path=os.path.join(args.input_dir , args.model_size ) , model_size=args.model_size , safe_serialization=args.safe_serialization , )
_lowerCAmelCase = os.path.join(args.input_dir , """tokenizer.model""" )
write_tokenizer(args.output_dir , __lowerCamelCase )
if __name__ == "__main__":
main()
| 5 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_lowercase = logging.get_logger(__name__)
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Any = ['''pixel_values''']
def __init__( self , _lowercase = True , _lowercase = 32 , _lowercase=PILImageResampling.BILINEAR , _lowercase = True , **_lowercase , ):
"""simple docstring"""
_lowerCAmelCase = do_resize
_lowerCAmelCase = do_rescale
_lowerCAmelCase = size_divisor
_lowerCAmelCase = resample
super().__init__(**_lowercase )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase = None , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = get_image_size(_lowercase )
# Rounds the height and width down to the closest multiple of size_divisor
_lowerCAmelCase = height // size_divisor * size_divisor
_lowerCAmelCase = width // size_divisor * size_divisor
_lowerCAmelCase = resize(_lowercase , (new_h, new_w) , resample=_lowercase , data_format=_lowercase , **_lowercase )
return image
def _lowercase ( self , _lowercase , _lowercase , _lowercase = None , **_lowercase ):
"""simple docstring"""
return rescale(image=_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase )
def _lowercase ( self , _lowercase , _lowercase = None , _lowercase = None , _lowercase=None , _lowercase = None , _lowercase = None , _lowercase = ChannelDimension.FIRST , **_lowercase , ):
"""simple docstring"""
_lowerCAmelCase = do_resize if do_resize is not None else self.do_resize
_lowerCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
_lowerCAmelCase = size_divisor if size_divisor is not None else self.size_divisor
_lowerCAmelCase = resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError("""size_divisor is required for resizing""" )
_lowerCAmelCase = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
raise ValueError("""Invalid image(s)""" )
# All transformations expect numpy arrays.
_lowerCAmelCase = [to_numpy_array(_lowercase ) for img in images]
if do_resize:
_lowerCAmelCase = [self.resize(_lowercase , size_divisor=_lowercase , resample=_lowercase ) for image in images]
if do_rescale:
_lowerCAmelCase = [self.rescale(_lowercase , scale=1 / 255 ) for image in images]
_lowerCAmelCase = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
_lowerCAmelCase = {"""pixel_values""": images}
return BatchFeature(data=_lowercase , tensor_type=_lowercase )
| 5 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Tuple = (DDPMScheduler,)
def _lowercase ( self , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_lowercase )
return config
def _lowercase ( self ):
"""simple docstring"""
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_lowercase )
def _lowercase ( self ):
"""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=_lowercase , beta_end=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
self.check_over_configs(thresholding=_lowercase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_lowercase , prediction_type=_lowercase , sample_max_value=_lowercase , )
def _lowercase ( self ):
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
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 _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = len(_lowercase )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter
_lowerCAmelCase = torch.manual_seed(0 )
for t in reversed(range(_lowercase ) ):
# 1. predict noise residual
_lowerCAmelCase = model(_lowercase , _lowercase )
# 2. predict previous mean of sample x_t-1
_lowerCAmelCase = scheduler.step(_lowercase , _lowercase , _lowercase , generator=_lowercase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_lowerCAmelCase = pred_prev_sample
_lowerCAmelCase = torch.sum(torch.abs(_lowercase ) )
_lowerCAmelCase = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 258.9606 ) < 1e-2
assert abs(result_mean.item() - 0.3372 ) < 1e-3
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config(prediction_type="""v_prediction""" )
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = len(_lowercase )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter
_lowerCAmelCase = torch.manual_seed(0 )
for t in reversed(range(_lowercase ) ):
# 1. predict noise residual
_lowerCAmelCase = model(_lowercase , _lowercase )
# 2. predict previous mean of sample x_t-1
_lowerCAmelCase = scheduler.step(_lowercase , _lowercase , _lowercase , generator=_lowercase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_lowerCAmelCase = pred_prev_sample
_lowerCAmelCase = torch.sum(torch.abs(_lowercase ) )
_lowerCAmelCase = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 202.0296 ) < 1e-2
assert abs(result_mean.item() - 0.2631 ) < 1e-3
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_lowercase )
_lowerCAmelCase = scheduler.timesteps
for i, timestep in enumerate(_lowercase ):
if i == len(_lowercase ) - 1:
_lowerCAmelCase = -1
else:
_lowerCAmelCase = timesteps[i + 1]
_lowerCAmelCase = scheduler.previous_timestep(_lowercase )
_lowerCAmelCase = prev_t.item()
self.assertEqual(_lowercase , _lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 51, 0]
with self.assertRaises(_lowercase , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 1, 0]
_lowerCAmelCase = len(_lowercase )
with self.assertRaises(_lowercase , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_lowercase , timesteps=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_lowercase , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_lowercase )
| 5 | 1 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
_lowercase = re.compile(R"""\s+""")
def A (__lowerCamelCase :Optional[int] ):
return {"hash": hashlib.mda(re.sub(__lowerCamelCase , """""" , example["""content"""] ).encode("""utf-8""" ) ).hexdigest()}
def A (__lowerCamelCase :Optional[Any] ):
_lowerCAmelCase = [len(__lowerCamelCase ) for line in example["""content"""].splitlines()]
return {"line_mean": np.mean(__lowerCamelCase ), "line_max": max(__lowerCamelCase )}
def A (__lowerCamelCase :Tuple ):
_lowerCAmelCase = np.mean([c.isalnum() for c in example["""content"""]] )
return {"alpha_frac": alpha_frac}
def A (__lowerCamelCase :Dict , __lowerCamelCase :str ):
if example["hash"] in uniques:
uniques.remove(example["""hash"""] )
return True
else:
return False
def A (__lowerCamelCase :Tuple , __lowerCamelCase :Optional[Any]=5 ):
_lowerCAmelCase = ["""auto-generated""", """autogenerated""", """automatically generated"""]
_lowerCAmelCase = example["""content"""].splitlines()
for _, line in zip(range(__lowerCamelCase ) , __lowerCamelCase ):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def A (__lowerCamelCase :Tuple , __lowerCamelCase :Union[str, Any]=5 , __lowerCamelCase :Dict=0.05 ):
_lowerCAmelCase = ["""unit tests""", """test file""", """configuration file"""]
_lowerCAmelCase = example["""content"""].splitlines()
_lowerCAmelCase = 0
_lowerCAmelCase = 0
# first test
for _, line in zip(range(__lowerCamelCase ) , __lowerCamelCase ):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
_lowerCAmelCase = example["""content"""].count("""\n""" )
_lowerCAmelCase = int(coeff * nlines )
for line in lines:
count_config += line.lower().count("""config""" )
count_test += line.lower().count("""test""" )
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def A (__lowerCamelCase :Tuple ):
_lowerCAmelCase = ["""def """, """class """, """for """, """while """]
_lowerCAmelCase = example["""content"""].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :int=4 ):
_lowerCAmelCase = example["""content"""].splitlines()
_lowerCAmelCase = 0
for line in lines:
counter += line.lower().count("""=""" )
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def A (__lowerCamelCase :Tuple ):
_lowerCAmelCase = tokenizer(example["""content"""] , truncation=__lowerCamelCase )["""input_ids"""]
_lowerCAmelCase = len(example["""content"""] ) / len(__lowerCamelCase )
return {"ratio": ratio}
def A (__lowerCamelCase :Any ):
_lowerCAmelCase = {}
results.update(get_hash(__lowerCamelCase ) )
results.update(line_stats(__lowerCamelCase ) )
results.update(alpha_stats(__lowerCamelCase ) )
results.update(char_token_ratio(__lowerCamelCase ) )
results.update(is_autogenerated(__lowerCamelCase ) )
results.update(is_config_or_test(__lowerCamelCase ) )
results.update(has_no_keywords(__lowerCamelCase ) )
results.update(has_few_assignments(__lowerCamelCase ) )
return results
def A (__lowerCamelCase :List[Any] , __lowerCamelCase :int , __lowerCamelCase :List[Any] ):
if not check_uniques(__lowerCamelCase , __lowerCamelCase ):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def A (__lowerCamelCase :int ):
with open(__lowerCamelCase , """rb""" ) as f_in:
with gzip.open(str(__lowerCamelCase ) + """.gz""" , """wb""" , compresslevel=6 ) as f_out:
shutil.copyfileobj(__lowerCamelCase , __lowerCamelCase )
os.unlink(__lowerCamelCase )
# Settings
_lowercase = HfArgumentParser(PreprocessingArguments)
_lowercase = parser.parse_args()
if args.num_workers is None:
_lowercase = multiprocessing.cpu_count()
_lowercase = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
_lowercase = time.time()
_lowercase = load_dataset(args.dataset_name, split="""train""")
print(F"""Time to load dataset: {time.time()-t_start:.2f}""")
# Run preprocessing
_lowercase = time.time()
_lowercase = ds.map(preprocess, num_proc=args.num_workers)
print(F"""Time to preprocess dataset: {time.time()-t_start:.2f}""")
# Deduplicate hashes
_lowercase = set(ds.unique("""hash"""))
_lowercase = len(uniques) / len(ds)
print(F"""Fraction of duplicates: {1-frac:.2%}""")
# Deduplicate data and apply heuristics
_lowercase = time.time()
_lowercase = ds.filter(filter, fn_kwargs={"""uniques""": uniques, """args""": args})
print(F"""Time to filter dataset: {time.time()-t_start:.2f}""")
print(F"""Size of filtered dataset: {len(ds_filter)}""")
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
_lowercase = time.time()
_lowercase , _lowercase = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(F"""Time to deduplicate dataset: {time.time()-t_start:.2f}""")
print(F"""Size of deduplicate dataset: {len(ds_filter)}""")
# Save data in batches of samples_per_file
_lowercase = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / """duplicate_clusters.json""", """w""") as f:
json.dump(duplicate_clusters, f)
_lowercase = output_dir / """data"""
data_dir.mkdir(exist_ok=True)
_lowercase = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
_lowercase = str(data_dir / F"""file-{file_number+1:012}.json""")
_lowercase = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(F"""Time to save dataset: {time.time()-t_start:.2f}""")
| 5 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_lowercase = logging.get_logger(__name__)
_lowercase = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_lowercase = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class UpperCAmelCase_ :
'''simple docstring'''
_lowercase : str = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(_SCREAMING_SNAKE_CASE )} )
_lowercase : str = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''} )
_lowercase : int = field(
default=1_2_8 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_lowercase : int = field(
default=1_2_8 , metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''} , )
_lowercase : int = field(
default=6_4 , metadata={
'''help''': (
'''The maximum number of tokens for the question. Questions longer than this will '''
'''be truncated to this length.'''
)
} , )
_lowercase : int = field(
default=3_0 , metadata={
'''help''': (
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
)
} , )
_lowercase : bool = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
_lowercase : bool = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''} )
_lowercase : float = field(
default=0.0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
_lowercase : int = field(
default=2_0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
_lowercase : int = field(
default=0 , metadata={
'''help''': (
'''language id of input for language-specific xlm models (see'''
''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
)
} , )
_lowercase : int = field(default=1 , metadata={'''help''': '''multiple threads for converting example to features'''} )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : str = '''train'''
_lowercase : Union[str, Any] = '''dev'''
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : SquadDataTrainingArguments
_lowercase : List[SquadFeatures]
_lowercase : Split
_lowercase : bool
def __init__( self , _lowercase , _lowercase , _lowercase = None , _lowercase = Split.train , _lowercase = False , _lowercase = None , _lowercase = "pt" , ):
"""simple docstring"""
_lowerCAmelCase = args
_lowerCAmelCase = is_language_sensitive
_lowerCAmelCase = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(_lowercase , _lowercase ):
try:
_lowerCAmelCase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
_lowerCAmelCase = mode
# Load data features from cache or dataset file
_lowerCAmelCase = """v2""" if args.version_2_with_negative else """v1"""
_lowerCAmelCase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
_lowerCAmelCase = cached_features_file + """.lock"""
with FileLock(_lowercase ):
if os.path.exists(_lowercase ) and not args.overwrite_cache:
_lowerCAmelCase = time.time()
_lowerCAmelCase = torch.load(_lowercase )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
_lowerCAmelCase = self.old_features["""features"""]
_lowerCAmelCase = self.old_features.get("""dataset""" , _lowercase )
_lowerCAmelCase = self.old_features.get("""examples""" , _lowercase )
logger.info(
F'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
F'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
""" future run""" )
else:
if mode == Split.dev:
_lowerCAmelCase = self.processor.get_dev_examples(args.data_dir )
else:
_lowerCAmelCase = self.processor.get_train_examples(args.data_dir )
_lowerCAmelCase , _lowerCAmelCase = squad_convert_examples_to_features(
examples=self.examples , tokenizer=_lowercase , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=_lowercase , )
_lowerCAmelCase = time.time()
torch.save(
{"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples} , _lowercase , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self ):
"""simple docstring"""
return len(self.features )
def __getitem__( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.features[i]
_lowerCAmelCase = torch.tensor(feature.input_ids , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.attention_mask , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.token_type_ids , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.cls_index , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.p_mask , dtype=torch.float )
_lowerCAmelCase = torch.tensor(feature.is_impossible , dtype=torch.float )
_lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": attention_mask,
"""token_type_ids""": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"""is_impossible""": is_impossible} )
if self.is_language_sensitive:
inputs.update({"""langs""": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
_lowerCAmelCase = torch.tensor(feature.start_position , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions} )
return inputs
| 5 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""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_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Union[str, Any] = '''wavlm'''
def __init__( self , _lowercase=32 , _lowercase=768 , _lowercase=12 , _lowercase=12 , _lowercase=3_072 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=0.1 , _lowercase=0.0 , _lowercase=0.1 , _lowercase=0.1 , _lowercase=0.02 , _lowercase=1e-5 , _lowercase="group" , _lowercase="gelu" , _lowercase=(512, 512, 512, 512, 512, 512, 512) , _lowercase=(5, 2, 2, 2, 2, 2, 2) , _lowercase=(10, 3, 3, 3, 3, 2, 2) , _lowercase=False , _lowercase=128 , _lowercase=16 , _lowercase=320 , _lowercase=800 , _lowercase=False , _lowercase=True , _lowercase=0.05 , _lowercase=10 , _lowercase=2 , _lowercase=0.0 , _lowercase=10 , _lowercase=320 , _lowercase=2 , _lowercase=0.1 , _lowercase=100 , _lowercase=256 , _lowercase=256 , _lowercase=0.1 , _lowercase="mean" , _lowercase=False , _lowercase=False , _lowercase=256 , _lowercase=(512, 512, 512, 512, 1_500) , _lowercase=(5, 3, 3, 1, 1) , _lowercase=(1, 2, 3, 1, 1) , _lowercase=512 , _lowercase=80 , _lowercase=0 , _lowercase=1 , _lowercase=2 , _lowercase=False , _lowercase=3 , _lowercase=2 , _lowercase=3 , _lowercase=None , **_lowercase , ):
"""simple docstring"""
super().__init__(**_lowercase , pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase )
_lowerCAmelCase = hidden_size
_lowerCAmelCase = feat_extract_norm
_lowerCAmelCase = feat_extract_activation
_lowerCAmelCase = list(_lowercase )
_lowerCAmelCase = list(_lowercase )
_lowerCAmelCase = list(_lowercase )
_lowerCAmelCase = conv_bias
_lowerCAmelCase = num_buckets
_lowerCAmelCase = max_bucket_distance
_lowerCAmelCase = num_conv_pos_embeddings
_lowerCAmelCase = num_conv_pos_embedding_groups
_lowerCAmelCase = len(self.conv_dim )
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = hidden_dropout
_lowerCAmelCase = attention_dropout
_lowerCAmelCase = activation_dropout
_lowerCAmelCase = feat_proj_dropout
_lowerCAmelCase = final_dropout
_lowerCAmelCase = layerdrop
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = initializer_range
_lowerCAmelCase = num_ctc_classes
_lowerCAmelCase = vocab_size
_lowerCAmelCase = do_stable_layer_norm
_lowerCAmelCase = use_weighted_layer_sum
_lowerCAmelCase = 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
_lowerCAmelCase = apply_spec_augment
_lowerCAmelCase = mask_time_prob
_lowerCAmelCase = mask_time_length
_lowerCAmelCase = mask_time_min_masks
_lowerCAmelCase = mask_feature_prob
_lowerCAmelCase = mask_feature_length
# parameters for pretraining with codevector quantized representations
_lowerCAmelCase = num_codevectors_per_group
_lowerCAmelCase = num_codevector_groups
_lowerCAmelCase = contrastive_logits_temperature
_lowerCAmelCase = num_negatives
_lowerCAmelCase = codevector_dim
_lowerCAmelCase = proj_codevector_dim
_lowerCAmelCase = diversity_loss_weight
# ctc loss
_lowerCAmelCase = ctc_loss_reduction
_lowerCAmelCase = ctc_zero_infinity
# adapter
_lowerCAmelCase = add_adapter
_lowerCAmelCase = adapter_kernel_size
_lowerCAmelCase = adapter_stride
_lowerCAmelCase = num_adapter_layers
_lowerCAmelCase = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_lowerCAmelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_lowerCAmelCase = list(_lowercase )
_lowerCAmelCase = list(_lowercase )
_lowerCAmelCase = list(_lowercase )
_lowerCAmelCase = xvector_output_dim
@property
def _lowercase ( self ):
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 5 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-question_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-reader-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-ctx_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
),
"""facebook/dpr-question_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
),
"""facebook/dpr-reader-multiset-base""": (
"""https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
),
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : str = '''dpr'''
def __init__( self , _lowercase=30_522 , _lowercase=768 , _lowercase=12 , _lowercase=12 , _lowercase=3_072 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=2 , _lowercase=0.02 , _lowercase=1e-12 , _lowercase=0 , _lowercase="absolute" , _lowercase = 0 , **_lowercase , ):
"""simple docstring"""
super().__init__(pad_token_id=_lowercase , **_lowercase )
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = hidden_act
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_vocab_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = projection_dim
_lowerCAmelCase = position_embedding_type
| 5 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
_lowercase : Optional[Any] = DiTPipeline
_lowercase : List[Any] = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
_lowercase : List[Any] = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
_lowercase : Optional[Any] = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
_lowercase : Optional[int] = False
def _lowercase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
_lowerCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=_lowercase , activation_fn="""gelu-approximate""" , num_embeds_ada_norm=1_000 , norm_type="""ada_norm_zero""" , norm_elementwise_affine=_lowercase , )
_lowerCAmelCase = AutoencoderKL()
_lowerCAmelCase = DDIMScheduler()
_lowerCAmelCase = {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
return components
def _lowercase ( self , _lowercase , _lowercase=0 ):
"""simple docstring"""
if str(_lowercase ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(_lowercase )
else:
_lowerCAmelCase = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
_lowerCAmelCase = {
"""class_labels""": [1],
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = """cpu"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = self.pipeline_class(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
_lowerCAmelCase = self.get_dummy_inputs(_lowercase )
_lowerCAmelCase = pipe(**_lowercase ).images
_lowerCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_lowerCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_lowerCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_lowercase , 1e-3 )
def _lowercase ( self ):
"""simple docstring"""
self._test_inference_batch_single_identical(relax_max_difference=_lowercase , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def _lowercase ( self ):
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""" )
pipe.to("""cuda""" )
_lowerCAmelCase = ["""vase""", """umbrella""", """white shark""", """white wolf"""]
_lowerCAmelCase = pipe.get_label_ids(_lowercase )
_lowerCAmelCase = pipe(_lowercase , generator=_lowercase , num_inference_steps=40 , output_type="""np""" ).images
for word, image in zip(_lowercase , _lowercase ):
_lowerCAmelCase = load_numpy(
F'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1e-2
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""" )
_lowerCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("""cuda""" )
_lowerCAmelCase = ["""vase""", """umbrella"""]
_lowerCAmelCase = pipe.get_label_ids(_lowercase )
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(_lowercase , generator=_lowercase , num_inference_steps=25 , output_type="""np""" ).images
for word, image in zip(_lowercase , _lowercase ):
_lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
F'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1e-1
| 5 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
_lowercase = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
_lowercase = """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""
_lowercase = """
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
\"raw_values\" : Returns a full set of errors in case of multioutput input.
\"uniform_average\" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric(\"mse\")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{'mse': 0.6123724356957945}
If you're using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{'mse': array([0.41666667, 1. ])}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"""
] , )
def _lowercase ( self ):
"""simple docstring"""
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value("""float""" ) ),
"references": datasets.Sequence(datasets.Value("""float""" ) ),
}
else:
return {
"predictions": datasets.Value("""float""" ),
"references": datasets.Value("""float""" ),
}
def _lowercase ( self , _lowercase , _lowercase , _lowercase=None , _lowercase="uniform_average" , _lowercase=True ):
"""simple docstring"""
_lowerCAmelCase = mean_squared_error(
_lowercase , _lowercase , sample_weight=_lowercase , multioutput=_lowercase , squared=_lowercase )
return {"mse": mse}
| 5 | 1 |
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, 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 (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase , _lowercase=13 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=99 , _lowercase=32 , _lowercase=2 , _lowercase=4 , _lowercase=37 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=16 , _lowercase=2 , _lowercase=0.02 , _lowercase=3 , _lowercase=4 , _lowercase=None , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = 13
_lowerCAmelCase = 7
_lowerCAmelCase = True
_lowerCAmelCase = True
_lowerCAmelCase = True
_lowerCAmelCase = True
_lowerCAmelCase = 99
_lowerCAmelCase = 384
_lowerCAmelCase = 2
_lowerCAmelCase = 4
_lowerCAmelCase = 37
_lowerCAmelCase = """gelu"""
_lowerCAmelCase = 0.1
_lowerCAmelCase = 0.1
_lowerCAmelCase = 512
_lowerCAmelCase = 16
_lowerCAmelCase = 2
_lowerCAmelCase = 0.02
_lowerCAmelCase = 3
_lowerCAmelCase = 4
_lowerCAmelCase = 128
_lowerCAmelCase = 2
_lowerCAmelCase = 9
_lowerCAmelCase = 1
_lowerCAmelCase = None
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = None
if self.use_input_mask:
_lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCAmelCase = None
if self.use_token_type_ids:
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowerCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
_lowerCAmelCase = ConvBertConfig(
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=_lowercase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = TFConvBertModel(config=_lowercase )
_lowerCAmelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
_lowerCAmelCase = [input_ids, input_mask]
_lowerCAmelCase = model(_lowercase )
_lowerCAmelCase = model(_lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = TFConvBertForMaskedLM(config=_lowercase )
_lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_lowerCAmelCase = model(_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = TFConvBertForSequenceClassification(config=_lowercase )
_lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_lowerCAmelCase = model(_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.num_choices
_lowerCAmelCase = TFConvBertForMultipleChoice(config=_lowercase )
_lowerCAmelCase = tf.tile(tf.expand_dims(_lowercase , 1 ) , (1, self.num_choices, 1) )
_lowerCAmelCase = tf.tile(tf.expand_dims(_lowercase , 1 ) , (1, self.num_choices, 1) )
_lowerCAmelCase = tf.tile(tf.expand_dims(_lowercase , 1 ) , (1, self.num_choices, 1) )
_lowerCAmelCase = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
_lowerCAmelCase = model(_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = TFConvBertForTokenClassification(config=_lowercase )
_lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_lowerCAmelCase = model(_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = TFConvBertForQuestionAnswering(config=_lowercase )
_lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_lowerCAmelCase = model(_lowercase )
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 _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = config_and_inputs
_lowerCAmelCase = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
_lowercase : Tuple = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
_lowercase : str = (
{
'''feature-extraction''': TFConvBertModel,
'''fill-mask''': TFConvBertForMaskedLM,
'''question-answering''': TFConvBertForQuestionAnswering,
'''text-classification''': TFConvBertForSequenceClassification,
'''token-classification''': TFConvBertForTokenClassification,
'''zero-shot''': TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
_lowercase : Optional[Any] = False
_lowercase : str = False
_lowercase : Any = False
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFConvBertModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=_lowercase , hidden_size=37 )
def _lowercase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_lowercase )
@slow
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCAmelCase = True
_lowerCAmelCase = True
if hasattr(_lowercase , """use_cache""" ):
_lowerCAmelCase = True
_lowerCAmelCase = getattr(self.model_tester , """encoder_seq_length""" , self.model_tester.seq_length )
_lowerCAmelCase = getattr(self.model_tester , """key_length""" , _lowercase )
for model_class in self.all_model_classes:
_lowerCAmelCase = self._prepare_for_class(_lowercase , _lowercase )
_lowerCAmelCase = model_class(_lowercase )
_lowerCAmelCase = len(model(_lowercase ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowercase , saved_model=_lowercase )
_lowerCAmelCase = os.path.join(_lowercase , """saved_model""" , """1""" )
_lowerCAmelCase = tf.keras.models.load_model(_lowercase )
_lowerCAmelCase = model(_lowercase )
if self.is_encoder_decoder:
_lowerCAmelCase = outputs["""encoder_hidden_states"""]
_lowerCAmelCase = outputs["""encoder_attentions"""]
else:
_lowerCAmelCase = outputs["""hidden_states"""]
_lowerCAmelCase = outputs["""attentions"""]
self.assertEqual(len(_lowercase ) , _lowercase )
_lowerCAmelCase = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(_lowercase ) , _lowercase )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(_lowercase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFConvBertModel.from_pretrained("""YituTech/conv-bert-base""" )
self.assertIsNotNone(_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCAmelCase = True
_lowerCAmelCase = getattr(self.model_tester , """decoder_seq_length""" , self.model_tester.seq_length )
_lowerCAmelCase = getattr(self.model_tester , """encoder_seq_length""" , self.model_tester.seq_length )
_lowerCAmelCase = getattr(self.model_tester , """key_length""" , _lowercase )
_lowerCAmelCase = getattr(self.model_tester , """key_length""" , _lowercase )
def check_decoder_attentions_output(_lowercase ):
_lowerCAmelCase = len(_lowercase )
self.assertEqual(out_len % 2 , 0 )
_lowerCAmelCase = outputs.decoder_attentions
self.assertEqual(len(_lowercase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(_lowercase ):
_lowerCAmelCase = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(_lowercase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
_lowerCAmelCase = True
_lowerCAmelCase = False
_lowerCAmelCase = model_class(_lowercase )
_lowerCAmelCase = model(self._prepare_for_class(_lowercase , _lowercase ) )
_lowerCAmelCase = len(_lowercase )
self.assertEqual(config.output_hidden_states , _lowercase )
check_encoder_attentions_output(_lowercase )
if self.is_encoder_decoder:
_lowerCAmelCase = model_class(_lowercase )
_lowerCAmelCase = model(self._prepare_for_class(_lowercase , _lowercase ) )
self.assertEqual(config.output_hidden_states , _lowercase )
check_decoder_attentions_output(_lowercase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
_lowerCAmelCase = True
_lowerCAmelCase = model_class(_lowercase )
_lowerCAmelCase = model(self._prepare_for_class(_lowercase , _lowercase ) )
self.assertEqual(config.output_hidden_states , _lowercase )
check_encoder_attentions_output(_lowercase )
# Check attention is always last and order is fine
_lowerCAmelCase = True
_lowerCAmelCase = True
_lowerCAmelCase = model_class(_lowercase )
_lowerCAmelCase = model(self._prepare_for_class(_lowercase , _lowercase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_lowercase ) )
self.assertEqual(model.config.output_hidden_states , _lowercase )
check_encoder_attentions_output(_lowercase )
@require_tf
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFConvBertModel.from_pretrained("""YituTech/conv-bert-base""" )
_lowerCAmelCase = tf.constant([[0, 1, 2, 3, 4, 5]] )
_lowerCAmelCase = model(_lowercase )[0]
_lowerCAmelCase = [1, 6, 768]
self.assertEqual(output.shape , _lowercase )
_lowerCAmelCase = tf.constant(
[
[
[-0.0347_5493, -0.468_6034, -0.3063_8832],
[0.2263_7248, -0.2698_8646, -0.742_3424],
[0.1032_4868, -0.4501_3508, -0.5828_0784],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , _lowercase , atol=1e-4 )
| 5 |
'''simple docstring'''
def A ():
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def A (__lowerCamelCase :List[Any] ):
_lowerCAmelCase = 1
_lowerCAmelCase = 2
while i * i <= n:
_lowerCAmelCase = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def A ():
return next(i for i in triangle_number_generator() if count_divisors(__lowerCamelCase ) > 500 )
if __name__ == "__main__":
print(solution())
| 5 | 1 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import 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,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
)
from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase , _lowercase=13 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=99 , _lowercase=16 , _lowercase=36 , _lowercase=6 , _lowercase=6 , _lowercase=6 , _lowercase=37 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=16 , _lowercase=2 , _lowercase=0.02 , _lowercase=3 , _lowercase=4 , _lowercase=None , ):
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = seq_length
_lowerCAmelCase = is_training
_lowerCAmelCase = use_input_mask
_lowerCAmelCase = use_token_type_ids
_lowerCAmelCase = use_labels
_lowerCAmelCase = vocab_size
_lowerCAmelCase = embedding_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_hidden_groups
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_vocab_size
_lowerCAmelCase = type_sequence_label_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = num_labels
_lowerCAmelCase = num_choices
_lowerCAmelCase = scope
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = None
if self.use_input_mask:
_lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCAmelCase = None
if self.use_token_type_ids:
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowerCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
_lowerCAmelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowercase ( self ):
"""simple docstring"""
return AlbertConfig(
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 , num_hidden_groups=self.num_hidden_groups , )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = AlbertModel(config=_lowercase )
model.to(_lowercase )
model.eval()
_lowerCAmelCase = model(_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase )
_lowerCAmelCase = model(_lowercase , token_type_ids=_lowercase )
_lowerCAmelCase = model(_lowercase )
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 _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = AlbertForPreTraining(config=_lowercase )
model.to(_lowercase )
model.eval()
_lowerCAmelCase = model(
_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase , sentence_order_label=_lowercase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.sop_logits.shape , (self.batch_size, config.num_labels) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = AlbertForMaskedLM(config=_lowercase )
model.to(_lowercase )
model.eval()
_lowerCAmelCase = model(_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = AlbertForQuestionAnswering(config=_lowercase )
model.to(_lowercase )
model.eval()
_lowerCAmelCase = model(
_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , start_positions=_lowercase , end_positions=_lowercase , )
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 _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = AlbertForSequenceClassification(_lowercase )
model.to(_lowercase )
model.eval()
_lowerCAmelCase = model(_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = AlbertForTokenClassification(config=_lowercase )
model.to(_lowercase )
model.eval()
_lowerCAmelCase = model(_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.num_choices
_lowerCAmelCase = AlbertForMultipleChoice(config=_lowercase )
model.to(_lowercase )
model.eval()
_lowerCAmelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowerCAmelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowerCAmelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowerCAmelCase = model(
_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) = config_and_inputs
_lowerCAmelCase = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
_lowercase : Optional[int] = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
_lowercase : Union[str, Any] = (
{
'''feature-extraction''': AlbertModel,
'''fill-mask''': AlbertForMaskedLM,
'''question-answering''': AlbertForQuestionAnswering,
'''text-classification''': AlbertForSequenceClassification,
'''token-classification''': AlbertForTokenClassification,
'''zero-shot''': AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
_lowercase : Any = True
def _lowercase ( self , _lowercase , _lowercase , _lowercase=False ):
"""simple docstring"""
_lowerCAmelCase = super()._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
if return_labels:
if model_class in get_values(_lowercase ):
_lowerCAmelCase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=_lowercase )
_lowerCAmelCase = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_lowercase )
return inputs_dict
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = AlbertModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=_lowercase , hidden_size=37 )
def _lowercase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_lowerCAmelCase = type
self.model_tester.create_and_check_model(*_lowercase )
@slow
def _lowercase ( self ):
"""simple docstring"""
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase = AlbertModel.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = AlbertModel.from_pretrained("""albert-base-v2""" )
_lowerCAmelCase = torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
_lowerCAmelCase = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
_lowerCAmelCase = model(_lowercase , attention_mask=_lowercase )[0]
_lowerCAmelCase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , _lowercase )
_lowerCAmelCase = torch.tensor(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _lowercase , atol=1e-4 ) )
| 5 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
_lowercase = logging.get_logger(__name__)
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , *_lowercase , **_lowercase ):
"""simple docstring"""
warnings.warn(
"""The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use DonutImageProcessor instead.""" , _lowercase , )
super().__init__(*_lowercase , **_lowercase )
| 5 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
_lowerCAmelCase = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
_lowerCAmelCase = model(_lowercase )["""last_hidden_state"""]
_lowerCAmelCase = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , _lowercase )
# compare the actual values for a slice.
_lowerCAmelCase = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 5 |
'''simple docstring'''
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_min_version,
require_tpu,
require_xpu,
skip,
slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU
from .scripts import test_script, test_sync, test_ops # isort: skip
| 5 | 1 |
'''simple docstring'''
from __future__ import annotations
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase , _lowercase ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = text, pattern
_lowerCAmelCase , _lowerCAmelCase = len(_lowercase ), len(_lowercase )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def _lowercase ( self , _lowercase ):
"""simple docstring"""
for i in range(self.patLen - 1 , -1 , -1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = []
for i in range(self.textLen - self.patLen + 1 ):
_lowerCAmelCase = self.mismatch_in_text(_lowercase )
if mismatch_index == -1:
positions.append(_lowercase )
else:
_lowerCAmelCase = self.match_in_pattern(self.text[mismatch_index] )
_lowerCAmelCase = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
_lowercase = """ABAABA"""
_lowercase = """AB"""
_lowercase = BoyerMooreSearch(text, pattern)
_lowercase = bms.bad_character_heuristic()
if len(positions) == 0:
print("""No match found""")
else:
print("""Pattern found in following positions: """)
print(positions)
| 5 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("""Googling.....""")
_lowercase = """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
_lowercase = requests.get(url, headers={"""UserAgent""": UserAgent().random})
# res.raise_for_status()
with open("""project1a.html""", """wb""") as out_file: # only for knowing the class
for data in res.iter_content(10000):
out_file.write(data)
_lowercase = BeautifulSoup(res.text, """html.parser""")
_lowercase = list(soup.select(""".eZt8xd"""))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get("""href"""))
else:
webbrowser.open(F"""https://google.com{link.get('href')}""")
| 5 | 1 |
'''simple docstring'''
_lowercase = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_lowercase = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
_lowercase = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 5 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_lowercase = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
def A ():
_lowerCAmelCase = Github(os.environ["""GITHUB_TOKEN"""] )
_lowerCAmelCase = g.get_repo("""huggingface/diffusers""" )
_lowerCAmelCase = repo.get_issues(state="""open""" )
for issue in open_issues:
_lowerCAmelCase = sorted(issue.get_comments() , key=lambda __lowerCamelCase : i.created_at , reverse=__lowerCamelCase )
_lowerCAmelCase = comments[0] if len(__lowerCamelCase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state="""closed""" )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state="""open""" )
issue.remove_from_labels("""stale""" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
"""This issue has been automatically marked as stale because it has not had """
"""recent activity. If you think this still needs to be addressed """
"""please comment on this thread.\n\nPlease note that issues that do not follow the """
"""[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) """
"""are likely to be ignored.""" )
issue.add_to_labels("""stale""" )
if __name__ == "__main__":
main()
| 5 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
_lowercase = list[list[float | int]]
def A (__lowerCamelCase :Matrix , __lowerCamelCase :Matrix ):
_lowerCAmelCase = len(__lowerCamelCase )
_lowerCAmelCase = [[0 for _ in range(size + 1 )] for _ in range(__lowerCamelCase )]
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
for row in range(__lowerCamelCase ):
for col in range(__lowerCamelCase ):
_lowerCAmelCase = matrix[row][col]
_lowerCAmelCase = vector[row][0]
_lowerCAmelCase = 0
_lowerCAmelCase = 0
while row < size and col < size:
# pivoting
_lowerCAmelCase = max((abs(augmented[rowa][col] ), rowa) for rowa in range(__lowerCamelCase , __lowerCamelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
_lowerCAmelCase , _lowerCAmelCase = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , __lowerCamelCase ):
_lowerCAmelCase = augmented[rowa][col] / augmented[row][col]
_lowerCAmelCase = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , __lowerCamelCase ):
for row in range(__lowerCamelCase ):
_lowerCAmelCase = augmented[row][col] / augmented[col][col]
for cola in range(__lowerCamelCase , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(__lowerCamelCase )
]
def A (__lowerCamelCase :list[int] ):
_lowerCAmelCase = len(__lowerCamelCase )
_lowerCAmelCase = [[0 for _ in range(__lowerCamelCase )] for _ in range(__lowerCamelCase )]
_lowerCAmelCase = [[0] for _ in range(__lowerCamelCase )]
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
for x_val, y_val in enumerate(__lowerCamelCase ):
for col in range(__lowerCamelCase ):
_lowerCAmelCase = (x_val + 1) ** (size - col - 1)
_lowerCAmelCase = y_val
_lowerCAmelCase = solve(__lowerCamelCase , __lowerCamelCase )
def interpolated_func(__lowerCamelCase :int ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(__lowerCamelCase ) )
return interpolated_func
def A (__lowerCamelCase :int ):
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def A (__lowerCamelCase :Callable[[int], int] = question_function , __lowerCamelCase :int = 10 ):
_lowerCAmelCase = [func(__lowerCamelCase ) for x_val in range(1 , order + 1 )]
_lowerCAmelCase = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
_lowerCAmelCase = 0
_lowerCAmelCase = 42
_lowerCAmelCase = 42
for poly in polynomials:
_lowerCAmelCase = 1
while func(__lowerCamelCase ) == poly(__lowerCamelCase ):
x_val += 1
ret += poly(__lowerCamelCase )
return ret
if __name__ == "__main__":
print(F"""{solution() = }""")
| 5 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
ImageTextPipelineOutput,
UniDiffuserPipeline,
)
else:
from .modeling_text_decoder import UniDiffuserTextDecoder
from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
| 5 | 1 |
'''simple docstring'''
import math
_lowercase = 10
_lowercase = 7
_lowercase = BALLS_PER_COLOUR * NUM_COLOURS
def A (__lowerCamelCase :int = 20 ):
_lowerCAmelCase = math.comb(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = math.comb(NUM_BALLS - BALLS_PER_COLOUR , __lowerCamelCase )
_lowerCAmelCase = NUM_COLOURS * (1 - missing_colour / total)
return f'{result:.9f}'
if __name__ == "__main__":
print(solution(20))
| 5 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowercase = {"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ViTMAEForPreTraining""",
"""ViTMAELayer""",
"""ViTMAEModel""",
"""ViTMAEPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""TFViTMAEForPreTraining""",
"""TFViTMAEModel""",
"""TFViTMAEPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 5 | 1 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
_lowercase = """examples/"""
_lowercase = {
"""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"""),
}
_lowercase = {
"""init""": """src/diffusers/__init__.py""",
"""setup""": """setup.py""",
}
_lowercase = """README.md"""
def A (__lowerCamelCase :Any , __lowerCamelCase :Optional[int] , __lowerCamelCase :Optional[Any] ):
with open(__lowerCamelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
_lowerCAmelCase = f.read()
_lowerCAmelCase , _lowerCAmelCase = REPLACE_PATTERNS[pattern]
_lowerCAmelCase = replace.replace("""VERSION""" , __lowerCamelCase )
_lowerCAmelCase = re_pattern.sub(__lowerCamelCase , __lowerCamelCase )
with open(__lowerCamelCase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.write(__lowerCamelCase )
def A (__lowerCamelCase :Optional[int] ):
for folder, directories, fnames in os.walk(__lowerCamelCase ):
# 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(__lowerCamelCase , __lowerCamelCase ) , __lowerCamelCase , pattern="""examples""" )
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Union[str, Any]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
if not patch:
update_version_in_examples(__lowerCamelCase )
def A ():
_lowerCAmelCase = """🤗 Transformers currently provides the following architectures"""
_lowerCAmelCase = """1. Want to contribute a new model?"""
with open(__lowerCamelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
_lowerCAmelCase = f.readlines()
# Find the start of the list.
_lowerCAmelCase = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
_lowerCAmelCase = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("""1.""" ):
_lowerCAmelCase = lines[index].replace(
"""https://huggingface.co/docs/diffusers/main/model_doc""" , """https://huggingface.co/docs/diffusers/model_doc""" , )
index += 1
with open(__lowerCamelCase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(__lowerCamelCase )
def A ():
with open(REPLACE_FILES["""init"""] , """r""" ) as f:
_lowerCAmelCase = f.read()
_lowerCAmelCase = REPLACE_PATTERNS["""init"""][0].search(__lowerCamelCase ).groups()[0]
return packaging.version.parse(__lowerCamelCase )
def A (__lowerCamelCase :Union[str, Any]=False ):
_lowerCAmelCase = 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:
_lowerCAmelCase = default_version.base_version
elif patch:
_lowerCAmelCase = f'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
else:
_lowerCAmelCase = f'{default_version.major}.{default_version.minor + 1}.0'
# Now let's ask nicely if that's the right one.
_lowerCAmelCase = input(f'Which version are you releasing? [{default_version}]' )
if len(__lowerCamelCase ) == 0:
_lowerCAmelCase = default_version
print(f'Updating version to {version}.' )
global_version_update(__lowerCamelCase , patch=__lowerCamelCase )
def A ():
_lowerCAmelCase = get_version()
_lowerCAmelCase = f'{current_version.major}.{current_version.minor + 1}.0.dev0'
_lowerCAmelCase = current_version.base_version
# Check with the user we got that right.
_lowerCAmelCase = input(f'Which version are we developing now? [{dev_version}]' )
if len(__lowerCamelCase ) == 0:
_lowerCAmelCase = dev_version
print(f'Updating version to {version}.' )
global_version_update(__lowerCamelCase )
# print("Cleaning main README, don't forget to run `make fix-copies`.")
# clean_main_ref_in_model_list()
if __name__ == "__main__":
_lowercase = 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.""")
_lowercase = 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()
| 5 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
_lowercase = logging.get_logger(__name__)
@add_end_docstrings(_SCREAMING_SNAKE_CASE )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , **_lowercase ):
"""simple docstring"""
super().__init__(**_lowercase )
if self.framework != "pt":
raise ValueError(F'The {self.__class__} is only available in PyTorch.' )
# No specific FOR_XXX available yet
def __call__( self , _lowercase , **_lowercase ):
"""simple docstring"""
return super().__call__(_lowercase , **_lowercase )
def _lowercase ( self , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase = {}
if "candidate_labels" in kwargs:
_lowerCAmelCase = kwargs["""candidate_labels"""]
if "hypothesis_template" in kwargs:
_lowerCAmelCase = kwargs["""hypothesis_template"""]
return preprocess_params, {}, {}
def _lowercase ( self , _lowercase , _lowercase=None , _lowercase="This is a sound of {}." ):
"""simple docstring"""
if isinstance(_lowercase , _lowercase ):
if audio.startswith("""http://""" ) or audio.startswith("""https://""" ):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
_lowerCAmelCase = requests.get(_lowercase ).content
else:
with open(_lowercase , """rb""" ) as f:
_lowerCAmelCase = f.read()
if isinstance(_lowercase , _lowercase ):
_lowerCAmelCase = ffmpeg_read(_lowercase , self.feature_extractor.sampling_rate )
if not isinstance(_lowercase , np.ndarray ):
raise ValueError("""We expect a numpy ndarray as input""" )
if len(audio.shape ) != 1:
raise ValueError("""We expect a single channel audio input for ZeroShotAudioClassificationPipeline""" )
_lowerCAmelCase = self.feature_extractor(
[audio] , sampling_rate=self.feature_extractor.sampling_rate , return_tensors="""pt""" )
_lowerCAmelCase = candidate_labels
_lowerCAmelCase = [hypothesis_template.format(_lowercase ) for x in candidate_labels]
_lowerCAmelCase = self.tokenizer(_lowercase , return_tensors=self.framework , padding=_lowercase )
_lowerCAmelCase = [text_inputs]
return inputs
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = model_inputs.pop("""candidate_labels""" )
_lowerCAmelCase = model_inputs.pop("""text_inputs""" )
if isinstance(text_inputs[0] , _lowercase ):
_lowerCAmelCase = text_inputs[0]
else:
# Batching case.
_lowerCAmelCase = text_inputs[0][0]
_lowerCAmelCase = self.model(**_lowercase , **_lowercase )
_lowerCAmelCase = {
"""candidate_labels""": candidate_labels,
"""logits""": outputs.logits_per_audio,
}
return model_outputs
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = model_outputs.pop("""candidate_labels""" )
_lowerCAmelCase = model_outputs["""logits"""][0]
if self.framework == "pt":
_lowerCAmelCase = logits.softmax(dim=0 )
_lowerCAmelCase = probs.tolist()
else:
raise ValueError("""`tf` framework not supported.""" )
_lowerCAmelCase = [
{"""score""": score, """label""": candidate_label}
for score, candidate_label in sorted(zip(_lowercase , _lowercase ) , key=lambda _lowercase : -x[0] )
]
return result
| 5 | 1 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_lowercase = """\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
booktitle = {},
year = {2002},
pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",
author = \"Lin, Chin-Yew and
Och, Franz Josef\",
booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",
month = \"aug 23{--}aug 27\",
year = \"2004\",
address = \"Geneva, Switzerland\",
publisher = \"COLING\",
url = \"https://www.aclweb.org/anthology/C04-1072\",
pages = \"501--507\",
}
"""
_lowercase = """\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality 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,
the 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
remains one of the most popular automated and inexpensive metrics.
Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].
BLEU'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
representing 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
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""
_lowercase = """
Computes BLEU score of translated segments against one or more references.
Args:
predictions: list of translations to score.
Each translation should be tokenized into a list of tokens.
references: list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
max_order: Maximum n-gram order to use when computing BLEU score.
smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
'bleu': bleu score,
'precisions': geometric mean of n-gram precisions,
'brevity_penalty': brevity penalty,
'length_ratio': ratio of lengths,
'translation_length': translation_length,
'reference_length': reference_length
Examples:
>>> predictions = [
... [\"hello\", \"there\", \"general\", \"kenobi\"], # tokenized prediction of the first sample
... [\"foo\", \"bar\", \"foobar\"] # tokenized prediction of the second sample
... ]
>>> references = [
... [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]], # tokenized references for the first sample (2 references)
... [[\"foo\", \"bar\", \"foobar\"]] # tokenized references for the second sample (1 reference)
... ]
>>> bleu = datasets.load_metric(\"bleu\")
>>> results = bleu.compute(predictions=predictions, references=references)
>>> print(results[\"bleu\"])
1.0
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
'''simple docstring'''
def _lowercase ( self ):
"""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 _lowercase ( self , _lowercase , _lowercase , _lowercase=4 , _lowercase=False ):
"""simple docstring"""
_lowerCAmelCase = compute_bleu(
reference_corpus=_lowercase , translation_corpus=_lowercase , max_order=_lowercase , smooth=_lowercase )
((_lowerCAmelCase) , (_lowerCAmelCase) , (_lowerCAmelCase) , (_lowerCAmelCase) , (_lowerCAmelCase) , (_lowerCAmelCase)) = score
return {
"bleu": bleu,
"precisions": precisions,
"brevity_penalty": bp,
"length_ratio": ratio,
"translation_length": translation_length,
"reference_length": reference_length,
}
| 5 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_lowercase = logging.get_logger(__name__)
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[str] = ['''input_values''', '''padding_mask''']
def __init__( self , _lowercase = 1 , _lowercase = 24_000 , _lowercase = 0.0 , _lowercase = None , _lowercase = None , **_lowercase , ):
"""simple docstring"""
super().__init__(feature_size=_lowercase , sampling_rate=_lowercase , padding_value=_lowercase , **_lowercase )
_lowerCAmelCase = chunk_length_s
_lowerCAmelCase = overlap
@property
def _lowercase ( self ):
"""simple docstring"""
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _lowercase ( self ):
"""simple docstring"""
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 ) )
def __call__( self , _lowercase , _lowercase = None , _lowercase = False , _lowercase = None , _lowercase = None , _lowercase = None , ):
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
F' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
if padding and truncation:
raise ValueError("""Both padding and truncation were set. Make sure you only set one.""" )
elif padding is None:
# by default let's pad the inputs
_lowerCAmelCase = True
_lowerCAmelCase = bool(
isinstance(_lowercase , (list, tuple) ) and (isinstance(raw_audio[0] , (np.ndarray, tuple, list) )) )
if is_batched:
_lowerCAmelCase = [np.asarray(_lowercase , dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(_lowercase , np.ndarray ):
_lowerCAmelCase = np.asarray(_lowercase , dtype=np.floataa )
elif isinstance(_lowercase , np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
_lowerCAmelCase = raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
_lowerCAmelCase = [np.asarray(_lowercase ).T]
# verify inputs are valid
for idx, example in enumerate(_lowercase ):
if example.ndim > 2:
raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}' )
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels' )
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels' )
_lowerCAmelCase = None
_lowerCAmelCase = BatchFeature({"""input_values""": raw_audio} )
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
_lowerCAmelCase = min(array.shape[0] for array in raw_audio )
_lowerCAmelCase = int(np.floor(max_length / self.chunk_stride ) )
_lowerCAmelCase = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
_lowerCAmelCase = max(array.shape[0] for array in raw_audio )
_lowerCAmelCase = int(np.ceil(max_length / self.chunk_stride ) )
_lowerCAmelCase = (nb_step - 1) * self.chunk_stride + self.chunk_length
_lowerCAmelCase = """max_length"""
else:
_lowerCAmelCase = input_values
# normal padding on batch
if padded_inputs is None:
_lowerCAmelCase = self.pad(
_lowercase , max_length=_lowercase , truncation=_lowercase , padding=_lowercase , return_attention_mask=_lowercase , )
if padding:
_lowerCAmelCase = padded_inputs.pop("""attention_mask""" )
_lowerCAmelCase = []
for example in padded_inputs.pop("""input_values""" ):
if self.feature_size == 1:
_lowerCAmelCase = example[..., None]
input_values.append(example.T )
_lowerCAmelCase = input_values
if return_tensors is not None:
_lowerCAmelCase = padded_inputs.convert_to_tensors(_lowercase )
return padded_inputs
| 5 | 1 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
if version.parse(nlp.__version__) != version.parse("""0.8.3"""):
raise Exception("""requires gluonnlp == 0.8.3""")
if version.parse(mx.__version__) != version.parse("""1.5.0"""):
raise Exception("""requires mxnet == 1.5.0""")
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
_lowercase = """The Nymphenburg Palace is a beautiful palace in Munich!"""
def A (__lowerCamelCase :str , __lowerCamelCase :str ):
_lowerCAmelCase = {
"""attention_cell""": """multi_head""",
"""num_layers""": 4,
"""units""": 1024,
"""hidden_size""": 768,
"""max_length""": 512,
"""num_heads""": 8,
"""scaled""": True,
"""dropout""": 0.1,
"""use_residual""": True,
"""embed_size""": 1024,
"""embed_dropout""": 0.1,
"""word_embed""": None,
"""layer_norm_eps""": 1e-5,
"""token_type_vocab_size""": 2,
}
_lowerCAmelCase = bort_4_8_768_1024_hparams
# Let's construct the original Bort model here
# Taken from official BERT implementation, see:
# https://github.com/alexa/bort/blob/master/bort/bort.py
_lowerCAmelCase = BERTEncoder(
attention_cell=predefined_args["""attention_cell"""] , num_layers=predefined_args["""num_layers"""] , units=predefined_args["""units"""] , hidden_size=predefined_args["""hidden_size"""] , max_length=predefined_args["""max_length"""] , num_heads=predefined_args["""num_heads"""] , scaled=predefined_args["""scaled"""] , dropout=predefined_args["""dropout"""] , output_attention=__lowerCamelCase , output_all_encodings=__lowerCamelCase , use_residual=predefined_args["""use_residual"""] , activation=predefined_args.get("""activation""" , """gelu""" ) , layer_norm_eps=predefined_args.get("""layer_norm_eps""" , __lowerCamelCase ) , )
# Vocab information needs to be fetched first
# It's the same as RoBERTa, so RobertaTokenizer can be used later
_lowerCAmelCase = """openwebtext_ccnews_stories_books_cased"""
# Specify download folder to Gluonnlp's vocab
_lowerCAmelCase = os.path.join(get_home_dir() , """models""" )
_lowerCAmelCase = _load_vocab(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , cls=__lowerCamelCase )
_lowerCAmelCase = nlp.model.BERTModel(
__lowerCamelCase , len(__lowerCamelCase ) , units=predefined_args["""units"""] , embed_size=predefined_args["""embed_size"""] , embed_dropout=predefined_args["""embed_dropout"""] , word_embed=predefined_args["""word_embed"""] , use_pooler=__lowerCamelCase , use_token_type_embed=__lowerCamelCase , token_type_vocab_size=predefined_args["""token_type_vocab_size"""] , use_classifier=__lowerCamelCase , use_decoder=__lowerCamelCase , )
original_bort.load_parameters(__lowerCamelCase , cast_dtype=__lowerCamelCase , ignore_extra=__lowerCamelCase )
_lowerCAmelCase = original_bort._collect_params_with_prefix()
# Build our config 🤗
_lowerCAmelCase = {
"""architectures""": ["""BertForMaskedLM"""],
"""attention_probs_dropout_prob""": predefined_args["""dropout"""],
"""hidden_act""": """gelu""",
"""hidden_dropout_prob""": predefined_args["""dropout"""],
"""hidden_size""": predefined_args["""embed_size"""],
"""initializer_range""": 0.02,
"""intermediate_size""": predefined_args["""hidden_size"""],
"""layer_norm_eps""": predefined_args["""layer_norm_eps"""],
"""max_position_embeddings""": predefined_args["""max_length"""],
"""model_type""": """bort""",
"""num_attention_heads""": predefined_args["""num_heads"""],
"""num_hidden_layers""": predefined_args["""num_layers"""],
"""pad_token_id""": 1, # 2 = BERT, 1 = RoBERTa
"""type_vocab_size""": 1, # 2 = BERT, 1 = RoBERTa
"""vocab_size""": len(__lowerCamelCase ),
}
_lowerCAmelCase = BertConfig.from_dict(__lowerCamelCase )
_lowerCAmelCase = BertForMaskedLM(__lowerCamelCase )
hf_bort_model.eval()
# Parameter mapping table (Gluonnlp to Transformers)
# * denotes layer index
#
# | Gluon Parameter | Transformers Parameter
# | -------------------------------------------------------------- | ----------------------
# | `encoder.layer_norm.beta` | `bert.embeddings.LayerNorm.bias`
# | `encoder.layer_norm.gamma` | `bert.embeddings.LayerNorm.weight`
# | `encoder.position_weight` | `bert.embeddings.position_embeddings.weight`
# | `word_embed.0.weight` | `bert.embeddings.word_embeddings.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_key.bias` | `bert.encoder.layer.*.attention.self.key.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_key.weight` | `bert.encoder.layer.*.attention.self.key.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_query.bias` | `bert.encoder.layer.*.attention.self.query.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_value.bias` | `bert.encoder.layer.*.attention.self.value.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
# | `encoder.transformer_cells.*.ffn.ffn_2.bias` | `bert.encoder.layer.*.attention.output.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_2.weight` | `bert.encoder.layer.*.attention.output.dense.weight`
# | `encoder.transformer_cells.*.layer_norm.beta` | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.layer_norm.gamma` | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.ffn.ffn_1.bias` | `bert.encoder.layer.*.intermediate.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_1.weight` | `bert.encoder.layer.*.intermediate.dense.weight`
# | `encoder.transformer_cells.*.ffn.layer_norm.beta` | `bert.encoder.layer.*.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.ffn.layer_norm.gamma` | `bert.encoder.layer.*.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.proj.bias` | `bert.encoder.layer.*.output.dense.bias`
# | `encoder.transformer_cells.*.proj.weight` | `bert.encoder.layer.*.output.dense.weight`
# Helper function to convert MXNET Arrays to PyTorch
def to_torch(__lowerCamelCase :Any ) -> nn.Parameter:
return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy() ) )
# Check param shapes and map new HF param back
def check_and_map_params(__lowerCamelCase :Any , __lowerCamelCase :Tuple ):
_lowerCAmelCase = hf_param.shape
_lowerCAmelCase = to_torch(params[gluon_param] )
_lowerCAmelCase = gluon_param.shape
assert (
shape_hf == shape_gluon
), f'The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'
return gluon_param
_lowerCAmelCase = check_and_map_params(
hf_bort_model.bert.embeddings.word_embeddings.weight , """word_embed.0.weight""" )
_lowerCAmelCase = check_and_map_params(
hf_bort_model.bert.embeddings.position_embeddings.weight , """encoder.position_weight""" )
_lowerCAmelCase = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.bias , """encoder.layer_norm.beta""" )
_lowerCAmelCase = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.weight , """encoder.layer_norm.gamma""" )
# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
_lowerCAmelCase = torch.zeros_like(
hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )
for i in range(hf_bort_config.num_hidden_layers ):
_lowerCAmelCase = hf_bort_model.bert.encoder.layer[i]
# self attention
_lowerCAmelCase = layer.attention.self
_lowerCAmelCase = check_and_map_params(
self_attn.key.bias.data , f'encoder.transformer_cells.{i}.attention_cell.proj_key.bias' )
_lowerCAmelCase = check_and_map_params(
self_attn.key.weight.data , f'encoder.transformer_cells.{i}.attention_cell.proj_key.weight' )
_lowerCAmelCase = check_and_map_params(
self_attn.query.bias.data , f'encoder.transformer_cells.{i}.attention_cell.proj_query.bias' )
_lowerCAmelCase = check_and_map_params(
self_attn.query.weight.data , f'encoder.transformer_cells.{i}.attention_cell.proj_query.weight' )
_lowerCAmelCase = check_and_map_params(
self_attn.value.bias.data , f'encoder.transformer_cells.{i}.attention_cell.proj_value.bias' )
_lowerCAmelCase = check_and_map_params(
self_attn.value.weight.data , f'encoder.transformer_cells.{i}.attention_cell.proj_value.weight' )
# self attention output
_lowerCAmelCase = layer.attention.output
_lowerCAmelCase = check_and_map_params(
self_output.dense.bias , f'encoder.transformer_cells.{i}.proj.bias' )
_lowerCAmelCase = check_and_map_params(
self_output.dense.weight , f'encoder.transformer_cells.{i}.proj.weight' )
_lowerCAmelCase = check_and_map_params(
self_output.LayerNorm.bias , f'encoder.transformer_cells.{i}.layer_norm.beta' )
_lowerCAmelCase = check_and_map_params(
self_output.LayerNorm.weight , f'encoder.transformer_cells.{i}.layer_norm.gamma' )
# intermediate
_lowerCAmelCase = layer.intermediate
_lowerCAmelCase = check_and_map_params(
intermediate.dense.bias , f'encoder.transformer_cells.{i}.ffn.ffn_1.bias' )
_lowerCAmelCase = check_and_map_params(
intermediate.dense.weight , f'encoder.transformer_cells.{i}.ffn.ffn_1.weight' )
# output
_lowerCAmelCase = layer.output
_lowerCAmelCase = check_and_map_params(
bert_output.dense.bias , f'encoder.transformer_cells.{i}.ffn.ffn_2.bias' )
_lowerCAmelCase = check_and_map_params(
bert_output.dense.weight , f'encoder.transformer_cells.{i}.ffn.ffn_2.weight' )
_lowerCAmelCase = check_and_map_params(
bert_output.LayerNorm.bias , f'encoder.transformer_cells.{i}.ffn.layer_norm.beta' )
_lowerCAmelCase = check_and_map_params(
bert_output.LayerNorm.weight , f'encoder.transformer_cells.{i}.ffn.layer_norm.gamma' )
# Save space and energy 🎄
hf_bort_model.half()
# Compare output of both models
_lowerCAmelCase = RobertaTokenizer.from_pretrained("""roberta-base""" )
_lowerCAmelCase = tokenizer.encode_plus(__lowerCamelCase )["""input_ids"""]
# Get gluon output
_lowerCAmelCase = mx.nd.array([input_ids] )
_lowerCAmelCase = original_bort(inputs=__lowerCamelCase , token_types=[] )
# Get Transformer output (save and reload model again)
hf_bort_model.save_pretrained(__lowerCamelCase )
_lowerCAmelCase = BertModel.from_pretrained(__lowerCamelCase )
hf_bort_model.eval()
_lowerCAmelCase = tokenizer.encode_plus(__lowerCamelCase , return_tensors="""pt""" )
_lowerCAmelCase = hf_bort_model(**__lowerCamelCase )[0]
_lowerCAmelCase = output_gluon[0].asnumpy()
_lowerCAmelCase = output_hf[0].detach().numpy()
_lowerCAmelCase = np.max(np.abs(hf_layer - gluon_layer ) ).item()
_lowerCAmelCase = np.allclose(__lowerCamelCase , __lowerCamelCase , atol=1e-3 )
if success:
print("""✔️ Both model do output the same tensors""" )
else:
print("""❌ Both model do **NOT** output the same tensors""" )
print("""Absolute difference is:""" , __lowerCamelCase )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--bort_checkpoint_path""", default=None, type=str, required=True, help="""Path the official Bort params file."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
_lowercase = parser.parse_args()
convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)
| 5 |
'''simple docstring'''
_lowercase = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_lowercase = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
_lowercase = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 5 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info
def A (__lowerCamelCase :Union[str, Any] ):
return {key.lstrip("""-""" ): value for key, value in zip(unknown_args[::2] , unknown_args[1::2] )}
def A ():
_lowerCAmelCase = ArgumentParser(
"""HuggingFace Datasets CLI tool""" , usage="""datasets-cli <command> [<args>]""" , allow_abbrev=__lowerCamelCase )
_lowerCAmelCase = parser.add_subparsers(help="""datasets-cli command helpers""" )
set_verbosity_info()
# Register commands
ConvertCommand.register_subcommand(__lowerCamelCase )
EnvironmentCommand.register_subcommand(__lowerCamelCase )
TestCommand.register_subcommand(__lowerCamelCase )
RunBeamCommand.register_subcommand(__lowerCamelCase )
DummyDataCommand.register_subcommand(__lowerCamelCase )
# Parse args
_lowerCAmelCase , _lowerCAmelCase = parser.parse_known_args()
if not hasattr(__lowerCamelCase , """func""" ):
parser.print_help()
exit(1 )
_lowerCAmelCase = parse_unknown_args(__lowerCamelCase )
# Run
_lowerCAmelCase = args.func(__lowerCamelCase , **__lowerCamelCase )
service.run()
if __name__ == "__main__":
main()
| 5 |
'''simple docstring'''
import functools
def A (__lowerCamelCase :list[int] , __lowerCamelCase :list[int] ):
# Validation
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not all(isinstance(__lowerCamelCase , __lowerCamelCase ) for day in days ):
raise ValueError("""The parameter days should be a list of integers""" )
if len(__lowerCamelCase ) != 3 or not all(isinstance(__lowerCamelCase , __lowerCamelCase ) for cost in costs ):
raise ValueError("""The parameter costs should be a list of three integers""" )
if len(__lowerCamelCase ) == 0:
return 0
if min(__lowerCamelCase ) <= 0:
raise ValueError("""All days elements should be greater than 0""" )
if max(__lowerCamelCase ) >= 366:
raise ValueError("""All days elements should be less than 366""" )
_lowerCAmelCase = set(__lowerCamelCase )
@functools.cache
def dynamic_programming(__lowerCamelCase :int ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
_lowercase = None
_lowercase = logging.get_logger(__name__)
_lowercase = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
_lowercase = {
"""vocab_file""": {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
"""xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
},
"""tokenizer_file""": {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
"""xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
},
}
_lowercase = {
"""xlnet-base-cased""": None,
"""xlnet-large-cased""": None,
}
_lowercase = """▁"""
# Segments (not really needed)
_lowercase = 0
_lowercase = 1
_lowercase = 2
_lowercase = 3
_lowercase = 4
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : str = VOCAB_FILES_NAMES
_lowercase : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
_lowercase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowercase : str = '''left'''
_lowercase : Optional[int] = XLNetTokenizer
def __init__( self , _lowercase=None , _lowercase=None , _lowercase=False , _lowercase=True , _lowercase=False , _lowercase="<s>" , _lowercase="</s>" , _lowercase="<unk>" , _lowercase="<sep>" , _lowercase="<pad>" , _lowercase="<cls>" , _lowercase="<mask>" , _lowercase=["<eop>", "<eod>"] , **_lowercase , ):
"""simple docstring"""
_lowerCAmelCase = AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase ) if isinstance(_lowercase , _lowercase ) else mask_token
super().__init__(
vocab_file=_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , remove_space=_lowercase , keep_accents=_lowercase , bos_token=_lowercase , eos_token=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , additional_special_tokens=_lowercase , **_lowercase , )
_lowerCAmelCase = 3
_lowerCAmelCase = do_lower_case
_lowerCAmelCase = remove_space
_lowerCAmelCase = keep_accents
_lowerCAmelCase = vocab_file
_lowerCAmelCase = False if not self.vocab_file else True
def _lowercase ( self , _lowercase , _lowercase = None ):
"""simple docstring"""
_lowerCAmelCase = [self.sep_token_id]
_lowerCAmelCase = [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 , _lowercase , _lowercase = None ):
"""simple docstring"""
_lowerCAmelCase = [self.sep_token_id]
_lowerCAmelCase = [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 , _lowercase , _lowercase = None ):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_lowercase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
_lowerCAmelCase = os.path.join(
_lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowercase ):
copyfile(self.vocab_file , _lowercase )
return (out_vocab_file,)
| 5 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def A (__lowerCamelCase :List[Any] ):
_lowerCAmelCase = 384
if "tiny" in model_name:
_lowerCAmelCase = [3, 3, 9, 3]
_lowerCAmelCase = [96, 192, 384, 768]
if "small" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [96, 192, 384, 768]
if "base" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [128, 256, 512, 1024]
_lowerCAmelCase = 512
if "large" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [192, 384, 768, 1536]
_lowerCAmelCase = 768
if "xlarge" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [256, 512, 1024, 2048]
_lowerCAmelCase = 1024
# set label information
_lowerCAmelCase = 150
_lowerCAmelCase = """huggingface/label-files"""
_lowerCAmelCase = """ade20k-id2label.json"""
_lowerCAmelCase = json.load(open(hf_hub_download(__lowerCamelCase , __lowerCamelCase , repo_type="""dataset""" ) , """r""" ) )
_lowerCAmelCase = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
_lowerCAmelCase = {v: k for k, v in idalabel.items()}
_lowerCAmelCase = ConvNextConfig(
depths=__lowerCamelCase , hidden_sizes=__lowerCamelCase , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
_lowerCAmelCase = UperNetConfig(
backbone_config=__lowerCamelCase , auxiliary_in_channels=__lowerCamelCase , num_labels=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid=__lowerCamelCase , )
return config
def A (__lowerCamelCase :Optional[Any] ):
_lowerCAmelCase = []
# fmt: off
# stem
rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""") )
rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""") )
rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""") )
rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f'backbone.stages.{i}.{j}.gamma', f'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter') )
rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.weight', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.bias', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias') )
rename_keys.append((f'backbone.stages.{i}.{j}.norm.weight', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.norm.bias', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias') )
if i > 0:
rename_keys.append((f'backbone.downsample_layers.{i}.0.weight', f'backbone.encoder.stages.{i}.downsampling_layer.0.weight') )
rename_keys.append((f'backbone.downsample_layers.{i}.0.bias', f'backbone.encoder.stages.{i}.downsampling_layer.0.bias') )
rename_keys.append((f'backbone.downsample_layers.{i}.1.weight', f'backbone.encoder.stages.{i}.downsampling_layer.1.weight') )
rename_keys.append((f'backbone.downsample_layers.{i}.1.bias', f'backbone.encoder.stages.{i}.downsampling_layer.1.bias') )
rename_keys.append((f'backbone.norm{i}.weight', f'backbone.hidden_states_norms.stage{i+1}.weight') )
rename_keys.append((f'backbone.norm{i}.bias', f'backbone.hidden_states_norms.stage{i+1}.bias') )
# decode head
rename_keys.extend(
[
("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
] )
# fmt: on
return rename_keys
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :Dict , __lowerCamelCase :Tuple ):
_lowerCAmelCase = dct.pop(__lowerCamelCase )
_lowerCAmelCase = val
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Optional[Any] , __lowerCamelCase :Any ):
_lowerCAmelCase = {
"""upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
"""upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
"""upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
"""upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
"""upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
}
_lowerCAmelCase = model_name_to_url[model_name]
_lowerCAmelCase = torch.hub.load_state_dict_from_url(__lowerCamelCase , map_location="""cpu""" )["""state_dict"""]
_lowerCAmelCase = get_upernet_config(__lowerCamelCase )
_lowerCAmelCase = UperNetForSemanticSegmentation(__lowerCamelCase )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
_lowerCAmelCase = state_dict.pop(__lowerCamelCase )
if "bn" in key:
_lowerCAmelCase = key.replace("""bn""" , """batch_norm""" )
_lowerCAmelCase = val
# rename keys
_lowerCAmelCase = create_rename_keys(__lowerCamelCase )
for src, dest in rename_keys:
rename_key(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
model.load_state_dict(__lowerCamelCase )
# verify on image
_lowerCAmelCase = """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
_lowerCAmelCase = Image.open(requests.get(__lowerCamelCase , stream=__lowerCamelCase ).raw ).convert("""RGB""" )
_lowerCAmelCase = SegformerImageProcessor()
_lowerCAmelCase = processor(__lowerCamelCase , return_tensors="""pt""" ).pixel_values
with torch.no_grad():
_lowerCAmelCase = model(__lowerCamelCase )
if model_name == "upernet-convnext-tiny":
_lowerCAmelCase = torch.tensor(
[[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]] )
elif model_name == "upernet-convnext-small":
_lowerCAmelCase = torch.tensor(
[[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]] )
elif model_name == "upernet-convnext-base":
_lowerCAmelCase = torch.tensor(
[[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]] )
elif model_name == "upernet-convnext-large":
_lowerCAmelCase = torch.tensor(
[[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]] )
elif model_name == "upernet-convnext-xlarge":
_lowerCAmelCase = torch.tensor(
[[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]] )
print("""Logits:""" , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , __lowerCamelCase , atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(f'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(__lowerCamelCase )
print(f'Saving processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(__lowerCamelCase )
if push_to_hub:
print(f'Pushing model and processor for {model_name} to hub' )
model.push_to_hub(f'openmmlab/{model_name}' )
processor.push_to_hub(f'openmmlab/{model_name}' )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""upernet-convnext-tiny""",
type=str,
choices=[F"""upernet-convnext-{size}""" for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
help="""Name of the ConvNext UperNet model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_lowercase = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 5 | 1 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
_lowercase = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
_lowercase = """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""
_lowercase = """
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
\"raw_values\" : Returns a full set of errors in case of multioutput input.
\"uniform_average\" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric(\"mse\")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{'mse': 0.6123724356957945}
If you're using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{'mse': array([0.41666667, 1. ])}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"""
] , )
def _lowercase ( self ):
"""simple docstring"""
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value("""float""" ) ),
"references": datasets.Sequence(datasets.Value("""float""" ) ),
}
else:
return {
"predictions": datasets.Value("""float""" ),
"references": datasets.Value("""float""" ),
}
def _lowercase ( self , _lowercase , _lowercase , _lowercase=None , _lowercase="uniform_average" , _lowercase=True ):
"""simple docstring"""
_lowerCAmelCase = mean_squared_error(
_lowercase , _lowercase , sample_weight=_lowercase , multioutput=_lowercase , squared=_lowercase )
return {"mse": mse}
| 5 |
'''simple docstring'''
from itertools import product
def A (__lowerCamelCase :int , __lowerCamelCase :int ):
_lowerCAmelCase = sides_number
_lowerCAmelCase = max_face_number * dice_number
_lowerCAmelCase = [0] * (max_total + 1)
_lowerCAmelCase = 1
_lowerCAmelCase = range(__lowerCamelCase , max_face_number + 1 )
for dice_numbers in product(__lowerCamelCase , repeat=__lowerCamelCase ):
_lowerCAmelCase = sum(__lowerCamelCase )
totals_frequencies[total] += 1
return totals_frequencies
def A ():
_lowerCAmelCase = total_frequency_distribution(
sides_number=4 , dice_number=9 )
_lowerCAmelCase = total_frequency_distribution(
sides_number=6 , dice_number=6 )
_lowerCAmelCase = 0
_lowerCAmelCase = 9
_lowerCAmelCase = 4 * 9
_lowerCAmelCase = 6
for peter_total in range(__lowerCamelCase , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
_lowerCAmelCase = (4**9) * (6**6)
_lowerCAmelCase = peter_wins_count / total_games_number
_lowerCAmelCase = round(__lowerCamelCase , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(F"""{solution() = }""")
| 5 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-question_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-reader-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-ctx_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
),
"""facebook/dpr-question_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
),
"""facebook/dpr-reader-multiset-base""": (
"""https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
),
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : str = '''dpr'''
def __init__( self , _lowercase=30_522 , _lowercase=768 , _lowercase=12 , _lowercase=12 , _lowercase=3_072 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=2 , _lowercase=0.02 , _lowercase=1e-12 , _lowercase=0 , _lowercase="absolute" , _lowercase = 0 , **_lowercase , ):
"""simple docstring"""
super().__init__(pad_token_id=_lowercase , **_lowercase )
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = hidden_act
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_vocab_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = projection_dim
_lowerCAmelCase = position_embedding_type
| 5 |
'''simple docstring'''
from manim import *
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = Rectangle(height=0.5 , width=0.5 )
_lowerCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = VGroup(_lowercase , _lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""CPU""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_lowercase )
_lowerCAmelCase = [mem.copy() for i in range(1 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""GPU""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
gpu.align_to(_lowercase , _lowercase )
gpu.set_x(gpu.get_x() - 1 )
self.add(_lowercase )
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""Model""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
model.move_to([3, -1.0, 0] )
self.play(
Create(_lowercase , run_time=1 ) , Create(_lowercase , run_time=1 ) , Create(_lowercase , run_time=1 ) , )
_lowerCAmelCase = MarkupText(
F'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.' , font_size=24 , )
_lowerCAmelCase = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_lowerCAmelCase = MarkupText(
F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(_lowercase , run_time=2.5 ) , Write(_lowercase ) , Write(_lowercase ) )
self.add(_lowercase )
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = []
for i, rect in enumerate(_lowercase ):
_lowerCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(_lowercase , opacity=0.7 )
cpu_target.move_to(_lowercase )
cpu_target.generate_target()
_lowerCAmelCase = 0.46 / 4
_lowerCAmelCase = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=_lowercase )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=_lowercase , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=_lowercase , buff=0.0 )
cpu_targs.append(_lowercase )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(_lowercase ) )
second_animations.append(MoveToTarget(_lowercase , run_time=1.5 ) )
self.play(*_lowercase )
self.play(*_lowercase )
self.wait()
| 5 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_SCREAMING_SNAKE_CASE )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : str = field(default='''text-classification''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
_lowercase : ClassVar[Features] = Features({'''text''': Value('''string''' )} )
_lowercase : ClassVar[Features] = Features({'''labels''': ClassLabel} )
_lowercase : str = "text"
_lowercase : str = "labels"
def _lowercase ( self , _lowercase ):
"""simple docstring"""
if self.label_column not in features:
raise ValueError(F'Column {self.label_column} is not present in features.' )
if not isinstance(features[self.label_column] , _lowercase ):
raise ValueError(F'Column {self.label_column} is not a ClassLabel.' )
_lowerCAmelCase = copy.deepcopy(self )
_lowerCAmelCase = self.label_schema.copy()
_lowerCAmelCase = features[self.label_column]
_lowerCAmelCase = label_schema
return task_template
@property
def _lowercase ( self ):
"""simple docstring"""
return {
self.text_column: "text",
self.label_column: "labels",
}
| 5 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_lowercase = False
try:
_lowercase = _is_package_available("""google.colab""")
except ModuleNotFoundError:
pass
@input.register
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase = None , _lowercase = [] ):
"""simple docstring"""
_lowerCAmelCase = 0
_lowerCAmelCase = choices
_lowerCAmelCase = prompt
if sys.platform == "win32":
_lowerCAmelCase = """*"""
else:
_lowerCAmelCase = """➔ """
def _lowercase ( self , _lowercase , _lowercase = "" ):
"""simple docstring"""
if sys.platform != "win32":
writeColor(self.choices[index] , 32 , _lowercase )
else:
forceWrite(self.choices[index] , _lowercase )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
if index == self.position:
forceWrite(F' {self.arrow_char} ' )
self.write_choice(_lowercase )
else:
forceWrite(F' {self.choices[index]}' )
reset_cursor()
def _lowercase ( self , _lowercase , _lowercase = 1 ):
"""simple docstring"""
_lowerCAmelCase = self.position
if direction == Direction.DOWN:
if self.position + 1 >= len(self.choices ):
return
self.position += num_spaces
else:
if self.position - 1 < 0:
return
self.position -= num_spaces
clear_line()
self.print_choice(_lowercase )
move_cursor(_lowercase , direction.name )
self.print_choice(self.position )
@input.mark(KEYMAP["""up"""] )
def _lowercase ( self ):
"""simple docstring"""
self.move_direction(Direction.UP )
@input.mark(KEYMAP["""down"""] )
def _lowercase ( self ):
"""simple docstring"""
self.move_direction(Direction.DOWN )
@input.mark(KEYMAP["""newline"""] )
def _lowercase ( self ):
"""simple docstring"""
move_cursor(len(self.choices ) - self.position , """DOWN""" )
return self.position
@input.mark(KEYMAP["""interrupt"""] )
def _lowercase ( self ):
"""simple docstring"""
move_cursor(len(self.choices ) - self.position , """DOWN""" )
raise KeyboardInterrupt
@input.mark_multiple(*[KEYMAP[str(_lowercase )] for number in range(10 )] )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = int(chr(self.current_selection ) )
_lowerCAmelCase = index - self.position
if index == self.position:
return
if index < len(self.choices ):
if self.position > index:
self.move_direction(Direction.UP , -movement )
elif self.position < index:
self.move_direction(Direction.DOWN , _lowercase )
else:
return
else:
return
def _lowercase ( self , _lowercase = 0 ):
"""simple docstring"""
if self.prompt:
linebreak()
forceWrite(self.prompt , """\n""" )
if in_colab:
forceWrite("""Please input a choice index (starting from 0), and press enter""" , """\n""" )
else:
forceWrite("""Please select a choice using the arrow or number keys, and selecting with enter""" , """\n""" )
_lowerCAmelCase = default_choice
for i in range(len(self.choices ) ):
self.print_choice(_lowercase )
forceWrite("""\n""" )
move_cursor(len(self.choices ) - self.position , """UP""" )
with cursor.hide():
while True:
if in_colab:
try:
_lowerCAmelCase = int(builtins.input() )
except ValueError:
_lowerCAmelCase = default_choice
else:
_lowerCAmelCase = self.handle_input()
if choice is not None:
reset_cursor()
for _ in range(len(self.choices ) + 1 ):
move_cursor(1 , """UP""" )
clear_line()
self.write_choice(_lowercase , """\n""" )
return choice
| 5 | 1 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
_lowercase = 16
_lowercase = 32
def A (__lowerCamelCase :Accelerator , __lowerCamelCase :int = 16 , __lowerCamelCase :str = "bert-base-cased" ):
_lowerCAmelCase = AutoTokenizer.from_pretrained(__lowerCamelCase )
_lowerCAmelCase = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(__lowerCamelCase :int ):
# max_length=None => use the model max length (it's actually the default)
_lowerCAmelCase = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=__lowerCamelCase , max_length=__lowerCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
_lowerCAmelCase = datasets.map(
__lowerCamelCase , batched=__lowerCamelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=__lowerCamelCase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_lowerCAmelCase = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(__lowerCamelCase :List[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__lowerCamelCase , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return tokenizer.pad(__lowerCamelCase , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
_lowerCAmelCase = DataLoader(
tokenized_datasets["""train"""] , shuffle=__lowerCamelCase , collate_fn=__lowerCamelCase , batch_size=__lowerCamelCase )
_lowerCAmelCase = DataLoader(
tokenized_datasets["""validation"""] , shuffle=__lowerCamelCase , collate_fn=__lowerCamelCase , batch_size=__lowerCamelCase )
return train_dataloader, eval_dataloader
def A (__lowerCamelCase :Any , __lowerCamelCase :Optional[int] , __lowerCamelCase :Tuple , __lowerCamelCase :Union[str, Any] ):
model.eval()
_lowerCAmelCase = 0
for step, batch in enumerate(__lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_lowerCAmelCase = model(**__lowerCamelCase )
_lowerCAmelCase = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
_lowerCAmelCase , _lowerCAmelCase = accelerator.gather(
(predictions, batch["""labels"""]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(__lowerCamelCase ) - 1:
_lowerCAmelCase = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_lowerCAmelCase = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__lowerCamelCase , references=__lowerCamelCase , )
_lowerCAmelCase = metric.compute()
return eval_metric["accuracy"]
def A (__lowerCamelCase :Tuple , __lowerCamelCase :Optional[Any] ):
# Initialize accelerator
_lowerCAmelCase = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_lowerCAmelCase = config["""lr"""]
_lowerCAmelCase = int(config["""num_epochs"""] )
_lowerCAmelCase = int(config["""seed"""] )
_lowerCAmelCase = int(config["""batch_size"""] )
_lowerCAmelCase = args.model_name_or_path
set_seed(__lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase = get_dataloaders(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_lowerCAmelCase = AutoModelForSequenceClassification.from_pretrained(__lowerCamelCase , return_dict=__lowerCamelCase )
# Instantiate optimizer
_lowerCAmelCase = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
_lowerCAmelCase = optimizer_cls(params=model.parameters() , lr=__lowerCamelCase )
if accelerator.state.deepspeed_plugin is not None:
_lowerCAmelCase = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
_lowerCAmelCase = 1
_lowerCAmelCase = (len(__lowerCamelCase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
_lowerCAmelCase = get_linear_schedule_with_warmup(
optimizer=__lowerCamelCase , num_warmup_steps=0 , num_training_steps=__lowerCamelCase , )
else:
_lowerCAmelCase = DummyScheduler(__lowerCamelCase , total_num_steps=__lowerCamelCase , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = accelerator.prepare(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# We need to keep track of how many total steps we have iterated over
_lowerCAmelCase = 0
# We also need to keep track of the stating epoch so files are named properly
_lowerCAmelCase = 0
_lowerCAmelCase = evaluate.load("""glue""" , """mrpc""" )
_lowerCAmelCase = num_epochs
if args.partial_train_epoch is not None:
_lowerCAmelCase = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
_lowerCAmelCase = args.resume_from_checkpoint.split("""epoch_""" )[1]
_lowerCAmelCase = """"""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
_lowerCAmelCase = int(__lowerCamelCase ) + 1
_lowerCAmelCase = evaluation_loop(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
accelerator.print("""resumed checkpoint performance:""" , __lowerCamelCase )
accelerator.print("""resumed checkpoint's scheduler's lr:""" , lr_scheduler.get_lr()[0] )
accelerator.print("""resumed optimizers's lr:""" , optimizer.param_groups[0]["""lr"""] )
with open(os.path.join(args.output_dir , f'state_{starting_epoch-1}.json' ) , """r""" ) as f:
_lowerCAmelCase = json.load(__lowerCamelCase )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
_lowerCAmelCase = {}
for epoch in range(__lowerCamelCase , __lowerCamelCase ):
model.train()
for step, batch in enumerate(__lowerCamelCase ):
_lowerCAmelCase = model(**__lowerCamelCase )
_lowerCAmelCase = outputs.loss
_lowerCAmelCase = loss / gradient_accumulation_steps
accelerator.backward(__lowerCamelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
_lowerCAmelCase = f'epoch_{epoch}'
_lowerCAmelCase = os.path.join(args.output_dir , __lowerCamelCase )
accelerator.save_state(__lowerCamelCase )
_lowerCAmelCase = evaluation_loop(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = accuracy
_lowerCAmelCase = lr_scheduler.get_lr()[0]
_lowerCAmelCase = optimizer.param_groups[0]["""lr"""]
_lowerCAmelCase = epoch
_lowerCAmelCase = overall_step
accelerator.print(f'epoch {epoch}:' , __lowerCamelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , f'state_{epoch}.json' ) , """w""" ) as f:
json.dump(__lowerCamelCase , __lowerCamelCase )
def A ():
_lowerCAmelCase = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=__lowerCamelCase , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=__lowerCamelCase , )
parser.add_argument(
"""--output_dir""" , type=__lowerCamelCase , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--resume_from_checkpoint""" , type=__lowerCamelCase , default=__lowerCamelCase , help="""If the training should continue from a checkpoint folder.""" , )
parser.add_argument(
"""--partial_train_epoch""" , type=__lowerCamelCase , default=__lowerCamelCase , help="""If passed, the training will stop after this number of epochs.""" , )
parser.add_argument(
"""--num_epochs""" , type=__lowerCamelCase , default=2 , help="""Number of train epochs.""" , )
_lowerCAmelCase = parser.parse_args()
_lowerCAmelCase = {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(__lowerCamelCase , __lowerCamelCase )
if __name__ == "__main__":
main()
| 5 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""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""",
"""adapter_layer""": """encoder.layers.*.adapter_layer""",
"""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""",
"""pooling_layer.linear""": """projector""",
"""pooling_layer.projection""": """classifier""",
}
_lowercase = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""projector""",
"""classifier""",
]
def A (__lowerCamelCase :Optional[int] ):
_lowerCAmelCase = {}
with open(__lowerCamelCase , """r""" ) as file:
for line_number, line in enumerate(__lowerCamelCase ):
_lowerCAmelCase = line.strip()
if line:
_lowerCAmelCase = line.split()
_lowerCAmelCase = line_number
_lowerCAmelCase = words[0]
_lowerCAmelCase = value
return result
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :Any , __lowerCamelCase :Tuple , __lowerCamelCase :List[Any] , __lowerCamelCase :List[str] ):
for attribute in key.split(""".""" ):
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(__lowerCamelCase ):
_lowerCAmelCase = PARAM_MAPPING[full_name.split(""".""" )[-1]]
_lowerCAmelCase = """param"""
if weight_type is not None and weight_type != "param":
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase ).shape
elif weight_type is not None and weight_type == "param":
_lowerCAmelCase = hf_pointer
for attribute in hf_param_name.split(""".""" ):
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = shape_pointer.shape
# let's reduce dimension
_lowerCAmelCase = value[0]
else:
_lowerCAmelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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":
_lowerCAmelCase = value
elif weight_type == "weight_g":
_lowerCAmelCase = value
elif weight_type == "weight_v":
_lowerCAmelCase = value
elif weight_type == "bias":
_lowerCAmelCase = value
elif weight_type == "param":
for attribute in hf_param_name.split(""".""" ):
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = value
else:
_lowerCAmelCase = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Tuple , __lowerCamelCase :Dict , __lowerCamelCase :List[Any] , __lowerCamelCase :int ):
_lowerCAmelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(__lowerCamelCase ):
_lowerCAmelCase = PARAM_MAPPING[full_name.split(""".""" )[-1]]
_lowerCAmelCase = """param"""
if weight_type is not None and weight_type != "param":
_lowerCAmelCase = """.""".join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
_lowerCAmelCase = """.""".join([key, hf_param_name] )
else:
_lowerCAmelCase = key
_lowerCAmelCase = value if """lm_head""" in full_key else value[0]
_lowercase = {
"""W_a""": """linear_1.weight""",
"""W_b""": """linear_2.weight""",
"""b_a""": """linear_1.bias""",
"""b_b""": """linear_2.bias""",
"""ln_W""": """norm.weight""",
"""ln_b""": """norm.bias""",
}
def A (__lowerCamelCase :Any , __lowerCamelCase :int , __lowerCamelCase :List[str]=None , __lowerCamelCase :List[Any]=None ):
_lowerCAmelCase = False
for key, mapped_key in MAPPING.items():
_lowerCAmelCase = """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
_lowerCAmelCase = True
if "*" in mapped_key:
_lowerCAmelCase = name.split(__lowerCamelCase )[0].split(""".""" )[-2]
_lowerCAmelCase = mapped_key.replace("""*""" , __lowerCamelCase )
if "weight_g" in name:
_lowerCAmelCase = """weight_g"""
elif "weight_v" in name:
_lowerCAmelCase = """weight_v"""
elif "bias" in name:
_lowerCAmelCase = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_lowerCAmelCase = """weight"""
else:
_lowerCAmelCase = None
if hf_dict is not None:
rename_dict(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
else:
set_recursively(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return is_used
return is_used
def A (__lowerCamelCase :Any , __lowerCamelCase :Dict , __lowerCamelCase :Dict ):
_lowerCAmelCase = []
_lowerCAmelCase = fairseq_model.state_dict()
_lowerCAmelCase = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
_lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , hf_model.config.feat_extract_norm == """group""" , )
_lowerCAmelCase = True
else:
_lowerCAmelCase = load_wavaveca_layer(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
if not is_used:
unused_weights.append(__lowerCamelCase )
logger.warning(f'Unused weights: {unused_weights}' )
def A (__lowerCamelCase :Tuple , __lowerCamelCase :Optional[int] , __lowerCamelCase :Any , __lowerCamelCase :List[Any] , __lowerCamelCase :List[Any] ):
_lowerCAmelCase = full_name.split("""conv_layers.""" )[-1]
_lowerCAmelCase = name.split(""".""" )
_lowerCAmelCase = int(items[0] )
_lowerCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
_lowerCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
_lowerCAmelCase = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.' )
_lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.' )
_lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(__lowerCamelCase )
@torch.no_grad()
def A (__lowerCamelCase :List[str] , __lowerCamelCase :Tuple , __lowerCamelCase :List[Any]=None , __lowerCamelCase :Union[str, Any]=None , __lowerCamelCase :str=True , __lowerCamelCase :str=False ):
if config_path is not None:
_lowerCAmelCase = WavaVecaConfig.from_pretrained(__lowerCamelCase )
else:
_lowerCAmelCase = WavaVecaConfig()
if is_seq_class:
_lowerCAmelCase = read_txt_into_dict(__lowerCamelCase )
_lowerCAmelCase = idalabel
_lowerCAmelCase = WavaVecaForSequenceClassification(__lowerCamelCase )
_lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__lowerCamelCase , return_attention_mask=__lowerCamelCase , )
feature_extractor.save_pretrained(__lowerCamelCase )
elif is_finetuned:
if dict_path:
_lowerCAmelCase = Dictionary.load(__lowerCamelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_lowerCAmelCase = target_dict.pad_index
_lowerCAmelCase = target_dict.bos_index
_lowerCAmelCase = target_dict.eos_index
_lowerCAmelCase = len(target_dict.symbols )
_lowerCAmelCase = os.path.join(__lowerCamelCase , """vocab.json""" )
if not os.path.isdir(__lowerCamelCase ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__lowerCamelCase ) )
return
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
_lowerCAmelCase = target_dict.indices
# fairseq has the <pad> and <s> switched
_lowerCAmelCase = 0
_lowerCAmelCase = 1
with open(__lowerCamelCase , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = WavaVecaCTCTokenizer(
__lowerCamelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=__lowerCamelCase , )
_lowerCAmelCase = True if config.feat_extract_norm == """layer""" else False
_lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__lowerCamelCase , return_attention_mask=__lowerCamelCase , )
_lowerCAmelCase = WavaVecaProcessor(feature_extractor=__lowerCamelCase , tokenizer=__lowerCamelCase )
processor.save_pretrained(__lowerCamelCase )
_lowerCAmelCase = WavaVecaForCTC(__lowerCamelCase )
else:
_lowerCAmelCase = WavaVecaForPreTraining(__lowerCamelCase )
if is_finetuned or is_seq_class:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
_lowerCAmelCase = argparse.Namespace(task="""audio_pretraining""" )
_lowerCAmelCase = fairseq.tasks.setup_task(__lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=__lowerCamelCase )
_lowerCAmelCase = model[0].eval()
recursively_load_weights(__lowerCamelCase , __lowerCamelCase , not is_finetuned )
hf_wavavec.save_pretrained(__lowerCamelCase )
if __name__ == "__main__":
_lowercase = 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_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
parser.add_argument(
"""--is_seq_class""",
action="""store_true""",
help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
)
_lowercase = parser.parse_args()
_lowercase = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 5 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class UpperCAmelCase_ :
'''simple docstring'''
_lowercase : int
_lowercase : Node | None = None
_lowercase : Node | None = None
def A ():
_lowerCAmelCase = Node(1 )
_lowerCAmelCase = Node(2 )
_lowerCAmelCase = Node(3 )
_lowerCAmelCase = Node(4 )
_lowerCAmelCase = Node(5 )
return tree
def A (__lowerCamelCase :Node | None ):
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def A (__lowerCamelCase :Node | None ):
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def A (__lowerCamelCase :Node | None ):
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def A (__lowerCamelCase :Node | None ):
return (max(height(root.left ) , height(root.right ) ) + 1) if root else 0
def A (__lowerCamelCase :Node | None ):
_lowerCAmelCase = []
if root is None:
return output
_lowerCAmelCase = deque([root] )
while process_queue:
_lowerCAmelCase = process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def A (__lowerCamelCase :Node | None , __lowerCamelCase :int ):
_lowerCAmelCase = []
def populate_output(__lowerCamelCase :Node | None , __lowerCamelCase :int ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left , level - 1 )
populate_output(root.right , level - 1 )
populate_output(__lowerCamelCase , __lowerCamelCase )
return output
def A (__lowerCamelCase :Node | None , __lowerCamelCase :int ):
_lowerCAmelCase = []
def populate_output(__lowerCamelCase :Node | None , __lowerCamelCase :int ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right , level - 1 )
populate_output(root.left , level - 1 )
populate_output(__lowerCamelCase , __lowerCamelCase )
return output
def A (__lowerCamelCase :Node | None ):
if root is None:
return []
_lowerCAmelCase = []
_lowerCAmelCase = 0
_lowerCAmelCase = height(__lowerCamelCase )
for h in range(1 , height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(__lowerCamelCase , __lowerCamelCase ) )
_lowerCAmelCase = 1
else:
output.append(get_nodes_from_right_to_left(__lowerCamelCase , __lowerCamelCase ) )
_lowerCAmelCase = 0
return output
def A (): # Main function for testing.
_lowerCAmelCase = make_tree()
print(f'In-order Traversal: {inorder(__lowerCamelCase )}' )
print(f'Pre-order Traversal: {preorder(__lowerCamelCase )}' )
print(f'Post-order Traversal: {postorder(__lowerCamelCase )}' , """\n""" )
print(f'Height of Tree: {height(__lowerCamelCase )}' , """\n""" )
print("""Complete Level Order Traversal: """ )
print(level_order(__lowerCamelCase ) , """\n""" )
print("""Level-wise order Traversal: """ )
for level in range(1 , height(__lowerCamelCase ) + 1 ):
print(f'Level {level}:' , get_nodes_from_left_to_right(__lowerCamelCase , level=__lowerCamelCase ) )
print("""\nZigZag order Traversal: """ )
print(zigzag(__lowerCamelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 5 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[str] = '''decision_transformer'''
_lowercase : Optional[Any] = ['''past_key_values''']
_lowercase : str = {
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , _lowercase=17 , _lowercase=4 , _lowercase=128 , _lowercase=4_096 , _lowercase=True , _lowercase=1 , _lowercase=1_024 , _lowercase=3 , _lowercase=1 , _lowercase=None , _lowercase="relu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=0.1 , _lowercase=1e-5 , _lowercase=0.02 , _lowercase=True , _lowercase=True , _lowercase=50_256 , _lowercase=50_256 , _lowercase=False , _lowercase=False , **_lowercase , ):
"""simple docstring"""
_lowerCAmelCase = state_dim
_lowerCAmelCase = act_dim
_lowerCAmelCase = hidden_size
_lowerCAmelCase = max_ep_len
_lowerCAmelCase = action_tanh
_lowerCAmelCase = vocab_size
_lowerCAmelCase = n_positions
_lowerCAmelCase = n_layer
_lowerCAmelCase = n_head
_lowerCAmelCase = n_inner
_lowerCAmelCase = activation_function
_lowerCAmelCase = resid_pdrop
_lowerCAmelCase = embd_pdrop
_lowerCAmelCase = attn_pdrop
_lowerCAmelCase = layer_norm_epsilon
_lowerCAmelCase = initializer_range
_lowerCAmelCase = scale_attn_weights
_lowerCAmelCase = use_cache
_lowerCAmelCase = scale_attn_by_inverse_layer_idx
_lowerCAmelCase = reorder_and_upcast_attn
_lowerCAmelCase = bos_token_id
_lowerCAmelCase = eos_token_id
super().__init__(bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase )
| 5 | 1 |
'''simple docstring'''
def A (__lowerCamelCase :int = 10 , __lowerCamelCase :int = 22 ):
_lowerCAmelCase = range(1 , __lowerCamelCase )
_lowerCAmelCase = range(1 , __lowerCamelCase )
return sum(
1 for power in powers for base in bases if len(str(base**power ) ) == power )
if __name__ == "__main__":
print(F"""{solution(10, 22) = }""")
| 5 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
"""The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"""
)
_lowercase = None
_lowercase = {
"""7B""": 11008,
"""13B""": 13824,
"""30B""": 17920,
"""65B""": 22016,
"""70B""": 28672,
}
_lowercase = {
"""7B""": 1,
"""7Bf""": 1,
"""13B""": 2,
"""13Bf""": 2,
"""30B""": 4,
"""65B""": 8,
"""70B""": 8,
"""70Bf""": 8,
}
def A (__lowerCamelCase :int , __lowerCamelCase :Optional[Any]=1 , __lowerCamelCase :List[Any]=256 ):
return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of)
def A (__lowerCamelCase :Any ):
with open(__lowerCamelCase , """r""" ) as f:
return json.load(__lowerCamelCase )
def A (__lowerCamelCase :List[Any] , __lowerCamelCase :int ):
with open(__lowerCamelCase , """w""" ) as f:
json.dump(__lowerCamelCase , __lowerCamelCase )
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :Tuple , __lowerCamelCase :Optional[Any] , __lowerCamelCase :Tuple=True ):
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
_lowerCAmelCase = os.path.join(__lowerCamelCase , """tmp""" )
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
_lowerCAmelCase = read_json(os.path.join(__lowerCamelCase , """params.json""" ) )
_lowerCAmelCase = NUM_SHARDS[model_size]
_lowerCAmelCase = params["""n_layers"""]
_lowerCAmelCase = params["""n_heads"""]
_lowerCAmelCase = n_heads // num_shards
_lowerCAmelCase = params["""dim"""]
_lowerCAmelCase = dim // n_heads
_lowerCAmelCase = 10_000.0
_lowerCAmelCase = 1.0 / (base ** (torch.arange(0 , __lowerCamelCase , 2 ).float() / dims_per_head))
if "n_kv_heads" in params:
_lowerCAmelCase = params["""n_kv_heads"""] # for GQA / MQA
_lowerCAmelCase = n_heads_per_shard // num_key_value_heads
_lowerCAmelCase = dim // num_key_value_heads
else: # compatibility with other checkpoints
_lowerCAmelCase = n_heads
_lowerCAmelCase = n_heads_per_shard
_lowerCAmelCase = dim
# permute for sliced rotary
def permute(__lowerCamelCase :Optional[int] , __lowerCamelCase :str=n_heads , __lowerCamelCase :str=dim , __lowerCamelCase :List[Any]=dim ):
return w.view(__lowerCamelCase , dima // n_heads // 2 , 2 , __lowerCamelCase ).transpose(1 , 2 ).reshape(__lowerCamelCase , __lowerCamelCase )
print(f'Fetching all parameters from the checkpoint at {input_base_path}.' )
# Load weights
if model_size == "7B":
# Not sharded
# (The sharded implementation would also work, but this is simpler.)
_lowerCAmelCase = torch.load(os.path.join(__lowerCamelCase , """consolidated.00.pth""" ) , map_location="""cpu""" )
else:
# Sharded
_lowerCAmelCase = [
torch.load(os.path.join(__lowerCamelCase , f'consolidated.{i:02d}.pth' ) , map_location="""cpu""" )
for i in range(__lowerCamelCase )
]
_lowerCAmelCase = 0
_lowerCAmelCase = {"""weight_map""": {}}
for layer_i in range(__lowerCamelCase ):
_lowerCAmelCase = f'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
if model_size == "7B":
# Unsharded
_lowerCAmelCase = {
f'model.layers.{layer_i}.self_attn.q_proj.weight': permute(
loaded[f'layers.{layer_i}.attention.wq.weight'] ),
f'model.layers.{layer_i}.self_attn.k_proj.weight': permute(
loaded[f'layers.{layer_i}.attention.wk.weight'] ),
f'model.layers.{layer_i}.self_attn.v_proj.weight': loaded[f'layers.{layer_i}.attention.wv.weight'],
f'model.layers.{layer_i}.self_attn.o_proj.weight': loaded[f'layers.{layer_i}.attention.wo.weight'],
f'model.layers.{layer_i}.mlp.gate_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w1.weight'],
f'model.layers.{layer_i}.mlp.down_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w2.weight'],
f'model.layers.{layer_i}.mlp.up_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w3.weight'],
f'model.layers.{layer_i}.input_layernorm.weight': loaded[f'layers.{layer_i}.attention_norm.weight'],
f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[f'layers.{layer_i}.ffn_norm.weight'],
}
else:
# Sharded
# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
_lowerCAmelCase = {
f'model.layers.{layer_i}.input_layernorm.weight': loaded[0][
f'layers.{layer_i}.attention_norm.weight'
].clone(),
f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[0][
f'layers.{layer_i}.ffn_norm.weight'
].clone(),
}
_lowerCAmelCase = permute(
torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wq.weight'].view(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for i in range(__lowerCamelCase )
] , dim=0 , ).reshape(__lowerCamelCase , __lowerCamelCase ) )
_lowerCAmelCase = permute(
torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wk.weight'].view(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for i in range(__lowerCamelCase )
] , dim=0 , ).reshape(__lowerCamelCase , __lowerCamelCase ) , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , )
_lowerCAmelCase = torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wv.weight'].view(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for i in range(__lowerCamelCase )
] , dim=0 , ).reshape(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.attention.wo.weight'] for i in range(__lowerCamelCase )] , dim=1 )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w1.weight'] for i in range(__lowerCamelCase )] , dim=0 )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w2.weight'] for i in range(__lowerCamelCase )] , dim=1 )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w3.weight'] for i in range(__lowerCamelCase )] , dim=0 )
_lowerCAmelCase = inv_freq
for k, v in state_dict.items():
_lowerCAmelCase = filename
param_count += v.numel()
torch.save(__lowerCamelCase , os.path.join(__lowerCamelCase , __lowerCamelCase ) )
_lowerCAmelCase = f'pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'
if model_size == "7B":
# Unsharded
_lowerCAmelCase = {
"""model.embed_tokens.weight""": loaded["""tok_embeddings.weight"""],
"""model.norm.weight""": loaded["""norm.weight"""],
"""lm_head.weight""": loaded["""output.weight"""],
}
else:
_lowerCAmelCase = {
"""model.norm.weight""": loaded[0]["""norm.weight"""],
"""model.embed_tokens.weight""": torch.cat(
[loaded[i]["""tok_embeddings.weight"""] for i in range(__lowerCamelCase )] , dim=1 ),
"""lm_head.weight""": torch.cat([loaded[i]["""output.weight"""] for i in range(__lowerCamelCase )] , dim=0 ),
}
for k, v in state_dict.items():
_lowerCAmelCase = filename
param_count += v.numel()
torch.save(__lowerCamelCase , os.path.join(__lowerCamelCase , __lowerCamelCase ) )
# Write configs
_lowerCAmelCase = {"""total_size""": param_count * 2}
write_json(__lowerCamelCase , os.path.join(__lowerCamelCase , """pytorch_model.bin.index.json""" ) )
_lowerCAmelCase = params["""ffn_dim_multiplier"""] if """ffn_dim_multiplier""" in params else 1
_lowerCAmelCase = params["""multiple_of"""] if """multiple_of""" in params else 256
_lowerCAmelCase = LlamaConfig(
hidden_size=__lowerCamelCase , intermediate_size=compute_intermediate_size(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) , num_attention_heads=params["""n_heads"""] , num_hidden_layers=params["""n_layers"""] , rms_norm_eps=params["""norm_eps"""] , num_key_value_heads=__lowerCamelCase , )
config.save_pretrained(__lowerCamelCase )
# Make space so we can load the model properly now.
del state_dict
del loaded
gc.collect()
print("""Loading the checkpoint in a Llama model.""" )
_lowerCAmelCase = LlamaForCausalLM.from_pretrained(__lowerCamelCase , torch_dtype=torch.floataa , low_cpu_mem_usage=__lowerCamelCase )
# Avoid saving this as part of the config.
del model.config._name_or_path
print("""Saving in the Transformers format.""" )
model.save_pretrained(__lowerCamelCase , safe_serialization=__lowerCamelCase )
shutil.rmtree(__lowerCamelCase )
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Union[str, Any] ):
# Initialize the tokenizer based on the `spm` model
_lowerCAmelCase = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
print(f'Saving a {tokenizer_class.__name__} to {tokenizer_path}.' )
_lowerCAmelCase = tokenizer_class(__lowerCamelCase )
tokenizer.save_pretrained(__lowerCamelCase )
def A ():
_lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--input_dir""" , help="""Location of LLaMA weights, which contains tokenizer.model and model folders""" , )
parser.add_argument(
"""--model_size""" , choices=["""7B""", """7Bf""", """13B""", """13Bf""", """30B""", """65B""", """70B""", """70Bf""", """tokenizer_only"""] , )
parser.add_argument(
"""--output_dir""" , help="""Location to write HF model and tokenizer""" , )
parser.add_argument("""--safe_serialization""" , type=__lowerCamelCase , help="""Whether or not to save using `safetensors`.""" )
_lowerCAmelCase = parser.parse_args()
if args.model_size != "tokenizer_only":
write_model(
model_path=args.output_dir , input_base_path=os.path.join(args.input_dir , args.model_size ) , model_size=args.model_size , safe_serialization=args.safe_serialization , )
_lowerCAmelCase = os.path.join(args.input_dir , """tokenizer.model""" )
write_tokenizer(args.output_dir , __lowerCamelCase )
if __name__ == "__main__":
main()
| 5 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
"""configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
"""processing_git""": ["""GitProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""GIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GitForCausalLM""",
"""GitModel""",
"""GitPreTrainedModel""",
"""GitVisionModel""",
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 5 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Tuple = (DDPMScheduler,)
def _lowercase ( self , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_lowercase )
return config
def _lowercase ( self ):
"""simple docstring"""
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_lowercase )
def _lowercase ( self ):
"""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=_lowercase , beta_end=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
self.check_over_configs(thresholding=_lowercase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_lowercase , prediction_type=_lowercase , sample_max_value=_lowercase , )
def _lowercase ( self ):
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
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 _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = len(_lowercase )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter
_lowerCAmelCase = torch.manual_seed(0 )
for t in reversed(range(_lowercase ) ):
# 1. predict noise residual
_lowerCAmelCase = model(_lowercase , _lowercase )
# 2. predict previous mean of sample x_t-1
_lowerCAmelCase = scheduler.step(_lowercase , _lowercase , _lowercase , generator=_lowercase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_lowerCAmelCase = pred_prev_sample
_lowerCAmelCase = torch.sum(torch.abs(_lowercase ) )
_lowerCAmelCase = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 258.9606 ) < 1e-2
assert abs(result_mean.item() - 0.3372 ) < 1e-3
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config(prediction_type="""v_prediction""" )
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = len(_lowercase )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter
_lowerCAmelCase = torch.manual_seed(0 )
for t in reversed(range(_lowercase ) ):
# 1. predict noise residual
_lowerCAmelCase = model(_lowercase , _lowercase )
# 2. predict previous mean of sample x_t-1
_lowerCAmelCase = scheduler.step(_lowercase , _lowercase , _lowercase , generator=_lowercase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_lowerCAmelCase = pred_prev_sample
_lowerCAmelCase = torch.sum(torch.abs(_lowercase ) )
_lowerCAmelCase = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 202.0296 ) < 1e-2
assert abs(result_mean.item() - 0.2631 ) < 1e-3
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_lowercase )
_lowerCAmelCase = scheduler.timesteps
for i, timestep in enumerate(_lowercase ):
if i == len(_lowercase ) - 1:
_lowerCAmelCase = -1
else:
_lowerCAmelCase = timesteps[i + 1]
_lowerCAmelCase = scheduler.previous_timestep(_lowercase )
_lowerCAmelCase = prev_t.item()
self.assertEqual(_lowercase , _lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 51, 0]
with self.assertRaises(_lowercase , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 1, 0]
_lowerCAmelCase = len(_lowercase )
with self.assertRaises(_lowercase , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_lowercase , timesteps=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_lowercase , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_lowercase )
| 5 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[Any] = '''Wav2Vec2FeatureExtractor'''
_lowercase : Optional[Any] = '''AutoTokenizer'''
def __init__( self , _lowercase , _lowercase ):
"""simple docstring"""
super().__init__(_lowercase , _lowercase )
_lowerCAmelCase = self.feature_extractor
_lowerCAmelCase = False
@classmethod
def _lowercase ( cls , _lowercase , **_lowercase ):
"""simple docstring"""
try:
return super().from_pretrained(_lowercase , **_lowercase )
except OSError:
warnings.warn(
F'Loading a tokenizer inside {cls.__name__} from a config that does not'
""" include a `tokenizer_class` attribute is deprecated and will be """
"""removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
""" attribute to either your `config.json` or `tokenizer_config.json` """
"""file to suppress this warning: """ , _lowercase , )
_lowerCAmelCase = WavaVecaFeatureExtractor.from_pretrained(_lowercase , **_lowercase )
_lowerCAmelCase = WavaVecaCTCTokenizer.from_pretrained(_lowercase , **_lowercase )
return cls(feature_extractor=_lowercase , tokenizer=_lowercase )
def __call__( self , *_lowercase , **_lowercase ):
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor(*_lowercase , **_lowercase )
if "raw_speech" in kwargs:
warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
_lowerCAmelCase = kwargs.pop("""raw_speech""" )
else:
_lowerCAmelCase = kwargs.pop("""audio""" , _lowercase )
_lowerCAmelCase = kwargs.pop("""sampling_rate""" , _lowercase )
_lowerCAmelCase = kwargs.pop("""text""" , _lowercase )
if len(_lowercase ) > 0:
_lowerCAmelCase = args[0]
_lowerCAmelCase = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if audio is not None:
_lowerCAmelCase = self.feature_extractor(_lowercase , *_lowercase , sampling_rate=_lowercase , **_lowercase )
if text is not None:
_lowerCAmelCase = self.tokenizer(_lowercase , **_lowercase )
if text is None:
return inputs
elif audio is None:
return encodings
else:
_lowerCAmelCase = encodings["""input_ids"""]
return inputs
def _lowercase ( self , *_lowercase , **_lowercase ):
"""simple docstring"""
if self._in_target_context_manager:
return self.current_processor.pad(*_lowercase , **_lowercase )
_lowerCAmelCase = kwargs.pop("""input_features""" , _lowercase )
_lowerCAmelCase = kwargs.pop("""labels""" , _lowercase )
if len(_lowercase ) > 0:
_lowerCAmelCase = args[0]
_lowerCAmelCase = args[1:]
if input_features is not None:
_lowerCAmelCase = self.feature_extractor.pad(_lowercase , *_lowercase , **_lowercase )
if labels is not None:
_lowerCAmelCase = self.tokenizer.pad(_lowercase , **_lowercase )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
_lowerCAmelCase = labels["""input_ids"""]
return input_features
def _lowercase ( self , *_lowercase , **_lowercase ):
"""simple docstring"""
return self.tokenizer.batch_decode(*_lowercase , **_lowercase )
def _lowercase ( self , *_lowercase , **_lowercase ):
"""simple docstring"""
return self.tokenizer.decode(*_lowercase , **_lowercase )
@contextmanager
def _lowercase ( self ):
"""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 audio inputs, or in a separate call.""" )
_lowerCAmelCase = True
_lowerCAmelCase = self.tokenizer
yield
_lowerCAmelCase = self.feature_extractor
_lowerCAmelCase = False
| 5 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_lowercase = logging.get_logger(__name__)
_lowercase = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_lowercase = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class UpperCAmelCase_ :
'''simple docstring'''
_lowercase : str = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(_SCREAMING_SNAKE_CASE )} )
_lowercase : str = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''} )
_lowercase : int = field(
default=1_2_8 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_lowercase : int = field(
default=1_2_8 , metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''} , )
_lowercase : int = field(
default=6_4 , metadata={
'''help''': (
'''The maximum number of tokens for the question. Questions longer than this will '''
'''be truncated to this length.'''
)
} , )
_lowercase : int = field(
default=3_0 , metadata={
'''help''': (
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
)
} , )
_lowercase : bool = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
_lowercase : bool = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''} )
_lowercase : float = field(
default=0.0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
_lowercase : int = field(
default=2_0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
_lowercase : int = field(
default=0 , metadata={
'''help''': (
'''language id of input for language-specific xlm models (see'''
''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
)
} , )
_lowercase : int = field(default=1 , metadata={'''help''': '''multiple threads for converting example to features'''} )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : str = '''train'''
_lowercase : Union[str, Any] = '''dev'''
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : SquadDataTrainingArguments
_lowercase : List[SquadFeatures]
_lowercase : Split
_lowercase : bool
def __init__( self , _lowercase , _lowercase , _lowercase = None , _lowercase = Split.train , _lowercase = False , _lowercase = None , _lowercase = "pt" , ):
"""simple docstring"""
_lowerCAmelCase = args
_lowerCAmelCase = is_language_sensitive
_lowerCAmelCase = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(_lowercase , _lowercase ):
try:
_lowerCAmelCase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
_lowerCAmelCase = mode
# Load data features from cache or dataset file
_lowerCAmelCase = """v2""" if args.version_2_with_negative else """v1"""
_lowerCAmelCase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
_lowerCAmelCase = cached_features_file + """.lock"""
with FileLock(_lowercase ):
if os.path.exists(_lowercase ) and not args.overwrite_cache:
_lowerCAmelCase = time.time()
_lowerCAmelCase = torch.load(_lowercase )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
_lowerCAmelCase = self.old_features["""features"""]
_lowerCAmelCase = self.old_features.get("""dataset""" , _lowercase )
_lowerCAmelCase = self.old_features.get("""examples""" , _lowercase )
logger.info(
F'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
F'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
""" future run""" )
else:
if mode == Split.dev:
_lowerCAmelCase = self.processor.get_dev_examples(args.data_dir )
else:
_lowerCAmelCase = self.processor.get_train_examples(args.data_dir )
_lowerCAmelCase , _lowerCAmelCase = squad_convert_examples_to_features(
examples=self.examples , tokenizer=_lowercase , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=_lowercase , )
_lowerCAmelCase = time.time()
torch.save(
{"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples} , _lowercase , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self ):
"""simple docstring"""
return len(self.features )
def __getitem__( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.features[i]
_lowerCAmelCase = torch.tensor(feature.input_ids , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.attention_mask , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.token_type_ids , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.cls_index , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.p_mask , dtype=torch.float )
_lowerCAmelCase = torch.tensor(feature.is_impossible , dtype=torch.float )
_lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": attention_mask,
"""token_type_ids""": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"""is_impossible""": is_impossible} )
if self.is_language_sensitive:
inputs.update({"""langs""": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
_lowerCAmelCase = torch.tensor(feature.start_position , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions} )
return inputs
| 5 | 1 |
'''simple docstring'''
import cva
import numpy as np
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase , _lowercase ):
"""simple docstring"""
if k in (0.04, 0.06):
_lowerCAmelCase = k
_lowerCAmelCase = window_size
else:
raise ValueError("""invalid k value""" )
def __str__( self ):
"""simple docstring"""
return str(self.k )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = cva.imread(_lowercase , 0 )
_lowerCAmelCase , _lowerCAmelCase = img.shape
_lowerCAmelCase = []
_lowerCAmelCase = img.copy()
_lowerCAmelCase = cva.cvtColor(_lowercase , cva.COLOR_GRAY2RGB )
_lowerCAmelCase , _lowerCAmelCase = np.gradient(_lowercase )
_lowerCAmelCase = dx**2
_lowerCAmelCase = dy**2
_lowerCAmelCase = dx * dy
_lowerCAmelCase = 0.04
_lowerCAmelCase = self.window_size // 2
for y in range(_lowercase , h - offset ):
for x in range(_lowercase , w - offset ):
_lowerCAmelCase = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowerCAmelCase = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowerCAmelCase = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowerCAmelCase = (wxx * wyy) - (wxy**2)
_lowerCAmelCase = wxx + wyy
_lowerCAmelCase = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) , 0 )
color_img.itemset((y, x, 1) , 0 )
color_img.itemset((y, x, 2) , 255 )
return color_img, corner_list
if __name__ == "__main__":
_lowercase = HarrisCorner(0.04, 3)
_lowercase , _lowercase = edge_detect.detect("""path_to_image""")
cva.imwrite("""detect.png""", color_img)
| 5 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-question_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-reader-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
),
"""facebook/dpr-ctx_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
),
"""facebook/dpr-question_encoder-multiset-base""": (
"""https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
),
"""facebook/dpr-reader-multiset-base""": (
"""https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
),
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : str = '''dpr'''
def __init__( self , _lowercase=30_522 , _lowercase=768 , _lowercase=12 , _lowercase=12 , _lowercase=3_072 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=2 , _lowercase=0.02 , _lowercase=1e-12 , _lowercase=0 , _lowercase="absolute" , _lowercase = 0 , **_lowercase , ):
"""simple docstring"""
super().__init__(pad_token_id=_lowercase , **_lowercase )
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = hidden_act
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_vocab_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = projection_dim
_lowerCAmelCase = position_embedding_type
| 5 | 1 |
'''simple docstring'''
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_lowercase = [
"""Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"""
""" final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"""
""" depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.""",
"""The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"""
""" accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"""
""" founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"""
""" body.""",
"""Amnesty International releases its annual report on the death penalty. The report catalogs the use of"""
""" state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"""
""" world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"""
""" punishment.""",
]
_lowercase = [
"""Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."""
""" Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"""
""" had informed his Lufthansa training school of an episode of severe depression, airline says .""",
"""Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."""
""" Israel and the United States opposed the move, which could open the door to war crimes investigations against"""
""" Israelis .""",
"""Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"""
""" death . Organization claims that governments around the world are using the threat of terrorism to advance"""
""" executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"""
""" sentences up by 28% .""",
]
def A ():
_lowerCAmelCase = calculate_rouge(__lowerCamelCase , __lowerCamelCase , bootstrap_aggregation=__lowerCamelCase , rouge_keys=["""rouge2""", """rougeL"""] )
assert isinstance(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = calculate_rouge(__lowerCamelCase , __lowerCamelCase , bootstrap_aggregation=__lowerCamelCase , rouge_keys=["""rouge2"""] )
assert (
pd.DataFrame(no_aggregation["""rouge2"""] ).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_ra["""rouge2"""] ).fmeasure.mean()
)
def A ():
_lowerCAmelCase = """rougeLsum"""
_lowerCAmelCase = calculate_rouge(__lowerCamelCase , __lowerCamelCase , newline_sep=__lowerCamelCase , rouge_keys=[k] )[k]
_lowerCAmelCase = calculate_rouge(__lowerCamelCase , __lowerCamelCase , newline_sep=__lowerCamelCase , rouge_keys=[k] )[k]
assert score > score_no_sep
def A ():
_lowerCAmelCase = ["""rouge1""", """rouge2""", """rougeL"""]
_lowerCAmelCase = calculate_rouge(__lowerCamelCase , __lowerCamelCase , newline_sep=__lowerCamelCase , rouge_keys=__lowerCamelCase )
_lowerCAmelCase = calculate_rouge(__lowerCamelCase , __lowerCamelCase , newline_sep=__lowerCamelCase , rouge_keys=__lowerCamelCase )
assert score_sep == score_no_sep
def A ():
_lowerCAmelCase = [
"""Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.""",
"""Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .""",
]
_lowerCAmelCase = [
"""Margot Frank, died in 1945, a month earlier than previously thought.""",
"""Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"""
""" the final seconds on board Flight 9525.""",
]
assert calculate_rouge(__lowerCamelCase , __lowerCamelCase , newline_sep=__lowerCamelCase ) == calculate_rouge(__lowerCamelCase , __lowerCamelCase , newline_sep=__lowerCamelCase )
def A ():
_lowerCAmelCase = [
"""\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" """
]
_lowerCAmelCase = [
""" Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."""
]
_lowerCAmelCase = calculate_rouge(__lowerCamelCase , __lowerCamelCase , rouge_keys=["""rougeLsum"""] , newline_sep=__lowerCamelCase )["""rougeLsum"""]
_lowerCAmelCase = calculate_rouge(__lowerCamelCase , __lowerCamelCase , rouge_keys=["""rougeLsum"""] )["""rougeLsum"""]
assert new_score > prev_score
def A ():
_lowerCAmelCase = Path("""examples/seq2seq/test_data/wmt_en_ro""" )
_lowerCAmelCase = calculate_rouge_path(data_dir.joinpath("""test.source""" ) , data_dir.joinpath("""test.target""" ) )
assert isinstance(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = calculate_rouge_path(
data_dir.joinpath("""test.source""" ) , data_dir.joinpath("""test.target""" ) , bootstrap_aggregation=__lowerCamelCase )
assert isinstance(__lowerCamelCase , __lowerCamelCase )
| 5 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
_lowercase = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
_lowercase = """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""
_lowercase = """
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
\"raw_values\" : Returns a full set of errors in case of multioutput input.
\"uniform_average\" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric(\"mse\")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{'mse': 0.6123724356957945}
If you're using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{'mse': array([0.41666667, 1. ])}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"""
] , )
def _lowercase ( self ):
"""simple docstring"""
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value("""float""" ) ),
"references": datasets.Sequence(datasets.Value("""float""" ) ),
}
else:
return {
"predictions": datasets.Value("""float""" ),
"references": datasets.Value("""float""" ),
}
def _lowercase ( self , _lowercase , _lowercase , _lowercase=None , _lowercase="uniform_average" , _lowercase=True ):
"""simple docstring"""
_lowerCAmelCase = mean_squared_error(
_lowercase , _lowercase , sample_weight=_lowercase , multioutput=_lowercase , squared=_lowercase )
return {"mse": mse}
| 5 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
DYNAMO_BACKENDS,
_ask_field,
_ask_options,
_convert_dynamo_backend,
_convert_mixed_precision,
_convert_sagemaker_distributed_mode,
_convert_yes_no_to_bool,
)
if is_botoa_available():
import botoa # noqa: F401
def A (__lowerCamelCase :int ):
_lowerCAmelCase = botoa.client("""iam""" )
_lowerCAmelCase = {
"""Version""": """2012-10-17""",
"""Statement""": [
{"""Effect""": """Allow""", """Principal""": {"""Service""": """sagemaker.amazonaws.com"""}, """Action""": """sts:AssumeRole"""}
],
}
try:
# create the role, associated with the chosen trust policy
iam_client.create_role(
RoleName=__lowerCamelCase , AssumeRolePolicyDocument=json.dumps(__lowerCamelCase , indent=2 ) )
_lowerCAmelCase = {
"""Version""": """2012-10-17""",
"""Statement""": [
{
"""Effect""": """Allow""",
"""Action""": [
"""sagemaker:*""",
"""ecr:GetDownloadUrlForLayer""",
"""ecr:BatchGetImage""",
"""ecr:BatchCheckLayerAvailability""",
"""ecr:GetAuthorizationToken""",
"""cloudwatch:PutMetricData""",
"""cloudwatch:GetMetricData""",
"""cloudwatch:GetMetricStatistics""",
"""cloudwatch:ListMetrics""",
"""logs:CreateLogGroup""",
"""logs:CreateLogStream""",
"""logs:DescribeLogStreams""",
"""logs:PutLogEvents""",
"""logs:GetLogEvents""",
"""s3:CreateBucket""",
"""s3:ListBucket""",
"""s3:GetBucketLocation""",
"""s3:GetObject""",
"""s3:PutObject""",
],
"""Resource""": """*""",
}
],
}
# attach policy to role
iam_client.put_role_policy(
RoleName=__lowerCamelCase , PolicyName=f'{role_name}_policy_permission' , PolicyDocument=json.dumps(__lowerCamelCase , indent=2 ) , )
except iam_client.exceptions.EntityAlreadyExistsException:
print(f'role {role_name} already exists. Using existing one' )
def A (__lowerCamelCase :List[str] ):
_lowerCAmelCase = botoa.client("""iam""" )
return iam_client.get_role(RoleName=__lowerCamelCase )["Role"]["Arn"]
def A ():
_lowerCAmelCase = _ask_options(
"""How do you want to authorize?""" , ["""AWS Profile""", """Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) """] , __lowerCamelCase , )
_lowerCAmelCase = None
if credentials_configuration == 0:
_lowerCAmelCase = _ask_field("""Enter your AWS Profile name: [default] """ , default="""default""" )
_lowerCAmelCase = aws_profile
else:
print(
"""Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,"""
"""`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`""" )
_lowerCAmelCase = _ask_field("""AWS Access Key ID: """ )
_lowerCAmelCase = aws_access_key_id
_lowerCAmelCase = _ask_field("""AWS Secret Access Key: """ )
_lowerCAmelCase = aws_secret_access_key
_lowerCAmelCase = _ask_field("""Enter your AWS Region: [us-east-1]""" , default="""us-east-1""" )
_lowerCAmelCase = aws_region
_lowerCAmelCase = _ask_options(
"""Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?""" , ["""Provide IAM Role name""", """Create new IAM role using credentials"""] , __lowerCamelCase , )
if role_management == 0:
_lowerCAmelCase = _ask_field("""Enter your IAM role name: """ )
else:
_lowerCAmelCase = """accelerate_sagemaker_execution_role"""
print(f'Accelerate will create an iam role "{iam_role_name}" using the provided credentials' )
_create_iam_role_for_sagemaker(__lowerCamelCase )
_lowerCAmelCase = _ask_field(
"""Do you want to use custom Docker image? [yes/NO]: """ , _convert_yes_no_to_bool , default=__lowerCamelCase , error_message="""Please enter yes or no.""" , )
_lowerCAmelCase = None
if is_custom_docker_image:
_lowerCAmelCase = _ask_field("""Enter your Docker image: """ , lambda __lowerCamelCase : str(__lowerCamelCase ).lower() )
_lowerCAmelCase = _ask_field(
"""Do you want to provide SageMaker input channels with data locations? [yes/NO]: """ , _convert_yes_no_to_bool , default=__lowerCamelCase , error_message="""Please enter yes or no.""" , )
_lowerCAmelCase = None
if is_sagemaker_inputs_enabled:
_lowerCAmelCase = _ask_field(
"""Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): """ , lambda __lowerCamelCase : str(__lowerCamelCase ).lower() , )
_lowerCAmelCase = _ask_field(
"""Do you want to enable SageMaker metrics? [yes/NO]: """ , _convert_yes_no_to_bool , default=__lowerCamelCase , error_message="""Please enter yes or no.""" , )
_lowerCAmelCase = None
if is_sagemaker_metrics_enabled:
_lowerCAmelCase = _ask_field(
"""Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): """ , lambda __lowerCamelCase : str(__lowerCamelCase ).lower() , )
_lowerCAmelCase = _ask_options(
"""What is the distributed mode?""" , ["""No distributed training""", """Data parallelism"""] , _convert_sagemaker_distributed_mode , )
_lowerCAmelCase = {}
_lowerCAmelCase = _ask_field(
"""Do you wish to optimize your script with torch dynamo?[yes/NO]:""" , _convert_yes_no_to_bool , default=__lowerCamelCase , error_message="""Please enter yes or no.""" , )
if use_dynamo:
_lowerCAmelCase = """dynamo_"""
_lowerCAmelCase = _ask_options(
"""Which dynamo backend would you like to use?""" , [x.lower() for x in DYNAMO_BACKENDS] , _convert_dynamo_backend , default=2 , )
_lowerCAmelCase = _ask_field(
"""Do you want to customize the defaults sent to torch.compile? [yes/NO]: """ , _convert_yes_no_to_bool , default=__lowerCamelCase , error_message="""Please enter yes or no.""" , )
if use_custom_options:
_lowerCAmelCase = _ask_options(
"""Which mode do you want to use?""" , __lowerCamelCase , lambda __lowerCamelCase : TORCH_DYNAMO_MODES[int(__lowerCamelCase )] , default="""default""" , )
_lowerCAmelCase = _ask_field(
"""Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: """ , _convert_yes_no_to_bool , default=__lowerCamelCase , error_message="""Please enter yes or no.""" , )
_lowerCAmelCase = _ask_field(
"""Do you want to enable dynamic shape tracing? [yes/NO]: """ , _convert_yes_no_to_bool , default=__lowerCamelCase , error_message="""Please enter yes or no.""" , )
_lowerCAmelCase = """Which EC2 instance type you want to use for your training?"""
if distributed_type != SageMakerDistributedType.NO:
_lowerCAmelCase = _ask_options(
__lowerCamelCase , __lowerCamelCase , lambda __lowerCamelCase : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(__lowerCamelCase )] )
else:
eca_instance_query += "? [ml.p3.2xlarge]:"
_lowerCAmelCase = _ask_field(__lowerCamelCase , lambda __lowerCamelCase : str(__lowerCamelCase ).lower() , default="""ml.p3.2xlarge""" )
_lowerCAmelCase = 1
if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
_lowerCAmelCase = _ask_field(
"""How many machines do you want use? [1]: """ , __lowerCamelCase , default=1 , )
_lowerCAmelCase = _ask_options(
"""Do you wish to use FP16 or BF16 (mixed precision)?""" , ["""no""", """fp16""", """bf16""", """fp8"""] , _convert_mixed_precision , )
if use_dynamo and mixed_precision == "no":
print(
"""Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.""" )
return SageMakerConfig(
image_uri=__lowerCamelCase , compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER , distributed_type=__lowerCamelCase , use_cpu=__lowerCamelCase , dynamo_config=__lowerCamelCase , eca_instance_type=__lowerCamelCase , profile=__lowerCamelCase , region=__lowerCamelCase , iam_role_name=__lowerCamelCase , mixed_precision=__lowerCamelCase , num_machines=__lowerCamelCase , sagemaker_inputs_file=__lowerCamelCase , sagemaker_metrics_file=__lowerCamelCase , )
| 5 |
'''simple docstring'''
def A ():
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def A (__lowerCamelCase :List[Any] ):
_lowerCAmelCase = 1
_lowerCAmelCase = 2
while i * i <= n:
_lowerCAmelCase = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def A ():
return next(i for i in triangle_number_generator() if count_divisors(__lowerCamelCase ) > 500 )
if __name__ == "__main__":
print(solution())
| 5 | 1 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def A ():
raise RuntimeError("""CUDA out of memory.""" )
class UpperCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self ):
"""simple docstring"""
super().__init__()
_lowerCAmelCase = nn.Linear(3 , 4 )
_lowerCAmelCase = nn.BatchNormad(4 )
_lowerCAmelCase = nn.Linear(4 , 5 )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(_lowercase ) ) )
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = []
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(_lowercase ):
nonlocal batch_sizes
batch_sizes.append(_lowercase )
if batch_size != 8:
raise_fake_out_of_memory()
mock_training_loop_function()
self.assertListEqual(_lowercase , [128, 64, 32, 16, 8] )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = []
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(_lowercase , _lowercase ):
nonlocal batch_sizes
batch_sizes.append(_lowercase )
if batch_size != 8:
raise_fake_out_of_memory()
return batch_size, arga
_lowerCAmelCase , _lowerCAmelCase = mock_training_loop_function("""hello""" )
self.assertListEqual(_lowercase , [128, 64, 32, 16, 8] )
self.assertListEqual([bs, arga] , [8, """hello"""] )
def _lowercase ( self ):
"""simple docstring"""
@find_executable_batch_size(starting_batch_size=0 )
def mock_training_loop_function(_lowercase ):
pass
with self.assertRaises(_lowercase ) as cm:
mock_training_loop_function()
self.assertIn("""No executable batch size found, reached zero.""" , cm.exception.args[0] )
def _lowercase ( self ):
"""simple docstring"""
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(_lowercase ):
if batch_size > 0:
raise_fake_out_of_memory()
pass
with self.assertRaises(_lowercase ) as cm:
mock_training_loop_function()
self.assertIn("""No executable batch size found, reached zero.""" , cm.exception.args[0] )
def _lowercase ( self ):
"""simple docstring"""
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(_lowercase , _lowercase , _lowercase ):
if batch_size != 8:
raise raise_fake_out_of_memory()
with self.assertRaises(_lowercase ) as cm:
mock_training_loop_function(128 , """hello""" , """world""" )
self.assertIn("""Batch size was passed into `f`""" , cm.exception.args[0] )
self.assertIn("""`f(arg1='hello', arg2='world')""" , cm.exception.args[0] )
def _lowercase ( self ):
"""simple docstring"""
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(_lowercase ):
raise ValueError("""Oops, we had an error!""" )
with self.assertRaises(_lowercase ) as cm:
mock_training_loop_function()
self.assertIn("""Oops, we had an error!""" , cm.exception.args[0] )
@require_cuda
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = torch.cuda.memory_allocated()
_lowerCAmelCase = ModelForTest()
model.cuda()
self.assertGreater(torch.cuda.memory_allocated() , _lowercase )
_lowerCAmelCase = release_memory(_lowercase )
self.assertEqual(torch.cuda.memory_allocated() , _lowercase )
| 5 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
_lowercase = logging.get_logger(__name__)
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , *_lowercase , **_lowercase ):
"""simple docstring"""
warnings.warn(
"""The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use DonutImageProcessor instead.""" , _lowercase , )
super().__init__(*_lowercase , **_lowercase )
| 5 | 1 |
'''simple docstring'''
from __future__ import annotations
def A (__lowerCamelCase :list[int] ):
return len(set(__lowerCamelCase ) ) == len(__lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 |
'''simple docstring'''
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_min_version,
require_tpu,
require_xpu,
skip,
slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU
from .scripts import test_script, test_sync, test_ops # isort: skip
| 5 | 1 |
'''simple docstring'''
import os
from pathlib import Path
def A ():
from torch.utils.cpp_extension import load
_lowerCAmelCase = Path(__lowerCamelCase ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
_lowerCAmelCase = [
root / filename
for filename in [
"""vision.cpp""",
os.path.join("""cpu""" , """ms_deform_attn_cpu.cpp""" ),
os.path.join("""cuda""" , """ms_deform_attn_cuda.cu""" ),
]
]
load(
"""MultiScaleDeformableAttention""" , __lowerCamelCase , with_cuda=__lowerCamelCase , extra_include_paths=[str(__lowerCamelCase )] , extra_cflags=["""-DWITH_CUDA=1"""] , extra_cuda_cflags=[
"""-DCUDA_HAS_FP16=1""",
"""-D__CUDA_NO_HALF_OPERATORS__""",
"""-D__CUDA_NO_HALF_CONVERSIONS__""",
"""-D__CUDA_NO_HALF2_OPERATORS__""",
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 5 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("""Googling.....""")
_lowercase = """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
_lowercase = requests.get(url, headers={"""UserAgent""": UserAgent().random})
# res.raise_for_status()
with open("""project1a.html""", """wb""") as out_file: # only for knowing the class
for data in res.iter_content(10000):
out_file.write(data)
_lowercase = BeautifulSoup(res.text, """html.parser""")
_lowercase = list(soup.select(""".eZt8xd"""))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get("""href"""))
else:
webbrowser.open(F"""https://google.com{link.get('href')}""")
| 5 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowercase = {
"""configuration_resnet""": ["""RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ResNetConfig""", """ResNetOnnxConfig"""]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ResNetForImageClassification""",
"""ResNetModel""",
"""ResNetPreTrainedModel""",
"""ResNetBackbone""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFResNetForImageClassification""",
"""TFResNetModel""",
"""TFResNetPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""FlaxResNetForImageClassification""",
"""FlaxResNetModel""",
"""FlaxResNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 5 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_lowercase = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
def A ():
_lowerCAmelCase = Github(os.environ["""GITHUB_TOKEN"""] )
_lowerCAmelCase = g.get_repo("""huggingface/diffusers""" )
_lowerCAmelCase = repo.get_issues(state="""open""" )
for issue in open_issues:
_lowerCAmelCase = sorted(issue.get_comments() , key=lambda __lowerCamelCase : i.created_at , reverse=__lowerCamelCase )
_lowerCAmelCase = comments[0] if len(__lowerCamelCase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state="""closed""" )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state="""open""" )
issue.remove_from_labels("""stale""" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
"""This issue has been automatically marked as stale because it has not had """
"""recent activity. If you think this still needs to be addressed """
"""please comment on this thread.\n\nPlease note that issues that do not follow the """
"""[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) """
"""are likely to be ignored.""" )
issue.add_to_labels("""stale""" )
if __name__ == "__main__":
main()
| 5 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=_SCREAMING_SNAKE_CASE )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : str = field(default='''question-answering-extractive''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
_lowercase : ClassVar[Features] = Features({'''question''': Value('''string''' ), '''context''': Value('''string''' )} )
_lowercase : ClassVar[Features] = Features(
{
'''answers''': Sequence(
{
'''text''': Value('''string''' ),
'''answer_start''': Value('''int32''' ),
} )
} )
_lowercase : str = "question"
_lowercase : str = "context"
_lowercase : str = "answers"
@property
def _lowercase ( self ):
"""simple docstring"""
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 5 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
ImageTextPipelineOutput,
UniDiffuserPipeline,
)
else:
from .modeling_text_decoder import UniDiffuserTextDecoder
from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
| 5 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
_lowercase : str = FunnelTokenizer
_lowercase : int = FunnelTokenizerFast
_lowercase : str = True
_lowercase : Tuple = True
def _lowercase ( self ):
"""simple docstring"""
super().setUp()
_lowerCAmelCase = [
"""<unk>""",
"""<cls>""",
"""<sep>""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
_lowerCAmelCase = 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] ) )
def _lowercase ( self , **_lowercase ):
"""simple docstring"""
return FunnelTokenizer.from_pretrained(self.tmpdirname , **_lowercase )
def _lowercase ( self , **_lowercase ):
"""simple docstring"""
return FunnelTokenizerFast.from_pretrained(self.tmpdirname , **_lowercase )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = """UNwant\u00E9d,running"""
_lowerCAmelCase = """unwanted, running"""
return input_text, output_text
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.tokenizer_class(self.vocab_file )
_lowerCAmelCase = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(_lowercase , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase ) , [7, 4, 5, 10, 8, 9] )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.get_tokenizers(do_lower_case=_lowercase )
for tokenizer in tokenizers:
_lowerCAmelCase = tokenizer("""UNwant\u00E9d,running""" )
_lowerCAmelCase = len(inputs["""input_ids"""] ) - 1
self.assertListEqual(inputs["""token_type_ids"""] , [2] + [0] * sentence_len )
_lowerCAmelCase = tokenizer("""UNwant\u00E9d,running""" , """UNwant\u00E9d,running""" )
self.assertListEqual(inputs["""token_type_ids"""] , [2] + [0] * sentence_len + [1] * sentence_len )
| 5 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowercase = {"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ViTMAEForPreTraining""",
"""ViTMAELayer""",
"""ViTMAEModel""",
"""ViTMAEPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""TFViTMAEForPreTraining""",
"""TFViTMAEModel""",
"""TFViTMAEPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 5 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(_SCREAMING_SNAKE_CASE ) , '''Tatoeba directory does not exist.''' )
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = tempfile.mkdtemp()
return TatoebaConverter(save_dir=_lowercase )
@slow
def _lowercase ( self ):
"""simple docstring"""
self.resolver.convert_models(["""heb-eng"""] )
@slow
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.resolver.write_model_card("""opus-mt-he-en""" , dry_run=_lowercase )
assert mmeta["long_pair"] == "heb-eng"
| 5 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
_lowercase = logging.get_logger(__name__)
@add_end_docstrings(_SCREAMING_SNAKE_CASE )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , **_lowercase ):
"""simple docstring"""
super().__init__(**_lowercase )
if self.framework != "pt":
raise ValueError(F'The {self.__class__} is only available in PyTorch.' )
# No specific FOR_XXX available yet
def __call__( self , _lowercase , **_lowercase ):
"""simple docstring"""
return super().__call__(_lowercase , **_lowercase )
def _lowercase ( self , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase = {}
if "candidate_labels" in kwargs:
_lowerCAmelCase = kwargs["""candidate_labels"""]
if "hypothesis_template" in kwargs:
_lowerCAmelCase = kwargs["""hypothesis_template"""]
return preprocess_params, {}, {}
def _lowercase ( self , _lowercase , _lowercase=None , _lowercase="This is a sound of {}." ):
"""simple docstring"""
if isinstance(_lowercase , _lowercase ):
if audio.startswith("""http://""" ) or audio.startswith("""https://""" ):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
_lowerCAmelCase = requests.get(_lowercase ).content
else:
with open(_lowercase , """rb""" ) as f:
_lowerCAmelCase = f.read()
if isinstance(_lowercase , _lowercase ):
_lowerCAmelCase = ffmpeg_read(_lowercase , self.feature_extractor.sampling_rate )
if not isinstance(_lowercase , np.ndarray ):
raise ValueError("""We expect a numpy ndarray as input""" )
if len(audio.shape ) != 1:
raise ValueError("""We expect a single channel audio input for ZeroShotAudioClassificationPipeline""" )
_lowerCAmelCase = self.feature_extractor(
[audio] , sampling_rate=self.feature_extractor.sampling_rate , return_tensors="""pt""" )
_lowerCAmelCase = candidate_labels
_lowerCAmelCase = [hypothesis_template.format(_lowercase ) for x in candidate_labels]
_lowerCAmelCase = self.tokenizer(_lowercase , return_tensors=self.framework , padding=_lowercase )
_lowerCAmelCase = [text_inputs]
return inputs
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = model_inputs.pop("""candidate_labels""" )
_lowerCAmelCase = model_inputs.pop("""text_inputs""" )
if isinstance(text_inputs[0] , _lowercase ):
_lowerCAmelCase = text_inputs[0]
else:
# Batching case.
_lowerCAmelCase = text_inputs[0][0]
_lowerCAmelCase = self.model(**_lowercase , **_lowercase )
_lowerCAmelCase = {
"""candidate_labels""": candidate_labels,
"""logits""": outputs.logits_per_audio,
}
return model_outputs
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = model_outputs.pop("""candidate_labels""" )
_lowerCAmelCase = model_outputs["""logits"""][0]
if self.framework == "pt":
_lowerCAmelCase = logits.softmax(dim=0 )
_lowerCAmelCase = probs.tolist()
else:
raise ValueError("""`tf` framework not supported.""" )
_lowerCAmelCase = [
{"""score""": score, """label""": candidate_label}
for score, candidate_label in sorted(zip(_lowercase , _lowercase ) , key=lambda _lowercase : -x[0] )
]
return result
| 5 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
_lowercase = logging.get_logger("""transformers.models.encodec""")
_lowercase = {
"""quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
"""quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
"""quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
"""quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
_lowercase = {
"""encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
"""encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
"""encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
"""encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
"""encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
"""encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
"""encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
"""encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
"""encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
"""encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
"""encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
"""encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
"""encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
"""encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
"""encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
"""encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
"""encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
"""encoder.model.13.lstm""": """encoder.layers.13.lstm""",
"""encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
_lowercase = {
"""encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
"""encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
"""encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
"""encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
"""encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
"""encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
"""encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
"""encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
"""encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
"""encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
"""encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
"""encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
"""encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
"""encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
"""encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
"""encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
"""encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
"""encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
_lowercase = {
"""decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
"""decoder.model.1.lstm""": """decoder.layers.1.lstm""",
"""decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
"""decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
"""decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
"""decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
"""decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
"""decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
"""decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
"""decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
"""decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
"""decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
"""decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
"""decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
"""decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
"""decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
"""decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
"""decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
"""decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
_lowercase = {
"""decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
"""decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
"""decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
"""decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
"""decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
"""decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
"""decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
"""decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
"""decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
"""decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
"""decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
"""decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
"""decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
"""decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
"""decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
"""decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
"""decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
"""decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
_lowercase = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
_lowercase = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
_lowercase = []
_lowercase = []
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Optional[Any] , __lowerCamelCase :Any , __lowerCamelCase :str , __lowerCamelCase :Optional[int] ):
for attribute in key.split(""".""" ):
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase )
if weight_type is not None:
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase ).shape
else:
_lowerCAmelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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":
_lowerCAmelCase = value
elif weight_type == "weight_g":
_lowerCAmelCase = value
elif weight_type == "weight_v":
_lowerCAmelCase = value
elif weight_type == "bias":
_lowerCAmelCase = value
elif weight_type == "running_mean":
_lowerCAmelCase = value
elif weight_type == "running_var":
_lowerCAmelCase = value
elif weight_type == "num_batches_tracked":
_lowerCAmelCase = value
elif weight_type == "weight_ih_l0":
_lowerCAmelCase = value
elif weight_type == "weight_hh_l0":
_lowerCAmelCase = value
elif weight_type == "bias_ih_l0":
_lowerCAmelCase = value
elif weight_type == "bias_hh_l0":
_lowerCAmelCase = value
elif weight_type == "weight_ih_l1":
_lowerCAmelCase = value
elif weight_type == "weight_hh_l1":
_lowerCAmelCase = value
elif weight_type == "bias_ih_l1":
_lowerCAmelCase = value
elif weight_type == "bias_hh_l1":
_lowerCAmelCase = value
else:
_lowerCAmelCase = value
logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.' )
def A (__lowerCamelCase :List[str] , __lowerCamelCase :str ):
for key in ignore_keys:
if key.endswith(""".*""" ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
_lowerCAmelCase , _lowerCAmelCase = key.split(""".*.""" )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def A (__lowerCamelCase :Any , __lowerCamelCase :Optional[Any] , __lowerCamelCase :Dict ):
_lowerCAmelCase = []
if model_name == "encodec_24khz" or "encodec_32khz":
_lowerCAmelCase = MAPPING_24K
elif model_name == "encodec_48khz":
_lowerCAmelCase = MAPPING_48K
else:
raise ValueError(f'Unsupported model: {model_name}' )
for name, value in orig_dict.items():
if should_ignore(__lowerCamelCase , __lowerCamelCase ):
logger.info(f'{name} was ignored' )
continue
_lowerCAmelCase = False
for key, mapped_key in MAPPING.items():
if "*" in key:
_lowerCAmelCase , _lowerCAmelCase = key.split(""".*.""" )
if prefix in name and suffix in name:
_lowerCAmelCase = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith("""embed""" ) and name.endswith("""embed_avg""" ):
continue
_lowerCAmelCase = True
if "*" in mapped_key:
_lowerCAmelCase = name.split(__lowerCamelCase )[0].split(""".""" )[-2]
_lowerCAmelCase = mapped_key.replace("""*""" , __lowerCamelCase )
if "weight_g" in name:
_lowerCAmelCase = """weight_g"""
elif "weight_v" in name:
_lowerCAmelCase = """weight_v"""
elif "weight_ih_l0" in name:
_lowerCAmelCase = """weight_ih_l0"""
elif "weight_hh_l0" in name:
_lowerCAmelCase = """weight_hh_l0"""
elif "bias_ih_l0" in name:
_lowerCAmelCase = """bias_ih_l0"""
elif "bias_hh_l0" in name:
_lowerCAmelCase = """bias_hh_l0"""
elif "weight_ih_l1" in name:
_lowerCAmelCase = """weight_ih_l1"""
elif "weight_hh_l1" in name:
_lowerCAmelCase = """weight_hh_l1"""
elif "bias_ih_l1" in name:
_lowerCAmelCase = """bias_ih_l1"""
elif "bias_hh_l1" in name:
_lowerCAmelCase = """bias_hh_l1"""
elif "bias" in name:
_lowerCAmelCase = """bias"""
elif "weight" in name:
_lowerCAmelCase = """weight"""
elif "running_mean" in name:
_lowerCAmelCase = """running_mean"""
elif "running_var" in name:
_lowerCAmelCase = """running_var"""
elif "num_batches_tracked" in name:
_lowerCAmelCase = """num_batches_tracked"""
else:
_lowerCAmelCase = None
set_recursively(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
continue
if not is_used:
unused_weights.append(__lowerCamelCase )
logger.warning(f'Unused weights: {unused_weights}' )
@torch.no_grad()
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :List[Any] , __lowerCamelCase :str , __lowerCamelCase :int=None , __lowerCamelCase :str=None , ):
if config_path is not None:
_lowerCAmelCase = EncodecConfig.from_pretrained(__lowerCamelCase )
else:
_lowerCAmelCase = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
_lowerCAmelCase = [8, 5, 4, 4]
_lowerCAmelCase = [2.2]
_lowerCAmelCase = 64
_lowerCAmelCase = 32000
_lowerCAmelCase = 2048
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
elif model_name == "encodec_48khz":
_lowerCAmelCase = [8, 5, 4, 2]
_lowerCAmelCase = [3.0, 6.0, 12.0, 24.0]
_lowerCAmelCase = 48000
_lowerCAmelCase = 2
_lowerCAmelCase = False
_lowerCAmelCase = """time_group_norm"""
_lowerCAmelCase = True
_lowerCAmelCase = 1.0
_lowerCAmelCase = 0.01
else:
raise ValueError(f'Unknown model name: {model_name}' )
_lowerCAmelCase = EncodecModel(__lowerCamelCase )
_lowerCAmelCase = EncodecFeatureExtractor(
feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , )
feature_extractor.save_pretrained(__lowerCamelCase )
_lowerCAmelCase = torch.load(__lowerCamelCase )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
_lowerCAmelCase = original_checkpoint["""best_state"""]
recursively_load_weights(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
model.save_pretrained(__lowerCamelCase )
if repo_id:
print("""Pushing to the hub...""" )
feature_extractor.push_to_hub(__lowerCamelCase )
model.push_to_hub(__lowerCamelCase )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument(
"""--model""",
default="""encodec_24khz""",
type=str,
help="""The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.""",
)
parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
_lowercase = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 5 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_lowercase = logging.get_logger(__name__)
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[str] = ['''input_values''', '''padding_mask''']
def __init__( self , _lowercase = 1 , _lowercase = 24_000 , _lowercase = 0.0 , _lowercase = None , _lowercase = None , **_lowercase , ):
"""simple docstring"""
super().__init__(feature_size=_lowercase , sampling_rate=_lowercase , padding_value=_lowercase , **_lowercase )
_lowerCAmelCase = chunk_length_s
_lowerCAmelCase = overlap
@property
def _lowercase ( self ):
"""simple docstring"""
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _lowercase ( self ):
"""simple docstring"""
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 ) )
def __call__( self , _lowercase , _lowercase = None , _lowercase = False , _lowercase = None , _lowercase = None , _lowercase = None , ):
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
F' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
if padding and truncation:
raise ValueError("""Both padding and truncation were set. Make sure you only set one.""" )
elif padding is None:
# by default let's pad the inputs
_lowerCAmelCase = True
_lowerCAmelCase = bool(
isinstance(_lowercase , (list, tuple) ) and (isinstance(raw_audio[0] , (np.ndarray, tuple, list) )) )
if is_batched:
_lowerCAmelCase = [np.asarray(_lowercase , dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(_lowercase , np.ndarray ):
_lowerCAmelCase = np.asarray(_lowercase , dtype=np.floataa )
elif isinstance(_lowercase , np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
_lowerCAmelCase = raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
_lowerCAmelCase = [np.asarray(_lowercase ).T]
# verify inputs are valid
for idx, example in enumerate(_lowercase ):
if example.ndim > 2:
raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}' )
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels' )
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels' )
_lowerCAmelCase = None
_lowerCAmelCase = BatchFeature({"""input_values""": raw_audio} )
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
_lowerCAmelCase = min(array.shape[0] for array in raw_audio )
_lowerCAmelCase = int(np.floor(max_length / self.chunk_stride ) )
_lowerCAmelCase = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
_lowerCAmelCase = max(array.shape[0] for array in raw_audio )
_lowerCAmelCase = int(np.ceil(max_length / self.chunk_stride ) )
_lowerCAmelCase = (nb_step - 1) * self.chunk_stride + self.chunk_length
_lowerCAmelCase = """max_length"""
else:
_lowerCAmelCase = input_values
# normal padding on batch
if padded_inputs is None:
_lowerCAmelCase = self.pad(
_lowercase , max_length=_lowercase , truncation=_lowercase , padding=_lowercase , return_attention_mask=_lowercase , )
if padding:
_lowerCAmelCase = padded_inputs.pop("""attention_mask""" )
_lowerCAmelCase = []
for example in padded_inputs.pop("""input_values""" ):
if self.feature_size == 1:
_lowerCAmelCase = example[..., None]
input_values.append(example.T )
_lowerCAmelCase = input_values
if return_tensors is not None:
_lowerCAmelCase = padded_inputs.convert_to_tensors(_lowercase )
return padded_inputs
| 5 | 1 |
'''simple docstring'''
import numpy as np
def A (__lowerCamelCase :np.array ):
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 |
'''simple docstring'''
_lowercase = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_lowercase = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
_lowercase = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 5 | 1 |
'''simple docstring'''
def A (__lowerCamelCase :int = 100 ):
_lowerCAmelCase = 0
_lowerCAmelCase = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(F"""{solution() = }""")
| 5 |
'''simple docstring'''
import functools
def A (__lowerCamelCase :list[int] , __lowerCamelCase :list[int] ):
# Validation
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not all(isinstance(__lowerCamelCase , __lowerCamelCase ) for day in days ):
raise ValueError("""The parameter days should be a list of integers""" )
if len(__lowerCamelCase ) != 3 or not all(isinstance(__lowerCamelCase , __lowerCamelCase ) for cost in costs ):
raise ValueError("""The parameter costs should be a list of three integers""" )
if len(__lowerCamelCase ) == 0:
return 0
if min(__lowerCamelCase ) <= 0:
raise ValueError("""All days elements should be greater than 0""" )
if max(__lowerCamelCase ) >= 366:
raise ValueError("""All days elements should be less than 366""" )
_lowerCAmelCase = set(__lowerCamelCase )
@functools.cache
def dynamic_programming(__lowerCamelCase :int ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 | 1 |
'''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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
_lowercase = logging.get_logger(__name__)
if is_vision_available():
import PIL
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[str] = ['''pixel_values''']
def __init__( self , _lowercase = True , _lowercase = None , _lowercase = PILImageResampling.BICUBIC , _lowercase = True , _lowercase = None , _lowercase = True , _lowercase = 1 / 255 , _lowercase = True , _lowercase = None , _lowercase = None , _lowercase = True , **_lowercase , ):
"""simple docstring"""
super().__init__(**_lowercase )
_lowerCAmelCase = size if size is not None else {"""shortest_edge""": 224}
_lowerCAmelCase = get_size_dict(_lowercase , default_to_square=_lowercase )
_lowerCAmelCase = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
_lowerCAmelCase = get_size_dict(_lowercase , default_to_square=_lowercase , param_name="""crop_size""" )
_lowerCAmelCase = do_resize
_lowerCAmelCase = size
_lowerCAmelCase = resample
_lowerCAmelCase = do_center_crop
_lowerCAmelCase = crop_size
_lowerCAmelCase = do_rescale
_lowerCAmelCase = rescale_factor
_lowerCAmelCase = do_normalize
_lowerCAmelCase = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
_lowerCAmelCase = image_std if image_std is not None else OPENAI_CLIP_STD
_lowerCAmelCase = do_convert_rgb
def _lowercase ( self , _lowercase , _lowercase , _lowercase = PILImageResampling.BICUBIC , _lowercase = None , **_lowercase , ):
"""simple docstring"""
_lowerCAmelCase = get_size_dict(_lowercase , default_to_square=_lowercase )
if "shortest_edge" not in size:
raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
_lowerCAmelCase = get_resize_output_image_size(_lowercase , size=size["""shortest_edge"""] , default_to_square=_lowercase )
return resize(_lowercase , size=_lowercase , resample=_lowercase , data_format=_lowercase , **_lowercase )
def _lowercase ( self , _lowercase , _lowercase , _lowercase = None , **_lowercase , ):
"""simple docstring"""
_lowerCAmelCase = get_size_dict(_lowercase )
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(_lowercase , size=(size["""height"""], size["""width"""]) , data_format=_lowercase , **_lowercase )
def _lowercase ( self , _lowercase , _lowercase , _lowercase = None , **_lowercase , ):
"""simple docstring"""
return rescale(_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase )
def _lowercase ( self , _lowercase , _lowercase , _lowercase , _lowercase = None , **_lowercase , ):
"""simple docstring"""
return normalize(_lowercase , mean=_lowercase , std=_lowercase , data_format=_lowercase , **_lowercase )
def _lowercase ( self , _lowercase , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = None , _lowercase = ChannelDimension.FIRST , **_lowercase , ):
"""simple docstring"""
_lowerCAmelCase = do_resize if do_resize is not None else self.do_resize
_lowerCAmelCase = size if size is not None else self.size
_lowerCAmelCase = get_size_dict(_lowercase , param_name="""size""" , default_to_square=_lowercase )
_lowerCAmelCase = resample if resample is not None else self.resample
_lowerCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
_lowerCAmelCase = crop_size if crop_size is not None else self.crop_size
_lowerCAmelCase = get_size_dict(_lowercase , param_name="""crop_size""" , default_to_square=_lowercase )
_lowerCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
_lowerCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
_lowerCAmelCase = image_mean if image_mean is not None else self.image_mean
_lowerCAmelCase = image_std if image_std is not None else self.image_std
_lowerCAmelCase = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
_lowerCAmelCase = make_list_of_images(_lowercase )
if not valid_images(_lowercase ):
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.""" )
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.""" )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
_lowerCAmelCase = [convert_to_rgb(_lowercase ) for image in images]
# All transformations expect numpy arrays.
_lowerCAmelCase = [to_numpy_array(_lowercase ) for image in images]
if do_resize:
_lowerCAmelCase = [self.resize(image=_lowercase , size=_lowercase , resample=_lowercase ) for image in images]
if do_center_crop:
_lowerCAmelCase = [self.center_crop(image=_lowercase , size=_lowercase ) for image in images]
if do_rescale:
_lowerCAmelCase = [self.rescale(image=_lowercase , scale=_lowercase ) for image in images]
if do_normalize:
_lowerCAmelCase = [self.normalize(image=_lowercase , mean=_lowercase , std=_lowercase ) for image in images]
_lowerCAmelCase = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images]
_lowerCAmelCase = {"""pixel_values""": images}
return BatchFeature(data=_lowercase , tensor_type=_lowercase )
| 5 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def A (__lowerCamelCase :List[Any] ):
_lowerCAmelCase = 384
if "tiny" in model_name:
_lowerCAmelCase = [3, 3, 9, 3]
_lowerCAmelCase = [96, 192, 384, 768]
if "small" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [96, 192, 384, 768]
if "base" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [128, 256, 512, 1024]
_lowerCAmelCase = 512
if "large" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [192, 384, 768, 1536]
_lowerCAmelCase = 768
if "xlarge" in model_name:
_lowerCAmelCase = [3, 3, 27, 3]
_lowerCAmelCase = [256, 512, 1024, 2048]
_lowerCAmelCase = 1024
# set label information
_lowerCAmelCase = 150
_lowerCAmelCase = """huggingface/label-files"""
_lowerCAmelCase = """ade20k-id2label.json"""
_lowerCAmelCase = json.load(open(hf_hub_download(__lowerCamelCase , __lowerCamelCase , repo_type="""dataset""" ) , """r""" ) )
_lowerCAmelCase = {int(__lowerCamelCase ): v for k, v in idalabel.items()}
_lowerCAmelCase = {v: k for k, v in idalabel.items()}
_lowerCAmelCase = ConvNextConfig(
depths=__lowerCamelCase , hidden_sizes=__lowerCamelCase , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
_lowerCAmelCase = UperNetConfig(
backbone_config=__lowerCamelCase , auxiliary_in_channels=__lowerCamelCase , num_labels=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid=__lowerCamelCase , )
return config
def A (__lowerCamelCase :Optional[Any] ):
_lowerCAmelCase = []
# fmt: off
# stem
rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""") )
rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""") )
rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""") )
rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f'backbone.stages.{i}.{j}.gamma', f'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter') )
rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.weight', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.bias', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias') )
rename_keys.append((f'backbone.stages.{i}.{j}.norm.weight', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.norm.bias', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight') )
rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias') )
if i > 0:
rename_keys.append((f'backbone.downsample_layers.{i}.0.weight', f'backbone.encoder.stages.{i}.downsampling_layer.0.weight') )
rename_keys.append((f'backbone.downsample_layers.{i}.0.bias', f'backbone.encoder.stages.{i}.downsampling_layer.0.bias') )
rename_keys.append((f'backbone.downsample_layers.{i}.1.weight', f'backbone.encoder.stages.{i}.downsampling_layer.1.weight') )
rename_keys.append((f'backbone.downsample_layers.{i}.1.bias', f'backbone.encoder.stages.{i}.downsampling_layer.1.bias') )
rename_keys.append((f'backbone.norm{i}.weight', f'backbone.hidden_states_norms.stage{i+1}.weight') )
rename_keys.append((f'backbone.norm{i}.bias', f'backbone.hidden_states_norms.stage{i+1}.bias') )
# decode head
rename_keys.extend(
[
("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
] )
# fmt: on
return rename_keys
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :Dict , __lowerCamelCase :Tuple ):
_lowerCAmelCase = dct.pop(__lowerCamelCase )
_lowerCAmelCase = val
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Optional[Any] , __lowerCamelCase :Any ):
_lowerCAmelCase = {
"""upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
"""upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
"""upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
"""upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
"""upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
}
_lowerCAmelCase = model_name_to_url[model_name]
_lowerCAmelCase = torch.hub.load_state_dict_from_url(__lowerCamelCase , map_location="""cpu""" )["""state_dict"""]
_lowerCAmelCase = get_upernet_config(__lowerCamelCase )
_lowerCAmelCase = UperNetForSemanticSegmentation(__lowerCamelCase )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
_lowerCAmelCase = state_dict.pop(__lowerCamelCase )
if "bn" in key:
_lowerCAmelCase = key.replace("""bn""" , """batch_norm""" )
_lowerCAmelCase = val
# rename keys
_lowerCAmelCase = create_rename_keys(__lowerCamelCase )
for src, dest in rename_keys:
rename_key(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
model.load_state_dict(__lowerCamelCase )
# verify on image
_lowerCAmelCase = """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
_lowerCAmelCase = Image.open(requests.get(__lowerCamelCase , stream=__lowerCamelCase ).raw ).convert("""RGB""" )
_lowerCAmelCase = SegformerImageProcessor()
_lowerCAmelCase = processor(__lowerCamelCase , return_tensors="""pt""" ).pixel_values
with torch.no_grad():
_lowerCAmelCase = model(__lowerCamelCase )
if model_name == "upernet-convnext-tiny":
_lowerCAmelCase = torch.tensor(
[[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]] )
elif model_name == "upernet-convnext-small":
_lowerCAmelCase = torch.tensor(
[[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]] )
elif model_name == "upernet-convnext-base":
_lowerCAmelCase = torch.tensor(
[[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]] )
elif model_name == "upernet-convnext-large":
_lowerCAmelCase = torch.tensor(
[[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]] )
elif model_name == "upernet-convnext-xlarge":
_lowerCAmelCase = torch.tensor(
[[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]] )
print("""Logits:""" , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , __lowerCamelCase , atol=1e-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(f'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(__lowerCamelCase )
print(f'Saving processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(__lowerCamelCase )
if push_to_hub:
print(f'Pushing model and processor for {model_name} to hub' )
model.push_to_hub(f'openmmlab/{model_name}' )
processor.push_to_hub(f'openmmlab/{model_name}' )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""upernet-convnext-tiny""",
type=str,
choices=[F"""upernet-convnext-{size}""" for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
help="""Name of the ConvNext UperNet model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_lowercase = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 5 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase = {
"""configuration_roc_bert""": ["""ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoCBertConfig"""],
"""tokenization_roc_bert""": ["""RoCBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RoCBertForCausalLM""",
"""RoCBertForMaskedLM""",
"""RoCBertForMultipleChoice""",
"""RoCBertForPreTraining""",
"""RoCBertForQuestionAnswering""",
"""RoCBertForSequenceClassification""",
"""RoCBertForTokenClassification""",
"""RoCBertLayer""",
"""RoCBertModel""",
"""RoCBertPreTrainedModel""",
"""load_tf_weights_in_roc_bert""",
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 5 |
'''simple docstring'''
from itertools import product
def A (__lowerCamelCase :int , __lowerCamelCase :int ):
_lowerCAmelCase = sides_number
_lowerCAmelCase = max_face_number * dice_number
_lowerCAmelCase = [0] * (max_total + 1)
_lowerCAmelCase = 1
_lowerCAmelCase = range(__lowerCamelCase , max_face_number + 1 )
for dice_numbers in product(__lowerCamelCase , repeat=__lowerCamelCase ):
_lowerCAmelCase = sum(__lowerCamelCase )
totals_frequencies[total] += 1
return totals_frequencies
def A ():
_lowerCAmelCase = total_frequency_distribution(
sides_number=4 , dice_number=9 )
_lowerCAmelCase = total_frequency_distribution(
sides_number=6 , dice_number=6 )
_lowerCAmelCase = 0
_lowerCAmelCase = 9
_lowerCAmelCase = 4 * 9
_lowerCAmelCase = 6
for peter_total in range(__lowerCamelCase , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
_lowerCAmelCase = (4**9) * (6**6)
_lowerCAmelCase = peter_wins_count / total_games_number
_lowerCAmelCase = round(__lowerCamelCase , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(F"""{solution() = }""")
| 5 | 1 |
'''simple docstring'''
import pprint
import requests
_lowercase = """https://zenquotes.io/api"""
def A ():
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def A ():
return requests.get(API_ENDPOINT_URL + """/random""" ).json()
if __name__ == "__main__":
_lowercase = random_quotes()
pprint.pprint(response)
| 5 |
'''simple docstring'''
from manim import *
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = Rectangle(height=0.5 , width=0.5 )
_lowerCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = VGroup(_lowercase , _lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""CPU""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(_lowercase )
_lowerCAmelCase = [mem.copy() for i in range(1 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""GPU""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
gpu.align_to(_lowercase , _lowercase )
gpu.set_x(gpu.get_x() - 1 )
self.add(_lowercase )
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = VGroup(*_lowercase ).arrange(_lowercase , buff=0 )
_lowerCAmelCase = Text("""Model""" , font_size=24 )
_lowerCAmelCase = Group(_lowercase , _lowercase ).arrange(_lowercase , buff=0.5 , aligned_edge=_lowercase )
model.move_to([3, -1.0, 0] )
self.play(
Create(_lowercase , run_time=1 ) , Create(_lowercase , run_time=1 ) , Create(_lowercase , run_time=1 ) , )
_lowerCAmelCase = MarkupText(
F'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.' , font_size=24 , )
_lowerCAmelCase = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_lowerCAmelCase = MarkupText(
F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(_lowercase , run_time=2.5 ) , Write(_lowercase ) , Write(_lowercase ) )
self.add(_lowercase )
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = []
for i, rect in enumerate(_lowercase ):
_lowerCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(_lowercase , opacity=0.7 )
cpu_target.move_to(_lowercase )
cpu_target.generate_target()
_lowerCAmelCase = 0.46 / 4
_lowerCAmelCase = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=_lowercase )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target , direction=_lowercase , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=_lowercase , buff=0.0 )
cpu_targs.append(_lowercase )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(_lowercase ) )
second_animations.append(MoveToTarget(_lowercase , run_time=1.5 ) )
self.play(*_lowercase )
self.play(*_lowercase )
self.wait()
| 5 | 1 |
'''simple docstring'''
def A (__lowerCamelCase :List[Any] ):
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
_lowerCAmelCase = len(__lowerCamelCase )
_lowerCAmelCase = max(__lowerCamelCase )
_lowerCAmelCase = min(__lowerCamelCase )
# create the counting array
_lowerCAmelCase = coll_max + 1 - coll_min
_lowerCAmelCase = [0] * counting_arr_length
# count how much a number appears in the collection
for number in collection:
counting_arr[number - coll_min] += 1
# sum each position with it's predecessors. now, counting_arr[i] tells
# us how many elements <= i has in the collection
for i in range(1 , __lowerCamelCase ):
_lowerCAmelCase = counting_arr[i] + counting_arr[i - 1]
# create the output collection
_lowerCAmelCase = [0] * coll_len
# place the elements in the output, respecting the original order (stable
# sort) from end to begin, updating counting_arr
for i in reversed(range(0 , __lowerCamelCase ) ):
_lowerCAmelCase = collection[i]
counting_arr[collection[i] - coll_min] -= 1
return ordered
def A (__lowerCamelCase :Optional[int] ):
return "".join([chr(__lowerCamelCase ) for i in counting_sort([ord(__lowerCamelCase ) for c in string] )] )
if __name__ == "__main__":
# Test string sort
assert counting_sort_string("""thisisthestring""") == "eghhiiinrsssttt"
_lowercase = input("""Enter numbers separated by a comma:\n""").strip()
_lowercase = [int(item) for item in user_input.split(""",""")]
print(counting_sort(unsorted))
| 5 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_lowercase = False
try:
_lowercase = _is_package_available("""google.colab""")
except ModuleNotFoundError:
pass
@input.register
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase = None , _lowercase = [] ):
"""simple docstring"""
_lowerCAmelCase = 0
_lowerCAmelCase = choices
_lowerCAmelCase = prompt
if sys.platform == "win32":
_lowerCAmelCase = """*"""
else:
_lowerCAmelCase = """➔ """
def _lowercase ( self , _lowercase , _lowercase = "" ):
"""simple docstring"""
if sys.platform != "win32":
writeColor(self.choices[index] , 32 , _lowercase )
else:
forceWrite(self.choices[index] , _lowercase )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
if index == self.position:
forceWrite(F' {self.arrow_char} ' )
self.write_choice(_lowercase )
else:
forceWrite(F' {self.choices[index]}' )
reset_cursor()
def _lowercase ( self , _lowercase , _lowercase = 1 ):
"""simple docstring"""
_lowerCAmelCase = self.position
if direction == Direction.DOWN:
if self.position + 1 >= len(self.choices ):
return
self.position += num_spaces
else:
if self.position - 1 < 0:
return
self.position -= num_spaces
clear_line()
self.print_choice(_lowercase )
move_cursor(_lowercase , direction.name )
self.print_choice(self.position )
@input.mark(KEYMAP["""up"""] )
def _lowercase ( self ):
"""simple docstring"""
self.move_direction(Direction.UP )
@input.mark(KEYMAP["""down"""] )
def _lowercase ( self ):
"""simple docstring"""
self.move_direction(Direction.DOWN )
@input.mark(KEYMAP["""newline"""] )
def _lowercase ( self ):
"""simple docstring"""
move_cursor(len(self.choices ) - self.position , """DOWN""" )
return self.position
@input.mark(KEYMAP["""interrupt"""] )
def _lowercase ( self ):
"""simple docstring"""
move_cursor(len(self.choices ) - self.position , """DOWN""" )
raise KeyboardInterrupt
@input.mark_multiple(*[KEYMAP[str(_lowercase )] for number in range(10 )] )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = int(chr(self.current_selection ) )
_lowerCAmelCase = index - self.position
if index == self.position:
return
if index < len(self.choices ):
if self.position > index:
self.move_direction(Direction.UP , -movement )
elif self.position < index:
self.move_direction(Direction.DOWN , _lowercase )
else:
return
else:
return
def _lowercase ( self , _lowercase = 0 ):
"""simple docstring"""
if self.prompt:
linebreak()
forceWrite(self.prompt , """\n""" )
if in_colab:
forceWrite("""Please input a choice index (starting from 0), and press enter""" , """\n""" )
else:
forceWrite("""Please select a choice using the arrow or number keys, and selecting with enter""" , """\n""" )
_lowerCAmelCase = default_choice
for i in range(len(self.choices ) ):
self.print_choice(_lowercase )
forceWrite("""\n""" )
move_cursor(len(self.choices ) - self.position , """UP""" )
with cursor.hide():
while True:
if in_colab:
try:
_lowerCAmelCase = int(builtins.input() )
except ValueError:
_lowerCAmelCase = default_choice
else:
_lowerCAmelCase = self.handle_input()
if choice is not None:
reset_cursor()
for _ in range(len(self.choices ) + 1 ):
move_cursor(1 , """UP""" )
clear_line()
self.write_choice(_lowercase , """\n""" )
return choice
| 5 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
ImageTextPipelineOutput,
UniDiffuserPipeline,
)
else:
from .modeling_text_decoder import UniDiffuserTextDecoder
from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
| 5 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""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""",
"""adapter_layer""": """encoder.layers.*.adapter_layer""",
"""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""",
"""pooling_layer.linear""": """projector""",
"""pooling_layer.projection""": """classifier""",
}
_lowercase = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""projector""",
"""classifier""",
]
def A (__lowerCamelCase :Optional[int] ):
_lowerCAmelCase = {}
with open(__lowerCamelCase , """r""" ) as file:
for line_number, line in enumerate(__lowerCamelCase ):
_lowerCAmelCase = line.strip()
if line:
_lowerCAmelCase = line.split()
_lowerCAmelCase = line_number
_lowerCAmelCase = words[0]
_lowerCAmelCase = value
return result
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :Any , __lowerCamelCase :Tuple , __lowerCamelCase :List[Any] , __lowerCamelCase :List[str] ):
for attribute in key.split(""".""" ):
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(__lowerCamelCase ):
_lowerCAmelCase = PARAM_MAPPING[full_name.split(""".""" )[-1]]
_lowerCAmelCase = """param"""
if weight_type is not None and weight_type != "param":
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase ).shape
elif weight_type is not None and weight_type == "param":
_lowerCAmelCase = hf_pointer
for attribute in hf_param_name.split(""".""" ):
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = shape_pointer.shape
# let's reduce dimension
_lowerCAmelCase = value[0]
else:
_lowerCAmelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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":
_lowerCAmelCase = value
elif weight_type == "weight_g":
_lowerCAmelCase = value
elif weight_type == "weight_v":
_lowerCAmelCase = value
elif weight_type == "bias":
_lowerCAmelCase = value
elif weight_type == "param":
for attribute in hf_param_name.split(""".""" ):
_lowerCAmelCase = getattr(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = value
else:
_lowerCAmelCase = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Tuple , __lowerCamelCase :Dict , __lowerCamelCase :List[Any] , __lowerCamelCase :int ):
_lowerCAmelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(__lowerCamelCase ):
_lowerCAmelCase = PARAM_MAPPING[full_name.split(""".""" )[-1]]
_lowerCAmelCase = """param"""
if weight_type is not None and weight_type != "param":
_lowerCAmelCase = """.""".join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
_lowerCAmelCase = """.""".join([key, hf_param_name] )
else:
_lowerCAmelCase = key
_lowerCAmelCase = value if """lm_head""" in full_key else value[0]
_lowercase = {
"""W_a""": """linear_1.weight""",
"""W_b""": """linear_2.weight""",
"""b_a""": """linear_1.bias""",
"""b_b""": """linear_2.bias""",
"""ln_W""": """norm.weight""",
"""ln_b""": """norm.bias""",
}
def A (__lowerCamelCase :Any , __lowerCamelCase :int , __lowerCamelCase :List[str]=None , __lowerCamelCase :List[Any]=None ):
_lowerCAmelCase = False
for key, mapped_key in MAPPING.items():
_lowerCAmelCase = """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
_lowerCAmelCase = True
if "*" in mapped_key:
_lowerCAmelCase = name.split(__lowerCamelCase )[0].split(""".""" )[-2]
_lowerCAmelCase = mapped_key.replace("""*""" , __lowerCamelCase )
if "weight_g" in name:
_lowerCAmelCase = """weight_g"""
elif "weight_v" in name:
_lowerCAmelCase = """weight_v"""
elif "bias" in name:
_lowerCAmelCase = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_lowerCAmelCase = """weight"""
else:
_lowerCAmelCase = None
if hf_dict is not None:
rename_dict(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
else:
set_recursively(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return is_used
return is_used
def A (__lowerCamelCase :Any , __lowerCamelCase :Dict , __lowerCamelCase :Dict ):
_lowerCAmelCase = []
_lowerCAmelCase = fairseq_model.state_dict()
_lowerCAmelCase = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
_lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , hf_model.config.feat_extract_norm == """group""" , )
_lowerCAmelCase = True
else:
_lowerCAmelCase = load_wavaveca_layer(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
if not is_used:
unused_weights.append(__lowerCamelCase )
logger.warning(f'Unused weights: {unused_weights}' )
def A (__lowerCamelCase :Tuple , __lowerCamelCase :Optional[int] , __lowerCamelCase :Any , __lowerCamelCase :List[Any] , __lowerCamelCase :List[Any] ):
_lowerCAmelCase = full_name.split("""conv_layers.""" )[-1]
_lowerCAmelCase = name.split(""".""" )
_lowerCAmelCase = int(items[0] )
_lowerCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
_lowerCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
_lowerCAmelCase = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.' )
_lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.' )
_lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(__lowerCamelCase )
@torch.no_grad()
def A (__lowerCamelCase :List[str] , __lowerCamelCase :Tuple , __lowerCamelCase :List[Any]=None , __lowerCamelCase :Union[str, Any]=None , __lowerCamelCase :str=True , __lowerCamelCase :str=False ):
if config_path is not None:
_lowerCAmelCase = WavaVecaConfig.from_pretrained(__lowerCamelCase )
else:
_lowerCAmelCase = WavaVecaConfig()
if is_seq_class:
_lowerCAmelCase = read_txt_into_dict(__lowerCamelCase )
_lowerCAmelCase = idalabel
_lowerCAmelCase = WavaVecaForSequenceClassification(__lowerCamelCase )
_lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__lowerCamelCase , return_attention_mask=__lowerCamelCase , )
feature_extractor.save_pretrained(__lowerCamelCase )
elif is_finetuned:
if dict_path:
_lowerCAmelCase = Dictionary.load(__lowerCamelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_lowerCAmelCase = target_dict.pad_index
_lowerCAmelCase = target_dict.bos_index
_lowerCAmelCase = target_dict.eos_index
_lowerCAmelCase = len(target_dict.symbols )
_lowerCAmelCase = os.path.join(__lowerCamelCase , """vocab.json""" )
if not os.path.isdir(__lowerCamelCase ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__lowerCamelCase ) )
return
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
_lowerCAmelCase = target_dict.indices
# fairseq has the <pad> and <s> switched
_lowerCAmelCase = 0
_lowerCAmelCase = 1
with open(__lowerCamelCase , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = WavaVecaCTCTokenizer(
__lowerCamelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=__lowerCamelCase , )
_lowerCAmelCase = True if config.feat_extract_norm == """layer""" else False
_lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=__lowerCamelCase , return_attention_mask=__lowerCamelCase , )
_lowerCAmelCase = WavaVecaProcessor(feature_extractor=__lowerCamelCase , tokenizer=__lowerCamelCase )
processor.save_pretrained(__lowerCamelCase )
_lowerCAmelCase = WavaVecaForCTC(__lowerCamelCase )
else:
_lowerCAmelCase = WavaVecaForPreTraining(__lowerCamelCase )
if is_finetuned or is_seq_class:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
_lowerCAmelCase = argparse.Namespace(task="""audio_pretraining""" )
_lowerCAmelCase = fairseq.tasks.setup_task(__lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=__lowerCamelCase )
_lowerCAmelCase = model[0].eval()
recursively_load_weights(__lowerCamelCase , __lowerCamelCase , not is_finetuned )
hf_wavavec.save_pretrained(__lowerCamelCase )
if __name__ == "__main__":
_lowercase = 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_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
parser.add_argument(
"""--is_seq_class""",
action="""store_true""",
help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
)
_lowercase = parser.parse_args()
_lowercase = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 5 | 1 |
'''simple docstring'''
import os
def A (__lowerCamelCase :Dict ):
_lowerCAmelCase = len(grid[0] )
_lowerCAmelCase = len(__lowerCamelCase )
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = 0
# Check vertically, horizontally, diagonally at the same time (only works
# for nxn grid)
for i in range(__lowerCamelCase ):
for j in range(n_rows - 3 ):
_lowerCAmelCase = grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
_lowerCAmelCase = grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]
# Left-to-right diagonal (\) product
if i < n_columns - 3:
_lowerCAmelCase = (
grid[i][j]
* grid[i + 1][j + 1]
* grid[i + 2][j + 2]
* grid[i + 3][j + 3]
)
# Right-to-left diagonal(/) product
if i > 2:
_lowerCAmelCase = (
grid[i][j]
* grid[i - 1][j + 1]
* grid[i - 2][j + 2]
* grid[i - 3][j + 3]
)
_lowerCAmelCase = max(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
if max_product > largest:
_lowerCAmelCase = max_product
return largest
def A ():
_lowerCAmelCase = []
with open(os.path.dirname(__lowerCamelCase ) + """/grid.txt""" ) as file:
for line in file:
grid.append(line.strip("""\n""" ).split(""" """ ) )
_lowerCAmelCase = [[int(__lowerCamelCase ) for i in grid[j]] for j in range(len(__lowerCamelCase ) )]
return largest_product(__lowerCamelCase )
if __name__ == "__main__":
print(solution())
| 5 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : List[str] = '''decision_transformer'''
_lowercase : Optional[Any] = ['''past_key_values''']
_lowercase : str = {
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , _lowercase=17 , _lowercase=4 , _lowercase=128 , _lowercase=4_096 , _lowercase=True , _lowercase=1 , _lowercase=1_024 , _lowercase=3 , _lowercase=1 , _lowercase=None , _lowercase="relu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=0.1 , _lowercase=1e-5 , _lowercase=0.02 , _lowercase=True , _lowercase=True , _lowercase=50_256 , _lowercase=50_256 , _lowercase=False , _lowercase=False , **_lowercase , ):
"""simple docstring"""
_lowerCAmelCase = state_dim
_lowerCAmelCase = act_dim
_lowerCAmelCase = hidden_size
_lowerCAmelCase = max_ep_len
_lowerCAmelCase = action_tanh
_lowerCAmelCase = vocab_size
_lowerCAmelCase = n_positions
_lowerCAmelCase = n_layer
_lowerCAmelCase = n_head
_lowerCAmelCase = n_inner
_lowerCAmelCase = activation_function
_lowerCAmelCase = resid_pdrop
_lowerCAmelCase = embd_pdrop
_lowerCAmelCase = attn_pdrop
_lowerCAmelCase = layer_norm_epsilon
_lowerCAmelCase = initializer_range
_lowerCAmelCase = scale_attn_weights
_lowerCAmelCase = use_cache
_lowerCAmelCase = scale_attn_by_inverse_layer_idx
_lowerCAmelCase = reorder_and_upcast_attn
_lowerCAmelCase = bos_token_id
_lowerCAmelCase = eos_token_id
super().__init__(bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase )
| 5 | 1 |
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_lowercase = False
try:
_lowercase = _is_package_available("""google.colab""")
except ModuleNotFoundError:
pass
@input.register
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase = None , _lowercase = [] ):
"""simple docstring"""
_lowerCAmelCase = 0
_lowerCAmelCase = choices
_lowerCAmelCase = prompt
if sys.platform == "win32":
_lowerCAmelCase = """*"""
else:
_lowerCAmelCase = """➔ """
def _lowercase ( self , _lowercase , _lowercase = "" ):
"""simple docstring"""
if sys.platform != "win32":
writeColor(self.choices[index] , 32 , _lowercase )
else:
forceWrite(self.choices[index] , _lowercase )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
if index == self.position:
forceWrite(F' {self.arrow_char} ' )
self.write_choice(_lowercase )
else:
forceWrite(F' {self.choices[index]}' )
reset_cursor()
def _lowercase ( self , _lowercase , _lowercase = 1 ):
"""simple docstring"""
_lowerCAmelCase = self.position
if direction == Direction.DOWN:
if self.position + 1 >= len(self.choices ):
return
self.position += num_spaces
else:
if self.position - 1 < 0:
return
self.position -= num_spaces
clear_line()
self.print_choice(_lowercase )
move_cursor(_lowercase , direction.name )
self.print_choice(self.position )
@input.mark(KEYMAP["""up"""] )
def _lowercase ( self ):
"""simple docstring"""
self.move_direction(Direction.UP )
@input.mark(KEYMAP["""down"""] )
def _lowercase ( self ):
"""simple docstring"""
self.move_direction(Direction.DOWN )
@input.mark(KEYMAP["""newline"""] )
def _lowercase ( self ):
"""simple docstring"""
move_cursor(len(self.choices ) - self.position , """DOWN""" )
return self.position
@input.mark(KEYMAP["""interrupt"""] )
def _lowercase ( self ):
"""simple docstring"""
move_cursor(len(self.choices ) - self.position , """DOWN""" )
raise KeyboardInterrupt
@input.mark_multiple(*[KEYMAP[str(_lowercase )] for number in range(10 )] )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = int(chr(self.current_selection ) )
_lowerCAmelCase = index - self.position
if index == self.position:
return
if index < len(self.choices ):
if self.position > index:
self.move_direction(Direction.UP , -movement )
elif self.position < index:
self.move_direction(Direction.DOWN , _lowercase )
else:
return
else:
return
def _lowercase ( self , _lowercase = 0 ):
"""simple docstring"""
if self.prompt:
linebreak()
forceWrite(self.prompt , """\n""" )
if in_colab:
forceWrite("""Please input a choice index (starting from 0), and press enter""" , """\n""" )
else:
forceWrite("""Please select a choice using the arrow or number keys, and selecting with enter""" , """\n""" )
_lowerCAmelCase = default_choice
for i in range(len(self.choices ) ):
self.print_choice(_lowercase )
forceWrite("""\n""" )
move_cursor(len(self.choices ) - self.position , """UP""" )
with cursor.hide():
while True:
if in_colab:
try:
_lowerCAmelCase = int(builtins.input() )
except ValueError:
_lowerCAmelCase = default_choice
else:
_lowerCAmelCase = self.handle_input()
if choice is not None:
reset_cursor()
for _ in range(len(self.choices ) + 1 ):
move_cursor(1 , """UP""" )
clear_line()
self.write_choice(_lowercase , """\n""" )
return choice
| 5 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
"""The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"""
)
_lowercase = None
_lowercase = {
"""7B""": 11008,
"""13B""": 13824,
"""30B""": 17920,
"""65B""": 22016,
"""70B""": 28672,
}
_lowercase = {
"""7B""": 1,
"""7Bf""": 1,
"""13B""": 2,
"""13Bf""": 2,
"""30B""": 4,
"""65B""": 8,
"""70B""": 8,
"""70Bf""": 8,
}
def A (__lowerCamelCase :int , __lowerCamelCase :Optional[Any]=1 , __lowerCamelCase :List[Any]=256 ):
return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of)
def A (__lowerCamelCase :Any ):
with open(__lowerCamelCase , """r""" ) as f:
return json.load(__lowerCamelCase )
def A (__lowerCamelCase :List[Any] , __lowerCamelCase :int ):
with open(__lowerCamelCase , """w""" ) as f:
json.dump(__lowerCamelCase , __lowerCamelCase )
def A (__lowerCamelCase :Optional[Any] , __lowerCamelCase :Tuple , __lowerCamelCase :Optional[Any] , __lowerCamelCase :Tuple=True ):
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
_lowerCAmelCase = os.path.join(__lowerCamelCase , """tmp""" )
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
_lowerCAmelCase = read_json(os.path.join(__lowerCamelCase , """params.json""" ) )
_lowerCAmelCase = NUM_SHARDS[model_size]
_lowerCAmelCase = params["""n_layers"""]
_lowerCAmelCase = params["""n_heads"""]
_lowerCAmelCase = n_heads // num_shards
_lowerCAmelCase = params["""dim"""]
_lowerCAmelCase = dim // n_heads
_lowerCAmelCase = 10_000.0
_lowerCAmelCase = 1.0 / (base ** (torch.arange(0 , __lowerCamelCase , 2 ).float() / dims_per_head))
if "n_kv_heads" in params:
_lowerCAmelCase = params["""n_kv_heads"""] # for GQA / MQA
_lowerCAmelCase = n_heads_per_shard // num_key_value_heads
_lowerCAmelCase = dim // num_key_value_heads
else: # compatibility with other checkpoints
_lowerCAmelCase = n_heads
_lowerCAmelCase = n_heads_per_shard
_lowerCAmelCase = dim
# permute for sliced rotary
def permute(__lowerCamelCase :Optional[int] , __lowerCamelCase :str=n_heads , __lowerCamelCase :str=dim , __lowerCamelCase :List[Any]=dim ):
return w.view(__lowerCamelCase , dima // n_heads // 2 , 2 , __lowerCamelCase ).transpose(1 , 2 ).reshape(__lowerCamelCase , __lowerCamelCase )
print(f'Fetching all parameters from the checkpoint at {input_base_path}.' )
# Load weights
if model_size == "7B":
# Not sharded
# (The sharded implementation would also work, but this is simpler.)
_lowerCAmelCase = torch.load(os.path.join(__lowerCamelCase , """consolidated.00.pth""" ) , map_location="""cpu""" )
else:
# Sharded
_lowerCAmelCase = [
torch.load(os.path.join(__lowerCamelCase , f'consolidated.{i:02d}.pth' ) , map_location="""cpu""" )
for i in range(__lowerCamelCase )
]
_lowerCAmelCase = 0
_lowerCAmelCase = {"""weight_map""": {}}
for layer_i in range(__lowerCamelCase ):
_lowerCAmelCase = f'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
if model_size == "7B":
# Unsharded
_lowerCAmelCase = {
f'model.layers.{layer_i}.self_attn.q_proj.weight': permute(
loaded[f'layers.{layer_i}.attention.wq.weight'] ),
f'model.layers.{layer_i}.self_attn.k_proj.weight': permute(
loaded[f'layers.{layer_i}.attention.wk.weight'] ),
f'model.layers.{layer_i}.self_attn.v_proj.weight': loaded[f'layers.{layer_i}.attention.wv.weight'],
f'model.layers.{layer_i}.self_attn.o_proj.weight': loaded[f'layers.{layer_i}.attention.wo.weight'],
f'model.layers.{layer_i}.mlp.gate_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w1.weight'],
f'model.layers.{layer_i}.mlp.down_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w2.weight'],
f'model.layers.{layer_i}.mlp.up_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w3.weight'],
f'model.layers.{layer_i}.input_layernorm.weight': loaded[f'layers.{layer_i}.attention_norm.weight'],
f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[f'layers.{layer_i}.ffn_norm.weight'],
}
else:
# Sharded
# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
_lowerCAmelCase = {
f'model.layers.{layer_i}.input_layernorm.weight': loaded[0][
f'layers.{layer_i}.attention_norm.weight'
].clone(),
f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[0][
f'layers.{layer_i}.ffn_norm.weight'
].clone(),
}
_lowerCAmelCase = permute(
torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wq.weight'].view(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for i in range(__lowerCamelCase )
] , dim=0 , ).reshape(__lowerCamelCase , __lowerCamelCase ) )
_lowerCAmelCase = permute(
torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wk.weight'].view(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for i in range(__lowerCamelCase )
] , dim=0 , ).reshape(__lowerCamelCase , __lowerCamelCase ) , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , )
_lowerCAmelCase = torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wv.weight'].view(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for i in range(__lowerCamelCase )
] , dim=0 , ).reshape(__lowerCamelCase , __lowerCamelCase )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.attention.wo.weight'] for i in range(__lowerCamelCase )] , dim=1 )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w1.weight'] for i in range(__lowerCamelCase )] , dim=0 )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w2.weight'] for i in range(__lowerCamelCase )] , dim=1 )
_lowerCAmelCase = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w3.weight'] for i in range(__lowerCamelCase )] , dim=0 )
_lowerCAmelCase = inv_freq
for k, v in state_dict.items():
_lowerCAmelCase = filename
param_count += v.numel()
torch.save(__lowerCamelCase , os.path.join(__lowerCamelCase , __lowerCamelCase ) )
_lowerCAmelCase = f'pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'
if model_size == "7B":
# Unsharded
_lowerCAmelCase = {
"""model.embed_tokens.weight""": loaded["""tok_embeddings.weight"""],
"""model.norm.weight""": loaded["""norm.weight"""],
"""lm_head.weight""": loaded["""output.weight"""],
}
else:
_lowerCAmelCase = {
"""model.norm.weight""": loaded[0]["""norm.weight"""],
"""model.embed_tokens.weight""": torch.cat(
[loaded[i]["""tok_embeddings.weight"""] for i in range(__lowerCamelCase )] , dim=1 ),
"""lm_head.weight""": torch.cat([loaded[i]["""output.weight"""] for i in range(__lowerCamelCase )] , dim=0 ),
}
for k, v in state_dict.items():
_lowerCAmelCase = filename
param_count += v.numel()
torch.save(__lowerCamelCase , os.path.join(__lowerCamelCase , __lowerCamelCase ) )
# Write configs
_lowerCAmelCase = {"""total_size""": param_count * 2}
write_json(__lowerCamelCase , os.path.join(__lowerCamelCase , """pytorch_model.bin.index.json""" ) )
_lowerCAmelCase = params["""ffn_dim_multiplier"""] if """ffn_dim_multiplier""" in params else 1
_lowerCAmelCase = params["""multiple_of"""] if """multiple_of""" in params else 256
_lowerCAmelCase = LlamaConfig(
hidden_size=__lowerCamelCase , intermediate_size=compute_intermediate_size(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) , num_attention_heads=params["""n_heads"""] , num_hidden_layers=params["""n_layers"""] , rms_norm_eps=params["""norm_eps"""] , num_key_value_heads=__lowerCamelCase , )
config.save_pretrained(__lowerCamelCase )
# Make space so we can load the model properly now.
del state_dict
del loaded
gc.collect()
print("""Loading the checkpoint in a Llama model.""" )
_lowerCAmelCase = LlamaForCausalLM.from_pretrained(__lowerCamelCase , torch_dtype=torch.floataa , low_cpu_mem_usage=__lowerCamelCase )
# Avoid saving this as part of the config.
del model.config._name_or_path
print("""Saving in the Transformers format.""" )
model.save_pretrained(__lowerCamelCase , safe_serialization=__lowerCamelCase )
shutil.rmtree(__lowerCamelCase )
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Union[str, Any] ):
# Initialize the tokenizer based on the `spm` model
_lowerCAmelCase = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
print(f'Saving a {tokenizer_class.__name__} to {tokenizer_path}.' )
_lowerCAmelCase = tokenizer_class(__lowerCamelCase )
tokenizer.save_pretrained(__lowerCamelCase )
def A ():
_lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--input_dir""" , help="""Location of LLaMA weights, which contains tokenizer.model and model folders""" , )
parser.add_argument(
"""--model_size""" , choices=["""7B""", """7Bf""", """13B""", """13Bf""", """30B""", """65B""", """70B""", """70Bf""", """tokenizer_only"""] , )
parser.add_argument(
"""--output_dir""" , help="""Location to write HF model and tokenizer""" , )
parser.add_argument("""--safe_serialization""" , type=__lowerCamelCase , help="""Whether or not to save using `safetensors`.""" )
_lowerCAmelCase = parser.parse_args()
if args.model_size != "tokenizer_only":
write_model(
model_path=args.output_dir , input_base_path=os.path.join(args.input_dir , args.model_size ) , model_size=args.model_size , safe_serialization=args.safe_serialization , )
_lowerCAmelCase = os.path.join(args.input_dir , """tokenizer.model""" )
write_tokenizer(args.output_dir , __lowerCamelCase )
if __name__ == "__main__":
main()
| 5 | 1 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("""Googling.....""")
_lowercase = """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
_lowercase = requests.get(url, headers={"""UserAgent""": UserAgent().random})
# res.raise_for_status()
with open("""project1a.html""", """wb""") as out_file: # only for knowing the class
for data in res.iter_content(10000):
out_file.write(data)
_lowercase = BeautifulSoup(res.text, """html.parser""")
_lowercase = list(soup.select(""".eZt8xd"""))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get("""href"""))
else:
webbrowser.open(F"""https://google.com{link.get('href')}""")
| 5 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Tuple = (DDPMScheduler,)
def _lowercase ( self , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_lowercase )
return config
def _lowercase ( self ):
"""simple docstring"""
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_lowercase )
def _lowercase ( self ):
"""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=_lowercase , beta_end=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
self.check_over_configs(thresholding=_lowercase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_lowercase , prediction_type=_lowercase , sample_max_value=_lowercase , )
def _lowercase ( self ):
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
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 _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = len(_lowercase )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter
_lowerCAmelCase = torch.manual_seed(0 )
for t in reversed(range(_lowercase ) ):
# 1. predict noise residual
_lowerCAmelCase = model(_lowercase , _lowercase )
# 2. predict previous mean of sample x_t-1
_lowerCAmelCase = scheduler.step(_lowercase , _lowercase , _lowercase , generator=_lowercase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_lowerCAmelCase = pred_prev_sample
_lowerCAmelCase = torch.sum(torch.abs(_lowercase ) )
_lowerCAmelCase = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 258.9606 ) < 1e-2
assert abs(result_mean.item() - 0.3372 ) < 1e-3
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config(prediction_type="""v_prediction""" )
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = len(_lowercase )
_lowerCAmelCase = self.dummy_model()
_lowerCAmelCase = self.dummy_sample_deter
_lowerCAmelCase = torch.manual_seed(0 )
for t in reversed(range(_lowercase ) ):
# 1. predict noise residual
_lowerCAmelCase = model(_lowercase , _lowercase )
# 2. predict previous mean of sample x_t-1
_lowerCAmelCase = scheduler.step(_lowercase , _lowercase , _lowercase , generator=_lowercase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
_lowerCAmelCase = pred_prev_sample
_lowerCAmelCase = torch.sum(torch.abs(_lowercase ) )
_lowerCAmelCase = torch.mean(torch.abs(_lowercase ) )
assert abs(result_sum.item() - 202.0296 ) < 1e-2
assert abs(result_mean.item() - 0.2631 ) < 1e-3
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_lowercase )
_lowerCAmelCase = scheduler.timesteps
for i, timestep in enumerate(_lowercase ):
if i == len(_lowercase ) - 1:
_lowerCAmelCase = -1
else:
_lowerCAmelCase = timesteps[i + 1]
_lowerCAmelCase = scheduler.previous_timestep(_lowercase )
_lowerCAmelCase = prev_t.item()
self.assertEqual(_lowercase , _lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 51, 0]
with self.assertRaises(_lowercase , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [100, 87, 50, 1, 0]
_lowerCAmelCase = len(_lowercase )
with self.assertRaises(_lowercase , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_lowercase , timesteps=_lowercase )
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.scheduler_classes[0]
_lowerCAmelCase = self.get_scheduler_config()
_lowerCAmelCase = scheduler_class(**_lowercase )
_lowerCAmelCase = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_lowercase , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_lowercase )
| 5 | 1 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
_lowercase = logging.get_logger(__name__)
@add_end_docstrings(_SCREAMING_SNAKE_CASE )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , **_lowercase ):
"""simple docstring"""
super().__init__(**_lowercase )
if self.framework != "pt":
raise ValueError(F'The {self.__class__} is only available in PyTorch.' )
# No specific FOR_XXX available yet
def __call__( self , _lowercase , **_lowercase ):
"""simple docstring"""
return super().__call__(_lowercase , **_lowercase )
def _lowercase ( self , **_lowercase ):
"""simple docstring"""
_lowerCAmelCase = {}
if "candidate_labels" in kwargs:
_lowerCAmelCase = kwargs["""candidate_labels"""]
if "hypothesis_template" in kwargs:
_lowerCAmelCase = kwargs["""hypothesis_template"""]
return preprocess_params, {}, {}
def _lowercase ( self , _lowercase , _lowercase=None , _lowercase="This is a sound of {}." ):
"""simple docstring"""
if isinstance(_lowercase , _lowercase ):
if audio.startswith("""http://""" ) or audio.startswith("""https://""" ):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
_lowerCAmelCase = requests.get(_lowercase ).content
else:
with open(_lowercase , """rb""" ) as f:
_lowerCAmelCase = f.read()
if isinstance(_lowercase , _lowercase ):
_lowerCAmelCase = ffmpeg_read(_lowercase , self.feature_extractor.sampling_rate )
if not isinstance(_lowercase , np.ndarray ):
raise ValueError("""We expect a numpy ndarray as input""" )
if len(audio.shape ) != 1:
raise ValueError("""We expect a single channel audio input for ZeroShotAudioClassificationPipeline""" )
_lowerCAmelCase = self.feature_extractor(
[audio] , sampling_rate=self.feature_extractor.sampling_rate , return_tensors="""pt""" )
_lowerCAmelCase = candidate_labels
_lowerCAmelCase = [hypothesis_template.format(_lowercase ) for x in candidate_labels]
_lowerCAmelCase = self.tokenizer(_lowercase , return_tensors=self.framework , padding=_lowercase )
_lowerCAmelCase = [text_inputs]
return inputs
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = model_inputs.pop("""candidate_labels""" )
_lowerCAmelCase = model_inputs.pop("""text_inputs""" )
if isinstance(text_inputs[0] , _lowercase ):
_lowerCAmelCase = text_inputs[0]
else:
# Batching case.
_lowerCAmelCase = text_inputs[0][0]
_lowerCAmelCase = self.model(**_lowercase , **_lowercase )
_lowerCAmelCase = {
"""candidate_labels""": candidate_labels,
"""logits""": outputs.logits_per_audio,
}
return model_outputs
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = model_outputs.pop("""candidate_labels""" )
_lowerCAmelCase = model_outputs["""logits"""][0]
if self.framework == "pt":
_lowerCAmelCase = logits.softmax(dim=0 )
_lowerCAmelCase = probs.tolist()
else:
raise ValueError("""`tf` framework not supported.""" )
_lowerCAmelCase = [
{"""score""": score, """label""": candidate_label}
for score, candidate_label in sorted(zip(_lowercase , _lowercase ) , key=lambda _lowercase : -x[0] )
]
return result
| 5 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_lowercase = logging.get_logger(__name__)
_lowercase = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_lowercase = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class UpperCAmelCase_ :
'''simple docstring'''
_lowercase : str = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(_SCREAMING_SNAKE_CASE )} )
_lowercase : str = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''} )
_lowercase : int = field(
default=1_2_8 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
_lowercase : int = field(
default=1_2_8 , metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''} , )
_lowercase : int = field(
default=6_4 , metadata={
'''help''': (
'''The maximum number of tokens for the question. Questions longer than this will '''
'''be truncated to this length.'''
)
} , )
_lowercase : int = field(
default=3_0 , metadata={
'''help''': (
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
)
} , )
_lowercase : bool = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
_lowercase : bool = field(
default=_SCREAMING_SNAKE_CASE , metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''} )
_lowercase : float = field(
default=0.0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
_lowercase : int = field(
default=2_0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
_lowercase : int = field(
default=0 , metadata={
'''help''': (
'''language id of input for language-specific xlm models (see'''
''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
)
} , )
_lowercase : int = field(default=1 , metadata={'''help''': '''multiple threads for converting example to features'''} )
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : str = '''train'''
_lowercase : Union[str, Any] = '''dev'''
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : SquadDataTrainingArguments
_lowercase : List[SquadFeatures]
_lowercase : Split
_lowercase : bool
def __init__( self , _lowercase , _lowercase , _lowercase = None , _lowercase = Split.train , _lowercase = False , _lowercase = None , _lowercase = "pt" , ):
"""simple docstring"""
_lowerCAmelCase = args
_lowerCAmelCase = is_language_sensitive
_lowerCAmelCase = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(_lowercase , _lowercase ):
try:
_lowerCAmelCase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
_lowerCAmelCase = mode
# Load data features from cache or dataset file
_lowerCAmelCase = """v2""" if args.version_2_with_negative else """v1"""
_lowerCAmelCase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
_lowerCAmelCase = cached_features_file + """.lock"""
with FileLock(_lowercase ):
if os.path.exists(_lowercase ) and not args.overwrite_cache:
_lowerCAmelCase = time.time()
_lowerCAmelCase = torch.load(_lowercase )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
_lowerCAmelCase = self.old_features["""features"""]
_lowerCAmelCase = self.old_features.get("""dataset""" , _lowercase )
_lowerCAmelCase = self.old_features.get("""examples""" , _lowercase )
logger.info(
F'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
F'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
""" future run""" )
else:
if mode == Split.dev:
_lowerCAmelCase = self.processor.get_dev_examples(args.data_dir )
else:
_lowerCAmelCase = self.processor.get_train_examples(args.data_dir )
_lowerCAmelCase , _lowerCAmelCase = squad_convert_examples_to_features(
examples=self.examples , tokenizer=_lowercase , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=_lowercase , )
_lowerCAmelCase = time.time()
torch.save(
{"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples} , _lowercase , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self ):
"""simple docstring"""
return len(self.features )
def __getitem__( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.features[i]
_lowerCAmelCase = torch.tensor(feature.input_ids , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.attention_mask , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.token_type_ids , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.cls_index , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.p_mask , dtype=torch.float )
_lowerCAmelCase = torch.tensor(feature.is_impossible , dtype=torch.float )
_lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": attention_mask,
"""token_type_ids""": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"""is_impossible""": is_impossible} )
if self.is_language_sensitive:
inputs.update({"""langs""": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
_lowerCAmelCase = torch.tensor(feature.start_position , dtype=torch.long )
_lowerCAmelCase = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions} )
return inputs
| 5 | 1 |
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