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"""simple docstring"""
import doctest
from collections import deque
import numpy as np
class lowerCamelCase :
def __init__( self : str ) -> None:
SCREAMING_SNAKE_CASE__ = [2, 1, 2, -1]
SCREAMING_SNAKE_CASE__ = [1, 2, 3, 4]
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> list[float]:
SCREAMING_SNAKE_CASE__ = len(self.first_signal )
SCREAMING_SNAKE_CASE__ = len(self.second_signal )
SCREAMING_SNAKE_CASE__ = max(__UpperCAmelCase , __UpperCAmelCase )
# create a zero matrix of max_length x max_length
SCREAMING_SNAKE_CASE__ = [[0] * max_length for i in range(__UpperCAmelCase )]
# fills the smaller signal with zeros to make both signals of same length
if length_first_signal < length_second_signal:
self.first_signal += [0] * (max_length - length_first_signal)
elif length_first_signal > length_second_signal:
self.second_signal += [0] * (max_length - length_second_signal)
for i in range(__UpperCAmelCase ):
SCREAMING_SNAKE_CASE__ = deque(self.second_signal )
rotated_signal.rotate(__UpperCAmelCase )
for j, item in enumerate(__UpperCAmelCase ):
matrix[i][j] += item
# multiply the matrix with the first signal
SCREAMING_SNAKE_CASE__ = np.matmul(np.transpose(__UpperCAmelCase ) , np.transpose(self.first_signal ) )
# rounding-off to two decimal places
return [round(__UpperCAmelCase , 2 ) for i in final_signal]
if __name__ == "__main__":
doctest.testmod()
| 165 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class lowerCamelCase (unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Dict:
SCREAMING_SNAKE_CASE__ = AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""" )
SCREAMING_SNAKE_CASE__ = AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""" )
model.to(__UpperCAmelCase )
from datasets import load_dataset
SCREAMING_SNAKE_CASE__ = load_dataset("""nielsr/rvlcdip-demo""" )
SCREAMING_SNAKE_CASE__ = dataset["""train"""][0]["""image"""].convert("""RGB""" )
SCREAMING_SNAKE_CASE__ = image_processor(__UpperCAmelCase , return_tensors="""pt""" ).to(__UpperCAmelCase )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE__ = model(**__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = outputs.logits
SCREAMING_SNAKE_CASE__ = torch.Size((1, 1_6) )
self.assertEqual(logits.shape , __UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = torch.tensor(
[-0.4_158, -0.4_092, -0.4_347] , device=__UpperCAmelCase , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , __UpperCAmelCase , atol=1e-4 ) )
| 165 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowercase : int = logging.get_logger(__name__)
_lowercase : Any = {
"salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''blip_2_vision_model'''
def __init__( self : Optional[int] , lowercase_ : Union[str, Any]=1408 , lowercase_ : List[str]=6144 , lowercase_ : Union[str, Any]=39 , lowercase_ : List[str]=16 , lowercase_ : Optional[Any]=224 , lowercase_ : int=14 , lowercase_ : str="gelu" , lowercase_ : int=0.0_00_01 , lowercase_ : List[Any]=0.0 , lowercase_ : int=1E-10 , lowercase_ : int=True , **lowercase_ : Tuple , ):
super().__init__(**lowercase_ )
lowercase_ : Any = hidden_size
lowercase_ : Union[str, Any] = intermediate_size
lowercase_ : Dict = num_hidden_layers
lowercase_ : int = num_attention_heads
lowercase_ : Optional[Any] = patch_size
lowercase_ : Tuple = image_size
lowercase_ : List[Any] = initializer_range
lowercase_ : Any = attention_dropout
lowercase_ : str = layer_norm_eps
lowercase_ : List[Any] = hidden_act
lowercase_ : Optional[Any] = qkv_bias
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Optional[int] , lowercase_ : Union[str, os.PathLike] , **lowercase_ : int ):
cls._set_token_in_kwargs(lowercase_ )
lowercase_ : str = cls.get_config_dict(lowercase_ , **lowercase_ )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get("""model_type""" ) == "blip-2":
lowercase_ : str = 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 __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''blip_2_qformer'''
def __init__( self : Optional[Any] , lowercase_ : Any=30522 , lowercase_ : Union[str, Any]=768 , lowercase_ : Any=12 , lowercase_ : str=12 , lowercase_ : List[str]=3072 , lowercase_ : str="gelu" , lowercase_ : Tuple=0.1 , lowercase_ : int=0.1 , lowercase_ : List[str]=512 , lowercase_ : Optional[int]=0.02 , lowercase_ : str=1E-12 , lowercase_ : str=0 , lowercase_ : Union[str, Any]="absolute" , lowercase_ : int=2 , lowercase_ : Any=1408 , **lowercase_ : Any , ):
super().__init__(pad_token_id=lowercase_ , **lowercase_ )
lowercase_ : Dict = vocab_size
lowercase_ : List[Any] = hidden_size
lowercase_ : Any = num_hidden_layers
lowercase_ : str = num_attention_heads
lowercase_ : List[str] = hidden_act
lowercase_ : Any = intermediate_size
lowercase_ : Dict = hidden_dropout_prob
lowercase_ : List[str] = attention_probs_dropout_prob
lowercase_ : Dict = max_position_embeddings
lowercase_ : Tuple = initializer_range
lowercase_ : str = layer_norm_eps
lowercase_ : int = position_embedding_type
lowercase_ : List[str] = cross_attention_frequency
lowercase_ : int = encoder_hidden_size
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[Any] , lowercase_ : Union[str, os.PathLike] , **lowercase_ : Union[str, Any] ):
cls._set_token_in_kwargs(lowercase_ )
lowercase_ : Dict = cls.get_config_dict(lowercase_ , **lowercase_ )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get("""model_type""" ) == "blip-2":
lowercase_ : Union[str, Any] = config_dict["""qformer_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(lowercase_ , **lowercase_ )
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''blip-2'''
UpperCamelCase__ = True
def __init__( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : List[Any]=None , lowercase_ : Dict=None , lowercase_ : Any=32 , **lowercase_ : Any ):
super().__init__(**lowercase_ )
if vision_config is None:
lowercase_ : Any = {}
logger.info("""vision_config is None. initializing the Blip2VisionConfig with default values.""" )
if qformer_config is None:
lowercase_ : str = {}
logger.info("""qformer_config is None. Initializing the Blip2QFormerConfig with default values.""" )
if text_config is None:
lowercase_ : int = {}
logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`).""" )
lowercase_ : List[Any] = BlipaVisionConfig(**lowercase_ )
lowercase_ : List[str] = BlipaQFormerConfig(**lowercase_ )
lowercase_ : int = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
lowercase_ : Tuple = CONFIG_MAPPING[text_model_type](**lowercase_ )
lowercase_ : Dict = self.text_config.tie_word_embeddings
lowercase_ : Tuple = self.text_config.is_encoder_decoder
lowercase_ : Any = num_query_tokens
lowercase_ : Tuple = self.vision_config.hidden_size
lowercase_ : List[Any] = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
lowercase_ : Optional[int] = 1.0
lowercase_ : Dict = 0.02
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : List[str] , lowercase_ : BlipaVisionConfig , lowercase_ : BlipaQFormerConfig , lowercase_ : PretrainedConfig , **lowercase_ : List[str] , ):
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **lowercase_ , )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Any = copy.deepcopy(self.__dict__ )
lowercase_ : List[str] = self.vision_config.to_dict()
lowercase_ : Optional[Any] = self.qformer_config.to_dict()
lowercase_ : List[Any] = self.text_config.to_dict()
lowercase_ : List[str] = self.__class__.model_type
return output
| 359 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
_lowercase : str = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
def __init__( self : str , *lowercase_ : int , **lowercase_ : Any ):
super().__init__(*lowercase_ , **lowercase_ )
requires_backends(self , """decord""" )
self.check_model_type(lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : str=None , lowercase_ : Union[str, Any]=None , lowercase_ : List[Any]=None ):
lowercase_ : Union[str, Any] = {}
if frame_sampling_rate is not None:
lowercase_ : Any = frame_sampling_rate
if num_frames is not None:
lowercase_ : Optional[Any] = num_frames
lowercase_ : Union[str, Any] = {}
if top_k is not None:
lowercase_ : Optional[Any] = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : str , lowercase_ : Union[str, List[str]] , **lowercase_ : str ):
return super().__call__(lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[str]=None , lowercase_ : Optional[int]=1 ):
if num_frames is None:
lowercase_ : List[Any] = self.model.config.num_frames
if video.startswith("""http://""" ) or video.startswith("""https://""" ):
lowercase_ : Union[str, Any] = BytesIO(requests.get(lowercase_ ).content )
lowercase_ : Optional[Any] = VideoReader(lowercase_ )
videoreader.seek(0 )
lowercase_ : Tuple = 0
lowercase_ : List[Any] = num_frames * frame_sampling_rate - 1
lowercase_ : Optional[int] = np.linspace(lowercase_ , lowercase_ , num=lowercase_ , dtype=np.intaa )
lowercase_ : Optional[int] = videoreader.get_batch(lowercase_ ).asnumpy()
lowercase_ : Union[str, Any] = list(lowercase_ )
lowercase_ : Optional[Any] = self.image_processor(lowercase_ , return_tensors=self.framework )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self : str , lowercase_ : str ):
lowercase_ : int = self.model(**lowercase_ )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : List[Any] , lowercase_ : Dict=5 ):
if top_k > self.model.config.num_labels:
lowercase_ : List[Any] = self.model.config.num_labels
if self.framework == "pt":
lowercase_ : str = model_outputs.logits.softmax(-1 )[0]
lowercase_ , lowercase_ : Optional[Any] = probs.topk(lowercase_ )
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
lowercase_ : Union[str, Any] = scores.tolist()
lowercase_ : Tuple = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowercase_ , lowercase_ )]
| 21 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowercase : Any = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE__ ( __A , __A=False ) -> Optional[int]:
_snake_case = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias') )
# projection layer + position embeddings
rename_keys.extend(
[
('cls_token', 'vit.embeddings.cls_token'),
('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
('pos_embed', 'vit.embeddings.position_embeddings'),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('norm.weight', 'layernorm.weight'),
('norm.bias', 'layernorm.bias'),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
_snake_case = [(pair[0], pair[1][4:]) if pair[1].startswith('vit' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('norm.weight', 'vit.layernorm.weight'),
('norm.bias', 'vit.layernorm.bias'),
('head.weight', 'classifier.weight'),
('head.bias', 'classifier.bias'),
] )
return rename_keys
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A=False ) -> int:
for i in range(config.num_hidden_layers ):
if base_model:
_snake_case = ''
else:
_snake_case = 'vit.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_snake_case = state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
_snake_case = state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_snake_case = in_proj_weight[
: config.hidden_size, :
]
_snake_case = in_proj_bias[: config.hidden_size]
_snake_case = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_snake_case = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_snake_case = in_proj_weight[
-config.hidden_size :, :
]
_snake_case = in_proj_bias[-config.hidden_size :]
def SCREAMING_SNAKE_CASE__ ( __A ) -> List[str]:
_snake_case = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A ) -> Any:
_snake_case = dct.pop(__SCREAMING_SNAKE_CASE )
_snake_case = val
def SCREAMING_SNAKE_CASE__ ( ) -> Any:
_snake_case = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_snake_case = Image.open(requests.get(__SCREAMING_SNAKE_CASE , stream=__SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A=True ) -> Optional[Any]:
_snake_case = ViTConfig()
# patch_size
if model_name[-1] == "8":
_snake_case = 8
# set labels if required
if not base_model:
_snake_case = 1_000
_snake_case = 'huggingface/label-files'
_snake_case = 'imagenet-1k-id2label.json'
_snake_case = json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , repo_type='dataset' ) , 'r' ) )
_snake_case = {int(__SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
_snake_case = idalabel
_snake_case = {v: k for k, v in idalabel.items()}
# size of the architecture
if model_name in ["dino_vits8", "dino_vits16"]:
_snake_case = 384
_snake_case = 1_536
_snake_case = 12
_snake_case = 6
# load original model from torch hub
_snake_case = torch.hub.load('facebookresearch/dino:main' , __SCREAMING_SNAKE_CASE )
original_model.eval()
# load state_dict of original model, remove and rename some keys
_snake_case = original_model.state_dict()
if base_model:
remove_classification_head_(__SCREAMING_SNAKE_CASE )
_snake_case = create_rename_keys(__SCREAMING_SNAKE_CASE , base_model=__SCREAMING_SNAKE_CASE )
for src, dest in rename_keys:
rename_key(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
read_in_q_k_v(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# load HuggingFace model
if base_model:
_snake_case = ViTModel(__SCREAMING_SNAKE_CASE , add_pooling_layer=__SCREAMING_SNAKE_CASE ).eval()
else:
_snake_case = ViTForImageClassification(__SCREAMING_SNAKE_CASE ).eval()
model.load_state_dict(__SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by ViTImageProcessor
_snake_case = ViTImageProcessor()
_snake_case = image_processor(images=prepare_img() , return_tensors='pt' )
_snake_case = encoding['pixel_values']
_snake_case = model(__SCREAMING_SNAKE_CASE )
if base_model:
_snake_case = original_model(__SCREAMING_SNAKE_CASE )
assert torch.allclose(__SCREAMING_SNAKE_CASE , outputs.last_hidden_state[:, 0, :] , atol=1e-1 )
else:
_snake_case = original_model(__SCREAMING_SNAKE_CASE )
assert logits.shape == outputs.logits.shape
assert torch.allclose(__SCREAMING_SNAKE_CASE , outputs.logits , atol=1e-3 )
Path(__SCREAMING_SNAKE_CASE ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(__SCREAMING_SNAKE_CASE )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowercase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="dino_vitb16",
type=str,
help="Name of the model trained with DINO 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(
"--base_model",
action="store_true",
help="Whether to only convert the base model (no projection head weights).",
)
parser.set_defaults(base_model=True)
lowercase : Any = parser.parse_args()
convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
| 42 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''salesforce/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''',
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = """blip_2_vision_model"""
def __init__( self , snake_case=1408 , snake_case=6144 , snake_case=39 , snake_case=16 , snake_case=224 , snake_case=14 , snake_case="gelu" , snake_case=0.00_001 , snake_case=0.0 , snake_case=1E-10 , snake_case=True , **snake_case , ):
super().__init__(**snake_case )
lowercase = hidden_size
lowercase = intermediate_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = patch_size
lowercase = image_size
lowercase = initializer_range
lowercase = attention_dropout
lowercase = layer_norm_eps
lowercase = hidden_act
lowercase = qkv_bias
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , snake_case , **snake_case ):
cls._set_token_in_kwargs(snake_case )
lowercase , lowercase = cls.get_config_dict(snake_case , **snake_case )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
lowercase = 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(snake_case , **snake_case )
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : int = """blip_2_qformer"""
def __init__( self , snake_case=3_0522 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=0.02 , snake_case=1E-12 , snake_case=0 , snake_case="absolute" , snake_case=2 , snake_case=1408 , **snake_case , ):
super().__init__(pad_token_id=snake_case , **snake_case )
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = hidden_act
lowercase = intermediate_size
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = initializer_range
lowercase = layer_norm_eps
lowercase = position_embedding_type
lowercase = cross_attention_frequency
lowercase = encoder_hidden_size
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , snake_case , **snake_case ):
cls._set_token_in_kwargs(snake_case )
lowercase , lowercase = cls.get_config_dict(snake_case , **snake_case )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
lowercase = config_dict['qformer_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(snake_case , **snake_case )
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[Any] = """blip-2"""
_UpperCamelCase : str = True
def __init__( self , snake_case=None , snake_case=None , snake_case=None , snake_case=32 , **snake_case ):
super().__init__(**snake_case )
if vision_config is None:
lowercase = {}
logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.' )
if qformer_config is None:
lowercase = {}
logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.' )
if text_config is None:
lowercase = {}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )
lowercase = BlipaVisionConfig(**snake_case )
lowercase = BlipaQFormerConfig(**snake_case )
lowercase = text_config['model_type'] if 'model_type' in text_config else 'opt'
lowercase = CONFIG_MAPPING[text_model_type](**snake_case )
lowercase = self.text_config.tie_word_embeddings
lowercase = self.text_config.is_encoder_decoder
lowercase = num_query_tokens
lowercase = self.vision_config.hidden_size
lowercase = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
lowercase = 1.0
lowercase = 0.02
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls , snake_case , snake_case , snake_case , **snake_case , ):
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **snake_case , )
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = copy.deepcopy(self.__dict__ )
lowercase = self.vision_config.to_dict()
lowercase = self.qformer_config.to_dict()
lowercase = self.text_config.to_dict()
lowercase = self.__class__.model_type
return output
| 195 | 0 |
'''simple docstring'''
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _A ( A__ , A__ , A__ ):
"""simple docstring"""
__lowercase = AlbertConfig.from_json_file(A__ )
print(F"Building PyTorch model from configuration: {config}" )
__lowercase = AlbertForPreTraining(A__ )
# Load weights from tf checkpoint
load_tf_weights_in_albert(A__ , A__ , A__ )
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict() , A__ )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--albert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained ALBERT 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.'''
)
lowerCAmelCase__ = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 52 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import OwlViTImageProcessor, OwlViTProcessor
@require_vision
class lowercase_ (unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : List[str] ):
__lowercase = tempfile.mkdtemp()
# fmt: off
__lowercase = ['''''', '''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
# fmt: on
__lowercase = dict(zip(lowercase__ ,range(len(lowercase__ ) ) ) )
__lowercase = ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
__lowercase = {'''unk_token''': '''<unk>'''}
__lowercase = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['''vocab_file'''] )
__lowercase = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file ,'''w''' ,encoding='''utf-8''' ) as fp:
fp.write(json.dumps(lowercase__ ) + '''\n''' )
with open(self.merges_file ,'''w''' ,encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(lowercase__ ) )
__lowercase = {
'''do_resize''': True,
'''size''': 2_0,
'''do_center_crop''': True,
'''crop_size''': 1_8,
'''do_normalize''': True,
'''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
'''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
}
__lowercase = os.path.join(self.tmpdirname ,lowercase__ )
with open(self.image_processor_file ,'''w''' ,encoding='''utf-8''' ) as fp:
json.dump(lowercase__ ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Tuple ,**lowercase__ : Optional[int] ):
return CLIPTokenizer.from_pretrained(self.tmpdirname ,pad_token='''!''' ,**lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ,**lowercase__ : Union[str, Any] ):
return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,pad_token='''!''' ,**lowercase__ )
def SCREAMING_SNAKE_CASE ( self : int ,**lowercase__ : int ):
return OwlViTImageProcessor.from_pretrained(self.tmpdirname ,**lowercase__ )
def SCREAMING_SNAKE_CASE ( self : int ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE ( self : int ):
__lowercase = [np.random.randint(2_5_5 ,size=(3, 3_0, 4_0_0) ,dtype=np.uinta )]
__lowercase = [Image.fromarray(np.moveaxis(lowercase__ ,0 ,-1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE ( self : str ):
__lowercase = self.get_tokenizer()
__lowercase = self.get_rust_tokenizer()
__lowercase = self.get_image_processor()
__lowercase = OwlViTProcessor(tokenizer=lowercase__ ,image_processor=lowercase__ )
processor_slow.save_pretrained(self.tmpdirname )
__lowercase = OwlViTProcessor.from_pretrained(self.tmpdirname ,use_fast=lowercase__ )
__lowercase = OwlViTProcessor(tokenizer=lowercase__ ,image_processor=lowercase__ )
processor_fast.save_pretrained(self.tmpdirname )
__lowercase = OwlViTProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() ,tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() ,tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() ,tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer ,lowercase__ )
self.assertIsInstance(processor_fast.tokenizer ,lowercase__ )
self.assertEqual(processor_slow.image_processor.to_json_string() ,image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() ,image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor ,lowercase__ )
self.assertIsInstance(processor_fast.image_processor ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : List[str] ):
__lowercase = OwlViTProcessor(tokenizer=self.get_tokenizer() ,image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__lowercase = self.get_tokenizer(bos_token='''(BOS)''' ,eos_token='''(EOS)''' )
__lowercase = self.get_image_processor(do_normalize=lowercase__ )
__lowercase = OwlViTProcessor.from_pretrained(
self.tmpdirname ,bos_token='''(BOS)''' ,eos_token='''(EOS)''' ,do_normalize=lowercase__ )
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer ,lowercase__ )
self.assertEqual(processor.image_processor.to_json_string() ,image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : int ):
__lowercase = self.get_image_processor()
__lowercase = self.get_tokenizer()
__lowercase = OwlViTProcessor(tokenizer=lowercase__ ,image_processor=lowercase__ )
__lowercase = self.prepare_image_inputs()
__lowercase = image_processor(lowercase__ ,return_tensors='''np''' )
__lowercase = processor(images=lowercase__ ,return_tensors='''np''' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() ,input_processor[key].sum() ,delta=1e-2 )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
__lowercase = self.get_image_processor()
__lowercase = self.get_tokenizer()
__lowercase = OwlViTProcessor(tokenizer=lowercase__ ,image_processor=lowercase__ )
__lowercase = '''lower newer'''
__lowercase = processor(text=lowercase__ ,return_tensors='''np''' )
__lowercase = tokenizer(lowercase__ ,return_tensors='''np''' )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key][0].tolist() ,encoded_processor[key][0].tolist() )
def SCREAMING_SNAKE_CASE ( self : str ):
__lowercase = self.get_image_processor()
__lowercase = self.get_tokenizer()
__lowercase = OwlViTProcessor(tokenizer=lowercase__ ,image_processor=lowercase__ )
__lowercase = '''lower newer'''
__lowercase = self.prepare_image_inputs()
__lowercase = processor(text=lowercase__ ,images=lowercase__ )
self.assertListEqual(list(inputs.keys() ) ,['''input_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(lowercase__ ):
processor()
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
__lowercase = '''google/owlvit-base-patch32'''
__lowercase = OwlViTProcessor.from_pretrained(lowercase__ )
__lowercase = ['''cat''', '''nasa badge''']
__lowercase = processor(text=lowercase__ )
__lowercase = 1_6
self.assertListEqual(list(inputs.keys() ) ,['''input_ids''', '''attention_mask'''] )
self.assertEqual(inputs['''input_ids'''].shape ,(2, seq_length) )
# test if it raises when no input is passed
with pytest.raises(lowercase__ ):
processor()
def SCREAMING_SNAKE_CASE ( self : List[str] ):
__lowercase = '''google/owlvit-base-patch32'''
__lowercase = OwlViTProcessor.from_pretrained(lowercase__ )
__lowercase = [['''cat''', '''nasa badge'''], ['''person''']]
__lowercase = processor(text=lowercase__ )
__lowercase = 1_6
__lowercase = len(lowercase__ )
__lowercase = max([len(lowercase__ ) for texts in input_texts] )
self.assertListEqual(list(inputs.keys() ) ,['''input_ids''', '''attention_mask'''] )
self.assertEqual(inputs['''input_ids'''].shape ,(batch_size * num_max_text_queries, seq_length) )
# test if it raises when no input is passed
with pytest.raises(lowercase__ ):
processor()
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
__lowercase = '''google/owlvit-base-patch32'''
__lowercase = OwlViTProcessor.from_pretrained(lowercase__ )
__lowercase = ['''cat''', '''nasa badge''']
__lowercase = processor(text=lowercase__ )
__lowercase = 1_6
__lowercase = inputs['''input_ids''']
__lowercase = [
[4_9_4_0_6, 2_3_6_8, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[4_9_4_0_6, 6_8_4_1, 1_1_3_0_1, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
self.assertListEqual(list(inputs.keys() ) ,['''input_ids''', '''attention_mask'''] )
self.assertEqual(inputs['''input_ids'''].shape ,(2, seq_length) )
self.assertListEqual(list(input_ids[0] ) ,predicted_ids[0] )
self.assertListEqual(list(input_ids[1] ) ,predicted_ids[1] )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
__lowercase = self.get_image_processor()
__lowercase = self.get_tokenizer()
__lowercase = OwlViTProcessor(tokenizer=lowercase__ ,image_processor=lowercase__ )
__lowercase = self.prepare_image_inputs()
__lowercase = self.prepare_image_inputs()
__lowercase = processor(images=lowercase__ ,query_images=lowercase__ )
self.assertListEqual(list(inputs.keys() ) ,['''query_pixel_values''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(lowercase__ ):
processor()
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
__lowercase = self.get_image_processor()
__lowercase = self.get_tokenizer()
__lowercase = OwlViTProcessor(tokenizer=lowercase__ ,image_processor=lowercase__ )
__lowercase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__lowercase = processor.batch_decode(lowercase__ )
__lowercase = tokenizer.batch_decode(lowercase__ )
self.assertListEqual(lowercase__ ,lowercase__ )
| 52 | 1 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class a ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE : torch.FloatTensor
SCREAMING_SNAKE_CASE : Optional[torch.FloatTensor] = None
def lowerCamelCase__ ( _lowerCamelCase : List[str] , _lowerCamelCase : List[str]=0.9_99 , _lowerCamelCase : Union[str, Any]="cosine" , ) -> Any:
if alpha_transform_type == "cosine":
def alpha_bar_fn(_lowerCamelCase : List[Any] ):
return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(_lowerCamelCase : Tuple ):
return math.exp(t * -12.0 )
else:
raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
lowerCamelCase_ = []
for i in range(__UpperCAmelCase ):
lowerCamelCase_ = i / num_diffusion_timesteps
lowerCamelCase_ = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase ) / alpha_bar_fn(__UpperCAmelCase ) , __UpperCAmelCase ) )
return torch.tensor(__UpperCAmelCase , dtype=torch.floataa )
class a ( _UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE : Tuple = 1
@register_to_config
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : Optional[Any] = 1000 , __SCREAMING_SNAKE_CASE : Union[str, Any] = 0.0_001 , __SCREAMING_SNAKE_CASE : List[str] = 0.02 , __SCREAMING_SNAKE_CASE : int = "linear" , __SCREAMING_SNAKE_CASE : List[Any] = None , __SCREAMING_SNAKE_CASE : Optional[int] = True , __SCREAMING_SNAKE_CASE : int = True , __SCREAMING_SNAKE_CASE : Any = 0 , __SCREAMING_SNAKE_CASE : Optional[int] = "epsilon" , __SCREAMING_SNAKE_CASE : Dict = 1.0 , **__SCREAMING_SNAKE_CASE : Any , ) -> List[Any]:
if kwargs.get('set_alpha_to_one' , _UpperCAmelCase ) is not None:
lowerCamelCase_ = (
'''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
)
deprecate('set_alpha_to_one' , '1.0.0' , _UpperCAmelCase , standard_warn=_UpperCAmelCase )
lowerCamelCase_ = kwargs['''set_alpha_to_one''']
if trained_betas is not None:
lowerCamelCase_ = torch.tensor(_UpperCAmelCase , dtype=torch.floataa )
elif beta_schedule == "linear":
lowerCamelCase_ = torch.linspace(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
lowerCamelCase_ = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , _UpperCAmelCase , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
lowerCamelCase_ = betas_for_alpha_bar(_UpperCAmelCase )
else:
raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''' )
lowerCamelCase_ = 1.0 - self.betas
lowerCamelCase_ = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
lowerCamelCase_ = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
lowerCamelCase_ = 1.0
# setable values
lowerCamelCase_ = None
lowerCamelCase_ = torch.from_numpy(np.arange(0 , _UpperCAmelCase ).copy().astype(np.intaa ) )
def UpperCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[int] = None ) -> List[Any]:
return sample
def UpperCamelCase ( self : str , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[Any] = None ) -> Optional[int]:
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
F''' maximal {self.config.num_train_timesteps} timesteps.''' )
lowerCamelCase_ = num_inference_steps
lowerCamelCase_ = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
lowerCamelCase_ = (np.arange(0 , _UpperCAmelCase ) * step_ratio).round().copy().astype(np.intaa )
lowerCamelCase_ = torch.from_numpy(_UpperCAmelCase ).to(_UpperCAmelCase )
self.timesteps += self.config.steps_offset
def UpperCamelCase ( self : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[Any] = 0.0 , __SCREAMING_SNAKE_CASE : int = False , __SCREAMING_SNAKE_CASE : Union[str, Any] = None , __SCREAMING_SNAKE_CASE : List[Any] = True , ) -> str:
# 1. get previous step value (=t+1)
lowerCamelCase_ = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
lowerCamelCase_ = self.alphas_cumprod[timestep]
lowerCamelCase_ = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
lowerCamelCase_ = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
lowerCamelCase_ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
lowerCamelCase_ = model_output
elif self.config.prediction_type == "sample":
lowerCamelCase_ = model_output
lowerCamelCase_ = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
lowerCamelCase_ = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
lowerCamelCase_ = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
' `v_prediction`' )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
lowerCamelCase_ = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
lowerCamelCase_ = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
lowerCamelCase_ = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=_UpperCAmelCase , pred_original_sample=_UpperCAmelCase )
def __len__( self : Optional[Any] ) -> str:
return self.config.num_train_timesteps
| 183 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase ) -> List[str]:
lowercase__: Optional[Any] = 0
lowercase__: List[Any] = len(__UpperCAmelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
lowercase__: Tuple = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(__UpperCAmelCase ):
return None
lowercase__: Optional[int] = sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
lowercase__: List[Any] = left
lowercase__: int = point
elif point > right:
lowercase__: Dict = right
lowercase__: List[str] = point
else:
if item < current_item:
lowercase__: int = point - 1
else:
lowercase__: int = point + 1
return None
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[str]:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
lowercase__: Optional[int] = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(__UpperCAmelCase ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
elif point > right:
return interpolation_search_by_recursion(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , point - 1 )
else:
return interpolation_search_by_recursion(
__UpperCAmelCase , __UpperCAmelCase , point + 1 , __UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> List[Any]:
if collection != sorted(__UpperCAmelCase ):
raise ValueError('''Collection must be ascending sorted''' )
return True
if __name__ == "__main__":
import sys
__A = 0
if debug == 1:
__A = [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
try:
__assert_sorted(collection)
except ValueError:
sys.exit("Sequence must be ascending sorted to apply interpolation search")
__A = 6_7
__A = interpolation_search(collection, target)
if result is not None:
print(f'''{target} found at positions: {result}''')
else:
print("Not found")
| 177 | 0 |
"""simple docstring"""
import unittest
import numpy as np
def _lowerCAmelCase ( UpperCAmelCase__ : np.ndarray, UpperCAmelCase__ : np.ndarray, UpperCAmelCase__ : np.ndarray, UpperCAmelCase__ : np.ndarray | None = None, ) ->np.ndarray:
A__ : List[Any] = np.shape(UpperCAmelCase__ )
A__ : List[str] = np.shape(UpperCAmelCase__ )
A__ : str = np.shape(UpperCAmelCase__ )
if shape_a[0] != shape_b[0]:
A__ : int = (
"""Expected the same number of rows for A and B. """
f'Instead found A of size {shape_a} and B of size {shape_b}'
)
raise ValueError(UpperCAmelCase__ )
if shape_b[1] != shape_c[1]:
A__ : int = (
"""Expected the same number of columns for B and C. """
f'Instead found B of size {shape_b} and C of size {shape_c}'
)
raise ValueError(UpperCAmelCase__ )
A__ : Any = pseudo_inv
if a_inv is None:
try:
A__ : List[str] = np.linalg.inv(UpperCAmelCase__ )
except np.linalg.LinAlgError:
raise ValueError(
"""Input matrix A is not invertible. Cannot compute Schur complement.""" )
return mat_c - mat_b.T @ a_inv @ mat_b
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def _UpperCamelCase ( self : Optional[int] ):
'''simple docstring'''
A__ : Optional[int] = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
A__ : Dict = np.array([[0, 3], [3, 0], [2, 3]] )
A__ : int = np.array([[2, 1], [6, 3]] )
A__ : List[str] = schur_complement(snake_case , snake_case , snake_case )
A__ : str = np.block([[a, b], [b.T, c]] )
A__ : Optional[Any] = np.linalg.det(snake_case )
A__ : Union[str, Any] = np.linalg.det(snake_case )
A__ : Optional[Any] = np.linalg.det(snake_case )
self.assertAlmostEqual(snake_case , det_a * det_s )
def _UpperCamelCase ( self : Any ):
'''simple docstring'''
A__ : Union[str, Any] = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
A__ : Dict = np.array([[0, 3], [3, 0], [2, 3]] )
A__ : Tuple = np.array([[2, 1], [6, 3]] )
with self.assertRaises(snake_case ):
schur_complement(snake_case , snake_case , snake_case )
def _UpperCamelCase ( self : int ):
'''simple docstring'''
A__ : Optional[int] = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
A__ : Any = np.array([[0, 3], [3, 0], [2, 3]] )
A__ : Optional[int] = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(snake_case ):
schur_complement(snake_case , snake_case , snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main()
| 296 |
"""simple docstring"""
import numpy as np
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[str] ):
'''simple docstring'''
A__ : Optional[int] = (0, 0)
A__ : Dict = None
A__ : int = 0
A__ : str = 0
A__ : Optional[Any] = 0
def __eq__( self : str , snake_case : Optional[int] ):
'''simple docstring'''
return self.position == cell.position
def _UpperCamelCase ( self : List[str] ):
'''simple docstring'''
print(self.position )
class __SCREAMING_SNAKE_CASE :
def __init__( self : int , snake_case : Any=(5, 5) ):
'''simple docstring'''
A__ : Optional[int] = np.zeros(snake_case )
A__ : List[Any] = world_size[0]
A__ : Dict = world_size[1]
def _UpperCamelCase ( self : Any ):
'''simple docstring'''
print(self.w )
def _UpperCamelCase ( self : Optional[int] , snake_case : List[Any] ):
'''simple docstring'''
A__ : int = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
A__ : int = cell.position[0]
A__ : str = cell.position[1]
A__ : Any = []
for n in neughbour_cord:
A__ : List[Any] = current_x + n[0]
A__ : Tuple = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
A__ : List[Any] = Cell()
A__ : str = (x, y)
A__ : Optional[Any] = cell
neighbours.append(snake_case )
return neighbours
def _lowerCAmelCase ( UpperCAmelCase__ : List[str], UpperCAmelCase__ : Optional[Any], UpperCAmelCase__ : Dict ) ->Dict:
A__ : Union[str, Any] = []
A__ : Optional[int] = []
_open.append(UpperCAmelCase__ )
while _open:
A__ : List[Any] = np.argmin([n.f for n in _open] )
A__ : Union[str, Any] = _open[min_f]
_closed.append(_open.pop(UpperCAmelCase__ ) )
if current == goal:
break
for n in world.get_neigbours(UpperCAmelCase__ ):
for c in _closed:
if c == n:
continue
A__ : Dict = current.g + 1
A__ , A__ : int = n.position
A__ , A__ : Optional[int] = goal.position
A__ : Union[str, Any] = (ya - ya) ** 2 + (xa - xa) ** 2
A__ : Optional[int] = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(UpperCAmelCase__ )
A__ : List[str] = []
while current.parent is not None:
path.append(current.position )
A__ : Union[str, Any] = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
A_ = Gridworld()
# Start position and goal
A_ = Cell()
A_ = (0, 0)
A_ = Cell()
A_ = (4, 4)
print(F'path from {start.position} to {goal.position}')
A_ = astar(world, start, goal)
# Just for visual reasons.
for i in s:
A_ = 1
print(world.w)
| 296 | 1 |
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
'''stable diffusion controlnet''',
'''0.22.0''',
'''Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.''',
standard_warn=False,
stacklevel=3,
)
| 313 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
_A : str = StableDiffusionSAGPipeline
_A : Optional[Any] = TEXT_TO_IMAGE_PARAMS
_A : Any = TEXT_TO_IMAGE_BATCH_PARAMS
_A : Tuple = TEXT_TO_IMAGE_IMAGE_PARAMS
_A : Tuple = TEXT_TO_IMAGE_IMAGE_PARAMS
_A : List[str] = False
def lowerCAmelCase_ ( self: Optional[Any] ) -> str:
torch.manual_seed(0 )
snake_case_ :Any = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
snake_case_ :Any = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="""scaled_linear""" , clip_sample=snake_case , set_alpha_to_one=snake_case , )
torch.manual_seed(0 )
snake_case_ :Optional[int] = 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 , )
torch.manual_seed(0 )
snake_case_ :Union[str, Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
snake_case_ :Tuple = CLIPTextModel(snake_case )
snake_case_ :str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
snake_case_ :Dict = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowerCAmelCase_ ( self: List[str] , snake_case: Tuple , snake_case: List[str]=0 ) -> str:
if str(snake_case ).startswith("""mps""" ):
snake_case_ :Tuple = torch.manual_seed(snake_case )
else:
snake_case_ :Optional[int] = torch.Generator(device=snake_case ).manual_seed(snake_case )
snake_case_ :Any = {
"""prompt""": """.""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 1.0,
"""sag_scale""": 1.0,
"""output_type""": """numpy""",
}
return inputs
def lowerCAmelCase_ ( self: Optional[int] ) -> str:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self: int ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase_ ( self: int ) -> List[str]:
snake_case_ :Any = StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""" )
snake_case_ :int = sag_pipe.to(snake_case )
sag_pipe.set_progress_bar_config(disable=snake_case )
snake_case_ :Union[str, Any] = """."""
snake_case_ :str = torch.manual_seed(0 )
snake_case_ :str = sag_pipe(
[prompt] , generator=snake_case , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="""np""" )
snake_case_ :List[Any] = output.images
snake_case_ :Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
snake_case_ :List[Any] = np.array([0.1_5_6_8, 0.1_7_3_8, 0.1_6_9_5, 0.1_6_9_3, 0.1_5_0_7, 0.1_7_0_5, 0.1_5_4_7, 0.1_7_5_1, 0.1_9_4_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def lowerCAmelCase_ ( self: Dict ) -> str:
snake_case_ :Tuple = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
snake_case_ :Optional[int] = sag_pipe.to(snake_case )
sag_pipe.set_progress_bar_config(disable=snake_case )
snake_case_ :Tuple = """."""
snake_case_ :Union[str, Any] = torch.manual_seed(0 )
snake_case_ :Tuple = sag_pipe(
[prompt] , generator=snake_case , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="""np""" )
snake_case_ :Optional[int] = output.images
snake_case_ :Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
snake_case_ :Tuple = np.array([0.3_4_5_9, 0.2_8_7_6, 0.2_5_3_7, 0.3_0_0_2, 0.2_6_7_1, 0.2_1_6_0, 0.3_0_2_6, 0.2_2_6_2, 0.2_3_7_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def lowerCAmelCase_ ( self: List[str] ) -> List[str]:
snake_case_ :Optional[int] = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
snake_case_ :int = sag_pipe.to(snake_case )
sag_pipe.set_progress_bar_config(disable=snake_case )
snake_case_ :Tuple = """."""
snake_case_ :Optional[int] = torch.manual_seed(0 )
snake_case_ :List[str] = sag_pipe(
[prompt] , width=768 , height=512 , generator=snake_case , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="""np""" , )
snake_case_ :Optional[Any] = output.images
assert image.shape == (1, 512, 768, 3)
| 66 | 0 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
lowerCamelCase_ = random.Random()
if is_torch_available():
import torch
def snake_case ( A__ ,A__=1.0 ,A__=None ,A__=None ):
if rng is None:
UpperCAmelCase_ : Any = global_rng
UpperCAmelCase_ : Optional[Any] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCamelCase_ (unittest.TestCase ):
def __init__( self : int , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[int]=7 , lowerCAmelCase_ : Optional[int]=400 , lowerCAmelCase_ : Optional[int]=2_000 , lowerCAmelCase_ : Any=1 , lowerCAmelCase_ : str=0.0 , lowerCAmelCase_ : int=16_000 , lowerCAmelCase_ : int=True , lowerCAmelCase_ : Optional[int]=True , ) -> Optional[Any]:
UpperCAmelCase_ : List[Any] = parent
UpperCAmelCase_ : Dict = batch_size
UpperCAmelCase_ : Union[str, Any] = min_seq_length
UpperCAmelCase_ : Dict = max_seq_length
UpperCAmelCase_ : str = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
UpperCAmelCase_ : List[Any] = feature_size
UpperCAmelCase_ : Tuple = padding_value
UpperCAmelCase_ : List[str] = sampling_rate
UpperCAmelCase_ : Any = return_attention_mask
UpperCAmelCase_ : Dict = do_normalize
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> int:
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def _SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase_ : Any=False , lowerCAmelCase_ : Any=False ) -> Union[str, Any]:
def _flatten(lowerCAmelCase_ : Dict ):
return list(itertools.chain(*__SCREAMING_SNAKE_CASE ) )
if equal_length:
UpperCAmelCase_ : Tuple = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
UpperCAmelCase_ : List[Any] = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
UpperCAmelCase_ : Optional[int] = [np.asarray(__SCREAMING_SNAKE_CASE ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCamelCase_ (_A , unittest.TestCase ):
__magic_name__ = ASTFeatureExtractor
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Optional[Any]:
UpperCAmelCase_ : List[Any] = ASTFeatureExtractionTester(self )
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Any:
# Tests that all call wrap to encode_plus and batch_encode_plus
UpperCAmelCase_ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
UpperCAmelCase_ : Dict = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
UpperCAmelCase_ : Optional[int] = [np.asarray(__SCREAMING_SNAKE_CASE ) for speech_input in speech_inputs]
# Test not batched input
UpperCAmelCase_ : int = feat_extract(speech_inputs[0] , return_tensors="np" ).input_values
UpperCAmelCase_ : int = feat_extract(np_speech_inputs[0] , return_tensors="np" ).input_values
self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=1e-3 ) )
# Test batched
UpperCAmelCase_ : Dict = feat_extract(__SCREAMING_SNAKE_CASE , padding=__SCREAMING_SNAKE_CASE , return_tensors="np" ).input_values
UpperCAmelCase_ : List[Any] = feat_extract(__SCREAMING_SNAKE_CASE , padding=__SCREAMING_SNAKE_CASE , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
UpperCAmelCase_ : Union[str, Any] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
UpperCAmelCase_ : int = np.asarray(__SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Any = feat_extract(__SCREAMING_SNAKE_CASE , return_tensors="np" ).input_values
UpperCAmelCase_ : str = feat_extract(__SCREAMING_SNAKE_CASE , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=1e-3 ) )
@require_torch
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[str]:
import torch
UpperCAmelCase_ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCAmelCase_ : Tuple = np.random.rand(100 ).astype(np.floataa )
UpperCAmelCase_ : List[Any] = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
UpperCAmelCase_ : str = feature_extractor.pad([{"input_values": inputs}] , return_tensors="np" )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
UpperCAmelCase_ : Dict = feature_extractor.pad([{"input_values": inputs}] , return_tensors="pt" )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , lowerCAmelCase_ : Dict ) -> Tuple:
from datasets import load_dataset
UpperCAmelCase_ : Tuple = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" )
# automatic decoding with librispeech
UpperCAmelCase_ : List[str] = ds.sort("id" ).select(range(__SCREAMING_SNAKE_CASE ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
@require_torch
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Tuple:
# fmt: off
UpperCAmelCase_ : List[str] = torch.tensor(
[-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
-1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
-1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
-0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9] )
# fmt: on
UpperCAmelCase_ : Dict = self._load_datasamples(1 )
UpperCAmelCase_ : Optional[Any] = ASTFeatureExtractor()
UpperCAmelCase_ : str = feature_extractor(__SCREAMING_SNAKE_CASE , return_tensors="pt" ).input_values
self.assertEquals(input_values.shape , (1, 1_024, 128) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , __SCREAMING_SNAKE_CASE , atol=1e-4 ) )
| 363 |
"""simple docstring"""
def snake_case ( A__ ):
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(A__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 253 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
_snake_case = logging.get_logger(__name__)
_snake_case = {'''vocab_file''': '''spiece.model'''}
_snake_case = {
'''vocab_file''': {
'''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
}
}
class UpperCAmelCase_ ( UpperCamelCase ):
'''simple docstring'''
def __init__( self , __A , __A=False , __A=True , __A=False , __A="<s>" , __A="</s>" , __A="<unk>" , __A="<sep>" , __A="<pad>" , __A="<cls>" , __A="<mask>" , __A=["<eop>", "<eod>"] , __A = None , **__A , ):
"""simple docstring"""
lowerCamelCase : List[str] = AddedToken(__A , lstrip=__A , rstrip=__A ) if isinstance(__A , __A ) else mask_token
lowerCamelCase : Tuple = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=__A , remove_space=__A , keep_accents=__A , bos_token=__A , eos_token=__A , unk_token=__A , sep_token=__A , pad_token=__A , cls_token=__A , mask_token=__A , additional_special_tokens=__A , sp_model_kwargs=self.sp_model_kwargs , **__A , )
lowerCamelCase : Dict = 3
lowerCamelCase : Dict = do_lower_case
lowerCamelCase : Optional[int] = remove_space
lowerCamelCase : Any = keep_accents
lowerCamelCase : List[Any] = vocab_file
lowerCamelCase : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__A )
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__(
"You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
"See https://pypi.org/project/jieba/ for installation." )
lowerCamelCase : Any = jieba
lowerCamelCase : int = str.maketrans(" \n" , "\u2582\u2583" )
@property
# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
def _snake_case ( self ):
"""simple docstring"""
return len(self.sp_model )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : int = {self.convert_ids_to_tokens(__A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
"""simple docstring"""
lowerCamelCase : str = self.__dict__.copy()
lowerCamelCase : Union[str, Any] = None
return state
def __setstate__( self , __A ):
"""simple docstring"""
lowerCamelCase : int = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
lowerCamelCase : int = {}
lowerCamelCase : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _snake_case ( self , __A ):
"""simple docstring"""
if self.remove_space:
lowerCamelCase : Tuple = " ".join(inputs.strip().split() )
else:
lowerCamelCase : List[Any] = inputs
lowerCamelCase : List[str] = outputs.replace("``" , "\"" ).replace("''" , "\"" )
if not self.keep_accents:
lowerCamelCase : int = unicodedata.normalize("NFKD" , __A )
lowerCamelCase : Dict = "".join([c for c in outputs if not unicodedata.combining(__A )] )
if self.do_lower_case:
lowerCamelCase : List[Any] = outputs.lower()
return outputs
def _snake_case ( self , __A ):
"""simple docstring"""
lowerCamelCase : Optional[int] = self.preprocess_text(__A )
lowerCamelCase : List[Any] = self.sp_model.encode(__A , out_type=__A )
lowerCamelCase : int = []
for piece in pieces:
if len(__A ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
lowerCamelCase : Dict = self.sp_model.EncodeAsPieces(piece[:-1].replace(__A , "" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
lowerCamelCase : List[str] = cur_pieces[1:]
else:
lowerCamelCase : List[Any] = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(__A )
else:
new_pieces.append(__A )
return new_pieces
def _snake_case ( self , __A ):
"""simple docstring"""
return self.sp_model.PieceToId(__A )
def _snake_case ( self , __A ):
"""simple docstring"""
return self.sp_model.IdToPiece(__A )
def _snake_case ( self , __A ):
"""simple docstring"""
lowerCamelCase : Any = "".join(__A ).replace(__A , " " ).strip()
return out_string
def _snake_case ( self , __A , __A = None ):
"""simple docstring"""
lowerCamelCase : List[str] = [self.sep_token_id]
lowerCamelCase : List[str] = [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 _snake_case ( self , __A , __A = None , __A = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__A , token_ids_a=__A , already_has_special_tokens=__A )
if token_ids_a is not None:
return ([0] * len(__A )) + [1] + ([0] * len(__A )) + [1, 1]
return ([0] * len(__A )) + [1, 1]
def _snake_case ( self , __A , __A = None ):
"""simple docstring"""
lowerCamelCase : Dict = [self.sep_token_id]
lowerCamelCase : Optional[int] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _snake_case ( self , __A , __A = None ):
"""simple docstring"""
if not os.path.isdir(__A ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowerCamelCase : List[str] = os.path.join(
__A , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __A )
elif not os.path.isfile(self.vocab_file ):
with open(__A , "wb" ) as fi:
lowerCamelCase : Optional[Any] = self.sp_model.serialized_model_proto()
fi.write(__A )
return (out_vocab_file,)
def _snake_case ( self , *__A , **__A ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = super()._decode(*__A , **__A )
lowerCamelCase : int = text.replace(" " , "" ).replace("\u2582" , " " ).replace("\u2583" , "\n" )
return text
| 283 |
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=UpperCamelCase ):
'''simple docstring'''
__A : Any = ["flax"]
def __init__( self , *__A , **__A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class UpperCAmelCase_ ( metaclass=UpperCamelCase ):
'''simple docstring'''
__A : Optional[int] = ["flax"]
def __init__( self , *__A , **__A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class UpperCAmelCase_ ( metaclass=UpperCamelCase ):
'''simple docstring'''
__A : str = ["flax"]
def __init__( self , *__A , **__A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class UpperCAmelCase_ ( metaclass=UpperCamelCase ):
'''simple docstring'''
__A : List[Any] = ["flax"]
def __init__( self , *__A , **__A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class UpperCAmelCase_ ( metaclass=UpperCamelCase ):
'''simple docstring'''
__A : Optional[int] = ["flax"]
def __init__( self , *__A , **__A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class UpperCAmelCase_ ( metaclass=UpperCamelCase ):
'''simple docstring'''
__A : Dict = ["flax"]
def __init__( self , *__A , **__A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class UpperCAmelCase_ ( metaclass=UpperCamelCase ):
'''simple docstring'''
__A : Dict = ["flax"]
def __init__( self , *__A , **__A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class UpperCAmelCase_ ( metaclass=UpperCamelCase ):
'''simple docstring'''
__A : Union[str, Any] = ["flax"]
def __init__( self , *__A , **__A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class UpperCAmelCase_ ( metaclass=UpperCamelCase ):
'''simple docstring'''
__A : Optional[int] = ["flax"]
def __init__( self , *__A , **__A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class UpperCAmelCase_ ( metaclass=UpperCamelCase ):
'''simple docstring'''
__A : Optional[Any] = ["flax"]
def __init__( self , *__A , **__A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class UpperCAmelCase_ ( metaclass=UpperCamelCase ):
'''simple docstring'''
__A : Any = ["flax"]
def __init__( self , *__A , **__A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class UpperCAmelCase_ ( metaclass=UpperCamelCase ):
'''simple docstring'''
__A : Optional[Any] = ["flax"]
def __init__( self , *__A , **__A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
class UpperCAmelCase_ ( metaclass=UpperCamelCase ):
'''simple docstring'''
__A : int = ["flax"]
def __init__( self , *__A , **__A ):
"""simple docstring"""
requires_backends(self , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
@classmethod
def _snake_case ( cls , *__A , **__A ):
"""simple docstring"""
requires_backends(cls , ["flax"] )
| 283 | 1 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
logging.set_verbosity_info()
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Optional[Any]:
def get_masked_lm_array(_UpperCAmelCase ):
lowerCamelCase =F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
lowerCamelCase =tf.train.load_variable(_UpperCAmelCase , _UpperCAmelCase )
if "kernel" in name:
lowerCamelCase =array.transpose()
return torch.from_numpy(_UpperCAmelCase )
def get_encoder_array(_UpperCAmelCase ):
lowerCamelCase =F"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
lowerCamelCase =tf.train.load_variable(_UpperCAmelCase , _UpperCAmelCase )
if "kernel" in name:
lowerCamelCase =array.transpose()
return torch.from_numpy(_UpperCAmelCase )
def get_encoder_layer_array(_UpperCAmelCase , _UpperCAmelCase ):
lowerCamelCase =F"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
lowerCamelCase =tf.train.load_variable(_UpperCAmelCase , _UpperCAmelCase )
if "kernel" in name:
lowerCamelCase =array.transpose()
return torch.from_numpy(_UpperCAmelCase )
def get_encoder_attention_layer_array(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
lowerCamelCase =F"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
lowerCamelCase =tf.train.load_variable(_UpperCAmelCase , _UpperCAmelCase )
lowerCamelCase =array.reshape(_UpperCAmelCase )
if "kernel" in name:
lowerCamelCase =array.transpose()
return torch.from_numpy(_UpperCAmelCase )
print(F"""Loading model based on config from {config_path}...""" )
lowerCamelCase =BertConfig.from_json_file(_UpperCAmelCase )
lowerCamelCase =BertForMaskedLM(_UpperCAmelCase )
# Layers
for layer_index in range(0 , config.num_hidden_layers ):
lowerCamelCase =model.bert.encoder.layer[layer_index]
# Self-attention
lowerCamelCase =layer.attention.self
lowerCamelCase =get_encoder_attention_layer_array(
_UpperCAmelCase , """_query_dense/kernel""" , self_attn.query.weight.data.shape )
lowerCamelCase =get_encoder_attention_layer_array(
_UpperCAmelCase , """_query_dense/bias""" , self_attn.query.bias.data.shape )
lowerCamelCase =get_encoder_attention_layer_array(
_UpperCAmelCase , """_key_dense/kernel""" , self_attn.key.weight.data.shape )
lowerCamelCase =get_encoder_attention_layer_array(
_UpperCAmelCase , """_key_dense/bias""" , self_attn.key.bias.data.shape )
lowerCamelCase =get_encoder_attention_layer_array(
_UpperCAmelCase , """_value_dense/kernel""" , self_attn.value.weight.data.shape )
lowerCamelCase =get_encoder_attention_layer_array(
_UpperCAmelCase , """_value_dense/bias""" , self_attn.value.bias.data.shape )
# Self-attention Output
lowerCamelCase =layer.attention.output
lowerCamelCase =get_encoder_attention_layer_array(
_UpperCAmelCase , """_output_dense/kernel""" , self_output.dense.weight.data.shape )
lowerCamelCase =get_encoder_attention_layer_array(
_UpperCAmelCase , """_output_dense/bias""" , self_output.dense.bias.data.shape )
lowerCamelCase =get_encoder_layer_array(_UpperCAmelCase , """_attention_layer_norm/gamma""" )
lowerCamelCase =get_encoder_layer_array(_UpperCAmelCase , """_attention_layer_norm/beta""" )
# Intermediate
lowerCamelCase =layer.intermediate
lowerCamelCase =get_encoder_layer_array(_UpperCAmelCase , """_intermediate_dense/kernel""" )
lowerCamelCase =get_encoder_layer_array(_UpperCAmelCase , """_intermediate_dense/bias""" )
# Output
lowerCamelCase =layer.output
lowerCamelCase =get_encoder_layer_array(_UpperCAmelCase , """_output_dense/kernel""" )
lowerCamelCase =get_encoder_layer_array(_UpperCAmelCase , """_output_dense/bias""" )
lowerCamelCase =get_encoder_layer_array(_UpperCAmelCase , """_output_layer_norm/gamma""" )
lowerCamelCase =get_encoder_layer_array(_UpperCAmelCase , """_output_layer_norm/beta""" )
# Embeddings
lowerCamelCase =get_encoder_array("""_position_embedding_layer/embeddings""" )
lowerCamelCase =get_encoder_array("""_type_embedding_layer/embeddings""" )
lowerCamelCase =get_encoder_array("""_embedding_norm_layer/gamma""" )
lowerCamelCase =get_encoder_array("""_embedding_norm_layer/beta""" )
# LM Head
lowerCamelCase =model.cls.predictions.transform
lowerCamelCase =get_masked_lm_array("""dense/kernel""" )
lowerCamelCase =get_masked_lm_array("""dense/bias""" )
lowerCamelCase =get_masked_lm_array("""layer_norm/gamma""" )
lowerCamelCase =get_masked_lm_array("""layer_norm/beta""" )
lowerCamelCase =get_masked_lm_array("""embedding_table""" )
# Pooling
lowerCamelCase =BertPooler(config=_UpperCAmelCase )
lowerCamelCase =get_encoder_array("""_pooler_layer/kernel""" )
lowerCamelCase =get_encoder_array("""_pooler_layer/bias""" )
# Export final model
model.save_pretrained(_UpperCAmelCase )
# Integration test - should load without any errors ;)
lowerCamelCase =BertForMaskedLM.from_pretrained(_UpperCAmelCase )
print(new_model.eval() )
print("""Model conversion was done sucessfully!""" )
if __name__ == "__main__":
UpperCAmelCase__ : str =argparse.ArgumentParser()
parser.add_argument(
'''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow Token Dropping checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
type=str,
required=True,
help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''',
type=str,
required=True,
help='''Path to the output PyTorch model.''',
)
UpperCAmelCase__ : Dict =parser.parse_args()
convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 364 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
UpperCAmelCase__ : Optional[int] =logging.getLogger(__name__)
UpperCAmelCase__ : Tuple =50 # max width of layer names
UpperCAmelCase__ : List[str] =70 # max width of quantizer names
def _lowercase ( _UpperCAmelCase ) -> List[str]:
lowerCamelCase =parser.add_argument_group("""quant_trainer arguments""" )
group.add_argument("""--wprec""" , type=_UpperCAmelCase , default=8 , help="""weight precision""" )
group.add_argument("""--aprec""" , type=_UpperCAmelCase , default=8 , help="""activation precision""" )
group.add_argument("""--quant-per-tensor""" , action="""store_true""" , help="""per tensor weight scaling""" )
group.add_argument("""--quant-disable""" , action="""store_true""" , help="""disable all quantizers""" )
group.add_argument("""--quant-disable-embeddings""" , action="""store_true""" , help="""disable all embeddings quantizers""" )
group.add_argument("""--quant-disable-keyword""" , type=_UpperCAmelCase , nargs="""+""" , help="""disable quantizers by keyword""" )
group.add_argument("""--quant-disable-layer-module""" , type=_UpperCAmelCase , help="""disable quantizers by keyword under layer.""" )
group.add_argument("""--quant-enable-layer-module""" , type=_UpperCAmelCase , help="""enable quantizers by keyword under layer""" )
group.add_argument("""--calibrator""" , default="""max""" , help="""which quantization range calibrator to use""" )
group.add_argument("""--percentile""" , default=_UpperCAmelCase , type=_UpperCAmelCase , help="""percentile for PercentileCalibrator""" )
group.add_argument("""--fuse-qkv""" , action="""store_true""" , help="""use the same scale factor for qkv""" )
group.add_argument("""--clip-gelu""" , metavar="""N""" , type=_UpperCAmelCase , help="""clip gelu output maximum value to N""" )
group.add_argument(
"""--recalibrate-weights""" , action="""store_true""" , help=(
"""recalibrate weight amaxes by taking the max of the weights."""
""" amaxes will be computed with the current quantization granularity (axis)."""
) , )
def _lowercase ( _UpperCAmelCase ) -> Dict:
if args.calibrator == "max":
lowerCamelCase ="""max"""
elif args.calibrator == "percentile":
if args.percentile is None:
raise ValueError("""Specify --percentile when using percentile calibrator""" )
lowerCamelCase ="""histogram"""
elif args.calibrator == "mse":
lowerCamelCase ="""histogram"""
else:
raise ValueError(F"""Invalid calibrator {args.calibrator}""" )
lowerCamelCase =QuantDescriptor(num_bits=args.aprec , calib_method=_UpperCAmelCase )
lowerCamelCase =QuantDescriptor(num_bits=args.wprec , axis=(None if args.quant_per_tensor else (0,)) )
quant_nn.QuantLinear.set_default_quant_desc_input(_UpperCAmelCase )
quant_nn.QuantLinear.set_default_quant_desc_weight(_UpperCAmelCase )
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=False , _UpperCAmelCase=False ) -> int:
logger.info("""Configuring Model for Quantization""" )
logger.info(F"""using quantization package {pytorch_quantization.__file__}""" )
if not calib:
if args.quant_disable_embeddings:
set_quantizer_by_name(_UpperCAmelCase , ["""embeddings"""] , which="""weight""" , _disabled=_UpperCAmelCase )
if args.quant_disable:
set_quantizer_by_name(_UpperCAmelCase , [""""""] , _disabled=_UpperCAmelCase )
if args.quant_disable_keyword:
set_quantizer_by_name(_UpperCAmelCase , args.quant_disable_keyword , _disabled=_UpperCAmelCase )
if args.quant_disable_layer_module:
set_quantizer_by_name(_UpperCAmelCase , [r"""layer.\d+.""" + args.quant_disable_layer_module] , _disabled=_UpperCAmelCase )
if args.quant_enable_layer_module:
set_quantizer_by_name(_UpperCAmelCase , [r"""layer.\d+.""" + args.quant_enable_layer_module] , _disabled=_UpperCAmelCase )
if args.recalibrate_weights:
recalibrate_weights(_UpperCAmelCase )
if args.fuse_qkv:
fuse_qkv(_UpperCAmelCase , _UpperCAmelCase )
if args.clip_gelu:
clip_gelu(_UpperCAmelCase , args.clip_gelu )
# if args.local_rank in [-1, 0] and not calib:
print_quant_summary(_UpperCAmelCase )
def _lowercase ( _UpperCAmelCase ) -> Optional[Any]:
logger.info("""Enabling Calibration""" )
for name, module in model.named_modules():
if name.endswith("""_quantizer""" ):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
logger.info(F"""{name:80}: {module}""" )
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase ) -> Optional[int]:
logger.info("""Loading calibrated amax""" )
for name, module in model.named_modules():
if name.endswith("""_quantizer""" ):
if module._calibrator is not None:
if isinstance(module._calibrator , calib.MaxCalibrator ):
module.load_calib_amax()
else:
module.load_calib_amax("""percentile""" , percentile=args.percentile )
module.enable_quant()
module.disable_calib()
else:
module.enable()
model.cuda()
print_quant_summary(_UpperCAmelCase )
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase ) -> Tuple:
def fusea(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
for mod in [qq, qk, qv]:
if not hasattr(_UpperCAmelCase , """_amax""" ):
print(""" WARNING: NO AMAX BUFFER""" )
return
lowerCamelCase =qq._amax.detach().item()
lowerCamelCase =qk._amax.detach().item()
lowerCamelCase =qv._amax.detach().item()
lowerCamelCase =max(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
qq._amax.fill_(_UpperCAmelCase )
qk._amax.fill_(_UpperCAmelCase )
qv._amax.fill_(_UpperCAmelCase )
logger.info(F""" q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}""" )
for name, mod in model.named_modules():
if name.endswith(""".attention.self""" ):
logger.info(F"""FUSE_QKV: {name:{name_width}}""" )
fusea(mod.matmul_q_input_quantizer , mod.matmul_k_input_quantizer , mod.matmul_v_input_quantizer )
if args.quant_per_tensor:
fusea(mod.query._weight_quantizer , mod.key._weight_quantizer , mod.value._weight_quantizer )
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase ) -> int:
for name, mod in model.named_modules():
if name.endswith(""".output.dense""" ) and not name.endswith("""attention.output.dense""" ):
lowerCamelCase =mod._input_quantizer._amax.data.detach().item()
mod._input_quantizer._amax.data.detach().clamp_(max=_UpperCAmelCase )
lowerCamelCase =mod._input_quantizer._amax.data.detach().item()
logger.info(F"""CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}""" )
def _lowercase ( _UpperCAmelCase ) -> Dict:
for name, mod in model.named_modules():
if hasattr(_UpperCAmelCase , """_weight_quantizer""" ) and mod._weight_quantizer.axis is not None:
lowerCamelCase =mod.weight.shape[0]
lowerCamelCase =mod._weight_quantizer._amax.detach()
lowerCamelCase =torch.ones(_UpperCAmelCase , dtype=amax.dtype , device=amax.device ) * amax
print(F"""expanding {name} {amax} -> {mod._weight_quantizer._amax}""" )
def _lowercase ( _UpperCAmelCase ) -> List[str]:
for name, mod in model.named_modules():
if hasattr(_UpperCAmelCase , """_weight_quantizer""" ):
if not hasattr(mod.weight_quantizer , """_amax""" ):
print("""RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER""" )
continue
# determine which axes to reduce across
# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
lowerCamelCase =set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
lowerCamelCase =set(range(len(mod.weight.size() ) ) ) - axis_set
lowerCamelCase =pytorch_quantization.utils.reduce_amax(mod.weight , axis=_UpperCAmelCase , keepdims=_UpperCAmelCase ).detach()
logger.info(F"""RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}""" )
lowerCamelCase =amax
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase=25 , _UpperCAmelCase=1_80 , _UpperCAmelCase=None ) -> Dict:
if ignore is None:
lowerCamelCase =[]
elif not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowerCamelCase =[ignore]
lowerCamelCase =0
for name, mod in model.named_modules():
if not hasattr(_UpperCAmelCase , """weight""" ):
continue
lowerCamelCase =max(_UpperCAmelCase , len(_UpperCAmelCase ) )
for name, mod in model.named_modules():
lowerCamelCase =getattr(_UpperCAmelCase , """_input_quantizer""" , _UpperCAmelCase )
lowerCamelCase =getattr(_UpperCAmelCase , """_weight_quantizer""" , _UpperCAmelCase )
if not hasattr(_UpperCAmelCase , """weight""" ):
continue
if type(_UpperCAmelCase ) in ignore:
continue
if [True for s in ignore if type(_UpperCAmelCase ) is str and s in name]:
continue
lowerCamelCase =F"""Act:{input_q.extra_repr()}"""
lowerCamelCase =F"""Wgt:{weight_q.extra_repr()}"""
lowerCamelCase =F"""{name:{name_width}} {act_str} {wgt_str}"""
if len(_UpperCAmelCase ) <= line_width:
logger.info(_UpperCAmelCase )
else:
logger.info(F"""{name:{name_width}} {act_str}""" )
logger.info(F"""{" ":{name_width}} {wgt_str}""" )
def _lowercase ( _UpperCAmelCase ) -> Dict:
lowerCamelCase =0
for name, mod in model.named_modules():
if isinstance(_UpperCAmelCase , pytorch_quantization.nn.TensorQuantizer ):
print(F"""{name:80} {mod}""" )
count += 1
print(F"""{count} TensorQuantizers found in model""" )
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Union[str, Any]:
lowerCamelCase =getattr(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
if quantizer_mod is not None:
assert hasattr(_UpperCAmelCase , _UpperCAmelCase )
setattr(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
else:
logger.warning(F"""{name} has no {quantizer}""" )
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase="both" , **_UpperCAmelCase ) -> List[str]:
lowerCamelCase =F"""Warning: changing {which} quantizers of {name:{qname_width}}"""
for k, v in kwargs.items():
s += F""" {k}={v}"""
if which in ["input", "both"]:
set_quantizer(_UpperCAmelCase , _UpperCAmelCase , """_input_quantizer""" , _UpperCAmelCase , _UpperCAmelCase )
if which in ["weight", "both"]:
set_quantizer(_UpperCAmelCase , _UpperCAmelCase , """_weight_quantizer""" , _UpperCAmelCase , _UpperCAmelCase )
logger.info(_UpperCAmelCase )
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ) -> int:
for name, mod in model.named_modules():
if hasattr(_UpperCAmelCase , """_input_quantizer""" ) or hasattr(_UpperCAmelCase , """_weight_quantizer""" ):
for n in names:
if re.search(_UpperCAmelCase , _UpperCAmelCase ):
set_quantizers(_UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase )
elif name.endswith("""_quantizer""" ):
for n in names:
if re.search(_UpperCAmelCase , _UpperCAmelCase ):
lowerCamelCase =F"""Warning: changing {name:{name_width}}"""
for k, v in kwargs.items():
s += F""" {k}={v}"""
setattr(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
logger.info(_UpperCAmelCase )
| 262 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
SCREAMING_SNAKE_CASE = {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}
class UpperCAmelCase_ ( A_ ):
lowercase__ = '''albert'''
def __init__( self : int , snake_case_ : Any=30_000 , snake_case_ : str=128 , snake_case_ : Tuple=4_096 , snake_case_ : int=12 , snake_case_ : str=1 , snake_case_ : Optional[int]=64 , snake_case_ : str=16_384 , snake_case_ : Tuple=1 , snake_case_ : List[Any]="gelu_new" , snake_case_ : Optional[Any]=0 , snake_case_ : Optional[int]=0 , snake_case_ : List[str]=512 , snake_case_ : List[Any]=2 , snake_case_ : Optional[Any]=0.02 , snake_case_ : Optional[Any]=1e-12 , snake_case_ : Tuple=0.1 , snake_case_ : str="absolute" , snake_case_ : int=0 , snake_case_ : Optional[int]=2 , snake_case_ : Dict=3 , **snake_case_ : Tuple , ) -> Optional[int]:
'''simple docstring'''
super().__init__(pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , **snake_case_ )
A__ = vocab_size
A__ = embedding_size
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_hidden_groups
A__ = num_attention_heads
A__ = inner_group_num
A__ = hidden_act
A__ = intermediate_size
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = max_position_embeddings
A__ = type_vocab_size
A__ = initializer_range
A__ = layer_norm_eps
A__ = classifier_dropout_prob
A__ = position_embedding_type
class UpperCAmelCase_ ( A_ ):
@property
def __magic_name__ ( self : str ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
A__ = {0: "batch", 1: "choice", 2: "sequence"}
else:
A__ = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 247 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class UpperCAmelCase_ ( A_ ):
lowercase__ = '''megatron-bert'''
def __init__( self : Optional[Any] , snake_case_ : Optional[Any]=29_056 , snake_case_ : int=1_024 , snake_case_ : Optional[int]=24 , snake_case_ : str=16 , snake_case_ : str=4_096 , snake_case_ : Tuple="gelu" , snake_case_ : List[str]=0.1 , snake_case_ : Optional[int]=0.1 , snake_case_ : List[str]=512 , snake_case_ : Optional[int]=2 , snake_case_ : Dict=0.02 , snake_case_ : Optional[Any]=1e-12 , snake_case_ : Optional[Any]=0 , snake_case_ : int="absolute" , snake_case_ : List[str]=True , **snake_case_ : Tuple , ) -> int:
'''simple docstring'''
super().__init__(pad_token_id=snake_case_ , **snake_case_ )
A__ = vocab_size
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = hidden_act
A__ = intermediate_size
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = max_position_embeddings
A__ = type_vocab_size
A__ = initializer_range
A__ = layer_norm_eps
A__ = position_embedding_type
A__ = use_cache
| 247 | 1 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
lowercase : List[str] = logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE__ )
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
super().__init__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
requires_backends(self , "vision" )
self.check_model_type(
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
if self.framework == "tf"
else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )
def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE=None ) -> int:
snake_case_ : List[str] = {}
if top_k is not None:
snake_case_ : Any = top_k
return {}, {}, postprocess_params
def __call__( self , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Tuple:
return super().__call__(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE ) -> int:
snake_case_ : List[Any] = load_image(_SCREAMING_SNAKE_CASE )
snake_case_ : Union[str, Any] = self.image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors=self.framework )
return model_inputs
def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE ) -> List[str]:
snake_case_ : str = self.model(**_SCREAMING_SNAKE_CASE )
return model_outputs
def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=5 ) -> int:
if top_k > self.model.config.num_labels:
snake_case_ : Optional[Any] = self.model.config.num_labels
if self.framework == "pt":
snake_case_ : Tuple = model_outputs.logits.softmax(-1 )[0]
snake_case_ , snake_case_ : int = probs.topk(_SCREAMING_SNAKE_CASE )
elif self.framework == "tf":
snake_case_ : int = stable_softmax(model_outputs.logits , axis=-1 )[0]
snake_case_ : Optional[Any] = tf.math.top_k(_SCREAMING_SNAKE_CASE , k=_SCREAMING_SNAKE_CASE )
snake_case_ , snake_case_ : List[str] = topk.values.numpy(), topk.indices.numpy()
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
snake_case_ : str = scores.tolist()
snake_case_ : Union[str, Any] = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )]
| 36 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : List[Any] = {
'''microsoft/conditional-detr-resnet-50''': (
'''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
),
}
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
A : Tuple = 'conditional_detr'
A : Optional[int] = ['past_key_values']
A : List[Any] = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=300 , _SCREAMING_SNAKE_CASE=6 , _SCREAMING_SNAKE_CASE=2048 , _SCREAMING_SNAKE_CASE=8 , _SCREAMING_SNAKE_CASE=6 , _SCREAMING_SNAKE_CASE=2048 , _SCREAMING_SNAKE_CASE=8 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE="relu" , _SCREAMING_SNAKE_CASE=256 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE="sine" , _SCREAMING_SNAKE_CASE="resnet50" , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.25 , **_SCREAMING_SNAKE_CASE , ) -> str:
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
snake_case_ : List[Any] = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
snake_case_ : Optional[int] = backbone_config.get("model_type" )
snake_case_ : str = CONFIG_MAPPING[backbone_model_type]
snake_case_ : Tuple = config_class.from_dict(_SCREAMING_SNAKE_CASE )
snake_case_ : Optional[Any] = use_timm_backbone
snake_case_ : Optional[Any] = backbone_config
snake_case_ : str = num_channels
snake_case_ : Optional[Any] = num_queries
snake_case_ : Optional[Any] = d_model
snake_case_ : Optional[Any] = encoder_ffn_dim
snake_case_ : str = encoder_layers
snake_case_ : int = encoder_attention_heads
snake_case_ : int = decoder_ffn_dim
snake_case_ : Optional[Any] = decoder_layers
snake_case_ : List[str] = decoder_attention_heads
snake_case_ : List[str] = dropout
snake_case_ : Optional[int] = attention_dropout
snake_case_ : Tuple = activation_dropout
snake_case_ : List[Any] = activation_function
snake_case_ : Dict = init_std
snake_case_ : str = init_xavier_std
snake_case_ : Tuple = encoder_layerdrop
snake_case_ : int = decoder_layerdrop
snake_case_ : List[Any] = encoder_layers
snake_case_ : int = auxiliary_loss
snake_case_ : int = position_embedding_type
snake_case_ : List[str] = backbone
snake_case_ : Union[str, Any] = use_pretrained_backbone
snake_case_ : Optional[Any] = dilation
# Hungarian matcher
snake_case_ : Tuple = class_cost
snake_case_ : Tuple = bbox_cost
snake_case_ : str = giou_cost
# Loss coefficients
snake_case_ : Union[str, Any] = mask_loss_coefficient
snake_case_ : Tuple = dice_loss_coefficient
snake_case_ : List[str] = cls_loss_coefficient
snake_case_ : List[str] = bbox_loss_coefficient
snake_case_ : List[str] = giou_loss_coefficient
snake_case_ : Any = focal_alpha
super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@property
def _lowerCAmelCase ( self ) -> int:
return self.encoder_attention_heads
@property
def _lowerCAmelCase ( self ) -> int:
return self.d_model
def _lowerCAmelCase ( self ) -> Optional[Any]:
snake_case_ : List[Any] = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
snake_case_ : Optional[int] = self.backbone_config.to_dict()
snake_case_ : Optional[int] = self.__class__.model_type
return output
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
A : Union[str, Any] = version.parse('1.11' )
@property
def _lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def _lowerCAmelCase ( self ) -> float:
return 1e-5
@property
def _lowerCAmelCase ( self ) -> int:
return 12
| 36 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__lowerCAmelCase = version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False , ):
output_path.parent.mkdir(parents=_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , f=output_path.as_posix() , input_names=_SCREAMING_SNAKE_CASE , output_names=_SCREAMING_SNAKE_CASE , dynamic_axes=_SCREAMING_SNAKE_CASE , do_constant_folding=_SCREAMING_SNAKE_CASE , use_external_data_format=_SCREAMING_SNAKE_CASE , enable_onnx_checker=_SCREAMING_SNAKE_CASE , opset_version=_SCREAMING_SNAKE_CASE , )
else:
export(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , f=output_path.as_posix() , input_names=_SCREAMING_SNAKE_CASE , output_names=_SCREAMING_SNAKE_CASE , dynamic_axes=_SCREAMING_SNAKE_CASE , do_constant_folding=_SCREAMING_SNAKE_CASE , opset_version=_SCREAMING_SNAKE_CASE , )
@torch.no_grad()
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ):
_snake_case = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
_snake_case = """cuda"""
elif fpaa and not torch.cuda.is_available():
raise ValueError("""`float16` model export is only supported on GPUs with CUDA""" )
else:
_snake_case = """cpu"""
_snake_case = Path(_SCREAMING_SNAKE_CASE )
# VAE DECODER
_snake_case = AutoencoderKL.from_pretrained(model_path + """/vae""" )
_snake_case = vae_decoder.config.latent_channels
# forward only through the decoder part
_snake_case = vae_decoder.decode
onnx_export(
_SCREAMING_SNAKE_CASE , model_args=(
torch.randn(1 , _SCREAMING_SNAKE_CASE , 25 , 25 ).to(device=_SCREAMING_SNAKE_CASE , dtype=_SCREAMING_SNAKE_CASE ),
False,
) , output_path=output_path / """vae_decoder""" / """model.onnx""" , ordered_input_names=["""latent_sample""", """return_dict"""] , output_names=["""sample"""] , dynamic_axes={
"""latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
} , opset=_SCREAMING_SNAKE_CASE , )
del vae_decoder
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--model_path',
type=str,
required=True,
help='Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).',
)
parser.add_argument('--output_path', type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--opset',
default=14,
type=int,
help='The version of the ONNX operator set to use.',
)
parser.add_argument('--fp16', action='store_true', default=False, help='Export the models in `float16` mode')
__lowerCAmelCase = parser.parse_args()
print(args.output_path)
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
print('SD: Done: ONNX')
| 341 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import UperNetForSemanticSegmentation
from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _lowerCAmelCase :
'''simple docstring'''
def __init__(self , UpperCAmelCase , UpperCAmelCase=13 , UpperCAmelCase=32 , UpperCAmelCase=3 , UpperCAmelCase=4 , UpperCAmelCase=[10, 20, 30, 40] , UpperCAmelCase=[2, 2, 3, 2] , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=37 , UpperCAmelCase="gelu" , UpperCAmelCase=10 , UpperCAmelCase=0.02 , UpperCAmelCase=["stage2", "stage3", "stage4"] , UpperCAmelCase=3 , UpperCAmelCase=None , ) -> List[Any]:
_snake_case = parent
_snake_case = batch_size
_snake_case = image_size
_snake_case = num_channels
_snake_case = num_stages
_snake_case = hidden_sizes
_snake_case = depths
_snake_case = is_training
_snake_case = use_labels
_snake_case = intermediate_size
_snake_case = hidden_act
_snake_case = type_sequence_label_size
_snake_case = initializer_range
_snake_case = out_features
_snake_case = num_labels
_snake_case = scope
_snake_case = num_stages
def lowercase (self ) -> List[Any]:
_snake_case = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_snake_case = None
if self.use_labels:
_snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case = self.get_config()
return config, pixel_values, labels
def lowercase (self ) -> Tuple:
return ConvNextConfig(
num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , )
def lowercase (self ) -> Any:
return UperNetConfig(
backbone_config=self.get_backbone_config() , hidden_size=512 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=UpperCAmelCase , auxiliary_loss_weight=0.4 , auxiliary_in_channels=40 , auxiliary_channels=256 , auxiliary_num_convs=1 , auxiliary_concat_input=UpperCAmelCase , loss_ignore_index=255 , num_labels=self.num_labels , )
def lowercase (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> str:
_snake_case = UperNetForSemanticSegmentation(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_snake_case = model(UpperCAmelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def lowercase (self ) -> Tuple:
_snake_case = self.prepare_config_and_inputs()
(
(
_snake_case
), (
_snake_case
), (
_snake_case
),
) = config_and_inputs
_snake_case = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( __snake_case , __snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
lowerCAmelCase_ = {"image-segmentation": UperNetForSemanticSegmentation} if is_torch_available() else {}
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def lowercase (self ) -> Optional[Any]:
_snake_case = UperNetModelTester(self )
_snake_case = ConfigTester(self , config_class=UpperCAmelCase , has_text_modality=UpperCAmelCase , hidden_size=37 )
def lowercase (self ) -> str:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowercase (self ) -> Union[str, Any]:
return
def lowercase (self ) -> Union[str, Any]:
_snake_case, _snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case = model_class(UpperCAmelCase )
_snake_case = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_snake_case = [*signature.parameters.keys()]
_snake_case = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCAmelCase )
def lowercase (self ) -> int:
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase )
@unittest.skip(reason="""UperNet does not use inputs_embeds""" )
def lowercase (self ) -> int:
pass
@unittest.skip(reason="""UperNet does not support input and output embeddings""" )
def lowercase (self ) -> List[str]:
pass
@unittest.skip(reason="""UperNet does not have a base model""" )
def lowercase (self ) -> Union[str, Any]:
pass
@unittest.skip(reason="""UperNet does not have a base model""" )
def lowercase (self ) -> Union[str, Any]:
pass
@require_torch_multi_gpu
@unittest.skip(reason="""UperNet has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def lowercase (self ) -> str:
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def lowercase (self ) -> int:
pass
def lowercase (self ) -> List[str]:
def check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
_snake_case = model_class(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
with torch.no_grad():
_snake_case = model(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) )
_snake_case = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_snake_case = self.model_tester.num_stages
self.assertEqual(len(UpperCAmelCase ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_snake_case, _snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case = True
check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_snake_case = True
check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def lowercase (self ) -> List[str]:
_snake_case, _snake_case = self.model_tester.prepare_config_and_inputs_for_common()
_snake_case = _config_zero_init(UpperCAmelCase )
_snake_case = _config_zero_init(configs_no_init.backbone_config )
for model_class in self.all_model_classes:
_snake_case = model_class(config=UpperCAmelCase )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip(reason="""UperNet does not have tied weights""" )
def lowercase (self ) -> Optional[Any]:
pass
@slow
def lowercase (self ) -> Tuple:
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case = UperNetForSemanticSegmentation.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( ):
_snake_case = hf_hub_download(
repo_id="""hf-internal-testing/fixtures_ade20k""" , repo_type="""dataset""" , filename="""ADE_val_00000001.jpg""" )
_snake_case = Image.open(_SCREAMING_SNAKE_CASE ).convert("""RGB""" )
return image
@require_torch
@require_vision
@slow
class _lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def lowercase (self ) -> Any:
_snake_case = AutoImageProcessor.from_pretrained("""openmmlab/upernet-swin-tiny""" )
_snake_case = UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-swin-tiny""" ).to(UpperCAmelCase )
_snake_case = prepare_img()
_snake_case = processor(images=UpperCAmelCase , return_tensors="""pt""" ).to(UpperCAmelCase )
with torch.no_grad():
_snake_case = model(**UpperCAmelCase )
_snake_case = torch.Size((1, model.config.num_labels, 512, 512) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase )
_snake_case = torch.tensor(
[[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]] ).to(UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , UpperCAmelCase , atol=1e-4 ) )
def lowercase (self ) -> Any:
_snake_case = AutoImageProcessor.from_pretrained("""openmmlab/upernet-convnext-tiny""" )
_snake_case = UperNetForSemanticSegmentation.from_pretrained("""openmmlab/upernet-convnext-tiny""" ).to(UpperCAmelCase )
_snake_case = prepare_img()
_snake_case = processor(images=UpperCAmelCase , return_tensors="""pt""" ).to(UpperCAmelCase )
with torch.no_grad():
_snake_case = model(**UpperCAmelCase )
_snake_case = torch.Size((1, model.config.num_labels, 512, 512) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase )
_snake_case = torch.tensor(
[[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]] ).to(UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , UpperCAmelCase , atol=1e-4 ) )
| 341 | 1 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def lowercase__ ( lowercase_ ) -> int:
"""simple docstring"""
if is_torch_version("<" ,"2.0.0" ) or not hasattr(lowercase_ ,"_dynamo" ):
return False
return isinstance(lowercase_ ,torch._dynamo.eval_frame.OptimizedModule )
def lowercase__ ( lowercase_ ,lowercase_ = True ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase : Optional[int] = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
_UpperCamelCase : int = is_compiled_module(lowercase_ )
if is_compiled:
_UpperCamelCase : Dict = model
_UpperCamelCase : Union[str, Any] = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(lowercase_ ,lowercase_ ):
_UpperCamelCase : Tuple = model.module
if not keep_fpaa_wrapper:
_UpperCamelCase : Tuple = getattr(lowercase_ ,"forward" )
_UpperCamelCase : str = model.__dict__.pop("_original_forward" ,lowercase_ )
if original_forward is not None:
while hasattr(lowercase_ ,"__wrapped__" ):
_UpperCamelCase : Optional[int] = forward.__wrapped__
if forward == original_forward:
break
_UpperCamelCase : int = forward
if getattr(lowercase_ ,"_converted_to_transformer_engine" ,lowercase_ ):
convert_model(lowercase_ ,to_transformer_engine=lowercase_ )
if is_compiled:
_UpperCamelCase : Dict = model
_UpperCamelCase : List[Any] = compiled_model
return model
def lowercase__ ( ) -> Union[str, Any]:
"""simple docstring"""
PartialState().wait_for_everyone()
def lowercase__ ( lowercase_ ,lowercase_ ) -> Optional[int]:
"""simple docstring"""
if PartialState().distributed_type == DistributedType.TPU:
xm.save(lowercase_ ,lowercase_ )
elif PartialState().local_process_index == 0:
torch.save(lowercase_ ,lowercase_ )
@contextmanager
def lowercase__ ( **lowercase_ ) -> Optional[Any]:
"""simple docstring"""
for key, value in kwargs.items():
_UpperCamelCase : Any = str(lowercase_ )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def lowercase__ ( lowercase_ ) -> Any:
"""simple docstring"""
if not hasattr(lowercase_ ,"__qualname__" ) and not hasattr(lowercase_ ,"__name__" ):
_UpperCamelCase : Union[str, Any] = getattr(lowercase_ ,"__class__" ,lowercase_ )
if hasattr(lowercase_ ,"__qualname__" ):
return obj.__qualname__
if hasattr(lowercase_ ,"__name__" ):
return obj.__name__
return str(lowercase_ )
def lowercase__ ( lowercase_ ,lowercase_ ) -> Optional[Any]:
"""simple docstring"""
for key, value in source.items():
if isinstance(lowercase_ ,lowercase_ ):
_UpperCamelCase : Any = destination.setdefault(lowercase_ ,{} )
merge_dicts(lowercase_ ,lowercase_ )
else:
_UpperCamelCase : Union[str, Any] = value
return destination
def lowercase__ ( lowercase_ = None ) -> bool:
"""simple docstring"""
if port is None:
_UpperCamelCase : Optional[int] = 29_500
with socket.socket(socket.AF_INET ,socket.SOCK_STREAM ) as s:
return s.connect_ex(("localhost", port) ) == 0
| 310 |
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowerCamelCase__ = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n"
lowerCamelCase__ = "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"
lowerCamelCase__ = "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n 'google_bleu': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.4\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE ( datasets.Metric ):
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( self : List[Any] ) -> MetricInfo:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ),
"references": datasets.Sequence(
datasets.Sequence(datasets.Value("string" , id="token" ) , id="sequence" ) , id="references" ),
} ) , )
def __SCREAMING_SNAKE_CASE ( self : Optional[Any] , __a : List[List[List[str]]] , __a : List[List[str]] , __a : int = 1 , __a : int = 4 , ) -> Dict[str, float]:
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=__a , hypotheses=__a , min_len=__a , max_len=__a )
}
| 310 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowercase_ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ['MLukeTokenizer']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 205 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def a ( A__ : bool = True , *A__ : int , **A__ : Union[str, Any] ) -> List[str]:
"""simple docstring"""
if not is_tqdm_available():
raise ImportError('Accelerate\'s `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.' )
_lowercase =False
if main_process_only:
_lowercase =PartialState().local_process_index == 0
return _tqdm(*A__ , **A__ , disable=A__ )
| 205 | 1 |
# Copyright 2023 The HuggingFace Inc. 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 torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class __lowerCAmelCase ( UpperCamelCase__):
_lowercase : Dict = """microsoft/speecht5_tts"""
_lowercase : str = (
"""This is a tool that reads an English text out loud. It takes an input named `text` which should contain the """
"""text to read (in English) and returns a waveform object containing the sound."""
)
_lowercase : Optional[int] = """text_reader"""
_lowercase : Any = SpeechTaProcessor
_lowercase : Union[str, Any] = SpeechTaForTextToSpeech
_lowercase : Tuple = SpeechTaHifiGan
_lowercase : Tuple = ["""text"""]
_lowercase : Tuple = ["""audio"""]
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
if self.post_processor is None:
a__ : List[str] ="microsoft/speecht5_hifigan"
super().setup()
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__=None ) -> Any:
'''simple docstring'''
a__ : List[str] =self.pre_processor(text=lowerCAmelCase__ , return_tensors="pt" , truncation=lowerCAmelCase__ )
if speaker_embeddings is None:
if not is_datasets_available():
raise ImportError("Datasets needs to be installed if not passing speaker embeddings." )
a__ : List[Any] =load_dataset("Matthijs/cmu-arctic-xvectors" , split="validation" )
a__ : Optional[Any] =torch.tensor(embeddings_dataset[7_3_0_5]["xvector"] ).unsqueeze(0 )
return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
def _lowercase ( self , lowerCAmelCase__ ) -> Union[str, Any]:
'''simple docstring'''
with torch.no_grad():
return self.model.generate_speech(**lowerCAmelCase__ )
def _lowercase ( self , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
with torch.no_grad():
return self.post_processor(lowerCAmelCase__ ).cpu().detach()
| 148 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class __lowerCAmelCase ( UpperCamelCase__):
_lowercase : Any = """char"""
_lowercase : str = """bpe"""
_lowercase : List[Any] = """wp"""
UpperCAmelCase : List[Any] = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class __lowerCAmelCase ( UpperCamelCase__):
_lowercase : Tuple = ["""image_processor""", """char_tokenizer"""]
_lowercase : Any = """ViTImageProcessor"""
_lowercase : Optional[Any] = """MgpstrTokenizer"""
def __init__( self , lowerCAmelCase__=None , lowerCAmelCase__=None , **lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
a__ : Optional[int] =None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , lowerCAmelCase__ , )
a__ : List[str] =kwargs.pop("feature_extractor" )
a__ : List[str] =image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
a__ : str =tokenizer
a__ : List[str] =AutoTokenizer.from_pretrained("gpt2" )
a__ : Optional[int] =AutoTokenizer.from_pretrained("bert-base-uncased" )
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
def __call__( self , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , **lowerCAmelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if images is None and text is None:
raise ValueError("You need to specify either an `images` or `text` input to process." )
if images is not None:
a__ : Union[str, Any] =self.image_processor(lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ )
if text is not None:
a__ : int =self.char_tokenizer(lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ )
if text is None:
return inputs
elif images is None:
return encodings
else:
a__ : Tuple =encodings["input_ids"]
return inputs
def _lowercase ( self , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
a__ , a__ , a__ : Any =sequences
a__ : Union[str, Any] =char_preds.size(0 )
a__ , a__ : Dict =self._decode_helper(lowerCAmelCase__ , "char" )
a__ , a__ : List[Any] =self._decode_helper(lowerCAmelCase__ , "bpe" )
a__ , a__ : Optional[int] =self._decode_helper(lowerCAmelCase__ , "wp" )
a__ : List[Any] =[]
a__ : Dict =[]
for i in range(lowerCAmelCase__ ):
a__ : int =[char_scores[i], bpe_scores[i], wp_scores[i]]
a__ : Tuple =[char_strs[i], bpe_strs[i], wp_strs[i]]
a__ : Any =scores.index(max(lowerCAmelCase__ ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
a__ : Dict ={}
a__ : str =final_strs
a__ : Optional[int] =final_scores
a__ : Union[str, Any] =char_strs
a__ : List[str] =bpe_strs
a__ : Union[str, Any] =wp_strs
return out
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Tuple:
'''simple docstring'''
if format == DecodeType.CHARACTER:
a__ : Optional[Any] =self.char_decode
a__ : Dict =1
a__ : Tuple ="[s]"
elif format == DecodeType.BPE:
a__ : str =self.bpe_decode
a__ : Dict =2
a__ : Optional[int] ="#"
elif format == DecodeType.WORDPIECE:
a__ : Union[str, Any] =self.wp_decode
a__ : List[Any] =1_0_2
a__ : Dict ="[SEP]"
else:
raise ValueError(F'''Format {format} is not supported.''' )
a__ , a__ : Any =[], []
a__ : str =pred_logits.size(0 )
a__ : Optional[Any] =pred_logits.size(1 )
a__ , a__ : Optional[int] =pred_logits.topk(1 , dim=-1 , largest=lowerCAmelCase__ , sorted=lowerCAmelCase__ )
a__ : Optional[Any] =preds_index.view(-1 , lowerCAmelCase__ )[:, 1:]
a__ : Dict =decoder(lowerCAmelCase__ )
a__ , a__ : Any =torch.nn.functional.softmax(lowerCAmelCase__ , dim=2 ).max(dim=2 )
a__ : int =preds_max_prob[:, 1:]
for index in range(lowerCAmelCase__ ):
a__ : Optional[Any] =preds_str[index].find(lowerCAmelCase__ )
a__ : Optional[int] =preds_str[index][:pred_eos]
a__ : List[Any] =preds_index[index].cpu().tolist()
a__ : List[Any] =pred_index.index(lowerCAmelCase__ ) if eos_token in pred_index else -1
a__ : Union[str, Any] =preds_max_prob[index][: pred_eos_index + 1]
a__ : List[Any] =pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(lowerCAmelCase__ )
conf_scores.append(lowerCAmelCase__ )
return dec_strs, conf_scores
def _lowercase ( self , lowerCAmelCase__ ) -> str:
'''simple docstring'''
a__ : int =[seq.replace(" " , "" ) for seq in self.char_tokenizer.batch_decode(lowerCAmelCase__ )]
return decode_strs
def _lowercase ( self , lowerCAmelCase__ ) -> Tuple:
'''simple docstring'''
return self.bpe_tokenizer.batch_decode(lowerCAmelCase__ )
def _lowercase ( self , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
a__ : List[Any] =[seq.replace(" " , "" ) for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase__ )]
return decode_strs
| 148 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = ViTImageProcessor if is_vision_available() else None
@property
def snake_case__ ( self : Tuple ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case__ ( self : Any ):
"""simple docstring"""
snake_case_ = (3, 32, 1_28)
snake_case_ = tempfile.mkdtemp()
# fmt: off
snake_case_ = ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
# fmt: on
snake_case_ = dict(zip(__lowercase , range(len(__lowercase ) ) ) )
snake_case_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(__lowercase ) + "\n" )
snake_case_ = {
'do_normalize': False,
'do_resize': True,
'image_processor_type': 'ViTImageProcessor',
'resample': 3,
'size': {'height': 32, 'width': 1_28},
}
snake_case_ = os.path.join(self.tmpdirname , __lowercase )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(__lowercase , __lowercase )
def snake_case__ ( self : Any , **__lowercase : List[Any] ):
"""simple docstring"""
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **__lowercase )
def snake_case__ ( self : int , **__lowercase : int ):
"""simple docstring"""
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__lowercase )
def snake_case__ ( self : List[str] ):
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def snake_case__ ( self : str ):
"""simple docstring"""
snake_case_ = np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )
snake_case_ = Image.fromarray(np.moveaxis(__lowercase , 0 , -1 ) )
return image_input
def snake_case__ ( self : List[str] ):
"""simple docstring"""
snake_case_ = self.get_tokenizer()
snake_case_ = self.get_image_processor()
snake_case_ = MgpstrProcessor(tokenizer=__lowercase , image_processor=__lowercase )
processor.save_pretrained(self.tmpdirname )
snake_case_ = MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=__lowercase )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , __lowercase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowercase )
def snake_case__ ( self : Dict ):
"""simple docstring"""
snake_case_ = self.get_tokenizer()
snake_case_ = self.get_image_processor()
snake_case_ = MgpstrProcessor(tokenizer=__lowercase , image_processor=__lowercase )
processor.save_pretrained(self.tmpdirname )
snake_case_ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
snake_case_ = self.get_image_processor(do_normalize=__lowercase , padding_value=1.0 )
snake_case_ = MgpstrProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=__lowercase , padding_value=1.0 )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , __lowercase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowercase )
def snake_case__ ( self : Optional[int] ):
"""simple docstring"""
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = MgpstrProcessor(tokenizer=__lowercase , image_processor=__lowercase )
snake_case_ = self.prepare_image_inputs()
snake_case_ = image_processor(__lowercase , return_tensors="np" )
snake_case_ = processor(images=__lowercase , return_tensors="np" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def snake_case__ ( self : List[str] ):
"""simple docstring"""
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = MgpstrProcessor(tokenizer=__lowercase , image_processor=__lowercase )
snake_case_ = 'test'
snake_case_ = processor(text=__lowercase )
snake_case_ = tokenizer(__lowercase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def snake_case__ ( self : int ):
"""simple docstring"""
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = MgpstrProcessor(tokenizer=__lowercase , image_processor=__lowercase )
snake_case_ = 'test'
snake_case_ = self.prepare_image_inputs()
snake_case_ = processor(text=__lowercase , images=__lowercase )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "labels"] )
# test if it raises when no input is passed
with pytest.raises(__lowercase ):
processor()
def snake_case__ ( self : Tuple ):
"""simple docstring"""
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = MgpstrProcessor(tokenizer=__lowercase , image_processor=__lowercase )
snake_case_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]
snake_case_ = processor.char_decode(__lowercase )
snake_case_ = tokenizer.batch_decode(__lowercase )
snake_case_ = [seq.replace(" " , "" ) for seq in decoded_tok]
self.assertListEqual(__lowercase , __lowercase )
def snake_case__ ( self : str ):
"""simple docstring"""
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = MgpstrProcessor(tokenizer=__lowercase , image_processor=__lowercase )
snake_case_ = None
snake_case_ = self.prepare_image_inputs()
snake_case_ = processor(text=__lowercase , images=__lowercase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
def snake_case__ ( self : int ):
"""simple docstring"""
snake_case_ = self.get_image_processor()
snake_case_ = self.get_tokenizer()
snake_case_ = MgpstrProcessor(tokenizer=__lowercase , image_processor=__lowercase )
snake_case_ = torch.randn(1 , 27 , 38 )
snake_case_ = torch.randn(1 , 27 , 5_02_57 )
snake_case_ = torch.randn(1 , 27 , 3_05_22 )
snake_case_ = processor.batch_decode([char_input, bpe_input, wp_input] )
self.assertListEqual(list(results.keys() ) , ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"] )
| 187 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[str] = {
"SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json",
# See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}
class _lowerCamelCase( _a ):
lowercase_ : Dict = """deformable_detr"""
lowercase_ : int = {
"""hidden_size""": """d_model""",
"""num_attention_heads""": """encoder_attention_heads""",
}
def __init__( self, lowerCamelCase=True, lowerCamelCase=None, lowerCamelCase=3, lowerCamelCase=3_00, lowerCamelCase=10_24, lowerCamelCase=6, lowerCamelCase=10_24, lowerCamelCase=8, lowerCamelCase=6, lowerCamelCase=10_24, lowerCamelCase=8, lowerCamelCase=0.0, lowerCamelCase=True, lowerCamelCase="relu", lowerCamelCase=2_56, lowerCamelCase=0.1, lowerCamelCase=0.0, lowerCamelCase=0.0, lowerCamelCase=0.0_2, lowerCamelCase=1.0, lowerCamelCase=True, lowerCamelCase=False, lowerCamelCase="sine", lowerCamelCase="resnet50", lowerCamelCase=True, lowerCamelCase=False, lowerCamelCase=4, lowerCamelCase=4, lowerCamelCase=4, lowerCamelCase=False, lowerCamelCase=3_00, lowerCamelCase=False, lowerCamelCase=1, lowerCamelCase=5, lowerCamelCase=2, lowerCamelCase=1, lowerCamelCase=1, lowerCamelCase=5, lowerCamelCase=2, lowerCamelCase=0.1, lowerCamelCase=0.2_5, lowerCamelCase=False, **lowerCamelCase, ) -> Optional[int]:
"""simple docstring"""
if backbone_config is not None and use_timm_backbone:
raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.')
if not use_timm_backbone:
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.')
_lowercase : List[str] = CONFIG_MAPPING['resnet'](out_features=['stage4'])
elif isinstance(lowerCamelCase, lowerCamelCase):
_lowercase : List[str] = backbone_config.get('model_type')
_lowercase : str = CONFIG_MAPPING[backbone_model_type]
_lowercase : Optional[int] = config_class.from_dict(lowerCamelCase)
_lowercase : Tuple = use_timm_backbone
_lowercase : List[str] = backbone_config
_lowercase : Tuple = num_channels
_lowercase : Optional[Any] = num_queries
_lowercase : Optional[Any] = max_position_embeddings
_lowercase : Optional[int] = d_model
_lowercase : int = encoder_ffn_dim
_lowercase : List[Any] = encoder_layers
_lowercase : str = encoder_attention_heads
_lowercase : str = decoder_ffn_dim
_lowercase : Optional[Any] = decoder_layers
_lowercase : List[str] = decoder_attention_heads
_lowercase : Optional[int] = dropout
_lowercase : Optional[Any] = attention_dropout
_lowercase : int = activation_dropout
_lowercase : Any = activation_function
_lowercase : Optional[int] = init_std
_lowercase : int = init_xavier_std
_lowercase : Union[str, Any] = encoder_layerdrop
_lowercase : Tuple = auxiliary_loss
_lowercase : Union[str, Any] = position_embedding_type
_lowercase : str = backbone
_lowercase : List[Any] = use_pretrained_backbone
_lowercase : Any = dilation
# deformable attributes
_lowercase : Any = num_feature_levels
_lowercase : Dict = encoder_n_points
_lowercase : Dict = decoder_n_points
_lowercase : Dict = two_stage
_lowercase : Union[str, Any] = two_stage_num_proposals
_lowercase : str = with_box_refine
if two_stage is True and with_box_refine is False:
raise ValueError('If two_stage is True, with_box_refine must be True.')
# Hungarian matcher
_lowercase : Tuple = class_cost
_lowercase : int = bbox_cost
_lowercase : Optional[int] = giou_cost
# Loss coefficients
_lowercase : Optional[Any] = mask_loss_coefficient
_lowercase : Dict = dice_loss_coefficient
_lowercase : Tuple = bbox_loss_coefficient
_lowercase : Optional[int] = giou_loss_coefficient
_lowercase : Union[str, Any] = eos_coefficient
_lowercase : Union[str, Any] = focal_alpha
_lowercase : Dict = disable_custom_kernels
super().__init__(is_encoder_decoder=lowerCamelCase, **lowerCamelCase)
@property
def UpperCamelCase ( self) -> int:
"""simple docstring"""
return self.encoder_attention_heads
@property
def UpperCamelCase ( self) -> int:
"""simple docstring"""
return self.d_model
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
_lowercase : Union[str, Any] = copy.deepcopy(self.__dict__)
if self.backbone_config is not None:
_lowercase : Union[str, Any] = self.backbone_config.to_dict()
_lowercase : Tuple = self.__class__.model_type
return output
| 21 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
a : Dict = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> None:
warnings.warn(
"""The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use CLIPImageProcessor instead.""" , A , )
super().__init__(*A , **A )
| 361 |
'''simple docstring'''
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
UpperCAmelCase : Optional[Any] = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
UpperCAmelCase : List[str] = """encrypt"""
UpperCAmelCase : List[str] = encrypt_message(_lowercase , _lowercase )
elif mode.lower().startswith("""d""" ):
UpperCAmelCase : Tuple = """decrypt"""
UpperCAmelCase : str = decrypt_message(_lowercase , _lowercase )
print(F'''\n{mode.title()}ed message:''' )
print(_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """encrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """decrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> str:
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : Optional[Any] = 0
UpperCAmelCase : Tuple = key.upper()
for symbol in message:
UpperCAmelCase : Dict = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(_lowercase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(_lowercase ):
UpperCAmelCase : Optional[int] = 0
else:
translated.append(_lowercase )
return "".join(_lowercase )
if __name__ == "__main__":
main()
| 338 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
UpperCamelCase_ = {
'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
'tokenization_gpt_neox_japanese': ['GPTNeoXJapaneseTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = [
'GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
'GPTNeoXJapaneseForCausalLM',
'GPTNeoXJapaneseLayer',
'GPTNeoXJapaneseModel',
'GPTNeoXJapanesePreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 243 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
__lowerCAmelCase : str = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = """openai-gpt"""
SCREAMING_SNAKE_CASE_ : Tuple = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : str , __lowerCamelCase : List[str]=4_04_78 , __lowerCamelCase : List[Any]=5_12 , __lowerCamelCase : List[str]=7_68 , __lowerCamelCase : List[str]=12 , __lowerCamelCase : Optional[Any]=12 , __lowerCamelCase : str="gelu" , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : Any=0.1 , __lowerCamelCase : Dict=0.1 , __lowerCamelCase : Any=1e-5 , __lowerCamelCase : Optional[int]=0.02 , __lowerCamelCase : Optional[int]="cls_index" , __lowerCamelCase : List[Any]=True , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : Optional[Any]=True , __lowerCamelCase : Tuple=0.1 , **__lowerCamelCase : Union[str, Any] , ) -> List[str]:
a = vocab_size
a = n_positions
a = n_embd
a = n_layer
a = n_head
a = afn
a = resid_pdrop
a = embd_pdrop
a = attn_pdrop
a = layer_norm_epsilon
a = initializer_range
a = summary_type
a = summary_use_proj
a = summary_activation
a = summary_first_dropout
a = summary_proj_to_labels
super().__init__(**__lowerCamelCase )
| 107 | 0 |
from __future__ import annotations
def __lowerCamelCase ( __a :list , __a :int , __a :int , __a :int ) -> list:
"""simple docstring"""
A__ = []
A__ , A__ = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0 ) )
A__ = result + left + right
return input_list
def __lowerCamelCase ( __a :list ) -> list:
"""simple docstring"""
if len(__a ) <= 1:
return input_list
A__ = list(__a )
# iteration for two-way merging
A__ = 2
while p <= len(__a ):
# getting low, high and middle value for merge-sort of single list
for i in range(0 , len(__a ) , __a ):
A__ = i
A__ = i + p - 1
A__ = (low + high + 1) // 2
A__ = merge(__a , __a , __a , __a )
# final merge of last two parts
if p * 2 >= len(__a ):
A__ = i
A__ = merge(__a , 0 , __a , len(__a ) - 1 )
break
p *= 2
return input_list
if __name__ == "__main__":
A : List[Any] = input('''Enter numbers separated by a comma:\n''').strip()
if user_input == "":
A : int = []
else:
A : Tuple = [int(item.strip()) for item in user_input.split(''',''')]
print(iter_merge_sort(unsorted))
| 276 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def __lowerCamelCase ( __a :int ) -> int:
"""simple docstring"""
A__ = prime_factors(__a )
if is_square_free(__a ):
return -1 if len(__a ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 276 | 1 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class A__ ( enum.Enum ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 2
@add_end_docstrings(a_ )
class A__ ( a_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = '\n In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n begging for his blessing. <eod> </s> <eos>\n '
def __init__( self: str , *_SCREAMING_SNAKE_CASE: Union[str, Any] , **_SCREAMING_SNAKE_CASE: int) -> List[str]:
"""simple docstring"""
super().__init__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE)
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING)
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
__lowerCAmelCase : Tuple = None
if self.model.config.prefix is not None:
__lowerCAmelCase : Any = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
__lowerCAmelCase : int = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Tuple = self._sanitize_parameters(prefix=_SCREAMING_SNAKE_CASE , **self._forward_params)
__lowerCAmelCase : List[str] = {**self._preprocess_params, **preprocess_params}
__lowerCAmelCase : Tuple = {**self._forward_params, **forward_params}
def _SCREAMING_SNAKE_CASE ( self: str , _SCREAMING_SNAKE_CASE: Union[str, Any]=None , _SCREAMING_SNAKE_CASE: Optional[Any]=None , _SCREAMING_SNAKE_CASE: Optional[Any]=None , _SCREAMING_SNAKE_CASE: List[Any]=None , _SCREAMING_SNAKE_CASE: List[Any]=None , _SCREAMING_SNAKE_CASE: Tuple=None , _SCREAMING_SNAKE_CASE: str=None , _SCREAMING_SNAKE_CASE: Optional[Any]=None , **_SCREAMING_SNAKE_CASE: Optional[Any] , ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : List[str] = {}
if prefix is not None:
__lowerCAmelCase : Optional[Any] = prefix
if prefix:
__lowerCAmelCase : Optional[int] = self.tokenizer(
_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE , return_tensors=self.framework)
__lowerCAmelCase : Any = prefix_inputs["input_ids"].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
F"""{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"""
" [None, \'hole\']")
__lowerCAmelCase : Optional[int] = handle_long_generation
preprocess_params.update(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Any = generate_kwargs
__lowerCAmelCase : Any = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_full_text`")
if return_tensors is not None:
raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`")
__lowerCAmelCase : List[str] = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_tensors`")
__lowerCAmelCase : Optional[Any] = ReturnType.TENSORS
if return_type is not None:
__lowerCAmelCase : int = return_type
if clean_up_tokenization_spaces is not None:
__lowerCAmelCase : Any = clean_up_tokenization_spaces
if stop_sequence is not None:
__lowerCAmelCase : Dict = self.tokenizer.encode(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE)
if len(_SCREAMING_SNAKE_CASE) > 1:
warnings.warn(
"Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
" the stop sequence will be used as the stop sequence string in the interim.")
__lowerCAmelCase : Optional[Any] = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def _SCREAMING_SNAKE_CASE ( self: Optional[int] , *_SCREAMING_SNAKE_CASE: Optional[int] , **_SCREAMING_SNAKE_CASE: int) -> str:
"""simple docstring"""
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({"add_space_before_punct_symbol": True})
return super()._parse_and_tokenize(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE)
def __call__( self: Optional[int] , _SCREAMING_SNAKE_CASE: List[Any] , **_SCREAMING_SNAKE_CASE: int) -> Optional[Any]:
"""simple docstring"""
return super().__call__(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE)
def _SCREAMING_SNAKE_CASE ( self: Dict , _SCREAMING_SNAKE_CASE: List[str] , _SCREAMING_SNAKE_CASE: List[Any]="" , _SCREAMING_SNAKE_CASE: Any=None , **_SCREAMING_SNAKE_CASE: Union[str, Any]) -> Any:
"""simple docstring"""
__lowerCAmelCase : List[str] = self.tokenizer(
prefix + prompt_text , padding=_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE , return_tensors=self.framework)
__lowerCAmelCase : List[str] = prompt_text
if handle_long_generation == "hole":
__lowerCAmelCase : Optional[int] = inputs["input_ids"].shape[-1]
if "max_new_tokens" in generate_kwargs:
__lowerCAmelCase : str = generate_kwargs["max_new_tokens"]
else:
__lowerCAmelCase : Any = generate_kwargs.get("max_length" , self.model.config.max_length) - cur_len
if new_tokens < 0:
raise ValueError("We cannot infer how many new tokens are expected")
if cur_len + new_tokens > self.tokenizer.model_max_length:
__lowerCAmelCase : List[Any] = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
"We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
" models max length")
__lowerCAmelCase : Optional[int] = inputs["input_ids"][:, -keep_length:]
if "attention_mask" in inputs:
__lowerCAmelCase : Optional[Any] = inputs["attention_mask"][:, -keep_length:]
return inputs
def _SCREAMING_SNAKE_CASE ( self: Tuple , _SCREAMING_SNAKE_CASE: Union[str, Any] , **_SCREAMING_SNAKE_CASE: Any) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : int = model_inputs["input_ids"]
__lowerCAmelCase : List[Any] = model_inputs.get("attention_mask" , _SCREAMING_SNAKE_CASE)
# Allow empty prompts
if input_ids.shape[1] == 0:
__lowerCAmelCase : Dict = None
__lowerCAmelCase : Dict = None
__lowerCAmelCase : int = 1
else:
__lowerCAmelCase : Dict = input_ids.shape[0]
__lowerCAmelCase : str = model_inputs.pop("prompt_text")
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
__lowerCAmelCase : List[str] = generate_kwargs.pop("prefix_length" , 0)
if prefix_length > 0:
__lowerCAmelCase : Tuple = "max_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].max_new_tokens is not None
)
if not has_max_new_tokens:
__lowerCAmelCase : Dict = generate_kwargs.get("max_length") or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
__lowerCAmelCase : Optional[int] = "min_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
__lowerCAmelCase : Optional[int] = self.model.generate(input_ids=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Any = generated_sequence.shape[0]
if self.framework == "pt":
__lowerCAmelCase : Any = generated_sequence.reshape(_SCREAMING_SNAKE_CASE , out_b // in_b , *generated_sequence.shape[1:])
elif self.framework == "tf":
__lowerCAmelCase : str = tf.reshape(_SCREAMING_SNAKE_CASE , (in_b, out_b // in_b, *generated_sequence.shape[1:]))
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def _SCREAMING_SNAKE_CASE ( self: Dict , _SCREAMING_SNAKE_CASE: Tuple , _SCREAMING_SNAKE_CASE: str=ReturnType.FULL_TEXT , _SCREAMING_SNAKE_CASE: Optional[Any]=True) -> str:
"""simple docstring"""
__lowerCAmelCase : Tuple = model_outputs["generated_sequence"][0]
__lowerCAmelCase : Optional[int] = model_outputs["input_ids"]
__lowerCAmelCase : Optional[Any] = model_outputs["prompt_text"]
__lowerCAmelCase : Tuple = generated_sequence.numpy().tolist()
__lowerCAmelCase : Any = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
__lowerCAmelCase : int = {"generated_token_ids": sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
__lowerCAmelCase : List[Any] = self.tokenizer.decode(
_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE , clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
__lowerCAmelCase : Any = 0
else:
__lowerCAmelCase : Any = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=_SCREAMING_SNAKE_CASE , clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE , ))
if return_type == ReturnType.FULL_TEXT:
__lowerCAmelCase : Optional[int] = prompt_text + text[prompt_length:]
else:
__lowerCAmelCase : Optional[Any] = text[prompt_length:]
__lowerCAmelCase : List[str] = {"generated_text": all_text}
records.append(_SCREAMING_SNAKE_CASE)
return records
| 269 |
def a_ ( _A = 1000 ) -> int:
"""simple docstring"""
return sum(e for e in range(3 , _A ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 307 | 0 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPTaConfig,
GPTaLMHeadModel,
GPTaTokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
_a = {
'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def __a ( __lowerCamelCase ):
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts )
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config )
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights )
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def __a ( __lowerCamelCase, __lowerCamelCase ):
if args.student_type == "roberta":
UpperCAmelCase_ : Optional[int] = False
elif args.student_type == "gpt2":
UpperCAmelCase_ : List[Any] = False
def __a ( __lowerCamelCase, __lowerCamelCase ):
if args.student_type == "roberta":
UpperCAmelCase_ : str = False
def __a ( ):
UpperCAmelCase_ : Union[str, Any] = argparse.ArgumentParser(description="Training" )
parser.add_argument("--force", action="store_true", help="Overwrite dump_path if it already exists." )
parser.add_argument(
"--dump_path", type=__lowerCamelCase, required=__lowerCamelCase, help="The output directory (log, checkpoints, parameters, etc.)" )
parser.add_argument(
"--data_file", type=__lowerCamelCase, required=__lowerCamelCase, help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence.", )
parser.add_argument(
"--student_type", type=__lowerCamelCase, choices=["distilbert", "roberta", "gpt2"], required=__lowerCamelCase, help="The student type (DistilBERT, RoBERTa).", )
parser.add_argument("--student_config", type=__lowerCamelCase, required=__lowerCamelCase, help="Path to the student configuration." )
parser.add_argument(
"--student_pretrained_weights", default=__lowerCamelCase, type=__lowerCamelCase, help="Load student initialization checkpoint." )
parser.add_argument(
"--teacher_type", choices=["bert", "roberta", "gpt2"], required=__lowerCamelCase, help="Teacher type (BERT, RoBERTa)." )
parser.add_argument("--teacher_name", type=__lowerCamelCase, required=__lowerCamelCase, help="The teacher model." )
parser.add_argument("--temperature", default=2.0, type=__lowerCamelCase, help="Temperature for the softmax temperature." )
parser.add_argument(
"--alpha_ce", default=0.5, type=__lowerCamelCase, help="Linear weight for the distillation loss. Must be >=0." )
parser.add_argument(
"--alpha_mlm", default=0.0, type=__lowerCamelCase, help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.", )
parser.add_argument("--alpha_clm", default=0.5, type=__lowerCamelCase, help="Linear weight for the CLM loss. Must be >=0." )
parser.add_argument("--alpha_mse", default=0.0, type=__lowerCamelCase, help="Linear weight of the MSE loss. Must be >=0." )
parser.add_argument(
"--alpha_cos", default=0.0, type=__lowerCamelCase, help="Linear weight of the cosine embedding loss. Must be >=0." )
parser.add_argument(
"--mlm", action="store_true", help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM." )
parser.add_argument(
"--mlm_mask_prop", default=0.15, type=__lowerCamelCase, help="Proportion of tokens for which we need to make a prediction.", )
parser.add_argument("--word_mask", default=0.8, type=__lowerCamelCase, help="Proportion of tokens to mask out." )
parser.add_argument("--word_keep", default=0.1, type=__lowerCamelCase, help="Proportion of tokens to keep." )
parser.add_argument("--word_rand", default=0.1, type=__lowerCamelCase, help="Proportion of tokens to randomly replace." )
parser.add_argument(
"--mlm_smoothing", default=0.7, type=__lowerCamelCase, help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).", )
parser.add_argument("--token_counts", type=__lowerCamelCase, help="The token counts in the data_file for MLM." )
parser.add_argument(
"--restrict_ce_to_mask", action="store_true", help="If true, compute the distillation loss only the [MLM] prediction distribution.", )
parser.add_argument(
"--freeze_pos_embs", action="store_true", help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.", )
parser.add_argument(
"--freeze_token_type_embds", action="store_true", help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.", )
parser.add_argument("--n_epoch", type=__lowerCamelCase, default=3, help="Number of pass on the whole dataset." )
parser.add_argument("--batch_size", type=__lowerCamelCase, default=5, help="Batch size (for each process)." )
parser.add_argument(
"--group_by_size", action="store_false", help="If true, group sequences that have similar length into the same batch. Default is true.", )
parser.add_argument(
"--gradient_accumulation_steps", type=__lowerCamelCase, default=50, help="Gradient accumulation for larger training batches.", )
parser.add_argument("--warmup_prop", default=0.05, type=__lowerCamelCase, help="Linear warmup proportion." )
parser.add_argument("--weight_decay", default=0.0, type=__lowerCamelCase, help="Weight decay if we apply some." )
parser.add_argument("--learning_rate", default=5E-4, type=__lowerCamelCase, help="The initial learning rate for Adam." )
parser.add_argument("--adam_epsilon", default=1E-6, type=__lowerCamelCase, help="Epsilon for Adam optimizer." )
parser.add_argument("--max_grad_norm", default=5.0, type=__lowerCamelCase, help="Max gradient norm." )
parser.add_argument("--initializer_range", default=0.02, type=__lowerCamelCase, help="Random initialization range." )
parser.add_argument(
"--fp16", action="store_true", help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", )
parser.add_argument(
"--fp16_opt_level", type=__lowerCamelCase, default="O1", help=(
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html"
), )
parser.add_argument("--n_gpu", type=__lowerCamelCase, default=1, help="Number of GPUs in the node." )
parser.add_argument("--local_rank", type=__lowerCamelCase, default=-1, help="Distributed training - Local rank" )
parser.add_argument("--seed", type=__lowerCamelCase, default=56, help="Random seed" )
parser.add_argument("--log_interval", type=__lowerCamelCase, default=500, help="Tensorboard logging interval." )
parser.add_argument("--checkpoint_interval", type=__lowerCamelCase, default=4000, help="Checkpoint interval." )
UpperCAmelCase_ : Tuple = parser.parse_args()
sanity_checks(__lowerCamelCase )
# ARGS #
init_gpu_params(__lowerCamelCase )
set_seed(__lowerCamelCase )
if args.is_master:
if os.path.exists(args.dump_path ):
if not args.force:
raise ValueError(
f"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
" itUse `--force` if you want to overwrite it" )
else:
shutil.rmtree(args.dump_path )
if not os.path.exists(args.dump_path ):
os.makedirs(args.dump_path )
logger.info(f"""Experiment will be dumped and logged in {args.dump_path}""" )
# SAVE PARAMS #
logger.info(f"""Param: {args}""" )
with open(os.path.join(args.dump_path, "parameters.json" ), "w" ) as f:
json.dump(vars(__lowerCamelCase ), __lowerCamelCase, indent=4 )
git_log(args.dump_path )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Tuple = MODEL_CLASSES[args.student_type]
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Tuple = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
UpperCAmelCase_ : str = teacher_tokenizer_class.from_pretrained(args.teacher_name )
UpperCAmelCase_ : List[Any] = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
UpperCAmelCase_ : Dict = tokenizer.all_special_tokens.index(__lowerCamelCase )
UpperCAmelCase_ : List[str] = tokenizer.all_special_ids[idx]
logger.info(f"""Special tokens {special_tok_ids}""" )
UpperCAmelCase_ : Tuple = special_tok_ids
UpperCAmelCase_ : List[str] = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(f"""Loading data from {args.data_file}""" )
with open(args.data_file, "rb" ) as fp:
UpperCAmelCase_ : List[Any] = pickle.load(__lowerCamelCase )
if args.mlm:
logger.info(f"""Loading token counts from {args.token_counts} (already pre-computed)""" )
with open(args.token_counts, "rb" ) as fp:
UpperCAmelCase_ : List[Any] = pickle.load(__lowerCamelCase )
UpperCAmelCase_ : List[Any] = np.maximum(__lowerCamelCase, 1 ) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
UpperCAmelCase_ : Any = 0.0 # do not predict special tokens
UpperCAmelCase_ : str = torch.from_numpy(__lowerCamelCase )
else:
UpperCAmelCase_ : Optional[int] = None
UpperCAmelCase_ : Dict = LmSeqsDataset(params=__lowerCamelCase, data=__lowerCamelCase )
logger.info("Data loader created." )
# STUDENT #
logger.info(f"""Loading student config from {args.student_config}""" )
UpperCAmelCase_ : List[str] = student_config_class.from_pretrained(args.student_config )
UpperCAmelCase_ : Dict = True
if args.student_pretrained_weights is not None:
logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}""" )
UpperCAmelCase_ : Union[str, Any] = student_model_class.from_pretrained(args.student_pretrained_weights, config=__lowerCamelCase )
else:
UpperCAmelCase_ : Optional[int] = student_model_class(__lowerCamelCase )
if args.n_gpu > 0:
student.to(f"""cuda:{args.local_rank}""" )
logger.info("Student loaded." )
# TEACHER #
UpperCAmelCase_ : str = teacher_model_class.from_pretrained(args.teacher_name, output_hidden_states=__lowerCamelCase )
if args.n_gpu > 0:
teacher.to(f"""cuda:{args.local_rank}""" )
logger.info(f"""Teacher loaded from {args.teacher_name}.""" )
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(__lowerCamelCase, __lowerCamelCase )
if args.freeze_token_type_embds:
freeze_token_type_embeddings(__lowerCamelCase, __lowerCamelCase )
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0 ) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
UpperCAmelCase_ : int = Distiller(
params=__lowerCamelCase, dataset=__lowerCamelCase, token_probs=__lowerCamelCase, student=__lowerCamelCase, teacher=__lowerCamelCase )
distiller.train()
logger.info("Let's go get some drinks." )
if __name__ == "__main__":
main()
| 23 |
"""simple docstring"""
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = (PNDMScheduler,)
SCREAMING_SNAKE_CASE__ : str = (("""num_inference_steps""", 50),)
def UpperCamelCase__ ( self , **lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : int = {
"num_train_timesteps": 1000,
"beta_start": 0.00_01,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**lowercase_ )
return config
def UpperCamelCase__ ( self , lowercase_=0 , **lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : str = dict(self.forward_default_kwargs )
UpperCAmelCase_ : List[str] = kwargs.pop("num_inference_steps" , lowercase_ )
UpperCAmelCase_ : Union[str, Any] = self.dummy_sample
UpperCAmelCase_ : Dict = 0.1 * sample
UpperCAmelCase_ : Dict = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ : List[Any] = self.get_scheduler_config(**lowercase_ )
UpperCAmelCase_ : Dict = scheduler_class(**lowercase_ )
scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals
UpperCAmelCase_ : List[Any] = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase_ )
UpperCAmelCase_ : Optional[int] = scheduler_class.from_pretrained(lowercase_ )
new_scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals
UpperCAmelCase_ : int = dummy_past_residuals[:]
UpperCAmelCase_ : List[str] = scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
UpperCAmelCase_ : str = new_scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
UpperCAmelCase_ : Optional[int] = scheduler.step_plms(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
UpperCAmelCase_ : Dict = new_scheduler.step_plms(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def UpperCamelCase__ ( self , lowercase_=0 , **lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = dict(self.forward_default_kwargs )
UpperCAmelCase_ : str = kwargs.pop("num_inference_steps" , lowercase_ )
UpperCAmelCase_ : Optional[int] = self.dummy_sample
UpperCAmelCase_ : List[str] = 0.1 * sample
UpperCAmelCase_ : Tuple = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ : str = self.get_scheduler_config()
UpperCAmelCase_ : Dict = scheduler_class(**lowercase_ )
scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals (must be after setting timesteps)
UpperCAmelCase_ : List[Any] = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase_ )
UpperCAmelCase_ : Dict = scheduler_class.from_pretrained(lowercase_ )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowercase_ )
# copy over dummy past residual (must be after setting timesteps)
UpperCAmelCase_ : Optional[Any] = dummy_past_residuals[:]
UpperCAmelCase_ : Union[str, Any] = scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
UpperCAmelCase_ : Dict = new_scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
UpperCAmelCase_ : List[str] = scheduler.step_plms(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
UpperCAmelCase_ : int = new_scheduler.step_plms(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCamelCase__ ( self , **lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : str = self.scheduler_classes[0]
UpperCAmelCase_ : Union[str, Any] = self.get_scheduler_config(**lowercase_ )
UpperCAmelCase_ : List[Any] = scheduler_class(**lowercase_ )
UpperCAmelCase_ : Tuple = 10
UpperCAmelCase_ : List[str] = self.dummy_model()
UpperCAmelCase_ : str = self.dummy_sample_deter
scheduler.set_timesteps(lowercase_ )
for i, t in enumerate(scheduler.prk_timesteps ):
UpperCAmelCase_ : Tuple = model(lowercase_ , lowercase_ )
UpperCAmelCase_ : Optional[int] = scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
UpperCAmelCase_ : Any = model(lowercase_ , lowercase_ )
UpperCAmelCase_ : Optional[Any] = scheduler.step_plms(lowercase_ , lowercase_ , lowercase_ ).prev_sample
return sample
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : str = dict(self.forward_default_kwargs )
UpperCAmelCase_ : Optional[Any] = kwargs.pop("num_inference_steps" , lowercase_ )
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ : Any = self.get_scheduler_config()
UpperCAmelCase_ : Tuple = scheduler_class(**lowercase_ )
UpperCAmelCase_ : str = self.dummy_sample
UpperCAmelCase_ : List[Any] = 0.1 * sample
if num_inference_steps is not None and hasattr(lowercase_ , "set_timesteps" ):
scheduler.set_timesteps(lowercase_ )
elif num_inference_steps is not None and not hasattr(lowercase_ , "set_timesteps" ):
UpperCAmelCase_ : List[Any] = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
UpperCAmelCase_ : List[str] = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
UpperCAmelCase_ : List[str] = dummy_past_residuals[:]
UpperCAmelCase_ : str = scheduler.step_prk(lowercase_ , 0 , lowercase_ , **lowercase_ ).prev_sample
UpperCAmelCase_ : Any = scheduler.step_prk(lowercase_ , 1 , lowercase_ , **lowercase_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
UpperCAmelCase_ : Optional[Any] = scheduler.step_plms(lowercase_ , 0 , lowercase_ , **lowercase_ ).prev_sample
UpperCAmelCase_ : Optional[Any] = scheduler.step_plms(lowercase_ , 1 , lowercase_ , **lowercase_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=lowercase_ )
UpperCAmelCase_ : Optional[int] = self.scheduler_classes[0]
UpperCAmelCase_ : int = self.get_scheduler_config(steps_offset=1 )
UpperCAmelCase_ : Optional[Any] = scheduler_class(**lowercase_ )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) , )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for beta_start, beta_end in zip([0.00_01, 0.0_01] , [0.0_02, 0.02] ):
self.check_over_configs(beta_start=lowercase_ , beta_end=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for t in [1, 5, 10]:
self.check_over_forward(time_step=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
UpperCAmelCase_ : List[Any] = 27
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ : List[Any] = self.dummy_sample
UpperCAmelCase_ : Optional[int] = 0.1 * sample
UpperCAmelCase_ : List[str] = self.get_scheduler_config()
UpperCAmelCase_ : List[str] = scheduler_class(**lowercase_ )
scheduler.set_timesteps(lowercase_ )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
UpperCAmelCase_ : List[str] = scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ ).prev_sample
def UpperCamelCase__ ( self ):
"""simple docstring"""
with self.assertRaises(lowercase_ ):
UpperCAmelCase_ : List[str] = self.scheduler_classes[0]
UpperCAmelCase_ : str = self.get_scheduler_config()
UpperCAmelCase_ : Tuple = scheduler_class(**lowercase_ )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = self.full_loop()
UpperCAmelCase_ : Any = torch.sum(torch.abs(lowercase_ ) )
UpperCAmelCase_ : Dict = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 1_98.13_18 ) < 1E-2
assert abs(result_mean.item() - 0.25_80 ) < 1E-3
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = self.full_loop(prediction_type="v_prediction" )
UpperCAmelCase_ : str = torch.sum(torch.abs(lowercase_ ) )
UpperCAmelCase_ : Tuple = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 67.39_86 ) < 1E-2
assert abs(result_mean.item() - 0.08_78 ) < 1E-3
def UpperCamelCase__ ( self ):
"""simple docstring"""
# We specify different beta, so that the first alpha is 0.99
UpperCAmelCase_ : Union[str, Any] = self.full_loop(set_alpha_to_one=lowercase_ , beta_start=0.01 )
UpperCAmelCase_ : List[Any] = torch.sum(torch.abs(lowercase_ ) )
UpperCAmelCase_ : int = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 2_30.03_99 ) < 1E-2
assert abs(result_mean.item() - 0.29_95 ) < 1E-3
def UpperCamelCase__ ( self ):
"""simple docstring"""
# We specify different beta, so that the first alpha is 0.99
UpperCAmelCase_ : Tuple = self.full_loop(set_alpha_to_one=lowercase_ , beta_start=0.01 )
UpperCAmelCase_ : int = torch.sum(torch.abs(lowercase_ ) )
UpperCAmelCase_ : Tuple = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 1_86.94_82 ) < 1E-2
assert abs(result_mean.item() - 0.24_34 ) < 1E-3
| 23 | 1 |
"""simple docstring"""
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import BaseTransformersCLICommand
def _snake_case ( UpperCamelCase : List[str] ):
return EnvironmentCommand()
def _snake_case ( UpperCamelCase : str ):
return EnvironmentCommand(args.accelerate_config_file )
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
@staticmethod
def SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : int = parser.add_parser("""env""" )
download_parser.set_defaults(func=UpperCAmelCase_ )
download_parser.add_argument(
"""--accelerate-config_file""" , default=UpperCAmelCase_ , help="""The accelerate config file to use for the default values in the launching script.""" , )
download_parser.set_defaults(func=UpperCAmelCase_ )
def __init__( self , _SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
UpperCAmelCase : Dict = accelerate_config_file
def SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = """not installed"""
if is_safetensors_available():
import safetensors
UpperCAmelCase : Dict = safetensors.__version__
elif importlib.util.find_spec("""safetensors""" ) is not None:
import safetensors
UpperCAmelCase : List[str] = F"{safetensors.__version__} but is ignored because of PyTorch version too old."
UpperCAmelCase : Tuple = """not installed"""
UpperCAmelCase : List[str] = """not found"""
if is_accelerate_available():
import accelerate
from accelerate.commands.config import default_config_file, load_config_from_file
UpperCAmelCase : str = accelerate.__version__
# Get the default from the config file.
if self._accelerate_config_file is not None or os.path.isfile(UpperCAmelCase_ ):
UpperCAmelCase : Union[str, Any] = load_config_from_file(self._accelerate_config_file ).to_dict()
UpperCAmelCase : Union[str, Any] = (
"""\n""".join([F"\t- {prop}: {val}" for prop, val in accelerate_config.items()] )
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
else F"\t{accelerate_config}"
)
UpperCAmelCase : Optional[Any] = """not installed"""
UpperCAmelCase : Optional[Any] = """NA"""
if is_torch_available():
import torch
UpperCAmelCase : List[Any] = torch.__version__
UpperCAmelCase : List[str] = torch.cuda.is_available()
UpperCAmelCase : Any = """not installed"""
UpperCAmelCase : Optional[Any] = """NA"""
if is_tf_available():
import tensorflow as tf
UpperCAmelCase : Union[str, Any] = tf.__version__
try:
# deprecated in v2.1
UpperCAmelCase : Union[str, Any] = tf.test.is_gpu_available()
except AttributeError:
# returns list of devices, convert to bool
UpperCAmelCase : Any = bool(tf.config.list_physical_devices("""GPU""" ) )
UpperCAmelCase : Dict = """not installed"""
UpperCAmelCase : Optional[int] = """not installed"""
UpperCAmelCase : List[Any] = """not installed"""
UpperCAmelCase : str = """NA"""
if is_flax_available():
import flax
import jax
import jaxlib
UpperCAmelCase : Optional[Any] = flax.__version__
UpperCAmelCase : List[Any] = jax.__version__
UpperCAmelCase : Optional[Any] = jaxlib.__version__
UpperCAmelCase : List[Any] = jax.lib.xla_bridge.get_backend().platform
UpperCAmelCase : Union[str, Any] = {
"""`transformers` version""": version,
"""Platform""": platform.platform(),
"""Python version""": platform.python_version(),
"""Huggingface_hub version""": huggingface_hub.__version__,
"""Safetensors version""": F"{safetensors_version}",
"""Accelerate version""": F"{accelerate_version}",
"""Accelerate config""": F"{accelerate_config_str}",
"""PyTorch version (GPU?)""": F"{pt_version} ({pt_cuda_available})",
"""Tensorflow version (GPU?)""": F"{tf_version} ({tf_cuda_available})",
"""Flax version (CPU?/GPU?/TPU?)""": F"{flax_version} ({jax_backend})",
"""Jax version""": F"{jax_version}",
"""JaxLib version""": F"{jaxlib_version}",
"""Using GPU in script?""": """<fill in>""",
"""Using distributed or parallel set-up in script?""": """<fill in>""",
}
print("""\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n""" )
print(self.format_dict(UpperCAmelCase_ ) )
return info
@staticmethod
def SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
return "\n".join([F"- {prop}: {val}" for prop, val in d.items()] ) + "\n"
| 109 |
def __lowerCamelCase ( snake_case__ ) -> list:
"""simple docstring"""
def merge(snake_case__ ,snake_case__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(snake_case__ ) <= 1:
return collection
_SCREAMING_SNAKE_CASE = len(snake_case__ ) // 2
return merge(merge_sort(collection[:mid] ) ,merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase = input('''Enter numbers separated by a comma:\n''').strip()
UpperCamelCase = [int(item) for item in user_input.split(''',''')]
print(*merge_sort(unsorted), sep=''',''')
| 306 | 0 |
"""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
A_ : int =logging.get_logger(__name__)
class __a ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[int] = "vision-encoder-decoder"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = True
def __init__( self , **a__ ):
super().__init__(**a__ )
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(a__ , **a__ )
_lowerCamelCase = AutoConfig.for_model(a__ , **a__ )
_lowerCamelCase = True
@classmethod
def snake_case_ ( cls , a__ , a__ , **a__ ):
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() , **a__ )
def snake_case_ ( self ):
_lowerCamelCase = copy.deepcopy(self.__dict__ )
_lowerCamelCase = self.encoder.to_dict()
_lowerCamelCase = self.decoder.to_dict()
_lowerCamelCase = self.__class__.model_type
return output
class __a ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE__ : int = version.parse("1.11" )
@property
def snake_case_ ( self ):
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def snake_case_ ( self ):
return 1e-4
@property
def snake_case_ ( self ):
return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}} )
class __a ( lowerCAmelCase__ ):
@property
def snake_case_ ( self ):
_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 snake_case_ ( self , a__ , a__ = -1 , a__ = -1 , a__ = False , a__ = None , ):
import torch
_lowerCamelCase = OrderedDict()
_lowerCamelCase = super().generate_dummy_inputs(
a__ , batch_size=a__ , seq_length=a__ , is_pair=a__ , framework=a__ )
_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(a__ )
return common_inputs
class __a ( lowerCAmelCase__ ):
@property
def snake_case_ ( self ):
pass
def snake_case_ ( self , a__ ):
return VisionEncoderDecoderEncoderOnnxConfig(a__ )
def snake_case_ ( self , a__ , a__ , a__ = "default" ):
_lowerCamelCase = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(a__ , a__ )
| 356 |
"""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.
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def SCREAMING_SNAKE_CASE_ ( )-> List[Any]:
_lowerCamelCase = ArgumentParser('Accelerate CLI tool' , usage='accelerate <command> [<args>]' , allow_abbrev=snake_case )
_lowerCamelCase = parser.add_subparsers(help='accelerate command helpers' )
# Register commands
get_config_parser(subparsers=snake_case )
env_command_parser(subparsers=snake_case )
launch_command_parser(subparsers=snake_case )
tpu_command_parser(subparsers=snake_case )
test_command_parser(subparsers=snake_case )
# Let's go
_lowerCamelCase = parser.parse_args()
if not hasattr(snake_case , 'func' ):
parser.print_help()
exit(1 )
# Run
args.func(snake_case )
if __name__ == "__main__":
main()
| 80 | 0 |
"""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 snake_case ( A__ ,A__ ,A__=None ):
# set parameter of one layer
assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
UpperCAmelCase_ : List[str] = nn.Parameter(A__ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
UpperCAmelCase_ : Any = nn.Parameter(A__ )
def snake_case ( A__ ,A__ ,A__ ):
# set torch weights for 1-to-1 comparison
UpperCAmelCase_ : Union[str, Any] = np.asarray(weights[0] )
UpperCAmelCase_ : Optional[int] = np.asarray(weights[1] )
UpperCAmelCase_ : Dict = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key ,torch.tensor(A__ ).transpose(1 ,2 ).contiguous().view(-1 ,A__ ) ,)
set_param(
torch_layer.self_attention.value ,torch.tensor(A__ ).transpose(1 ,2 ).contiguous().view(-1 ,A__ ) ,)
set_param(
torch_layer.output.dense ,torch.tensor(A__ ).view(-1 ,A__ ).contiguous().transpose(0 ,1 ) ,)
def snake_case ( A__ ,A__ ,A__ ):
# set torch weights for 1-to-1 comparison
UpperCAmelCase_ : Optional[int] = np.asarray(weights[0] )
UpperCAmelCase_ : Union[str, Any] = np.asarray(weights[1] )
UpperCAmelCase_ : str = np.asarray(weights[2] )
UpperCAmelCase_ : Dict = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query ,torch.tensor(A__ ).transpose(1 ,2 ).contiguous().view(-1 ,A__ ) ,)
set_param(
torch_layer.self_attention.key ,torch.tensor(A__ ).transpose(1 ,2 ).contiguous().view(-1 ,A__ ) ,)
set_param(
torch_layer.self_attention.value ,torch.tensor(A__ ).transpose(1 ,2 ).contiguous().view(-1 ,A__ ) ,)
set_param(
torch_layer.output.dense ,torch.tensor(A__ ).view(-1 ,A__ ).contiguous().transpose(0 ,1 ) ,)
def snake_case ( A__ ,A__ ,A__ ):
# layernorm 1
UpperCAmelCase_ : int = weights[0][0][0]
UpperCAmelCase_ : Union[str, Any] = np.asarray(layer_norm_a[0] )
UpperCAmelCase_ : Dict = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm ,torch.tensor(A__ ) ,torch.tensor(A__ ) ,)
# lsh weights + output
UpperCAmelCase_ : List[str] = weights[0][1]
if len(A__ ) < 4:
set_layer_weights_in_torch_lsh(A__ ,torch_block.attention ,A__ )
else:
set_layer_weights_in_torch_local(A__ ,torch_block.attention ,A__ )
# intermediate weighs
UpperCAmelCase_ : List[str] = weights[2][0][1][2]
# Chunked Feed Forward
if len(A__ ) == 4:
UpperCAmelCase_ : Union[str, Any] = intermediate_weights[2]
# layernorm 2
UpperCAmelCase_ : str = np.asarray(intermediate_weights[0][0] )
UpperCAmelCase_ : Optional[Any] = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm ,torch.tensor(A__ ) ,torch.tensor(A__ ) ,)
# intermediate dense
UpperCAmelCase_ : int = np.asarray(intermediate_weights[1][0] )
UpperCAmelCase_ : Tuple = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense ,torch.tensor(A__ ).transpose(0 ,1 ).contiguous() ,torch.tensor(A__ ) ,)
# intermediate out
UpperCAmelCase_ : List[Any] = np.asarray(intermediate_weights[4][0] )
UpperCAmelCase_ : Optional[int] = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense ,torch.tensor(A__ ).transpose(0 ,1 ).contiguous() ,torch.tensor(A__ ) ,)
def snake_case ( A__ ,A__ ,A__ ):
# reformer model
UpperCAmelCase_ : Tuple = torch_model.reformer
# word embeds
UpperCAmelCase_ : Tuple = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings ,torch.tensor(A__ ) ,)
if isinstance(weights[3] ,A__ ):
UpperCAmelCase_ : Union[str, Any] = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
UpperCAmelCase_ : Any = 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"""
UpperCAmelCase_ : Dict = nn.Parameter(torch.tensor(A__ ) )
UpperCAmelCase_ : List[Any] = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
A__ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
UpperCAmelCase_ : Union[str, Any] = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(A__ ,A__ ,A__ )
# output layer norm
UpperCAmelCase_ : str = np.asarray(weights[7][0] )
UpperCAmelCase_ : List[Any] = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm ,torch.tensor(A__ ) ,torch.tensor(A__ ) ,)
# output embeddings
UpperCAmelCase_ : Union[str, Any] = np.asarray(weights[9][0] )
UpperCAmelCase_ : List[Any] = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder ,torch.tensor(A__ ).transpose(0 ,1 ).contiguous() ,torch.tensor(A__ ) ,)
def snake_case ( A__ ,A__ ,A__ ):
# Initialise PyTorch model
UpperCAmelCase_ : int = ReformerConfig.from_json_file(A__ )
print(F"""Building PyTorch model from configuration: {config}""" )
UpperCAmelCase_ : List[str] = ReformerModelWithLMHead(A__ )
with open(A__ ,"rb" ) as f:
UpperCAmelCase_ : Any = pickle.load(A__ )["weights"]
set_model_weights_in_torch(A__ ,A__ ,config.hidden_size )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() ,A__ )
if __name__ == "__main__":
lowerCamelCase_ = 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.'''
)
lowerCamelCase_ = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 268 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
'''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
'''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
'''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
'''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}
class UpperCamelCase_ (__A ):
__magic_name__ = '''rwkv'''
__magic_name__ = {'''max_position_embeddings''': '''context_length'''}
def __init__( self : str , lowerCAmelCase_ : str=50_277 , lowerCAmelCase_ : Optional[int]=1_024 , lowerCAmelCase_ : Optional[int]=4_096 , lowerCAmelCase_ : Union[str, Any]=32 , lowerCAmelCase_ : List[Any]=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : List[Any]=1e-5 , lowerCAmelCase_ : str=0 , lowerCAmelCase_ : Tuple=0 , lowerCAmelCase_ : int=6 , lowerCAmelCase_ : Optional[Any]=False , lowerCAmelCase_ : Any=True , **lowerCAmelCase_ : List[Any] , ) -> List[str]:
UpperCAmelCase_ : Tuple = vocab_size
UpperCAmelCase_ : List[str] = context_length
UpperCAmelCase_ : Dict = hidden_size
UpperCAmelCase_ : Optional[int] = num_hidden_layers
UpperCAmelCase_ : Optional[int] = attention_hidden_size if attention_hidden_size is not None else hidden_size
UpperCAmelCase_ : Dict = intermediate_size if intermediate_size is not None else 4 * hidden_size
UpperCAmelCase_ : Any = layer_norm_epsilon
UpperCAmelCase_ : List[Any] = rescale_every
UpperCAmelCase_ : List[str] = use_cache
UpperCAmelCase_ : List[str] = bos_token_id
UpperCAmelCase_ : Union[str, Any] = eos_token_id
super().__init__(
tie_word_embeddings=lowerCAmelCase_ , bos_token_id=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ , **lowerCAmelCase_ )
| 268 | 1 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
UpperCAmelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
UpperCAmelCase_ = '\n Examples:\n ```py\n >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n >>> pipe_prior.to("cuda")\n >>> prompt = "red cat, 4k photo"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> zero_image_emb = out.negative_image_embeds\n >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n >>> pipe.to("cuda")\n >>> image = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=50,\n ... ).images\n >>> image[0].save("cat.png")\n ```\n'
def lowerCamelCase__ ( A__ : Any , A__ : Optional[int] , A__ : str=8 ):
'''simple docstring'''
__lowerCamelCase = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
__lowerCamelCase = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class lowerCamelCase__( __lowerCamelCase):
def __init__( self: Optional[Any] , UpperCamelCase_: UNetaDConditionModel , UpperCamelCase_: DDPMScheduler , UpperCamelCase_: VQModel , ):
super().__init__()
self.register_modules(
unet=UpperCamelCase_ , scheduler=UpperCamelCase_ , movq=UpperCamelCase_ , )
__lowerCamelCase = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def lowerCAmelCase__ ( self: List[Any] , UpperCamelCase_: Optional[int] , UpperCamelCase_: Tuple , UpperCamelCase_: List[Any] , UpperCamelCase_: Optional[int] , UpperCamelCase_: Optional[int] , UpperCamelCase_: int ):
if latents is None:
__lowerCamelCase = randn_tensor(UpperCamelCase_ , generator=UpperCamelCase_ , device=UpperCamelCase_ , dtype=UpperCamelCase_ )
else:
if latents.shape != shape:
raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}' )
__lowerCamelCase = latents.to(UpperCamelCase_ )
__lowerCamelCase = latents * scheduler.init_noise_sigma
return latents
def lowerCAmelCase__ ( self: Optional[int] , UpperCamelCase_: Union[str, Any]=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
__lowerCamelCase = torch.device(F'cuda:{gpu_id}' )
__lowerCamelCase = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(UpperCamelCase_ , UpperCamelCase_ )
def lowerCAmelCase__ ( self: str , UpperCamelCase_: Union[str, Any]=0 ):
if is_accelerate_available() and is_accelerate_version(""">=""" , """0.17.0.dev0""" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" )
__lowerCamelCase = torch.device(F'cuda:{gpu_id}' )
if self.device.type != "cpu":
self.to("""cpu""" , silence_dtype_warnings=UpperCamelCase_ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
__lowerCamelCase = None
for cpu_offloaded_model in [self.unet, self.movq]:
__lowerCamelCase, __lowerCamelCase = cpu_offload_with_hook(UpperCamelCase_ , UpperCamelCase_ , prev_module_hook=UpperCamelCase_ )
# We'll offload the last model manually.
__lowerCamelCase = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def lowerCAmelCase__ ( self: Any ):
if not hasattr(self.unet , """_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(UpperCamelCase_ , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(UpperCamelCase_ )
def __call__( self: Optional[Any] , UpperCamelCase_: Union[torch.FloatTensor, List[torch.FloatTensor]] , UpperCamelCase_: Union[torch.FloatTensor, List[torch.FloatTensor]] , UpperCamelCase_: int = 5_12 , UpperCamelCase_: int = 5_12 , UpperCamelCase_: int = 1_00 , UpperCamelCase_: float = 4.0 , UpperCamelCase_: int = 1 , UpperCamelCase_: Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCamelCase_: Optional[torch.FloatTensor] = None , UpperCamelCase_: Optional[str] = "pil" , UpperCamelCase_: bool = True , ):
__lowerCamelCase = self._execution_device
__lowerCamelCase = guidance_scale > 1.0
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
__lowerCamelCase = torch.cat(UpperCamelCase_ , dim=0 )
__lowerCamelCase = image_embeds.shape[0] * num_images_per_prompt
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
__lowerCamelCase = torch.cat(UpperCamelCase_ , dim=0 )
if do_classifier_free_guidance:
__lowerCamelCase = image_embeds.repeat_interleave(UpperCamelCase_ , dim=0 )
__lowerCamelCase = negative_image_embeds.repeat_interleave(UpperCamelCase_ , dim=0 )
__lowerCamelCase = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=UpperCamelCase_ )
self.scheduler.set_timesteps(UpperCamelCase_ , device=UpperCamelCase_ )
__lowerCamelCase = self.scheduler.timesteps
__lowerCamelCase = self.unet.config.in_channels
__lowerCamelCase, __lowerCamelCase = downscale_height_and_width(UpperCamelCase_ , UpperCamelCase_ , self.movq_scale_factor )
# create initial latent
__lowerCamelCase = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , self.scheduler , )
for i, t in enumerate(self.progress_bar(UpperCamelCase_ ) ):
# expand the latents if we are doing classifier free guidance
__lowerCamelCase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
__lowerCamelCase = {"""image_embeds""": image_embeds}
__lowerCamelCase = self.unet(
sample=UpperCamelCase_ , timestep=UpperCamelCase_ , encoder_hidden_states=UpperCamelCase_ , added_cond_kwargs=UpperCamelCase_ , return_dict=UpperCamelCase_ , )[0]
if do_classifier_free_guidance:
__lowerCamelCase, __lowerCamelCase = noise_pred.split(latents.shape[1] , dim=1 )
__lowerCamelCase, __lowerCamelCase = noise_pred.chunk(2 )
__lowerCamelCase, __lowerCamelCase = variance_pred.chunk(2 )
__lowerCamelCase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
__lowerCamelCase = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , """variance_type""" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
__lowerCamelCase, __lowerCamelCase = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
__lowerCamelCase = self.scheduler.step(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , generator=UpperCamelCase_ , )[0]
# post-processing
__lowerCamelCase = self.movq.decode(UpperCamelCase_ , force_not_quantize=UpperCamelCase_ )["""sample"""]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}' )
if output_type in ["np", "pil"]:
__lowerCamelCase = image * 0.5 + 0.5
__lowerCamelCase = image.clamp(0 , 1 )
__lowerCamelCase = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
__lowerCamelCase = self.numpy_to_pil(UpperCamelCase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=UpperCamelCase_ )
| 29 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCamelCase__ ( A__ : Tuple , A__ : Optional[int]=0.999 , A__ : Any="cosine" , ):
'''simple docstring'''
if alpha_transform_type == "cosine":
def alpha_bar_fn(A__ : Any ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(A__ : Optional[int] ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}' )
__lowerCamelCase = []
for i in range(A__ ):
__lowerCamelCase = i / num_diffusion_timesteps
__lowerCamelCase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(A__ ) / alpha_bar_fn(A__ ) , A__ ) )
return torch.tensor(A__ , dtype=torch.floataa )
class lowerCamelCase__( __lowerCamelCase , __lowerCamelCase):
UpperCAmelCase__ : List[str] = [e.name for e in KarrasDiffusionSchedulers]
UpperCAmelCase__ : Any = 2
@register_to_config
def __init__( self: List[str] , UpperCamelCase_: int = 10_00 , UpperCamelCase_: float = 0.0_0085 , UpperCamelCase_: float = 0.012 , UpperCamelCase_: str = "linear" , UpperCamelCase_: Optional[Union[np.ndarray, List[float]]] = None , UpperCamelCase_: str = "epsilon" , UpperCamelCase_: str = "linspace" , UpperCamelCase_: int = 0 , ):
if trained_betas is not None:
__lowerCamelCase = torch.tensor(UpperCamelCase_ , dtype=torch.floataa )
elif beta_schedule == "linear":
__lowerCamelCase = torch.linspace(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__lowerCamelCase = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , UpperCamelCase_ , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
__lowerCamelCase = betas_for_alpha_bar(UpperCamelCase_ )
else:
raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}' )
__lowerCamelCase = 1.0 - self.betas
__lowerCamelCase = torch.cumprod(self.alphas , dim=0 )
# set all values
self.set_timesteps(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
def lowerCAmelCase__ ( self: int , UpperCamelCase_: List[str] , UpperCamelCase_: Optional[Any]=None ):
if schedule_timesteps is None:
__lowerCamelCase = self.timesteps
__lowerCamelCase = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
__lowerCamelCase = 1 if len(UpperCamelCase_ ) > 1 else 0
else:
__lowerCamelCase = timestep.cpu().item() if torch.is_tensor(UpperCamelCase_ ) else timestep
__lowerCamelCase = self._index_counter[timestep_int]
return indices[pos].item()
@property
def lowerCAmelCase__ ( self: Optional[int] ):
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def lowerCAmelCase__ ( self: List[str] , UpperCamelCase_: torch.FloatTensor , UpperCamelCase_: Union[float, torch.FloatTensor] , ):
__lowerCamelCase = self.index_for_timestep(UpperCamelCase_ )
if self.state_in_first_order:
__lowerCamelCase = self.sigmas[step_index]
else:
__lowerCamelCase = self.sigmas_interpol[step_index]
__lowerCamelCase = sample / ((sigma**2 + 1) ** 0.5)
return sample
def lowerCAmelCase__ ( self: Optional[Any] , UpperCamelCase_: int , UpperCamelCase_: Union[str, torch.device] = None , UpperCamelCase_: Optional[int] = None , ):
__lowerCamelCase = num_inference_steps
__lowerCamelCase = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
__lowerCamelCase = np.linspace(0 , num_train_timesteps - 1 , UpperCamelCase_ , dtype=UpperCamelCase_ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
__lowerCamelCase = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
__lowerCamelCase = (np.arange(0 , UpperCamelCase_ ) * step_ratio).round()[::-1].copy().astype(UpperCamelCase_ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
__lowerCamelCase = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
__lowerCamelCase = (np.arange(UpperCamelCase_ , 0 , -step_ratio )).round().copy().astype(UpperCamelCase_ )
timesteps -= 1
else:
raise ValueError(
F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.' )
__lowerCamelCase = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
__lowerCamelCase = torch.from_numpy(np.log(UpperCamelCase_ ) ).to(UpperCamelCase_ )
__lowerCamelCase = np.interp(UpperCamelCase_ , np.arange(0 , len(UpperCamelCase_ ) ) , UpperCamelCase_ )
__lowerCamelCase = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
__lowerCamelCase = torch.from_numpy(UpperCamelCase_ ).to(device=UpperCamelCase_ )
# interpolate sigmas
__lowerCamelCase = sigmas.log().lerp(sigmas.roll(1 ).log() , 0.5 ).exp()
__lowerCamelCase = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
__lowerCamelCase = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(UpperCamelCase_ ).startswith("""mps""" ):
# mps does not support float64
__lowerCamelCase = torch.from_numpy(UpperCamelCase_ ).to(UpperCamelCase_ , dtype=torch.floataa )
else:
__lowerCamelCase = torch.from_numpy(UpperCamelCase_ ).to(UpperCamelCase_ )
# interpolate timesteps
__lowerCamelCase = self.sigma_to_t(UpperCamelCase_ ).to(UpperCamelCase_ , dtype=timesteps.dtype )
__lowerCamelCase = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) , dim=-1 ).flatten()
__lowerCamelCase = torch.cat([timesteps[:1], interleaved_timesteps] )
__lowerCamelCase = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
__lowerCamelCase = defaultdict(UpperCamelCase_ )
def lowerCAmelCase__ ( self: Optional[Any] , UpperCamelCase_: str ):
# get log sigma
__lowerCamelCase = sigma.log()
# get distribution
__lowerCamelCase = log_sigma - self.log_sigmas[:, None]
# get sigmas range
__lowerCamelCase = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
__lowerCamelCase = low_idx + 1
__lowerCamelCase = self.log_sigmas[low_idx]
__lowerCamelCase = self.log_sigmas[high_idx]
# interpolate sigmas
__lowerCamelCase = (low - log_sigma) / (low - high)
__lowerCamelCase = w.clamp(0 , 1 )
# transform interpolation to time range
__lowerCamelCase = (1 - w) * low_idx + w * high_idx
__lowerCamelCase = t.view(sigma.shape )
return t
@property
def lowerCAmelCase__ ( self: Dict ):
return self.sample is None
def lowerCAmelCase__ ( self: List[Any] , UpperCamelCase_: Union[torch.FloatTensor, np.ndarray] , UpperCamelCase_: Union[float, torch.FloatTensor] , UpperCamelCase_: Union[torch.FloatTensor, np.ndarray] , UpperCamelCase_: bool = True , ):
__lowerCamelCase = self.index_for_timestep(UpperCamelCase_ )
# advance index counter by 1
__lowerCamelCase = timestep.cpu().item() if torch.is_tensor(UpperCamelCase_ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
__lowerCamelCase = self.sigmas[step_index]
__lowerCamelCase = self.sigmas_interpol[step_index + 1]
__lowerCamelCase = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
__lowerCamelCase = self.sigmas[step_index - 1]
__lowerCamelCase = self.sigmas_interpol[step_index]
__lowerCamelCase = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
__lowerCamelCase = 0
__lowerCamelCase = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
__lowerCamelCase = sigma_hat if self.state_in_first_order else sigma_interpol
__lowerCamelCase = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
__lowerCamelCase = sigma_hat if self.state_in_first_order else sigma_interpol
__lowerCamelCase = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError("""prediction_type not implemented yet: sample""" )
else:
raise ValueError(
F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`' )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
__lowerCamelCase = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
__lowerCamelCase = sigma_interpol - sigma_hat
# store for 2nd order step
__lowerCamelCase = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
__lowerCamelCase = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
__lowerCamelCase = sigma_next - sigma_hat
__lowerCamelCase = self.sample
__lowerCamelCase = None
__lowerCamelCase = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=UpperCamelCase_ )
def lowerCAmelCase__ ( self: List[str] , UpperCamelCase_: torch.FloatTensor , UpperCamelCase_: torch.FloatTensor , UpperCamelCase_: torch.FloatTensor , ):
# Make sure sigmas and timesteps have the same device and dtype as original_samples
__lowerCamelCase = self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(UpperCamelCase_ ):
# mps does not support float64
__lowerCamelCase = self.timesteps.to(original_samples.device , dtype=torch.floataa )
__lowerCamelCase = timesteps.to(original_samples.device , dtype=torch.floataa )
else:
__lowerCamelCase = self.timesteps.to(original_samples.device )
__lowerCamelCase = timesteps.to(original_samples.device )
__lowerCamelCase = [self.index_for_timestep(UpperCamelCase_ , UpperCamelCase_ ) for t in timesteps]
__lowerCamelCase = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
__lowerCamelCase = sigma.unsqueeze(-1 )
__lowerCamelCase = original_samples + noise * sigma
return noisy_samples
def __len__( self: Tuple ):
return self.config.num_train_timesteps
| 29 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : str = logging.get_logger(__name__)
snake_case : Optional[int] = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
'''microsoft/markuplm-large''': '''https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json''',
}
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = 'markuplm'
def __init__( self , _lowerCamelCase=3_0522 , _lowerCamelCase=768 , _lowerCamelCase=12 , _lowerCamelCase=12 , _lowerCamelCase=3072 , _lowerCamelCase="gelu" , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase=512 , _lowerCamelCase=2 , _lowerCamelCase=0.02 , _lowerCamelCase=1e-12 , _lowerCamelCase=0 , _lowerCamelCase=0 , _lowerCamelCase=2 , _lowerCamelCase=256 , _lowerCamelCase=1024 , _lowerCamelCase=216 , _lowerCamelCase=1001 , _lowerCamelCase=32 , _lowerCamelCase=50 , _lowerCamelCase="absolute" , _lowerCamelCase=True , _lowerCamelCase=None , **_lowerCamelCase , ):
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , **_lowerCamelCase , )
a :Optional[Any] = vocab_size
a :int = hidden_size
a :List[Any] = num_hidden_layers
a :str = num_attention_heads
a :Tuple = hidden_act
a :Any = intermediate_size
a :Optional[int] = hidden_dropout_prob
a :Optional[Any] = attention_probs_dropout_prob
a :Any = max_position_embeddings
a :Union[str, Any] = type_vocab_size
a :Optional[int] = initializer_range
a :Any = layer_norm_eps
a :Any = position_embedding_type
a :Optional[Any] = use_cache
a :Optional[Any] = classifier_dropout
# additional properties
a :Optional[int] = max_depth
a :int = max_xpath_tag_unit_embeddings
a :Optional[Any] = max_xpath_subs_unit_embeddings
a :Union[str, Any] = tag_pad_id
a :str = subs_pad_id
a :List[str] = xpath_unit_hidden_size
| 94 |
"""simple docstring"""
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
lowerCamelCase_ : Tuple = logging.get_logger(__name__)
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : Optional[Any] = R'\w+[.]\d+'
A_ : int = re.findall(_UpperCAmelCase , _UpperCAmelCase )
for pat in pats:
A_ : Optional[int] = key.replace(_UpperCAmelCase , '_'.join(pat.split('.' ) ) )
return key
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : List[Any] = pt_tuple_key[:-1] + ('scale',)
if (
any('norm' in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
A_ : Union[str, Any] = pt_tuple_key[:-1] + ('scale',)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
A_ : List[str] = pt_tuple_key[:-1] + ('scale',)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
A_ : Optional[Any] = pt_tuple_key[:-1] + ('embedding',)
return renamed_pt_tuple_key, pt_tensor
# conv layer
A_ : int = pt_tuple_key[:-1] + ('kernel',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
A_ : str = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
A_ : Optional[Any] = pt_tuple_key[:-1] + ('kernel',)
if pt_tuple_key[-1] == "weight":
A_ : Optional[Any] = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
A_ : Tuple = pt_tuple_key[:-1] + ('weight',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
A_ : Optional[int] = pt_tuple_key[:-1] + ('bias',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=42 ):
"""simple docstring"""
A_ : int = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
A_ : Union[str, Any] = flax_model.init_weights(PRNGKey(_UpperCAmelCase ) )
A_ : Optional[Any] = flatten_dict(_UpperCAmelCase )
A_ : Tuple = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
A_ : Any = rename_key(_UpperCAmelCase )
A_ : List[str] = tuple(renamed_pt_key.split('.' ) )
# Correctly rename weight parameters
A_ , A_ : Union[str, Any] = rename_key_and_reshape_tensor(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
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}.""" )
# also add unexpected weight so that warning is thrown
A_ : str = jnp.asarray(_UpperCAmelCase )
return unflatten_dict(_UpperCAmelCase )
| 286 | 0 |
'''simple docstring'''
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask
lowerCamelCase :Tuple = logging.getLogger(__name__)
class _lowerCAmelCase ( __UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = 'token-classification'
def __init__(self , lowercase ):
if type(lowercase ) == dict:
A_ : Optional[int] = Namespace(**lowercase )
A_ : Dict = import_module("""tasks""" )
try:
A_ : str = getattr(lowercase , hparams.task_type )
A_ : TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
F'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
F'Available tasks classes are: {TokenClassificationTask.__subclasses__()}' )
A_ : Any = self.token_classification_task.get_labels(hparams.labels )
A_ : str = CrossEntropyLoss().ignore_index
super().__init__(lowercase , len(self.labels ) , self.mode )
def _a (self , **lowercase ):
return self.model(**lowercase )
def _a (self , lowercase , lowercase ):
A_ : Optional[Any] = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
A_ : List[str] = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
A_ : List[Any] = self(**lowercase )
A_ : Optional[int] = outputs[0]
# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
return {"loss": loss}
def _a (self ):
A_ : int = self.hparams
for mode in ["train", "dev", "test"]:
A_ : List[Any] = self._feature_file(lowercase )
if os.path.exists(lowercase ) and not args.overwrite_cache:
logger.info("""Loading features from cached file %s""" , lowercase )
A_ : Tuple = torch.load(lowercase )
else:
logger.info("""Creating features from dataset file at %s""" , args.data_dir )
A_ : Optional[Any] = self.token_classification_task.read_examples_from_file(args.data_dir , lowercase )
A_ : Tuple = self.token_classification_task.convert_examples_to_features(
lowercase , self.labels , args.max_seq_length , self.tokenizer , cls_token_at_end=bool(self.config.model_type in ["""xlnet"""] ) , cls_token=self.tokenizer.cls_token , cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0 , sep_token=self.tokenizer.sep_token , sep_token_extra=lowercase , pad_on_left=bool(self.config.model_type in ["""xlnet"""] ) , pad_token=self.tokenizer.pad_token_id , pad_token_segment_id=self.tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info("""Saving features into cached file %s""" , lowercase )
torch.save(lowercase , lowercase )
def _a (self , lowercase , lowercase , lowercase = False ):
A_ : List[Any] = self._feature_file(lowercase )
logger.info("""Loading features from cached file %s""" , lowercase )
A_ : int = torch.load(lowercase )
A_ : str = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
A_ : Optional[int] = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
if features[0].token_type_ids is not None:
A_ : Tuple = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
else:
A_ : Dict = torch.tensor([0 for f in features] , dtype=torch.long )
# HACK(we will not use this anymore soon)
A_ : Tuple = torch.tensor([f.label_ids for f in features] , dtype=torch.long )
return DataLoader(
TensorDataset(lowercase , lowercase , lowercase , lowercase ) , batch_size=lowercase )
def _a (self , lowercase , lowercase ):
"""Compute validation""" ""
A_ : Optional[int] = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
A_ : int = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
A_ : List[str] = self(**lowercase )
A_ : List[Any] = outputs[:2]
A_ : Tuple = logits.detach().cpu().numpy()
A_ : List[Any] = inputs["""labels"""].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _a (self , lowercase ):
A_ : Union[str, Any] = torch.stack([x["""val_loss"""] for x in outputs] ).mean()
A_ : Any = np.concatenate([x["""pred"""] for x in outputs] , axis=0 )
A_ : Optional[int] = np.argmax(lowercase , axis=2 )
A_ : int = np.concatenate([x["""target"""] for x in outputs] , axis=0 )
A_ : Dict = dict(enumerate(self.labels ) )
A_ : Tuple = [[] for _ in range(out_label_ids.shape[0] )]
A_ : int = [[] for _ in range(out_label_ids.shape[0] )]
for i in range(out_label_ids.shape[0] ):
for j in range(out_label_ids.shape[1] ):
if out_label_ids[i, j] != self.pad_token_label_id:
out_label_list[i].append(label_map[out_label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
A_ : Tuple = {
"""val_loss""": val_loss_mean,
"""accuracy_score""": accuracy_score(lowercase , lowercase ),
"""precision""": precision_score(lowercase , lowercase ),
"""recall""": recall_score(lowercase , lowercase ),
"""f1""": fa_score(lowercase , lowercase ),
}
A_ : Optional[Any] = dict(results.items() )
A_ : Optional[int] = results
return ret, preds_list, out_label_list
def _a (self , lowercase ):
# when stable
A_ : Optional[Any] = self._eval_end(lowercase )
A_ : str = ret["""log"""]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _a (self , lowercase ):
# updating to test_epoch_end instead of deprecated test_end
A_ : List[str] = self._eval_end(lowercase )
# Converting to the dict required by pl
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
# pytorch_lightning/trainer/logging.py#L139
A_ : Dict = ret["""log"""]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def _a (lowercase , lowercase ):
# Add NER specific options
BaseTransformer.add_model_specific_args(lowercase , lowercase )
parser.add_argument(
"""--task_type""" , default="""NER""" , type=lowercase , help="""Task type to fine tune in training (e.g. NER, POS, etc)""" )
parser.add_argument(
"""--max_seq_length""" , default=128 , type=lowercase , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--labels""" , default="""""" , type=lowercase , help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.""" , )
parser.add_argument(
"""--gpus""" , default=0 , type=lowercase , help="""The number of GPUs allocated for this, it is by default 0 meaning none""" , )
parser.add_argument(
"""--overwrite_cache""" , action="""store_true""" , help="""Overwrite the cached training and evaluation sets""" )
return parser
if __name__ == "__main__":
lowerCamelCase :Any = argparse.ArgumentParser()
add_generic_args(parser, os.getcwd())
lowerCamelCase :Optional[int] = NERTransformer.add_model_specific_args(parser, os.getcwd())
lowerCamelCase :List[str] = parser.parse_args()
lowerCamelCase :Union[str, Any] = NERTransformer(args)
lowerCamelCase :Union[str, Any] = generic_train(model, args)
if args.do_predict:
# See https://github.com/huggingface/transformers/issues/3159
# pl use this default format to create a checkpoint:
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
# /pytorch_lightning/callbacks/model_checkpoint.py#L322
lowerCamelCase :str = sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
lowerCamelCase :List[str] = model.load_from_checkpoint(checkpoints[-1])
trainer.test(model)
| 366 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
lowerCamelCase :Tuple = logging.get_logger(__name__)
lowerCamelCase :Optional[int] = {'''vocab_file''': '''spiece.model'''}
lowerCamelCase :int = {
'''vocab_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
}
}
# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase :Tuple = {
'''t5-small''': 5_1_2,
'''t5-base''': 5_1_2,
'''t5-large''': 5_1_2,
'''t5-3b''': 5_1_2,
'''t5-11b''': 5_1_2,
}
lowerCamelCase :str = '''▁'''
class _lowerCAmelCase ( __UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : Any = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['input_ids', 'attention_mask']
def __init__(self , lowercase , lowercase="</s>" , lowercase="<unk>" , lowercase="<pad>" , lowercase=100 , lowercase=None , lowercase = None , lowercase=True , **lowercase , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
A_ : Any = [F'<extra_id_{i}>' for i in range(lowercase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
A_ : Tuple = len(set(filter(lambda lowercase : bool("""extra_id""" in str(lowercase ) ) , lowercase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
""" provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
""" tokens""" )
if legacy:
logger.warning_once(
F'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
""" read the related pull request available at https://github.com/huggingface/transformers/pull/24565""" )
A_ : str = legacy
A_ : Dict = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=lowercase , unk_token=lowercase , pad_token=lowercase , extra_ids=lowercase , additional_special_tokens=lowercase , sp_model_kwargs=self.sp_model_kwargs , legacy=lowercase , **lowercase , )
A_ : List[str] = vocab_file
A_ : Tuple = extra_ids
A_ : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowercase )
@staticmethod
def _a (lowercase , lowercase , lowercase ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
A_ : Union[str, Any] = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
"""This tokenizer was incorrectly instantiated with a model max length of"""
F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
""" behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
""" `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
F' {pretrained_model_name_or_path} automatically truncating your input to'
F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
""" `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
""" instantiate this tokenizer with `model_max_length` set to your preferred value.""" , lowercase , )
return max_model_length
@property
def _a (self ):
return self.sp_model.get_piece_size() + self._extra_ids
def _a (self ):
A_ : Union[str, Any] = {self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _a (self , lowercase , lowercase = None , lowercase = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase , token_ids_a=lowercase , already_has_special_tokens=lowercase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(lowercase )) + [1]
return ([0] * len(lowercase )) + [1] + ([0] * len(lowercase )) + [1]
def _a (self ):
return list(
set(filter(lambda lowercase : bool(re.search(R"""<extra_id_\d+>""" , lowercase ) ) is not None , self.additional_special_tokens ) ) )
def _a (self ):
return [self._convert_token_to_id(lowercase ) for token in self.get_sentinel_tokens()]
def _a (self , lowercase ):
if len(lowercase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
""" eos tokens being added.""" )
return token_ids
else:
return token_ids + [self.eos_token_id]
def _a (self , lowercase , lowercase = None ):
A_ : Dict = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def _a (self , lowercase , lowercase = None ):
A_ : Optional[Any] = self._add_eos_if_not_present(lowercase )
if token_ids_a is None:
return token_ids_a
else:
A_ : List[Any] = self._add_eos_if_not_present(lowercase )
return token_ids_a + token_ids_a
def __getstate__(self ):
A_ : int = self.__dict__.copy()
A_ : Tuple = None
return state
def __setstate__(self , lowercase ):
A_ : Tuple = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
A_ : Dict = {}
A_ : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _a (self , lowercase , **lowercase ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
A_ : Tuple = SPIECE_UNDERLINE + text.replace(lowercase , """ """ )
return super().tokenize(lowercase , **lowercase )
def _a (self , lowercase , **lowercase ):
if not self.legacy:
A_ : Dict = text.startswith(lowercase )
if is_first:
A_ : str = text[1:]
A_ : Optional[int] = self.sp_model.encode(lowercase , out_type=lowercase )
if not self.legacy and not is_first and not text.startswith(""" """ ) and tokens[0].startswith(lowercase ):
A_ : Optional[int] = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def _a (self , lowercase ):
if token.startswith("""<extra_id_""" ):
A_ : Union[str, Any] = re.match(R"""<extra_id_(\d+)>""" , lowercase )
A_ : str = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(lowercase )
def _a (self , lowercase ):
if index < self.sp_model.get_piece_size():
A_ : List[Any] = self.sp_model.IdToPiece(lowercase )
else:
A_ : Dict = F'<extra_id_{self.vocab_size - 1 - index}>'
return token
def _a (self , lowercase ):
A_ : Union[str, Any] = []
A_ : int = """"""
A_ : Any = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowercase ) + token
A_ : Dict = True
A_ : Union[str, Any] = []
else:
current_sub_tokens.append(lowercase )
A_ : Optional[Any] = False
out_string += self.sp_model.decode(lowercase )
return out_string.strip()
def _a (self , lowercase , lowercase = None ):
if not os.path.isdir(lowercase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
A_ : Optional[Any] = 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 ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowercase )
elif not os.path.isfile(self.vocab_file ):
with open(lowercase , """wb""" ) as fi:
A_ : Union[str, Any] = self.sp_model.serialized_model_proto()
fi.write(lowercase )
return (out_vocab_file,)
| 135 | 0 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = 0 # The first color of the flag.
SCREAMING_SNAKE_CASE_ : int = 1 # The second color of the flag.
SCREAMING_SNAKE_CASE_ : Dict = 2 # The third color of the flag.
SCREAMING_SNAKE_CASE_ : Tuple = (red, white, blue)
def _snake_case ( UpperCAmelCase_ : list ):
if not sequence:
return []
if len(UpperCAmelCase_ ) == 1:
return list(UpperCAmelCase_ )
A__ = 0
A__ = len(UpperCAmelCase_ ) - 1
A__ = 0
while mid <= high:
if sequence[mid] == colors[0]:
A__ , A__ = sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
A__ , A__ = sequence[high], sequence[mid]
high -= 1
else:
A__ = F"""The elements inside the sequence must contains only {colors} values"""
raise ValueError(UpperCAmelCase_ )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = input('Enter numbers separated by commas:\n').strip()
SCREAMING_SNAKE_CASE_ : Tuple = [int(item.strip()) for item in user_input.split(',')]
print(f"""{dutch_national_flag_sort(unsorted)}""")
| 335 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _snake_case ( UpperCAmelCase_ : List[Any] ):
A__ = FileLock(str(tmpdir / """foo.lock""" ) )
A__ = FileLock(str(tmpdir / """foo.lock""" ) )
A__ = 0.01
with locka.acquire():
with pytest.raises(UpperCAmelCase_ ):
A__ = time.time()
locka.acquire(UpperCAmelCase_ )
assert time.time() - _start > timeout
def _snake_case ( UpperCAmelCase_ : List[Any] ):
A__ = """a""" * 1000 + """.lock"""
A__ = FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith(""".lock""" )
assert not locka._lock_file.endswith(UpperCAmelCase_ )
assert len(os.path.basename(locka._lock_file ) ) <= 255
A__ = FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(UpperCAmelCase_ ):
locka.acquire(0 )
| 335 | 1 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
_SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
class A__ ( snake_case__ ):
"""simple docstring"""
def __init__( self , *__snake_case , **__snake_case ):
warnings.warn(
'''The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use PerceiverImageProcessor instead.''' , __snake_case , )
super().__init__(*__snake_case , **__snake_case )
| 362 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Optional[int] = {
"google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}
class A__ ( snake_case__ ):
"""simple docstring"""
__magic_name__ = 'efficientnet'
def __init__( self , __snake_case = 3 , __snake_case = 6_0_0 , __snake_case = 2.0 , __snake_case = 3.1 , __snake_case = 8 , __snake_case = [3, 3, 5, 3, 5, 5, 3] , __snake_case = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2] , __snake_case = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0] , __snake_case = [] , __snake_case = [1, 2, 2, 2, 1, 2, 1] , __snake_case = [1, 2, 2, 3, 3, 4, 1] , __snake_case = [1, 6, 6, 6, 6, 6, 6] , __snake_case = 0.25 , __snake_case = "swish" , __snake_case = 2_5_6_0 , __snake_case = "mean" , __snake_case = 0.02 , __snake_case = 0.001 , __snake_case = 0.99 , __snake_case = 0.5 , __snake_case = 0.2 , **__snake_case , ):
super().__init__(**__snake_case )
snake_case = num_channels
snake_case = image_size
snake_case = width_coefficient
snake_case = depth_coefficient
snake_case = depth_divisor
snake_case = kernel_sizes
snake_case = in_channels
snake_case = out_channels
snake_case = depthwise_padding
snake_case = strides
snake_case = num_block_repeats
snake_case = expand_ratios
snake_case = squeeze_expansion_ratio
snake_case = hidden_act
snake_case = hidden_dim
snake_case = pooling_type
snake_case = initializer_range
snake_case = batch_norm_eps
snake_case = batch_norm_momentum
snake_case = dropout_rate
snake_case = drop_connect_rate
snake_case = sum(__snake_case ) * 4
class A__ ( snake_case__ ):
"""simple docstring"""
__magic_name__ = version.parse('1.11' )
@property
def a_ ( self ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def a_ ( self ):
return 1E-5
| 213 | 0 |
import os
def __snake_case ( __UpperCamelCase : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(__UpperCamelCase ) ,__UpperCamelCase ) ) as input_file:
A_ = [
[int(__UpperCamelCase ) for element in line.split("," )]
for line in input_file.readlines()
]
A_ = len(__UpperCamelCase )
A_ = len(matrix[0] )
A_ = [[-1 for _ in range(__UpperCamelCase )] for _ in range(__UpperCamelCase )]
for i in range(__UpperCamelCase ):
A_ = matrix[i][0]
for j in range(1 ,__UpperCamelCase ):
for i in range(__UpperCamelCase ):
A_ = minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 ,__UpperCamelCase ):
A_ = min(
minimal_path_sums[i][j] ,minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2 ,-1 ,-1 ):
A_ = min(
minimal_path_sums[i][j] ,minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(F"{solution() = }")
| 312 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
__a :Dict = get_logger(__name__)
__a :Union[str, Any] = Path(__file__).parent / 'model_card_template.md'
__a :Tuple = uuida().hex
__a :List[Any] = os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
__a :Union[str, Any] = os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
__a :Tuple = HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'
def __snake_case ( __UpperCamelCase : Union[Dict, str, None] = None ):
"""simple docstring"""
A_ = f'''diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'''
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += f'''; torch/{_torch_version}'''
if is_flax_available():
ua += f'''; jax/{_jax_version}'''
ua += f'''; flax/{_flax_version}'''
if is_onnx_available():
ua += f'''; onnxruntime/{_onnxruntime_version}'''
# CI will set this value to True
if os.environ.get("DIFFUSERS_IS_CI" ,"" ).upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
ua += "; " + "; ".join(f'''{k}/{v}''' for k, v in user_agent.items() )
elif isinstance(__UpperCamelCase ,__UpperCamelCase ):
ua += "; " + user_agent
return ua
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : Optional[str] = None ,__UpperCamelCase : Optional[str] = None ):
"""simple docstring"""
if token is None:
A_ = HfFolder.get_token()
if organization is None:
A_ = whoami(__UpperCamelCase )["name"]
return f'''{username}/{model_id}'''
else:
return f'''{organization}/{model_id}'''
def __snake_case ( __UpperCamelCase : Tuple ,__UpperCamelCase : Union[str, Any] ):
"""simple docstring"""
if not is_jinja_available():
raise ValueError(
"Modelcard rendering is based on Jinja templates."
" Please make sure to have `jinja` installed before using `create_model_card`."
" To install it, please run `pip install Jinja2`." )
if hasattr(__UpperCamelCase ,"local_rank" ) and args.local_rank not in [-1, 0]:
return
A_ = args.hub_token if hasattr(__UpperCamelCase ,"hub_token" ) else None
A_ = get_full_repo_name(__UpperCamelCase ,token=__UpperCamelCase )
A_ = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language="en" ,license="apache-2.0" ,library_name="diffusers" ,tags=[] ,datasets=args.dataset_name ,metrics=[] ,) ,template_path=__UpperCamelCase ,model_name=__UpperCamelCase ,repo_name=__UpperCamelCase ,dataset_name=args.dataset_name if hasattr(__UpperCamelCase ,"dataset_name" ) else None ,learning_rate=args.learning_rate ,train_batch_size=args.train_batch_size ,eval_batch_size=args.eval_batch_size ,gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(__UpperCamelCase ,"gradient_accumulation_steps" ) else None
) ,adam_betaa=args.adam_betaa if hasattr(__UpperCamelCase ,"adam_beta1" ) else None ,adam_betaa=args.adam_betaa if hasattr(__UpperCamelCase ,"adam_beta2" ) else None ,adam_weight_decay=args.adam_weight_decay if hasattr(__UpperCamelCase ,"adam_weight_decay" ) else None ,adam_epsilon=args.adam_epsilon if hasattr(__UpperCamelCase ,"adam_epsilon" ) else None ,lr_scheduler=args.lr_scheduler if hasattr(__UpperCamelCase ,"lr_scheduler" ) else None ,lr_warmup_steps=args.lr_warmup_steps if hasattr(__UpperCamelCase ,"lr_warmup_steps" ) else None ,ema_inv_gamma=args.ema_inv_gamma if hasattr(__UpperCamelCase ,"ema_inv_gamma" ) else None ,ema_power=args.ema_power if hasattr(__UpperCamelCase ,"ema_power" ) else None ,ema_max_decay=args.ema_max_decay if hasattr(__UpperCamelCase ,"ema_max_decay" ) else None ,mixed_precision=args.mixed_precision ,)
A_ = os.path.join(args.output_dir ,"README.md" )
model_card.save(__UpperCamelCase )
def __snake_case ( __UpperCamelCase : Optional[str] ,__UpperCamelCase : Optional[str] = None ):
"""simple docstring"""
if resolved_file is None or commit_hash is not None:
return commit_hash
A_ = str(Path(__UpperCamelCase ).as_posix() )
A_ = re.search(R"snapshots/([^/]+)/" ,__UpperCamelCase )
if search is None:
return None
A_ = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(__UpperCamelCase ) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
__a :str = os.path.expanduser(
os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
__a :List[Any] = os.path.join(hf_cache_home, 'diffusers')
def __snake_case ( __UpperCamelCase : Optional[str] = None ,__UpperCamelCase : Optional[str] = None ):
"""simple docstring"""
if new_cache_dir is None:
A_ = DIFFUSERS_CACHE
if old_cache_dir is None:
A_ = old_diffusers_cache
A_ = Path(__UpperCamelCase ).expanduser()
A_ = Path(__UpperCamelCase ).expanduser()
for old_blob_path in old_cache_dir.glob("**/blobs/*" ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
A_ = new_cache_dir / old_blob_path.relative_to(__UpperCamelCase )
new_blob_path.parent.mkdir(parents=__UpperCamelCase ,exist_ok=__UpperCamelCase )
os.replace(__UpperCamelCase ,__UpperCamelCase )
try:
os.symlink(__UpperCamelCase ,__UpperCamelCase )
except OSError:
logger.warning(
"Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded." )
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
__a :Dict = os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
__a :Optional[int] = 0
else:
with open(cache_version_file) as f:
try:
__a :Dict = int(f.read())
except ValueError:
__a :str = 0
if cache_version < 1:
__a :Optional[Any] = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
'The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '
'existing cached models. This is a one-time operation, you can interrupt it or run it '
'later by calling `diffusers.utils.hub_utils.move_cache()`.'
)
try:
move_cache()
except Exception as e:
__a :Optional[Any] = '\n'.join(traceback.format_tb(e.__traceback__))
logger.error(
F"There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease "
'file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '
'message and we will do our best to help.'
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, 'w') as f:
f.write('1')
except Exception:
logger.warning(
F"There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure "
'the directory exists and can be written to.'
)
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : Optional[str] = None ):
"""simple docstring"""
if variant is not None:
A_ = weights_name.split("." )
A_ = splits[:-1] + [variant] + splits[-1:]
A_ = ".".join(__UpperCamelCase )
return weights_name
def __snake_case ( __UpperCamelCase : Optional[Any] ,*,
__UpperCamelCase : Union[str, Any] ,__UpperCamelCase : Any ,__UpperCamelCase : Tuple ,__UpperCamelCase : Union[str, Any] ,__UpperCamelCase : str ,__UpperCamelCase : int ,__UpperCamelCase : Union[str, Any] ,__UpperCamelCase : int ,__UpperCamelCase : Optional[int] ,__UpperCamelCase : Tuple ,__UpperCamelCase : Optional[int]=None ,):
"""simple docstring"""
A_ = str(__UpperCamelCase )
if os.path.isfile(__UpperCamelCase ):
return pretrained_model_name_or_path
elif os.path.isdir(__UpperCamelCase ):
if os.path.isfile(os.path.join(__UpperCamelCase ,__UpperCamelCase ) ):
# Load from a PyTorch checkpoint
A_ = os.path.join(__UpperCamelCase ,__UpperCamelCase )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) ):
A_ = os.path.join(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
return model_file
else:
raise EnvironmentError(
f'''Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.''' )
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(__UpperCamelCase ).base_version ) >= version.parse("0.20.0" )
):
try:
A_ = hf_hub_download(
__UpperCamelCase ,filename=_add_variant(__UpperCamelCase ,__UpperCamelCase ) ,cache_dir=__UpperCamelCase ,force_download=__UpperCamelCase ,proxies=__UpperCamelCase ,resume_download=__UpperCamelCase ,local_files_only=__UpperCamelCase ,use_auth_token=__UpperCamelCase ,user_agent=__UpperCamelCase ,subfolder=__UpperCamelCase ,revision=revision or commit_hash ,)
warnings.warn(
f'''Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.''' ,__UpperCamelCase ,)
return model_file
except: # noqa: E722
warnings.warn(
f'''You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(__UpperCamelCase ,__UpperCamelCase )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(__UpperCamelCase ,__UpperCamelCase )}\' so that the correct variant file can be added.''' ,__UpperCamelCase ,)
try:
# 2. Load model file as usual
A_ = hf_hub_download(
__UpperCamelCase ,filename=__UpperCamelCase ,cache_dir=__UpperCamelCase ,force_download=__UpperCamelCase ,proxies=__UpperCamelCase ,resume_download=__UpperCamelCase ,local_files_only=__UpperCamelCase ,use_auth_token=__UpperCamelCase ,user_agent=__UpperCamelCase ,subfolder=__UpperCamelCase ,revision=revision or commit_hash ,)
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
f'''{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '''
"listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
"token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
"login`." )
except RevisionNotFoundError:
raise EnvironmentError(
f'''{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '''
"this model name. Check the model page at "
f'''\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.''' )
except EntryNotFoundError:
raise EnvironmentError(
f'''{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.''' )
except HTTPError as err:
raise EnvironmentError(
f'''There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}''' )
except ValueError:
raise EnvironmentError(
f'''We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'''
f''' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'''
f''' directory containing a file named {weights_name} or'''
" \nCheckout your internet connection or see how to run the library in"
" offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'." )
except EnvironmentError:
raise EnvironmentError(
f'''Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '''
"'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
f'''Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '''
f'''containing a file named {weights_name}''' )
| 312 | 1 |
"""simple docstring"""
import numpy as np
def snake_case_ ( A_ : Optional[int], A_ : Optional[int], A_ : Optional[int], A_ : Optional[Any], A_ : int ):
'''simple docstring'''
_lowerCamelCase : int = int(np.ceil((x_end - xa) / h ) )
_lowerCamelCase : Tuple = np.zeros((n + 1,) )
_lowerCamelCase : Union[str, Any] = ya
_lowerCamelCase : Union[str, Any] = xa
for k in range(A_ ):
_lowerCamelCase : Tuple = f(A_, y[k] )
_lowerCamelCase : List[str] = f(x + 0.5 * h, y[k] + 0.5 * h * ka )
_lowerCamelCase : str = f(x + 0.5 * h, y[k] + 0.5 * h * ka )
_lowerCamelCase : Dict = f(x + h, y[k] + h * ka )
_lowerCamelCase : int = y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 175 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( _lowercase , unittest.TestCase):
snake_case__ : Optional[int] = BlenderbotSmallTokenizer
snake_case__ : List[str] = False
def SCREAMING_SNAKE_CASE ( self : Dict ):
"""simple docstring"""
super().setUp()
_lowerCamelCase : str = ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
_lowerCamelCase : Any = dict(zip(__lowerCAmelCase , range(len(__lowerCAmelCase ) ) ) )
_lowerCamelCase : Any = ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
_lowerCamelCase : List[str] = {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}
_lowerCamelCase : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
_lowerCamelCase : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__lowerCAmelCase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__lowerCAmelCase ) )
def SCREAMING_SNAKE_CASE ( self : Tuple , **__lowerCAmelCase : List[str] ):
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname , **__lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[Any] , __lowerCAmelCase : List[str] ):
"""simple docstring"""
_lowerCamelCase : int = '''adapt act apte'''
_lowerCamelCase : Tuple = '''adapt act apte'''
return input_text, output_text
def SCREAMING_SNAKE_CASE ( self : int ):
"""simple docstring"""
_lowerCamelCase : Tuple = BlenderbotSmallTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_lowerCamelCase : int = '''adapt act apte'''
_lowerCamelCase : Optional[Any] = ['''adapt''', '''act''', '''ap@@''', '''te''']
_lowerCamelCase : Optional[Any] = tokenizer.tokenize(__lowerCAmelCase )
self.assertListEqual(__lowerCAmelCase , __lowerCAmelCase )
_lowerCamelCase : List[Any] = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
_lowerCamelCase : Dict = [0, 1, 2, 3, 4, 5]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase ) , __lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[str] ):
"""simple docstring"""
_lowerCamelCase : str = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
assert tok('''sam''' ).input_ids == [1_3_8_4]
_lowerCamelCase : List[str] = '''I am a small frog.'''
_lowerCamelCase : str = tok([src_text] , padding=__lowerCAmelCase , truncation=__lowerCAmelCase )['''input_ids''']
_lowerCamelCase : Any = tok.batch_decode(__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase , clean_up_tokenization_spaces=__lowerCAmelCase )[0]
assert src_text != decoded # I wish it did!
assert decoded == "i am a small frog ."
def SCREAMING_SNAKE_CASE ( self : Any ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
_lowerCamelCase : Optional[Any] = '''I am a small frog .'''
_lowerCamelCase : str = '''.'''
_lowerCamelCase : str = tok(__lowerCAmelCase )['''input_ids''']
_lowerCamelCase : Dict = tok(__lowerCAmelCase )['''input_ids''']
assert encoded[-1] == encoded_dot[0]
| 175 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
# General docstring
_SCREAMING_SNAKE_CASE = """MobileNetV1Config"""
# Base docstring
_SCREAMING_SNAKE_CASE = """google/mobilenet_v1_1.0_224"""
_SCREAMING_SNAKE_CASE = [1, 10_24, 7, 7]
# Image classification docstring
_SCREAMING_SNAKE_CASE = """google/mobilenet_v1_1.0_224"""
_SCREAMING_SNAKE_CASE = """tabby, tabby cat"""
_SCREAMING_SNAKE_CASE = [
"""google/mobilenet_v1_1.0_224""",
"""google/mobilenet_v1_0.75_192""",
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a=None ):
snake_case_ : Union[str, Any] = {}
if isinstance(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Optional[Any] = model.mobilenet_va
else:
snake_case_ : Optional[Any] = model
snake_case_ : Optional[int] = '''MobilenetV1/Conv2d_0/'''
snake_case_ : List[str] = backbone.conv_stem.convolution.weight
snake_case_ : Any = backbone.conv_stem.normalization.bias
snake_case_ : List[str] = backbone.conv_stem.normalization.weight
snake_case_ : Dict = backbone.conv_stem.normalization.running_mean
snake_case_ : Optional[int] = backbone.conv_stem.normalization.running_var
for i in range(13 ):
snake_case_ : str = i + 1
snake_case_ : List[Any] = i * 2
snake_case_ : Tuple = backbone.layer[pt_index]
snake_case_ : List[str] = f"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
snake_case_ : int = pointer.convolution.weight
snake_case_ : Tuple = pointer.normalization.bias
snake_case_ : Optional[int] = pointer.normalization.weight
snake_case_ : int = pointer.normalization.running_mean
snake_case_ : List[Any] = pointer.normalization.running_var
snake_case_ : List[Any] = backbone.layer[pt_index + 1]
snake_case_ : Dict = f"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
snake_case_ : List[Any] = pointer.convolution.weight
snake_case_ : Optional[int] = pointer.normalization.bias
snake_case_ : List[str] = pointer.normalization.weight
snake_case_ : Union[str, Any] = pointer.normalization.running_mean
snake_case_ : Optional[Any] = pointer.normalization.running_var
if isinstance(_UpperCamelCase , _UpperCamelCase ):
snake_case_ : Optional[int] = '''MobilenetV1/Logits/Conv2d_1c_1x1/'''
snake_case_ : int = model.classifier.weight
snake_case_ : Tuple = model.classifier.bias
return tf_to_pt_map
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ):
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '
'https://www.tensorflow.org/install/ for installation instructions.' )
raise
# Load weights from TF model
snake_case_ : List[str] = tf.train.list_variables(_UpperCamelCase )
snake_case_ : Dict = {}
for name, shape in init_vars:
logger.info(f"""Loading TF weight {name} with shape {shape}""" )
snake_case_ : Dict = tf.train.load_variable(_UpperCamelCase , _UpperCamelCase )
snake_case_ : Optional[int] = array
# Build TF to PyTorch weights loading map
snake_case_ : str = _build_tf_to_pytorch_map(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
for name, pointer in tf_to_pt_map.items():
logger.info(f"""Importing {name}""" )
if name not in tf_weights:
logger.info(f"""{name} not in tf pre-trained weights, skipping""" )
continue
snake_case_ : str = tf_weights[name]
if "depthwise_weights" in name:
logger.info('Transposing depthwise' )
snake_case_ : List[str] = np.transpose(_UpperCamelCase , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('Transposing' )
if len(pointer.shape ) == 2: # copying into linear layer
snake_case_ : int = array.squeeze().transpose()
else:
snake_case_ : Union[str, Any] = np.transpose(_UpperCamelCase , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(f"""Pointer shape {pointer.shape} and array shape {array.shape} mismatched""" )
logger.info(f"""Initialize PyTorch weight {name} {array.shape}""" )
snake_case_ : Any = torch.from_numpy(_UpperCamelCase )
tf_weights.pop(_UpperCamelCase , _UpperCamelCase )
tf_weights.pop(name + '/RMSProp' , _UpperCamelCase )
tf_weights.pop(name + '/RMSProp_1' , _UpperCamelCase )
tf_weights.pop(name + '/ExponentialMovingAverage' , _UpperCamelCase )
logger.info(f"""Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}""" )
return model
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : Any = features.shape[-2:]
snake_case_ : str = conv_layer.stride
snake_case_ : List[Any] = conv_layer.kernel_size
if in_height % stride_height == 0:
snake_case_ : Tuple = max(kernel_height - stride_height , 0 )
else:
snake_case_ : Union[str, Any] = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
snake_case_ : Optional[int] = max(kernel_width - stride_width , 0 )
else:
snake_case_ : Dict = max(kernel_width - (in_width % stride_width) , 0 )
snake_case_ : Union[str, Any] = pad_along_width // 2
snake_case_ : Optional[Any] = pad_along_width - pad_left
snake_case_ : Optional[Any] = pad_along_height // 2
snake_case_ : Union[str, Any] = pad_along_height - pad_top
snake_case_ : Tuple = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(_UpperCamelCase , _UpperCamelCase , 'constant' , 0.0 )
class SCREAMING_SNAKE_CASE_ ( nn.Module ):
def __init__( self : Any , _A : Dict , _A : List[Any] , _A : List[str] , _A : List[Any] , _A : Optional[int] = 1 , _A : List[Any] = 1 , _A : Tuple = False , _A : Optional[int] = True , _A : Optional[Any] = True , ) -> None:
"""simple docstring"""
super().__init__()
snake_case_ : Optional[Any] = config
if in_channels % groups != 0:
raise ValueError(F"""Input channels ({in_channels}) are not divisible by {groups} groups.""" )
if out_channels % groups != 0:
raise ValueError(F"""Output channels ({out_channels}) are not divisible by {groups} groups.""" )
snake_case_ : List[str] = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
snake_case_ : List[str] = nn.Convad(
in_channels=_A , out_channels=_A , kernel_size=_A , stride=_A , padding=_A , groups=_A , bias=_A , padding_mode='zeros' , )
if use_normalization:
snake_case_ : Tuple = nn.BatchNormad(
num_features=_A , eps=config.layer_norm_eps , momentum=0.9_9_9_7 , affine=_A , track_running_stats=_A , )
else:
snake_case_ : str = None
if use_activation:
if isinstance(_A , _A ):
snake_case_ : Optional[Any] = ACTaFN[use_activation]
elif isinstance(config.hidden_act , _A ):
snake_case_ : Any = ACTaFN[config.hidden_act]
else:
snake_case_ : List[str] = config.hidden_act
else:
snake_case_ : Any = None
def UpperCAmelCase_ ( self : Dict , _A : Any ) -> torch.Tensor:
"""simple docstring"""
if self.config.tf_padding:
snake_case_ : Union[str, Any] = apply_tf_padding(_A , self.convolution )
snake_case_ : Union[str, Any] = self.convolution(_A )
if self.normalization is not None:
snake_case_ : int = self.normalization(_A )
if self.activation is not None:
snake_case_ : List[Any] = self.activation(_A )
return features
class SCREAMING_SNAKE_CASE_ ( _a ):
__magic_name__: Tuple = MobileNetVaConfig
__magic_name__: Optional[int] = load_tf_weights_in_mobilenet_va
__magic_name__: int = '''mobilenet_v1'''
__magic_name__: str = '''pixel_values'''
__magic_name__: List[str] = False
def UpperCAmelCase_ ( self : List[str] , _A : Tuple ) -> None:
"""simple docstring"""
if isinstance(_A , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(_A , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
_SCREAMING_SNAKE_CASE = R"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
_SCREAMING_SNAKE_CASE = R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`MobileNetV1ImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare MobileNetV1 model outputting raw hidden-states without any specific head on top." , _a , )
class SCREAMING_SNAKE_CASE_ ( _a ):
def __init__( self : Tuple , _A : Dict , _A : Dict = True ) -> List[Any]:
"""simple docstring"""
super().__init__(_A )
snake_case_ : Union[str, Any] = config
snake_case_ : str = 32
snake_case_ : Optional[int] = max(int(depth * config.depth_multiplier ) , config.min_depth )
snake_case_ : List[str] = MobileNetVaConvLayer(
_A , in_channels=config.num_channels , out_channels=_A , kernel_size=3 , stride=2 , )
snake_case_ : Any = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
snake_case_ : Union[str, Any] = nn.ModuleList()
for i in range(13 ):
snake_case_ : List[str] = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
snake_case_ : Dict = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
_A , in_channels=_A , out_channels=_A , kernel_size=3 , stride=strides[i] , groups=_A , ) )
self.layer.append(
MobileNetVaConvLayer(
_A , in_channels=_A , out_channels=_A , kernel_size=1 , ) )
snake_case_ : List[str] = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def UpperCAmelCase_ ( self : Tuple , _A : Tuple ) -> Dict:
"""simple docstring"""
raise NotImplementedError
@add_start_docstrings_to_model_forward(_A )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=_A , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def UpperCAmelCase_ ( self : Tuple , _A : Optional[int] = None , _A : Any = None , _A : Dict = None , ) -> Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
"""simple docstring"""
snake_case_ : Tuple = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
snake_case_ : Dict = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
snake_case_ : Union[str, Any] = self.conv_stem(_A )
snake_case_ : Union[str, Any] = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
snake_case_ : List[str] = layer_module(_A )
if output_hidden_states:
snake_case_ : Union[str, Any] = all_hidden_states + (hidden_states,)
snake_case_ : Any = hidden_states
if self.pooler is not None:
snake_case_ : Optional[int] = torch.flatten(self.pooler(_A ) , start_dim=1 )
else:
snake_case_ : Tuple = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=_A , pooler_output=_A , hidden_states=_A , )
@add_start_docstrings(
"\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n " , _a , )
class SCREAMING_SNAKE_CASE_ ( _a ):
def __init__( self : Any , _A : Optional[int] ) -> None:
"""simple docstring"""
super().__init__(_A )
snake_case_ : Dict = config.num_labels
snake_case_ : List[str] = MobileNetVaModel(_A )
snake_case_ : Optional[int] = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
snake_case_ : Union[str, Any] = nn.Dropout(config.classifier_dropout_prob , inplace=_A )
snake_case_ : Any = nn.Linear(_A , config.num_labels ) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(_A )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=_A , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def UpperCAmelCase_ ( self : Optional[int] , _A : Optional[Any] = None , _A : List[Any] = None , _A : Optional[Any] = None , _A : List[str] = None , ) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
"""simple docstring"""
snake_case_ : List[Any] = return_dict if return_dict is not None else self.config.use_return_dict
snake_case_ : Any = self.mobilenet_va(_A , output_hidden_states=_A , return_dict=_A )
snake_case_ : Optional[Any] = outputs.pooler_output if return_dict else outputs[1]
snake_case_ : List[Any] = self.classifier(self.dropout(_A ) )
snake_case_ : Optional[Any] = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
snake_case_ : List[str] = '''regression'''
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
snake_case_ : List[str] = '''single_label_classification'''
else:
snake_case_ : Optional[Any] = '''multi_label_classification'''
if self.config.problem_type == "regression":
snake_case_ : Optional[Any] = MSELoss()
if self.num_labels == 1:
snake_case_ : str = loss_fct(logits.squeeze() , labels.squeeze() )
else:
snake_case_ : str = loss_fct(_A , _A )
elif self.config.problem_type == "single_label_classification":
snake_case_ : List[str] = CrossEntropyLoss()
snake_case_ : Union[str, Any] = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
snake_case_ : Tuple = BCEWithLogitsLoss()
snake_case_ : Optional[Any] = loss_fct(_A , _A )
if not return_dict:
snake_case_ : int = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=_A , logits=_A , hidden_states=outputs.hidden_states , )
| 327 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowerCAmelCase_ = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS 5S 9S AC''',
'''KD 6S 9D TH AD''',
'''KS 8D 4D 9S 4S''', # pair
'''8C 4S KH JS 4D''', # pair
'''QH 8H KD JH 8S''', # pair
'''KC 4H KS 2H 8D''', # pair
'''KD 4S KC 3H 8S''', # pair
'''AH 8S AS KC JH''', # pair
'''3H 4C 4H 3S 2H''', # 2 pairs
'''5S 5D 2C KH KH''', # 2 pairs
'''3C KH 5D 5S KH''', # 2 pairs
'''AS 3C KH AD KH''', # 2 pairs
'''7C 7S 3S 7H 5S''', # 3 of a kind
'''7C 7S KH 2H 7H''', # 3 of a kind
'''AC KH QH AH AS''', # 3 of a kind
'''2H 4D 3C AS 5S''', # straight (low ace)
'''3C 5C 4C 2C 6H''', # straight
'''6S 8S 7S 5H 9H''', # straight
'''JS QS 9H TS KH''', # straight
'''QC KH TS JS AH''', # straight (high ace)
'''8C 9C 5C 3C TC''', # flush
'''3S 8S 9S 5S KS''', # flush
'''4C 5C 9C 8C KC''', # flush
'''JH 8H AH KH QH''', # flush
'''3D 2H 3H 2C 2D''', # full house
'''2H 2C 3S 3H 3D''', # full house
'''KH KC 3S 3H 3D''', # full house
'''JC 6H JS JD JH''', # 4 of a kind
'''JC 7H JS JD JH''', # 4 of a kind
'''JC KH JS JD JH''', # 4 of a kind
'''2S AS 4S 5S 3S''', # straight flush (low ace)
'''2D 6D 3D 4D 5D''', # straight flush
'''5C 6C 3C 7C 4C''', # straight flush
'''JH 9H TH KH QH''', # straight flush
'''JH AH TH KH QH''', # royal flush (high ace straight flush)
)
lowerCAmelCase_ = (
('''2H 3H 4H 5H 6H''', '''KS AS TS QS JS''', '''Loss'''),
('''2H 3H 4H 5H 6H''', '''AS AD AC AH JD''', '''Win'''),
('''AS AH 2H AD AC''', '''JS JD JC JH 3D''', '''Win'''),
('''2S AH 2H AS AC''', '''JS JD JC JH AD''', '''Loss'''),
('''2S AH 2H AS AC''', '''2H 3H 5H 6H 7H''', '''Win'''),
('''AS 3S 4S 8S 2S''', '''2H 3H 5H 6H 7H''', '''Win'''),
('''2H 3H 5H 6H 7H''', '''2S 3H 4H 5S 6C''', '''Win'''),
('''2S 3H 4H 5S 6C''', '''3D 4C 5H 6H 2S''', '''Tie'''),
('''2S 3H 4H 5S 6C''', '''AH AC 5H 6H AS''', '''Win'''),
('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H AS''', '''Loss'''),
('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H 7S''', '''Win'''),
('''6S AD 7H 4S AS''', '''AH AC 5H 6H 7S''', '''Loss'''),
('''2S AH 4H 5S KC''', '''AH AC 5H 6H 7S''', '''Loss'''),
('''2S 3H 6H 7S 9C''', '''7H 3C TH 6H 9S''', '''Loss'''),
('''4S 5H 6H TS AC''', '''3S 5H 6H TS AC''', '''Win'''),
('''2S AH 4H 5S 6C''', '''AD 4C 5H 6H 2C''', '''Tie'''),
('''AS AH 3H AD AC''', '''AS AH 2H AD AC''', '''Win'''),
('''AH AC 5H 5C QS''', '''AH AC 5H 5C KS''', '''Loss'''),
('''AH AC 5H 5C QS''', '''KH KC 5H 5C QS''', '''Win'''),
('''7C 7S KH 2H 7H''', '''3C 3S AH 2H 3H''', '''Win'''),
('''3C 3S AH 2H 3H''', '''7C 7S KH 2H 7H''', '''Loss'''),
('''6H 5H 4H 3H 2H''', '''5H 4H 3H 2H AH''', '''Win'''),
('''5H 4H 3H 2H AH''', '''5H 4H 3H 2H AH''', '''Tie'''),
('''5H 4H 3H 2H AH''', '''6H 5H 4H 3H 2H''', '''Loss'''),
('''AH AD KS KC AC''', '''AH KD KH AC KC''', '''Win'''),
('''2H 4D 3C AS 5S''', '''2H 4D 3C 6S 5S''', '''Loss'''),
('''2H 3S 3C 3H 2S''', '''3S 3C 2S 2H 2D''', '''Win'''),
('''4D 6D 5D 2D JH''', '''3S 8S 3H TC KH''', '''Loss'''),
('''4S 6C 8S 3S 7S''', '''AD KS 2D 7D 7C''', '''Loss'''),
('''6S 4C 7H 8C 3H''', '''5H JC AH 9D 9C''', '''Loss'''),
('''9D 9H JH TC QH''', '''3C 2S JS 5C 7H''', '''Win'''),
('''2H TC 8S AD 9S''', '''4H TS 7H 2C 5C''', '''Win'''),
('''9D 3S 2C 7S 7C''', '''JC TD 3C TC 9H''', '''Loss'''),
)
lowerCAmelCase_ = (
('''2H 3H 4H 5H 6H''', True),
('''AS AH 2H AD AC''', False),
('''2H 3H 5H 6H 7H''', True),
('''KS AS TS QS JS''', True),
('''8H 9H QS JS TH''', False),
('''AS 3S 4S 8S 2S''', True),
)
lowerCAmelCase_ = (
('''2H 3H 4H 5H 6H''', True),
('''AS AH 2H AD AC''', False),
('''2H 3H 5H 6H 7H''', False),
('''KS AS TS QS JS''', True),
('''8H 9H QS JS TH''', True),
)
lowerCAmelCase_ = (
('''2H 4D 3C AS 5S''', True, [5, 4, 3, 2, 1_4]),
('''2H 5D 3C AS 5S''', False, [1_4, 5, 5, 3, 2]),
('''JH QD KC AS TS''', False, [1_4, 1_3, 1_2, 1_1, 1_0]),
('''9D 3S 2C 7S 7C''', False, [9, 7, 7, 3, 2]),
)
lowerCAmelCase_ = (
('''JH AH TH KH QH''', 0),
('''JH 9H TH KH QH''', 0),
('''JC KH JS JD JH''', 7),
('''KH KC 3S 3H 3D''', 6),
('''8C 9C 5C 3C TC''', 0),
('''JS QS 9H TS KH''', 0),
('''7C 7S KH 2H 7H''', 3),
('''3C KH 5D 5S KH''', 2),
('''QH 8H KD JH 8S''', 1),
('''2D 6D 9D TH 7D''', 0),
)
lowerCAmelCase_ = (
('''JH AH TH KH QH''', 2_3),
('''JH 9H TH KH QH''', 2_2),
('''JC KH JS JD JH''', 2_1),
('''KH KC 3S 3H 3D''', 2_0),
('''8C 9C 5C 3C TC''', 1_9),
('''JS QS 9H TS KH''', 1_8),
('''7C 7S KH 2H 7H''', 1_7),
('''3C KH 5D 5S KH''', 1_6),
('''QH 8H KD JH 8S''', 1_5),
('''2D 6D 9D TH 7D''', 1_4),
)
def lowerCamelCase_ ( ) -> Dict:
"""simple docstring"""
snake_case_ , snake_case_ : Any = randrange(len(_UpperCamelCase ) ), randrange(len(_UpperCamelCase ) )
snake_case_ : Any = ['''Loss''', '''Tie''', '''Win'''][(play >= oppo) + (play > oppo)]
snake_case_ , snake_case_ : Tuple = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def lowerCamelCase_ ( _UpperCamelCase = 100 ) -> str:
"""simple docstring"""
return (generate_random_hand() for _ in range(_UpperCamelCase ))
@pytest.mark.parametrize('''hand, expected''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
assert PokerHand(_UpperCamelCase )._is_flush() == expected
@pytest.mark.parametrize('''hand, expected''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
assert PokerHand(_UpperCamelCase )._is_straight() == expected
@pytest.mark.parametrize('''hand, expected, card_values''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Tuple:
"""simple docstring"""
snake_case_ : str = PokerHand(_UpperCamelCase )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize('''hand, expected''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
assert PokerHand(_UpperCamelCase )._is_same_kind() == expected
@pytest.mark.parametrize('''hand, expected''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
assert PokerHand(_UpperCamelCase )._hand_type == expected
@pytest.mark.parametrize('''hand, other, expected''' , _UpperCamelCase )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
assert PokerHand(_UpperCamelCase ).compare_with(PokerHand(_UpperCamelCase ) ) == expected
@pytest.mark.parametrize('''hand, other, expected''' , generate_random_hands() )
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
assert PokerHand(_UpperCamelCase ).compare_with(PokerHand(_UpperCamelCase ) ) == expected
def lowerCamelCase_ ( ) -> str:
"""simple docstring"""
snake_case_ : Dict = [PokerHand(_UpperCamelCase ) for hand in SORTED_HANDS]
snake_case_ : str = poker_hands.copy()
shuffle(_UpperCamelCase )
snake_case_ : List[str] = chain(sorted(_UpperCamelCase ) )
for index, hand in enumerate(_UpperCamelCase ):
assert hand == poker_hands[index]
def lowerCamelCase_ ( ) -> Dict:
"""simple docstring"""
snake_case_ : Union[str, Any] = [PokerHand('''2D AC 3H 4H 5S''' ), PokerHand('''2S 3H 4H 5S 6C''' )]
pokerhands.sort(reverse=_UpperCamelCase )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def lowerCamelCase_ ( ) -> str:
"""simple docstring"""
snake_case_ : Dict = PokerHand('''2C 4S AS 3D 5C''' )
snake_case_ : str = True
snake_case_ : Tuple = [5, 4, 3, 2, 14]
for _ in range(10 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def lowerCamelCase_ ( ) -> List[str]:
"""simple docstring"""
snake_case_ : List[str] = 0
snake_case_ : Union[str, Any] = os.path.abspath(os.path.dirname(_UpperCamelCase ) )
snake_case_ : Dict = os.path.join(_UpperCamelCase , '''poker_hands.txt''' )
with open(_UpperCamelCase ) as file_hand:
for line in file_hand:
snake_case_ : Dict = line[:14].strip()
snake_case_ : List[str] = line[15:].strip()
snake_case_ , snake_case_ : str = PokerHand(_UpperCamelCase ), PokerHand(_UpperCamelCase )
snake_case_ : int = player.compare_with(_UpperCamelCase )
if output == "Win":
answer += 1
assert answer == 376
| 279 | 0 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
UpperCAmelCase: Union[str, Any] = logging.getLogger(__name__)
UpperCAmelCase: List[Any] = """pytorch_model.bin"""
@dataclasses.dataclass
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = dataclasses.field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."} )
SCREAMING_SNAKE_CASE_ : Optional[str] = dataclasses.field(
default=snake_case , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."} , )
@dataclasses.dataclass
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = dataclasses.field(metadata={"help": "A csv or a json file containing the training data."} )
SCREAMING_SNAKE_CASE_ : str = dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."} )
SCREAMING_SNAKE_CASE_ : Optional[str] = dataclasses.field(
default=snake_case , metadata={"help": "A csv or a json file containing the validation data."} )
SCREAMING_SNAKE_CASE_ : Optional[str] = dataclasses.field(
default=snake_case , metadata={"help": "The name of the task to train on."} , )
SCREAMING_SNAKE_CASE_ : Optional[List[str]] = dataclasses.field(
default=snake_case , metadata={"help": "The list of labels for the task."} )
@dataclasses.dataclass
class UpperCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = dataclasses.field(
metadata={"help": "The output directory where the model predictions and checkpoints will be written."} )
SCREAMING_SNAKE_CASE_ : Optional[str] = dataclasses.field(
default="accuracy" , metadata={"help": "The evaluation metric used for the task."} )
SCREAMING_SNAKE_CASE_ : Optional[str] = dataclasses.field(
default="no" , metadata={
"help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
} , )
SCREAMING_SNAKE_CASE_ : Optional[int] = dataclasses.field(
default=1_0 , metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} , )
SCREAMING_SNAKE_CASE_ : Optional[float] = dataclasses.field(
default=0.0 , metadata={
"help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
} , )
SCREAMING_SNAKE_CASE_ : Optional[bool] = dataclasses.field(
default=snake_case , metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."} , )
SCREAMING_SNAKE_CASE_ : Optional[bool] = dataclasses.field(
default=snake_case , metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."} , )
SCREAMING_SNAKE_CASE_ : Optional[bool] = dataclasses.field(
default=snake_case , metadata={"help": "Whether to fine-tune on labeled data after pseudo training."} , )
SCREAMING_SNAKE_CASE_ : Optional[float] = dataclasses.field(
default=0.0 , metadata={"help": "Confidence threshold for pseudo-labeled data filtering."} , )
SCREAMING_SNAKE_CASE_ : Optional[int] = dataclasses.field(
default=1_0_0 , metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} , )
SCREAMING_SNAKE_CASE_ : Optional[int] = dataclasses.field(
default=snake_case , metadata={"help": "Random seed for initialization."} , )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Any = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
_lowercase : Optional[Any] = dataset.filter(lambda __UpperCAmelCase : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
_lowercase : int = int(eval_result * len(__UpperCAmelCase ) )
print(__UpperCAmelCase )
_lowercase : Optional[Any] = dataset.sort("""probability""" , reverse=__UpperCAmelCase )
_lowercase : Dict = dataset.select(range(__UpperCAmelCase ) )
_lowercase : Any = dataset.remove_columns(["""label""", """probability"""] )
_lowercase : List[str] = dataset.rename_column("""prediction""" , """label""" )
_lowercase : List[str] = dataset.map(lambda __UpperCAmelCase : {"label": idalabel[example["label"]]} )
_lowercase : Optional[int] = dataset.shuffle(seed=args.seed )
_lowercase : List[Any] = os.path.join(__UpperCAmelCase , F"""train_pseudo.{args.data_file_extension}""" )
if args.data_file_extension == "csv":
dataset.to_csv(__UpperCAmelCase , index=__UpperCAmelCase )
else:
dataset.to_json(__UpperCAmelCase )
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase ):
_lowercase : Any = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
_lowercase : Union[str, Any] = STModelArguments(model_name_or_path=__UpperCAmelCase )
_lowercase : int = STDataArguments(train_file=__UpperCAmelCase , infer_file=__UpperCAmelCase )
_lowercase : Any = STTrainingArguments(output_dir=__UpperCAmelCase )
_lowercase : int = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(__UpperCAmelCase ).items():
setattr(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
for key, value in kwargs.items():
if hasattr(__UpperCAmelCase , __UpperCAmelCase ):
setattr(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# Sanity checks
_lowercase : Any = {}
_lowercase : Optional[Any] = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
_lowercase : Optional[Any] = args.train_file
_lowercase : List[str] = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
_lowercase : Dict = args.eval_file
for key in data_files:
_lowercase : Tuple = data_files[key].split(""".""" )[-1]
assert extension in ["csv", "json"], F"""`{key}_file` should be a csv or a json file."""
if args.data_file_extension is None:
_lowercase : int = extension
else:
assert extension == args.data_file_extension, F"""`{key}_file` should be a {args.data_file_extension} file`."""
assert (
args.eval_metric in datasets.list_metrics()
), F"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info("""Creating the initial data directory for self-training...""" )
_lowercase : Any = F"""{args.output_dir}/self-train_iter-{{}}""".format
_lowercase : List[str] = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=__UpperCAmelCase )
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
accelerator.wait_for_everyone()
_lowercase : Optional[int] = None
_lowercase : List[Any] = None
_lowercase : Dict = 0
_lowercase : Union[str, Any] = False
# Show the progress bar
_lowercase : Tuple = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
_lowercase : int = data_dir_format(__UpperCAmelCase )
assert os.path.exists(__UpperCAmelCase )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
_lowercase : Any = os.path.join(__UpperCAmelCase , """stage-1""" )
_lowercase : Any = {
"""accelerator""": accelerator,
"""model_name_or_path""": args.model_name_or_path,
"""cache_dir""": args.cache_dir,
"""do_train""": True,
"""train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
"""do_eval""": True if args.eval_file is not None else False,
"""eval_file""": data_files["""eval"""],
"""do_predict""": True,
"""infer_file""": data_files["""infer"""],
"""task_name""": args.task_name,
"""label_list""": args.label_list,
"""output_dir""": current_output_dir,
"""eval_metric""": args.eval_metric,
"""evaluation_strategy""": args.evaluation_strategy,
"""early_stopping_patience""": args.early_stopping_patience,
"""early_stopping_threshold""": args.early_stopping_threshold,
"""seed""": args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(__UpperCAmelCase , __UpperCAmelCase ):
arguments_dict.update({key: value} )
_lowercase : Optional[Any] = os.path.join(__UpperCAmelCase , """best-checkpoint""" , __UpperCAmelCase )
if os.path.exists(__UpperCAmelCase ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.""" , __UpperCAmelCase , __UpperCAmelCase , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 1 *****""" , __UpperCAmelCase )
finetune(**__UpperCAmelCase )
accelerator.wait_for_everyone()
assert os.path.exists(__UpperCAmelCase )
logger.info("""Self-training job completed: iteration: %d, stage: 1.""" , __UpperCAmelCase )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
_lowercase : List[str] = os.path.join(__UpperCAmelCase , """best-checkpoint""" )
_lowercase : Optional[Any] = os.path.join(__UpperCAmelCase , """stage-2""" )
# Update arguments_dict
_lowercase : Union[str, Any] = model_path
_lowercase : Optional[Any] = data_files["""train"""]
_lowercase : str = current_output_dir
_lowercase : int = os.path.join(__UpperCAmelCase , """best-checkpoint""" , __UpperCAmelCase )
if os.path.exists(__UpperCAmelCase ):
logger.info(
"""Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.""" , __UpperCAmelCase , __UpperCAmelCase , )
else:
logger.info("""***** Running self-training: iteration: %d, stage: 2 *****""" , __UpperCAmelCase )
finetune(**__UpperCAmelCase )
accelerator.wait_for_everyone()
assert os.path.exists(__UpperCAmelCase )
logger.info("""Self-training job completed: iteration: %d, stage: 2.""" , __UpperCAmelCase )
_lowercase : List[str] = iteration
_lowercase : Optional[Any] = data_dir_format(iteration + 1 )
_lowercase : Any = AutoConfig.from_pretrained(os.path.join(__UpperCAmelCase , """best-checkpoint""" ) )
_lowercase : int = config.idalabel
_lowercase : Optional[Any] = os.path.join(__UpperCAmelCase , """eval_results_best-checkpoint.json""" )
_lowercase : Tuple = os.path.join(__UpperCAmelCase , """test_results_best-checkpoint.json""" )
assert os.path.exists(__UpperCAmelCase )
with open(__UpperCAmelCase , """r""" ) as f:
_lowercase : Tuple = float(json.load(__UpperCAmelCase )[args.eval_metric] )
_lowercase : str = os.path.join(__UpperCAmelCase , """infer_output_best-checkpoint.csv""" )
assert os.path.exists(__UpperCAmelCase )
# Loading the dataset from local csv or json files.
_lowercase : str = load_dataset(args.data_file_extension , data_files={"""data""": data_files["""infer"""]} )["""data"""]
_lowercase : Optional[Any] = load_dataset("""csv""" , data_files={"""data""": infer_output_file} )["""data"""]
if accelerator.is_main_process:
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
shutil.copy(__UpperCAmelCase , os.path.join(__UpperCAmelCase , F"""eval_results_iter-{iteration}.json""" ) )
if os.path.exists(__UpperCAmelCase ):
shutil.copy(__UpperCAmelCase , os.path.join(__UpperCAmelCase , F"""test_results_iter-{iteration}.json""" ) )
create_pseudo_labeled_data(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
accelerator.wait_for_everyone()
_lowercase : Optional[int] = os.path.join(__UpperCAmelCase , F"""train_pseudo.{args.data_file_extension}""" )
if args.evaluation_strategy != IntervalStrategy.NO.value:
_lowercase : Union[str, Any] = eval_result
if best_iteration is None:
_lowercase : str = new_iteration
_lowercase : Any = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
_lowercase : str = new_iteration
_lowercase : Union[str, Any] = new_eval_result
_lowercase : Optional[Any] = 0
else:
if new_eval_result == best_eval_result:
_lowercase : Optional[int] = new_iteration
_lowercase : Dict = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
_lowercase : List[Any] = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info("""Best iteration: %d""" , __UpperCAmelCase )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , __UpperCAmelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(__UpperCAmelCase , F"""eval_results_iter-{iteration}.json""" ) , os.path.join(__UpperCAmelCase , """eval_results_best-iteration.json""" ) , )
else:
# Assume that the last iteration is the best
logger.info("""Best iteration: %d""" , args.max_selftrain_iterations - 1 )
logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , __UpperCAmelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(__UpperCAmelCase , F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json""" ) , os.path.join(__UpperCAmelCase , """eval_results_best-iteration.json""" ) , )
| 336 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCamelCase ( snake_case , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = LEDTokenizer
SCREAMING_SNAKE_CASE_ : List[str] = LEDTokenizerFast
SCREAMING_SNAKE_CASE_ : List[str] = True
def lowerCamelCase__ ( self ):
super().setUp()
_lowercase : Union[str, Any] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
_lowercase : List[Any] = dict(zip(UpperCAmelCase_ ,range(len(UpperCAmelCase_ ) ) ) )
_lowercase : Optional[int] = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
_lowercase : Dict = {"""unk_token""": """<unk>"""}
_lowercase : Optional[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
_lowercase : List[str] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(UpperCAmelCase_ ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(UpperCAmelCase_ ) )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,**UpperCAmelCase_ ):
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return "lower newer", "lower newer"
@cached_property
def lowerCamelCase__ ( self ):
return LEDTokenizer.from_pretrained("""allenai/led-base-16384""" )
@cached_property
def lowerCamelCase__ ( self ):
return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384""" )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Tuple = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
_lowercase : Any = [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Tuple = tokenizer(UpperCAmelCase_ ,max_length=len(UpperCAmelCase_ ) ,padding=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIsInstance(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertEqual((2, 9) ,batch.input_ids.shape )
self.assertEqual((2, 9) ,batch.attention_mask.shape )
_lowercase : Optional[Any] = batch.input_ids.tolist()[0]
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Tuple = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Dict = tokenizer(UpperCAmelCase_ ,padding=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIn("""input_ids""" ,UpperCAmelCase_ )
self.assertIn("""attention_mask""" ,UpperCAmelCase_ )
self.assertNotIn("""labels""" ,UpperCAmelCase_ )
self.assertNotIn("""decoder_attention_mask""" ,UpperCAmelCase_ )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : Dict = [
"""Summary of the text.""",
"""Another summary.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Tuple = tokenizer(text_target=UpperCAmelCase_ ,max_length=32 ,padding="""max_length""" ,return_tensors="""pt""" )
self.assertEqual(32 ,targets["""input_ids"""].shape[1] )
@require_torch
def lowerCamelCase__ ( self ):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : List[Any] = tokenizer(
["""I am a small frog""" * 10_24, """I am a small frog"""] ,padding=UpperCAmelCase_ ,truncation=UpperCAmelCase_ ,return_tensors="""pt""" )
self.assertIsInstance(UpperCAmelCase_ ,UpperCAmelCase_ )
self.assertEqual(batch.input_ids.shape ,(2, 51_22) )
@require_torch
def lowerCamelCase__ ( self ):
_lowercase : List[Any] = ["""A long paragraph for summarization."""]
_lowercase : Dict = [
"""Summary of the text.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : Dict = tokenizer(UpperCAmelCase_ ,return_tensors="""pt""" )
_lowercase : List[str] = tokenizer(text_target=UpperCAmelCase_ ,return_tensors="""pt""" )
_lowercase : Union[str, Any] = inputs["""input_ids"""]
_lowercase : List[str] = targets["""input_ids"""]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
@require_torch
def lowerCamelCase__ ( self ):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_lowercase : str = ["""Summary of the text.""", """Another summary."""]
_lowercase : Optional[int] = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
_lowercase : Any = tokenizer(UpperCAmelCase_ ,padding=UpperCAmelCase_ )
_lowercase : str = [[0] * len(UpperCAmelCase_ ) for x in encoded_output["""input_ids"""]]
_lowercase : Optional[int] = tokenizer.pad(UpperCAmelCase_ )
self.assertSequenceEqual(outputs["""global_attention_mask"""] ,UpperCAmelCase_ )
def lowerCamelCase__ ( self ):
pass
def lowerCamelCase__ ( self ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_lowercase : int = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Optional[int] = self.tokenizer_class.from_pretrained(UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Dict = """A, <mask> AllenNLP sentence."""
_lowercase : List[Any] = tokenizer_r.encode_plus(UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ )
_lowercase : Any = tokenizer_p.encode_plus(UpperCAmelCase_ ,add_special_tokens=UpperCAmelCase_ ,return_token_type_ids=UpperCAmelCase_ )
self.assertEqual(sum(tokens_r["""token_type_ids"""] ) ,sum(tokens_p["""token_type_ids"""] ) )
self.assertEqual(
sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] ) ,sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] ) ,)
_lowercase : str = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
_lowercase : str = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )
self.assertSequenceEqual(tokens_p["""input_ids"""] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r["""input_ids"""] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
UpperCAmelCase_ ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
self.assertSequenceEqual(
UpperCAmelCase_ ,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
| 336 | 1 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowercase : int = logging.get_logger(__name__)
lowercase : Any = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
lowercase : str = {
'tokenizer_file': {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
},
}
lowercase : Optional[Any] = {
'gpt-neox-20b': 20_48,
}
class A ( __snake_case ):
__magic_name__ = VOCAB_FILES_NAMES
__magic_name__ = PRETRAINED_VOCAB_FILES_MAP
__magic_name__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__magic_name__ = ['''input_ids''', '''attention_mask''']
def __init__( self , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE="<|endoftext|>" , SCREAMING_SNAKE_CASE="<|endoftext|>" , SCREAMING_SNAKE_CASE="<|endoftext|>" , SCREAMING_SNAKE_CASE=False , **SCREAMING_SNAKE_CASE , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , tokenizer_file=SCREAMING_SNAKE_CASE , unk_token=SCREAMING_SNAKE_CASE , bos_token=SCREAMING_SNAKE_CASE , eos_token=SCREAMING_SNAKE_CASE , add_prefix_space=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , )
A : Tuple = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , SCREAMING_SNAKE_CASE ) != add_prefix_space:
A : Optional[int] = getattr(SCREAMING_SNAKE_CASE , pre_tok_state.pop('''type''' ) )
A : Dict = add_prefix_space
A : Dict = pre_tok_class(**SCREAMING_SNAKE_CASE )
A : List[str] = add_prefix_space
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None ) -> Tuple[str]:
"""simple docstring"""
A : Tuple = self._tokenizer.model.save(SCREAMING_SNAKE_CASE , name=SCREAMING_SNAKE_CASE )
return tuple(SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> List[int]:
"""simple docstring"""
A : Any = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE ) + [self.eos_token_id] )
if len(SCREAMING_SNAKE_CASE ) > self.model_max_length:
A : List[str] = input_ids[-self.model_max_length :]
return input_ids
| 3 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
A : Optional[List[str]] = None
A : str = "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
A : str = [
np.dtype("|b1"),
np.dtype("|u1"),
np.dtype("<u2"),
np.dtype(">u2"),
np.dtype("<i2"),
np.dtype(">i2"),
np.dtype("<u4"),
np.dtype(">u4"),
np.dtype("<i4"),
np.dtype(">i4"),
np.dtype("<f4"),
np.dtype(">f4"),
np.dtype("<f8"),
np.dtype(">f8"),
]
@dataclass
class _lowercase :
"""simple docstring"""
A__ = True
A__ = None
# Automatically constructed
A__ = "PIL.Image.Image"
A__ = pa.struct({"bytes": pa.binary(), "path": pa.string()})
A__ = field(default="Image" , init=lowercase__ , repr=lowercase__)
def __call__( self : Any ):
'''simple docstring'''
return self.pa_type
def lowerCAmelCase ( self : Optional[Any] , __lowerCamelCase : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ):
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'." )
if isinstance(__lowerCamelCase , __lowerCamelCase ):
lowerCamelCase__ : str = np.array(__lowerCamelCase )
if isinstance(__lowerCamelCase , __lowerCamelCase ):
return {"path": value, "bytes": None}
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
return {"path": None, "bytes": value}
elif isinstance(__lowerCamelCase , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(__lowerCamelCase )
elif isinstance(__lowerCamelCase , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(__lowerCamelCase )
elif value.get("path" ) is not None and os.path.isfile(value["path"] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("path" )}
elif value.get("bytes" ) is not None or value.get("path" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("bytes" ), "path": value.get("path" )}
else:
raise ValueError(
f"An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}." )
def lowerCAmelCase ( self : Any , __lowerCamelCase : dict , __lowerCamelCase : List[Any]=None ):
'''simple docstring'''
if not self.decode:
raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead." )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support decoding images, please install 'Pillow'." )
if token_per_repo_id is None:
lowerCamelCase__ : Union[str, Any] = {}
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = value["path"], value["bytes"]
if bytes_ is None:
if path is None:
raise ValueError(f"An image should have one of 'path' or 'bytes' but both are None in {value}." )
else:
if is_local_path(__lowerCamelCase ):
lowerCamelCase__ : Union[str, Any] = PIL.Image.open(__lowerCamelCase )
else:
lowerCamelCase__ : Tuple = path.split("::" )[-1]
try:
lowerCamelCase__ : str = string_to_dict(__lowerCamelCase , config.HUB_DATASETS_URL )["repo_id"]
lowerCamelCase__ : Any = token_per_repo_id.get(__lowerCamelCase )
except ValueError:
lowerCamelCase__ : int = None
with xopen(__lowerCamelCase , "rb" , use_auth_token=__lowerCamelCase ) as f:
lowerCamelCase__ : List[str] = BytesIO(f.read() )
lowerCamelCase__ : Optional[int] = PIL.Image.open(bytes_ )
else:
lowerCamelCase__ : Dict = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def lowerCAmelCase ( self : Dict ):
'''simple docstring'''
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("binary" ),
"path": Value("string" ),
}
)
def lowerCAmelCase ( self : Optional[Any] , __lowerCamelCase : Union[pa.StringArray, pa.StructArray, pa.ListArray] ):
'''simple docstring'''
if pa.types.is_string(storage.type ):
lowerCamelCase__ : Dict = pa.array([None] * len(__lowerCamelCase ) , type=pa.binary() )
lowerCamelCase__ : List[str] = pa.StructArray.from_arrays([bytes_array, storage] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowerCamelCase__ : List[Any] = pa.array([None] * len(__lowerCamelCase ) , type=pa.string() )
lowerCamelCase__ : Any = pa.StructArray.from_arrays([storage, path_array] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("bytes" ) >= 0:
lowerCamelCase__ : Dict = storage.field("bytes" )
else:
lowerCamelCase__ : Optional[int] = pa.array([None] * len(__lowerCamelCase ) , type=pa.binary() )
if storage.type.get_field_index("path" ) >= 0:
lowerCamelCase__ : Dict = storage.field("path" )
else:
lowerCamelCase__ : Dict = pa.array([None] * len(__lowerCamelCase ) , type=pa.string() )
lowerCamelCase__ : int = pa.StructArray.from_arrays([bytes_array, path_array] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
lowerCamelCase__ : Union[str, Any] = pa.array(
[encode_np_array(np.array(__lowerCamelCase ) )["bytes"] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
lowerCamelCase__ : Dict = pa.array([None] * len(__lowerCamelCase ) , type=pa.string() )
lowerCamelCase__ : Dict = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["bytes", "path"] , mask=bytes_array.is_null() )
return array_cast(__lowerCamelCase , self.pa_type )
def lowerCAmelCase ( self : int , __lowerCamelCase : pa.StructArray ):
'''simple docstring'''
@no_op_if_value_is_null
def path_to_bytes(__lowerCamelCase : Union[str, Any] ):
with xopen(__lowerCamelCase , "rb" ) as f:
lowerCamelCase__ : str = f.read()
return bytes_
lowerCamelCase__ : List[Any] = pa.array(
[
(path_to_bytes(x["path"] ) if x["bytes"] is None else x["bytes"]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
lowerCamelCase__ : Optional[int] = pa.array(
[os.path.basename(__lowerCamelCase ) if path is not None else None for path in storage.field("path" ).to_pylist()] , type=pa.string() , )
lowerCamelCase__ : Tuple = pa.StructArray.from_arrays([bytes_array, path_array] , ["bytes", "path"] , mask=bytes_array.is_null() )
return array_cast(__lowerCamelCase , self.pa_type )
def lowercase_ ( ):
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'." )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
lowerCamelCase__ : List[str] = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def lowercase_ ( _A : "PIL.Image.Image" ):
"""simple docstring"""
lowerCamelCase__ : Optional[Any] = BytesIO()
if image.format in list_image_compression_formats():
lowerCamelCase__ : int = image.format
else:
lowerCamelCase__ : int = "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
image.save(_A , format=_A )
return buffer.getvalue()
def lowercase_ ( _A : "PIL.Image.Image" ):
"""simple docstring"""
if hasattr(_A , "filename" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(_A )}
def lowercase_ ( _A : np.ndarray ):
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'." )
lowerCamelCase__ : int = array.dtype
lowerCamelCase__ : List[str] = dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
lowerCamelCase__ : List[str] = dtype.kind
lowerCamelCase__ : Optional[Any] = dtype.itemsize
lowerCamelCase__ : Dict = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
lowerCamelCase__ : List[Any] = np.dtype("|u1" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F"Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays." )
if dtype is not dest_dtype:
warnings.warn(F"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'" )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
lowerCamelCase__ : Any = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
lowerCamelCase__ : Optional[Any] = dtype_byteorder + dtype_kind + str(_A )
lowerCamelCase__ : int = np.dtype(_A )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'" )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F"Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}" )
lowerCamelCase__ : List[Any] = PIL.Image.fromarray(array.astype(_A ) )
return {"path": None, "bytes": image_to_bytes(_A )}
def lowercase_ ( _A : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ):
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'." )
if objs:
lowerCamelCase__ , lowerCamelCase__ : int = first_non_null_value(_A )
if isinstance(_A , _A ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(_A , np.ndarray ):
lowerCamelCase__ : Optional[Any] = no_op_if_value_is_null(_A )
return [obj_to_image_dict_func(_A ) for obj in objs]
elif isinstance(_A , PIL.Image.Image ):
lowerCamelCase__ : int = no_op_if_value_is_null(_A )
return [obj_to_image_dict_func(_A ) for obj in objs]
else:
return objs
else:
return objs
| 184 | 0 |
from __future__ import annotations
from cmath import sqrt
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> tuple[complex, complex]:
'''simple docstring'''
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
_UpperCAmelCase = b * b - 4 * a * c
_UpperCAmelCase = (-b + sqrt(_UpperCAmelCase )) / (2 * a)
_UpperCAmelCase = (-b - sqrt(_UpperCAmelCase )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def A ( ) -> str:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = quadratic_roots(a=5 , b=6 , c=1 )
print(F"The solutions are: {solutiona} and {solutiona}" )
if __name__ == "__main__":
main()
| 290 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class __lowerCAmelCase ( A ):
def _lowerCamelCase ( self : List[str]) -> int:
"""simple docstring"""
_UpperCAmelCase = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(A , 'hidden_sizes'))
self.parent.assertTrue(hasattr(A , 'neck_hidden_sizes'))
self.parent.assertTrue(hasattr(A , 'num_attention_heads'))
class __lowerCAmelCase :
def __init__( self : int , A : Tuple , A : List[str]=13 , A : List[str]=32 , A : List[str]=2 , A : List[str]=3 , A : List[Any]=6_40 , A : Any=4 , A : int="silu" , A : int=3 , A : Dict=32 , A : List[Any]=0.1 , A : Optional[Any]=0.1 , A : Optional[int]=0.1 , A : List[str]=0.0_2 , A : int=True , A : Any=True , A : List[str]=10 , A : Tuple=None , ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = last_hidden_size
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = conv_kernel_size
_UpperCAmelCase = output_stride
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = classifier_dropout_prob
_UpperCAmelCase = use_labels
_UpperCAmelCase = is_training
_UpperCAmelCase = num_labels
_UpperCAmelCase = initializer_range
_UpperCAmelCase = scope
def _lowerCamelCase ( self : Union[str, Any]) -> Any:
"""simple docstring"""
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_UpperCAmelCase = None
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels)
_UpperCAmelCase = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels)
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels, pixel_labels
def _lowerCamelCase ( self : str) -> int:
"""simple docstring"""
return MobileViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def _lowerCamelCase ( self : List[Any] , A : Dict , A : Tuple , A : int , A : Tuple) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = MobileViTModel(config=A)
model.to(A)
model.eval()
_UpperCAmelCase = model(A)
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def _lowerCamelCase ( self : int , A : Any , A : List[Any] , A : List[Any] , A : Optional[int]) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = MobileViTForImageClassification(A)
model.to(A)
model.eval()
_UpperCAmelCase = model(A , labels=A)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def _lowerCamelCase ( self : int , A : Tuple , A : Optional[Any] , A : Union[str, Any] , A : List[Any]) -> int:
"""simple docstring"""
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = MobileViTForSemanticSegmentation(A)
model.to(A)
model.eval()
_UpperCAmelCase = model(A)
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
_UpperCAmelCase = model(A , labels=A)
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def _lowerCamelCase ( self : int) -> Any:
"""simple docstring"""
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( A , A , unittest.TestCase ):
UpperCamelCase = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
UpperCamelCase = (
{
'''feature-extraction''': MobileViTModel,
'''image-classification''': MobileViTForImageClassification,
'''image-segmentation''': MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
UpperCamelCase = False
def _lowerCamelCase ( self : str) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = MobileViTModelTester(self)
_UpperCAmelCase = MobileViTConfigTester(self , config_class=A , has_text_modality=A)
def _lowerCamelCase ( self : Optional[int]) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='MobileViT does not use inputs_embeds')
def _lowerCamelCase ( self : Tuple) -> Dict:
"""simple docstring"""
pass
@unittest.skip(reason='MobileViT does not support input and output embeddings')
def _lowerCamelCase ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason='MobileViT does not output attentions')
def _lowerCamelCase ( self : Any) -> Optional[Any]:
"""simple docstring"""
pass
def _lowerCamelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(A)
_UpperCAmelCase = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , A)
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
def _lowerCamelCase ( self : Union[str, Any]) -> List[str]:
"""simple docstring"""
pass
def _lowerCamelCase ( self : Tuple) -> str:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A)
def _lowerCamelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
def check_hidden_states_output(A : List[str] , A : Union[str, Any] , A : int):
_UpperCAmelCase = model_class(A)
model.to(A)
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(A , A))
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = 5
self.assertEqual(len(A) , A)
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
_UpperCAmelCase = 2
for i in range(len(A)):
self.assertListEqual(
list(hidden_states[i].shape[-2:]) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2)
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = True
check_hidden_states_output(A , A , A)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
check_hidden_states_output(A , A , A)
def _lowerCamelCase ( self : List[str]) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A)
def _lowerCamelCase ( self : int) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*A)
@slow
def _lowerCamelCase ( self : Optional[int]) -> Union[str, Any]:
"""simple docstring"""
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = MobileViTModel.from_pretrained(A)
self.assertIsNotNone(A)
def A ( ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
@cached_property
def _lowerCamelCase ( self : Tuple) -> Dict:
"""simple docstring"""
return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small') if is_vision_available() else None
@slow
def _lowerCamelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small').to(A)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=A , return_tensors='pt').to(A)
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**A)
# verify the logits
_UpperCAmelCase = torch.Size((1, 10_00))
self.assertEqual(outputs.logits.shape , A)
_UpperCAmelCase = torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]).to(A)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , A , atol=1E-4))
@slow
def _lowerCamelCase ( self : List[Any]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small')
_UpperCAmelCase = model.to(A)
_UpperCAmelCase = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small')
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=A , return_tensors='pt').to(A)
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**A)
_UpperCAmelCase = outputs.logits
# verify the logits
_UpperCAmelCase = torch.Size((1, 21, 32, 32))
self.assertEqual(logits.shape , A)
_UpperCAmelCase = torch.tensor(
[
[[6.9_7_1_3, 6.9_7_8_6, 7.2_4_2_2], [7.2_8_9_3, 7.2_8_2_5, 7.4_4_4_6], [7.6_5_8_0, 7.8_7_9_7, 7.9_4_2_0]],
[[-1_0.6_8_6_9, -1_0.3_2_5_0, -1_0.3_4_7_1], [-1_0.4_2_2_8, -9.9_8_6_8, -9.7_1_3_2], [-1_1.0_4_0_5, -1_1.0_2_2_1, -1_0.7_3_1_8]],
[[-3.3_0_8_9, -2.8_5_3_9, -2.6_7_4_0], [-3.2_7_0_6, -2.5_6_2_1, -2.5_1_0_8], [-3.2_5_3_4, -2.6_6_1_5, -2.6_6_5_1]],
] , device=A , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , A , atol=1E-4))
@slow
def _lowerCamelCase ( self : List[Any]) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small')
_UpperCAmelCase = model.to(A)
_UpperCAmelCase = MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small')
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=A , return_tensors='pt').to(A)
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**A)
_UpperCAmelCase = outputs.logits.detach().cpu()
_UpperCAmelCase = image_processor.post_process_semantic_segmentation(outputs=A , target_sizes=[(50, 60)])
_UpperCAmelCase = torch.Size((50, 60))
self.assertEqual(segmentation[0].shape , A)
_UpperCAmelCase = image_processor.post_process_semantic_segmentation(outputs=A)
_UpperCAmelCase = torch.Size((32, 32))
self.assertEqual(segmentation[0].shape , A)
| 290 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: Optional[int] = logging.get_logger(__name__)
UpperCamelCase__: str = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class SCREAMING_SNAKE_CASE( A__ ):
"""simple docstring"""
lowerCamelCase__ = """ctrl"""
lowerCamelCase__ = ["""past_key_values"""]
lowerCamelCase__ = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : str , __snake_case : Optional[int]=246534 , __snake_case : Any=256 , __snake_case : Optional[Any]=1280 , __snake_case : List[Any]=8192 , __snake_case : Tuple=48 , __snake_case : Union[str, Any]=16 , __snake_case : Optional[int]=0.1 , __snake_case : List[Any]=0.1 , __snake_case : Optional[int]=1E-6 , __snake_case : Any=0.02 , __snake_case : Optional[Any]=True , **__snake_case : Dict , ) -> Optional[int]:
UpperCAmelCase : Dict = vocab_size
UpperCAmelCase : Optional[int] = n_positions
UpperCAmelCase : str = n_embd
UpperCAmelCase : Any = n_layer
UpperCAmelCase : Tuple = n_head
UpperCAmelCase : int = dff
UpperCAmelCase : Any = resid_pdrop
UpperCAmelCase : Any = embd_pdrop
UpperCAmelCase : Tuple = layer_norm_epsilon
UpperCAmelCase : int = initializer_range
UpperCAmelCase : Any = use_cache
super().__init__(**__snake_case )
| 23 |
'''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()
UpperCamelCase__: int = logging.get_logger(__name__)
UpperCamelCase__: Dict = {
"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",
}
UpperCamelCase__: Optional[Any] = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
"projector",
"classifier",
]
def snake_case_ ( _lowerCAmelCase : str ) -> Union[str, Any]:
UpperCAmelCase : Optional[int] = {}
with open(_lowerCAmelCase , '''r''' ) as file:
for line_number, line in enumerate(_lowerCAmelCase ):
UpperCAmelCase : List[str] = line.strip()
if line:
UpperCAmelCase : str = line.split()
UpperCAmelCase : Union[str, Any] = line_number
UpperCAmelCase : List[Any] = words[0]
UpperCAmelCase : Union[str, Any] = value
return result
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : str ) -> int:
for attribute in key.split('''.''' ):
UpperCAmelCase : Dict = getattr(_lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase : Dict = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(_lowerCAmelCase ):
UpperCAmelCase : Any = PARAM_MAPPING[full_name.split('''.''' )[-1]]
UpperCAmelCase : Dict = '''param'''
if weight_type is not None and weight_type != "param":
UpperCAmelCase : Optional[int] = getattr(_lowerCAmelCase , _lowerCAmelCase ).shape
elif weight_type is not None and weight_type == "param":
UpperCAmelCase : List[Any] = hf_pointer
for attribute in hf_param_name.split('''.''' ):
UpperCAmelCase : Optional[Any] = getattr(_lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase : int = shape_pointer.shape
# let's reduce dimension
UpperCAmelCase : Union[str, Any] = value[0]
else:
UpperCAmelCase : List[Any] = 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":
UpperCAmelCase : int = value
elif weight_type == "weight_g":
UpperCAmelCase : str = value
elif weight_type == "weight_v":
UpperCAmelCase : Dict = value
elif weight_type == "bias":
UpperCAmelCase : str = value
elif weight_type == "param":
for attribute in hf_param_name.split('''.''' ):
UpperCAmelCase : int = getattr(_lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase : Optional[int] = value
else:
UpperCAmelCase : Tuple = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[Any] ) -> List[Any]:
UpperCAmelCase : List[str] = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(_lowerCAmelCase ):
UpperCAmelCase : List[str] = PARAM_MAPPING[full_name.split('''.''' )[-1]]
UpperCAmelCase : Any = '''param'''
if weight_type is not None and weight_type != "param":
UpperCAmelCase : Optional[int] = '''.'''.join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
UpperCAmelCase : Optional[int] = '''.'''.join([key, hf_param_name] )
else:
UpperCAmelCase : List[Any] = key
UpperCAmelCase : Tuple = value if '''lm_head''' in full_key else value[0]
UpperCamelCase__: Tuple = {
"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 snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : List[str] , _lowerCAmelCase : Any=None , _lowerCAmelCase : Optional[Any]=None ) -> int:
UpperCAmelCase : List[Any] = False
for key, mapped_key in MAPPING.items():
UpperCAmelCase : int = '''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]:
UpperCAmelCase : Optional[Any] = True
if "*" in mapped_key:
UpperCAmelCase : Tuple = name.split(_lowerCAmelCase )[0].split('''.''' )[-2]
UpperCAmelCase : List[Any] = mapped_key.replace('''*''' , _lowerCAmelCase )
if "weight_g" in name:
UpperCAmelCase : str = '''weight_g'''
elif "weight_v" in name:
UpperCAmelCase : int = '''weight_v'''
elif "bias" in name:
UpperCAmelCase : int = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
UpperCAmelCase : List[str] = '''weight'''
else:
UpperCAmelCase : Dict = 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 snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ) -> Any:
UpperCAmelCase : Dict = []
UpperCAmelCase : Dict = fairseq_model.state_dict()
UpperCAmelCase : Union[str, Any] = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
UpperCAmelCase : Dict = False
if "conv_layers" in name:
load_conv_layer(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , hf_model.config.feat_extract_norm == '''group''' , )
UpperCAmelCase : Any = True
else:
UpperCAmelCase : Optional[Any] = load_wavaveca_layer(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
if not is_used:
unused_weights.append(_lowerCAmelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int] ) -> Union[str, Any]:
UpperCAmelCase : Any = full_name.split('''conv_layers.''' )[-1]
UpperCAmelCase : Optional[int] = name.split('''.''' )
UpperCAmelCase : Tuple = int(items[0] )
UpperCAmelCase : Tuple = 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.""" )
UpperCAmelCase : Tuple = 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.""" )
UpperCAmelCase : Union[str, Any] = 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.""" )
UpperCAmelCase : Union[str, Any] = 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.""" )
UpperCAmelCase : List[str] = 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 snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict=None , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : int=True , _lowerCAmelCase : Optional[int]=False ) -> Dict:
if config_path is not None:
UpperCAmelCase : List[str] = WavaVecaConfig.from_pretrained(_lowerCAmelCase )
else:
UpperCAmelCase : List[Any] = WavaVecaConfig()
if is_seq_class:
UpperCAmelCase : Optional[Any] = read_txt_into_dict(_lowerCAmelCase )
UpperCAmelCase : Optional[int] = idalabel
UpperCAmelCase : Optional[Any] = WavaVecaForSequenceClassification(_lowerCAmelCase )
UpperCAmelCase : Dict = 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:
UpperCAmelCase : Dict = Dictionary.load(_lowerCAmelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
UpperCAmelCase : Any = target_dict.pad_index
UpperCAmelCase : Tuple = target_dict.bos_index
UpperCAmelCase : Optional[int] = target_dict.eos_index
UpperCAmelCase : Union[str, Any] = len(target_dict.symbols )
UpperCAmelCase : Dict = 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 )
UpperCAmelCase : List[Any] = target_dict.indices
# fairseq has the <pad> and <s> switched
UpperCAmelCase : List[str] = 0
UpperCAmelCase : List[str] = 1
with open(_lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase : Optional[int] = 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 , )
UpperCAmelCase : int = True if config.feat_extract_norm == '''layer''' else False
UpperCAmelCase : int = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , )
UpperCAmelCase : str = WavaVecaProcessor(feature_extractor=_lowerCAmelCase , tokenizer=_lowerCAmelCase )
processor.save_pretrained(_lowerCAmelCase )
UpperCAmelCase : Union[str, Any] = WavaVecaForCTC(_lowerCAmelCase )
else:
UpperCAmelCase : Dict = WavaVecaForPreTraining(_lowerCAmelCase )
if is_finetuned or is_seq_class:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
UpperCAmelCase : Optional[Any] = argparse.Namespace(task='''audio_pretraining''' )
UpperCAmelCase : List[Any] = fairseq.tasks.setup_task(_lowerCAmelCase )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=_lowerCAmelCase )
UpperCAmelCase : Optional[int] = model[0].eval()
recursively_load_weights(_lowerCAmelCase , _lowerCAmelCase , not is_finetuned )
hf_wavavec.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
UpperCamelCase__: Dict = 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",
)
UpperCamelCase__: Any = parser.parse_args()
UpperCamelCase__: int = 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,
)
| 23 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def SCREAMING_SNAKE_CASE__ ( snake_case : str , snake_case : str )-> str | Literal[False]:
'''simple docstring'''
UpperCAmelCase__ : int = list(snake_case )
UpperCAmelCase__ : Dict = list(snake_case )
UpperCAmelCase__ : Optional[Any] = 0
for i in range(len(snake_case ) ):
if lista[i] != lista[i]:
count += 1
UpperCAmelCase__ : Any = "_"
if count > 1:
return False
else:
return "".join(snake_case )
def SCREAMING_SNAKE_CASE__ ( snake_case : list[str] )-> list[str]:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = []
while True:
UpperCAmelCase__ : List[Any] = ["$"] * len(snake_case )
UpperCAmelCase__ : Tuple = []
for i in range(len(snake_case ) ):
for j in range(i + 1 , len(snake_case ) ):
UpperCAmelCase__ : Dict = compare_string(binary[i] , binary[j] )
if k is False:
UpperCAmelCase__ : List[Any] = "*"
UpperCAmelCase__ : Dict = "*"
temp.append("X" )
for i in range(len(snake_case ) ):
if checka[i] == "$":
pi.append(binary[i] )
if len(snake_case ) == 0:
return pi
UpperCAmelCase__ : Any = list(set(snake_case ) )
def SCREAMING_SNAKE_CASE__ ( snake_case : int , snake_case : Sequence[float] )-> list[str]:
'''simple docstring'''
UpperCAmelCase__ : str = []
for minterm in minterms:
UpperCAmelCase__ : str = ""
for _ in range(snake_case ):
UpperCAmelCase__ : List[Any] = str(minterm % 2 ) + string
minterm //= 2
temp.append(snake_case )
return temp
def SCREAMING_SNAKE_CASE__ ( snake_case : str , snake_case : str , snake_case : int )-> bool:
'''simple docstring'''
UpperCAmelCase__ : List[str] = list(snake_case )
UpperCAmelCase__ : Optional[Any] = list(snake_case )
UpperCAmelCase__ : int = 0
for i in range(len(snake_case ) ):
if lista[i] != lista[i]:
count_n += 1
return count_n == count
def SCREAMING_SNAKE_CASE__ ( snake_case : list[list[int]] , snake_case : list[str] )-> list[str]:
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = []
UpperCAmelCase__ : Tuple = [0] * len(snake_case )
for i in range(len(chart[0] ) ):
UpperCAmelCase__ : Dict = 0
UpperCAmelCase__ : List[str] = -1
for j in range(len(snake_case ) ):
if chart[j][i] == 1:
count += 1
UpperCAmelCase__ : Tuple = j
if count == 1:
UpperCAmelCase__ : Optional[Any] = 1
for i in range(len(snake_case ) ):
if select[i] == 1:
for j in range(len(chart[0] ) ):
if chart[i][j] == 1:
for k in range(len(snake_case ) ):
UpperCAmelCase__ : str = 0
temp.append(prime_implicants[i] )
while True:
UpperCAmelCase__ : int = 0
UpperCAmelCase__ : Any = -1
UpperCAmelCase__ : Any = 0
for i in range(len(snake_case ) ):
UpperCAmelCase__ : str = chart[i].count(1 )
if count_n > max_n:
UpperCAmelCase__ : Dict = count_n
UpperCAmelCase__ : List[Any] = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem] )
for i in range(len(chart[0] ) ):
if chart[rem][i] == 1:
for j in range(len(snake_case ) ):
UpperCAmelCase__ : Optional[int] = 0
def SCREAMING_SNAKE_CASE__ ( snake_case : list[str] , snake_case : list[str] )-> list[list[int]]:
'''simple docstring'''
UpperCAmelCase__ : Tuple = [[0 for x in range(len(snake_case ) )] for x in range(len(snake_case ) )]
for i in range(len(snake_case ) ):
UpperCAmelCase__ : Any = prime_implicants[i].count("_" )
for j in range(len(snake_case ) ):
if is_for_table(prime_implicants[i] , binary[j] , snake_case ):
UpperCAmelCase__ : Dict = 1
return chart
def SCREAMING_SNAKE_CASE__ ( )-> None:
'''simple docstring'''
UpperCAmelCase__ : Tuple = int(input("Enter the no. of variables\n" ) )
UpperCAmelCase__ : List[str] = [
float(snake_case )
for x in input(
"Enter the decimal representation of Minterms 'Spaces Separated'\n" ).split()
]
UpperCAmelCase__ : List[Any] = decimal_to_binary(snake_case , snake_case )
UpperCAmelCase__ : Tuple = check(snake_case )
print("Prime Implicants are:" )
print(snake_case )
UpperCAmelCase__ : int = prime_implicant_chart(snake_case , snake_case )
UpperCAmelCase__ : Any = selection(snake_case , snake_case )
print("Essential Prime Implicants are:" )
print(snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 298 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase__ ( __magic_name__ , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ =XLMTokenizer
SCREAMING_SNAKE_CASE_ =False
def __a ( self : Dict ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCAmelCase__ : Optional[int] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
UpperCAmelCase__ : Any = dict(zip(snake_case__ , range(len(snake_case__ ) ) ) )
UpperCAmelCase__ : Tuple = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
UpperCAmelCase__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase__ : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" ) as fp:
fp.write(json.dumps(snake_case__ ) )
with open(self.merges_file , "w" ) as fp:
fp.write("\n".join(snake_case__ ) )
def __a ( self : Union[str, Any] , snake_case__ : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = "lower newer"
UpperCAmelCase__ : Optional[Any] = "lower newer"
return input_text, output_text
def __a ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = XLMTokenizer(self.vocab_file , self.merges_file )
UpperCAmelCase__ : List[Any] = "lower"
UpperCAmelCase__ : Any = ["low", "er</w>"]
UpperCAmelCase__ : Any = tokenizer.tokenize(snake_case__ )
self.assertListEqual(snake_case__ , snake_case__ )
UpperCAmelCase__ : Optional[Any] = tokens + ["<unk>"]
UpperCAmelCase__ : List[Any] = [1_4, 1_5, 2_0]
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ ) , snake_case__ )
@slow
def __a ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Any = XLMTokenizer.from_pretrained("xlm-mlm-en-2048" )
UpperCAmelCase__ : str = tokenizer.encode("sequence builders" , add_special_tokens=snake_case__ )
UpperCAmelCase__ : Dict = tokenizer.encode("multi-sequence build" , add_special_tokens=snake_case__ )
UpperCAmelCase__ : Any = tokenizer.build_inputs_with_special_tokens(snake_case__ )
UpperCAmelCase__ : Optional[Any] = tokenizer.build_inputs_with_special_tokens(snake_case__ , snake_case__ )
assert encoded_sentence == [0] + text + [1]
assert encoded_pair == [0] + text + [1] + text_a + [1]
| 298 | 1 |
'''simple docstring'''
lowerCamelCase : Dict = "Alexander Joslin"
import operator as op
from .stack import Stack
def _lowerCAmelCase ( _UpperCamelCase : str ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE ={'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
_SCREAMING_SNAKE_CASE =Stack()
_SCREAMING_SNAKE_CASE =Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(_UpperCamelCase ) )
elif i in operators:
# RULE 2
operator_stack.push(_UpperCamelCase )
elif i == ")":
# RULE 4
_SCREAMING_SNAKE_CASE =operator_stack.peek()
operator_stack.pop()
_SCREAMING_SNAKE_CASE =operand_stack.peek()
operand_stack.pop()
_SCREAMING_SNAKE_CASE =operand_stack.peek()
operand_stack.pop()
_SCREAMING_SNAKE_CASE =operators[opr](_UpperCamelCase , _UpperCamelCase )
operand_stack.push(_UpperCamelCase )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
lowerCamelCase : Optional[Any] = "(5 + ((4 * 2) * (2 + 3)))"
# answer = 45
print(f'''{equation} = {dijkstras_two_stack_algorithm(equation)}''')
| 47 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase : Dict = logging.get_logger(__name__)
lowerCamelCase : List[Any] = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class A__ ( A__ ):
A__ = 'deta'
A__ = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self : Tuple , _a : Optional[int]=None , _a : int=900 , _a : Optional[Any]=2048 , _a : int=6 , _a : Tuple=2048 , _a : Optional[int]=8 , _a : Any=6 , _a : str=1024 , _a : int=8 , _a : int=0.0 , _a : Optional[Any]=True , _a : Tuple="relu" , _a : Union[str, Any]=256 , _a : Tuple=0.1 , _a : str=0.0 , _a : Dict=0.0 , _a : Tuple=0.02 , _a : Union[str, Any]=1.0 , _a : Any=True , _a : Tuple=False , _a : List[Any]="sine" , _a : str=5 , _a : List[Any]=4 , _a : str=4 , _a : Union[str, Any]=True , _a : Optional[int]=300 , _a : Dict=True , _a : List[Any]=True , _a : List[Any]=1 , _a : List[str]=5 , _a : int=2 , _a : Dict=1 , _a : str=1 , _a : Optional[Any]=5 , _a : Union[str, Any]=2 , _a : List[str]=0.1 , _a : List[Any]=0.25 , **_a : Union[str, Any] , ) -> List[str]:
'''simple docstring'''
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
_SCREAMING_SNAKE_CASE =CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'] )
else:
if isinstance(_a , _a ):
_SCREAMING_SNAKE_CASE =backbone_config.pop('model_type' )
_SCREAMING_SNAKE_CASE =CONFIG_MAPPING[backbone_model_type]
_SCREAMING_SNAKE_CASE =config_class.from_dict(_a )
_SCREAMING_SNAKE_CASE =backbone_config
_SCREAMING_SNAKE_CASE =num_queries
_SCREAMING_SNAKE_CASE =max_position_embeddings
_SCREAMING_SNAKE_CASE =d_model
_SCREAMING_SNAKE_CASE =encoder_ffn_dim
_SCREAMING_SNAKE_CASE =encoder_layers
_SCREAMING_SNAKE_CASE =encoder_attention_heads
_SCREAMING_SNAKE_CASE =decoder_ffn_dim
_SCREAMING_SNAKE_CASE =decoder_layers
_SCREAMING_SNAKE_CASE =decoder_attention_heads
_SCREAMING_SNAKE_CASE =dropout
_SCREAMING_SNAKE_CASE =attention_dropout
_SCREAMING_SNAKE_CASE =activation_dropout
_SCREAMING_SNAKE_CASE =activation_function
_SCREAMING_SNAKE_CASE =init_std
_SCREAMING_SNAKE_CASE =init_xavier_std
_SCREAMING_SNAKE_CASE =encoder_layerdrop
_SCREAMING_SNAKE_CASE =auxiliary_loss
_SCREAMING_SNAKE_CASE =position_embedding_type
# deformable attributes
_SCREAMING_SNAKE_CASE =num_feature_levels
_SCREAMING_SNAKE_CASE =encoder_n_points
_SCREAMING_SNAKE_CASE =decoder_n_points
_SCREAMING_SNAKE_CASE =two_stage
_SCREAMING_SNAKE_CASE =two_stage_num_proposals
_SCREAMING_SNAKE_CASE =with_box_refine
_SCREAMING_SNAKE_CASE =assign_first_stage
if two_stage is True and with_box_refine is False:
raise ValueError('If two_stage is True, with_box_refine must be True.' )
# Hungarian matcher
_SCREAMING_SNAKE_CASE =class_cost
_SCREAMING_SNAKE_CASE =bbox_cost
_SCREAMING_SNAKE_CASE =giou_cost
# Loss coefficients
_SCREAMING_SNAKE_CASE =mask_loss_coefficient
_SCREAMING_SNAKE_CASE =dice_loss_coefficient
_SCREAMING_SNAKE_CASE =bbox_loss_coefficient
_SCREAMING_SNAKE_CASE =giou_loss_coefficient
_SCREAMING_SNAKE_CASE =eos_coefficient
_SCREAMING_SNAKE_CASE =focal_alpha
super().__init__(is_encoder_decoder=_a , **_a )
@property
def A ( self : Dict ) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def A ( self : List[Any] ) -> int:
'''simple docstring'''
return self.d_model
def A ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =copy.deepcopy(self.__dict__ )
_SCREAMING_SNAKE_CASE =self.backbone_config.to_dict()
_SCREAMING_SNAKE_CASE =self.__class__.model_type
return output
| 47 | 1 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
a__: Optional[int] = get_logger(__name__)
class SCREAMING_SNAKE_CASE__ :
__SCREAMING_SNAKE_CASE = "dummy_data"
__SCREAMING_SNAKE_CASE = "datasets"
__SCREAMING_SNAKE_CASE = False
def __init__( self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = False,__lowerCamelCase = True,__lowerCamelCase = None,):
A__ = 0
A__ = dataset_name
A__ = cache_dir
A__ = use_local_dummy_data
A__ = config
# download_callbacks take a single url as input
A__ = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
A__ = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
A__ = str(_a )
# to be downloaded
A__ = None
A__ = None
@property
def UpperCamelCase ( self ):
if self._dummy_file is None:
A__ = self.download_dummy_data()
return self._dummy_file
@property
def UpperCamelCase ( self ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join('''dummy''',self.config.name,self.version_name )
# structure is dummy / version_name
return os.path.join('''dummy''',self.version_name )
@property
def UpperCamelCase ( self ):
return os.path.join(self.dummy_data_folder,'''dummy_data.zip''' )
def UpperCamelCase ( self ):
A__ = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
A__ = cached_path(
_a,cache_dir=self.cache_dir,extract_compressed_file=_a,force_extract=_a )
return os.path.join(_a,self.dummy_file_name )
@property
def UpperCamelCase ( self ):
return os.path.join(self.datasets_scripts_dir,self.dataset_name,self.dummy_zip_file )
@property
def UpperCamelCase ( self ):
if self._bucket_url is None:
A__ = hf_github_url(self.dataset_name,self.dummy_zip_file.replace(os.sep,'''/''' ) )
return self._bucket_url
@property
def UpperCamelCase ( self ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep,'''/''' ).split('''/''' )[:-1] )
def UpperCamelCase ( self,__lowerCamelCase,*__lowerCamelCase ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
A__ = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
A__ = self.dummy_file_name
# special case when data_url is a dict
if isinstance(_a,_a ):
return self.create_dummy_data_dict(_a,_a )
elif isinstance(_a,(list, tuple) ):
return self.create_dummy_data_list(_a,_a )
else:
return self.create_dummy_data_single(_a,_a )
def UpperCamelCase ( self,__lowerCamelCase,*__lowerCamelCase ):
return self.download_and_extract(_a )
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase ):
return self.download_and_extract(_a )
def UpperCamelCase ( self,__lowerCamelCase,*__lowerCamelCase,**__lowerCamelCase ):
return path
def UpperCamelCase ( self ):
return {}
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase ):
A__ = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(_a,_a ):
for single_url in single_urls:
download_callback(_a )
else:
A__ = single_urls
download_callback(_a )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(_a,_a ):
A__ = [os.path.join(_a,urllib.parse.quote_plus(Path(_a ).name ) ) for x in single_urls]
else:
A__ = single_urls
A__ = os.path.join(_a,urllib.parse.quote_plus(Path(_a ).name ) )
A__ = value
# make sure that values are unique
if all(isinstance(_a,_a ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
A__ = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase ):
A__ = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
A__ = all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}''',_a ) ) for url in data_url )
A__ = all(
url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed''' ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
A__ = [data_url[0]] * len(_a )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(_a )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
A__ = os.path.join(_a,urllib.parse.quote_plus(single_url.split('''/''' )[-1] ) )
dummy_data_list.append(_a )
return dummy_data_list
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase ):
for download_callback in self.download_callbacks:
download_callback(_a )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
A__ = os.path.join(_a,urllib.parse.quote_plus(data_url.split('''/''' )[-1] ) )
if os.path.exists(_a ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def UpperCamelCase ( self ):
pass
def UpperCamelCase ( self ):
pass
def UpperCamelCase ( self,__lowerCamelCase ):
def _iter_archive_members(__lowerCamelCase ):
# this preserves the order of the members inside the ZIP archive
A__ = Path(self.dummy_file ).parent
A__ = path.relative_to(_a )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
A__ = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(_a )
A__ = Path(_a )
A__ = _iter_archive_members(_a ) if self.use_local_dummy_data else path.rglob('''*''' )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''') ):
yield file_path.relative_to(_a ).as_posix(), file_path.open('''rb''' )
def UpperCamelCase ( self,__lowerCamelCase ):
if not isinstance(_a,_a ):
A__ = [paths]
for path in paths:
if os.path.isfile(_a ):
if os.path.basename(_a ).startswith(('''.''', '''__''') ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(_a ):
if os.path.basename(_a ).startswith(('''.''', '''__''') ):
continue
dirnames.sort()
for filename in sorted(_a ):
if filename.startswith(('''.''', '''__''') ):
continue
yield os.path.join(_a,_a )
| 365 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class SCREAMING_SNAKE_CASE__ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
def __init__( self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = 1.0,__lowerCamelCase = None,):
super().__init__()
A__ = initial_learning_rate
A__ = warmup_steps
A__ = power
A__ = decay_schedule_fn
A__ = name
def __call__( self,__lowerCamelCase ):
with tf.name_scope(self.name or '''WarmUp''' ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
A__ = tf.cast(__lowerCamelCase,tf.floataa )
A__ = tf.cast(self.warmup_steps,tf.floataa )
A__ = global_step_float / warmup_steps_float
A__ = self.initial_learning_rate * tf.math.pow(__lowerCamelCase,self.power )
return tf.cond(
global_step_float < warmup_steps_float,lambda: warmup_learning_rate,lambda: self.decay_schedule_fn(step - self.warmup_steps ),name=__lowerCamelCase,)
def UpperCamelCase ( self ):
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def UpperCamelCase__( UpperCamelCase__ : float , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : float = 0.0 , UpperCamelCase__ : float = 0.9 , UpperCamelCase__ : float = 0.999 , UpperCamelCase__ : float = 1e-8 , UpperCamelCase__ : Optional[float] = None , UpperCamelCase__ : Optional[float] = None , UpperCamelCase__ : float = 0.0 , UpperCamelCase__ : float = 1.0 , UpperCamelCase__ : Optional[List[str]] = None , )->int:
A__ = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=UpperCamelCase__ , decay_steps=num_train_steps - num_warmup_steps , end_learning_rate=init_lr * min_lr_ratio , power=UpperCamelCase__ , )
if num_warmup_steps:
A__ = WarmUp(
initial_learning_rate=UpperCamelCase__ , decay_schedule_fn=UpperCamelCase__ , warmup_steps=UpperCamelCase__ , )
if weight_decay_rate > 0.0:
A__ = AdamWeightDecay(
learning_rate=UpperCamelCase__ , weight_decay_rate=UpperCamelCase__ , beta_a=UpperCamelCase__ , beta_a=UpperCamelCase__ , epsilon=UpperCamelCase__ , clipnorm=UpperCamelCase__ , global_clipnorm=UpperCamelCase__ , exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias'''] , include_in_weight_decay=UpperCamelCase__ , )
else:
A__ = tf.keras.optimizers.Adam(
learning_rate=UpperCamelCase__ , beta_a=UpperCamelCase__ , beta_a=UpperCamelCase__ , epsilon=UpperCamelCase__ , clipnorm=UpperCamelCase__ , global_clipnorm=UpperCamelCase__ , )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
def __init__( self,__lowerCamelCase = 0.001,__lowerCamelCase = 0.9,__lowerCamelCase = 0.999,__lowerCamelCase = 1E-7,__lowerCamelCase = False,__lowerCamelCase = 0.0,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = "AdamWeightDecay",**__lowerCamelCase,):
super().__init__(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase )
A__ = weight_decay_rate
A__ = include_in_weight_decay
A__ = exclude_from_weight_decay
@classmethod
def UpperCamelCase ( cls,__lowerCamelCase ):
A__ = {'''WarmUp''': WarmUp}
return super(__lowerCamelCase,cls ).from_config(__lowerCamelCase,custom_objects=__lowerCamelCase )
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
super(__lowerCamelCase,self )._prepare_local(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )
A__ = tf.constant(
self.weight_decay_rate,name='''adam_weight_decay_rate''' )
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
A__ = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate'''],use_locking=self._use_locking,)
return tf.no_op()
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase=None,**__lowerCamelCase ):
A__ , A__ = list(zip(*__lowerCamelCase ) )
return super(__lowerCamelCase,self ).apply_gradients(zip(__lowerCamelCase,__lowerCamelCase ),name=__lowerCamelCase,**__lowerCamelCase )
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
A__ = apply_state or {}
A__ = apply_state.get((var_device, var_dtype) )
if coefficients is None:
A__ = self._fallback_apply_state(__lowerCamelCase,__lowerCamelCase )
A__ = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=None ):
A__ , A__ = self._get_lr(var.device,var.dtype.base_dtype,__lowerCamelCase )
A__ = self._decay_weights_op(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )
with tf.control_dependencies([decay] ):
return super(__lowerCamelCase,self )._resource_apply_dense(__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase )
def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=None ):
A__ , A__ = self._get_lr(var.device,var.dtype.base_dtype,__lowerCamelCase )
A__ = self._decay_weights_op(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )
with tf.control_dependencies([decay] ):
return super(__lowerCamelCase,self )._resource_apply_sparse(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase )
def UpperCamelCase ( self ):
A__ = super().get_config()
config.update({'''weight_decay_rate''': self.weight_decay_rate} )
return config
def UpperCamelCase ( self,__lowerCamelCase ):
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(__lowerCamelCase,__lowerCamelCase ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(__lowerCamelCase,__lowerCamelCase ) is not None:
return False
return True
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
def __init__( self ):
A__ = []
A__ = None
@property
def UpperCamelCase ( self ):
if self._accum_steps is None:
A__ = tf.Variable(
tf.constant(0,dtype=tf.intaa ),trainable=__lowerCamelCase,synchronization=tf.VariableSynchronization.ON_READ,aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,)
return self._accum_steps.value()
@property
def UpperCamelCase ( self ):
if not self._gradients:
raise ValueError('''The accumulator should be called first to initialize the gradients''' )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self,__lowerCamelCase ):
if not self._gradients:
A__ = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(__lowerCamelCase ),trainable=__lowerCamelCase,synchronization=tf.VariableSynchronization.ON_READ,aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,)
if gradient is not None
else gradient
for gradient in gradients
] )
if len(__lowerCamelCase ) != len(self._gradients ):
raise ValueError(f"Expected {len(self._gradients )} gradients, but got {len(__lowerCamelCase )}" )
for accum_gradient, gradient in zip(self._gradients,__lowerCamelCase ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(__lowerCamelCase )
self._accum_steps.assign_add(1 )
def UpperCamelCase ( self ):
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(__lowerCamelCase ) )
| 39 | 0 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class lowerCamelCase (unittest.TestCase ):
'''simple docstring'''
@parameterized.expand([(None,), ('foo.json',)] )
def __UpperCAmelCase ( self , _UpperCamelCase ) -> List[Any]:
UpperCAmelCase_ : Union[str, Any] = GenerationConfig(
do_sample=_UpperCamelCase , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_UpperCamelCase , config_name=_UpperCamelCase )
UpperCAmelCase_ : str = GenerationConfig.from_pretrained(_UpperCamelCase , config_name=_UpperCamelCase )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , _UpperCamelCase )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 5_0 )
self.assertEqual(loaded_config.max_length , 2_0 )
self.assertEqual(loaded_config.max_time , _UpperCamelCase )
def __UpperCAmelCase ( self ) -> List[str]:
UpperCAmelCase_ : Optional[int] = AutoConfig.from_pretrained('gpt2' )
UpperCAmelCase_ : Tuple = GenerationConfig.from_model_config(_UpperCamelCase )
UpperCAmelCase_ : int = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(_UpperCamelCase , _UpperCamelCase )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def __UpperCAmelCase ( self ) -> str:
UpperCAmelCase_ : List[str] = GenerationConfig()
UpperCAmelCase_ : int = {
'max_new_tokens': 1_0_2_4,
'foo': 'bar',
}
UpperCAmelCase_ : List[Any] = copy.deepcopy(_UpperCamelCase )
UpperCAmelCase_ : Tuple = generation_config.update(**_UpperCamelCase )
# update_kwargs was not modified (no side effects)
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1_0_2_4 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(_UpperCamelCase , {'foo': 'bar'} )
def __UpperCAmelCase ( self ) -> int:
UpperCAmelCase_ : int = GenerationConfig()
UpperCAmelCase_ : Union[str, Any] = 'bar'
with tempfile.TemporaryDirectory('test-generation-config' ) as tmp_dir:
generation_config.save_pretrained(_UpperCamelCase )
UpperCAmelCase_ : List[str] = GenerationConfig.from_pretrained(_UpperCamelCase )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , 'bar' )
UpperCAmelCase_ : Tuple = GenerationConfig.from_model_config(_UpperCamelCase )
assert not hasattr(_UpperCamelCase , 'foo' ) # no new kwargs should be initialized if from config
def __UpperCAmelCase ( self ) -> Dict:
UpperCAmelCase_ : List[Any] = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , _UpperCamelCase )
self.assertEqual(default_config.num_beams , 1 )
UpperCAmelCase_ : List[Any] = GenerationConfig(
do_sample=_UpperCamelCase , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , _UpperCamelCase )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_UpperCamelCase )
UpperCAmelCase_ : str = GenerationConfig.from_pretrained(_UpperCamelCase , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , _UpperCamelCase )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class lowerCamelCase (unittest.TestCase ):
'''simple docstring'''
@classmethod
def __UpperCAmelCase ( cls ) -> Optional[int]:
UpperCAmelCase_ : Dict = TOKEN
HfFolder.save_token(_UpperCamelCase )
@classmethod
def __UpperCAmelCase ( cls ) -> List[Any]:
try:
delete_repo(token=cls._token , repo_id='test-generation-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-generation-config-org' )
except HTTPError:
pass
def __UpperCAmelCase ( self ) -> Union[str, Any]:
UpperCAmelCase_ : List[str] = GenerationConfig(
do_sample=_UpperCamelCase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('test-generation-config' , use_auth_token=self._token )
UpperCAmelCase_ : Optional[int] = GenerationConfig.from_pretrained(f"{USER}/test-generation-config" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_UpperCamelCase , getattr(_UpperCamelCase , _UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-generation-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
_UpperCamelCase , repo_id='test-generation-config' , push_to_hub=_UpperCamelCase , use_auth_token=self._token )
UpperCAmelCase_ : Optional[int] = GenerationConfig.from_pretrained(f"{USER}/test-generation-config" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_UpperCamelCase , getattr(_UpperCamelCase , _UpperCamelCase ) )
def __UpperCAmelCase ( self ) -> int:
UpperCAmelCase_ : List[str] = GenerationConfig(
do_sample=_UpperCamelCase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('valid_org/test-generation-config-org' , use_auth_token=self._token )
UpperCAmelCase_ : Union[str, Any] = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_UpperCamelCase , getattr(_UpperCamelCase , _UpperCamelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-generation-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
_UpperCamelCase , repo_id='valid_org/test-generation-config-org' , push_to_hub=_UpperCamelCase , use_auth_token=self._token )
UpperCAmelCase_ : Union[str, Any] = GenerationConfig.from_pretrained('valid_org/test-generation-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_UpperCamelCase , getattr(_UpperCamelCase , _UpperCamelCase ) )
| 29 |
__UpperCAmelCase = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'dataclasses': 'dataclasses',
'datasets': 'datasets!=2.5.0',
'decord': 'decord==0.6.0',
'deepspeed': 'deepspeed>=0.9.3',
'diffusers': 'diffusers',
'dill': 'dill<0.3.5',
'evaluate': 'evaluate>=0.2.0',
'fairscale': 'fairscale>0.3',
'faiss-cpu': 'faiss-cpu',
'fastapi': 'fastapi',
'filelock': 'filelock',
'flax': 'flax>=0.4.1,<=0.7.0',
'ftfy': 'ftfy',
'fugashi': 'fugashi>=1.0',
'GitPython': 'GitPython<3.1.19',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
'huggingface-hub': 'huggingface-hub>=0.14.1,<1.0',
'importlib_metadata': 'importlib_metadata',
'ipadic': 'ipadic>=1.0.0,<2.0',
'isort': 'isort>=5.5.4',
'jax': 'jax>=0.2.8,!=0.3.2,<=0.4.13',
'jaxlib': 'jaxlib>=0.1.65,<=0.4.13',
'jieba': 'jieba',
'kenlm': 'kenlm',
'keras-nlp': 'keras-nlp>=0.3.1',
'librosa': 'librosa',
'nltk': 'nltk',
'natten': 'natten>=0.14.6',
'numpy': 'numpy>=1.17',
'onnxconverter-common': 'onnxconverter-common',
'onnxruntime-tools': 'onnxruntime-tools>=1.4.2',
'onnxruntime': 'onnxruntime>=1.4.0',
'opencv-python': 'opencv-python',
'optuna': 'optuna',
'optax': 'optax>=0.0.8,<=0.1.4',
'packaging': 'packaging>=20.0',
'parameterized': 'parameterized',
'phonemizer': 'phonemizer',
'protobuf': 'protobuf',
'psutil': 'psutil',
'pyyaml': 'pyyaml>=5.1',
'pydantic': 'pydantic<2',
'pytest': 'pytest>=7.2.0',
'pytest-timeout': 'pytest-timeout',
'pytest-xdist': 'pytest-xdist',
'python': 'python>=3.8.0',
'ray[tune]': 'ray[tune]',
'regex': 'regex!=2019.12.17',
'requests': 'requests',
'rhoknp': 'rhoknp>=1.1.0,<1.3.1',
'rjieba': 'rjieba',
'rouge-score': 'rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1',
'ruff': 'ruff>=0.0.241,<=0.0.259',
'sacrebleu': 'sacrebleu>=1.4.12,<2.0.0',
'sacremoses': 'sacremoses',
'safetensors': 'safetensors>=0.3.1',
'sagemaker': 'sagemaker>=2.31.0',
'scikit-learn': 'scikit-learn',
'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
'sigopt': 'sigopt',
'starlette': 'starlette',
'sudachipy': 'sudachipy>=0.6.6',
'sudachidict_core': 'sudachidict_core>=20220729',
'tensorflow-cpu': 'tensorflow-cpu>=2.6,<2.14',
'tensorflow': 'tensorflow>=2.6,<2.14',
'tensorflow-text': 'tensorflow-text<2.14',
'tf2onnx': 'tf2onnx',
'timeout-decorator': 'timeout-decorator',
'timm': 'timm',
'tokenizers': 'tokenizers>=0.11.1,!=0.11.3,<0.14',
'torch': 'torch>=1.9,!=1.12.0',
'torchaudio': 'torchaudio',
'torchvision': 'torchvision',
'pyctcdecode': 'pyctcdecode>=0.4.0',
'tqdm': 'tqdm>=4.27',
'unidic': 'unidic>=1.0.2',
'unidic_lite': 'unidic_lite>=1.0.7',
'urllib3': 'urllib3<2.0.0',
'uvicorn': 'uvicorn',
}
| 29 | 1 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( lowercase_ = 4 ) -> list[list[int]]:
A__ = abs(lowercase_ ) or 4
return [[1 + x + y * row_size for x in range(lowercase_ )] for y in range(lowercase_ )]
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[list[int]]:
return reverse_row(transpose(lowercase_ ) )
# OR.. transpose(reverse_column(matrix))
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[list[int]]:
return reverse_row(reverse_column(lowercase_ ) )
# OR.. reverse_column(reverse_row(matrix))
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[list[int]]:
return reverse_column(transpose(lowercase_ ) )
# OR.. transpose(reverse_row(matrix))
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[list[int]]:
A__ = [list(lowercase_ ) for x in zip(*lowercase_ )]
return matrix
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[list[int]]:
A__ = matrix[::-1]
return matrix
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[list[int]]:
A__ = [x[::-1] for x in matrix]
return matrix
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> None:
for i in matrix:
print(*lowercase_ )
if __name__ == "__main__":
lowercase = make_matrix()
print("\norigin:\n")
print_matrix(matrix)
print("\nrotate 90 counterclockwise:\n")
print_matrix(rotate_aa(matrix))
lowercase = make_matrix()
print("\norigin:\n")
print_matrix(matrix)
print("\nrotate 180:\n")
print_matrix(rotate_aaa(matrix))
lowercase = make_matrix()
print("\norigin:\n")
print_matrix(matrix)
print("\nrotate 270 counterclockwise:\n")
print_matrix(rotate_aaa(matrix))
| 370 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
SCREAMING_SNAKE_CASE = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
),
},
}
SCREAMING_SNAKE_CASE = {
"facebook/nllb-large-en-ro": 1024,
"facebook/nllb-200-distilled-600M": 1024,
}
# fmt: off
SCREAMING_SNAKE_CASE = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class UpperCAmelCase_ ( A_ ):
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = ['''input_ids''', '''attention_mask''']
lowercase__ = NllbTokenizer
lowercase__ = []
lowercase__ = []
def __init__( self : int , snake_case_ : int=None , snake_case_ : Any=None , snake_case_ : int="<s>" , snake_case_ : List[Any]="</s>" , snake_case_ : Optional[int]="</s>" , snake_case_ : int="<s>" , snake_case_ : str="<unk>" , snake_case_ : str="<pad>" , snake_case_ : Optional[int]="<mask>" , snake_case_ : str=None , snake_case_ : List[Any]=None , snake_case_ : Tuple=None , snake_case_ : Optional[int]=False , **snake_case_ : List[str] , ) -> Tuple:
'''simple docstring'''
A__ = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else mask_token
A__ = legacy_behaviour
super().__init__(
vocab_file=snake_case_ , tokenizer_file=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , sep_token=snake_case_ , cls_token=snake_case_ , unk_token=snake_case_ , pad_token=snake_case_ , mask_token=snake_case_ , src_lang=snake_case_ , tgt_lang=snake_case_ , additional_special_tokens=snake_case_ , legacy_behaviour=snake_case_ , **snake_case_ , )
A__ = vocab_file
A__ = False if not self.vocab_file else True
A__ = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"additional_special_tokens": _additional_special_tokens} )
A__ = {
lang_code: self.convert_tokens_to_ids(snake_case_ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
A__ = src_lang if src_lang is not None else "eng_Latn"
A__ = self.convert_tokens_to_ids(self._src_lang )
A__ = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def __magic_name__ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
return self._src_lang
@src_lang.setter
def __magic_name__ ( self : Optional[int] , snake_case_ : str ) -> None:
'''simple docstring'''
A__ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __magic_name__ ( self : Optional[int] , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __magic_name__ ( self : Any , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
A__ = [self.sep_token_id]
A__ = [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 __magic_name__ ( self : int , snake_case_ : Tuple , snake_case_ : str , snake_case_ : Optional[str] , snake_case_ : Optional[str] , **snake_case_ : Tuple ) -> List[Any]:
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
A__ = src_lang
A__ = self(snake_case_ , add_special_tokens=snake_case_ , return_tensors=snake_case_ , **snake_case_ )
A__ = self.convert_tokens_to_ids(snake_case_ )
A__ = tgt_lang_id
return inputs
def __magic_name__ ( self : int , snake_case_ : List[str] , snake_case_ : str = "eng_Latn" , snake_case_ : Optional[List[str]] = None , snake_case_ : str = "fra_Latn" , **snake_case_ : Dict , ) -> BatchEncoding:
'''simple docstring'''
A__ = src_lang
A__ = tgt_lang
return super().prepare_seqaseq_batch(snake_case_ , snake_case_ , **snake_case_ )
def __magic_name__ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
return self.set_src_lang_special_tokens(self.src_lang )
def __magic_name__ ( self : Tuple ) -> Dict:
'''simple docstring'''
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def __magic_name__ ( self : List[Any] , snake_case_ : Dict ) -> None:
'''simple docstring'''
A__ = self.convert_tokens_to_ids(snake_case_ )
if self.legacy_behaviour:
A__ = []
A__ = [self.eos_token_id, self.cur_lang_code]
else:
A__ = [self.cur_lang_code]
A__ = [self.eos_token_id]
A__ = self.convert_ids_to_tokens(self.prefix_tokens )
A__ = self.convert_ids_to_tokens(self.suffix_tokens )
A__ = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def __magic_name__ ( self : List[Any] , snake_case_ : str ) -> None:
'''simple docstring'''
A__ = self.convert_tokens_to_ids(snake_case_ )
if self.legacy_behaviour:
A__ = []
A__ = [self.eos_token_id, self.cur_lang_code]
else:
A__ = [self.cur_lang_code]
A__ = [self.eos_token_id]
A__ = self.convert_ids_to_tokens(self.prefix_tokens )
A__ = self.convert_ids_to_tokens(self.suffix_tokens )
A__ = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def __magic_name__ ( self : List[str] , snake_case_ : str , snake_case_ : Optional[str] = None ) -> Tuple[str]:
'''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(snake_case_ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
return
A__ = os.path.join(
snake_case_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ):
copyfile(self.vocab_file , snake_case_ )
return (out_vocab_file,)
| 230 | 0 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class lowerCamelCase_ :
'''simple docstring'''
@staticmethod
def _A ( *A : int , **A : Tuple ):
pass
@is_pipeline_test
@require_vision
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
@require_torch
def _A ( self : str ):
_UpperCAmelCase : Tuple = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , )
_UpperCAmelCase : str = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
_UpperCAmelCase : Optional[Any] = image_classifier(A , candidate_labels=["a", "b", "c"] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(A ) , [
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}],
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}, {"score": 0.333, "label": "b"}],
] , )
_UpperCAmelCase : Tuple = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(A ) , [
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
] , )
@require_tf
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Tuple = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , framework="tf" )
_UpperCAmelCase : Any = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
_UpperCAmelCase : List[str] = image_classifier(A , candidate_labels=["a", "b", "c"] )
self.assertEqual(
nested_simplify(A ) , [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}] , )
_UpperCAmelCase : Any = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(A ) , [
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
] , )
@slow
@require_torch
def _A ( self : int ):
_UpperCAmelCase : Tuple = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , )
# This is an image of 2 cats with remotes and no planes
_UpperCAmelCase : Any = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
_UpperCAmelCase : List[str] = image_classifier(A , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(A ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
_UpperCAmelCase : Optional[Any] = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(A ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , )
@slow
@require_tf
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Tuple = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , framework="tf" )
# This is an image of 2 cats with remotes and no planes
_UpperCAmelCase : Optional[int] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
_UpperCAmelCase : Optional[int] = image_classifier(A , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(A ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
_UpperCAmelCase : Dict = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(A ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , )
| 31 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def lowercase ():
# Get the sagemaker specific mp parameters from smp_options variable.
__lowerCAmelCase = os.getenv("""SM_HP_MP_PARAMETERS""" , """{}""" )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
__lowerCAmelCase = json.loads(_lowerCAmelCase )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
__lowerCAmelCase = os.getenv("""SM_FRAMEWORK_PARAMS""" , """{}""" )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
__lowerCAmelCase = json.loads(_lowerCAmelCase )
if not mpi_options.get("""sagemaker_mpi_enabled""" , _lowerCAmelCase ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec("""smdistributed""" ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class lowerCAmelCase_ ( A__ ):
'''simple docstring'''
_snake_case = field(
default='''''' , metadata={'''help''': '''Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer'''} , )
def A__ ( self ) -> Tuple:
super().__post_init__()
warnings.warn(
"""`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
"""`TrainingArguments` instead.""" , snake_case_ , )
@cached_property
def A__ ( self ) -> "torch.device":
logger.info("""PyTorch: setting up devices""" )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
"""torch.distributed process group is initialized, but local_rank == -1. """
"""In order to use Torch DDP, launch your script with `python -m torch.distributed.launch""" )
if self.no_cuda:
__lowerCAmelCase = torch.device("""cpu""" )
__lowerCAmelCase = 0
elif is_sagemaker_model_parallel_available():
__lowerCAmelCase = smp.local_rank()
__lowerCAmelCase = torch.device("""cuda""" , snake_case_ )
__lowerCAmelCase = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend="""smddp""" , timeout=self.ddp_timeout_delta )
__lowerCAmelCase = int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK""" ) )
__lowerCAmelCase = torch.device("""cuda""" , self.local_rank )
__lowerCAmelCase = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
__lowerCAmelCase = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
__lowerCAmelCase = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend="""nccl""" , timeout=self.ddp_timeout_delta )
__lowerCAmelCase = torch.device("""cuda""" , self.local_rank )
__lowerCAmelCase = 1
if device.type == "cuda":
torch.cuda.set_device(snake_case_ )
return device
@property
def A__ ( self ) -> Dict:
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def A__ ( self ) -> Optional[int]:
return not is_sagemaker_model_parallel_available()
@property
def A__ ( self ) -> Tuple:
return False
| 301 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"""google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
"""google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class SCREAMING_SNAKE_CASE_ ( _a ):
__magic_name__: Any = "mobilenet_v1"
def __init__( self : int , _A : Tuple=3 , _A : str=224 , _A : Dict=1.0 , _A : List[Any]=8 , _A : Tuple="relu6" , _A : Dict=True , _A : Optional[int]=0.9_9_9 , _A : List[Any]=0.0_2 , _A : Optional[Any]=0.0_0_1 , **_A : Optional[int] , ) -> Dict:
"""simple docstring"""
super().__init__(**_a )
if depth_multiplier <= 0:
raise ValueError('depth_multiplier must be greater than zero.' )
snake_case_ : str = num_channels
snake_case_ : Union[str, Any] = image_size
snake_case_ : Dict = depth_multiplier
snake_case_ : Union[str, Any] = min_depth
snake_case_ : Any = hidden_act
snake_case_ : int = tf_padding
snake_case_ : Dict = classifier_dropout_prob
snake_case_ : Any = initializer_range
snake_case_ : List[str] = layer_norm_eps
class SCREAMING_SNAKE_CASE_ ( _a ):
__magic_name__: Any = version.parse("1.11" )
@property
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
"""simple docstring"""
return OrderedDict([('pixel_values', {0: 'batch'})] )
@property
def UpperCAmelCase_ ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
if self.task == "image-classification":
return OrderedDict([('logits', {0: 'batch'})] )
else:
return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})] )
@property
def UpperCAmelCase_ ( self : Dict ) -> Any:
"""simple docstring"""
return 1E-4
| 370 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE_ ( snake_case_ , snake_case_ ):
@register_to_config
def __init__( self : Optional[Any] , *,
_A : int = 4 , _A : int = 768 , _A : int , _A : Tuple , ) -> Dict:
"""simple docstring"""
super().__init__()
snake_case_ : int = nn.Parameter(torch.zeros(_A ) )
# parameters for additional clip time embeddings
snake_case_ : Tuple = nn.Linear(_A , _A )
snake_case_ : List[Any] = nn.Linear(_A , _A )
# parameters for encoder hidden states
snake_case_ : Union[str, Any] = clip_extra_context_tokens
snake_case_ : str = nn.Linear(
_A , self.clip_extra_context_tokens * cross_attention_dim )
snake_case_ : Any = nn.Linear(_A , _A )
snake_case_ : Tuple = nn.LayerNorm(_A )
def UpperCAmelCase_ ( self : List[str] , *, _A : Tuple , _A : List[Any] , _A : str , _A : Optional[Any] ) -> List[Any]:
"""simple docstring"""
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
snake_case_ : Optional[int] = image_embeddings.shape[0]
snake_case_ : Optional[Any] = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
snake_case_ : Optional[Any] = classifier_free_guidance_embeddings.expand(
_A , -1 )
snake_case_ : Any = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
snake_case_ : str = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
snake_case_ : str = self.embedding_proj(_A )
snake_case_ : Dict = self.clip_image_embeddings_project_to_time_embeddings(_A )
snake_case_ : Tuple = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
snake_case_ : List[str] = self.clip_extra_context_tokens_proj(_A )
snake_case_ : Optional[Any] = clip_extra_context_tokens.reshape(_A , -1 , self.clip_extra_context_tokens )
snake_case_ : int = clip_extra_context_tokens.permute(0 , 2 , 1 )
snake_case_ : Optional[int] = self.encoder_hidden_states_proj(_A )
snake_case_ : Any = self.text_encoder_hidden_states_norm(_A )
snake_case_ : Dict = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 88 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'microsoft/table-transformer-detection': (
'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
),
}
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :List[str] = '''table-transformer'''
lowerCamelCase :Tuple = ['''past_key_values''']
lowerCamelCase :Optional[int] = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
}
def __init__( self , lowerCAmelCase_=True , lowerCAmelCase_=None , lowerCAmelCase_=3 , lowerCAmelCase_=1_00 , lowerCAmelCase_=6 , lowerCAmelCase_=20_48 , lowerCAmelCase_=8 , lowerCAmelCase_=6 , lowerCAmelCase_=20_48 , lowerCAmelCase_=8 , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.0 , lowerCAmelCase_=True , lowerCAmelCase_="relu" , lowerCAmelCase_=2_56 , lowerCAmelCase_=0.1 , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.0 , lowerCAmelCase_=0.02 , lowerCAmelCase_=1.0 , lowerCAmelCase_=False , lowerCAmelCase_="sine" , lowerCAmelCase_="resnet50" , lowerCAmelCase_=True , lowerCAmelCase_=False , lowerCAmelCase_=1 , lowerCAmelCase_=5 , lowerCAmelCase_=2 , lowerCAmelCase_=1 , lowerCAmelCase_=1 , lowerCAmelCase_=5 , lowerCAmelCase_=2 , lowerCAmelCase_=0.1 , **lowerCAmelCase_ , ) -> Any:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
_A = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_A = backbone_config.get("""model_type""" )
_A = CONFIG_MAPPING[backbone_model_type]
_A = config_class.from_dict(lowerCAmelCase_ )
# set timm attributes to None
_A , _A , _A = None, None, None
_A = use_timm_backbone
_A = backbone_config
_A = num_channels
_A = num_queries
_A = d_model
_A = encoder_ffn_dim
_A = encoder_layers
_A = encoder_attention_heads
_A = decoder_ffn_dim
_A = decoder_layers
_A = decoder_attention_heads
_A = dropout
_A = attention_dropout
_A = activation_dropout
_A = activation_function
_A = init_std
_A = init_xavier_std
_A = encoder_layerdrop
_A = decoder_layerdrop
_A = encoder_layers
_A = auxiliary_loss
_A = position_embedding_type
_A = backbone
_A = use_pretrained_backbone
_A = dilation
# Hungarian matcher
_A = class_cost
_A = bbox_cost
_A = giou_cost
# Loss coefficients
_A = mask_loss_coefficient
_A = dice_loss_coefficient
_A = bbox_loss_coefficient
_A = giou_loss_coefficient
_A = eos_coefficient
super().__init__(is_encoder_decoder=lowerCAmelCase_ , **lowerCAmelCase_ )
@property
def UpperCAmelCase ( self ) -> int:
return self.encoder_attention_heads
@property
def UpperCAmelCase ( self ) -> int:
return self.d_model
class a ( __lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase :Dict = version.parse('''1.11''' )
@property
def UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def UpperCAmelCase ( self ) -> float:
return 1E-5
@property
def UpperCAmelCase ( self ) -> int:
return 12
| 180 |
import math
import sys
def snake_case ( snake_case__ :int) -> int:
if number != int(snake_case__):
raise ValueError("""the value of input must be a natural number""")
if number < 0:
raise ValueError("""the value of input must not be a negative number""")
if number == 0:
return 1
_A = [-1] * (number + 1)
_A = 0
for i in range(1 , number + 1):
_A = sys.maxsize
_A = int(math.sqrt(snake_case__))
for j in range(1 , root + 1):
_A = 1 + answers[i - (j**2)]
_A = min(snake_case__ , snake_case__)
_A = answer
return answers[number]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 180 | 1 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score # noqa: E402 # isort:skip
A__ : int =logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
return max(metric_fn(lowerCAmelCase , lowerCAmelCase ) for gt in ground_truths )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = [line.strip() for line in open(lowerCAmelCase , """r""" ).readlines()]
_lowerCAmelCase = []
if args.gold_data_mode == "qa":
_lowerCAmelCase = pd.read_csv(lowerCAmelCase , sep="""\t""" , header=lowerCAmelCase )
for answer_list in data[1]:
_lowerCAmelCase = ast.literal_eval(lowerCAmelCase )
answers.append(lowerCAmelCase )
else:
_lowerCAmelCase = [line.strip() for line in open(lowerCAmelCase , """r""" ).readlines()]
_lowerCAmelCase = [[reference] for reference in references]
_lowerCAmelCase = _lowerCAmelCase = _lowerCAmelCase = 0
for prediction, ground_truths in zip(lowerCAmelCase , lowerCAmelCase ):
total += 1
em += metric_max_over_ground_truths(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
fa += metric_max_over_ground_truths(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
_lowerCAmelCase = 100.0 * em / total
_lowerCAmelCase = 100.0 * fa / total
logger.info(f"F1: {fa:.2f}" )
logger.info(f"EM: {em:.2f}" )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = args.k
_lowerCAmelCase = [line.strip() for line in open(lowerCAmelCase , """r""" ).readlines()]
_lowerCAmelCase = [line.strip() for line in open(lowerCAmelCase , """r""" ).readlines()]
_lowerCAmelCase = _lowerCAmelCase = 0
for hypo, reference in zip(lowerCAmelCase , lowerCAmelCase ):
_lowerCAmelCase = set(hypo.split("""\t""" )[:k] )
_lowerCAmelCase = set(reference.split("""\t""" ) )
total += 1
em += len(hypo_provenance & ref_provenance ) / k
_lowerCAmelCase = 100.0 * em / total
logger.info(f"Precision@{k}: {em: .2f}" )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
def strip_title(lowerCAmelCase ):
if title.startswith("""\"""" ):
_lowerCAmelCase = title[1:]
if title.endswith("""\"""" ):
_lowerCAmelCase = title[:-1]
return title
_lowerCAmelCase = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
lowerCAmelCase , return_tensors="""pt""" , padding=lowerCAmelCase , truncation=lowerCAmelCase , )["""input_ids"""].to(args.device )
_lowerCAmelCase = rag_model.rag.question_encoder(lowerCAmelCase )
_lowerCAmelCase = question_enc_outputs[0]
_lowerCAmelCase = rag_model.retriever(
lowerCAmelCase , question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy() , prefix=rag_model.rag.generator.config.prefix , n_docs=rag_model.config.n_docs , return_tensors="""pt""" , )
_lowerCAmelCase = rag_model.retriever.index.get_doc_dicts(result.doc_ids )
_lowerCAmelCase = []
for docs in all_docs:
_lowerCAmelCase = [strip_title(lowerCAmelCase ) for title in docs["""title"""]]
provenance_strings.append("""\t""".join(lowerCAmelCase ) )
return provenance_strings
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
with torch.no_grad():
_lowerCAmelCase = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
lowerCAmelCase , return_tensors="""pt""" , padding=lowerCAmelCase , truncation=lowerCAmelCase )
_lowerCAmelCase = inputs_dict.input_ids.to(args.device )
_lowerCAmelCase = inputs_dict.attention_mask.to(args.device )
_lowerCAmelCase = rag_model.generate( # rag_model overwrites generate
lowerCAmelCase , attention_mask=lowerCAmelCase , num_beams=args.num_beams , min_length=args.min_length , max_length=args.max_length , early_stopping=lowerCAmelCase , num_return_sequences=1 , bad_words_ids=[[0, 0]] , )
_lowerCAmelCase = rag_model.retriever.generator_tokenizer.batch_decode(lowerCAmelCase , skip_special_tokens=lowerCAmelCase )
if args.print_predictions:
for q, a in zip(lowerCAmelCase , lowerCAmelCase ):
logger.info("""Q: {} - A: {}""".format(lowerCAmelCase , lowerCAmelCase ) )
return answers
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--model_type""" , choices=["""rag_sequence""", """rag_token""", """bart"""] , type=lowerCAmelCase , help=(
"""RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
""" model_name_or_path"""
) , )
parser.add_argument(
"""--index_name""" , default=lowerCAmelCase , choices=["""exact""", """compressed""", """legacy"""] , type=lowerCAmelCase , help="""RAG model retriever type""" , )
parser.add_argument(
"""--index_path""" , default=lowerCAmelCase , type=lowerCAmelCase , help="""Path to the retrieval index""" , )
parser.add_argument("""--n_docs""" , default=5 , type=lowerCAmelCase , help="""Number of retrieved docs""" )
parser.add_argument(
"""--model_name_or_path""" , default=lowerCAmelCase , type=lowerCAmelCase , required=lowerCAmelCase , help="""Path to pretrained checkpoints or model identifier from huggingface.co/models""" , )
parser.add_argument(
"""--eval_mode""" , choices=["""e2e""", """retrieval"""] , default="""e2e""" , type=lowerCAmelCase , help=(
"""Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
""" precision@k."""
) , )
parser.add_argument("""--k""" , default=1 , type=lowerCAmelCase , help="""k for the precision@k calculation""" )
parser.add_argument(
"""--evaluation_set""" , default=lowerCAmelCase , type=lowerCAmelCase , required=lowerCAmelCase , help="""Path to a file containing evaluation samples""" , )
parser.add_argument(
"""--gold_data_path""" , default=lowerCAmelCase , type=lowerCAmelCase , required=lowerCAmelCase , help="""Path to a tab-separated file with gold samples""" , )
parser.add_argument(
"""--gold_data_mode""" , default="""qa""" , type=lowerCAmelCase , choices=["""qa""", """ans"""] , help=(
"""Format of the gold data file"""
"""qa - a single line in the following format: question [tab] answer_list"""
"""ans - a single line of the gold file contains the expected answer string"""
) , )
parser.add_argument(
"""--predictions_path""" , type=lowerCAmelCase , default="""predictions.txt""" , help="""Name of the predictions file, to be stored in the checkpoints directory""" , )
parser.add_argument(
"""--eval_all_checkpoints""" , action="""store_true""" , help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number""" , )
parser.add_argument(
"""--eval_batch_size""" , default=8 , type=lowerCAmelCase , help="""Batch size per GPU/CPU for evaluation.""" , )
parser.add_argument(
"""--recalculate""" , help="""Recalculate predictions even if the prediction file exists""" , action="""store_true""" , )
parser.add_argument(
"""--num_beams""" , default=4 , type=lowerCAmelCase , help="""Number of beams to be used when generating answers""" , )
parser.add_argument("""--min_length""" , default=1 , type=lowerCAmelCase , help="""Min length of the generated answers""" )
parser.add_argument("""--max_length""" , default=50 , type=lowerCAmelCase , help="""Max length of the generated answers""" )
parser.add_argument(
"""--print_predictions""" , action="""store_true""" , help="""If True, prints predictions while evaluating.""" , )
parser.add_argument(
"""--print_docs""" , action="""store_true""" , help="""If True, prints docs retried while generating.""" , )
_lowerCAmelCase = parser.parse_args()
_lowerCAmelCase = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
return args
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = {}
if args.model_type is None:
_lowerCAmelCase = infer_model_type(args.model_name_or_path )
assert args.model_type is not None
if args.model_type.startswith("""rag""" ):
_lowerCAmelCase = RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
_lowerCAmelCase = args.n_docs
if args.index_name is not None:
_lowerCAmelCase = args.index_name
if args.index_path is not None:
_lowerCAmelCase = args.index_path
else:
_lowerCAmelCase = BartForConditionalGeneration
_lowerCAmelCase = (
[f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info("""Evaluate the following checkpoints: %s""" , lowerCAmelCase )
_lowerCAmelCase = get_scores if args.eval_mode == """e2e""" else get_precision_at_k
_lowerCAmelCase = evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path ) and (not args.recalculate):
logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path ) )
score_fn(lowerCAmelCase , args.predictions_path , args.gold_data_path )
continue
logger.info("""***** Running evaluation for {} *****""".format(lowerCAmelCase ) )
logger.info(""" Batch size = %d""" , args.eval_batch_size )
logger.info(""" Predictions will be stored under {}""".format(args.predictions_path ) )
if args.model_type.startswith("""rag""" ):
_lowerCAmelCase = RagRetriever.from_pretrained(lowerCAmelCase , **lowerCAmelCase )
_lowerCAmelCase = model_class.from_pretrained(lowerCAmelCase , retriever=lowerCAmelCase , **lowerCAmelCase )
model.retriever.init_retrieval()
else:
_lowerCAmelCase = model_class.from_pretrained(lowerCAmelCase , **lowerCAmelCase )
model.to(args.device )
with open(args.evaluation_set , """r""" ) as eval_file, open(args.predictions_path , """w""" ) as preds_file:
_lowerCAmelCase = []
for line in tqdm(lowerCAmelCase ):
questions.append(line.strip() )
if len(lowerCAmelCase ) == args.eval_batch_size:
_lowerCAmelCase = evaluate_batch_fn(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
preds_file.write("""\n""".join(lowerCAmelCase ) + """\n""" )
preds_file.flush()
_lowerCAmelCase = []
if len(lowerCAmelCase ) > 0:
_lowerCAmelCase = evaluate_batch_fn(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
preds_file.write("""\n""".join(lowerCAmelCase ) )
preds_file.flush()
score_fn(lowerCAmelCase , args.predictions_path , args.gold_data_path )
if __name__ == "__main__":
A__ : List[Any] =get_args()
main(args)
| 356 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class UpperCAmelCase ( unittest.TestCase ):
def lowercase__ ( self : int ) -> Union[str, Any]:
_lowerCAmelCase = inspect.getfile(accelerate.test_utils )
_lowerCAmelCase = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ["""scripts""", """external_deps""", """test_metrics.py"""] )
from accelerate.test_utils.scripts.external_deps import test_metrics # noqa: F401
_lowerCAmelCase = test_metrics
@require_cpu
def lowercase__ ( self : Optional[Any] ) -> Optional[Any]:
debug_launcher(self.test_metrics.main , num_processes=1 )
@require_cpu
def lowercase__ ( self : Tuple ) -> Tuple:
debug_launcher(self.test_metrics.main )
@require_single_gpu
def lowercase__ ( self : Union[str, Any] ) -> str:
self.test_metrics.main()
@require_multi_gpu
def lowercase__ ( self : str ) -> List[str]:
print(f"Found {torch.cuda.device_count()} devices." )
_lowerCAmelCase = ["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(__snake_case , env=os.environ.copy() )
| 220 | 0 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
SCREAMING_SNAKE_CASE :Union[str, Any] = False
SCREAMING_SNAKE_CASE :Any = True
SCREAMING_SNAKE_CASE :Tuple = False
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Tuple = argparse.ArgumentParser()
parser.add_argument(
'--repo_path',
default=None,
type=str,
required=True,
help='The config json file corresponding to the architecture.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
SCREAMING_SNAKE_CASE :Union[str, Any] = parser.parse_args()
SCREAMING_SNAKE_CASE :Dict = {
'image_size': 'sample_size',
'num_res_blocks': 'layers_per_block',
'block_channels': 'block_out_channels',
'down_blocks': 'down_block_types',
'up_blocks': 'up_block_types',
'downscale_freq_shift': 'freq_shift',
'resnet_num_groups': 'norm_num_groups',
'resnet_act_fn': 'act_fn',
'resnet_eps': 'norm_eps',
'num_head_channels': 'attention_head_dim',
}
SCREAMING_SNAKE_CASE :Optional[int] = {
'time_steps': 'time_proj',
'mid': 'mid_block',
'downsample_blocks': 'down_blocks',
'upsample_blocks': 'up_blocks',
}
SCREAMING_SNAKE_CASE :int = '' if has_file(args.repo_path, 'config.json') else 'unet'
with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
SCREAMING_SNAKE_CASE :Dict = reader.read()
SCREAMING_SNAKE_CASE :List[str] = json.loads(text)
if do_only_config:
for key in config_parameters_to_change.keys():
config.pop(key, None)
if has_file(args.repo_path, 'config.json'):
SCREAMING_SNAKE_CASE :Optional[int] = UNetaDModel(**config)
else:
SCREAMING_SNAKE_CASE :Optional[Any] = UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
SCREAMING_SNAKE_CASE :List[str] = class_name(**config)
if do_only_config:
model.save_config(os.path.join(args.repo_path, subfolder))
SCREAMING_SNAKE_CASE :List[str] = dict(model.config)
if do_only_renaming:
for key, value in config_parameters_to_change.items():
if key in config:
SCREAMING_SNAKE_CASE :Optional[Any] = config[key]
del config[key]
SCREAMING_SNAKE_CASE :Optional[Any] = [k.replace('UNetRes', '') for k in config['down_block_types']]
SCREAMING_SNAKE_CASE :List[Any] = [k.replace('UNetRes', '') for k in config['up_block_types']]
if do_only_weights:
SCREAMING_SNAKE_CASE :Tuple = torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))
SCREAMING_SNAKE_CASE :Any = {}
for param_key, param_value in state_dict.items():
if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
continue
SCREAMING_SNAKE_CASE :List[str] = False
for key, new_key in key_parameters_to_change.items():
if not has_changed and param_key.split('.')[0] == key:
SCREAMING_SNAKE_CASE :List[Any] = param_value
SCREAMING_SNAKE_CASE :str = True
if not has_changed:
SCREAMING_SNAKE_CASE :List[str] = param_value
model.load_state_dict(new_state_dict)
model.save_pretrained(os.path.join(args.repo_path, subfolder))
| 15 |
lowercase = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowercase = [{"type": "code", "content": INSTALL_CONTENT}]
lowercase = {
"{processor_class}": "FakeProcessorClass",
"{model_class}": "FakeModelClass",
"{object_class}": "FakeObjectClass",
}
| 178 | 0 |
"""simple docstring"""
import functools
def __UpperCAmelCase ( UpperCAmelCase_ : list[int] , UpperCAmelCase_ : list[int] ) -> int:
'''simple docstring'''
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or not all(isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) for day in days ):
raise ValueError('The parameter days should be a list of integers' )
if len(UpperCAmelCase_ ) != 3 or not all(isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) for cost in costs ):
raise ValueError('The parameter costs should be a list of three integers' )
if len(UpperCAmelCase_ ) == 0:
return 0
if min(UpperCAmelCase_ ) <= 0:
raise ValueError('All days elements should be greater than 0' )
if max(UpperCAmelCase_ ) >= 3_66:
raise ValueError('All days elements should be less than 366' )
__snake_case : Optional[Any] = set(UpperCAmelCase_ )
@functools.cache
def dynamic_programming(UpperCAmelCase_ : int ) -> int:
if index > 3_65:
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()
| 95 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class UpperCamelCase ( lowercase ):
@require_torch
def _lowercase (self : Union[str, Any]) -> Optional[Any]:
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
__snake_case : Any = '\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n '
__snake_case : Tuple = '\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n '
__snake_case : int = '\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n '
# Force fetching the files so that we can use the cache
__snake_case : int = 'hf-internal-testing/tiny-random-bert'
BertConfig.from_pretrained(_A)
BertModel.from_pretrained(_A)
BertTokenizer.from_pretrained(_A)
pipeline(task='fill-mask' , model=_A)
# baseline - just load from_pretrained with normal network
__snake_case : Union[str, Any] = [sys.executable, '-c', '\n'.join([load, run, mock])]
# should succeed
__snake_case : Union[str, Any] = self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
__snake_case : str = '1'
__snake_case : Union[str, Any] = subprocess.run(_A , env=_A , check=_A , capture_output=_A)
self.assertEqual(result.returncode , 0 , result.stderr)
self.assertIn('success' , result.stdout.decode())
@require_torch
def _lowercase (self : Union[str, Any]) -> Union[str, Any]:
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
__snake_case : Optional[Any] = '\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n '
__snake_case : Optional[Any] = '\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n '
__snake_case : Union[str, Any] = '\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n '
# Force fetching the files so that we can use the cache
__snake_case : str = 'hf-internal-testing/tiny-random-bert'
BertConfig.from_pretrained(_A)
BertModel.from_pretrained(_A)
BertTokenizer.from_pretrained(_A)
pipeline(task='fill-mask' , model=_A)
# baseline - just load from_pretrained with normal network
__snake_case : Any = [sys.executable, '-c', '\n'.join([load, run, mock])]
# should succeed
__snake_case : int = self.get_env()
__snake_case : Tuple = subprocess.run(_A , env=_A , check=_A , capture_output=_A)
self.assertEqual(result.returncode , 0 , result.stderr)
self.assertIn('success' , result.stdout.decode())
@require_torch
def _lowercase (self : int) -> Any:
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
__snake_case : int = '\nfrom transformers import BertConfig, BertModel, BertTokenizer\n '
__snake_case : int = '\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n '
__snake_case : Optional[int] = '\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n '
# baseline - just load from_pretrained with normal network
__snake_case : Optional[Any] = [sys.executable, '-c', '\n'.join([load, run])]
# should succeed
__snake_case : Optional[int] = self.get_env()
__snake_case : Dict = subprocess.run(_A , env=_A , check=_A , capture_output=_A)
self.assertEqual(result.returncode , 0 , result.stderr)
self.assertIn('success' , result.stdout.decode())
# next emulate no network
__snake_case : Optional[Any] = [sys.executable, '-c', '\n'.join([load, mock, run])]
# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
# env["TRANSFORMERS_OFFLINE"] = "0"
# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
# self.assertEqual(result.returncode, 1, result.stderr)
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
__snake_case : Union[str, Any] = '1'
__snake_case : str = subprocess.run(_A , env=_A , check=_A , capture_output=_A)
self.assertEqual(result.returncode , 0 , result.stderr)
self.assertIn('success' , result.stdout.decode())
@require_torch
def _lowercase (self : str) -> Dict:
__snake_case : Dict = '\nfrom transformers import pipeline\n '
__snake_case : List[Any] = '\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n '
__snake_case : List[str] = '\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n '
__snake_case : str = self.get_env()
__snake_case : Tuple = '1'
__snake_case : Tuple = [sys.executable, '-c', '\n'.join([load, mock, run])]
__snake_case : Optional[Any] = subprocess.run(_A , env=_A , check=_A , capture_output=_A)
self.assertEqual(result.returncode , 1 , result.stderr)
self.assertIn(
'You cannot infer task automatically within `pipeline` when using offline mode' , result.stderr.decode().replace('\n' , '') , )
@require_torch
def _lowercase (self : int) -> Optional[Any]:
__snake_case : int = '\nfrom transformers import AutoModel\n '
__snake_case : str = '\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n '
# baseline - just load from_pretrained with normal network
__snake_case : str = [sys.executable, '-c', '\n'.join([load, run])]
# should succeed
__snake_case : str = self.get_env()
__snake_case : Dict = subprocess.run(_A , env=_A , check=_A , capture_output=_A)
self.assertEqual(result.returncode , 0 , result.stderr)
self.assertIn('success' , result.stdout.decode())
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
__snake_case : List[str] = '1'
__snake_case : Optional[int] = subprocess.run(_A , env=_A , check=_A , capture_output=_A)
self.assertEqual(result.returncode , 0 , result.stderr)
self.assertIn('success' , result.stdout.decode())
| 95 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
a__ = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
"""SpeechT5Config""",
"""SpeechT5HifiGanConfig""",
],
"""feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
"""processing_speecht5""": ["""SpeechT5Processor"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = ["""SpeechT5Tokenizer"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = [
"""SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SpeechT5ForSpeechToText""",
"""SpeechT5ForSpeechToSpeech""",
"""SpeechT5ForTextToSpeech""",
"""SpeechT5Model""",
"""SpeechT5PreTrainedModel""",
"""SpeechT5HifiGan""",
]
if TYPE_CHECKING:
from .configuration_speechta import (
SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
SpeechTaConfig,
SpeechTaHifiGanConfig,
)
from .feature_extraction_speechta import SpeechTaFeatureExtractor
from .processing_speechta import SpeechTaProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speechta import SpeechTaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speechta import (
SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaHifiGan,
SpeechTaModel,
SpeechTaPreTrainedModel,
)
else:
import sys
a__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 317 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
a__ = logging.get_logger(__name__)
class snake_case ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def __init__( self : List[Any] , *lowerCAmelCase : List[Any] , **lowerCAmelCase : Dict) -> None:
"""simple docstring"""
warnings.warn(
"""The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use YolosImageProcessor instead.""" , lowerCAmelCase , )
super().__init__(*lowerCAmelCase , **lowerCAmelCase)
| 317 | 1 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs
__a = imread(R"""digital_image_processing/image_data/lena_small.jpg""")
__a = cvtColor(img, COLOR_BGR2GRAY)
def __snake_case( ) -> Union[str, Any]:
snake_case__ : Optional[int] = cn.convert_to_negative(_lowerCAmelCase )
# assert negative_img array for at least one True
assert negative_img.any()
def __snake_case( ) -> int:
with Image.open("""digital_image_processing/image_data/lena_small.jpg""" ) as img:
# Work around assertion for response
assert str(cc.change_contrast(_lowerCAmelCase , 110 ) ).startswith(
"""<PIL.Image.Image image mode=RGB size=100x100 at""" )
def __snake_case( ) -> str:
snake_case__ : List[Any] = canny.gen_gaussian_kernel(9 , sigma=1.4 )
# Assert ambiguous array
assert resp.all()
def __snake_case( ) -> Union[str, Any]:
snake_case__ : Optional[Any] = imread("""digital_image_processing/image_data/lena_small.jpg""" , 0 )
# assert ambiguous array for all == True
assert canny_img.all()
snake_case__ : Any = canny.canny(_lowerCAmelCase )
# assert canny array for at least one True
assert canny_array.any()
def __snake_case( ) -> Optional[Any]:
assert gg.gaussian_filter(_lowerCAmelCase , 5 , sigma=0.9 ).all()
def __snake_case( ) -> Union[str, Any]:
# laplace diagonals
snake_case__ : List[Any] = array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
snake_case__ : Optional[int] = conv.img_convolve(_lowerCAmelCase , _lowerCAmelCase ).astype(_lowerCAmelCase )
assert res.any()
def __snake_case( ) -> List[Any]:
assert med.median_filter(_lowerCAmelCase , 3 ).any()
def __snake_case( ) -> int:
snake_case__ , snake_case__ : Optional[int] = sob.sobel_filter(_lowerCAmelCase )
assert grad.any() and theta.any()
def __snake_case( ) -> List[Any]:
snake_case__ : int = sp.make_sepia(_lowerCAmelCase , 20 )
assert sepia.all()
def __snake_case( _lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg" ) -> Dict:
snake_case__ : Tuple = bs.Burkes(imread(_lowerCAmelCase , 1 ) , 120 )
burkes.process()
assert burkes.output_img.any()
def __snake_case( _lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg" , ) -> Dict:
snake_case__ : Tuple = rs.NearestNeighbour(imread(_lowerCAmelCase , 1 ) , 400 , 200 )
nn.process()
assert nn.output.any()
def __snake_case( ) -> Dict:
snake_case__ : Union[str, Any] = """digital_image_processing/image_data/lena.jpg"""
# Reading the image and converting it to grayscale.
snake_case__ : Optional[int] = imread(_lowerCAmelCase , 0 )
# Test for get_neighbors_pixel function() return not None
snake_case__ : Any = 0
snake_case__ : Tuple = 0
snake_case__ : Dict = image[x_coordinate][y_coordinate]
snake_case__ : int = lbp.get_neighbors_pixel(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
assert neighbors_pixels is not None
# Test for local_binary_pattern function()
# Create a numpy array as the same height and width of read image
snake_case__ : Union[str, Any] = np.zeros((image.shape[0], image.shape[1]) )
# Iterating through the image and calculating the local binary pattern value
# for each pixel.
for i in range(0 , image.shape[0] ):
for j in range(0 , image.shape[1] ):
snake_case__ : Optional[Any] = lbp.local_binary_value(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
assert lbp_image.any()
| 359 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Any , snake_case_ : str , snake_case_ : Dict=7 , snake_case_ : str=3 , snake_case_ : List[str]=18 , snake_case_ : Tuple=30 , snake_case_ : int=400 , snake_case_ : Any=True , snake_case_ : List[str]=None , snake_case_ : List[str]=True , snake_case_ : Union[str, Any]=None , snake_case_ : Dict=True , ):
snake_case__ : List[str] = size if size is not None else {"""shortest_edge""": 20}
snake_case__ : Union[str, Any] = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
snake_case__ : Tuple = parent
snake_case__ : Tuple = batch_size
snake_case__ : List[str] = num_channels
snake_case__ : Any = image_size
snake_case__ : str = min_resolution
snake_case__ : Dict = max_resolution
snake_case__ : Optional[int] = do_resize
snake_case__ : int = size
snake_case__ : List[Any] = do_center_crop
snake_case__ : int = crop_size
snake_case__ : Dict = do_flip_channel_order
def lowerCamelCase ( self : str ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class UpperCAmelCase_ ( _a , unittest.TestCase ):
"""simple docstring"""
lowercase = MobileViTImageProcessor if is_vision_available() else None
def lowerCamelCase ( self : List[str] ):
snake_case__ : List[str] = MobileViTImageProcessingTester(self )
@property
def lowerCamelCase ( self : Optional[Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase ( self : Optional[Any] ):
snake_case__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case_ , """do_resize""" ) )
self.assertTrue(hasattr(snake_case_ , """size""" ) )
self.assertTrue(hasattr(snake_case_ , """do_center_crop""" ) )
self.assertTrue(hasattr(snake_case_ , """center_crop""" ) )
self.assertTrue(hasattr(snake_case_ , """do_flip_channel_order""" ) )
def lowerCamelCase ( self : List[str] ):
snake_case__ : Any = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 20} )
self.assertEqual(image_processor.crop_size , {"""height""": 18, """width""": 18} )
snake_case__ : Dict = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42} )
self.assertEqual(image_processor.crop_size , {"""height""": 84, """width""": 84} )
def lowerCamelCase ( self : str ):
pass
def lowerCamelCase ( self : int ):
# Initialize image_processing
snake_case__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case__ : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case_ )
for image in image_inputs:
self.assertIsInstance(snake_case_ , Image.Image )
# Test not batched input
snake_case__ : Dict = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
snake_case__ : List[str] = image_processing(snake_case_ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def lowerCamelCase ( self : int ):
# Initialize image_processing
snake_case__ : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case__ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case_ , numpify=snake_case_ )
for image in image_inputs:
self.assertIsInstance(snake_case_ , np.ndarray )
# Test not batched input
snake_case__ : Optional[Any] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
snake_case__ : str = image_processing(snake_case_ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def lowerCamelCase ( self : List[Any] ):
# Initialize image_processing
snake_case__ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case__ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case_ , torchify=snake_case_ )
for image in image_inputs:
self.assertIsInstance(snake_case_ , torch.Tensor )
# Test not batched input
snake_case__ : List[Any] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
snake_case__ : Optional[Any] = image_processing(snake_case_ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
| 43 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class UpperCamelCase_ ( UpperCamelCase , UpperCamelCase , unittest.TestCase):
"""simple docstring"""
snake_case__ : Any = CycleDiffusionPipeline
snake_case__ : Dict = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"negative_prompt",
"height",
"width",
"negative_prompt_embeds",
}
snake_case__ : List[Any] = PipelineTesterMixin.required_optional_params - {"latents"}
snake_case__ : Any = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"})
snake_case__ : Union[str, Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
snake_case__ : str = IMAGE_TO_IMAGE_IMAGE_PARAMS
def UpperCAmelCase_ ( self : Tuple ) -> str:
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=3_2 , )
__SCREAMING_SNAKE_CASE = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="scaled_linear" , num_train_timesteps=1_0_0_0 , clip_sample=UpperCAmelCase__ , set_alpha_to_one=UpperCAmelCase__ , )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
__SCREAMING_SNAKE_CASE = CLIPTextModel(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
__SCREAMING_SNAKE_CASE = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def UpperCAmelCase_ ( self : Optional[int] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[int]=0 ) -> Any:
__SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(UpperCAmelCase__ ) ).to(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = image / 2 + 0.5
if str(UpperCAmelCase__ ).startswith("mps" ):
__SCREAMING_SNAKE_CASE = torch.manual_seed(UpperCAmelCase__ )
else:
__SCREAMING_SNAKE_CASE = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = {
"prompt": "An astronaut riding an elephant",
"source_prompt": "An astronaut riding a horse",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"eta": 0.1,
"strength": 0.8,
"guidance_scale": 3,
"source_guidance_scale": 1,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase_ ( self : Tuple ) -> List[Any]:
__SCREAMING_SNAKE_CASE = "cpu" # ensure determinism for the device-dependent torch.Generator
__SCREAMING_SNAKE_CASE = self.get_dummy_components()
__SCREAMING_SNAKE_CASE = CycleDiffusionPipeline(**UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = self.get_dummy_inputs(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = pipe(**UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = output.images
__SCREAMING_SNAKE_CASE = images[0, -3:, -3:, -1]
assert images.shape == (1, 3_2, 3_2, 3)
__SCREAMING_SNAKE_CASE = np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@unittest.skipIf(torch_device != "cuda" , "This test requires a GPU" )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Tuple:
__SCREAMING_SNAKE_CASE = self.get_dummy_components()
for name, module in components.items():
if hasattr(UpperCAmelCase__ , "half" ):
__SCREAMING_SNAKE_CASE = module.half()
__SCREAMING_SNAKE_CASE = CycleDiffusionPipeline(**UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = self.get_dummy_inputs(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = pipe(**UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = output.images
__SCREAMING_SNAKE_CASE = images[0, -3:, -3:, -1]
assert images.shape == (1, 3_2, 3_2, 3)
__SCREAMING_SNAKE_CASE = np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def UpperCAmelCase_ ( self : int ) -> Tuple:
return super().test_save_load_local()
@unittest.skip("non-deterministic pipeline" )
def UpperCAmelCase_ ( self : List[str] ) -> Union[str, Any]:
return super().test_inference_batch_single_identical()
@skip_mps
def UpperCAmelCase_ ( self : Optional[Any] ) -> Tuple:
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def UpperCAmelCase_ ( self : Dict ) -> List[Any]:
return super().test_save_load_optional_components()
@skip_mps
def UpperCAmelCase_ ( self : int ) -> str:
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class UpperCamelCase_ ( unittest.TestCase):
"""simple docstring"""
def UpperCAmelCase_ ( self : Tuple ) -> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase_ ( self : Optional[int] ) -> Tuple:
__SCREAMING_SNAKE_CASE = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/cycle-diffusion/black_colored_car.png" )
__SCREAMING_SNAKE_CASE = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy" )
__SCREAMING_SNAKE_CASE = init_image.resize((5_1_2, 5_1_2) )
__SCREAMING_SNAKE_CASE = "CompVis/stable-diffusion-v1-4"
__SCREAMING_SNAKE_CASE = DDIMScheduler.from_pretrained(UpperCAmelCase__ , subfolder="scheduler" )
__SCREAMING_SNAKE_CASE = CycleDiffusionPipeline.from_pretrained(
UpperCAmelCase__ , scheduler=UpperCAmelCase__ , safety_checker=UpperCAmelCase__ , torch_dtype=torch.floataa , revision="fp16" )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
pipe.enable_attention_slicing()
__SCREAMING_SNAKE_CASE = "A black colored car"
__SCREAMING_SNAKE_CASE = "A blue colored car"
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(
prompt=UpperCAmelCase__ , source_prompt=UpperCAmelCase__ , image=UpperCAmelCase__ , num_inference_steps=1_0_0 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=UpperCAmelCase__ , output_type="np" , )
__SCREAMING_SNAKE_CASE = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5E-1
def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]:
__SCREAMING_SNAKE_CASE = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/cycle-diffusion/black_colored_car.png" )
__SCREAMING_SNAKE_CASE = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy" )
__SCREAMING_SNAKE_CASE = init_image.resize((5_1_2, 5_1_2) )
__SCREAMING_SNAKE_CASE = "CompVis/stable-diffusion-v1-4"
__SCREAMING_SNAKE_CASE = DDIMScheduler.from_pretrained(UpperCAmelCase__ , subfolder="scheduler" )
__SCREAMING_SNAKE_CASE = CycleDiffusionPipeline.from_pretrained(UpperCAmelCase__ , scheduler=UpperCAmelCase__ , safety_checker=UpperCAmelCase__ )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
pipe.enable_attention_slicing()
__SCREAMING_SNAKE_CASE = "A black colored car"
__SCREAMING_SNAKE_CASE = "A blue colored car"
__SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(
prompt=UpperCAmelCase__ , source_prompt=UpperCAmelCase__ , image=UpperCAmelCase__ , num_inference_steps=1_0_0 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=UpperCAmelCase__ , output_type="np" , )
__SCREAMING_SNAKE_CASE = output.images
assert np.abs(image - expected_image ).max() < 2E-2
| 54 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
if upper_limit < 0:
raise ValueError("Limit for the Catalan sequence must be ≥ 0" )
__SCREAMING_SNAKE_CASE = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
__SCREAMING_SNAKE_CASE = 1
if upper_limit > 0:
__SCREAMING_SNAKE_CASE = 1
# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
for i in range(2 , upper_limit + 1 ):
for j in range(lowerCAmelCase_ ):
catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]
return catalan_list
if __name__ == "__main__":
print('''\n********* Catalan Numbers Using Dynamic Programming ************\n''')
print('''\n*** Enter -1 at any time to quit ***''')
print('''\nEnter the upper limit (≥ 0) for the Catalan number sequence: ''', end='''''')
try:
while True:
a__ : List[str] = int(input().strip())
if N < 0:
print('''\n********* Goodbye!! ************''')
break
else:
print(F"The Catalan numbers from 0 through {N} are:")
print(catalan_numbers(N))
print('''Try another upper limit for the sequence: ''', end='''''')
except (NameError, ValueError):
print('''\n********* Invalid input, goodbye! ************\n''')
import doctest
doctest.testmod()
| 54 | 1 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
lowercase_ = get_logger(__name__)
lowercase_ = Path(__file__).parent / '''model_card_template.md'''
lowercase_ = uuida().hex
lowercase_ = os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
lowercase_ = os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
lowercase_ = HUGGINGFACE_CO_RESOLVE_ENDPOINT + '''/api/telemetry/'''
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : Union[Dict, str, None] = None ):
'''simple docstring'''
__snake_case : List[str] = F'''diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'''
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += F'''; torch/{_torch_version}'''
if is_flax_available():
ua += F'''; jax/{_jax_version}'''
ua += F'''; flax/{_flax_version}'''
if is_onnx_available():
ua += F'''; onnxruntime/{_onnxruntime_version}'''
# CI will set this value to True
if os.environ.get("""DIFFUSERS_IS_CI""" , """""" ).upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
ua += "; " + "; ".join(F'''{k}/{v}''' for k, v in user_agent.items() )
elif isinstance(_UpperCAmelCase , _UpperCAmelCase ):
ua += "; " + user_agent
return ua
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[str] = None , __SCREAMING_SNAKE_CASE : Optional[str] = None ):
'''simple docstring'''
if token is None:
__snake_case : Optional[int] = HfFolder.get_token()
if organization is None:
__snake_case : Union[str, Any] = whoami(_UpperCAmelCase )['name']
return F'''{username}/{model_id}'''
else:
return F'''{organization}/{model_id}'''
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
if not is_jinja_available():
raise ValueError(
"""Modelcard rendering is based on Jinja templates."""
""" Please make sure to have `jinja` installed before using `create_model_card`."""
""" To install it, please run `pip install Jinja2`.""" )
if hasattr(_UpperCAmelCase , """local_rank""" ) and args.local_rank not in [-1, 0]:
return
__snake_case : str = args.hub_token if hasattr(_UpperCAmelCase , """hub_token""" ) else None
__snake_case : Optional[int] = get_full_repo_name(_UpperCAmelCase , token=_UpperCAmelCase )
__snake_case : int = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language="""en""" , license="""apache-2.0""" , library_name="""diffusers""" , tags=[] , datasets=args.dataset_name , metrics=[] , ) , template_path=_UpperCAmelCase , model_name=_UpperCAmelCase , repo_name=_UpperCAmelCase , dataset_name=args.dataset_name if hasattr(_UpperCAmelCase , """dataset_name""" ) else None , learning_rate=args.learning_rate , train_batch_size=args.train_batch_size , eval_batch_size=args.eval_batch_size , gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(_UpperCAmelCase , """gradient_accumulation_steps""" ) else None
) , adam_betaa=args.adam_betaa if hasattr(_UpperCAmelCase , """adam_beta1""" ) else None , adam_betaa=args.adam_betaa if hasattr(_UpperCAmelCase , """adam_beta2""" ) else None , adam_weight_decay=args.adam_weight_decay if hasattr(_UpperCAmelCase , """adam_weight_decay""" ) else None , adam_epsilon=args.adam_epsilon if hasattr(_UpperCAmelCase , """adam_epsilon""" ) else None , lr_scheduler=args.lr_scheduler if hasattr(_UpperCAmelCase , """lr_scheduler""" ) else None , lr_warmup_steps=args.lr_warmup_steps if hasattr(_UpperCAmelCase , """lr_warmup_steps""" ) else None , ema_inv_gamma=args.ema_inv_gamma if hasattr(_UpperCAmelCase , """ema_inv_gamma""" ) else None , ema_power=args.ema_power if hasattr(_UpperCAmelCase , """ema_power""" ) else None , ema_max_decay=args.ema_max_decay if hasattr(_UpperCAmelCase , """ema_max_decay""" ) else None , mixed_precision=args.mixed_precision , )
__snake_case : List[Any] = os.path.join(args.output_dir , """README.md""" )
model_card.save(_UpperCAmelCase )
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : Optional[str] , __SCREAMING_SNAKE_CASE : Optional[str] = None ):
'''simple docstring'''
if resolved_file is None or commit_hash is not None:
return commit_hash
__snake_case : List[str] = str(Path(_UpperCAmelCase ).as_posix() )
__snake_case : List[Any] = re.search(R"""snapshots/([^/]+)/""" , _UpperCAmelCase )
if search is None:
return None
__snake_case : str = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(_UpperCAmelCase ) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
lowercase_ = os.path.expanduser(
os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
lowercase_ = os.path.join(hf_cache_home, "diffusers")
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : Optional[str] = None , __SCREAMING_SNAKE_CASE : Optional[str] = None ):
'''simple docstring'''
if new_cache_dir is None:
__snake_case : Tuple = DIFFUSERS_CACHE
if old_cache_dir is None:
__snake_case : str = old_diffusers_cache
__snake_case : str = Path(_UpperCAmelCase ).expanduser()
__snake_case : int = Path(_UpperCAmelCase ).expanduser()
for old_blob_path in old_cache_dir.glob("""**/blobs/*""" ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
__snake_case : Optional[Any] = new_cache_dir / old_blob_path.relative_to(_UpperCAmelCase )
new_blob_path.parent.mkdir(parents=_UpperCAmelCase , exist_ok=_UpperCAmelCase )
os.replace(_UpperCAmelCase , _UpperCAmelCase )
try:
os.symlink(_UpperCAmelCase , _UpperCAmelCase )
except OSError:
logger.warning(
"""Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.""" )
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
lowercase_ = os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
lowercase_ = 0
else:
with open(cache_version_file) as f:
try:
lowercase_ = int(f.read())
except ValueError:
lowercase_ = 0
if cache_version < 1:
lowercase_ = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
"The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
"existing cached models. This is a one-time operation, you can interrupt it or run it "
"later by calling `diffusers.utils.hub_utils.move_cache()`."
)
try:
move_cache()
except Exception as e:
lowercase_ = '''\n'''.join(traceback.format_tb(e.__traceback__))
logger.error(
F'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
"file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
"message and we will do our best to help."
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, "w") as f:
f.write("1")
except Exception:
logger.warning(
F'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
"the directory exists and can be written to."
)
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[str] = None ):
'''simple docstring'''
if variant is not None:
__snake_case : List[Any] = weights_name.split(""".""" )
__snake_case : Dict = splits[:-1] + [variant] + splits[-1:]
__snake_case : Optional[Any] = '.'.join(_UpperCAmelCase )
return weights_name
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : Dict , *,
__SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[str]=None , ):
'''simple docstring'''
__snake_case : Optional[Any] = str(_UpperCAmelCase )
if os.path.isfile(_UpperCAmelCase ):
return pretrained_model_name_or_path
elif os.path.isdir(_UpperCAmelCase ):
if os.path.isfile(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) ):
# Load from a PyTorch checkpoint
__snake_case : Tuple = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) ):
__snake_case : List[Any] = os.path.join(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
return model_file
else:
raise EnvironmentError(
F'''Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.''' )
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(_UpperCAmelCase ).base_version ) >= version.parse("""0.20.0""" )
):
try:
__snake_case : Optional[Any] = hf_hub_download(
_UpperCAmelCase , filename=_add_variant(_UpperCAmelCase , _UpperCAmelCase ) , cache_dir=_UpperCAmelCase , force_download=_UpperCAmelCase , proxies=_UpperCAmelCase , resume_download=_UpperCAmelCase , local_files_only=_UpperCAmelCase , use_auth_token=_UpperCAmelCase , user_agent=_UpperCAmelCase , subfolder=_UpperCAmelCase , revision=revision or commit_hash , )
warnings.warn(
F'''Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.''' , _UpperCAmelCase , )
return model_file
except: # noqa: E722
warnings.warn(
F'''You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(_UpperCAmelCase , _UpperCAmelCase )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(_UpperCAmelCase , _UpperCAmelCase )}\' so that the correct variant file can be added.''' , _UpperCAmelCase , )
try:
# 2. Load model file as usual
__snake_case : List[Any] = hf_hub_download(
_UpperCAmelCase , filename=_UpperCAmelCase , cache_dir=_UpperCAmelCase , force_download=_UpperCAmelCase , proxies=_UpperCAmelCase , resume_download=_UpperCAmelCase , local_files_only=_UpperCAmelCase , use_auth_token=_UpperCAmelCase , user_agent=_UpperCAmelCase , subfolder=_UpperCAmelCase , revision=revision or commit_hash , )
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
F'''{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '''
"""listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a """
"""token having permission to this repo with `use_auth_token` or log in with `huggingface-cli """
"""login`.""" )
except RevisionNotFoundError:
raise EnvironmentError(
F'''{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '''
"""this model name. Check the model page at """
F'''\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.''' )
except EntryNotFoundError:
raise EnvironmentError(
F'''{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.''' )
except HTTPError as err:
raise EnvironmentError(
F'''There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}''' )
except ValueError:
raise EnvironmentError(
F'''We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'''
F''' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'''
F''' directory containing a file named {weights_name} or'''
""" \nCheckout your internet connection or see how to run the library in"""
""" offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.""" )
except EnvironmentError:
raise EnvironmentError(
F'''Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '''
"""\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. """
F'''Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '''
F'''containing a file named {weights_name}''' )
| 350 |
import random
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : list , __SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
__snake_case , __snake_case , __snake_case : Tuple = [], [], []
for element in data:
if element < pivot:
less.append(__SCREAMING_SNAKE_CASE )
elif element > pivot:
greater.append(__SCREAMING_SNAKE_CASE )
else:
equal.append(__SCREAMING_SNAKE_CASE )
return less, equal, greater
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : list , __SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
# index = len(items) // 2 when trying to find the median
# (value of index when items is sorted)
# invalid input
if index >= len(__SCREAMING_SNAKE_CASE ) or index < 0:
return None
__snake_case : int = items[random.randint(0 , len(__SCREAMING_SNAKE_CASE ) - 1 )]
__snake_case : Tuple = 0
__snake_case , __snake_case , __snake_case : List[str] = _partition(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__snake_case : Optional[Any] = len(__SCREAMING_SNAKE_CASE )
__snake_case : int = len(__SCREAMING_SNAKE_CASE )
# index is the pivot
if m <= index < m + count:
return pivot
# must be in smaller
elif m > index:
return quick_select(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# must be in larger
else:
return quick_select(__SCREAMING_SNAKE_CASE , index - (m + count) )
| 20 | 0 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
UpperCAmelCase : Tuple = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
UpperCAmelCase : Any = {
"""vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
"""merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
"""tokenizer_config_file""": {
"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
},
}
UpperCAmelCase : Optional[Any] = {"""facebook/blenderbot-3B""": 128}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _A ( ):
"""simple docstring"""
a__ : Optional[int] =(
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
a__ : Tuple =bs[:]
a__ : List[str] =0
for b in range(2**8 ):
if b not in bs:
bs.append(SCREAMING_SNAKE_CASE__ )
cs.append(2**8 + n )
n += 1
a__ : List[str] =[chr(SCREAMING_SNAKE_CASE__ ) for n in cs]
return dict(zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
def _A ( SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
a__ : List[str] =set()
a__ : int =word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
a__ : List[str] =char
return pairs
class __lowerCAmelCase ( A_):
_lowercase : List[str] = VOCAB_FILES_NAMES
_lowercase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
_lowercase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowercase : Dict = ["input_ids", "attention_mask"]
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__="replace" , lowerCAmelCase__="<s>" , lowerCAmelCase__="</s>" , lowerCAmelCase__="</s>" , lowerCAmelCase__="<s>" , lowerCAmelCase__="<unk>" , lowerCAmelCase__="<pad>" , lowerCAmelCase__="<mask>" , lowerCAmelCase__=False , **lowerCAmelCase__ , ) -> Union[str, Any]:
'''simple docstring'''
a__ : Dict =AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else bos_token
a__ : Tuple =AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else eos_token
a__ : Optional[int] =AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else sep_token
a__ : List[str] =AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else cls_token
a__ : Optional[int] =AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else unk_token
a__ : List[str] =AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
a__ : Dict =AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else mask_token
super().__init__(
errors=A_ , bos_token=A_ , eos_token=A_ , unk_token=A_ , sep_token=A_ , cls_token=A_ , pad_token=A_ , mask_token=A_ , add_prefix_space=A_ , **A_ , )
with open(A_ , encoding="utf-8" ) as vocab_handle:
a__ : int =json.load(A_ )
a__ : Union[str, Any] ={v: k for k, v in self.encoder.items()}
a__ : List[Any] =errors # how to handle errors in decoding
a__ : List[str] =bytes_to_unicode()
a__ : List[Any] ={v: k for k, v in self.byte_encoder.items()}
with open(A_ , encoding="utf-8" ) as merges_handle:
a__ : Dict =merges_handle.read().split("\n" )[1:-1]
a__ : Tuple =[tuple(merge.split() ) for merge in bpe_merges]
a__ : Any =dict(zip(A_ , range(len(A_ ) ) ) )
a__ : Any ={}
a__ : List[Any] =add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
a__ : int =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.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def _lowercase ( self ) -> Union[str, Any]:
'''simple docstring'''
return len(self.encoder )
def _lowercase ( self ) -> Union[str, Any]:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def _lowercase ( self , lowerCAmelCase__ ) -> List[str]:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
a__ : Dict =tuple(A_ )
a__ : Optional[Any] =get_pairs(A_ )
if not pairs:
return token
while True:
a__ : Union[str, Any] =min(A_ , key=lambda lowerCAmelCase__ : self.bpe_ranks.get(A_ , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
a__ , a__ : Union[str, Any] =bigram
a__ : int =[]
a__ : Union[str, Any] =0
while i < len(A_ ):
try:
a__ : Optional[int] =word.index(A_ , A_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
a__ : List[str] =j
if word[i] == first and i < len(A_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
a__ : Optional[Any] =tuple(A_ )
a__ : Tuple =new_word
if len(A_ ) == 1:
break
else:
a__ : List[Any] =get_pairs(A_ )
a__ : List[Any] =" ".join(A_ )
a__ : int =word
return word
def _lowercase ( self , lowerCAmelCase__ ) -> Tuple:
'''simple docstring'''
a__ : List[Any] =[]
for token in re.findall(self.pat , A_ ):
a__ : Union[str, Any] ="".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(A_ ).split(" " ) )
return bpe_tokens
def _lowercase ( self , lowerCAmelCase__ ) -> Dict:
'''simple docstring'''
return self.encoder.get(A_ , self.encoder.get(self.unk_token ) )
def _lowercase ( self , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
return self.decoder.get(A_ )
def _lowercase ( self , lowerCAmelCase__ ) -> int:
'''simple docstring'''
a__ : Dict ="".join(A_ )
a__ : Optional[Any] =bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(A_ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
a__ : Tuple =os.path.join(
A_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
a__ : Tuple =os.path.join(
A_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(A_ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=A_ , ensure_ascii=A_ ) + "\n" )
a__ : Tuple =0
with open(A_ , "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 lowerCAmelCase__ : 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!" )
a__ : Any =token_index
writer.write(" ".join(A_ ) + "\n" )
index += 1
return vocab_file, merge_file
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A_ , token_ids_a=A_ , already_has_special_tokens=A_ )
if token_ids_a is None:
return [1] + ([0] * len(A_ )) + [1]
return [1] + ([0] * len(A_ )) + [1, 1] + ([0] * len(A_ )) + [1]
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> List[int]:
'''simple docstring'''
a__ : Optional[int] =[self.sep_token_id]
a__ : Union[str, Any] =[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 , lowerCAmelCase__ , lowerCAmelCase__=False , **lowerCAmelCase__ ) -> Tuple:
'''simple docstring'''
a__ : str =kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(A_ ) > 0 and not text[0].isspace()):
a__ : Tuple =" " + text
return (text, kwargs)
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> Tuple:
'''simple docstring'''
return token_ids_a + [self.eos_token_id]
def _lowercase ( self , lowerCAmelCase__ ) -> List[int]:
'''simple docstring'''
a__ : str =[]
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(" " + text )
else:
# Generated responses should contain them already.
inputs.append(A_ )
a__ : Optional[int] =" ".join(A_ )
a__ : str =self.encode(A_ )
if len(A_ ) > self.model_max_length:
a__ : str =input_ids[-self.model_max_length :]
logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''' )
return input_ids
| 95 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class UpperCAmelCase__ ( A_ ):
"""simple docstring"""
UpperCAmelCase__ : Optional[int] = "mvp"
UpperCAmelCase__ : Tuple = ["past_key_values"]
UpperCAmelCase__ : Union[str, Any] = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self , A_=50267 , A_=1024 , A_=12 , A_=4096 , A_=16 , A_=12 , A_=4096 , A_=16 , A_=0.0 , A_=0.0 , A_="gelu" , A_=1024 , A_=0.1 , A_=0.0 , A_=0.0 , A_=0.02 , A_=0.0 , A_=False , A_=True , A_=1 , A_=0 , A_=2 , A_=True , A_=2 , A_=2 , A_=False , A_=100 , A_=800 , **A_ , ) -> Union[str, Any]:
__UpperCamelCase =vocab_size
__UpperCamelCase =max_position_embeddings
__UpperCamelCase =d_model
__UpperCamelCase =encoder_ffn_dim
__UpperCamelCase =encoder_layers
__UpperCamelCase =encoder_attention_heads
__UpperCamelCase =decoder_ffn_dim
__UpperCamelCase =decoder_layers
__UpperCamelCase =decoder_attention_heads
__UpperCamelCase =dropout
__UpperCamelCase =attention_dropout
__UpperCamelCase =activation_dropout
__UpperCamelCase =activation_function
__UpperCamelCase =init_std
__UpperCamelCase =encoder_layerdrop
__UpperCamelCase =decoder_layerdrop
__UpperCamelCase =classifier_dropout
__UpperCamelCase =use_cache
__UpperCamelCase =encoder_layers
__UpperCamelCase =scale_embedding # scale factor will be sqrt(d_model) if True
__UpperCamelCase =use_prompt
__UpperCamelCase =prompt_length
__UpperCamelCase =prompt_mid_dim
super().__init__(
pad_token_id=A_ , bos_token_id=A_ , eos_token_id=A_ , is_encoder_decoder=A_ , decoder_start_token_id=A_ , forced_eos_token_id=A_ , **A_ , )
if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated' , A_ ):
__UpperCamelCase =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.' )
| 62 | 0 |
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
__a :int = [
'python',
'tqdm',
'regex',
'requests',
'packaging',
'filelock',
'numpy',
'tokenizers',
'huggingface-hub',
'safetensors',
'accelerate',
'pyyaml',
]
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
elif pkg == "accelerate":
# must be loaded here, or else tqdm check may fail
from .utils import is_accelerate_available
# Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
# Transformers with PyTorch
if not is_accelerate_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(F"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")
def __snake_case ( __UpperCamelCase : Optional[Any] ,__UpperCamelCase : List[Any]=None ):
"""simple docstring"""
require_version(deps[pkg] ,__UpperCamelCase )
| 329 |
from __future__ import annotations
def __snake_case ( __UpperCamelCase : int = 4 ):
"""simple docstring"""
A_ = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
return reverse_row(transpose(__UpperCamelCase ) )
# OR.. transpose(reverse_column(matrix))
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
return reverse_row(reverse_column(__UpperCamelCase ) )
# OR.. reverse_column(reverse_row(matrix))
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
return reverse_column(transpose(__UpperCamelCase ) )
# OR.. transpose(reverse_row(matrix))
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
A_ = [list(__UpperCamelCase ) for x in zip(*__UpperCamelCase )]
return matrix
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
A_ = matrix[::-1]
return matrix
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
A_ = [x[::-1] for x in matrix]
return matrix
def __snake_case ( __UpperCamelCase : list[list[int]] ):
"""simple docstring"""
for i in matrix:
print(*__UpperCamelCase )
if __name__ == "__main__":
__a :Any = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 90 counterclockwise:\n')
print_matrix(rotate_aa(matrix))
__a :Any = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 180:\n')
print_matrix(rotate_aaa(matrix))
__a :Any = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 270 counterclockwise:\n')
print_matrix(rotate_aaa(matrix))
| 329 | 1 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False ):
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : List[Any] = f'''Expected string as input, found {type(SCREAMING_SNAKE_CASE )}'''
raise ValueError(SCREAMING_SNAKE_CASE )
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : List[str] = f'''Expected boolean as use_pascal parameter, found {type(SCREAMING_SNAKE_CASE )}'''
raise ValueError(SCREAMING_SNAKE_CASE )
A_ : int = input_str.split('''_''' )
A_ : Any = 0 if use_pascal else 1
A_ : List[str] = words[start_index:]
A_ : Optional[int] = [word[0].upper() + word[1:] for word in words_to_capitalize]
A_ : List[Any] = '''''' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 186 |
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import RegNetForImageClassification, RegNetModel
from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _lowerCamelCase :
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=10 , _SCREAMING_SNAKE_CASE=[10, 20, 30, 40] , _SCREAMING_SNAKE_CASE=[1, 1, 2, 1] , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE="relu" , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=None , )->List[str]:
'''simple docstring'''
A_ : str = parent
A_ : int = batch_size
A_ : List[str] = image_size
A_ : Dict = num_channels
A_ : Tuple = embeddings_size
A_ : Union[str, Any] = hidden_sizes
A_ : Dict = depths
A_ : str = is_training
A_ : Union[str, Any] = use_labels
A_ : Union[str, Any] = hidden_act
A_ : Optional[Any] = num_labels
A_ : Tuple = scope
A_ : Optional[int] = len(_SCREAMING_SNAKE_CASE )
def _snake_case ( self )->Optional[Any]:
'''simple docstring'''
A_ : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : str = None
if self.use_labels:
A_ : Union[str, Any] = ids_tensor([self.batch_size] , self.num_labels )
A_ : Optional[Any] = self.get_config()
return config, pixel_values, labels
def _snake_case ( self )->Union[str, Any]:
'''simple docstring'''
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->Union[str, Any]:
'''simple docstring'''
A_ : Dict = RegNetModel(config=_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
A_ : Any = model(_SCREAMING_SNAKE_CASE )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->Union[str, Any]:
'''simple docstring'''
A_ : Union[str, Any] = self.num_labels
A_ : Dict = RegNetForImageClassification(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
A_ : int = model(_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self )->Union[str, Any]:
'''simple docstring'''
A_ : Tuple = self.prepare_config_and_inputs()
A_ , A_ , A_ : str = config_and_inputs
A_ : Any = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _lowerCamelCase ( UpperCamelCase , UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
snake_case = (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
snake_case = (
{"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
if is_torch_available()
else {}
)
snake_case = False
snake_case = False
snake_case = False
snake_case = False
def _snake_case ( self )->Union[str, Any]:
'''simple docstring'''
A_ : Union[str, Any] = RegNetModelTester(self )
A_ : Union[str, Any] = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , has_text_modality=_SCREAMING_SNAKE_CASE )
def _snake_case ( self )->Dict:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _snake_case ( self )->Tuple:
'''simple docstring'''
return
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def _snake_case ( self )->Dict:
'''simple docstring'''
pass
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def _snake_case ( self )->str:
'''simple docstring'''
pass
def _snake_case ( self )->List[Any]:
'''simple docstring'''
A_ , A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : str = model_class(_SCREAMING_SNAKE_CASE )
A_ : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : Any = [*signature.parameters.keys()]
A_ : Any = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _SCREAMING_SNAKE_CASE )
def _snake_case ( self )->Any:
'''simple docstring'''
A_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def _snake_case ( self )->Optional[Any]:
'''simple docstring'''
A_ , A_ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Union[str, Any] = model_class(config=_SCREAMING_SNAKE_CASE )
for name, module in model.named_modules():
if isinstance(_SCREAMING_SNAKE_CASE , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
def _snake_case ( self )->List[Any]:
'''simple docstring'''
def check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
A_ : str = model_class(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
A_ : Tuple = model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
A_ : Union[str, Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
A_ : Optional[int] = self.model_tester.num_stages
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
A_ , A_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
A_ : int = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
A_ : int = layer_type
A_ : List[Any] = True
check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A_ : str = True
check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def _snake_case ( self )->Dict:
'''simple docstring'''
A_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE )
@slow
def _snake_case ( self )->str:
'''simple docstring'''
for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Dict = RegNetModel.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
def _SCREAMING_SNAKE_CASE ( ):
A_ : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def _snake_case ( self )->List[str]:
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _snake_case ( self )->Tuple:
'''simple docstring'''
A_ : List[Any] = RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(_SCREAMING_SNAKE_CASE )
A_ : Optional[Any] = self.default_image_processor
A_ : Any = prepare_img()
A_ : Optional[Any] = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).to(_SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
A_ : Union[str, Any] = model(**_SCREAMING_SNAKE_CASE )
# verify the logits
A_ : Union[str, Any] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _SCREAMING_SNAKE_CASE )
A_ : Optional[int] = torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6] ).to(_SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 ) )
| 186 | 1 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class lowercase ( a ):
lowercase__ : jnp.ndarray
@flax_register_to_config
class lowercase ( nn.Module , a , a ):
lowercase__ : int = 32
lowercase__ : int = 4
lowercase__ : int = 4
lowercase__ : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
lowercase__ : Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
lowercase__ : Union[bool, Tuple[bool]] = False
lowercase__ : Tuple[int] = (320, 640, 1_280, 1_280)
lowercase__ : int = 2
lowercase__ : Union[int, Tuple[int]] = 8
lowercase__ : Optional[Union[int, Tuple[int]]] = None
lowercase__ : int = 1_280
lowercase__ : float = 0.0
lowercase__ : bool = False
lowercase__ : jnp.dtype = jnp.floataa
lowercase__ : bool = True
lowercase__ : int = 0
lowercase__ : bool = False
def __snake_case( self : Optional[int] , _UpperCamelCase : jax.random.KeyArray ) -> FrozenDict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = (1, self.in_channels, self.sample_size, self.sample_size)
SCREAMING_SNAKE_CASE = jnp.zeros(_UpperCamelCase , dtype=jnp.floataa )
SCREAMING_SNAKE_CASE = jnp.ones((1,) , dtype=jnp.intaa )
SCREAMING_SNAKE_CASE = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = jax.random.split(_UpperCamelCase )
SCREAMING_SNAKE_CASE = {"params": params_rng, "dropout": dropout_rng}
return self.init(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )["params"]
def __snake_case( self : Dict ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.block_out_channels
SCREAMING_SNAKE_CASE = block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
"At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19." )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
SCREAMING_SNAKE_CASE = self.num_attention_heads or self.attention_head_dim
# input
SCREAMING_SNAKE_CASE = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
SCREAMING_SNAKE_CASE = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
SCREAMING_SNAKE_CASE = FlaxTimestepEmbedding(_UpperCamelCase , dtype=self.dtype )
SCREAMING_SNAKE_CASE = self.only_cross_attention
if isinstance(_UpperCamelCase , _UpperCamelCase ):
SCREAMING_SNAKE_CASE = (only_cross_attention,) * len(self.down_block_types )
if isinstance(_UpperCamelCase , _UpperCamelCase ):
SCREAMING_SNAKE_CASE = (num_attention_heads,) * len(self.down_block_types )
# down
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
SCREAMING_SNAKE_CASE = output_channel
SCREAMING_SNAKE_CASE = block_out_channels[i]
SCREAMING_SNAKE_CASE = i == len(_UpperCamelCase ) - 1
if down_block_type == "CrossAttnDownBlock2D":
SCREAMING_SNAKE_CASE = FlaxCrossAttnDownBlockaD(
in_channels=_UpperCamelCase , out_channels=_UpperCamelCase , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
SCREAMING_SNAKE_CASE = FlaxDownBlockaD(
in_channels=_UpperCamelCase , out_channels=_UpperCamelCase , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(_UpperCamelCase )
SCREAMING_SNAKE_CASE = down_blocks
# mid
SCREAMING_SNAKE_CASE = FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
# up
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = list(reversed(_UpperCamelCase ) )
SCREAMING_SNAKE_CASE = list(reversed(_UpperCamelCase ) )
SCREAMING_SNAKE_CASE = list(reversed(_UpperCamelCase ) )
SCREAMING_SNAKE_CASE = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
SCREAMING_SNAKE_CASE = output_channel
SCREAMING_SNAKE_CASE = reversed_block_out_channels[i]
SCREAMING_SNAKE_CASE = reversed_block_out_channels[min(i + 1 , len(_UpperCamelCase ) - 1 )]
SCREAMING_SNAKE_CASE = i == len(_UpperCamelCase ) - 1
if up_block_type == "CrossAttnUpBlock2D":
SCREAMING_SNAKE_CASE = FlaxCrossAttnUpBlockaD(
in_channels=_UpperCamelCase , out_channels=_UpperCamelCase , prev_output_channel=_UpperCamelCase , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
SCREAMING_SNAKE_CASE = FlaxUpBlockaD(
in_channels=_UpperCamelCase , out_channels=_UpperCamelCase , prev_output_channel=_UpperCamelCase , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(_UpperCamelCase )
SCREAMING_SNAKE_CASE = output_channel
SCREAMING_SNAKE_CASE = up_blocks
# out
SCREAMING_SNAKE_CASE = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
SCREAMING_SNAKE_CASE = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : str , _UpperCamelCase : int , _UpperCamelCase : Tuple , _UpperCamelCase : List[str] , _UpperCamelCase : Any=None , _UpperCamelCase : Optional[Any]=None , _UpperCamelCase : bool = True , _UpperCamelCase : bool = False , ) -> Union[FlaxUNetaDConditionOutput, Tuple]:
'''simple docstring'''
if not isinstance(_UpperCamelCase , jnp.ndarray ):
SCREAMING_SNAKE_CASE = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(_UpperCamelCase , jnp.ndarray ) and len(timesteps.shape ) == 0:
SCREAMING_SNAKE_CASE = timesteps.astype(dtype=jnp.floataa )
SCREAMING_SNAKE_CASE = jnp.expand_dims(_UpperCamelCase , 0 )
SCREAMING_SNAKE_CASE = self.time_proj(_UpperCamelCase )
SCREAMING_SNAKE_CASE = self.time_embedding(_UpperCamelCase )
# 2. pre-process
SCREAMING_SNAKE_CASE = jnp.transpose(_UpperCamelCase , (0, 2, 3, 1) )
SCREAMING_SNAKE_CASE = self.conv_in(_UpperCamelCase )
# 3. down
SCREAMING_SNAKE_CASE = (sample,)
for down_block in self.down_blocks:
if isinstance(_UpperCamelCase , _UpperCamelCase ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = down_block(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , deterministic=not train )
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = down_block(_UpperCamelCase , _UpperCamelCase , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
SCREAMING_SNAKE_CASE = ()
for down_block_res_sample, down_block_additional_residual in zip(
_UpperCamelCase , _UpperCamelCase ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
SCREAMING_SNAKE_CASE = new_down_block_res_samples
# 4. mid
SCREAMING_SNAKE_CASE = self.mid_block(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
SCREAMING_SNAKE_CASE = down_block_res_samples[-(self.layers_per_block + 1) :]
SCREAMING_SNAKE_CASE = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(_UpperCamelCase , _UpperCamelCase ):
SCREAMING_SNAKE_CASE = up_block(
_UpperCamelCase , temb=_UpperCamelCase , encoder_hidden_states=_UpperCamelCase , res_hidden_states_tuple=_UpperCamelCase , deterministic=not train , )
else:
SCREAMING_SNAKE_CASE = up_block(_UpperCamelCase , temb=_UpperCamelCase , res_hidden_states_tuple=_UpperCamelCase , deterministic=not train )
# 6. post-process
SCREAMING_SNAKE_CASE = self.conv_norm_out(_UpperCamelCase )
SCREAMING_SNAKE_CASE = nn.silu(_UpperCamelCase )
SCREAMING_SNAKE_CASE = self.conv_out(_UpperCamelCase )
SCREAMING_SNAKE_CASE = jnp.transpose(_UpperCamelCase , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=_UpperCamelCase )
| 364 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
class lowercase :
lowercase__ : str = None
@experimental
def __lowerCamelCase (UpperCAmelCase__ : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Any ):
if ParallelBackendConfig.backend_name is None:
return _map_with_multiprocessing_pool(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
return _map_with_joblib(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
def __lowerCamelCase (UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[Any] ):
SCREAMING_SNAKE_CASE = num_proc if num_proc <= len(UpperCAmelCase__ ) else len(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE = [] # We organize the splits ourselve (contiguous splits)
for index in range(UpperCAmelCase__ ):
SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ ) // num_proc
SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ ) % num_proc
SCREAMING_SNAKE_CASE = div * index + min(UpperCAmelCase__ , UpperCAmelCase__ )
SCREAMING_SNAKE_CASE = start + div + (1 if index < mod else 0)
split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) )
if len(UpperCAmelCase__ ) != sum(len(i[1] ) for i in split_kwds ):
raise ValueError(
F"Error dividing inputs iterable among processes. "
F"Total number of objects {len(UpperCAmelCase__ )}, "
F"length: {sum(len(i[1] ) for i in split_kwds )}" )
logger.info(
F"Spawning {num_proc} processes for {len(UpperCAmelCase__ )} objects in slices of {[len(i[1] ) for i in split_kwds]}" )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None, None
if not disable_tqdm:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = (RLock(),), tqdm.set_lock
with Pool(UpperCAmelCase__ , initargs=UpperCAmelCase__ , initializer=UpperCAmelCase__ ) as pool:
SCREAMING_SNAKE_CASE = pool.map(UpperCAmelCase__ , UpperCAmelCase__ )
logger.info(F"Finished {num_proc} processes" )
SCREAMING_SNAKE_CASE = [obj for proc_res in mapped for obj in proc_res]
logger.info(F"Unpacked {len(UpperCAmelCase__ )} objects" )
return mapped
def __lowerCamelCase (UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Any , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Any ):
# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
# and it requires monkey-patching joblib internal classes which is subject to change
import joblib
with joblib.parallel_backend(ParallelBackendConfig.backend_name , n_jobs=UpperCAmelCase__ ):
return joblib.Parallel()(
joblib.delayed(UpperCAmelCase__ )((function, obj, types, None, True, None) ) for obj in iterable )
@experimental
@contextlib.contextmanager
def __lowerCamelCase (UpperCAmelCase__ : str ):
SCREAMING_SNAKE_CASE = backend_name
if backend_name == "spark":
from joblibspark import register_spark
register_spark()
# TODO: call create_cache_and_write_probe if "download" in steps
# TODO: raise NotImplementedError when Dataset.map etc is called
try:
yield
finally:
SCREAMING_SNAKE_CASE = None
| 206 | 0 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
A_ : Union[str, Any] = logging.getLogger(__name__)
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """masked_bert"""
def __init__( self ,a_=30_522 ,a_=768 ,a_=12 ,a_=12 ,a_=3_072 ,a_="gelu" ,a_=0.1 ,a_=0.1 ,a_=512 ,a_=2 ,a_=0.02 ,a_=1E-1_2 ,a_=0 ,a_="topK" ,a_="constant" ,a_=0.0 ,**a_ ,) -> Optional[int]:
super().__init__(pad_token_id=a_ ,**a_ )
_UpperCAmelCase : Dict = vocab_size
_UpperCAmelCase : Optional[int] = hidden_size
_UpperCAmelCase : Any = num_hidden_layers
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Tuple = hidden_act
_UpperCAmelCase : int = intermediate_size
_UpperCAmelCase : Optional[Any] = hidden_dropout_prob
_UpperCAmelCase : str = attention_probs_dropout_prob
_UpperCAmelCase : str = max_position_embeddings
_UpperCAmelCase : Optional[Any] = type_vocab_size
_UpperCAmelCase : str = initializer_range
_UpperCAmelCase : Optional[int] = layer_norm_eps
_UpperCAmelCase : int = pruning_method
_UpperCAmelCase : str = mask_init
_UpperCAmelCase : List[str] = mask_scale
| 215 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : Union[str, Any] = [
("""bert.bert""", """visual_bert"""),
("""bert.cls""", """cls"""),
("""bert.classifier""", """cls"""),
("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
("""position_embeddings_visual""", """visual_position_embeddings"""),
("""projection""", """visual_projection"""),
]
A_ : int = [
"""nlvr2_coco_pre_trained.th""",
"""nlvr2_fine_tuned.th""",
"""nlvr2_pre_trained.th""",
"""vcr_coco_pre_train.th""",
"""vcr_fine_tune.th""",
"""vcr_pre_train.th""",
"""vqa_coco_pre_trained.th""",
"""vqa_fine_tuned.th""",
"""vqa_pre_trained.th""",
]
def snake_case_ ( lowerCAmelCase_ )-> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : Dict = torch.load(lowerCAmelCase_ , map_location="""cpu""" )
return sd
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=rename_keys_prefix )-> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : Any = OrderedDict()
_UpperCAmelCase : Any = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
_UpperCAmelCase : Dict = key
for name_pair in rename_keys_prefix:
_UpperCAmelCase : str = new_key.replace(name_pair[0] , name_pair[1] )
_UpperCAmelCase : str = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
_UpperCAmelCase : int = new_d["""cls.predictions.bias"""]
return new_d
@torch.no_grad()
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Any:
'''simple docstring'''
assert (
checkpoint_path.split("""/""" )[-1] in ACCEPTABLE_CHECKPOINTS
), F'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''
# Get Config
if "pre" in checkpoint_path:
_UpperCAmelCase : Optional[int] = """pretraining"""
if "vcr" in checkpoint_path:
_UpperCAmelCase : Optional[int] = {"""visual_embedding_dim""": 512}
elif "vqa_advanced" in checkpoint_path:
_UpperCAmelCase : List[Any] = {"""visual_embedding_dim""": 2048}
elif "vqa" in checkpoint_path:
_UpperCAmelCase : Any = {"""visual_embedding_dim""": 2048}
elif "nlvr" in checkpoint_path:
_UpperCAmelCase : Any = {"""visual_embedding_dim""": 1024}
else:
raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.''' )
else:
if "vcr" in checkpoint_path:
_UpperCAmelCase : str = {"""visual_embedding_dim""": 512}
_UpperCAmelCase : int = """multichoice"""
elif "vqa_advanced" in checkpoint_path:
_UpperCAmelCase : str = {"""visual_embedding_dim""": 2048}
_UpperCAmelCase : int = """vqa_advanced"""
elif "vqa" in checkpoint_path:
_UpperCAmelCase : List[str] = {"""visual_embedding_dim""": 2048, """num_labels""": 3129}
_UpperCAmelCase : int = """vqa"""
elif "nlvr" in checkpoint_path:
_UpperCAmelCase : int = {
"""visual_embedding_dim""": 1024,
"""num_labels""": 2,
}
_UpperCAmelCase : Optional[Any] = """nlvr"""
_UpperCAmelCase : int = VisualBertConfig(**lowerCAmelCase_ )
# Load State Dict
_UpperCAmelCase : Any = load_state_dict(lowerCAmelCase_ )
_UpperCAmelCase : Optional[int] = get_new_dict(lowerCAmelCase_ , lowerCAmelCase_ )
if model_type == "pretraining":
_UpperCAmelCase : List[str] = VisualBertForPreTraining(lowerCAmelCase_ )
elif model_type == "vqa":
_UpperCAmelCase : Optional[int] = VisualBertForQuestionAnswering(lowerCAmelCase_ )
elif model_type == "nlvr":
_UpperCAmelCase : str = VisualBertForVisualReasoning(lowerCAmelCase_ )
elif model_type == "multichoice":
_UpperCAmelCase : Dict = VisualBertForMultipleChoice(lowerCAmelCase_ )
model.load_state_dict(lowerCAmelCase_ )
# Save Checkpoints
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
A_ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
A_ : int = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 215 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""LongformerConfig""",
"""LongformerOnnxConfig""",
],
"""tokenization_longformer""": ["""LongformerTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["""LongformerTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LongformerForMaskedLM""",
"""LongformerForMultipleChoice""",
"""LongformerForQuestionAnswering""",
"""LongformerForSequenceClassification""",
"""LongformerForTokenClassification""",
"""LongformerModel""",
"""LongformerPreTrainedModel""",
"""LongformerSelfAttention""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
"""TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFLongformerForMaskedLM""",
"""TFLongformerForMultipleChoice""",
"""TFLongformerForQuestionAnswering""",
"""TFLongformerForSequenceClassification""",
"""TFLongformerForTokenClassification""",
"""TFLongformerModel""",
"""TFLongformerPreTrainedModel""",
"""TFLongformerSelfAttention""",
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 139 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Tuple , lowerCAmelCase : Tuple , lowerCAmelCase : str=13 , lowerCAmelCase : Optional[Any]=7 , lowerCAmelCase : Optional[Any]=True , lowerCAmelCase : Any=True , lowerCAmelCase : str=True , lowerCAmelCase : Optional[Any]=True , lowerCAmelCase : Dict=99 , lowerCAmelCase : Any=32 , lowerCAmelCase : int=5 , lowerCAmelCase : Tuple=4 , lowerCAmelCase : Optional[Any]=37 , lowerCAmelCase : str="gelu" , lowerCAmelCase : str=0.1 , lowerCAmelCase : str=0.1 , lowerCAmelCase : Any=5_12 , lowerCAmelCase : Optional[Any]=16 , lowerCAmelCase : Dict=2 , lowerCAmelCase : Tuple=0.02 , lowerCAmelCase : Optional[int]=4 , ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : int = parent
__lowerCAmelCase : Dict = batch_size
__lowerCAmelCase : Dict = seq_length
__lowerCAmelCase : Union[str, Any] = is_training
__lowerCAmelCase : List[Any] = use_attention_mask
__lowerCAmelCase : List[Any] = use_token_type_ids
__lowerCAmelCase : Optional[int] = use_labels
__lowerCAmelCase : str = vocab_size
__lowerCAmelCase : Any = hidden_size
__lowerCAmelCase : Optional[int] = num_hidden_layers
__lowerCAmelCase : Optional[int] = num_attention_heads
__lowerCAmelCase : Dict = intermediate_size
__lowerCAmelCase : Tuple = hidden_act
__lowerCAmelCase : Dict = hidden_dropout_prob
__lowerCAmelCase : Any = attention_probs_dropout_prob
__lowerCAmelCase : Union[str, Any] = max_position_embeddings
__lowerCAmelCase : int = type_vocab_size
__lowerCAmelCase : Tuple = type_sequence_label_size
__lowerCAmelCase : int = initializer_range
__lowerCAmelCase : Optional[int] = num_choices
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Dict:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase : Dict = None
if self.use_attention_mask:
__lowerCAmelCase : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase : Union[str, Any] = None
if self.use_token_type_ids:
__lowerCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCAmelCase : List[str] = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCAmelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : Any = self.prepare_config_and_inputs()
__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase : List[str] = config_and_inputs
__lowerCAmelCase : Any = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : List[str] = self.prepare_config_and_inputs()
__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase : Dict = config_and_inputs
__lowerCAmelCase : Any = True
__lowerCAmelCase : str = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class SCREAMING_SNAKE_CASE ( a_ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : int =True
lowerCamelCase : Any =(
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def SCREAMING_SNAKE_CASE ( self : str ) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = FlaxBertModelTester(self )
@slow
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Dict:
"""simple docstring"""
__lowerCAmelCase : int = FlaxBertModel.from_pretrained("""bert-base-cased""" )
__lowerCAmelCase : str = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCAmelCase )
| 139 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
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 (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class __snake_case :
def __init__( self , lowercase , lowercase=13 , lowercase=7 , lowercase=True , lowercase=True , lowercase=True , lowercase=True , lowercase=99 , lowercase=[1, 1, 2] , lowercase=1 , lowercase=32 , lowercase=4 , lowercase=8 , lowercase=37 , lowercase="gelu_new" , lowercase=0.1 , lowercase=0.1 , lowercase=0.0 , lowercase=5_12 , lowercase=3 , lowercase=0.02 , lowercase=3 , lowercase=4 , lowercase=None , lowercase=False , ) -> Tuple:
'''simple docstring'''
a__: Tuple = parent
a__: Any = batch_size
a__: List[Any] = seq_length
a__: Any = is_training
a__: Any = use_input_mask
a__: Union[str, Any] = use_token_type_ids
a__: Optional[Any] = use_labels
a__: List[Any] = vocab_size
a__: Dict = block_sizes
a__: List[str] = num_decoder_layers
a__: List[Any] = d_model
a__: Optional[Any] = n_head
a__: Dict = d_head
a__: str = d_inner
a__: Any = hidden_act
a__: Tuple = hidden_dropout
a__: str = attention_dropout
a__: Optional[Any] = activation_dropout
a__: List[Any] = max_position_embeddings
a__: int = type_vocab_size
a__: Dict = 2
a__: Dict = num_labels
a__: Dict = num_choices
a__: str = scope
a__: List[str] = initializer_std
# Used in the tests to check the size of the first attention layer
a__: Union[str, Any] = n_head
# Used in the tests to check the size of the first hidden state
a__: Optional[int] = self.d_model
# Used in the tests to check the number of output hidden states/attentions
a__: Dict = sum(self.block_sizes) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
a__: List[Any] = self.num_hidden_layers + 2
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
a__: Dict = None
if self.use_input_mask:
a__: Any = random_attention_mask([self.batch_size, self.seq_length])
a__: str = None
if self.use_token_type_ids:
a__: Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
a__: int = None
a__: Dict = None
a__: str = None
if self.use_labels:
a__: Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size)
a__: List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
a__: Tuple = ids_tensor([self.batch_size] , self.num_choices)
a__: Any = FunnelConfig(
vocab_size=self.vocab_size , block_sizes=self.block_sizes , num_decoder_layers=self.num_decoder_layers , d_model=self.d_model , n_head=self.n_head , d_head=self.d_head , d_inner=self.d_inner , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , activation_dropout=self.activation_dropout , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_std=self.initializer_std , )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , ) -> Optional[int]:
'''simple docstring'''
a__: Union[str, Any] = TFFunnelModel(config=lowercase)
a__: List[str] = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
a__: List[Any] = model(lowercase)
a__: int = [input_ids, input_mask]
a__: Dict = model(lowercase)
a__: Dict = model(lowercase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model))
a__: Dict = False
a__: str = TFFunnelModel(config=lowercase)
a__: List[Any] = model(lowercase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model))
a__: Optional[int] = False
a__: Optional[int] = TFFunnelModel(config=lowercase)
a__: Any = model(lowercase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , ) -> Union[str, Any]:
'''simple docstring'''
a__: List[Any] = TFFunnelBaseModel(config=lowercase)
a__: int = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
a__: int = model(lowercase)
a__: List[Any] = [input_ids, input_mask]
a__: str = model(lowercase)
a__: Any = model(lowercase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 2, self.d_model))
a__: str = False
a__: Any = TFFunnelBaseModel(config=lowercase)
a__: int = model(lowercase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 3, self.d_model))
a__: Union[str, Any] = False
a__: Tuple = TFFunnelBaseModel(config=lowercase)
a__: int = model(lowercase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 2, self.d_model))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , ) -> str:
'''simple docstring'''
a__: List[str] = TFFunnelForPreTraining(config=lowercase)
a__: int = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
a__: List[Any] = model(lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , ) -> Dict:
'''simple docstring'''
a__: Optional[Any] = TFFunnelForMaskedLM(config=lowercase)
a__: Any = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
a__: str = model(lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , ) -> Any:
'''simple docstring'''
a__: int = self.num_labels
a__: Tuple = TFFunnelForSequenceClassification(config=lowercase)
a__: Tuple = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
a__: Optional[Any] = model(lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , ) -> List[Any]:
'''simple docstring'''
a__: List[Any] = self.num_choices
a__: int = TFFunnelForMultipleChoice(config=lowercase)
a__: Tuple = tf.tile(tf.expand_dims(lowercase , 1) , (1, self.num_choices, 1))
a__: Tuple = tf.tile(tf.expand_dims(lowercase , 1) , (1, self.num_choices, 1))
a__: Union[str, Any] = tf.tile(tf.expand_dims(lowercase , 1) , (1, self.num_choices, 1))
a__: List[str] = {
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
a__: List[Any] = model(lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , ) -> str:
'''simple docstring'''
a__: Optional[Any] = self.num_labels
a__: str = TFFunnelForTokenClassification(config=lowercase)
a__: Dict = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
a__: Dict = model(lowercase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , ) -> List[Any]:
'''simple docstring'''
a__: Tuple = TFFunnelForQuestionAnswering(config=lowercase)
a__: str = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
a__: Optional[Any] = 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 lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Any = self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) ,
): int = config_and_inputs
a__: List[Any] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class __snake_case ( __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
a__ = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
a__ = (
{
"""feature-extraction""": (TFFunnelBaseModel, TFFunnelModel),
"""fill-mask""": TFFunnelForMaskedLM,
"""question-answering""": TFFunnelForQuestionAnswering,
"""text-classification""": TFFunnelForSequenceClassification,
"""token-classification""": TFFunnelForTokenClassification,
"""zero-shot""": TFFunnelForSequenceClassification,
}
if is_tf_available()
else {}
)
a__ = False
a__ = False
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Tuple = TFFunnelModelTester(self)
a__: Tuple = ConfigTester(self , config_class=lowercase)
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self) -> str:
'''simple docstring'''
a__: Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase)
def lowerCamelCase_ ( self) -> Union[str, Any]:
'''simple docstring'''
a__: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowercase)
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowercase)
def lowerCamelCase_ ( self) -> Any:
'''simple docstring'''
a__: Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase)
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowercase)
@require_tf
class __snake_case ( __lowerCAmelCase , unittest.TestCase ):
a__ = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
a__ = False
a__ = False
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: List[Any] = TFFunnelModelTester(self , base=lowercase)
a__: int = ConfigTester(self , config_class=lowercase)
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*lowercase)
def lowerCamelCase_ ( self) -> List[str]:
'''simple docstring'''
a__: int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowercase)
def lowerCamelCase_ ( self) -> int:
'''simple docstring'''
a__: List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowercase)
| 290 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
),
}
class __snake_case ( __lowerCAmelCase ):
a__ = """audio-spectrogram-transformer"""
def __init__( self , lowercase=7_68 , lowercase=12 , lowercase=12 , lowercase=30_72 , lowercase="gelu" , lowercase=0.0 , lowercase=0.0 , lowercase=0.02 , lowercase=1e-12 , lowercase=16 , lowercase=True , lowercase=10 , lowercase=10 , lowercase=10_24 , lowercase=1_28 , **lowercase , ) -> str:
'''simple docstring'''
super().__init__(**lowercase)
a__: Any = hidden_size
a__: int = num_hidden_layers
a__: Union[str, Any] = num_attention_heads
a__: Any = intermediate_size
a__: Union[str, Any] = hidden_act
a__: int = hidden_dropout_prob
a__: str = attention_probs_dropout_prob
a__: str = initializer_range
a__: Tuple = layer_norm_eps
a__: Any = patch_size
a__: int = qkv_bias
a__: Optional[Any] = frequency_stride
a__: int = time_stride
a__: List[str] = max_length
a__: Tuple = num_mel_bins
| 290 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase_ : str = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : Union[str, Any] = ["""YolosFeatureExtractor"""]
lowerCamelCase_ : Dict = ["""YolosImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : Dict = [
"""YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""YolosForObjectDetection""",
"""YolosModel""",
"""YolosPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
lowerCamelCase_ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 352 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
lowerCamelCase_ : Any = random.Random()
def _A ( lowercase , lowercase=1.0 , lowercase=None , lowercase=None ):
"""simple docstring"""
if rng is None:
a =global_rng
a =[]
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class __A ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , __A , __A=7 , __A=400 , __A=2000 , __A=10 , __A=160 , __A=8 , __A=0.0 , __A=4000 , __A=False , __A=True , ) -> Optional[Any]:
a =parent
a =batch_size
a =min_seq_length
a =max_seq_length
a =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
a =padding_value
a =sampling_rate
a =return_attention_mask
a =do_normalize
a =feature_size
a =chunk_length
a =hop_length
def SCREAMING_SNAKE_CASE ( self ) -> str:
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def SCREAMING_SNAKE_CASE ( self , __A=False , __A=False ) -> str:
def _flatten(__A ):
return list(itertools.chain(*__A ) )
if equal_length:
a =[floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
a =[
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
a =[np.asarray(__A ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __A ( _SCREAMING_SNAKE_CASE, unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase = WhisperFeatureExtractor if is_speech_available() else None
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
a =WhisperFeatureExtractionTester(self )
def SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
a =self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
a =feat_extract_first.save_pretrained(__A )[0]
check_json_file_has_correct_format(__A )
a =self.feature_extraction_class.from_pretrained(__A )
a =feat_extract_first.to_dict()
a =feat_extract_second.to_dict()
a =feat_extract_first.mel_filters
a =feat_extract_second.mel_filters
self.assertTrue(np.allclose(__A , __A ) )
self.assertEqual(__A , __A )
def SCREAMING_SNAKE_CASE ( self ) -> str:
a =self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
a =os.path.join(__A , '''feat_extract.json''' )
feat_extract_first.to_json_file(__A )
a =self.feature_extraction_class.from_json_file(__A )
a =feat_extract_first.to_dict()
a =feat_extract_second.to_dict()
a =feat_extract_first.mel_filters
a =feat_extract_second.mel_filters
self.assertTrue(np.allclose(__A , __A ) )
self.assertEqual(__A , __A )
def SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
# Tests that all call wrap to encode_plus and batch_encode_plus
a =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
a =[floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
a =[np.asarray(__A ) for speech_input in speech_inputs]
# Test feature size
a =feature_extractor(__A , padding='''max_length''' , return_tensors='''np''' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
a =feature_extractor(speech_inputs[0] , return_tensors='''np''' ).input_features
a =feature_extractor(np_speech_inputs[0] , return_tensors='''np''' ).input_features
self.assertTrue(np.allclose(__A , __A , atol=1E-3 ) )
# Test batched
a =feature_extractor(__A , return_tensors='''np''' ).input_features
a =feature_extractor(__A , return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(__A , __A ):
self.assertTrue(np.allclose(__A , __A , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
a =[floats_list((1, x) )[0] for x in (800, 800, 800)]
a =np.asarray(__A )
a =feature_extractor(__A , return_tensors='''np''' ).input_features
a =feature_extractor(__A , return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(__A , __A ):
self.assertTrue(np.allclose(__A , __A , atol=1E-3 ) )
# Test truncation required
a =[floats_list((1, x) )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200 )]
a =[np.asarray(__A ) for speech_input in speech_inputs]
a =[x[: feature_extractor.n_samples] for x in speech_inputs]
a =[np.asarray(__A ) for speech_input in speech_inputs_truncated]
a =feature_extractor(__A , return_tensors='''np''' ).input_features
a =feature_extractor(__A , return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(__A , __A ):
self.assertTrue(np.allclose(__A , __A , atol=1E-3 ) )
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
import torch
a =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
a =np.random.rand(100 , 32 ).astype(np.floataa )
a =np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
a =feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''np''' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
a =feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''pt''' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def SCREAMING_SNAKE_CASE ( self , __A ) -> Dict:
a =load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation''' )
# automatic decoding with librispeech
a =ds.sort('''id''' ).select(range(__A ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def SCREAMING_SNAKE_CASE ( self ) -> Any:
# fmt: off
a =torch.tensor(
[
0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
-0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
] )
# fmt: on
a =self._load_datasamples(1 )
a =WhisperFeatureExtractor()
a =feature_extractor(__A , return_tensors='''pt''' ).input_features
self.assertEqual(input_features.shape , (1, 80, 3000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , __A , atol=1E-4 ) )
def SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
a =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
a =self._load_datasamples(1 )[0]
a =((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535 # Rescale to [0, 65535] to show issue
a =feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=__A )[0]
self.assertTrue(np.all(np.mean(__A ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(__A ) - 1 ) < 1E-3 ) )
| 215 | 0 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
UpperCamelCase = 0
UpperCamelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
UpperCamelCase = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
UpperCamelCase = tuple[int, int]
class snake_case_ :
def __init__( self : Any , lowercase_ : int , lowercase_ : int , lowercase_ : int , lowercase_ : int , lowercase_ : int , lowercase_ : Node | None , ) -> None:
lowercase__ : List[str] = pos_x
lowercase__ : Optional[int] = pos_y
lowercase__ : Tuple = (pos_y, pos_x)
lowercase__ : Union[str, Any] = goal_x
lowercase__ : int = goal_y
lowercase__ : Any = g_cost
lowercase__ : int = parent
lowercase__ : Any = self.calculate_heuristic()
lowercase__ : Union[str, Any] = self.g_cost + self.h_cost
def __UpperCamelCase ( self : Optional[int] ) -> float:
lowercase__ : Tuple = self.pos_x - self.goal_x
lowercase__ : int = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(lowercase_ ) + abs(lowercase_ )
else:
return sqrt(dy**2 + dx**2 )
def __lt__( self : Tuple , lowercase_ : Node ) -> bool:
return self.f_cost < other.f_cost
class snake_case_ :
def __init__( self : Union[str, Any] , lowercase_ : TPosition , lowercase_ : TPosition ) -> Optional[int]:
lowercase__ : int = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , lowercase_ )
lowercase__ : List[str] = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_99_99 , lowercase_ )
lowercase__ : Dict = [self.start]
lowercase__ : list[Node] = []
lowercase__ : Tuple = False
def __UpperCamelCase ( self : Dict ) -> list[TPosition]:
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
lowercase__ : str = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
return self.retrace_path(lowercase_ )
self.closed_nodes.append(lowercase_ )
lowercase__ : str = self.get_successors(lowercase_ )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(lowercase_ )
else:
# retrieve the best current path
lowercase__ : Union[str, Any] = self.open_nodes.pop(self.open_nodes.index(lowercase_ ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(lowercase_ )
else:
self.open_nodes.append(lowercase_ )
return [self.start.pos]
def __UpperCamelCase ( self : Optional[int] , lowercase_ : Node ) -> list[Node]:
lowercase__ : List[str] = []
for action in delta:
lowercase__ : List[Any] = parent.pos_x + action[1]
lowercase__ : Dict = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(lowercase_ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
lowercase_ , lowercase_ , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , lowercase_ , ) )
return successors
def __UpperCamelCase ( self : str , lowercase_ : Node | None ) -> list[TPosition]:
lowercase__ : List[str] = node
lowercase__ : Dict = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
lowercase__ : Any = current_node.parent
path.reverse()
return path
class snake_case_ :
def __init__( self : Dict , lowercase_ : TPosition , lowercase_ : TPosition ) -> None:
lowercase__ : Union[str, Any] = AStar(lowercase_ , lowercase_ )
lowercase__ : Tuple = AStar(lowercase_ , lowercase_ )
lowercase__ : str = False
def __UpperCamelCase ( self : Tuple ) -> list[TPosition]:
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
lowercase__ : List[str] = self.fwd_astar.open_nodes.pop(0 )
lowercase__ : Union[str, Any] = self.bwd_astar.open_nodes.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
lowercase_ , lowercase_ )
self.fwd_astar.closed_nodes.append(lowercase_ )
self.bwd_astar.closed_nodes.append(lowercase_ )
lowercase__ : List[str] = current_bwd_node
lowercase__ : Tuple = current_fwd_node
lowercase__ : List[str] = {
self.fwd_astar: self.fwd_astar.get_successors(lowercase_ ),
self.bwd_astar: self.bwd_astar.get_successors(lowercase_ ),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(lowercase_ )
else:
# retrieve the best current path
lowercase__ : List[str] = astar.open_nodes.pop(
astar.open_nodes.index(lowercase_ ) )
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(lowercase_ )
else:
astar.open_nodes.append(lowercase_ )
return [self.fwd_astar.start.pos]
def __UpperCamelCase ( self : Tuple , lowercase_ : Node , lowercase_ : Node ) -> list[TPosition]:
lowercase__ : str = self.fwd_astar.retrace_path(lowercase_ )
lowercase__ : Dict = self.bwd_astar.retrace_path(lowercase_ )
bwd_path.pop()
bwd_path.reverse()
lowercase__ : Union[str, Any] = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
UpperCamelCase = (0, 0)
UpperCamelCase = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
UpperCamelCase = time.time()
UpperCamelCase = AStar(init, goal)
UpperCamelCase = a_star.search()
UpperCamelCase = time.time() - start_time
print(f"AStar execution time = {end_time:f} seconds")
UpperCamelCase = time.time()
UpperCamelCase = BidirectionalAStar(init, goal)
UpperCamelCase = time.time() - bd_start_time
print(f"BidirectionalAStar execution time = {bd_end_time:f} seconds")
| 87 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class snake_case_ ( unittest.TestCase ):
@require_torch
def __UpperCamelCase ( self : Optional[int] ) -> List[Any]:
lowercase__ : Union[str, Any] = pipeline(
task="zero-shot-audio-classification" , model="hf-internal-testing/tiny-clap-htsat-unfused" )
lowercase__ : List[str] = load_dataset("ashraq/esc50" )
lowercase__ : List[Any] = dataset["train"]["audio"][-1]["array"]
lowercase__ : Dict = audio_classifier(lowercase_ , candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] )
self.assertEqual(
nested_simplify(lowercase_ ) , [{"score": 0.5_01, "label": "Sound of a dog"}, {"score": 0.4_99, "label": "Sound of vaccum cleaner"}] , )
@unittest.skip("No models are available in TF" )
def __UpperCamelCase ( self : str ) -> Optional[int]:
pass
@slow
@require_torch
def __UpperCamelCase ( self : List[str] ) -> int:
lowercase__ : Tuple = pipeline(
task="zero-shot-audio-classification" , model="laion/clap-htsat-unfused" , )
# This is an audio of a dog
lowercase__ : Union[str, Any] = load_dataset("ashraq/esc50" )
lowercase__ : Tuple = dataset["train"]["audio"][-1]["array"]
lowercase__ : List[Any] = audio_classifier(lowercase_ , candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] )
self.assertEqual(
nested_simplify(lowercase_ ) , [
{"score": 0.9_99, "label": "Sound of a dog"},
{"score": 0.0_01, "label": "Sound of vaccum cleaner"},
] , )
lowercase__ : int = audio_classifier([audio] * 5 , candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] )
self.assertEqual(
nested_simplify(lowercase_ ) , [
[
{"score": 0.9_99, "label": "Sound of a dog"},
{"score": 0.0_01, "label": "Sound of vaccum cleaner"},
],
]
* 5 , )
lowercase__ : Tuple = audio_classifier(
[audio] * 5 , candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"] , batch_size=5 )
self.assertEqual(
nested_simplify(lowercase_ ) , [
[
{"score": 0.9_99, "label": "Sound of a dog"},
{"score": 0.0_01, "label": "Sound of vaccum cleaner"},
],
]
* 5 , )
@unittest.skip("No models are available in TF" )
def __UpperCamelCase ( self : Union[str, Any] ) -> Dict:
pass
| 87 | 1 |
'''simple docstring'''
def _a ( ) -> int:
"""simple docstring"""
__snake_case : Union[str, Any] = 0
for i in range(1 , 1001 ):
total += i**i
return str(_lowerCamelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 13 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
# See all BART models at https://huggingface.co/models?filter=bart
__UpperCamelCase = {
"vocab_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
},
"merges_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
},
"tokenizer_file": {
"facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
"facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
"facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
"facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
"facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
"yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
},
}
__UpperCamelCase = {
"facebook/bart-base": 1024,
"facebook/bart-large": 1024,
"facebook/bart-large-mnli": 1024,
"facebook/bart-large-cnn": 1024,
"facebook/bart-large-xsum": 1024,
"yjernite/bart_eli5": 1024,
}
class _A ( __lowercase ):
lowercase__: Any = VOCAB_FILES_NAMES
lowercase__: List[Any] = PRETRAINED_VOCAB_FILES_MAP
lowercase__: Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__: Optional[Any] = ['''input_ids''', '''attention_mask''']
lowercase__: List[str] = BartTokenizer
def __init__( self : Union[str, Any] , __magic_name__ : int=None , __magic_name__ : Tuple=None , __magic_name__ : Dict=None , __magic_name__ : Optional[Any]="replace" , __magic_name__ : int="<s>" , __magic_name__ : Dict="</s>" , __magic_name__ : Union[str, Any]="</s>" , __magic_name__ : Union[str, Any]="<s>" , __magic_name__ : str="<unk>" , __magic_name__ : List[Any]="<pad>" , __magic_name__ : Union[str, Any]="<mask>" , __magic_name__ : Optional[int]=False , __magic_name__ : str=True , **__magic_name__ : Tuple , ) -> List[str]:
"""simple docstring"""
super().__init__(
__magic_name__ , __magic_name__ , tokenizer_file=__magic_name__ , errors=__magic_name__ , bos_token=__magic_name__ , eos_token=__magic_name__ , sep_token=__magic_name__ , cls_token=__magic_name__ , unk_token=__magic_name__ , pad_token=__magic_name__ , mask_token=__magic_name__ , add_prefix_space=__magic_name__ , trim_offsets=__magic_name__ , **__magic_name__ , )
__snake_case : Any = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , __magic_name__ ) != add_prefix_space:
__snake_case : str = getattr(__magic_name__ , pre_tok_state.pop("""type""" ) )
__snake_case : str = add_prefix_space
__snake_case : Union[str, Any] = pre_tok_class(**__magic_name__ )
__snake_case : str = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
__snake_case : Any = """post_processor"""
__snake_case : Any = getattr(self.backend_tokenizer , __magic_name__ , __magic_name__ )
if tokenizer_component_instance:
__snake_case : str = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
__snake_case : Tuple = tuple(state["""sep"""] )
if "cls" in state:
__snake_case : int = tuple(state["""cls"""] )
__snake_case : Optional[int] = False
if state.get("""add_prefix_space""" , __magic_name__ ) != add_prefix_space:
__snake_case : Optional[Any] = add_prefix_space
__snake_case : List[str] = True
if state.get("""trim_offsets""" , __magic_name__ ) != trim_offsets:
__snake_case : Optional[int] = trim_offsets
__snake_case : Any = True
if changes_to_apply:
__snake_case : str = getattr(__magic_name__ , state.pop("""type""" ) )
__snake_case : List[Any] = component_class(**__magic_name__ )
setattr(self.backend_tokenizer , __magic_name__ , __magic_name__ )
@property
def lowercase__ ( self : List[Any] ) -> str:
"""simple docstring"""
if self._mask_token is None:
if self.verbose:
logger.error("""Using mask_token, but it is not set yet.""" )
return None
return str(self._mask_token )
@mask_token.setter
def lowercase__ ( self : Union[str, Any] , __magic_name__ : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__snake_case : Dict = AddedToken(__magic_name__ , lstrip=__magic_name__ , rstrip=__magic_name__ ) if isinstance(__magic_name__ , __magic_name__ ) else value
__snake_case : Union[str, Any] = value
def lowercase__ ( self : Any , *__magic_name__ : Union[str, Any] , **__magic_name__ : Tuple ) -> BatchEncoding:
"""simple docstring"""
__snake_case : Union[str, Any] = kwargs.get("""is_split_into_words""" , __magic_name__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"""to use it with pretokenized inputs.""" )
return super()._batch_encode_plus(*__magic_name__ , **__magic_name__ )
def lowercase__ ( self : Dict , *__magic_name__ : Optional[int] , **__magic_name__ : List[Any] ) -> BatchEncoding:
"""simple docstring"""
__snake_case : Optional[Any] = kwargs.get("""is_split_into_words""" , __magic_name__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"""to use it with pretokenized inputs.""" )
return super()._encode_plus(*__magic_name__ , **__magic_name__ )
def lowercase__ ( self : List[Any] , __magic_name__ : str , __magic_name__ : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
__snake_case : List[str] = self._tokenizer.model.save(__magic_name__ , name=__magic_name__ )
return tuple(__magic_name__ )
def lowercase__ ( self : Dict , __magic_name__ : List[str] , __magic_name__ : Optional[Any]=None ) -> Optional[Any]:
"""simple docstring"""
__snake_case : Optional[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowercase__ ( self : str , __magic_name__ : List[int] , __magic_name__ : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
__snake_case : Optional[int] = [self.sep_token_id]
__snake_case : Tuple = [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]
| 13 | 1 |
'''simple docstring'''
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case ( __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int =PhobertTokenizer
SCREAMING_SNAKE_CASE_ : Union[str, Any] =False
def _lowerCamelCase ( self : Any ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__UpperCamelCase = ['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
__UpperCamelCase = dict(zip(__A , range(len(__A ) ) ) )
__UpperCamelCase = ['#version: 0.2', 'l à</w>']
__UpperCamelCase = {'unk_token': '<unk>'}
__UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
for token in vocab_tokens:
fp.write(f'''{token} {vocab_tokens[token]}\n''' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__A ) )
def _lowerCamelCase ( self : Dict , **__A : Tuple ):
kwargs.update(self.special_tokens_map )
return PhobertTokenizer.from_pretrained(self.tmpdirname , **__A )
def _lowerCamelCase ( self : str , __A : List[Any] ):
__UpperCamelCase = 'Tôi là VinAI Research'
__UpperCamelCase = 'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
return input_text, output_text
def _lowerCamelCase ( self : Optional[Any] ):
__UpperCamelCase = PhobertTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
__UpperCamelCase = 'Tôi là VinAI Research'
__UpperCamelCase = 'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
__UpperCamelCase = tokenizer.tokenize(__A )
print(__A )
self.assertListEqual(__A , __A )
__UpperCamelCase = tokens + [tokenizer.unk_token]
__UpperCamelCase = [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
| 53 |
'''simple docstring'''
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
def __init__( self : Any , __A : Dict , __A : str , __A : List[Any]=1_0_2_4 , __A : Tuple=1_0_2_4 , __A : str=3.6 ):
__UpperCamelCase = tokenizer
__UpperCamelCase = tokenizer.bos_token_id
__UpperCamelCase = dataset
__UpperCamelCase = seq_length
__UpperCamelCase = seq_length * chars_per_token * num_of_sequences
def __iter__( self : Any ):
__UpperCamelCase = iter(self.dataset )
__UpperCamelCase = True
while more_examples:
__UpperCamelCase , __UpperCamelCase = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(__A )['content'] )
buffer_len += len(buffer[-1] )
except StopIteration:
__UpperCamelCase = False
break
__UpperCamelCase = tokenizer(__A , truncation=__A )['input_ids']
__UpperCamelCase = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(__A ) , self.seq_length ):
__UpperCamelCase = all_token_ids[i : i + self.seq_length]
if len(__A ) == self.seq_length:
yield torch.tensor(__A )
def lowercase__ ( __lowercase : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__UpperCamelCase = {'streaming': True}
__UpperCamelCase = load_dataset(args.dataset_name , split='train' , **__lowercase )
__UpperCamelCase = ConstantLengthDataset(__lowercase , __lowercase , seq_length=args.seq_length )
__UpperCamelCase = DataLoader(__lowercase , batch_size=args.batch_size )
return eval_dataloader
def lowercase__ ( __lowercase : Tuple ) -> Optional[Any]:
"""simple docstring"""
model.eval()
__UpperCamelCase = []
for step, batch in enumerate(__lowercase ):
with torch.no_grad():
__UpperCamelCase = model(__lowercase , labels=__lowercase )
__UpperCamelCase = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(__lowercase ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
__UpperCamelCase = torch.mean(torch.cat(__lowercase ) )
try:
__UpperCamelCase = torch.exp(__lowercase )
except OverflowError:
__UpperCamelCase = float('inf' )
return loss.item(), perplexity.item()
# Setup Accelerator
a__ : int =Accelerator()
# Parse configuration
a__ : Dict =HfArgumentParser(EvaluationArguments)
a__ : Union[str, Any] =parser.parse_args()
set_seed(args.seed)
# Logging
a__ : List[Any] =logging.getLogger(__name__)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
# Load model and tokenizer
a__ : Union[str, Any] =AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a__ : List[Any] =AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
a__ : Union[str, Any] =create_dataloader(args)
# Prepare everything with our `accelerator`.
a__ , a__ : List[str] =accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
a__ , a__ : Any =evaluate(args)
logger.info(f'loss/eval: {eval_loss}, perplexity: {perplexity}')
| 53 | 1 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
lowercase_ = {
'/attention/': '/0/SelfAttention/',
'/self_attention/': '/0/SelfAttention/',
'/encoder_decoder_attention/': '/1/EncDecAttention/',
'value': 'v',
'query': 'q',
'key': 'k',
'out': 'o',
'pre_self_attention_layer_norm': '0/layer_norm',
'pre_cross_attention_layer_norm': '1/layer_norm',
'pre_attention_layer_norm': '0/layer_norm', # previously 1, but seems wrong
'token_embedder': 'shared',
'encoder_norm': 'final_layer_norm',
'decoder_norm': 'final_layer_norm',
'relpos_bias/rel_embedding': 'block/0/layer/0/SelfAttention/relative_attention_bias/weight',
'router/router_weights/w/': 'router/classifier/',
'roer/roer_weights/w/': 'router/classifier/',
'logits_dense': 'lm_head',
}
def _snake_case( SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> List[Any]:
'''simple docstring'''
A__ = list(s_dict.keys() )
for key in keys:
A__ = R'.*/layers_(\d+)'
A__ = key
if re.match(_UpperCamelCase , _UpperCamelCase ):
A__ = re.sub(R'layers_(\d+)' , R'block/\1/layer' , _UpperCamelCase )
A__ = R'(encoder|decoder)\/'
if re.match(_UpperCamelCase , _UpperCamelCase ):
A__ = re.match(_UpperCamelCase , _UpperCamelCase ).groups()
if groups[0] == "encoder":
A__ = re.sub(R'/mlp/' , R'/1/mlp/' , _UpperCamelCase )
A__ = re.sub(R'/pre_mlp_layer_norm/' , R'/1/layer_norm/' , _UpperCamelCase )
elif groups[0] == "decoder":
A__ = re.sub(R'/mlp/' , R'/2/mlp/' , _UpperCamelCase )
A__ = re.sub(R'/pre_mlp_layer_norm/' , R'/2/layer_norm/' , _UpperCamelCase )
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
A__ = new_key.replace(_UpperCamelCase , _UpperCamelCase )
print(f'{key} -> {new_key}' )
A__ = s_dict.pop(_UpperCamelCase )
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
A__ = s_dict[
'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
A__ = s_dict[
'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys() ):
if "expert" in key:
A__ = s_dict[key].shape[0]
A__ = s_dict[key]
for idx in range(_UpperCamelCase ):
A__ = expert_weihts[idx]
print(f'{key} -> {key.replace("expert/" , "nested fstring" )}' )
s_dict.pop(_UpperCamelCase )
return s_dict
lowercase_ = {
'NUM_ENCODER_LAYERS': 'num_layers',
'NUM_DECODER_LAYERS': 'num_decoder_layers',
'NUM_HEADS': 'num_heads',
'HEAD_DIM': 'd_kv',
'EMBED_DIM': 'd_model',
'MLP_DIM': 'd_ff',
'NUM_SELECTED_EXPERTS': 'num_selected_experts',
'NUM_ENCODER_SPARSE_LAYERS': 'num_sparse_encoder_layers',
'NUM_DECODER_SPARSE_LAYERS': 'num_sparse_decoder_layers',
'dense.MlpBlock.activations': 'feed_forward_proj',
}
def _snake_case( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : str ) -> int:
'''simple docstring'''
import regex as re
with open(_UpperCamelCase , 'r' ) as f:
A__ = f.read()
A__ = re.findall(R'(.*) = ([0-9.]*)' , _UpperCamelCase )
A__ = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
A__ = float(_UpperCamelCase ) if '.' in value else int(_UpperCamelCase )
A__ = re.findall(R'(.*activations) = \(\'(.*)\',\)' , _UpperCamelCase )[0]
A__ = str(activation[1] )
A__ = num_experts
A__ = SwitchTransformersConfig(**_UpperCamelCase )
return config
def _snake_case( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[str]=None , SCREAMING_SNAKE_CASE__ : Any="./" , SCREAMING_SNAKE_CASE__ : Any=8 ) -> Any:
'''simple docstring'''
print(f'Loading flax weights from : {flax_checkpoint_path}' )
A__ = checkpoints.load_tax_checkpoint(_UpperCamelCase )
if gin_file is not None:
A__ = convert_gin_to_config(_UpperCamelCase , _UpperCamelCase )
else:
A__ = SwitchTransformersConfig.from_pretrained(_UpperCamelCase )
A__ = SwitchTransformersForConditionalGeneration(_UpperCamelCase )
A__ = flax_params['target']
A__ = flatten_dict(_UpperCamelCase , sep='/' )
A__ = rename_keys(_UpperCamelCase )
A__ = unflatten_dict(_UpperCamelCase , sep='/' )
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(_UpperCamelCase , _UpperCamelCase )
print(f'Save PyTorch model to {pytorch_dump_path}' )
pt_model.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
" model architecture. If not provided, a `gin_file` has to be provided."
),
)
parser.add_argument(
"--gin_file",
default=None,
type=str,
required=False,
help="Path to the gin config file. If not provided, a `config_file` has to be passed ",
)
parser.add_argument(
"--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
)
parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
lowercase_ = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 368 |
import argparse
import struct
import unittest
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : bytes )-> None:
'''simple docstring'''
A__ = data
# Initialize hash values
A__ = [
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
A__ = [
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,
]
A__ = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def snake_case__ ( lowercase_ : bytes )-> bytes:
'''simple docstring'''
A__ = B'\x80' + (B'\x00' * (6_3 - (len(lowercase_ ) + 8) % 6_4))
A__ = struct.pack('>Q',(len(lowercase_ ) * 8) )
return data + padding + big_endian_integer
def snake_case__ ( self : Optional[int] )-> None:
'''simple docstring'''
A__ = [
self.preprocessed_data[x : x + 6_4]
for x in range(0,len(self.preprocessed_data ),6_4 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
A__ = list(struct.unpack('>16L',lowercase_ ) )
# add 48 0-ed integers
words += [0] * 4_8
A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ = self.hashes
for index in range(0,6_4 ):
if index > 1_5:
# modify the zero-ed indexes at the end of the array
A__ = (
self.ror(words[index - 1_5],7 )
^ self.ror(words[index - 1_5],1_8 )
^ (words[index - 1_5] >> 3)
)
A__ = (
self.ror(words[index - 2],1_7 )
^ self.ror(words[index - 2],1_9 )
^ (words[index - 2] >> 1_0)
)
A__ = (
words[index - 1_6] + sa + words[index - 7] + sa
) % 0X1_0_0_0_0_0_0_0_0
# Compression
A__ = self.ror(lowercase_,6 ) ^ self.ror(lowercase_,1_1 ) ^ self.ror(lowercase_,2_5 )
A__ = (e & f) ^ ((~e & 0Xf_f_f_f_f_f_f_f) & g)
A__ = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0X1_0_0_0_0_0_0_0_0
A__ = self.ror(lowercase_,2 ) ^ self.ror(lowercase_,1_3 ) ^ self.ror(lowercase_,2_2 )
A__ = (a & b) ^ (a & c) ^ (b & c)
A__ = (sa + maj) % 0X1_0_0_0_0_0_0_0_0
A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ = (
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),
)
A__ = [a, b, c, d, e, f, g, h]
# Modify final values
A__ = [
((element + mutated_hash_values[index]) % 0X1_0_0_0_0_0_0_0_0)
for index, element in enumerate(self.hashes )
]
A__ = ''.join([hex(lowercase_ )[2:].zfill(8 ) for value in self.hashes] )
def snake_case__ ( self : Union[str, Any],lowercase_ : int,lowercase_ : int )-> int:
'''simple docstring'''
return 0Xf_f_f_f_f_f_f_f & (value << (3_2 - rotations)) | (value >> rotations)
class A ( unittest.TestCase ):
"""simple docstring"""
def snake_case__ ( self : List[str] )-> None:
'''simple docstring'''
import hashlib
A__ = bytes('Test String','utf-8' )
self.assertEqual(SHAaaa(lowercase_ ).hash,hashlib.shaaaa(lowercase_ ).hexdigest() )
def _snake_case( ) -> None:
'''simple docstring'''
import doctest
doctest.testmod()
A__ = 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' )
A__ = parser.parse_args()
A__ = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , 'rb' ) as f:
A__ = f.read()
else:
A__ = bytes(SCREAMING_SNAKE_CASE__ , 'utf-8' )
print(SHAaaa(SCREAMING_SNAKE_CASE__ ).hash )
if __name__ == "__main__":
main()
| 282 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class _UpperCAmelCase ( _A , _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : List[Any] = IFPipeline
SCREAMING_SNAKE_CASE_ : Optional[int] = TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
SCREAMING_SNAKE_CASE_ : int = TEXT_TO_IMAGE_BATCH_PARAMS
SCREAMING_SNAKE_CASE_ : Any = PipelineTesterMixin.required_optional_params - {"latents"}
def A ( self : Optional[int] ) -> Tuple:
return self._get_dummy_components()
def A ( self : Dict , A : Union[str, Any] , A : Optional[Any]=0 ) -> Optional[Any]:
if str(A ).startswith('''mps''' ):
lowercase_ : Optional[Any] = torch.manual_seed(A )
else:
lowercase_ : Optional[int] = torch.Generator(device=A ).manual_seed(A )
lowercase_ : Dict = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def A ( self : int ) -> Dict:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def A ( self : Union[str, Any] ) -> int:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def A ( self : int ) -> Union[str, Any]:
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def A ( self : int ) -> Optional[int]:
self._test_save_load_local()
def A ( self : Tuple ) -> Optional[Any]:
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def A ( self : Tuple ) -> Any:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
def A ( self : Any ) -> Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : Tuple ) -> Optional[Any]:
# if
lowercase_ : Union[str, Any] = IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0''' , variant='''fp16''' , torch_dtype=torch.floataa )
lowercase_ : str = IFSuperResolutionPipeline.from_pretrained(
'''DeepFloyd/IF-II-L-v1.0''' , variant='''fp16''' , torch_dtype=torch.floataa , text_encoder=A , tokenizer=A )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to('''cuda''' )
lowercase_ , lowercase_ : List[str] = pipe_a.encode_prompt('''anime turtle''' , device='''cuda''' )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
lowercase_ : Optional[int] = None
lowercase_ : List[str] = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(A , A , A , A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
lowercase_ : List[Any] = IFImgaImgPipeline(**pipe_a.components )
lowercase_ : Optional[int] = IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(A , A , A , A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
lowercase_ : str = IFInpaintingPipeline(**pipe_a.components )
lowercase_ : int = IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(A , A , A , A )
def A ( self : Optional[Any] , A : int , A : List[str] , A : Optional[int] , A : List[Any] ) -> str:
# pipeline 1
_start_torch_memory_measurement()
lowercase_ : Optional[int] = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase_ : Optional[int] = pipe_a(
prompt_embeds=A , negative_prompt_embeds=A , num_inference_steps=2 , generator=A , output_type='''np''' , )
lowercase_ : Optional[Any] = output.images[0]
assert image.shape == (64, 64, 3)
lowercase_ : Dict = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
lowercase_ : str = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy''' )
assert_mean_pixel_difference(A , A )
# pipeline 2
_start_torch_memory_measurement()
lowercase_ : Dict = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase_ : Union[str, Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(A )
lowercase_ : str = pipe_a(
prompt_embeds=A , negative_prompt_embeds=A , image=A , generator=A , num_inference_steps=2 , output_type='''np''' , )
lowercase_ : Optional[int] = output.images[0]
assert image.shape == (2_56, 2_56, 3)
lowercase_ : Optional[Any] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
lowercase_ : int = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy''' )
assert_mean_pixel_difference(A , A )
def A ( self : List[Any] , A : str , A : Tuple , A : Optional[int] , A : Optional[Any] ) -> int:
# pipeline 1
_start_torch_memory_measurement()
lowercase_ : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(A )
lowercase_ : Optional[Any] = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase_ : List[Any] = pipe_a(
prompt_embeds=A , negative_prompt_embeds=A , image=A , num_inference_steps=2 , generator=A , output_type='''np''' , )
lowercase_ : Any = output.images[0]
assert image.shape == (64, 64, 3)
lowercase_ : Dict = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
lowercase_ : Dict = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy''' )
assert_mean_pixel_difference(A , A )
# pipeline 2
_start_torch_memory_measurement()
lowercase_ : str = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase_ : Any = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(0 ) ).to(A )
lowercase_ : int = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(A )
lowercase_ : Any = pipe_a(
prompt_embeds=A , negative_prompt_embeds=A , image=A , original_image=A , generator=A , num_inference_steps=2 , output_type='''np''' , )
lowercase_ : Optional[int] = output.images[0]
assert image.shape == (2_56, 2_56, 3)
lowercase_ : Union[str, Any] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
lowercase_ : Tuple = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy''' )
assert_mean_pixel_difference(A , A )
def A ( self : str , A : int , A : Any , A : Tuple , A : Dict ) -> List[Any]:
# pipeline 1
_start_torch_memory_measurement()
lowercase_ : Any = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(A )
lowercase_ : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(A )
lowercase_ : int = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase_ : Union[str, Any] = pipe_a(
prompt_embeds=A , negative_prompt_embeds=A , image=A , mask_image=A , num_inference_steps=2 , generator=A , output_type='''np''' , )
lowercase_ : Tuple = output.images[0]
assert image.shape == (64, 64, 3)
lowercase_ : str = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
lowercase_ : Union[str, Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy''' )
assert_mean_pixel_difference(A , A )
# pipeline 2
_start_torch_memory_measurement()
lowercase_ : int = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowercase_ : List[Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(A )
lowercase_ : List[Any] = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(0 ) ).to(A )
lowercase_ : str = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(1 ) ).to(A )
lowercase_ : List[str] = pipe_a(
prompt_embeds=A , negative_prompt_embeds=A , image=A , mask_image=A , original_image=A , generator=A , num_inference_steps=2 , output_type='''np''' , )
lowercase_ : List[Any] = output.images[0]
assert image.shape == (2_56, 2_56, 3)
lowercase_ : Optional[Any] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
lowercase_ : int = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy''' )
assert_mean_pixel_difference(A , A )
def lowercase ( ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 33 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
A : Optional[int] = 1_0_0
A : int = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100 )
def UpperCamelCase ( __magic_name__ : int ) -> set[int]:
"""simple docstring"""
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
lowercase__ = set()
lowercase__ = 42
lowercase__ = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def UpperCamelCase ( __magic_name__ : int = 5000 ) -> int | None:
"""simple docstring"""
for number_to_partition in range(1 , __magic_name__ ):
if len(partition(__magic_name__ ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F'{solution() = }')
| 305 | 0 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_a = logging.get_logger(__name__)
_a = {
'Visual-Attention-Network/van-base': (
'https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'
),
}
class _lowerCAmelCase ( lowercase ):
"""simple docstring"""
__UpperCAmelCase : Any = "van"
def __init__( self : str, UpperCAmelCase__ : Dict=2_2_4, UpperCAmelCase__ : Dict=3, UpperCAmelCase__ : List[Any]=[7, 3, 3, 3], UpperCAmelCase__ : Optional[Any]=[4, 2, 2, 2], UpperCAmelCase__ : int=[6_4, 1_2_8, 3_2_0, 5_1_2], UpperCAmelCase__ : List[Any]=[3, 3, 1_2, 3], UpperCAmelCase__ : Dict=[8, 8, 4, 4], UpperCAmelCase__ : List[Any]="gelu", UpperCAmelCase__ : Optional[Any]=0.02, UpperCAmelCase__ : Dict=1E-6, UpperCAmelCase__ : Union[str, Any]=1E-2, UpperCAmelCase__ : Optional[int]=0.0, UpperCAmelCase__ : Dict=0.0, **UpperCAmelCase__ : int, ):
super().__init__(**UpperCAmelCase__ )
__lowercase = image_size
__lowercase = num_channels
__lowercase = patch_sizes
__lowercase = strides
__lowercase = hidden_sizes
__lowercase = depths
__lowercase = mlp_ratios
__lowercase = hidden_act
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = layer_scale_init_value
__lowercase = drop_path_rate
__lowercase = dropout_rate
| 144 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
_a = True
except (ImportError, ModuleNotFoundError):
_a = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def _A ( UpperCamelCase_ : str) -> str:
'''simple docstring'''
re.sub("<n>", "", UpperCamelCase_) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(UpperCamelCase_))
| 144 | 1 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[Any]:
if isinstance(SCREAMING_SNAKE_CASE__ , torch.Tensor ):
return image
elif isinstance(SCREAMING_SNAKE_CASE__ , PIL.Image.Image ):
lowercase : Tuple = [image]
if isinstance(image[0] , PIL.Image.Image ):
lowercase : List[str] = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image]
lowercase : Optional[Any] = np.concatenate(SCREAMING_SNAKE_CASE__ , axis=0 )
lowercase : Optional[int] = np.array(SCREAMING_SNAKE_CASE__ ).astype(np.floataa ) / 255.0
lowercase : Tuple = image.transpose(0 , 3 , 1 , 2 )
lowercase : Dict = 2.0 * image - 1.0
lowercase : Optional[Any] = torch.from_numpy(SCREAMING_SNAKE_CASE__ )
elif isinstance(image[0] , torch.Tensor ):
lowercase : Any = torch.cat(SCREAMING_SNAKE_CASE__ , dim=0 )
return image
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0.9995 ) -> Any:
if not isinstance(SCREAMING_SNAKE_CASE__ , np.ndarray ):
lowercase : Optional[Any] = True
lowercase : Any = va.device
lowercase : Tuple = va.cpu().numpy()
lowercase : Dict = va.cpu().numpy()
lowercase : Any = np.sum(va * va / (np.linalg.norm(SCREAMING_SNAKE_CASE__ ) * np.linalg.norm(SCREAMING_SNAKE_CASE__ )) )
if np.abs(SCREAMING_SNAKE_CASE__ ) > DOT_THRESHOLD:
lowercase : Any = (1 - t) * va + t * va
else:
lowercase : int = np.arccos(SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = np.sin(SCREAMING_SNAKE_CASE__ )
lowercase : str = theta_a * t
lowercase : List[Any] = np.sin(SCREAMING_SNAKE_CASE__ )
lowercase : int = np.sin(theta_a - theta_t ) / sin_theta_a
lowercase : int = sin_theta_t / sin_theta_a
lowercase : Dict = sa * va + sa * va
if inputs_are_torch:
lowercase : Optional[int] = torch.from_numpy(SCREAMING_SNAKE_CASE__ ).to(SCREAMING_SNAKE_CASE__ )
return va
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
lowercase : Union[str, Any] = F.normalize(SCREAMING_SNAKE_CASE__ , dim=-1 )
lowercase : List[Any] = F.normalize(SCREAMING_SNAKE_CASE__ , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
for param in model.parameters():
lowercase : List[str] = value
class __snake_case ( lowerCAmelCase ):
def __init__( self ,snake_case ,snake_case ,snake_case ,snake_case ,snake_case ,snake_case ,snake_case ,snake_case=None ,snake_case=None ,snake_case=None ,):
'''simple docstring'''
super().__init__()
self.register_modules(
vae=snake_case ,text_encoder=snake_case ,clip_model=snake_case ,tokenizer=snake_case ,unet=snake_case ,scheduler=snake_case ,feature_extractor=snake_case ,coca_model=snake_case ,coca_tokenizer=snake_case ,coca_transform=snake_case ,)
lowercase : Optional[int] = (
feature_extractor.size
if isinstance(feature_extractor.size ,snake_case )
else feature_extractor.size["""shortest_edge"""]
)
lowercase : Dict = transforms.Normalize(mean=feature_extractor.image_mean ,std=feature_extractor.image_std )
set_requires_grad(self.text_encoder ,snake_case )
set_requires_grad(self.clip_model ,snake_case )
def _SCREAMING_SNAKE_CASE ( self ,snake_case = "auto" ):
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowercase : List[Any] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
self.enable_attention_slicing(snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
set_requires_grad(self.vae ,snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
set_requires_grad(self.vae ,snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
set_requires_grad(self.unet ,snake_case )
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
set_requires_grad(self.unet ,snake_case )
def _SCREAMING_SNAKE_CASE ( self ,snake_case ,snake_case ,snake_case ):
'''simple docstring'''
lowercase : Optional[int] = min(int(num_inference_steps * strength ) ,snake_case )
lowercase : List[Any] = max(num_inference_steps - init_timestep ,0 )
lowercase : int = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def _SCREAMING_SNAKE_CASE ( self ,snake_case ,snake_case ,snake_case ,snake_case ,snake_case ,snake_case=None ):
'''simple docstring'''
if not isinstance(snake_case ,torch.Tensor ):
raise ValueError(f"`image` has to be of type `torch.Tensor` but is {type(snake_case )}" )
lowercase : List[str] = image.to(device=snake_case ,dtype=snake_case )
if isinstance(snake_case ,snake_case ):
lowercase : Optional[int] = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(snake_case )
]
lowercase : Tuple = torch.cat(snake_case ,dim=0 )
else:
lowercase : List[str] = self.vae.encode(snake_case ).latent_dist.sample(snake_case )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowercase : Any = 0.18_215 * init_latents
lowercase : Dict = init_latents.repeat_interleave(snake_case ,dim=0 )
lowercase : List[str] = randn_tensor(init_latents.shape ,generator=snake_case ,device=snake_case ,dtype=snake_case )
# get latents
lowercase : Optional[int] = self.scheduler.add_noise(snake_case ,snake_case ,snake_case )
lowercase : Optional[Any] = init_latents
return latents
def _SCREAMING_SNAKE_CASE ( self ,snake_case ):
'''simple docstring'''
lowercase : Dict = self.coca_transform(snake_case ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
lowercase : List[Any] = self.coca_model.generate(transformed_image.to(device=self.device ,dtype=self.coca_model.dtype ) )
lowercase : int = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split("""<end_of_text>""" )[0].replace("""<start_of_text>""" ,"""""" ).rstrip(""" .,""" )
def _SCREAMING_SNAKE_CASE ( self ,snake_case ,snake_case ):
'''simple docstring'''
lowercase : Optional[int] = self.feature_extractor.preprocess(snake_case )
lowercase : Optional[int] = torch.from_numpy(clip_image_input["""pixel_values"""][0] ).unsqueeze(0 ).to(self.device ).half()
lowercase : List[Any] = self.clip_model.get_image_features(snake_case )
lowercase : List[str] = image_embeddings_clip / image_embeddings_clip.norm(p=2 ,dim=-1 ,keepdim=snake_case )
lowercase : Tuple = image_embeddings_clip.repeat_interleave(snake_case ,dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def _SCREAMING_SNAKE_CASE ( self ,snake_case ,snake_case ,snake_case ,snake_case ,snake_case ,snake_case ,snake_case ,):
'''simple docstring'''
lowercase : Optional[int] = latents.detach().requires_grad_()
lowercase : Optional[int] = self.scheduler.scale_model_input(snake_case ,snake_case )
# predict the noise residual
lowercase : Optional[int] = self.unet(snake_case ,snake_case ,encoder_hidden_states=snake_case ).sample
if isinstance(self.scheduler ,(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
lowercase : Optional[int] = self.scheduler.alphas_cumprod[timestep]
lowercase : Union[str, Any] = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
lowercase : Tuple = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
lowercase : int = torch.sqrt(snake_case )
lowercase : Any = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler ,snake_case ):
lowercase : Dict = self.scheduler.sigmas[index]
lowercase : Tuple = latents - sigma * noise_pred
else:
raise ValueError(f"scheduler type {type(self.scheduler )} not supported" )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowercase : Optional[Any] = 1 / 0.18_215 * sample
lowercase : Union[str, Any] = self.vae.decode(snake_case ).sample
lowercase : str = (image / 2 + 0.5).clamp(0 ,1 )
lowercase : int = transforms.Resize(self.feature_extractor_size )(snake_case )
lowercase : Tuple = self.normalize(snake_case ).to(latents.dtype )
lowercase : Tuple = self.clip_model.get_image_features(snake_case )
lowercase : List[str] = image_embeddings_clip / image_embeddings_clip.norm(p=2 ,dim=-1 ,keepdim=snake_case )
lowercase : str = spherical_dist_loss(snake_case ,snake_case ).mean() * clip_guidance_scale
lowercase : List[Any] = -torch.autograd.grad(snake_case ,snake_case )[0]
if isinstance(self.scheduler ,snake_case ):
lowercase : Any = latents.detach() + grads * (sigma**2)
lowercase : Optional[Any] = noise_pred_original
else:
lowercase : int = noise_pred_original - torch.sqrt(snake_case ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self ,snake_case ,snake_case ,snake_case = None ,snake_case = None ,snake_case = 512 ,snake_case = 512 ,snake_case = 0.6 ,snake_case = 50 ,snake_case = 7.5 ,snake_case = 1 ,snake_case = 0.0 ,snake_case = 100 ,snake_case = None ,snake_case = "pil" ,snake_case = True ,snake_case = 0.8 ,snake_case = 0.1 ,snake_case = 0.1 ,):
'''simple docstring'''
if isinstance(snake_case ,snake_case ) and len(snake_case ) != batch_size:
raise ValueError(f"You have passed {batch_size} batch_size, but only {len(snake_case )} generators." )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}." )
if isinstance(snake_case ,torch.Generator ) and batch_size > 1:
lowercase : str = [generator] + [None] * (batch_size - 1)
lowercase : Union[str, Any] = [
("""model""", self.coca_model is None),
("""tokenizer""", self.coca_tokenizer is None),
("""transform""", self.coca_transform is None),
]
lowercase : Optional[int] = [x[0] for x in coca_is_none if x[1]]
lowercase : int = """, """.join(snake_case )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(snake_case ):
raise ValueError(
f"Content prompt is None and CoCa [{coca_is_none_str}] is None."
f"Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline." )
lowercase : Optional[int] = self.get_image_description(snake_case )
if style_prompt is None:
if len(snake_case ):
raise ValueError(
f"Style prompt is None and CoCa [{coca_is_none_str}] is None."
f" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline." )
lowercase : str = self.get_image_description(snake_case )
# get prompt text embeddings for content and style
lowercase : List[Any] = self.tokenizer(
snake_case ,padding="""max_length""" ,max_length=self.tokenizer.model_max_length ,truncation=snake_case ,return_tensors="""pt""" ,)
lowercase : List[Any] = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
lowercase : Optional[int] = self.tokenizer(
snake_case ,padding="""max_length""" ,max_length=self.tokenizer.model_max_length ,truncation=snake_case ,return_tensors="""pt""" ,)
lowercase : Tuple = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
lowercase : Optional[Any] = slerp(snake_case ,snake_case ,snake_case )
# duplicate text embeddings for each generation per prompt
lowercase : str = text_embeddings.repeat_interleave(snake_case ,dim=0 )
# set timesteps
lowercase : str = """offset""" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
lowercase : Tuple = {}
if accepts_offset:
lowercase : int = 1
self.scheduler.set_timesteps(snake_case ,**snake_case )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
lowercase , lowercase : Optional[int] = self.get_timesteps(snake_case ,snake_case ,self.device )
lowercase : Tuple = timesteps[:1].repeat(snake_case )
# Preprocess image
lowercase : str = preprocess(snake_case ,snake_case ,snake_case )
lowercase : int = self.prepare_latents(
snake_case ,snake_case ,snake_case ,text_embeddings.dtype ,self.device ,snake_case )
lowercase : List[Any] = preprocess(snake_case ,snake_case ,snake_case )
lowercase : Tuple = self.prepare_latents(
snake_case ,snake_case ,snake_case ,text_embeddings.dtype ,self.device ,snake_case )
lowercase : List[str] = slerp(snake_case ,snake_case ,snake_case )
if clip_guidance_scale > 0:
lowercase : Union[str, Any] = self.get_clip_image_embeddings(snake_case ,snake_case )
lowercase : Optional[int] = self.get_clip_image_embeddings(snake_case ,snake_case )
lowercase : Optional[int] = slerp(
snake_case ,snake_case ,snake_case )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
lowercase : str = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowercase : List[str] = content_text_input.input_ids.shape[-1]
lowercase : Optional[Any] = self.tokenizer([""""""] ,padding="""max_length""" ,max_length=snake_case ,return_tensors="""pt""" )
lowercase : Any = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
lowercase : Any = uncond_embeddings.repeat_interleave(snake_case ,dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowercase : List[str] = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
lowercase : Tuple = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
lowercase : Tuple = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
lowercase : str = torch.randn(snake_case ,generator=snake_case ,device="""cpu""" ,dtype=snake_case ).to(
self.device )
else:
lowercase : Optional[int] = torch.randn(snake_case ,generator=snake_case ,device=self.device ,dtype=snake_case )
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}" )
lowercase : Any = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowercase : Dict = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
lowercase : Union[str, Any] = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowercase : str = {}
if accepts_eta:
lowercase : str = eta
# check if the scheduler accepts generator
lowercase : Optional[Any] = """generator""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
lowercase : Tuple = generator
with self.progress_bar(total=snake_case ):
for i, t in enumerate(snake_case ):
# expand the latents if we are doing classifier free guidance
lowercase : Optional[Any] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowercase : Optional[int] = self.scheduler.scale_model_input(snake_case ,snake_case )
# predict the noise residual
lowercase : Dict = self.unet(snake_case ,snake_case ,encoder_hidden_states=snake_case ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
lowercase , lowercase : str = noise_pred.chunk(2 )
lowercase : Optional[int] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
lowercase : int = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
lowercase , lowercase : Union[str, Any] = self.cond_fn(
snake_case ,snake_case ,snake_case ,snake_case ,snake_case ,snake_case ,snake_case ,)
# compute the previous noisy sample x_t -> x_t-1
lowercase : Any = self.scheduler.step(snake_case ,snake_case ,snake_case ,**snake_case ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowercase : Optional[Any] = 1 / 0.18_215 * latents
lowercase : Any = self.vae.decode(snake_case ).sample
lowercase : Optional[Any] = (image / 2 + 0.5).clamp(0 ,1 )
lowercase : Optional[Any] = image.cpu().permute(0 ,2 ,3 ,1 ).numpy()
if output_type == "pil":
lowercase : List[str] = self.numpy_to_pil(snake_case )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=snake_case ,nsfw_content_detected=snake_case )
| 20 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
lowercase : str = """\
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
note={In the Proceedings of ICLR.},
year={2019}
}
"""
lowercase : Dict = """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""
lowercase : int = """
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
predictions: list of predictions 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.
Returns: depending on the GLUE subset, one or several of:
\"accuracy\": Accuracy
\"f1\": F1 score
\"pearson\": Pearson Correlation
\"spearmanr\": Spearman Correlation
\"matthews_correlation\": Matthew Correlation
Examples:
>>> glue_metric = datasets.load_metric('glue', 'sst2') # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
>>> glue_metric = datasets.load_metric('glue', 'mrpc') # 'mrpc' or 'qqp'
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0, 'f1': 1.0}
>>> glue_metric = datasets.load_metric('glue', 'stsb')
>>> references = [0., 1., 2., 3., 4., 5.]
>>> predictions = [0., 1., 2., 3., 4., 5.]
>>> results = glue_metric.compute(predictions=predictions, references=references)
>>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
{'pearson': 1.0, 'spearmanr': 1.0}
>>> glue_metric = datasets.load_metric('glue', 'cola')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'matthews_correlation': 1.0}
"""
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
return float((preds == labels).mean() )
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Optional[Any]:
lowercase : Any = simple_accuracy(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : Union[str, Any] = float(fa_score(y_true=SCREAMING_SNAKE_CASE__ , y_pred=SCREAMING_SNAKE_CASE__ ) )
return {
"accuracy": acc,
"f1": fa,
}
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[Any]:
lowercase : Union[str, Any] = float(pearsonr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )[0] )
lowercase : Dict = float(spearmanr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )[0] )
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __snake_case ( datasets.Metric ):
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
if self.config_name not in [
"sst2",
"mnli",
"mnli_mismatched",
"mnli_matched",
"cola",
"stsb",
"mrpc",
"qqp",
"qnli",
"rte",
"wnli",
"hans",
]:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
"""\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]""" )
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"""predictions""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32""" ),
"""references""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32""" ),
} ) ,codebase_urls=[] ,reference_urls=[] ,format="""numpy""" ,)
def _SCREAMING_SNAKE_CASE ( self ,snake_case ,snake_case ):
'''simple docstring'''
if self.config_name == "cola":
return {"matthews_correlation": matthews_corrcoef(snake_case ,snake_case )}
elif self.config_name == "stsb":
return pearson_and_spearman(snake_case ,snake_case )
elif self.config_name in ["mrpc", "qqp"]:
return acc_and_fa(snake_case ,snake_case )
elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
return {"accuracy": simple_accuracy(snake_case ,snake_case )}
else:
raise KeyError(
"""You should supply a configuration name selected in """
"""[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
"""\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]""" )
| 20 | 1 |
from maths.prime_factors import prime_factors
def snake_case_ ( A_ : int ):
'''simple docstring'''
if not isinstance(A_, A_ ):
_lowerCamelCase : str = F'''Input value of [number={number}] must be an integer'''
raise TypeError(A_ )
if number < 1:
raise ValueError('''Input must be a positive integer''' )
return -1 if len(prime_factors(A_ ) ) % 2 else 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 370 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
# See all XGLM models at https://huggingface.co/models?filter=xglm
}
class __snake_case ( _lowercase):
snake_case__ : List[Any] = "xglm"
snake_case__ : Dict = ["past_key_values"]
snake_case__ : str = {
"num_attention_heads": "attention_heads",
"hidden_size": "d_model",
"num_hidden_layers": "num_layers",
}
def __init__( self : List[str] , __lowerCAmelCase : List[Any]=2_5_6_0_0_8 , __lowerCAmelCase : int=2_0_4_8 , __lowerCAmelCase : Dict=1_0_2_4 , __lowerCAmelCase : List[str]=4_0_9_6 , __lowerCAmelCase : Tuple=2_4 , __lowerCAmelCase : Dict=1_6 , __lowerCAmelCase : Tuple="gelu" , __lowerCAmelCase : Tuple=0.1 , __lowerCAmelCase : Tuple=0.1 , __lowerCAmelCase : Optional[Any]=0.0 , __lowerCAmelCase : List[Any]=0.0 , __lowerCAmelCase : int=0.02 , __lowerCAmelCase : Any=True , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : str=2 , __lowerCAmelCase : Dict=1 , __lowerCAmelCase : Dict=0 , __lowerCAmelCase : List[Any]=2 , **__lowerCAmelCase : Optional[Any] , ):
"""simple docstring"""
_lowerCamelCase : List[Any] = vocab_size
_lowerCamelCase : List[Any] = max_position_embeddings
_lowerCamelCase : int = d_model
_lowerCamelCase : Optional[Any] = ffn_dim
_lowerCamelCase : Any = num_layers
_lowerCamelCase : Union[str, Any] = attention_heads
_lowerCamelCase : List[str] = activation_function
_lowerCamelCase : Union[str, Any] = dropout
_lowerCamelCase : int = attention_dropout
_lowerCamelCase : Optional[int] = activation_dropout
_lowerCamelCase : Any = layerdrop
_lowerCamelCase : List[str] = init_std
_lowerCamelCase : Union[str, Any] = scale_embedding # scale factor will be sqrt(d_model) if True
_lowerCamelCase : str = use_cache
super().__init__(
pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , decoder_start_token_id=__lowerCAmelCase , **__lowerCAmelCase , )
| 175 | 0 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__A : Dict = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase : List[Any] = nn.functional.normalize(_UpperCAmelCase )
lowerCAmelCase : int = nn.functional.normalize(_UpperCAmelCase )
return torch.mm(_UpperCAmelCase, normalized_text_embeds.t() )
class __A ( lowerCAmelCase ):
lowerCAmelCase_ : Any = CLIPConfig
lowerCAmelCase_ : Optional[Any] = ["CLIPEncoderLayer"]
def __init__( self : str , UpperCAmelCase_ : CLIPConfig ):
super().__init__(UpperCAmelCase_ )
lowerCAmelCase : int = CLIPVisionModel(config.vision_config )
lowerCAmelCase : List[Any] = nn.Linear(config.vision_config.hidden_size , config.projection_dim , bias=UpperCAmelCase_ )
lowerCAmelCase : str = nn.Parameter(torch.ones(17 , config.projection_dim ) , requires_grad=UpperCAmelCase_ )
lowerCAmelCase : Dict = nn.Parameter(torch.ones(3 , config.projection_dim ) , requires_grad=UpperCAmelCase_ )
lowerCAmelCase : Tuple = nn.Parameter(torch.ones(17 ) , requires_grad=UpperCAmelCase_ )
lowerCAmelCase : Tuple = nn.Parameter(torch.ones(3 ) , requires_grad=UpperCAmelCase_ )
@torch.no_grad()
def lowercase__ ( self : int , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Any ):
lowerCAmelCase : Any = self.vision_model(UpperCAmelCase_ )[1] # pooled_output
lowerCAmelCase : Optional[Any] = self.visual_projection(UpperCAmelCase_ )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowerCAmelCase : Dict = cosine_distance(UpperCAmelCase_ , self.special_care_embeds ).cpu().float().numpy()
lowerCAmelCase : Optional[int] = cosine_distance(UpperCAmelCase_ , self.concept_embeds ).cpu().float().numpy()
lowerCAmelCase : Dict = []
lowerCAmelCase : Any = image_embeds.shape[0]
for i in range(UpperCAmelCase_ ):
lowerCAmelCase : Tuple = {'special_scores': {}, 'special_care': [], 'concept_scores': {}, 'bad_concepts': []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
lowerCAmelCase : Optional[int] = 0.0
for concept_idx in range(len(special_cos_dist[0] ) ):
lowerCAmelCase : Tuple = special_cos_dist[i][concept_idx]
lowerCAmelCase : Optional[int] = self.special_care_embeds_weights[concept_idx].item()
lowerCAmelCase : str = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img['special_scores'][concept_idx]} )
lowerCAmelCase : int = 0.01
for concept_idx in range(len(cos_dist[0] ) ):
lowerCAmelCase : Union[str, Any] = cos_dist[i][concept_idx]
lowerCAmelCase : Union[str, Any] = self.concept_embeds_weights[concept_idx].item()
lowerCAmelCase : Optional[int] = round(concept_cos - concept_threshold + adjustment , 3 )
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(UpperCAmelCase_ )
result.append(UpperCAmelCase_ )
lowerCAmelCase : Optional[int] = [len(res['bad_concepts'] ) > 0 for res in result]
return images, has_nsfw_concepts
@torch.no_grad()
def lowercase__ ( self : Optional[Any] , UpperCAmelCase_ : torch.FloatTensor , UpperCAmelCase_ : torch.FloatTensor ):
lowerCAmelCase : Tuple = self.vision_model(UpperCAmelCase_ )[1] # pooled_output
lowerCAmelCase : int = self.visual_projection(UpperCAmelCase_ )
lowerCAmelCase : List[Any] = cosine_distance(UpperCAmelCase_ , self.special_care_embeds )
lowerCAmelCase : List[str] = cosine_distance(UpperCAmelCase_ , self.concept_embeds )
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
lowerCAmelCase : Optional[int] = 0.0
lowerCAmelCase : Any = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
lowerCAmelCase : str = torch.any(special_scores > 0 , dim=1 )
lowerCAmelCase : str = special_care * 0.01
lowerCAmelCase : Any = special_adjustment.unsqueeze(1 ).expand(-1 , cos_dist.shape[1] )
lowerCAmelCase : List[Any] = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
lowerCAmelCase : Dict = torch.any(concept_scores > 0 , dim=1 )
return images, has_nsfw_concepts
| 138 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> str:
'''simple docstring'''
return " ".join(
''.join(word[::-1] ) if len(_UpperCAmelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sroirraw'''))
| 138 | 1 |
'''simple docstring'''
import math
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = 10001 ):
"""simple docstring"""
try:
lowerCAmelCase__ : int = int(SCREAMING_SNAKE_CASE_ )
except (TypeError, ValueError):
raise TypeError("""Parameter nth must be int or castable to int.""" ) from None
if nth <= 0:
raise ValueError("""Parameter nth must be greater than or equal to one.""" )
lowerCAmelCase__ : str = []
lowerCAmelCase__ : Any = 2
while len(SCREAMING_SNAKE_CASE_ ) < nth:
if is_prime(SCREAMING_SNAKE_CASE_ ):
primes.append(SCREAMING_SNAKE_CASE_ )
num += 1
else:
num += 1
return primes[len(SCREAMING_SNAKE_CASE_ ) - 1]
if __name__ == "__main__":
print(F"""{solution() = }""")
| 364 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
def count_of_possible_combinations(UpperCamelCase ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
def count_of_possible_combinations_with_dp_array(
UpperCamelCase , UpperCamelCase ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
lowerCAmelCase__ : Any = sum(
count_of_possible_combinations_with_dp_array(target - item , UpperCamelCase )
for item in array )
lowerCAmelCase__ : Tuple = answer
return answer
lowerCAmelCase__ : Optional[int] = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(UpperCamelCase , UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : str = [0] * (target + 1)
lowerCAmelCase__ : List[Any] = 1
for i in range(1 , target + 1 ):
for j in range(UpperCamelCase ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCAmelCase = 3
_lowerCAmelCase = 5
_lowerCAmelCase = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 184 | 0 |
def a_ ( _A ) -> list:
"""simple docstring"""
snake_case__ = len(_A )
for i in range(1 , _A ):
snake_case__ = collection[i]
snake_case__ = 0
snake_case__ = i - 1
while low <= high:
snake_case__ = (low + high) // 2
if val < collection[mid]:
snake_case__ = mid - 1
else:
snake_case__ = mid + 1
for j in range(_A , _A , -1 ):
snake_case__ = collection[j - 1]
snake_case__ = val
return collection
if __name__ == "__main__":
__UpperCamelCase : List[Any] = input("""Enter numbers separated by a comma:\n""").strip()
__UpperCamelCase : Union[str, Any] = [int(item) for item in user_input.split(""",""")]
print(binary_insertion_sort(unsorted))
| 307 |
class __SCREAMING_SNAKE_CASE( a_ ):
pass
class __SCREAMING_SNAKE_CASE( a_ ):
pass
class __SCREAMING_SNAKE_CASE:
def __init__( self: List[str] ) -> Union[str, Any]:
snake_case__ = [
[],
[],
[],
]
def lowerCAmelCase_ ( self: Union[str, Any] , UpperCamelCase: int , UpperCamelCase: int ) -> None:
try:
if len(self.queues[priority] ) >= 1_00:
raise OverflowError('Maximum queue size is 100' )
self.queues[priority].append(UpperCamelCase )
except IndexError:
raise ValueError('Valid priorities are 0, 1, and 2' )
def lowerCAmelCase_ ( self: List[Any] ) -> int:
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError('All queues are empty' )
def __str__( self: Union[str, Any] ) -> str:
return "\n".join(F'''Priority {i}: {q}''' for i, q in enumerate(self.queues ) )
class __SCREAMING_SNAKE_CASE:
def __init__( self: Union[str, Any] ) -> Any:
snake_case__ = []
def lowerCAmelCase_ ( self: str , UpperCamelCase: int ) -> None:
if len(self.queue ) == 1_00:
raise OverFlowError('Maximum queue size is 100' )
self.queue.append(UpperCamelCase )
def lowerCAmelCase_ ( self: int ) -> int:
if not self.queue:
raise UnderFlowError('The queue is empty' )
else:
snake_case__ = min(self.queue )
self.queue.remove(UpperCamelCase )
return data
def __str__( self: Optional[Any] ) -> str:
return str(self.queue )
def a_ ( ) -> List[Any]:
"""simple docstring"""
snake_case__ = FixedPriorityQueue()
fpq.enqueue(0 , 10 )
fpq.enqueue(1 , 70 )
fpq.enqueue(0 , 100 )
fpq.enqueue(2 , 1 )
fpq.enqueue(2 , 5 )
fpq.enqueue(1 , 7 )
fpq.enqueue(2 , 4 )
fpq.enqueue(1 , 64 )
fpq.enqueue(0 , 128 )
print(_A )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(_A )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def a_ ( ) -> List[Any]:
"""simple docstring"""
snake_case__ = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(100 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(128 )
print(_A )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(_A )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 307 | 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 ShapEPipeline
else:
from .camera import create_pan_cameras
from .pipeline_shap_e import ShapEPipeline
from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
from .renderer import (
BoundingBoxVolume,
ImportanceRaySampler,
MLPNeRFModelOutput,
MLPNeRSTFModel,
ShapEParamsProjModel,
ShapERenderer,
StratifiedRaySampler,
VoidNeRFModel,
)
| 11 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
"RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
"RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
"RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
"RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
"RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
"RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
"RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
"RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCAmelCase : Any = 'rwkv'
__UpperCAmelCase : Optional[Any] = {'max_position_embeddings': 'context_length'}
def __init__( self , _a=50_277 , _a=1_024 , _a=4_096 , _a=32 , _a=None , _a=None , _a=1E-5 , _a=0 , _a=0 , _a=6 , _a=False , _a=True , **_a , ):
__a = vocab_size
__a = context_length
__a = hidden_size
__a = num_hidden_layers
__a = attention_hidden_size if attention_hidden_size is not None else hidden_size
__a = intermediate_size if intermediate_size is not None else 4 * hidden_size
__a = layer_norm_epsilon
__a = rescale_every
__a = use_cache
__a = bos_token_id
__a = eos_token_id
super().__init__(
tie_word_embeddings=_a , bos_token_id=_a , eos_token_id=_a , **_a )
| 11 | 1 |
'''simple docstring'''
import cmath
import math
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
_UpperCamelCase : Tuple = math.radians(UpperCAmelCase_ )
_UpperCamelCase : Optional[int] = math.radians(UpperCAmelCase_ )
# Convert voltage and current to rectangular form
_UpperCamelCase : int = cmath.rect(UpperCAmelCase_ , UpperCAmelCase_ )
_UpperCamelCase : int = cmath.rect(UpperCAmelCase_ , UpperCAmelCase_ )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod()
| 83 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : list, lowercase__ : list, lowercase__ : int ):
'''simple docstring'''
__lowercase =len(lowercase__ )
__lowercase =[[0] * n for i in range(lowercase__ )]
for i in range(lowercase__ ):
__lowercase =y_points[i]
for i in range(2, lowercase__ ):
for j in range(lowercase__, lowercase__ ):
__lowercase =(
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 141 | 0 |
import math
def lowerCamelCase_ ( _a ):
"""simple docstring"""
return math.sqrt(_lowercase ) * math.sqrt(_lowercase ) == num
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : int = 0
lowerCAmelCase__ : str = n
while left <= right:
lowerCAmelCase__ : Optional[Any] = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
lowerCAmelCase__ : Optional[Any] = mid - 1
else:
lowerCAmelCase__ : List[str] = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 359 |
from __future__ import annotations
def lowerCamelCase_ ( _a , _a , _a , _a ): # noqa: E741
"""simple docstring"""
while r - l > 1:
lowerCAmelCase__ : Any = (l + r) // 2
if v[m] >= key:
lowerCAmelCase__ : int = m
else:
lowerCAmelCase__ : Tuple = m # noqa: E741
return r
def lowerCamelCase_ ( _a ):
"""simple docstring"""
if len(_a ) == 0:
return 0
lowerCAmelCase__ : Optional[int] = [0] * len(_a )
lowerCAmelCase__ : List[Any] = 1
lowerCAmelCase__ : int = v[0]
for i in range(1 , len(_a ) ):
if v[i] < tail[0]:
lowerCAmelCase__ : str = v[i]
elif v[i] > tail[length - 1]:
lowerCAmelCase__ : Any = v[i]
length += 1
else:
lowerCAmelCase__ : int = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 211 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class _a :
def __init__( self : Union[str, Any] , lowercase : int , lowercase : MutableSequence[float] ):
'''simple docstring'''
if len(lowercase ) != degree + 1:
raise ValueError(
'''The number of coefficients should be equal to the degree + 1.''' )
UpperCAmelCase = list(lowercase )
UpperCAmelCase = degree
def __add__( self : List[Any] , lowercase : Polynomial ):
'''simple docstring'''
if self.degree > polynomial_a.degree:
UpperCAmelCase = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , lowercase )
else:
UpperCAmelCase = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , lowercase )
def __sub__( self : str , lowercase : Polynomial ):
'''simple docstring'''
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self : Optional[int] ):
'''simple docstring'''
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self : List[Any] , lowercase : Polynomial ):
'''simple docstring'''
UpperCAmelCase = [0] * (self.degree + polynomial_a.degree + 1)
for i in range(self.degree + 1 ):
for j in range(polynomial_a.degree + 1 ):
coefficients[i + j] += (
self.coefficients[i] * polynomial_a.coefficients[j]
)
return Polynomial(self.degree + polynomial_a.degree , lowercase )
def A ( self : Optional[int] , lowercase : int | float ):
'''simple docstring'''
UpperCAmelCase = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self : str ):
'''simple docstring'''
UpperCAmelCase = ''''''
for i in range(self.degree , -1 , -1 ):
if self.coefficients[i] == 0:
continue
elif self.coefficients[i] > 0:
if polynomial:
polynomial += " + "
else:
polynomial += " - "
if i == 0:
polynomial += str(abs(self.coefficients[i] ) )
elif i == 1:
polynomial += str(abs(self.coefficients[i] ) ) + "x"
else:
polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(lowercase )
return polynomial
def __repr__( self : List[Any] ):
'''simple docstring'''
return self.__str__()
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = [0] * self.degree
for i in range(self.degree ):
UpperCAmelCase = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , lowercase )
def A ( self : str , lowercase : int | float = 0 ):
'''simple docstring'''
UpperCAmelCase = [0] * (self.degree + 2)
UpperCAmelCase = constant
for i in range(self.degree + 1 ):
UpperCAmelCase = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , lowercase )
def __eq__( self : List[Any] , lowercase : object ):
'''simple docstring'''
if not isinstance(lowercase , lowercase ):
return False
if self.degree != polynomial_a.degree:
return False
for i in range(self.degree + 1 ):
if self.coefficients[i] != polynomial_a.coefficients[i]:
return False
return True
def __ne__( self : Tuple , lowercase : object ):
'''simple docstring'''
return not self.__eq__(lowercase )
| 34 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
A =logging.get_logger(__name__)
def snake_case_ (_a : List[str] ):
UpperCAmelCase = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
UpperCAmelCase = 1_2_8
elif "12-12" in model_name:
UpperCAmelCase = 1_2
UpperCAmelCase = 1_2
elif "14-14" in model_name:
UpperCAmelCase = 1_4
UpperCAmelCase = 1_4
elif "16-16" in model_name:
UpperCAmelCase = 1_6
UpperCAmelCase = 1_6
else:
raise ValueError('''Model not supported''' )
UpperCAmelCase = '''huggingface/label-files'''
if "speech-commands" in model_name:
UpperCAmelCase = 3_5
UpperCAmelCase = '''speech-commands-v2-id2label.json'''
else:
UpperCAmelCase = 5_2_7
UpperCAmelCase = '''audioset-id2label.json'''
UpperCAmelCase = json.load(open(hf_hub_download(_a , _a , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase = {int(_a ): v for k, v in idalabel.items()}
UpperCAmelCase = idalabel
UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def snake_case_ (_a : Tuple ):
if "module.v" in name:
UpperCAmelCase = name.replace('''module.v''' , '''audio_spectrogram_transformer''' )
if "cls_token" in name:
UpperCAmelCase = name.replace('''cls_token''' , '''embeddings.cls_token''' )
if "dist_token" in name:
UpperCAmelCase = name.replace('''dist_token''' , '''embeddings.distillation_token''' )
if "pos_embed" in name:
UpperCAmelCase = name.replace('''pos_embed''' , '''embeddings.position_embeddings''' )
if "patch_embed.proj" in name:
UpperCAmelCase = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
# transformer blocks
if "blocks" in name:
UpperCAmelCase = name.replace('''blocks''' , '''encoder.layer''' )
if "attn.proj" in name:
UpperCAmelCase = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
UpperCAmelCase = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
UpperCAmelCase = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
UpperCAmelCase = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
UpperCAmelCase = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
UpperCAmelCase = name.replace('''mlp.fc2''' , '''output.dense''' )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
UpperCAmelCase = name.replace('''audio_spectrogram_transformer.norm''' , '''audio_spectrogram_transformer.layernorm''' )
# classifier head
if "module.mlp_head.0" in name:
UpperCAmelCase = name.replace('''module.mlp_head.0''' , '''classifier.layernorm''' )
if "module.mlp_head.1" in name:
UpperCAmelCase = name.replace('''module.mlp_head.1''' , '''classifier.dense''' )
return name
def snake_case_ (_a : Dict , _a : List[Any] ):
for key in orig_state_dict.copy().keys():
UpperCAmelCase = orig_state_dict.pop(_a )
if "qkv" in key:
UpperCAmelCase = key.split('''.''' )
UpperCAmelCase = int(key_split[3] )
UpperCAmelCase = config.hidden_size
if "weight" in key:
UpperCAmelCase = val[:dim, :]
UpperCAmelCase = val[dim : dim * 2, :]
UpperCAmelCase = val[-dim:, :]
else:
UpperCAmelCase = val[:dim]
UpperCAmelCase = val[dim : dim * 2]
UpperCAmelCase = val[-dim:]
else:
UpperCAmelCase = val
return orig_state_dict
def snake_case_ (_a : Tuple ):
UpperCAmelCase = [
'''module.v.head.weight''',
'''module.v.head.bias''',
'''module.v.head_dist.weight''',
'''module.v.head_dist.bias''',
]
for k in ignore_keys:
state_dict.pop(_a , _a )
@torch.no_grad()
def snake_case_ (_a : int , _a : Union[str, Any] , _a : Dict=False ):
UpperCAmelCase = get_audio_spectrogram_transformer_config(_a )
UpperCAmelCase = {
'''ast-finetuned-audioset-10-10-0.4593''': (
'''https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.450''': (
'''https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448''': (
'''https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'''
),
'''ast-finetuned-audioset-10-10-0.448-v2''': (
'''https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'''
),
'''ast-finetuned-audioset-12-12-0.447''': (
'''https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'''
),
'''ast-finetuned-audioset-14-14-0.443''': (
'''https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'''
),
'''ast-finetuned-audioset-16-16-0.442''': (
'''https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'''
),
'''ast-finetuned-speech-commands-v2''': (
'''https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'''
),
}
# load original state_dict
UpperCAmelCase = model_name_to_url[model_name]
UpperCAmelCase = torch.hub.load_state_dict_from_url(_a , map_location='''cpu''' )
# remove some keys
remove_keys(_a )
# rename some keys
UpperCAmelCase = convert_state_dict(_a , _a )
# load 🤗 model
UpperCAmelCase = ASTForAudioClassification(_a )
model.eval()
model.load_state_dict(_a )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
UpperCAmelCase = -4.267_7393 if '''speech-commands''' not in model_name else -6.84_5978
UpperCAmelCase = 4.568_9974 if '''speech-commands''' not in model_name else 5.565_4526
UpperCAmelCase = 1_0_2_4 if '''speech-commands''' not in model_name else 1_2_8
UpperCAmelCase = ASTFeatureExtractor(mean=_a , std=_a , max_length=_a )
if "speech-commands" in model_name:
UpperCAmelCase = load_dataset('''speech_commands''' , '''v0.02''' , split='''validation''' )
UpperCAmelCase = dataset[0]['''audio''']['''array''']
else:
UpperCAmelCase = hf_hub_download(
repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' , filename='''sample_audio.flac''' , repo_type='''dataset''' , )
UpperCAmelCase , UpperCAmelCase = torchaudio.load(_a )
UpperCAmelCase = waveform.squeeze().numpy()
UpperCAmelCase = feature_extractor(_a , sampling_rate=1_6_0_0_0 , return_tensors='''pt''' )
# forward pass
UpperCAmelCase = model(**_a )
UpperCAmelCase = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
UpperCAmelCase = torch.tensor([-0.8760, -7.0042, -8.6602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
UpperCAmelCase = torch.tensor([-1.1986, -7.0903, -8.2718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
UpperCAmelCase = torch.tensor([-2.6128, -8.0080, -9.4344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
UpperCAmelCase = torch.tensor([-1.5080, -7.4534, -8.8917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
UpperCAmelCase = torch.tensor([-0.5050, -6.5833, -8.0843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
UpperCAmelCase = torch.tensor([-0.3826, -7.0336, -8.2413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
UpperCAmelCase = torch.tensor([-1.2113, -6.9101, -8.3470] )
elif model_name == "ast-finetuned-speech-commands-v2":
UpperCAmelCase = torch.tensor([6.1589, -8.0566, -8.7984] )
else:
raise ValueError('''Unknown model name''' )
if not torch.allclose(logits[0, :3] , _a , atol=1E-4 ):
raise ValueError('''Logits don\'t match''' )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(_a ).mkdir(exist_ok=_a )
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_a )
print(F"Saving feature extractor to {pytorch_dump_folder_path}" )
feature_extractor.save_pretrained(_a )
if push_to_hub:
print('''Pushing model and feature extractor to the hub...''' )
model.push_to_hub(F"MIT/{model_name}" )
feature_extractor.push_to_hub(F"MIT/{model_name}" )
if __name__ == "__main__":
A =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='ast-finetuned-audioset-10-10-0.4593',
type=str,
help='Name of the Audio Spectrogram Transformer 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.'
)
A =parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 34 | 1 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class __lowerCAmelCase :
@staticmethod
def UpperCAmelCase ( *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
pass
def A ( snake_case :Image ) -> str:
__UpperCamelCase = hashlib.mda(image.tobytes() )
return m.hexdigest()[:1_0]
def A ( snake_case :Image ) -> Dict:
__UpperCamelCase = np.array(snake_case )
__UpperCamelCase = npimg.shape
return {"hash": hashimage(snake_case ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
lowercase = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
lowercase = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase = MaskGenerationPipeline(model=__UpperCAmelCase , image_processor=__UpperCAmelCase )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
pass
@require_tf
@unittest.skip('Image segmentation not implemented in TF' )
def UpperCAmelCase ( self ):
'''simple docstring'''
pass
@slow
@require_torch
def UpperCAmelCase ( self ):
'''simple docstring'''
__UpperCamelCase = pipeline('mask-generation' , model='facebook/sam-vit-huge' )
__UpperCamelCase = image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg' , points_per_batch=256 )
# Shortening by hashing
__UpperCamelCase = []
for i, o in enumerate(outputs['masks'] ):
new_outupt += [{"mask": mask_to_test_readable(__UpperCAmelCase ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
{'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1},
{'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
{'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
{'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
{'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9_9_6_7},
{'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_9_3},
{'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9_9_0_9},
{'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9_8_7_9},
{'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9_8_3_4},
{'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9_7_1_6},
{'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9_6_1_2},
{'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9_5_9_9},
{'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9_5_5_2},
{'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9_5_3_2},
{'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9_5_1_6},
{'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9_4_9_9},
{'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9_4_8_3},
{'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9_4_6_4},
{'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_4_3},
{'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_4_3},
{'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9_4_0_8},
{'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9_3_3_5},
{'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9_3_2_6},
{'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9_2_6_2},
{'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8_9_9_9},
{'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8_9_8_6},
{'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8_9_8_4},
{'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8_8_7_3},
{'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8_8_7_1}
] , )
# fmt: on
@require_torch
@slow
def UpperCAmelCase ( self ):
'''simple docstring'''
__UpperCamelCase = 'facebook/sam-vit-huge'
__UpperCamelCase = pipeline('mask-generation' , model=__UpperCAmelCase )
__UpperCamelCase = image_segmenter(
'http://images.cocodataset.org/val2017/000000039769.jpg' , pred_iou_thresh=1 , points_per_batch=256 )
# Shortening by hashing
__UpperCamelCase = []
for i, o in enumerate(outputs['masks'] ):
new_outupt += [{"mask": mask_to_test_readable(__UpperCAmelCase ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=4 ) , [
{'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0_4_4_4},
{'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_2_1_0},
{'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0_1_6_7},
{'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0_1_3_2},
{'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0_0_5_3},
] , )
| 365 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : str = logging.get_logger(__name__)
UpperCamelCase : Dict = {
"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 __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
lowercase = "wavlm"
def __init__( self , __UpperCAmelCase=32 , __UpperCAmelCase=768 , __UpperCAmelCase=12 , __UpperCAmelCase=12 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.0_2 , __UpperCAmelCase=1E-5 , __UpperCAmelCase="group" , __UpperCAmelCase="gelu" , __UpperCAmelCase=(512, 512, 512, 512, 512, 512, 512) , __UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2) , __UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2) , __UpperCAmelCase=False , __UpperCAmelCase=128 , __UpperCAmelCase=16 , __UpperCAmelCase=320 , __UpperCAmelCase=800 , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=0.0_5 , __UpperCAmelCase=10 , __UpperCAmelCase=2 , __UpperCAmelCase=0.0 , __UpperCAmelCase=10 , __UpperCAmelCase=320 , __UpperCAmelCase=2 , __UpperCAmelCase=0.1 , __UpperCAmelCase=100 , __UpperCAmelCase=256 , __UpperCAmelCase=256 , __UpperCAmelCase=0.1 , __UpperCAmelCase="mean" , __UpperCAmelCase=False , __UpperCAmelCase=False , __UpperCAmelCase=256 , __UpperCAmelCase=(512, 512, 512, 512, 1500) , __UpperCAmelCase=(5, 3, 3, 1, 1) , __UpperCAmelCase=(1, 2, 3, 1, 1) , __UpperCAmelCase=512 , __UpperCAmelCase=80 , __UpperCAmelCase=0 , __UpperCAmelCase=1 , __UpperCAmelCase=2 , __UpperCAmelCase=False , __UpperCAmelCase=3 , __UpperCAmelCase=2 , __UpperCAmelCase=3 , __UpperCAmelCase=None , **__UpperCAmelCase , ):
'''simple docstring'''
super().__init__(**__UpperCAmelCase , pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase )
__UpperCamelCase = hidden_size
__UpperCamelCase = feat_extract_norm
__UpperCamelCase = feat_extract_activation
__UpperCamelCase = list(__UpperCAmelCase )
__UpperCamelCase = list(__UpperCAmelCase )
__UpperCamelCase = list(__UpperCAmelCase )
__UpperCamelCase = conv_bias
__UpperCamelCase = num_buckets
__UpperCamelCase = max_bucket_distance
__UpperCamelCase = num_conv_pos_embeddings
__UpperCamelCase = num_conv_pos_embedding_groups
__UpperCamelCase = len(self.conv_dim )
__UpperCamelCase = num_hidden_layers
__UpperCamelCase = intermediate_size
__UpperCamelCase = hidden_act
__UpperCamelCase = num_attention_heads
__UpperCamelCase = hidden_dropout
__UpperCamelCase = attention_dropout
__UpperCamelCase = activation_dropout
__UpperCamelCase = feat_proj_dropout
__UpperCamelCase = final_dropout
__UpperCamelCase = layerdrop
__UpperCamelCase = layer_norm_eps
__UpperCamelCase = initializer_range
__UpperCamelCase = num_ctc_classes
__UpperCamelCase = vocab_size
__UpperCamelCase = do_stable_layer_norm
__UpperCamelCase = use_weighted_layer_sum
__UpperCamelCase = classifier_proj_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
F' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'
F' `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__UpperCamelCase = apply_spec_augment
__UpperCamelCase = mask_time_prob
__UpperCamelCase = mask_time_length
__UpperCamelCase = mask_time_min_masks
__UpperCamelCase = mask_feature_prob
__UpperCamelCase = mask_feature_length
# parameters for pretraining with codevector quantized representations
__UpperCamelCase = num_codevectors_per_group
__UpperCamelCase = num_codevector_groups
__UpperCamelCase = contrastive_logits_temperature
__UpperCamelCase = num_negatives
__UpperCamelCase = codevector_dim
__UpperCamelCase = proj_codevector_dim
__UpperCamelCase = diversity_loss_weight
# ctc loss
__UpperCamelCase = ctc_loss_reduction
__UpperCamelCase = ctc_zero_infinity
# adapter
__UpperCamelCase = add_adapter
__UpperCamelCase = adapter_kernel_size
__UpperCamelCase = adapter_stride
__UpperCamelCase = num_adapter_layers
__UpperCamelCase = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__UpperCamelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__UpperCamelCase = list(__UpperCAmelCase )
__UpperCamelCase = list(__UpperCAmelCase )
__UpperCamelCase = list(__UpperCAmelCase )
__UpperCamelCase = xvector_output_dim
@property
def UpperCAmelCase ( self ):
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 263 | 0 |
def A_ ( ):
SCREAMING_SNAKE_CASE_: List[Any] = 0
for i in range(1 , 10_01 ):
total += i**i
return str(_UpperCAmelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 13 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase = False ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: str = f"Expected string as input, found {type(_UpperCAmelCase )}"
raise ValueError(_UpperCAmelCase )
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Optional[Any] = f"Expected boolean as use_pascal parameter, found {type(_UpperCAmelCase )}"
raise ValueError(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: Tuple = input_str.split("_" )
SCREAMING_SNAKE_CASE_: str = 0 if use_pascal else 1
SCREAMING_SNAKE_CASE_: int = words[start_index:]
SCREAMING_SNAKE_CASE_: List[str] = [word[0].upper() + word[1:] for word in words_to_capitalize]
SCREAMING_SNAKE_CASE_: List[Any] = "" if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 13 | 1 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a ( A_ , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase :Dict = LayoutLMTokenizer
lowerCamelCase :Dict = LayoutLMTokenizerFast
lowerCamelCase :int = True
lowerCamelCase :str = True
def UpperCAmelCase ( self ) -> Tuple:
super().setUp()
_A = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def UpperCAmelCase ( self , **lowerCAmelCase_ ) -> List[Any]:
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def UpperCAmelCase ( self , lowerCAmelCase_ ) -> Union[str, Any]:
_A = """UNwant\u00E9d,running"""
_A = """unwanted, running"""
return input_text, output_text
def UpperCAmelCase ( self ) -> Tuple:
_A = self.tokenizer_class(self.vocab_file )
_A = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(_lowerCamelCase , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , [7, 4, 5, 10, 8, 9] )
def UpperCAmelCase ( self ) -> Optional[int]:
pass
| 368 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = ['FNetTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = ['FNetTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'FNetForMaskedLM',
'FNetForMultipleChoice',
'FNetForNextSentencePrediction',
'FNetForPreTraining',
'FNetForQuestionAnswering',
'FNetForSequenceClassification',
'FNetForTokenClassification',
'FNetLayer',
'FNetModel',
'FNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 81 | 0 |
'''simple docstring'''
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
__SCREAMING_SNAKE_CASE : str = 2
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : List[Any] , *, # begin keyword-only arguments
A : Union[str, Any]="<s>" , A : Dict="<pad>" , A : Any="</s>" , A : Tuple="<unk>" , A : List[Any]=None , ):
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = bos, unk, pad, eos
_UpperCAmelCase : int = []
_UpperCAmelCase : Dict = []
_UpperCAmelCase : Dict = {}
_UpperCAmelCase : Optional[Any] = self.add_symbol(A )
_UpperCAmelCase : Dict = self.add_symbol(A )
_UpperCAmelCase : int = self.add_symbol(A )
_UpperCAmelCase : List[Any] = self.add_symbol(A )
if extra_special_symbols:
for s in extra_special_symbols:
self.add_symbol(A )
_UpperCAmelCase : Tuple = len(self.symbols )
def __eq__( self : Optional[Any] , A : Tuple ):
return self.indices == other.indices
def __getitem__( self : int , A : Optional[Any] ):
if idx < len(self.symbols ):
return self.symbols[idx]
return self.unk_word
def __len__( self : Union[str, Any] ):
return len(self.symbols )
def __contains__( self : List[Any] , A : Dict ):
return sym in self.indices
@classmethod
def _A ( cls : int , A : Union[str, Any] ):
_UpperCAmelCase : List[Any] = cls()
d.add_from_file(A )
return d
def _A ( self : int , A : Tuple , A : Optional[Any]=1 , A : str=False ):
if word in self.indices and not overwrite:
_UpperCAmelCase : Union[str, Any] = self.indices[word]
_UpperCAmelCase : Tuple = self.count[idx] + n
return idx
else:
_UpperCAmelCase : List[Any] = len(self.symbols )
_UpperCAmelCase : int = idx
self.symbols.append(A )
self.count.append(A )
return idx
def _A ( self : int , A : List[Any] ):
return 0
def _A ( self : Dict , A : Optional[int] ):
if isinstance(A , A ):
try:
with open(A , "r" , encoding="utf-8" ) as fd:
self.add_from_file(A )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(A ) )
return
_UpperCAmelCase : Union[str, Any] = f.readlines()
_UpperCAmelCase : Optional[Any] = self._load_meta(A )
for line in lines[indices_start_line:]:
try:
_UpperCAmelCase , _UpperCAmelCase : Any = line.rstrip().rsplit(" " , 1 )
if field == "#fairseq:overwrite":
_UpperCAmelCase : Tuple = True
_UpperCAmelCase , _UpperCAmelCase : List[Any] = line.rsplit(" " , 1 )
else:
_UpperCAmelCase : Optional[Any] = False
_UpperCAmelCase : str = int(A )
_UpperCAmelCase : Any = line
if word in self and not overwrite:
raise RuntimeError(
"Duplicate word found when loading Dictionary: '{}'. "
"Duplicate words can overwrite earlier ones by adding the "
"#fairseq:overwrite flag at the end of the corresponding row "
"in the dictionary file. If using the Camembert model, please "
"download an updated copy of the model file.".format(A ) )
self.add_symbol(A , n=A , overwrite=A )
except ValueError:
raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'" )
def UpperCamelCase_ ( _UpperCAmelCase : Tuple ) -> Any:
"""simple docstring"""
_UpperCAmelCase : str = dict((re.sub(R"@@$" , "" , _UpperCAmelCase ), v) if k.endswith("@@" ) else (re.sub(R"$" , "</w>" , _UpperCAmelCase ), v) for k, v in d.items() )
_UpperCAmelCase : str = "<s> <pad> </s> <unk>".split()
# restore the special tokens
for k in keep_keys:
del da[F"""{k}</w>"""]
_UpperCAmelCase : Any = d[k] # restore
return da
def UpperCamelCase_ ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
if not os.path.exists(_UpperCAmelCase ):
raise ValueError(F"""path {biogpt_checkpoint_path} does not exist!""" )
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase )
print(F"""Writing results to {pytorch_dump_folder_path}""" )
# handle various types of models
_UpperCAmelCase : List[str] = os.path.join(_UpperCAmelCase , "checkpoint.pt" )
if not os.path.isfile(_UpperCAmelCase ):
raise ValueError(F"""path to the file {checkpoint_file} does not exist!""" )
_UpperCAmelCase : List[str] = torch.load(_UpperCAmelCase , map_location="cpu" )
_UpperCAmelCase : Any = chkpt["cfg"]["model"]
# dicts
_UpperCAmelCase : Tuple = os.path.join(_UpperCAmelCase , "dict.txt" )
if not os.path.isfile(_UpperCAmelCase ):
raise ValueError(F"""path to the file {dict_file} does not exist!""" )
_UpperCAmelCase : Any = Dictionary.load(_UpperCAmelCase )
_UpperCAmelCase : Dict = rewrite_dict_keys(src_dict.indices )
_UpperCAmelCase : Dict = len(_UpperCAmelCase )
_UpperCAmelCase : Any = os.path.join(_UpperCAmelCase , VOCAB_FILES_NAMES["vocab_file"] )
print(F"""Generating {src_vocab_file} of {src_vocab_size} records""" )
with open(_UpperCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(_UpperCAmelCase , ensure_ascii=_UpperCAmelCase , indent=_UpperCAmelCase ) )
# merges_file (bpecodes)
_UpperCAmelCase : Tuple = os.path.join(_UpperCAmelCase , "bpecodes" )
if not os.path.isfile(_UpperCAmelCase ):
raise ValueError(F"""path to the file {bpecodes_file} does not exist!""" )
_UpperCAmelCase : Optional[int] = os.path.join(_UpperCAmelCase , VOCAB_FILES_NAMES["merges_file"] )
shutil.copyfile(_UpperCAmelCase , _UpperCAmelCase )
# model config
_UpperCAmelCase : Any = os.path.join(_UpperCAmelCase , "config.json" )
_UpperCAmelCase : Optional[int] = {
"activation_dropout": args["activation_dropout"],
"architectures": ["BioGptForCausalLM"],
"attention_probs_dropout_prob": args["attention_dropout"],
"bos_token_id": 0,
"eos_token_id": 2,
"hidden_act": args["activation_fn"],
"hidden_dropout_prob": args["dropout"],
"hidden_size": args["decoder_embed_dim"],
"initializer_range": 0.0_2,
"intermediate_size": args["decoder_ffn_embed_dim"],
"layer_norm_eps": 1e-12,
"layerdrop": args["decoder_layerdrop"],
"max_position_embeddings": args["max_target_positions"],
"model_type": "biogpt",
"num_attention_heads": args["decoder_attention_heads"],
"num_hidden_layers": args["decoder_layers"],
"pad_token_id": 1,
"scale_embedding": not args["no_scale_embedding"],
"tie_word_embeddings": args["share_decoder_input_output_embed"],
"vocab_size": src_vocab_size,
}
# good hparam defaults to start with
print(F"""Generating {biogpt_model_config_file}""" )
with open(_UpperCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(_UpperCAmelCase , ensure_ascii=_UpperCAmelCase , indent=_UpperCAmelCase ) )
# tokenizer config
_UpperCAmelCase : Tuple = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase : List[Any] = {
"bos_token": "<s>",
"eos_token": "</s>",
"model_max_length": 1_024,
"pad_token": "<pad>",
"special_tokens_map_file": None,
"tokenizer_class": "BioGptTokenizer",
"unk_token": "<unk>",
}
print(F"""Generating {biogpt_tokenizer_config_file}""" )
with open(_UpperCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(_UpperCAmelCase , ensure_ascii=_UpperCAmelCase , indent=_UpperCAmelCase ) )
# model
_UpperCAmelCase : str = chkpt["model"]
# remove unneeded keys
_UpperCAmelCase : Optional[Any] = [
"decoder.version",
]
for k in ignore_keys:
model_state_dict.pop(_UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase : Any = list(model_state_dict.keys() )
for layer_name in layer_names:
if layer_name.endswith("output_projection.weight" ):
_UpperCAmelCase : Optional[Any] = model_state_dict.pop(_UpperCAmelCase )
else:
_UpperCAmelCase : Optional[Any] = model_state_dict.pop(_UpperCAmelCase )
_UpperCAmelCase : Any = BioGptConfig.from_pretrained(_UpperCAmelCase )
_UpperCAmelCase : str = BioGptForCausalLM(_UpperCAmelCase )
# check that it loads ok
model_new.load_state_dict(_UpperCAmelCase )
# save
_UpperCAmelCase : Tuple = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
print(F"""Generating {pytorch_weights_dump_path}""" )
torch.save(_UpperCAmelCase , _UpperCAmelCase )
print("Conversion is done!" )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--biogpt_checkpoint_path""",
default=None,
type=str,
required=True,
help=(
"""Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
""" bpecodes, etc."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
__SCREAMING_SNAKE_CASE : List[str] = parser.parse_args()
convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
| 31 |
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self : List[str] , lowercase : list[int] ):
'''simple docstring'''
_snake_case = len(lowercase )
_snake_case = [0] * len_array
if len_array > 0:
_snake_case = array[0]
for i in range(1 , lowercase ):
_snake_case = self.prefix_sum[i - 1] + array[i]
def A ( self : Optional[Any] , lowercase : int , lowercase : int ):
'''simple docstring'''
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def A ( self : Union[str, Any] , lowercase : int ):
'''simple docstring'''
_snake_case = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(lowercase )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 282 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_:List[str] = {
"""configuration_blenderbot""": [
"""BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BlenderbotConfig""",
"""BlenderbotOnnxConfig""",
],
"""tokenization_blenderbot""": ["""BlenderbotTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_:Any = ["""BlenderbotTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_:Optional[Any] = [
"""BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BlenderbotForCausalLM""",
"""BlenderbotForConditionalGeneration""",
"""BlenderbotModel""",
"""BlenderbotPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_:str = [
"""TFBlenderbotForConditionalGeneration""",
"""TFBlenderbotModel""",
"""TFBlenderbotPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_:Optional[int] = [
"""FlaxBlenderbotForConditionalGeneration""",
"""FlaxBlenderbotModel""",
"""FlaxBlenderbotPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_:int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 115 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_:Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:Any = {
"""xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
"""xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
"""xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
"""xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
"""xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
"""xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
"""xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
"""xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
"""xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
"""xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : List[str] = "xlm"
__lowerCamelCase : Tuple = {
"hidden_size": "emb_dim",
"num_attention_heads": "n_heads",
"num_hidden_layers": "n_layers",
"n_words": "vocab_size", # For backward compatibility
}
def __init__( self, lowerCamelCase__=3_0145, lowerCamelCase__=2048, lowerCamelCase__=12, lowerCamelCase__=16, lowerCamelCase__=0.1, lowerCamelCase__=0.1, lowerCamelCase__=True, lowerCamelCase__=False, lowerCamelCase__=False, lowerCamelCase__=False, lowerCamelCase__=1, lowerCamelCase__=True, lowerCamelCase__=512, lowerCamelCase__=2048**-0.5, lowerCamelCase__=1e-12, lowerCamelCase__=0.02, lowerCamelCase__=0, lowerCamelCase__=1, lowerCamelCase__=2, lowerCamelCase__=3, lowerCamelCase__=5, lowerCamelCase__=True, lowerCamelCase__="first", lowerCamelCase__=True, lowerCamelCase__=None, lowerCamelCase__=True, lowerCamelCase__=0.1, lowerCamelCase__=5, lowerCamelCase__=5, lowerCamelCase__=0, lowerCamelCase__=0, lowerCamelCase__=2, lowerCamelCase__=0, **lowerCamelCase__, ):
A : Dict = vocab_size
A : int = emb_dim
A : str = n_layers
A : Union[str, Any] = n_heads
A : Optional[int] = dropout
A : Union[str, Any] = attention_dropout
A : Optional[Any] = gelu_activation
A : Dict = sinusoidal_embeddings
A : int = causal
A : Optional[Any] = asm
A : Any = n_langs
A : List[str] = use_lang_emb
A : Union[str, Any] = layer_norm_eps
A : str = bos_index
A : int = eos_index
A : Tuple = pad_index
A : str = unk_index
A : Optional[Any] = mask_index
A : Union[str, Any] = is_encoder
A : Tuple = max_position_embeddings
A : List[str] = embed_init_std
A : Tuple = init_std
A : Tuple = summary_type
A : int = summary_use_proj
A : List[Any] = summary_activation
A : Optional[Any] = summary_proj_to_labels
A : Optional[Any] = summary_first_dropout
A : Optional[int] = start_n_top
A : Optional[Any] = end_n_top
A : List[str] = mask_token_id
A : Tuple = lang_id
if "n_words" in kwargs:
A : List[str] = kwargs["""n_words"""]
super().__init__(pad_token_id=lowerCamelCase__, bos_token_id=lowerCamelCase__, **lowerCamelCase__ )
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
@property
def _lowerCAmelCase ( self ):
if self.task == "multiple-choice":
A : Any = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
A : Union[str, Any] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 115 | 1 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
A__ : List[str] = pytest.mark.integration
A__ : Any = {'comet'}
A__ : str = importlib.util.find_spec('fairseq') is not None
A__ : Optional[int] = {'code_eval'}
A__ : Optional[int] = os.name == 'nt'
A__ : Union[str, Any] = {'bertscore', 'frugalscore', 'perplexity'}
A__ : int = importlib.util.find_spec('transformers') is not None
def _snake_case ( lowerCamelCase__ : Dict ) -> Any:
@wraps(lowerCamelCase__ )
def wrapper(self : int , lowerCamelCase__ : Optional[int] ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest("\"test requires Fairseq\"" )
else:
test_case(self , lowerCamelCase__ )
return wrapper
def _snake_case ( lowerCamelCase__ : str ) -> Dict:
@wraps(lowerCamelCase__ )
def wrapper(self : str , lowerCamelCase__ : Optional[int] ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest("\"test requires transformers\"" )
else:
test_case(self , lowerCamelCase__ )
return wrapper
def _snake_case ( lowerCamelCase__ : Dict ) -> Optional[Any]:
@wraps(lowerCamelCase__ )
def wrapper(self : List[str] , lowerCamelCase__ : str ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest("\"test not supported on Windows\"" )
else:
test_case(self , lowerCamelCase__ )
return wrapper
def _snake_case ( ) -> Any:
lowerCamelCase_ : List[Any] =[metric_dir.split(os.sep )[-2] for metric_dir in glob.glob("./metrics/*/" )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
snake_case__, snake_case__, snake_case__ )
@local
class lowercase__ ( parameterized.TestCase ):
_UpperCAmelCase :Union[str, Any] = {}
_UpperCAmelCase :Optional[int] = None
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning" )
@pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning" )
def UpperCAmelCase__ ( self : Optional[Any] , snake_case__ : Tuple ):
lowerCamelCase_ : int ="[...]"
lowerCamelCase_ : List[Any] =importlib.import_module(
datasets.load.metric_module_factory(os.path.join("metrics" , snake_case__ ) ).module_path )
lowerCamelCase_ : int =datasets.load.import_main_class(metric_module.__name__ , dataset=snake_case__ )
# check parameters
lowerCamelCase_ : int =inspect.signature(metric._compute ).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values() ) ) # no **kwargs
# run doctest
with self.patch_intensive_calls(snake_case__ , metric_module.__name__ ):
with self.use_local_metrics():
try:
lowerCamelCase_ : Dict =doctest.testmod(snake_case__ , verbose=snake_case__ , raise_on_error=snake_case__ )
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@slow
def UpperCAmelCase__ ( self : Optional[Any] , snake_case__ : Any ):
lowerCamelCase_ : List[str] ="[...]"
lowerCamelCase_ : List[str] =importlib.import_module(
datasets.load.metric_module_factory(os.path.join("metrics" , snake_case__ ) ).module_path )
# run doctest
with self.use_local_metrics():
lowerCamelCase_ : Any =doctest.testmod(snake_case__ , verbose=snake_case__ , raise_on_error=snake_case__ )
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@contextmanager
def UpperCAmelCase__ ( self : Any , snake_case__ : Optional[Any] , snake_case__ : Union[str, Any] ):
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](snake_case__ ):
yield
else:
yield
@contextmanager
def UpperCAmelCase__ ( self : Tuple ):
def load_local_metric(snake_case__ : Tuple , *snake_case__ : int , **snake_case__ : Dict ):
return load_metric(os.path.join("metrics" , snake_case__ ) , *snake_case__ , **snake_case__ )
with patch("datasets.load_metric" ) as mock_load_metric:
lowerCamelCase_ : List[str] =load_local_metric
yield
@classmethod
def UpperCAmelCase__ ( cls : Dict , snake_case__ : int ):
def wrapper(snake_case__ : Optional[int] ):
lowerCamelCase_ : int =contextmanager(snake_case__ )
lowerCamelCase_ : Dict =patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher("bleurt" )
def _snake_case ( lowerCamelCase__ : Optional[int] ) -> Dict:
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string("sv" , "" , "" ) # handle pytest cli flags
class lowercase__ ( snake_case__ ):
def UpperCAmelCase__ ( self : List[str] , snake_case__ : Union[str, Any] ):
assert len(input_dict["input_ids"] ) == 2
return np.array([1.03, 1.04] )
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch("bleurt.score._create_predictor" ) as mock_create_predictor:
lowerCamelCase_ : int =MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher("bertscore" )
def _snake_case ( lowerCamelCase__ : Tuple ) -> Optional[Any]:
import torch
def bert_cos_score_idf(lowerCamelCase__ : List[Any] , lowerCamelCase__ : Tuple , *lowerCamelCase__ : int , **lowerCamelCase__ : Dict ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(lowerCamelCase__ ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch("bert_score.scorer.get_model" ), patch(
"bert_score.scorer.bert_cos_score_idf" ) as mock_bert_cos_score_idf:
lowerCamelCase_ : Optional[int] =bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher("comet" )
def _snake_case ( lowerCamelCase__ : str ) -> Optional[Any]:
def load_from_checkpoint(lowerCamelCase__ : Optional[int] ):
class lowercase__ :
def UpperCAmelCase__ ( self : Tuple , snake_case__ : List[str] , *snake_case__ : List[str] , **snake_case__ : Dict ):
assert len(snake_case__ ) == 2
lowerCamelCase_ : int =[0.19, 0.92]
return scores, sum(snake_case__ ) / len(snake_case__ )
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch("comet.download_model" ) as mock_download_model:
lowerCamelCase_ : Optional[int] =None
with patch("comet.load_from_checkpoint" ) as mock_load_from_checkpoint:
lowerCamelCase_ : Union[str, Any] =load_from_checkpoint
yield
def _snake_case ( ) -> List[str]:
lowerCamelCase_ : Any =load_metric(os.path.join("metrics" , "seqeval" ) )
lowerCamelCase_ : int ="ERROR"
lowerCamelCase_ : int =F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
with pytest.raises(lowerCamelCase__ , match=re.escape(lowerCamelCase__ ) ):
metric.compute(predictions=[] , references=[] , scheme=lowerCamelCase__ )
| 144 |
"""simple docstring"""
def _snake_case ( lowerCamelCase__ : str ) -> str:
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the function" )
lowerCamelCase_ : Optional[Any] =""
while len(lowerCamelCase__ ) % 3 != 0:
lowerCamelCase_ : Any ="0" + bin_string
lowerCamelCase_ : int =[
bin_string[index : index + 3]
for index in range(len(lowerCamelCase__ ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
lowerCamelCase_ : int =0
for index, val in enumerate(lowerCamelCase__ ):
oct_val += int(2 ** (2 - index) * int(lowerCamelCase__ ) )
oct_string += str(lowerCamelCase__ )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 144 | 1 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
__UpperCamelCase = 1E-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class _A :
def __init__( self : int , __magic_name__ : Optional[int] , __magic_name__ : Optional[Any]=16 , __magic_name__ : Dict=13 , __magic_name__ : Optional[Any]=7 , __magic_name__ : Optional[Any]=14 , __magic_name__ : Optional[int]=10 , __magic_name__ : int=19 , __magic_name__ : Tuple=5 , __magic_name__ : List[str]=4 , __magic_name__ : Tuple=True , __magic_name__ : int=16 , __magic_name__ : Union[str, Any]=2 , __magic_name__ : Any=4 , __magic_name__ : str=4 , __magic_name__ : Tuple="gelu" , __magic_name__ : Tuple=0.1 , __magic_name__ : Optional[Any]=0.1 , __magic_name__ : Optional[Any]=[1, 2, 3, 4, 5] , __magic_name__ : Optional[int]=25 , __magic_name__ : List[Any]=5 , ) -> Tuple:
"""simple docstring"""
__snake_case : List[Any] = d_model
__snake_case : str = parent
__snake_case : int = batch_size
__snake_case : Optional[Any] = prediction_length
__snake_case : Tuple = context_length
__snake_case : Tuple = cardinality
__snake_case : List[Any] = num_time_features
__snake_case : Optional[int] = lags_sequence
__snake_case : List[str] = embedding_dimension
__snake_case : List[str] = is_training
__snake_case : Any = hidden_size
__snake_case : List[Any] = num_hidden_layers
__snake_case : Tuple = num_attention_heads
__snake_case : List[Any] = intermediate_size
__snake_case : Dict = hidden_act
__snake_case : str = hidden_dropout_prob
__snake_case : List[Any] = attention_probs_dropout_prob
__snake_case : Any = context_length
__snake_case : Optional[int] = prediction_length + label_length
__snake_case : Tuple = label_length
__snake_case : List[Any] = moving_average
__snake_case : Union[str, Any] = autocorrelation_factor
def lowercase__ ( self : Any ) -> int:
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def lowercase__ ( self : Tuple , __magic_name__ : Any ) -> Any:
"""simple docstring"""
__snake_case : Union[str, Any] = config.context_length + max(config.lags_sequence )
__snake_case : List[str] = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
__snake_case : int = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
__snake_case : Union[str, Any] = floats_tensor([self.batch_size, _past_length] )
__snake_case : Dict = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
__snake_case : List[Any] = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
__snake_case : int = floats_tensor([self.batch_size, config.prediction_length] )
__snake_case : List[Any] = {
"""past_values""": past_values,
"""static_categorical_features""": static_categorical_features,
"""past_time_features""": past_time_features,
"""past_observed_mask""": past_observed_mask,
"""future_time_features""": future_time_features,
"""future_values""": future_values,
}
return inputs_dict
def lowercase__ ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__snake_case : int = self.get_config()
__snake_case : Any = self.prepare_autoformer_inputs_dict(__A )
return config, inputs_dict
def lowercase__ ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__snake_case , __snake_case : List[str] = self.prepare_config_and_inputs()
return config, inputs_dict
def lowercase__ ( self : Dict , __magic_name__ : Any , __magic_name__ : Optional[int] ) -> List[str]:
"""simple docstring"""
__snake_case : Union[str, Any] = AutoformerModel(config=__A ).to(__A ).eval()
__snake_case : Optional[int] = model(**__A )
__snake_case : str = outputs.encoder_last_hidden_state
__snake_case : List[Any] = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : Optional[int] = model.get_encoder()
encoder.save_pretrained(__A )
__snake_case : Tuple = AutoformerEncoder.from_pretrained(__A ).to(__A )
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case : Dict = model.create_network_inputs(**__A )
__snake_case , __snake_case : List[Any] = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
__snake_case : Any = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
__snake_case : Optional[int] = encoder(inputs_embeds=__A )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
__snake_case : str = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
__snake_case : int = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
__snake_case : Tuple = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
__snake_case : int = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : List[str] = model.get_decoder()
decoder.save_pretrained(__A )
__snake_case : Optional[int] = AutoformerDecoder.from_pretrained(__A ).to(__A )
__snake_case : str = decoder(
trend=__A , inputs_embeds=__A , encoder_hidden_states=__A , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class _A ( __lowercase , __lowercase , unittest.TestCase ):
lowercase__: str = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
lowercase__: Tuple = (AutoformerForPrediction,) if is_torch_available() else ()
lowercase__: str = {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
lowercase__: Optional[int] = False
lowercase__: List[str] = False
lowercase__: Union[str, Any] = False
lowercase__: int = False
lowercase__: str = False
lowercase__: Optional[Any] = False
def lowercase__ ( self : int ) -> Optional[Any]:
"""simple docstring"""
__snake_case : str = AutoformerModelTester(self )
__snake_case : int = ConfigTester(self , config_class=__A , has_text_modality=__A )
def lowercase__ ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowercase__ ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__snake_case , __snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
__snake_case : Dict = model_class(__A )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__A )
__snake_case , __snake_case : Any = model_class.from_pretrained(__A , output_loading_info=__A )
self.assertEqual(info["""missing_keys"""] , [] )
def lowercase__ ( self : str ) -> Dict:
"""simple docstring"""
__snake_case : Any = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*__A )
@unittest.skip(reason="""Model has no tokens embeddings""" )
def lowercase__ ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
pass
def lowercase__ ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
__snake_case : Tuple = inspect.signature(getattr(__A , """forward""" ) )
# The main input is the name of the argument after `self`
__snake_case : Dict = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , __A )
def lowercase__ ( self : Optional[int] ) -> str:
"""simple docstring"""
__snake_case , __snake_case : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__snake_case : Union[str, Any] = model_class(__A )
__snake_case : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__snake_case : str = [*signature.parameters.keys()]
__snake_case : Optional[int] = [
"""past_values""",
"""past_time_features""",
"""past_observed_mask""",
"""static_categorical_features""",
"""static_real_features""",
"""future_values""",
"""future_time_features""",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("""future_observed_mask""" )
expected_arg_names.extend(
[
"""decoder_attention_mask""",
"""head_mask""",
"""decoder_head_mask""",
"""cross_attn_head_mask""",
"""encoder_outputs""",
"""past_key_values""",
"""output_hidden_states""",
"""output_attentions""",
"""use_cache""",
"""return_dict""",
] )
self.assertListEqual(arg_names[: len(__A )] , __A )
def lowercase__ ( self : Any ) -> List[str]:
"""simple docstring"""
__snake_case , __snake_case : Any = self.model_tester.prepare_config_and_inputs_for_common()
__snake_case : List[Any] = True
__snake_case : Optional[int] = getattr(self.model_tester , """seq_length""" , __A )
__snake_case : Any = getattr(self.model_tester , """decoder_seq_length""" , __A )
__snake_case : str = getattr(self.model_tester , """encoder_seq_length""" , __A )
__snake_case : Any = getattr(self.model_tester , """d_model""" , __A )
__snake_case : List[str] = getattr(self.model_tester , """num_attention_heads""" , __A )
__snake_case : Dict = d_model // num_attention_heads
for model_class in self.all_model_classes:
__snake_case : Any = True
__snake_case : Dict = False
__snake_case : Optional[int] = True
__snake_case : Optional[int] = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
__snake_case : str = model(**self._prepare_for_class(__A , __A ) )
__snake_case : Optional[Any] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(__A ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__snake_case : List[str] = True
__snake_case : str = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
__snake_case : str = model(**self._prepare_for_class(__A , __A ) )
__snake_case : Tuple = outputs.encoder_attentions
self.assertEqual(len(__A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
__snake_case : Any = len(__A )
__snake_case : List[Any] = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(__A , __A )
# decoder attentions
__snake_case : int = outputs.decoder_attentions
self.assertIsInstance(__A , (list, tuple) )
self.assertEqual(len(__A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
__snake_case : Any = outputs.cross_attentions
self.assertIsInstance(__A , (list, tuple) )
self.assertEqual(len(__A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
__snake_case : str = True
__snake_case : Tuple = True
__snake_case : Optional[int] = model_class(__A )
model.to(__A )
model.eval()
with torch.no_grad():
__snake_case : List[Any] = model(**self._prepare_for_class(__A , __A ) )
self.assertEqual(out_len + 2 , len(__A ) )
__snake_case : List[str] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(__A ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def lowercase__ ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def _a ( _lowerCamelCase : Union[str, Any]="train-batch.pt" ) -> List[str]:
"""simple docstring"""
__snake_case : Optional[Any] = hf_hub_download(repo_id="""hf-internal-testing/tourism-monthly-batch""" , filename=_lowercase , repo_type="""dataset""" )
__snake_case : Union[str, Any] = torch.load(_lowercase , map_location=_lowercase )
return batch
@require_torch
@slow
class _A ( unittest.TestCase ):
def lowercase__ ( self : Dict ) -> str:
"""simple docstring"""
__snake_case : Optional[Any] = AutoformerModel.from_pretrained("""huggingface/autoformer-tourism-monthly""" ).to(__A )
__snake_case : Union[str, Any] = prepare_batch()
with torch.no_grad():
__snake_case : Tuple = model(
past_values=batch["""past_values"""] , past_time_features=batch["""past_time_features"""] , past_observed_mask=batch["""past_observed_mask"""] , static_categorical_features=batch["""static_categorical_features"""] , future_values=batch["""future_values"""] , future_time_features=batch["""future_time_features"""] , )[0]
__snake_case : List[Any] = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , __A )
__snake_case : Any = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=__A )
self.assertTrue(torch.allclose(output[0, :3, :3] , __A , atol=__A ) )
def lowercase__ ( self : Any ) -> str:
"""simple docstring"""
__snake_case : Union[str, Any] = AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly""" ).to(__A )
__snake_case : Dict = prepare_batch("""val-batch.pt""" )
with torch.no_grad():
__snake_case : List[str] = model(
past_values=batch["""past_values"""] , past_time_features=batch["""past_time_features"""] , past_observed_mask=batch["""past_observed_mask"""] , static_categorical_features=batch["""static_categorical_features"""] , ).encoder_last_hidden_state
__snake_case : Union[str, Any] = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , __A )
__snake_case : Tuple = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=__A )
self.assertTrue(torch.allclose(output[0, :3, :3] , __A , atol=__A ) )
def lowercase__ ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__snake_case : Any = AutoformerForPrediction.from_pretrained("""huggingface/autoformer-tourism-monthly""" ).to(__A )
__snake_case : Tuple = prepare_batch("""val-batch.pt""" )
with torch.no_grad():
__snake_case : str = model.generate(
static_categorical_features=batch["""static_categorical_features"""] , past_time_features=batch["""past_time_features"""] , past_values=batch["""past_values"""] , future_time_features=batch["""future_time_features"""] , past_observed_mask=batch["""past_observed_mask"""] , )
__snake_case : Tuple = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , __A )
__snake_case : Optional[int] = torch.tensor([31_30.67_63, 40_56.52_93, 70_53.07_86] , device=__A )
__snake_case : List[str] = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , __A , rtol=1E-1 ) )
| 358 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
"Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
"Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
"Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
"Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
"Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
"Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
"Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
"Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
"Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
"Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
"Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}
class _A ( __lowercase ):
lowercase__: str = '''codegen'''
lowercase__: Optional[int] = {
'''max_position_embeddings''': '''n_positions''',
'''hidden_size''': '''n_embd''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : Union[str, Any] , __magic_name__ : Optional[Any]=5_04_00 , __magic_name__ : Any=20_48 , __magic_name__ : List[str]=20_48 , __magic_name__ : Union[str, Any]=40_96 , __magic_name__ : Tuple=28 , __magic_name__ : Dict=16 , __magic_name__ : List[str]=64 , __magic_name__ : str=None , __magic_name__ : Tuple="gelu_new" , __magic_name__ : Tuple=0.0 , __magic_name__ : Tuple=0.0 , __magic_name__ : Dict=0.0 , __magic_name__ : Optional[Any]=1E-5 , __magic_name__ : int=0.02 , __magic_name__ : List[Any]=True , __magic_name__ : int=5_02_56 , __magic_name__ : int=5_02_56 , __magic_name__ : Any=False , **__magic_name__ : Optional[int] , ) -> int:
"""simple docstring"""
__snake_case : List[str] = vocab_size
__snake_case : Union[str, Any] = n_ctx
__snake_case : int = n_positions
__snake_case : str = n_embd
__snake_case : Dict = n_layer
__snake_case : List[Any] = n_head
__snake_case : Any = n_inner
__snake_case : str = rotary_dim
__snake_case : List[str] = activation_function
__snake_case : Tuple = resid_pdrop
__snake_case : Dict = embd_pdrop
__snake_case : int = attn_pdrop
__snake_case : Tuple = layer_norm_epsilon
__snake_case : Union[str, Any] = initializer_range
__snake_case : Optional[Any] = use_cache
__snake_case : Dict = bos_token_id
__snake_case : Union[str, Any] = eos_token_id
super().__init__(
bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , tie_word_embeddings=__magic_name__ , **__magic_name__ )
class _A ( __lowercase ):
def __init__( self : int , __magic_name__ : PretrainedConfig , __magic_name__ : str = "default" , __magic_name__ : List[PatchingSpec] = None , __magic_name__ : bool = False , ) -> Tuple:
"""simple docstring"""
super().__init__(__magic_name__ , task=__magic_name__ , patching_specs=__magic_name__ , use_past=__magic_name__ )
if not getattr(self._config , """pad_token_id""" , __magic_name__ ):
# TODO: how to do that better?
__snake_case : List[str] = 0
@property
def lowercase__ ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
__snake_case : Dict = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(__magic_name__ , direction="""inputs""" )
__snake_case : Optional[Any] = {0: """batch""", 1: """past_sequence + sequence"""}
else:
__snake_case : Union[str, Any] = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def lowercase__ ( self : Tuple ) -> int:
"""simple docstring"""
return self._config.n_layer
@property
def lowercase__ ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return self._config.n_head
def lowercase__ ( self : Dict , __magic_name__ : PreTrainedTokenizer , __magic_name__ : int = -1 , __magic_name__ : int = -1 , __magic_name__ : bool = False , __magic_name__ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
"""simple docstring"""
__snake_case : Tuple = super(__magic_name__ , self ).generate_dummy_inputs(
__magic_name__ , batch_size=__magic_name__ , seq_length=__magic_name__ , is_pair=__magic_name__ , framework=__magic_name__ )
# We need to order the input in the way they appears in the forward()
__snake_case : Union[str, Any] = 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
__snake_case , __snake_case : str = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
__snake_case : Tuple = seqlen + 2
__snake_case : Union[str, Any] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
__snake_case : List[str] = [
(torch.zeros(__magic_name__ ), torch.zeros(__magic_name__ )) for _ in range(self.num_layers )
]
__snake_case : Optional[int] = common_inputs["""attention_mask"""]
if self.use_past:
__snake_case : Union[str, Any] = ordered_inputs["""attention_mask"""].dtype
__snake_case : Optional[Any] = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(__magic_name__ , __magic_name__ , dtype=__magic_name__ )] , dim=1 )
return ordered_inputs
@property
def lowercase__ ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return 13
| 13 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : list[int | str] ) -> None:
create_state_space_tree(SCREAMING_SNAKE_CASE__, [], 0, [0 for i in range(len(SCREAMING_SNAKE_CASE__ ) )] )
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : list[int | str], SCREAMING_SNAKE_CASE__ : list[int | str], SCREAMING_SNAKE_CASE__ : int, SCREAMING_SNAKE_CASE__ : list[int], ) -> None:
if index == len(SCREAMING_SNAKE_CASE__ ):
print(SCREAMING_SNAKE_CASE__ )
return
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
if not index_used[i]:
current_sequence.append(sequence[i] )
UpperCAmelCase_ : str = True
create_state_space_tree(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, index + 1, SCREAMING_SNAKE_CASE__ )
current_sequence.pop()
UpperCAmelCase_ : Tuple = False
snake_case_ : list[int | str] = [3, 1, 2, 4]
generate_all_permutations(sequence)
snake_case_ : list[int | str] = ["A", "B", "C"]
generate_all_permutations(sequence_a)
| 125 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
snake_case_ : Any = logging.get_logger(__name__) # pylint: disable=invalid-name
class __a (lowerCamelCase ):
def __init__( self : str , __magic_name__ : CLIPSegForImageSegmentation , __magic_name__ : CLIPSegProcessor , __magic_name__ : AutoencoderKL , __magic_name__ : CLIPTextModel , __magic_name__ : CLIPTokenizer , __magic_name__ : UNetaDConditionModel , __magic_name__ : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , __magic_name__ : StableDiffusionSafetyChecker , __magic_name__ : CLIPImageProcessor , ) -> str:
"""simple docstring"""
super().__init__()
if hasattr(scheduler.config , '''steps_offset''' ) and scheduler.config.steps_offset != 1:
UpperCAmelCase_ : Dict = (
F"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
F""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
'''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
''' file'''
)
deprecate('''steps_offset!=1''' , '''1.0.0''' , __magic_name__ , standard_warn=__magic_name__ )
UpperCAmelCase_ : Optional[int] = dict(scheduler.config )
UpperCAmelCase_ : str = 1
UpperCAmelCase_ : List[str] = FrozenDict(__magic_name__ )
if hasattr(scheduler.config , '''skip_prk_steps''' ) and scheduler.config.skip_prk_steps is False:
UpperCAmelCase_ : Dict = (
F"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
''' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'''
''' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'''
''' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'''
''' Hub, it would be very nice if you could open a Pull request for the'''
''' `scheduler/scheduler_config.json` file'''
)
deprecate('''skip_prk_steps not set''' , '''1.0.0''' , __magic_name__ , standard_warn=__magic_name__ )
UpperCAmelCase_ : Dict = dict(scheduler.config )
UpperCAmelCase_ : str = True
UpperCAmelCase_ : Tuple = FrozenDict(__magic_name__ )
if safety_checker is None:
logger.warning(
F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .''' )
self.register_modules(
segmentation_model=__magic_name__ , segmentation_processor=__magic_name__ , vae=__magic_name__ , text_encoder=__magic_name__ , tokenizer=__magic_name__ , unet=__magic_name__ , scheduler=__magic_name__ , safety_checker=__magic_name__ , feature_extractor=__magic_name__ , )
def UpperCAmelCase__ ( self : List[str] , __magic_name__ : Optional[Union[str, int]] = "auto" ) -> List[str]:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
UpperCAmelCase_ : Union[str, Any] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__magic_name__ )
def UpperCAmelCase__ ( self : List[str] ) -> str:
"""simple docstring"""
self.enable_attention_slicing(__magic_name__ )
def UpperCAmelCase__ ( self : Dict ) -> Optional[int]:
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
UpperCAmelCase_ : Tuple = torch.device('''cuda''' )
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(__magic_name__ , __magic_name__ )
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def UpperCAmelCase__ ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
if self.device != torch.device('''meta''' ) or not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(__magic_name__ , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
def __call__( self : Union[str, Any] , __magic_name__ : Union[str, List[str]] , __magic_name__ : Union[torch.FloatTensor, PIL.Image.Image] , __magic_name__ : str , __magic_name__ : int = 5_12 , __magic_name__ : int = 5_12 , __magic_name__ : int = 50 , __magic_name__ : float = 7.5 , __magic_name__ : Optional[Union[str, List[str]]] = None , __magic_name__ : Optional[int] = 1 , __magic_name__ : float = 0.0 , __magic_name__ : Optional[torch.Generator] = None , __magic_name__ : Optional[torch.FloatTensor] = None , __magic_name__ : Optional[str] = "pil" , __magic_name__ : bool = True , __magic_name__ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , __magic_name__ : int = 1 , **__magic_name__ : Tuple , ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = self.segmentation_processor(
text=[text] , images=[image] , padding='''max_length''' , return_tensors='''pt''' ).to(self.device )
UpperCAmelCase_ : int = self.segmentation_model(**__magic_name__ )
UpperCAmelCase_ : Dict = torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
UpperCAmelCase_ : List[Any] = self.numpy_to_pil(__magic_name__ )[0].resize(image.size )
# Run inpainting pipeline with the generated mask
UpperCAmelCase_ : int = StableDiffusionInpaintPipeline(
vae=self.vae , text_encoder=self.text_encoder , tokenizer=self.tokenizer , unet=self.unet , scheduler=self.scheduler , safety_checker=self.safety_checker , feature_extractor=self.feature_extractor , )
return inpainting_pipeline(
prompt=__magic_name__ , image=__magic_name__ , mask_image=__magic_name__ , height=__magic_name__ , width=__magic_name__ , num_inference_steps=__magic_name__ , guidance_scale=__magic_name__ , negative_prompt=__magic_name__ , num_images_per_prompt=__magic_name__ , eta=__magic_name__ , generator=__magic_name__ , latents=__magic_name__ , output_type=__magic_name__ , return_dict=__magic_name__ , callback=__magic_name__ , callback_steps=__magic_name__ , )
| 125 | 1 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class _snake_case :
def __init__( self: str , __lowerCamelCase: list[tuple[float, float]] ) -> Optional[int]:
__UpperCAmelCase : int = list_of_points
# Degree determines the flexibility of the curve.
# Degree = 1 will produce a straight line.
__UpperCAmelCase : Dict = len(__lowerCamelCase ) - 1
def _lowerCamelCase ( self: List[str] , __lowerCamelCase: float ) -> list[float]:
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__UpperCAmelCase : list[float] = []
for i in range(len(self.list_of_points ) ):
# basis function for each i
output_values.append(
comb(self.degree , __lowerCamelCase ) * ((1 - t) ** (self.degree - i)) * (t**i) )
# the basis must sum up to 1 for it to produce a valid Bezier curve.
assert round(sum(__lowerCamelCase ) , 5 ) == 1
return output_values
def _lowerCamelCase ( self: Union[str, Any] , __lowerCamelCase: float ) -> tuple[float, float]:
assert 0 <= t <= 1, "Time t must be between 0 and 1."
__UpperCAmelCase : Optional[int] = self.basis_function(__lowerCamelCase )
__UpperCAmelCase : Tuple = 0.0
__UpperCAmelCase : Dict = 0.0
for i in range(len(self.list_of_points ) ):
# For all points, sum up the product of i-th basis function and i-th point.
x += basis_function[i] * self.list_of_points[i][0]
y += basis_function[i] * self.list_of_points[i][1]
return (x, y)
def _lowerCamelCase ( self: Union[str, Any] , __lowerCamelCase: float = 0.01 ) -> Optional[Any]:
from matplotlib import pyplot as plt # type: ignore
__UpperCAmelCase : list[float] = [] # x coordinates of points to plot
__UpperCAmelCase : list[float] = [] # y coordinates of points to plot
__UpperCAmelCase : int = 0.0
while t <= 1:
__UpperCAmelCase : Optional[Any] = self.bezier_curve_function(__lowerCamelCase )
to_plot_x.append(value[0] )
to_plot_y.append(value[1] )
t += step_size
__UpperCAmelCase : str = [i[0] for i in self.list_of_points]
__UpperCAmelCase : str = [i[1] for i in self.list_of_points]
plt.plot(
__lowerCamelCase , __lowerCamelCase , color="blue" , label="Curve of Degree " + str(self.degree ) , )
plt.scatter(__lowerCamelCase , __lowerCamelCase , color="red" , label="Control Points" )
plt.legend()
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod()
BezierCurve([(1, 2), (3, 5)]).plot_curve() # degree 1
BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve() # degree 2
BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve() # degree 3
| 342 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
_snake_case = {
'''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
'''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
'''tokenizer_config_file''': {
'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
},
}
_snake_case = {'''facebook/blenderbot-3B''': 128}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _UpperCamelCase ( ) -> Dict:
__UpperCAmelCase : Tuple = (
list(range(ord("!" ), ord("~" ) + 1 ) ) + list(range(ord("¡" ), ord("¬" ) + 1 ) ) + list(range(ord("®" ), ord("ÿ" ) + 1 ) )
)
__UpperCAmelCase : str = bs[:]
__UpperCAmelCase : Any = 0
for b in range(2**8 ):
if b not in bs:
bs.append(snake_case__ )
cs.append(2**8 + n )
n += 1
__UpperCAmelCase : Optional[Any] = [chr(snake_case__ ) for n in cs]
return dict(zip(snake_case__, snake_case__ ) )
def _UpperCamelCase ( snake_case__ ) -> Any:
__UpperCAmelCase : List[Any] = set()
__UpperCAmelCase : Any = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__UpperCAmelCase : Union[str, Any] = char
return pairs
class _snake_case ( _lowercase ):
lowerCamelCase__: str = VOCAB_FILES_NAMES
lowerCamelCase__: List[Any] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__: Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__: Dict = ["input_ids", "attention_mask"]
def __init__( self: Tuple , __lowerCamelCase: Union[str, Any] , __lowerCamelCase: Optional[int] , __lowerCamelCase: List[str]="replace" , __lowerCamelCase: List[str]="<s>" , __lowerCamelCase: List[str]="</s>" , __lowerCamelCase: str="</s>" , __lowerCamelCase: Tuple="<s>" , __lowerCamelCase: Optional[int]="<unk>" , __lowerCamelCase: Any="<pad>" , __lowerCamelCase: List[str]="<mask>" , __lowerCamelCase: List[str]=False , **__lowerCamelCase: int , ) -> List[str]:
__UpperCAmelCase : int = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else bos_token
__UpperCAmelCase : List[Any] = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else eos_token
__UpperCAmelCase : Any = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else sep_token
__UpperCAmelCase : Tuple = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else cls_token
__UpperCAmelCase : Optional[Any] = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else unk_token
__UpperCAmelCase : List[Any] = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
__UpperCAmelCase : Dict = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else mask_token
super().__init__(
errors=__lowerCamelCase , bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , cls_token=__lowerCamelCase , pad_token=__lowerCamelCase , mask_token=__lowerCamelCase , add_prefix_space=__lowerCamelCase , **__lowerCamelCase , )
with open(__lowerCamelCase , encoding="utf-8" ) as vocab_handle:
__UpperCAmelCase : List[Any] = json.load(__lowerCamelCase )
__UpperCAmelCase : Optional[Any] = {v: k for k, v in self.encoder.items()}
__UpperCAmelCase : Dict = errors # how to handle errors in decoding
__UpperCAmelCase : Optional[int] = bytes_to_unicode()
__UpperCAmelCase : Dict = {v: k for k, v in self.byte_encoder.items()}
with open(__lowerCamelCase , encoding="utf-8" ) as merges_handle:
__UpperCAmelCase : List[Any] = merges_handle.read().split("\n" )[1:-1]
__UpperCAmelCase : Union[str, Any] = [tuple(merge.split() ) for merge in bpe_merges]
__UpperCAmelCase : int = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) )
__UpperCAmelCase : List[Any] = {}
__UpperCAmelCase : Tuple = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__UpperCAmelCase : int = 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.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def _lowerCamelCase ( self: Dict ) -> Any:
return len(self.encoder )
def _lowerCamelCase ( self: Optional[Any] ) -> List[str]:
return dict(self.encoder , **self.added_tokens_encoder )
def _lowerCamelCase ( self: int , __lowerCamelCase: List[Any] ) -> Union[str, Any]:
if token in self.cache:
return self.cache[token]
__UpperCAmelCase : List[Any] = tuple(__lowerCamelCase )
__UpperCAmelCase : Dict = get_pairs(__lowerCamelCase )
if not pairs:
return token
while True:
__UpperCAmelCase : Optional[int] = min(__lowerCamelCase , key=lambda __lowerCamelCase : self.bpe_ranks.get(__lowerCamelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = bigram
__UpperCAmelCase : Optional[int] = []
__UpperCAmelCase : str = 0
while i < len(__lowerCamelCase ):
try:
__UpperCAmelCase : Union[str, Any] = word.index(__lowerCamelCase , __lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__UpperCAmelCase : Union[str, Any] = j
if word[i] == first and i < len(__lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__UpperCAmelCase : List[Any] = tuple(__lowerCamelCase )
__UpperCAmelCase : str = new_word
if len(__lowerCamelCase ) == 1:
break
else:
__UpperCAmelCase : Optional[Any] = get_pairs(__lowerCamelCase )
__UpperCAmelCase : Optional[Any] = " ".join(__lowerCamelCase )
__UpperCAmelCase : Union[str, Any] = word
return word
def _lowerCamelCase ( self: Dict , __lowerCamelCase: Optional[Any] ) -> Dict:
__UpperCAmelCase : Any = []
for token in re.findall(self.pat , __lowerCamelCase ):
__UpperCAmelCase : int = "".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(__lowerCamelCase ).split(" " ) )
return bpe_tokens
def _lowerCamelCase ( self: int , __lowerCamelCase: str ) -> Dict:
return self.encoder.get(__lowerCamelCase , self.encoder.get(self.unk_token ) )
def _lowerCamelCase ( self: Tuple , __lowerCamelCase: List[Any] ) -> List[str]:
return self.decoder.get(__lowerCamelCase )
def _lowerCamelCase ( self: Any , __lowerCamelCase: Any ) -> int:
__UpperCAmelCase : Dict = "".join(__lowerCamelCase )
__UpperCAmelCase : Optional[int] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def _lowerCamelCase ( self: List[Any] , __lowerCamelCase: str , __lowerCamelCase: Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__lowerCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__UpperCAmelCase : Any = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
__UpperCAmelCase : Dict = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(__lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowerCamelCase , ensure_ascii=__lowerCamelCase ) + "\n" )
__UpperCAmelCase : Optional[Any] = 0
with open(__lowerCamelCase , "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 __lowerCamelCase : 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!" )
__UpperCAmelCase : Optional[Any] = token_index
writer.write(" ".join(__lowerCamelCase ) + "\n" )
index += 1
return vocab_file, merge_file
def _lowerCamelCase ( self: Dict , __lowerCamelCase: List[int] , __lowerCamelCase: Optional[List[int]] = None , __lowerCamelCase: bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCamelCase , token_ids_a=__lowerCamelCase , already_has_special_tokens=__lowerCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(__lowerCamelCase )) + [1]
return [1] + ([0] * len(__lowerCamelCase )) + [1, 1] + ([0] * len(__lowerCamelCase )) + [1]
def _lowerCamelCase ( self: Tuple , __lowerCamelCase: List[int] , __lowerCamelCase: Optional[List[int]] = None ) -> List[int]:
__UpperCAmelCase : int = [self.sep_token_id]
__UpperCAmelCase : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _lowerCamelCase ( self: str , __lowerCamelCase: Optional[int] , __lowerCamelCase: List[str]=False , **__lowerCamelCase: int ) -> List[Any]:
__UpperCAmelCase : Optional[Any] = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase ) > 0 and not text[0].isspace()):
__UpperCAmelCase : Optional[Any] = " " + text
return (text, kwargs)
def _lowerCamelCase ( self: List[str] , __lowerCamelCase: List[int] , __lowerCamelCase: Optional[List[int]] = None ) -> List[str]:
return token_ids_a + [self.eos_token_id]
def _lowerCamelCase ( self: List[str] , __lowerCamelCase: "Conversation" ) -> List[int]:
__UpperCAmelCase : Tuple = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(" " + text )
else:
# Generated responses should contain them already.
inputs.append(__lowerCamelCase )
__UpperCAmelCase : Optional[int] = " ".join(__lowerCamelCase )
__UpperCAmelCase : Optional[Any] = self.encode(__lowerCamelCase )
if len(__lowerCamelCase ) > self.model_max_length:
__UpperCAmelCase : List[Any] = input_ids[-self.model_max_length :]
logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''' )
return input_ids
| 342 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {'vocab_file': 'sentencepiece.bpe.model'}
a : Union[str, Any] = {
'vocab_file': {
'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
'moussaKam/barthez-orangesum-title': (
'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
),
},
}
a : List[Any] = {
'moussaKam/mbarthez': 1024,
'moussaKam/barthez': 1024,
'moussaKam/barthez-orangesum-title': 1024,
}
a : Dict = '▁'
class a ( snake_case__ ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ["""input_ids""", """attention_mask"""]
def __init__( self : Optional[int] , lowercase_ : List[str] , lowercase_ : List[Any]="<s>" , lowercase_ : Optional[Any]="</s>" , lowercase_ : List[Any]="</s>" , lowercase_ : List[Any]="<s>" , lowercase_ : int="<unk>" , lowercase_ : str="<pad>" , lowercase_ : List[Any]="<mask>" , lowercase_ : List[str] = None , **lowercase_ : List[Any] , ):
snake_case_ = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else mask_token
snake_case_ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_A , eos_token=_A , unk_token=_A , sep_token=_A , cls_token=_A , pad_token=_A , mask_token=_A , sp_model_kwargs=self.sp_model_kwargs , **_A , )
snake_case_ = vocab_file
snake_case_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_A ) )
snake_case_ = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
snake_case_ = len(self.sp_model ) - 1
snake_case_ = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def A_ ( self : str , lowercase_ : List[Any] , lowercase_ : int = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
snake_case_ = [self.cls_token_id]
snake_case_ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def A_ ( self : Optional[int] , lowercase_ : List[str] , lowercase_ : int = None , lowercase_ : int = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_A , token_ids_a=_A , already_has_special_tokens=_A )
if token_ids_a is None:
return [1] + ([0] * len(_A )) + [1]
return [1] + ([0] * len(_A )) + [1, 1] + ([0] * len(_A )) + [1]
def A_ ( self : int , lowercase_ : Tuple , lowercase_ : str = None ):
snake_case_ = [self.sep_token_id]
snake_case_ = [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]
@property
def A_ ( self : List[Any] ):
return len(self.sp_model )
def A_ ( self : Optional[int] ):
snake_case_ = {self.convert_ids_to_tokens(_A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def A_ ( self : Tuple , lowercase_ : Optional[Any] ):
return self.sp_model.encode(_A , out_type=_A )
def A_ ( self : int , lowercase_ : Tuple ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
snake_case_ = self.sp_model.PieceToId(_A )
return spm_id if spm_id else self.unk_token_id
def A_ ( self : Dict , lowercase_ : Tuple ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(_A )
def A_ ( self : int , lowercase_ : Dict ):
snake_case_ = []
snake_case_ = ''''''
snake_case_ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_A ) + token
snake_case_ = True
snake_case_ = []
else:
current_sub_tokens.append(_A )
snake_case_ = False
out_string += self.sp_model.decode(_A )
return out_string.strip()
def __getstate__( self : Union[str, Any] ):
snake_case_ = self.__dict__.copy()
snake_case_ = None
return state
def __setstate__( self : Optional[int] , lowercase_ : Union[str, Any] ):
snake_case_ = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
snake_case_ = {}
snake_case_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def A_ ( self : Optional[int] , lowercase_ : Union[str, Any] , lowercase_ : Optional[int] = None ):
if not os.path.isdir(_A ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
snake_case_ = os.path.join(
_A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _A )
elif not os.path.isfile(self.vocab_file ):
with open(_A , '''wb''' ) as fi:
snake_case_ = self.sp_model.serialized_model_proto()
fi.write(_A )
return (out_vocab_file,)
| 56 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
UpperCamelCase__ = get_logger(__name__)
def _a ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : str=0 ):
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
__lowerCAmelCase = model.state_dict()
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
__lowerCAmelCase = F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if accelerator.process_index == 0:
logger.info(F"""Saving model to {output_model_file}""" )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model saved to {output_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
__lowerCAmelCase = (
F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
if model_index == 0
else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving model to {output_model_file}""" )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model saved to {output_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , F"""{MODEL_NAME}_{model_index}""" )
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving model to {ckpt_dir}""" )
__lowerCAmelCase = {"model": state_dict}
dist_cp.save_state_dict(
state_dict=SCREAMING_SNAKE_CASE_ , storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE_ ) , planner=DefaultSavePlanner() , )
logger.info(F"""Model saved to {ckpt_dir}""" )
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Any=0 ):
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if type(SCREAMING_SNAKE_CASE_ ) != FSDP and accelerator.process_index != 0:
if not fsdp_plugin.sync_module_states:
raise ValueError(
"Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
"initializing FSDP object" )
return
__lowerCAmelCase = F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Loading model from {input_model_file}""" )
__lowerCAmelCase = torch.load(SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model loaded from {input_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
__lowerCAmelCase = (
F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
if model_index == 0
else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Loading model from {input_model_file}""" )
__lowerCAmelCase = torch.load(SCREAMING_SNAKE_CASE_ )
logger.info(F"""Model loaded from {input_model_file}""" )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
__lowerCAmelCase = (
os.path.join(SCREAMING_SNAKE_CASE_ , F"""{MODEL_NAME}_{model_index}""" )
if F"""{MODEL_NAME}""" not in input_dir
else input_dir
)
logger.info(F"""Loading model from {ckpt_dir}""" )
__lowerCAmelCase = {"model": model.state_dict()}
dist_cp.load_state_dict(
state_dict=SCREAMING_SNAKE_CASE_ , storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_ ) , planner=DefaultLoadPlanner() , )
__lowerCAmelCase = state_dict["model"]
logger.info(F"""Model loaded from {ckpt_dir}""" )
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : str=0 ):
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
__lowerCAmelCase = FSDP.optim_state_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if accelerator.process_index == 0:
__lowerCAmelCase = (
F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving Optimizer state to {output_optimizer_file}""" )
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Optimizer state saved in {output_optimizer_file}""" )
else:
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , F"""{OPTIMIZER_NAME}_{optimizer_index}""" )
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
logger.info(F"""Saving Optimizer state to {ckpt_dir}""" )
dist_cp.save_state_dict(
state_dict={"optimizer": optim_state} , storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE_ ) , planner=DefaultSavePlanner() , )
logger.info(F"""Optimizer state saved in {ckpt_dir}""" )
def _a ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Dict=0 ):
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
SCREAMING_SNAKE_CASE_ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
__lowerCAmelCase = None
# below check should work but currently it isn't working (mostly opytorch issue),
# in the meantime disabling it at the cost of excess memory usage
# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
__lowerCAmelCase = (
F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
)
__lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(F"""Loading Optimizer state from {input_optimizer_file}""" )
__lowerCAmelCase = torch.load(SCREAMING_SNAKE_CASE_ )
logger.info(F"""Optimizer state loaded from {input_optimizer_file}""" )
else:
__lowerCAmelCase = (
os.path.join(SCREAMING_SNAKE_CASE_ , F"""{OPTIMIZER_NAME}_{optimizer_index}""" )
if F"""{OPTIMIZER_NAME}""" not in input_dir
else input_dir
)
logger.info(F"""Loading Optimizer from {ckpt_dir}""" )
__lowerCAmelCase = load_sharded_optimizer_state_dict(
model_state_dict=model.state_dict() , optimizer_key="optimizer" , storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_ ) , )
__lowerCAmelCase = optim_state["optimizer"]
logger.info(F"""Optimizer loaded from {ckpt_dir}""" )
__lowerCAmelCase = FSDP.optim_state_dict_to_load(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
optimizer.load_state_dict(SCREAMING_SNAKE_CASE_ )
| 92 | 0 |
class __lowerCAmelCase :
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
__UpperCamelCase = arr.split(',' )
def UpperCAmelCase ( self ):
'''simple docstring'''
__UpperCamelCase = [int(self.array[0] )] * len(self.array )
__UpperCamelCase = [int(self.array[0] )] * len(self.array )
for i in range(1 , len(self.array ) ):
__UpperCamelCase = max(
int(self.array[i] ) + sum_value[i - 1] , int(self.array[i] ) )
__UpperCamelCase = max(sum_value[i] , rear[i - 1] )
return rear[len(self.array ) - 1]
if __name__ == "__main__":
UpperCamelCase : List[Any] = input("please input some numbers:")
UpperCamelCase : List[Any] = SubArray(whole_array)
UpperCamelCase : Union[str, Any] = array.solve_sub_array()
print(("the results is:", re))
| 369 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase : int = {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
),
"distilbert-base-uncased-finetuned-sst-2-english": (
"https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
),
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
lowercase = "distilbert"
lowercase = {
"hidden_size": "dim",
"num_attention_heads": "n_heads",
"num_hidden_layers": "n_layers",
}
def __init__( self , __UpperCAmelCase=3_0522 , __UpperCAmelCase=512 , __UpperCAmelCase=False , __UpperCAmelCase=6 , __UpperCAmelCase=12 , __UpperCAmelCase=768 , __UpperCAmelCase=4 * 768 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.0_2 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.2 , __UpperCAmelCase=0 , **__UpperCAmelCase , ):
'''simple docstring'''
__UpperCamelCase = vocab_size
__UpperCamelCase = max_position_embeddings
__UpperCamelCase = sinusoidal_pos_embds
__UpperCamelCase = n_layers
__UpperCamelCase = n_heads
__UpperCamelCase = dim
__UpperCamelCase = hidden_dim
__UpperCamelCase = dropout
__UpperCamelCase = attention_dropout
__UpperCamelCase = activation
__UpperCamelCase = initializer_range
__UpperCamelCase = qa_dropout
__UpperCamelCase = seq_classif_dropout
super().__init__(**__UpperCAmelCase , pad_token_id=__UpperCAmelCase )
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
@property
def UpperCAmelCase ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
__UpperCamelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
__UpperCamelCase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 263 | 0 |
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 ShapEPipeline
else:
from .camera import create_pan_cameras
from .pipeline_shap_e import ShapEPipeline
from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
from .renderer import (
BoundingBoxVolume,
ImportanceRaySampler,
MLPNeRFModelOutput,
MLPNeRSTFModel,
ShapEParamsProjModel,
ShapERenderer,
StratifiedRaySampler,
VoidNeRFModel,
)
| 11 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def _UpperCAmelCase (UpperCamelCase__ : Union[str, Any] ):
_A , _A : Any = image.size
_A , _A : str = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
_A : List[str] = image.resize((w, h) , resample=PIL_INTERPOLATION["lanczos"] )
_A : Any = np.array(UpperCamelCase__ ).astype(np.floataa ) / 2_55.0
_A : Optional[Any] = image[None].transpose(0 , 3 , 1 , 2 )
_A : Union[str, Any] = torch.from_numpy(UpperCamelCase__ )
return 2.0 * image - 1.0
class lowerCAmelCase__ ( a):
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , ) -> Optional[int]:
super().__init__()
self.register_modules(vqvae=__lowerCamelCase , unet=__lowerCamelCase , scheduler=__lowerCamelCase)
@torch.no_grad()
def __call__( self , __lowerCamelCase = None , __lowerCamelCase = 1 , __lowerCamelCase = 1_0_0 , __lowerCamelCase = 0.0 , __lowerCamelCase = None , __lowerCamelCase = "pil" , __lowerCamelCase = True , ) -> Union[Tuple, ImagePipelineOutput]:
if isinstance(__lowerCamelCase , PIL.Image.Image):
_A : Tuple = 1
elif isinstance(__lowerCamelCase , torch.Tensor):
_A : Union[str, Any] = image.shape[0]
else:
raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__lowerCamelCase)}")
if isinstance(__lowerCamelCase , PIL.Image.Image):
_A : Union[str, Any] = preprocess(__lowerCamelCase)
_A , _A : Union[str, Any] = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
_A : Optional[Any] = (batch_size, self.unet.config.in_channels // 2, height, width)
_A : str = next(self.unet.parameters()).dtype
_A : Union[str, Any] = randn_tensor(__lowerCamelCase , generator=__lowerCamelCase , device=self.device , dtype=__lowerCamelCase)
_A : List[Any] = image.to(device=self.device , dtype=__lowerCamelCase)
# set timesteps and move to the correct device
self.scheduler.set_timesteps(__lowerCamelCase , device=self.device)
_A : Any = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
_A : List[str] = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_A : str = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
_A : Optional[int] = {}
if accepts_eta:
_A : List[Any] = eta
for t in self.progress_bar(__lowerCamelCase):
# concat latents and low resolution image in the channel dimension.
_A : List[Any] = torch.cat([latents, image] , dim=1)
_A : str = self.scheduler.scale_model_input(__lowerCamelCase , __lowerCamelCase)
# predict the noise residual
_A : Any = self.unet(__lowerCamelCase , __lowerCamelCase).sample
# compute the previous noisy sample x_t -> x_t-1
_A : Optional[int] = self.scheduler.step(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , **__lowerCamelCase).prev_sample
# decode the image latents with the VQVAE
_A : Union[str, Any] = self.vqvae.decode(__lowerCamelCase).sample
_A : Dict = torch.clamp(__lowerCamelCase , -1.0 , 1.0)
_A : Tuple = image / 2 + 0.5
_A : int = image.cpu().permute(0 , 2 , 3 , 1).numpy()
if output_type == "pil":
_A : Optional[int] = self.numpy_to_pil(__lowerCamelCase)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__lowerCamelCase)
| 11 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class snake_case ( __snake_case, unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : Any = KandinskyVaaControlnetPipeline
SCREAMING_SNAKE_CASE_ : Any = ["""image_embeds""", """negative_image_embeds""", """hint"""]
SCREAMING_SNAKE_CASE_ : int = ["""image_embeds""", """negative_image_embeds""", """hint"""]
SCREAMING_SNAKE_CASE_ : Any = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
SCREAMING_SNAKE_CASE_ : str = False
@property
def lowercase_ ( self : Any)-> Union[str, Any]:
'''simple docstring'''
return 3_2
@property
def lowercase_ ( self : int)-> List[Any]:
'''simple docstring'''
return 3_2
@property
def lowercase_ ( self : Any)-> int:
'''simple docstring'''
return self.time_input_dim
@property
def lowercase_ ( self : List[str])-> Tuple:
'''simple docstring'''
return self.time_input_dim * 4
@property
def lowercase_ ( self : int)-> Optional[int]:
'''simple docstring'''
return 1_0_0
@property
def lowercase_ ( self : Dict)-> Optional[int]:
'''simple docstring'''
torch.manual_seed(0)
__lowerCAmelCase: List[Any] = {
"in_channels": 8,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "image_hint",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
__lowerCAmelCase: str = UNetaDConditionModel(**UpperCamelCase__)
return model
@property
def lowercase_ ( self : List[Any])-> Optional[Any]:
'''simple docstring'''
return {
"block_out_channels": [3_2, 3_2, 6_4, 6_4],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def lowercase_ ( self : Dict)-> Tuple:
'''simple docstring'''
torch.manual_seed(0)
__lowerCAmelCase: str = VQModel(**self.dummy_movq_kwargs)
return model
def lowercase_ ( self : List[str])-> Optional[int]:
'''simple docstring'''
__lowerCAmelCase: Any = self.dummy_unet
__lowerCAmelCase: Any = self.dummy_movq
__lowerCAmelCase: List[Any] = DDIMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule="linear" , beta_start=0.00085 , beta_end=0.012 , clip_sample=UpperCamelCase__ , set_alpha_to_one=UpperCamelCase__ , steps_offset=1 , prediction_type="epsilon" , thresholding=UpperCamelCase__ , )
__lowerCAmelCase: Optional[int] = {
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def lowercase_ ( self : str , UpperCamelCase__ : Tuple , UpperCamelCase__ : int=0)-> str:
'''simple docstring'''
__lowerCAmelCase: Any = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(UpperCamelCase__)).to(UpperCamelCase__)
__lowerCAmelCase: int = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1)).to(
UpperCamelCase__)
# create hint
__lowerCAmelCase: str = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(UpperCamelCase__)).to(UpperCamelCase__)
if str(UpperCamelCase__).startswith("mps"):
__lowerCAmelCase: Optional[Any] = torch.manual_seed(UpperCamelCase__)
else:
__lowerCAmelCase: str = torch.Generator(device=UpperCamelCase__).manual_seed(UpperCamelCase__)
__lowerCAmelCase: List[Any] = {
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"hint": hint,
"generator": generator,
"height": 6_4,
"width": 6_4,
"guidance_scale": 4.0,
"num_inference_steps": 2,
"output_type": "np",
}
return inputs
def lowercase_ ( self : int)-> List[str]:
'''simple docstring'''
__lowerCAmelCase: Dict = "cpu"
__lowerCAmelCase: Optional[int] = self.get_dummy_components()
__lowerCAmelCase: Optional[Any] = self.pipeline_class(**UpperCamelCase__)
__lowerCAmelCase: List[Any] = pipe.to(UpperCamelCase__)
pipe.set_progress_bar_config(disable=UpperCamelCase__)
__lowerCAmelCase: Union[str, Any] = pipe(**self.get_dummy_inputs(UpperCamelCase__))
__lowerCAmelCase: int = output.images
__lowerCAmelCase: int = pipe(
**self.get_dummy_inputs(UpperCamelCase__) , return_dict=UpperCamelCase__ , )[0]
__lowerCAmelCase: str = image[0, -3:, -3:, -1]
__lowerCAmelCase: List[str] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
__lowerCAmelCase: Any = np.array(
[0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595])
assert (
np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
@slow
@require_torch_gpu
class snake_case ( unittest.TestCase ):
def lowercase_ ( self : List[Any])-> str:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase_ ( self : Optional[int])-> List[str]:
'''simple docstring'''
__lowerCAmelCase: Optional[int] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy")
__lowerCAmelCase: Optional[int] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinskyv22/hint_image_cat.png")
__lowerCAmelCase: Union[str, Any] = torch.from_numpy(np.array(UpperCamelCase__)).float() / 255.0
__lowerCAmelCase: List[Any] = hint.permute(2 , 0 , 1).unsqueeze(0)
__lowerCAmelCase: List[Any] = KandinskyVaaPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior" , torch_dtype=torch.floataa)
pipe_prior.to(UpperCamelCase__)
__lowerCAmelCase: List[str] = KandinskyVaaControlnetPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-controlnet-depth" , torch_dtype=torch.floataa)
__lowerCAmelCase: Union[str, Any] = pipeline.to(UpperCamelCase__)
pipeline.set_progress_bar_config(disable=UpperCamelCase__)
__lowerCAmelCase: Optional[Any] = "A robot, 4k photo"
__lowerCAmelCase: Tuple = torch.Generator(device="cuda").manual_seed(0)
__lowerCAmelCase , __lowerCAmelCase: Dict = pipe_prior(
UpperCamelCase__ , generator=UpperCamelCase__ , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
__lowerCAmelCase: Tuple = torch.Generator(device="cuda").manual_seed(0)
__lowerCAmelCase: List[Any] = pipeline(
image_embeds=UpperCamelCase__ , negative_image_embeds=UpperCamelCase__ , hint=UpperCamelCase__ , generator=UpperCamelCase__ , num_inference_steps=1_0_0 , output_type="np" , )
__lowerCAmelCase: Tuple = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert_mean_pixel_difference(UpperCamelCase__ , UpperCamelCase__)
| 108 |
"""simple docstring"""
import math
import sys
def a__ ( __SCREAMING_SNAKE_CASE ) -> int:
if number != int(__SCREAMING_SNAKE_CASE ):
raise ValueError("the value of input must be a natural number" )
if number < 0:
raise ValueError("the value of input must not be a negative number" )
if number == 0:
return 1
__lowerCAmelCase: int = [-1] * (number + 1)
__lowerCAmelCase: Optional[Any] = 0
for i in range(1 , number + 1 ):
__lowerCAmelCase: List[str] = sys.maxsize
__lowerCAmelCase: int = int(math.sqrt(__SCREAMING_SNAKE_CASE ) )
for j in range(1 , root + 1 ):
__lowerCAmelCase: Optional[Any] = 1 + answers[i - (j**2)]
__lowerCAmelCase: List[Any] = min(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[Any] = answer
return answers[number]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 108 | 1 |
# 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.
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
snake_case : int = '''Create a default config file for Accelerate with only a few flags set.'''
def __lowerCamelCase ( UpperCAmelCase_ : Optional[Any]="no" , UpperCAmelCase_ : str = default_json_config_file , UpperCAmelCase_ : bool = False ):
"""simple docstring"""
a :List[str] = Path(UpperCAmelCase_ )
path.parent.mkdir(parents=UpperCAmelCase_ , exist_ok=UpperCAmelCase_ )
if path.exists():
print(
F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.''' )
return False
a :Optional[Any] = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}''' )
a :List[Any] = {
'''compute_environment''': '''LOCAL_MACHINE''',
'''mixed_precision''': mixed_precision,
}
if torch.cuda.is_available():
a :Dict = torch.cuda.device_count()
a :Tuple = num_gpus
a :int = False
if num_gpus > 1:
a :str = '''MULTI_GPU'''
else:
a :List[Any] = '''NO'''
elif is_xpu_available() and use_xpu:
a :List[Any] = torch.xpu.device_count()
a :Optional[int] = num_xpus
a :List[Any] = False
if num_xpus > 1:
a :int = '''MULTI_XPU'''
else:
a :str = '''NO'''
elif is_npu_available():
a :List[str] = torch.npu.device_count()
a :Any = num_npus
a :Optional[int] = False
if num_npus > 1:
a :List[str] = '''MULTI_NPU'''
else:
a :Dict = '''NO'''
else:
a :str = 0
a :Optional[Any] = True
a :Optional[Any] = 1
a :str = '''NO'''
a :List[str] = ClusterConfig(**UpperCAmelCase_ )
config.to_json_file(UpperCAmelCase_ )
return path
def __lowerCamelCase ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Union[str, Any] ):
"""simple docstring"""
a :List[Any] = parser.add_parser('''default''' , parents=UpperCAmelCase_ , help=UpperCAmelCase_ , formatter_class=UpperCAmelCase_ )
parser.add_argument(
'''--config_file''' , default=UpperCAmelCase_ , help=(
'''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
'''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
'''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
'''with \'huggingface\'.'''
) , dest='''save_location''' , )
parser.add_argument(
'''--mixed_precision''' , choices=['''no''', '''fp16''', '''bf16'''] , type=UpperCAmelCase_ , help='''Whether or not to use mixed precision training. '''
'''Choose between FP16 and BF16 (bfloat16) training. '''
'''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.''' , default='''no''' , )
parser.set_defaults(func=UpperCAmelCase_ )
return parser
def __lowerCamelCase ( UpperCAmelCase_ : int ):
"""simple docstring"""
a :Optional[Any] = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(F'''accelerate configuration saved at {config_file}''' )
| 94 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCamelCase ( UpperCAmelCase_ : str ):
"""simple docstring"""
return "".join(sorted(UpperCAmelCase_ ) )
def __lowerCamelCase ( UpperCAmelCase_ : str ):
"""simple docstring"""
return word_by_signature[signature(UpperCAmelCase_ )]
snake_case : str = Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
snake_case : Optional[int] = sorted({word.strip().lower() for word in data.splitlines()})
snake_case : str = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
snake_case : Optional[int] = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open('''anagrams.txt''', '''w''') as file:
file.write('''all_anagrams = \n ''')
file.write(pprint.pformat(all_anagrams))
| 94 | 1 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def __snake_case ( __UpperCamelCase : Features ):
"""simple docstring"""
A_ = np.inf
def set_batch_size(__UpperCamelCase : FeatureType ) -> None:
nonlocal batch_size
if isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = min(__UpperCamelCase ,config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = min(__UpperCamelCase ,config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(__UpperCamelCase ,__UpperCamelCase ) and feature.dtype == "binary":
A_ = min(__UpperCamelCase ,config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(__UpperCamelCase ,__UpperCamelCase )
return None if batch_size is np.inf else batch_size
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : Tuple , UpperCAmelCase : NestedDataStructureLike[PathLike] , UpperCAmelCase : Optional[NamedSplit] = None , UpperCAmelCase : Optional[Features] = None , UpperCAmelCase : str = None , UpperCAmelCase : bool = False , UpperCAmelCase : bool = False , UpperCAmelCase : Optional[int] = None , **UpperCAmelCase : Tuple , ):
super().__init__(
UpperCAmelCase , split=UpperCAmelCase , features=UpperCAmelCase , cache_dir=UpperCAmelCase , keep_in_memory=UpperCAmelCase , streaming=UpperCAmelCase , num_proc=UpperCAmelCase , **UpperCAmelCase , )
A_ = path_or_paths if isinstance(UpperCAmelCase , UpperCAmelCase ) else {self.split: path_or_paths}
A_ = _PACKAGED_DATASETS_MODULES["parquet"][1]
A_ = Parquet(
cache_dir=UpperCAmelCase , data_files=UpperCAmelCase , features=UpperCAmelCase , hash=UpperCAmelCase , **UpperCAmelCase , )
def __A ( self : Optional[Any] ):
# Build iterable dataset
if self.streaming:
A_ = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
A_ = None
A_ = None
A_ = None
A_ = None
self.builder.download_and_prepare(
download_config=UpperCAmelCase , download_mode=UpperCAmelCase , verification_mode=UpperCAmelCase , base_path=UpperCAmelCase , num_proc=self.num_proc , )
A_ = self.builder.as_dataset(
split=self.split , verification_mode=UpperCAmelCase , in_memory=self.keep_in_memory )
return dataset
class _a :
"""simple docstring"""
def __init__( self : Any , UpperCAmelCase : Dataset , UpperCAmelCase : Union[PathLike, BinaryIO] , UpperCAmelCase : Optional[int] = None , **UpperCAmelCase : List[Any] , ):
A_ = dataset
A_ = path_or_buf
A_ = batch_size or get_writer_batch_size(dataset.features )
A_ = parquet_writer_kwargs
def __A ( self : int ):
A_ = self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with open(self.path_or_buf , "wb+" ) as buffer:
A_ = self._write(file_obj=UpperCAmelCase , batch_size=UpperCAmelCase , **self.parquet_writer_kwargs )
else:
A_ = self._write(file_obj=self.path_or_buf , batch_size=UpperCAmelCase , **self.parquet_writer_kwargs )
return written
def __A ( self : Tuple , UpperCAmelCase : BinaryIO , UpperCAmelCase : int , **UpperCAmelCase : Optional[Any] ):
A_ = 0
A_ = parquet_writer_kwargs.pop("path_or_buf" , UpperCAmelCase )
A_ = self.dataset.features.arrow_schema
A_ = pq.ParquetWriter(UpperCAmelCase , schema=UpperCAmelCase , **UpperCAmelCase )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , UpperCAmelCase ) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating parquet from Arrow format" , ):
A_ = query_table(
table=self.dataset._data , key=slice(UpperCAmelCase , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(UpperCAmelCase )
written += batch.nbytes
writer.close()
return written
| 329 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = ['torch', 'transformers', 'onnx']
def __init__( self : List[Any] , *UpperCAmelCase : Union[str, Any] , **UpperCAmelCase : str ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Tuple , *UpperCAmelCase : Tuple , **UpperCAmelCase : Union[str, Any] ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Dict , *UpperCAmelCase : List[Any] , **UpperCAmelCase : Tuple ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Tuple = ['torch', 'transformers', 'onnx']
def __init__( self : Optional[Any] , *UpperCAmelCase : List[Any] , **UpperCAmelCase : List[Any] ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : List[Any] , *UpperCAmelCase : Union[str, Any] , **UpperCAmelCase : str ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Tuple , *UpperCAmelCase : Union[str, Any] , **UpperCAmelCase : int ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Any = ['torch', 'transformers', 'onnx']
def __init__( self : Dict , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : Optional[int] ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Union[str, Any] , *UpperCAmelCase : Tuple , **UpperCAmelCase : Optional[int] ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Tuple , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : int ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : List[str] = ['torch', 'transformers', 'onnx']
def __init__( self : List[Any] , *UpperCAmelCase : List[Any] , **UpperCAmelCase : int ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Any , *UpperCAmelCase : List[Any] , **UpperCAmelCase : str ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Optional[int] , *UpperCAmelCase : str , **UpperCAmelCase : int ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : Dict = ['torch', 'transformers', 'onnx']
def __init__( self : str , *UpperCAmelCase : int , **UpperCAmelCase : Tuple ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Optional[int] , *UpperCAmelCase : str , **UpperCAmelCase : Dict ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : int , *UpperCAmelCase : Optional[int] , **UpperCAmelCase : List[str] ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
class _a ( metaclass=snake_case_ ):
"""simple docstring"""
_lowerCamelCase : List[Any] = ['torch', 'transformers', 'onnx']
def __init__( self : str , *UpperCAmelCase : str , **UpperCAmelCase : List[Any] ):
requires_backends(self , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : List[Any] , *UpperCAmelCase : Optional[Any] , **UpperCAmelCase : List[Any] ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
@classmethod
def __A ( cls : Optional[int] , *UpperCAmelCase : List[str] , **UpperCAmelCase : int ):
requires_backends(cls , ["torch", "transformers", "onnx"] )
| 329 | 1 |
from __future__ import annotations
__snake_case :str = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
__a = [
[0 for col in range(len(grid[0] ) )] for row in range(len(_UpperCAmelCase ) )
] # the reference grid
__a = 1
__a = [
[0 for col in range(len(grid[0] ) )] for row in range(len(_UpperCAmelCase ) )
] # the action grid
__a = init[0]
__a = init[1]
__a = 0
__a = g + heuristic[x][y] # cost from starting cell to destination cell
__a = [[f, g, x, y]]
__a = False # flag that is set when search is complete
__a = False # flag set if we can't find expand
while not found and not resign:
if len(_UpperCAmelCase ) == 0:
raise ValueError('''Algorithm is unable to find solution''' )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
__a = cell.pop()
__a = next_cell[2]
__a = next_cell[3]
__a = next_cell[1]
if x == goal[0] and y == goal[1]:
__a = True
else:
for i in range(len(_UpperCAmelCase ) ): # to try out different valid actions
__a = x + DIRECTIONS[i][0]
__a = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(_UpperCAmelCase ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
__a = g + cost
__a = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
__a = 1
__a = i
__a = []
__a = goal[0]
__a = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
__a = x - DIRECTIONS[action[x][y]][0]
__a = y - DIRECTIONS[action[x][y]][1]
__a = xa
__a = ya
invpath.append([x, y] )
__a = []
for i in range(len(_UpperCAmelCase ) ):
path.append(invpath[len(_UpperCAmelCase ) - 1 - i] )
return path, action
if __name__ == "__main__":
__snake_case :Dict = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
__snake_case :List[Any] = [0, 0]
# all coordinates are given in format [y,x]
__snake_case :Tuple = [len(grid) - 1, len(grid[0]) - 1]
__snake_case :Any = 1
# the cost map which pushes the path closer to the goal
__snake_case :Optional[int] = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
__snake_case :Union[str, Any] = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
__snake_case :int = 99
__snake_case ,__snake_case :int = search(grid, init, goal, cost, heuristic)
print('''ACTION MAP''')
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 49 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class _A ( __UpperCAmelCase ):
def __init__( self : List[Any] , *__SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : Union[str, Any]=None , **__SCREAMING_SNAKE_CASE : str):
'''simple docstring'''
super().__init__(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE)
__a = eval_examples
__a = post_process_function
def _lowerCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : Optional[Dataset] = None , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Optional[List[str]] = None , __SCREAMING_SNAKE_CASE : str = "eval" , **__SCREAMING_SNAKE_CASE : Any , ):
'''simple docstring'''
__a = gen_kwargs.copy()
__a = (
gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length''') is not None else self.args.generation_max_length
)
__a = (
gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams''') is not None else self.args.generation_num_beams
)
__a = gen_kwargs
__a = self.eval_dataset if eval_dataset is None else eval_dataset
__a = self.get_eval_dataloader(__SCREAMING_SNAKE_CASE)
__a = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
__a = self.compute_metrics
__a = None
__a = time.time()
__a = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
__a = eval_loop(
__SCREAMING_SNAKE_CASE , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__SCREAMING_SNAKE_CASE , metric_key_prefix=__SCREAMING_SNAKE_CASE , )
finally:
__a = compute_metrics
__a = self.args.eval_batch_size * self.args.world_size
if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
output.metrics.update(
speed_metrics(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size) , ))
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
__a = self.post_process_function(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
__a = self.compute_metrics(__SCREAMING_SNAKE_CASE)
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys()):
if not key.startswith(F'{metric_key_prefix}_'):
__a = metrics.pop(__SCREAMING_SNAKE_CASE)
metrics.update(output.metrics)
else:
__a = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(__SCREAMING_SNAKE_CASE)
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report())
__a = self.callback_handler.on_evaluate(self.args , self.state , self.control , __SCREAMING_SNAKE_CASE)
return metrics
def _lowerCamelCase ( self : Tuple , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Tuple=None , __SCREAMING_SNAKE_CASE : str = "test" , **__SCREAMING_SNAKE_CASE : Dict):
'''simple docstring'''
__a = gen_kwargs.copy()
__a = self.get_test_dataloader(__SCREAMING_SNAKE_CASE)
# Temporarily disable metric computation, we will do it in the loop here.
__a = self.compute_metrics
__a = None
__a = time.time()
__a = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
__a = eval_loop(
__SCREAMING_SNAKE_CASE , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__SCREAMING_SNAKE_CASE , metric_key_prefix=__SCREAMING_SNAKE_CASE , )
finally:
__a = compute_metrics
__a = self.args.eval_batch_size * self.args.world_size
if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
output.metrics.update(
speed_metrics(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size) , ))
if self.post_process_function is None or self.compute_metrics is None:
return output
__a = self.post_process_function(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , '''predict''')
__a = self.compute_metrics(__SCREAMING_SNAKE_CASE)
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys()):
if not key.startswith(F'{metric_key_prefix}_'):
__a = metrics.pop(__SCREAMING_SNAKE_CASE)
metrics.update(output.metrics)
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=__SCREAMING_SNAKE_CASE)
| 49 | 1 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _SCREAMING_SNAKE_CASE ( lowercase : NDArray[floataa] , lowercase : NDArray[floataa] , lowercase : list[int] , lowercase : int , ):
'''simple docstring'''
lowerCamelCase_ , lowerCamelCase_ = coefficient_matrix.shape
lowerCamelCase_ , lowerCamelCase_ = constant_matrix.shape
if rowsa != colsa:
lowerCamelCase_ = f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
raise ValueError(lowercase )
if colsa != 1:
lowerCamelCase_ = f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
raise ValueError(lowercase )
if rowsa != rowsa:
lowerCamelCase_ = (
'Coefficient and constant matrices dimensions must be nxn and nx1 but '
f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
)
raise ValueError(lowercase )
if len(lowercase ) != rowsa:
lowerCamelCase_ = (
'Number of initial values must be equal to number of rows in coefficient '
f"""matrix but received {len(lowercase )} and {rowsa}"""
)
raise ValueError(lowercase )
if iterations <= 0:
raise ValueError('Iterations must be at least 1' )
lowerCamelCase_ = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
lowerCamelCase_ , lowerCamelCase_ = table.shape
strictly_diagonally_dominant(lowercase )
# Iterates the whole matrix for given number of times
for _ in range(lowercase ):
lowerCamelCase_ = []
for row in range(lowercase ):
lowerCamelCase_ = 0
for col in range(lowercase ):
if col == row:
lowerCamelCase_ = table[row][col]
elif col == cols - 1:
lowerCamelCase_ = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
lowerCamelCase_ = (temp + val) / denom
new_val.append(lowercase )
lowerCamelCase_ = new_val
return [float(lowercase ) for i in new_val]
def _SCREAMING_SNAKE_CASE ( lowercase : NDArray[floataa] ):
'''simple docstring'''
lowerCamelCase_ , lowerCamelCase_ = table.shape
lowerCamelCase_ = True
for i in range(0 , lowercase ):
lowerCamelCase_ = 0
for j in range(0 , cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError('Coefficient matrix is not strictly diagonally dominant' )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 352 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class A( UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = 42
class A( UpperCamelCase , UpperCamelCase ):
'''simple docstring'''
@register_to_config
def __init__( self : Tuple , A_ : int = 32 , A_ : int = 64 , A_ : int = 20 , A_ : int = 768 , A_ : Optional[Any]=77 , A_ : Optional[int]=4 , A_ : float = 0.0 , A_ : str = "silu" , A_ : Optional[str] = None , A_ : Optional[str] = None , A_ : Optional[str] = "linear" , A_ : Optional[str] = "prd" , A_ : Optional[int] = None , A_ : Optional[int] = None , A_ : Optional[int] = None , ) -> List[Any]:
"""simple docstring"""
super().__init__()
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = attention_head_dim
lowerCamelCase_ = num_attention_heads * attention_head_dim
lowerCamelCase_ = additional_embeddings
lowerCamelCase_ = time_embed_dim or inner_dim
lowerCamelCase_ = embedding_proj_dim or embedding_dim
lowerCamelCase_ = clip_embed_dim or embedding_dim
lowerCamelCase_ = Timesteps(A_ , A_ , 0 )
lowerCamelCase_ = TimestepEmbedding(A_ , A_ , out_dim=A_ , act_fn=A_ )
lowerCamelCase_ = nn.Linear(A_ , A_ )
if embedding_proj_norm_type is None:
lowerCamelCase_ = None
elif embedding_proj_norm_type == "layer":
lowerCamelCase_ = nn.LayerNorm(A_ )
else:
raise ValueError(f"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}""" )
lowerCamelCase_ = nn.Linear(A_ , A_ )
if encoder_hid_proj_type is None:
lowerCamelCase_ = None
elif encoder_hid_proj_type == "linear":
lowerCamelCase_ = nn.Linear(A_ , A_ )
else:
raise ValueError(f"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}""" )
lowerCamelCase_ = nn.Parameter(torch.zeros(1 , num_embeddings + additional_embeddings , A_ ) )
if added_emb_type == "prd":
lowerCamelCase_ = nn.Parameter(torch.zeros(1 , 1 , A_ ) )
elif added_emb_type is None:
lowerCamelCase_ = None
else:
raise ValueError(
f"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`.""" )
lowerCamelCase_ = nn.ModuleList(
[
BasicTransformerBlock(
A_ , A_ , A_ , dropout=A_ , activation_fn='gelu' , attention_bias=A_ , )
for d in range(A_ )
] )
if norm_in_type == "layer":
lowerCamelCase_ = nn.LayerNorm(A_ )
elif norm_in_type is None:
lowerCamelCase_ = None
else:
raise ValueError(f"""Unsupported norm_in_type: {norm_in_type}.""" )
lowerCamelCase_ = nn.LayerNorm(A_ )
lowerCamelCase_ = nn.Linear(A_ , A_ )
lowerCamelCase_ = torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] , -10000.0 )
causal_attention_mask.triu_(1 )
lowerCamelCase_ = causal_attention_mask[None, ...]
self.register_buffer('causal_attention_mask' , A_ , persistent=A_ )
lowerCamelCase_ = nn.Parameter(torch.zeros(1 , A_ ) )
lowerCamelCase_ = nn.Parameter(torch.zeros(1 , A_ ) )
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def a__ ( self : str ) -> Dict[str, AttentionProcessor]:
"""simple docstring"""
lowerCamelCase_ = {}
def fn_recursive_add_processors(A_ : str , A_ : torch.nn.Module , A_ : Dict[str, AttentionProcessor] ):
if hasattr(A_ , 'set_processor' ):
lowerCamelCase_ = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f"""{name}.{sub_name}""" , A_ , A_ )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(A_ , A_ , A_ )
return processors
def a__ ( self : List[Any] , A_ : Union[AttentionProcessor, Dict[str, AttentionProcessor]] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ = len(self.attn_processors.keys() )
if isinstance(A_ , A_ ) and len(A_ ) != count:
raise ValueError(
f"""A dict of processors was passed, but the number of processors {len(A_ )} does not match the"""
f""" number of attention layers: {count}. Please make sure to pass {count} processor classes.""" )
def fn_recursive_attn_processor(A_ : str , A_ : torch.nn.Module , A_ : Union[str, Any] ):
if hasattr(A_ , 'set_processor' ):
if not isinstance(A_ , A_ ):
module.set_processor(A_ )
else:
module.set_processor(processor.pop(f"""{name}.processor""" ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f"""{name}.{sub_name}""" , A_ , A_ )
for name, module in self.named_children():
fn_recursive_attn_processor(A_ , A_ , A_ )
def a__ ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
self.set_attn_processor(AttnProcessor() )
def a__ ( self : Dict , A_ : List[Any] , A_ : Union[torch.Tensor, float, int] , A_ : torch.FloatTensor , A_ : Optional[torch.FloatTensor] = None , A_ : Optional[torch.BoolTensor] = None , A_ : bool = True , ) -> str:
"""simple docstring"""
lowerCamelCase_ = hidden_states.shape[0]
lowerCamelCase_ = timestep
if not torch.is_tensor(A_ ):
lowerCamelCase_ = torch.tensor([timesteps] , dtype=torch.long , device=hidden_states.device )
elif torch.is_tensor(A_ ) and len(timesteps.shape ) == 0:
lowerCamelCase_ = timesteps[None].to(hidden_states.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
lowerCamelCase_ = timesteps * torch.ones(A_ , dtype=timesteps.dtype , device=timesteps.device )
lowerCamelCase_ = self.time_proj(A_ )
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
lowerCamelCase_ = timesteps_projected.to(dtype=self.dtype )
lowerCamelCase_ = self.time_embedding(A_ )
if self.embedding_proj_norm is not None:
lowerCamelCase_ = self.embedding_proj_norm(A_ )
lowerCamelCase_ = self.embedding_proj(A_ )
if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
lowerCamelCase_ = self.encoder_hidden_states_proj(A_ )
elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set' )
lowerCamelCase_ = self.proj_in(A_ )
lowerCamelCase_ = self.positional_embedding.to(hidden_states.dtype )
lowerCamelCase_ = []
lowerCamelCase_ = 0
if encoder_hidden_states is not None:
additional_embeds.append(A_ )
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape ) == 2:
lowerCamelCase_ = proj_embeddings[:, None, :]
if len(hidden_states.shape ) == 2:
lowerCamelCase_ = hidden_states[:, None, :]
lowerCamelCase_ = additional_embeds + [
proj_embeddings,
time_embeddings[:, None, :],
hidden_states,
]
if self.prd_embedding is not None:
lowerCamelCase_ = self.prd_embedding.to(hidden_states.dtype ).expand(A_ , -1 , -1 )
additional_embeds.append(A_ )
lowerCamelCase_ = torch.cat(
A_ , dim=1 , )
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
lowerCamelCase_ = additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
lowerCamelCase_ = F.pad(
A_ , (
0,
0,
additional_embeddings_len,
self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
) , value=0.0 , )
lowerCamelCase_ = hidden_states + positional_embeddings
if attention_mask is not None:
lowerCamelCase_ = (1 - attention_mask.to(hidden_states.dtype )) * -10000.0
lowerCamelCase_ = F.pad(A_ , (0, self.additional_embeddings) , value=0.0 )
lowerCamelCase_ = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype )
lowerCamelCase_ = attention_mask.repeat_interleave(self.config.num_attention_heads , dim=0 )
if self.norm_in is not None:
lowerCamelCase_ = self.norm_in(A_ )
for block in self.transformer_blocks:
lowerCamelCase_ = block(A_ , attention_mask=A_ )
lowerCamelCase_ = self.norm_out(A_ )
if self.prd_embedding is not None:
lowerCamelCase_ = hidden_states[:, -1]
else:
lowerCamelCase_ = hidden_states[:, additional_embeddings_len:]
lowerCamelCase_ = self.proj_to_clip_embeddings(A_ )
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=A_ )
def a__ ( self : Tuple , A_ : List[Any] ) -> Any:
"""simple docstring"""
lowerCamelCase_ = (prior_latents * self.clip_std) + self.clip_mean
return prior_latents
| 208 | 0 |
"""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
lowerCamelCase__ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class A__ ( _lowerCamelCase):
A_ : Optional[Any] = ['pixel_values']
def __init__( self , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = 1 / 2_55 , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = True , **_SCREAMING_SNAKE_CASE , ):
super().__init__(**_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : str = size if size is not None else {'shortest_edge': 2_24}
__lowerCAmelCase : List[str] = get_size_dict(_SCREAMING_SNAKE_CASE , default_to_square=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Any = crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
__lowerCAmelCase : Optional[int] = get_size_dict(_SCREAMING_SNAKE_CASE , default_to_square=_SCREAMING_SNAKE_CASE , param_name='crop_size' )
__lowerCAmelCase : Tuple = do_resize
__lowerCAmelCase : Union[str, Any] = size
__lowerCAmelCase : str = resample
__lowerCAmelCase : Optional[int] = do_center_crop
__lowerCAmelCase : List[Any] = crop_size
__lowerCAmelCase : Any = do_rescale
__lowerCAmelCase : List[str] = rescale_factor
__lowerCAmelCase : Tuple = do_normalize
__lowerCAmelCase : Dict = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
__lowerCAmelCase : Optional[Any] = image_std if image_std is not None else OPENAI_CLIP_STD
__lowerCAmelCase : str = do_convert_rgb
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ):
__lowerCAmelCase : str = get_size_dict(_SCREAMING_SNAKE_CASE , default_to_square=_SCREAMING_SNAKE_CASE )
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
__lowerCAmelCase : List[Any] = get_resize_output_image_size(_SCREAMING_SNAKE_CASE , size=size['shortest_edge'] , default_to_square=_SCREAMING_SNAKE_CASE )
return resize(_SCREAMING_SNAKE_CASE , size=_SCREAMING_SNAKE_CASE , resample=_SCREAMING_SNAKE_CASE , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ):
__lowerCAmelCase : Dict = get_size_dict(_SCREAMING_SNAKE_CASE )
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(_SCREAMING_SNAKE_CASE , size=(size['height'], size['width']) , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ):
return rescale(_SCREAMING_SNAKE_CASE , scale=_SCREAMING_SNAKE_CASE , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ):
return normalize(_SCREAMING_SNAKE_CASE , mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE , data_format=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST , **_SCREAMING_SNAKE_CASE , ):
__lowerCAmelCase : List[str] = do_resize if do_resize is not None else self.do_resize
__lowerCAmelCase : Dict = size if size is not None else self.size
__lowerCAmelCase : Optional[int] = get_size_dict(_SCREAMING_SNAKE_CASE , param_name='size' , default_to_square=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : str = resample if resample is not None else self.resample
__lowerCAmelCase : Dict = do_center_crop if do_center_crop is not None else self.do_center_crop
__lowerCAmelCase : Optional[Any] = crop_size if crop_size is not None else self.crop_size
__lowerCAmelCase : Any = get_size_dict(_SCREAMING_SNAKE_CASE , param_name='crop_size' , default_to_square=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Any = do_rescale if do_rescale is not None else self.do_rescale
__lowerCAmelCase : int = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowerCAmelCase : Optional[int] = do_normalize if do_normalize is not None else self.do_normalize
__lowerCAmelCase : List[str] = image_mean if image_mean is not None else self.image_mean
__lowerCAmelCase : int = image_std if image_std is not None else self.image_std
__lowerCAmelCase : str = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__lowerCAmelCase : Optional[Any] = make_list_of_images(_SCREAMING_SNAKE_CASE )
if not valid_images(_SCREAMING_SNAKE_CASE ):
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 : Union[str, Any] = [convert_to_rgb(_SCREAMING_SNAKE_CASE ) for image in images]
# All transformations expect numpy arrays.
__lowerCAmelCase : Dict = [to_numpy_array(_SCREAMING_SNAKE_CASE ) for image in images]
if do_resize:
__lowerCAmelCase : int = [self.resize(image=_SCREAMING_SNAKE_CASE , size=_SCREAMING_SNAKE_CASE , resample=_SCREAMING_SNAKE_CASE ) for image in images]
if do_center_crop:
__lowerCAmelCase : Any = [self.center_crop(image=_SCREAMING_SNAKE_CASE , size=_SCREAMING_SNAKE_CASE ) for image in images]
if do_rescale:
__lowerCAmelCase : str = [self.rescale(image=_SCREAMING_SNAKE_CASE , scale=_SCREAMING_SNAKE_CASE ) for image in images]
if do_normalize:
__lowerCAmelCase : List[Any] = [self.normalize(image=_SCREAMING_SNAKE_CASE , mean=_SCREAMING_SNAKE_CASE , std=_SCREAMING_SNAKE_CASE ) for image in images]
__lowerCAmelCase : Dict = [to_channel_dimension_format(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for image in images]
__lowerCAmelCase : Tuple = {'pixel_values': images}
return BatchFeature(data=_SCREAMING_SNAKE_CASE , tensor_type=_SCREAMING_SNAKE_CASE )
| 86 |
"""simple docstring"""
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
from transformers import WavLMConfig, WavLMModel, logging
logging.set_verbosity_info()
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""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.grep_linear""": """encoder.layers.*.attention.gru_rel_pos_linear""",
"""self_attn.relative_attention_bias""": """encoder.layers.*.attention.rel_attn_embed""",
"""self_attn.grep_a""": """encoder.layers.*.attention.gru_rel_pos_const""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """ctc_proj""",
"""mask_emb""": """masked_spec_embed""",
}
lowerCamelCase__ = [
"""ctc_proj""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
for attribute in key.split('.' ):
__lowerCAmelCase : str = getattr(_UpperCamelCase , _UpperCamelCase )
if weight_type is not None:
__lowerCAmelCase : Tuple = getattr(_UpperCamelCase , _UpperCamelCase ).shape
else:
__lowerCAmelCase : Dict = hf_pointer.shape
assert hf_shape == value.shape, (
F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
F" {value.shape} for {full_name}"
)
if weight_type == "weight":
__lowerCAmelCase : Union[str, Any] = value
elif weight_type == "weight_g":
__lowerCAmelCase : List[Any] = value
elif weight_type == "weight_v":
__lowerCAmelCase : Any = value
elif weight_type == "bias":
__lowerCAmelCase : List[str] = value
else:
__lowerCAmelCase : List[Any] = value
logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : Any = []
__lowerCAmelCase : Optional[int] = fairseq_model.state_dict()
__lowerCAmelCase : Union[str, Any] = hf_model.feature_extractor
for name, value in fairseq_dict.items():
__lowerCAmelCase : Union[str, Any] = False
if "conv_layers" in name:
load_conv_layer(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , hf_model.config.feat_extract_norm == 'group' , )
__lowerCAmelCase : str = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
__lowerCAmelCase : int = True
if "*" in mapped_key:
__lowerCAmelCase : List[str] = name.split(_UpperCamelCase )[0].split('.' )[-2]
__lowerCAmelCase : Optional[Any] = mapped_key.replace('*' , _UpperCamelCase )
if "weight_g" in name:
__lowerCAmelCase : Union[str, Any] = 'weight_g'
elif "weight_v" in name:
__lowerCAmelCase : int = 'weight_v'
elif "bias" in name and "relative_attention_bias" not in name:
__lowerCAmelCase : Optional[Any] = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowerCAmelCase : List[str] = 'weight'
else:
__lowerCAmelCase : Optional[Any] = None
set_recursively(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
continue
if not is_used:
unused_weights.append(_UpperCamelCase )
logger.warning(F"Unused weights: {unused_weights}" )
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : List[Any] = full_name.split('conv_layers.' )[-1]
__lowerCAmelCase : Any = name.split('.' )
__lowerCAmelCase : List[Any] = int(items[0] )
__lowerCAmelCase : Tuple = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
)
__lowerCAmelCase : Tuple = value
logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F"{full_name} has size {value.shape}, but"
F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
)
__lowerCAmelCase : int = value
logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
" found."
)
__lowerCAmelCase : Optional[Any] = value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F"{full_name} has size {value.shape}, but"
F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
)
__lowerCAmelCase : Any = value
logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(_UpperCamelCase )
@torch.no_grad()
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase=None ):
# load the pre-trained checkpoints
__lowerCAmelCase : Any = torch.load(_UpperCamelCase )
__lowerCAmelCase : List[str] = WavLMConfigOrig(checkpoint['cfg'] )
__lowerCAmelCase : Optional[Any] = WavLMOrig(_UpperCamelCase )
model.load_state_dict(checkpoint['model'] )
model.eval()
if config_path is not None:
__lowerCAmelCase : Dict = WavLMConfig.from_pretrained(_UpperCamelCase )
else:
__lowerCAmelCase : List[str] = WavLMConfig()
__lowerCAmelCase : List[str] = WavLMModel(_UpperCamelCase )
recursively_load_weights(_UpperCamelCase , _UpperCamelCase )
hf_wavlm.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
lowerCamelCase__ = 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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
lowerCamelCase__ = parser.parse_args()
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 86 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def lowerCAmelCase( )-> None:
"""simple docstring"""
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 350 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __magic_name__ ( snake_case , unittest.TestCase ):
UpperCamelCase_ :int = KandinskyVaaImgaImgPipeline
UpperCamelCase_ :Union[str, Any] = ["""image_embeds""", """negative_image_embeds""", """image"""]
UpperCamelCase_ :Dict = [
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
UpperCamelCase_ :Tuple = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
UpperCamelCase_ :int = False
@property
def UpperCAmelCase_ ( self )-> List[str]:
return 32
@property
def UpperCAmelCase_ ( self )-> List[Any]:
return 32
@property
def UpperCAmelCase_ ( self )-> Tuple:
return self.time_input_dim
@property
def UpperCAmelCase_ ( self )-> Optional[Any]:
return self.time_input_dim * 4
@property
def UpperCAmelCase_ ( self )-> Any:
return 100
@property
def UpperCAmelCase_ ( self )-> Tuple:
torch.manual_seed(0 )
UpperCamelCase_ = {
"in_channels": 4,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "image",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
UpperCamelCase_ = UNetaDConditionModel(**_lowercase )
return model
@property
def UpperCAmelCase_ ( self )-> List[str]:
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def UpperCAmelCase_ ( self )-> Any:
torch.manual_seed(0 )
UpperCamelCase_ = VQModel(**self.dummy_movq_kwargs )
return model
def UpperCAmelCase_ ( self )-> Tuple:
UpperCamelCase_ = self.dummy_unet
UpperCamelCase_ = self.dummy_movq
UpperCamelCase_ = {
"num_train_timesteps": 1_000,
"beta_schedule": "linear",
"beta_start": 0.00_085,
"beta_end": 0.012,
"clip_sample": False,
"set_alpha_to_one": False,
"steps_offset": 0,
"prediction_type": "epsilon",
"thresholding": False,
}
UpperCamelCase_ = DDIMScheduler(**_lowercase )
UpperCamelCase_ = {
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def UpperCAmelCase_ ( self , _lowercase , _lowercase=0 )-> Tuple:
UpperCamelCase_ = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(_lowercase ) ).to(_lowercase )
UpperCamelCase_ = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
_lowercase )
# create init_image
UpperCamelCase_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(_lowercase ) ).to(_lowercase )
UpperCamelCase_ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCamelCase_ = Image.fromarray(np.uinta(_lowercase ) ).convert("RGB" ).resize((256, 256) )
if str(_lowercase ).startswith("mps" ):
UpperCamelCase_ = torch.manual_seed(_lowercase )
else:
UpperCamelCase_ = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
UpperCamelCase_ = {
"image": init_image,
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"generator": generator,
"height": 64,
"width": 64,
"num_inference_steps": 10,
"guidance_scale": 7.0,
"strength": 0.2,
"output_type": "np",
}
return inputs
def UpperCAmelCase_ ( self )-> Optional[int]:
UpperCamelCase_ = "cpu"
UpperCamelCase_ = self.get_dummy_components()
UpperCamelCase_ = self.pipeline_class(**_lowercase )
UpperCamelCase_ = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
UpperCamelCase_ = pipe(**self.get_dummy_inputs(_lowercase ) )
UpperCamelCase_ = output.images
UpperCamelCase_ = pipe(
**self.get_dummy_inputs(_lowercase ) , return_dict=_lowercase , )[0]
UpperCamelCase_ = image[0, -3:, -3:, -1]
UpperCamelCase_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCamelCase_ = np.array(
[0.6_199_778, 0.63_984_406, 0.46_145_785, 0.62_944_984, 0.5_622_215, 0.47_306_132, 0.47_441_456, 0.4_607_606, 0.48_719_263] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
@slow
@require_torch_gpu
class __magic_name__ ( unittest.TestCase ):
def UpperCAmelCase_ ( self )-> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase_ ( self )-> List[str]:
UpperCamelCase_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinskyv22/kandinskyv22_img2img_frog.npy" )
UpperCamelCase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
UpperCamelCase_ = "A red cartoon frog, 4k"
UpperCamelCase_ = KandinskyVaaPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior" , torch_dtype=torch.floataa )
pipe_prior.to(_lowercase )
UpperCamelCase_ = KandinskyVaaImgaImgPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-decoder" , torch_dtype=torch.floataa )
UpperCamelCase_ = pipeline.to(_lowercase )
pipeline.set_progress_bar_config(disable=_lowercase )
UpperCamelCase_ = torch.Generator(device="cpu" ).manual_seed(0 )
UpperCamelCase_ , UpperCamelCase_ = pipe_prior(
_lowercase , generator=_lowercase , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
UpperCamelCase_ = pipeline(
image=_lowercase , image_embeds=_lowercase , negative_image_embeds=_lowercase , generator=_lowercase , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type="np" , )
UpperCamelCase_ = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_lowercase , _lowercase )
| 60 | 0 |
from abc import ABC, abstractmethod
from typing import List, Optional
class _snake_case ( snake_case ):
def __init__( self ):
# test for the above condition
self.test()
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Union[str, Any] = 0
__magic_name__ : Dict = False
while not completed:
if counter == 1:
self.reset()
__magic_name__ : Dict = self.advance()
if not self.does_advance(_a ):
raise Exception(
"Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true." )
__magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] = self.update(_a )
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 SCREAMING_SNAKE_CASE ( self ):
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def SCREAMING_SNAKE_CASE ( self , _a ):
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def SCREAMING_SNAKE_CASE ( self , _a ):
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def SCREAMING_SNAKE_CASE ( self ):
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def SCREAMING_SNAKE_CASE ( self ):
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def SCREAMING_SNAKE_CASE ( self , _a=False ):
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class _snake_case ( snake_case ):
def __init__( self , _a ):
super(_a , self ).__init__()
if not isinstance(_a , _a ) or len(_a ) == 0:
raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.''' )
if any((not isinstance(_a , _a ) 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}.''' )
__magic_name__ : List[str] = token_ids
__magic_name__ : List[str] = len(self.token_ids )
__magic_name__ : Dict = -1 # the index of the currently fulfilled step
__magic_name__ : Optional[int] = False
def SCREAMING_SNAKE_CASE ( self ):
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def SCREAMING_SNAKE_CASE ( self , _a ):
if not isinstance(_a , _a ):
raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_a )}''' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def SCREAMING_SNAKE_CASE ( self , _a ):
if not isinstance(_a , _a ):
raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_a )}''' )
__magic_name__ : Tuple = False
__magic_name__ : List[str] = False
__magic_name__ : Any = False
if self.does_advance(_a ):
self.fulfilled_idx += 1
__magic_name__ : Optional[int] = True
if self.fulfilled_idx == (self.seqlen - 1):
__magic_name__ : Any = True
__magic_name__ : Optional[int] = completed
else:
# failed to make progress.
__magic_name__ : Any = True
self.reset()
return stepped, completed, reset
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = False
__magic_name__ : str = 0
def SCREAMING_SNAKE_CASE ( self ):
return self.seqlen - (self.fulfilled_idx + 1)
def SCREAMING_SNAKE_CASE ( self , _a=False ):
__magic_name__ : str = PhrasalConstraint(self.token_ids )
if stateful:
__magic_name__ : str = self.seqlen
__magic_name__ : Any = self.fulfilled_idx
__magic_name__ : Tuple = self.completed
return new_constraint
class _snake_case :
def __init__( self , _a , _a=True ):
__magic_name__ : Union[str, Any] = max([len(_a ) for one in nested_token_ids] )
__magic_name__ : List[Any] = {}
for token_ids in nested_token_ids:
__magic_name__ : Optional[int] = root
for tidx, token_id in enumerate(_a ):
if token_id not in level:
__magic_name__ : List[Any] = {}
__magic_name__ : Optional[int] = level[token_id]
if no_subsets and self.has_subsets(_a , _a ):
raise ValueError(
"Each list in `nested_token_ids` can't be a complete subset of another list, but is"
f''' {nested_token_ids}.''' )
__magic_name__ : int = root
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : int = self.trie
for current_token in current_seq:
__magic_name__ : Union[str, Any] = start[current_token]
__magic_name__ : int = list(start.keys() )
return next_tokens
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : int = self.next_tokens(_a )
return len(_a ) == 0
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : Tuple = list(root.values() )
if len(_a ) == 0:
return 1
else:
return sum([self.count_leaves(_a ) for nn in next_nodes] )
def SCREAMING_SNAKE_CASE ( self , _a , _a ):
__magic_name__ : List[Any] = self.count_leaves(_a )
return len(_a ) != leaf_count
class _snake_case ( snake_case ):
def __init__( self , _a ):
super(_a , self ).__init__()
if not isinstance(_a , _a ) or len(_a ) == 0:
raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' )
if any(not isinstance(_a , _a ) 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(_a , _a ) 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}.''' )
__magic_name__ : Any = DisjunctiveTrie(_a )
__magic_name__ : str = nested_token_ids
__magic_name__ : List[str] = self.trie.max_height
__magic_name__ : Optional[Any] = []
__magic_name__ : Union[str, Any] = False
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Any = self.trie.next_tokens(self.current_seq )
if len(_a ) == 0:
return None
else:
return token_list
def SCREAMING_SNAKE_CASE ( self , _a ):
if not isinstance(_a , _a ):
raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_a )}''' )
__magic_name__ : List[Any] = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def SCREAMING_SNAKE_CASE ( self , _a ):
if not isinstance(_a , _a ):
raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_a )}''' )
__magic_name__ : str = False
__magic_name__ : List[str] = False
__magic_name__ : Tuple = False
if self.does_advance(_a ):
self.current_seq.append(_a )
__magic_name__ : Optional[int] = True
else:
__magic_name__ : Optional[int] = True
self.reset()
__magic_name__ : str = self.trie.reached_leaf(self.current_seq )
__magic_name__ : int = completed
return stepped, completed, reset
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = False
__magic_name__ : Dict = []
def SCREAMING_SNAKE_CASE ( self ):
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def SCREAMING_SNAKE_CASE ( self , _a=False ):
__magic_name__ : Union[str, Any] = DisjunctiveConstraint(self.token_ids )
if stateful:
__magic_name__ : Any = self.seqlen
__magic_name__ : Dict = self.current_seq
__magic_name__ : Optional[int] = self.completed
return new_constraint
class _snake_case :
def __init__( self , _a ):
__magic_name__ : Optional[int] = constraints
# max # of steps required to fulfill a given constraint
__magic_name__ : int = max([c.seqlen for c in constraints] )
__magic_name__ : List[Any] = len(_a )
__magic_name__ : Tuple = False
self.init_state()
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Any = []
__magic_name__ : Any = None
__magic_name__ : Optional[Any] = [constraint.copy(stateful=_a ) for constraint in self.constraints]
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[Any] = 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 SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
__magic_name__ : Optional[Any] = constraint.advance()
if isinstance(_a , _a ):
token_list.append(_a )
elif isinstance(_a , _a ):
token_list.extend(_a )
else:
__magic_name__ : List[str] = self.inprogress_constraint.advance()
if isinstance(_a , _a ):
token_list.append(_a )
elif isinstance(_a , _a ):
token_list.extend(_a )
if len(_a ) == 0:
return None
else:
return token_list
def SCREAMING_SNAKE_CASE ( self , _a ):
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
__magic_name__ , __magic_name__ : Optional[Any] = self.add(_a )
# the entire list of constraints are fulfilled
if self.completed:
break
def SCREAMING_SNAKE_CASE ( self , _a ):
if not isinstance(_a , _a ):
raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.''' )
__magic_name__ , __magic_name__ : int = False, False
if self.completed:
__magic_name__ : str = True
__magic_name__ : List[Any] = 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
__magic_name__ , __magic_name__ , __magic_name__ : List[str] = self.inprogress_constraint.update(_a )
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=_a ) )
__magic_name__ : Optional[int] = 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 )
__magic_name__ : Dict = None
if len(self.pending_constraints ) == 0:
# we're done!
__magic_name__ : Union[str, Any] = 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(_a ):
__magic_name__ , __magic_name__ , __magic_name__ : str = pending_constraint.update(_a )
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(_a )
__magic_name__ : Any = None
if not complete and stepped:
__magic_name__ : str = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
__magic_name__ : Optional[int] = (
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.
__magic_name__ : Optional[Any] = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def SCREAMING_SNAKE_CASE ( self , _a=True ):
__magic_name__ : Optional[Any] = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
__magic_name__ : Optional[Any] = [
constraint.copy(stateful=_a ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
__magic_name__ : Optional[int] = self.inprogress_constraint.copy(stateful=_a )
__magic_name__ : str = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 281 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class _snake_case ( unittest.TestCase ):
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ):
__magic_name__ : List[Any] = parent
__magic_name__ : Optional[Any] = batch_size
__magic_name__ : Dict = seq_length
__magic_name__ : Union[str, Any] = is_training
__magic_name__ : Optional[Any] = use_attention_mask
__magic_name__ : Optional[Any] = use_token_type_ids
__magic_name__ : int = use_labels
__magic_name__ : List[Any] = vocab_size
__magic_name__ : Union[str, Any] = hidden_size
__magic_name__ : Optional[Any] = num_hidden_layers
__magic_name__ : int = num_attention_heads
__magic_name__ : Any = intermediate_size
__magic_name__ : List[Any] = hidden_act
__magic_name__ : List[Any] = hidden_dropout_prob
__magic_name__ : Optional[int] = attention_probs_dropout_prob
__magic_name__ : List[Any] = max_position_embeddings
__magic_name__ : Tuple = type_vocab_size
__magic_name__ : List[str] = type_sequence_label_size
__magic_name__ : Dict = initializer_range
__magic_name__ : List[Any] = num_choices
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__magic_name__ : List[Any] = None
if self.use_attention_mask:
__magic_name__ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
__magic_name__ : str = None
if self.use_token_type_ids:
__magic_name__ : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__magic_name__ : List[str] = RobertaPreLayerNormConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : int = self.prepare_config_and_inputs()
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : List[Any] = config_and_inputs
__magic_name__ : List[str] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = self.prepare_config_and_inputs()
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ : Union[str, Any] = config_and_inputs
__magic_name__ : Tuple = True
__magic_name__ : int = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__magic_name__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class _snake_case ( snake_case , unittest.TestCase ):
UpperCamelCase__ = True
UpperCamelCase__ = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[Any] = FlaxRobertaPreLayerNormModelTester(self )
@slow
def SCREAMING_SNAKE_CASE ( self ):
for model_class_name in self.all_model_classes:
__magic_name__ : Optional[Any] = model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_a )
__magic_name__ : Dict = model(np.ones((1, 1) ) )
self.assertIsNotNone(_a )
@require_flax
class _snake_case ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Dict = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_a )
__magic_name__ : Union[str, Any] = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__magic_name__ : List[str] = model(_a )[0]
__magic_name__ : str = [1, 11, 50_265]
self.assertEqual(list(output.shape ) , _a )
# compare the actual values for a slice.
__magic_name__ : List[str] = np.array(
[[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _a , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=_a )
__magic_name__ : Tuple = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
__magic_name__ : Tuple = model(_a )[0]
# compare the actual values for a slice.
__magic_name__ : Dict = np.array(
[[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _a , atol=1e-4 ) )
| 281 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/config.json''',
'''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/config.json''',
'''xlm-roberta-large-finetuned-conll02-dutch''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'''
),
'''xlm-roberta-large-finetuned-conll02-spanish''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'''
),
'''xlm-roberta-large-finetuned-conll03-english''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'''
),
'''xlm-roberta-large-finetuned-conll03-german''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'''
),
}
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : List[str] = 'xlm-roberta'
def __init__( self , lowerCamelCase=30522 , lowerCamelCase=768 , lowerCamelCase=12 , lowerCamelCase=12 , lowerCamelCase=3072 , lowerCamelCase="gelu" , lowerCamelCase=0.1 , lowerCamelCase=0.1 , lowerCamelCase=512 , lowerCamelCase=2 , lowerCamelCase=0.02 , lowerCamelCase=1e-12 , lowerCamelCase=1 , lowerCamelCase=0 , lowerCamelCase=2 , lowerCamelCase="absolute" , lowerCamelCase=True , lowerCamelCase=None , **lowerCamelCase , ) -> List[Any]:
super().__init__(pad_token_id=lowerCamelCase , bos_token_id=lowerCamelCase , eos_token_id=lowerCamelCase , **lowerCamelCase )
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = hidden_act
snake_case_ = intermediate_size
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = initializer_range
snake_case_ = layer_norm_eps
snake_case_ = position_embedding_type
snake_case_ = use_cache
snake_case_ = classifier_dropout
class __lowerCamelCase ( __snake_case ):
@property
def lowerCAmelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
snake_case_ = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
snake_case_ = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 34 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCamelCase_ = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
lowerCamelCase_ = None
def UpperCamelCase( ) -> List[Any]:
'''simple docstring'''
snake_case_ = argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0.""" )
parser.add_argument("""data_file""" , metavar="""data.json""" , help="""Input data JSON file.""" )
parser.add_argument("""pred_file""" , metavar="""pred.json""" , help="""Model predictions.""" )
parser.add_argument(
"""--out-file""" , """-o""" , metavar="""eval.json""" , help="""Write accuracy metrics to file (default is stdout).""" )
parser.add_argument(
"""--na-prob-file""" , """-n""" , metavar="""na_prob.json""" , help="""Model estimates of probability of no answer.""" )
parser.add_argument(
"""--na-prob-thresh""" , """-t""" , type=lowercase_ , default=1.0 , help="""Predict \"\" if no-answer probability exceeds this (default = 1.0).""" , )
parser.add_argument(
"""--out-image-dir""" , """-p""" , metavar="""out_images""" , default=lowercase_ , help="""Save precision-recall curves to directory.""" )
parser.add_argument("""--verbose""" , """-v""" , action="""store_true""" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def UpperCamelCase( lowercase_ ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case_ = bool(qa["""answers"""]["""text"""] )
return qid_to_has_ans
def UpperCamelCase( lowercase_ ) -> Tuple:
'''simple docstring'''
def remove_articles(lowercase_ ):
return ARTICLES_REGEX.sub(""" """ , lowercase_ )
def white_space_fix(lowercase_ ):
return " ".join(text.split() )
def remove_punc(lowercase_ ):
snake_case_ = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase_ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def UpperCamelCase( lowercase_ ) -> Dict:
'''simple docstring'''
if not s:
return []
return normalize_answer(lowercase_ ).split()
def UpperCamelCase( lowercase_ , lowercase_ ) -> Dict:
'''simple docstring'''
return int(normalize_answer(lowercase_ ) == normalize_answer(lowercase_ ) )
def UpperCamelCase( lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ = get_tokens(lowercase_ )
snake_case_ = get_tokens(lowercase_ )
snake_case_ = collections.Counter(lowercase_ ) & collections.Counter(lowercase_ )
snake_case_ = sum(common.values() )
if len(lowercase_ ) == 0 or len(lowercase_ ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
snake_case_ = 1.0 * num_same / len(lowercase_ )
snake_case_ = 1.0 * num_same / len(lowercase_ )
snake_case_ = (2 * precision * recall) / (precision + recall)
return fa
def UpperCamelCase( lowercase_ , lowercase_ ) -> List[str]:
'''simple docstring'''
snake_case_ = {}
snake_case_ = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case_ = qa["""id"""]
snake_case_ = [t for t in qa["""answers"""]["""text"""] if normalize_answer(lowercase_ )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
snake_case_ = [""""""]
if qid not in preds:
print(f'''Missing prediction for {qid}''' )
continue
snake_case_ = preds[qid]
# Take max over all gold answers
snake_case_ = max(compute_exact(lowercase_ , lowercase_ ) for a in gold_answers )
snake_case_ = max(compute_fa(lowercase_ , lowercase_ ) for a in gold_answers )
return exact_scores, fa_scores
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = {}
for qid, s in scores.items():
snake_case_ = na_probs[qid] > na_prob_thresh
if pred_na:
snake_case_ = float(not qid_to_has_ans[qid] )
else:
snake_case_ = s
return new_scores
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_=None ) -> Dict:
'''simple docstring'''
if not qid_list:
snake_case_ = len(lowercase_ )
return collections.OrderedDict(
[
("""exact""", 1_00.0 * sum(exact_scores.values() ) / total),
("""f1""", 1_00.0 * sum(fa_scores.values() ) / total),
("""total""", total),
] )
else:
snake_case_ = len(lowercase_ )
return collections.OrderedDict(
[
("""exact""", 1_00.0 * sum(exact_scores[k] for k in qid_list ) / total),
("""f1""", 1_00.0 * sum(fa_scores[k] for k in qid_list ) / total),
("""total""", total),
] )
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
for k in new_eval:
snake_case_ = new_eval[k]
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Dict:
'''simple docstring'''
plt.step(lowercase_ , lowercase_ , color="""b""" , alpha=0.2 , where="""post""" )
plt.fill_between(lowercase_ , lowercase_ , step="""post""" , alpha=0.2 , color="""b""" )
plt.xlabel("""Recall""" )
plt.ylabel("""Precision""" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(lowercase_ )
plt.savefig(lowercase_ )
plt.clf()
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_=None , lowercase_=None ) -> Dict:
'''simple docstring'''
snake_case_ = sorted(lowercase_ , key=lambda lowercase_ : na_probs[k] )
snake_case_ = 0.0
snake_case_ = 1.0
snake_case_ = 0.0
snake_case_ = [1.0]
snake_case_ = [0.0]
snake_case_ = 0.0
for i, qid in enumerate(lowercase_ ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
snake_case_ = true_pos / float(i + 1 )
snake_case_ = true_pos / float(lowercase_ )
if i == len(lowercase_ ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(lowercase_ )
recalls.append(lowercase_ )
if out_image:
plot_pr_curve(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
return {"ap": 1_00.0 * avg_prec}
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
if out_image_dir and not os.path.exists(lowercase_ ):
os.makedirs(lowercase_ )
snake_case_ = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
snake_case_ = make_precision_recall_eval(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , out_image=os.path.join(lowercase_ , """pr_exact.png""" ) , title="""Precision-Recall curve for Exact Match score""" , )
snake_case_ = make_precision_recall_eval(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , out_image=os.path.join(lowercase_ , """pr_f1.png""" ) , title="""Precision-Recall curve for F1 score""" , )
snake_case_ = {k: float(lowercase_ ) for k, v in qid_to_has_ans.items()}
snake_case_ = make_precision_recall_eval(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , out_image=os.path.join(lowercase_ , """pr_oracle.png""" ) , title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)""" , )
merge_eval(lowercase_ , lowercase_ , """pr_exact""" )
merge_eval(lowercase_ , lowercase_ , """pr_f1""" )
merge_eval(lowercase_ , lowercase_ , """pr_oracle""" )
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> int:
'''simple docstring'''
if not qid_list:
return
snake_case_ = [na_probs[k] for k in qid_list]
snake_case_ = np.ones_like(lowercase_ ) / float(len(lowercase_ ) )
plt.hist(lowercase_ , weights=lowercase_ , bins=20 , range=(0.0, 1.0) )
plt.xlabel("""Model probability of no-answer""" )
plt.ylabel("""Proportion of dataset""" )
plt.title(f'''Histogram of no-answer probability: {name}''' )
plt.savefig(os.path.join(lowercase_ , f'''na_prob_hist_{name}.png''' ) )
plt.clf()
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Optional[int]:
'''simple docstring'''
snake_case_ = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
snake_case_ = num_no_ans
snake_case_ = cur_score
snake_case_ = 0.0
snake_case_ = sorted(lowercase_ , key=lambda lowercase_ : na_probs[k] )
for i, qid in enumerate(lowercase_ ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
snake_case_ = scores[qid]
else:
if preds[qid]:
snake_case_ = -1
else:
snake_case_ = 0
cur_score += diff
if cur_score > best_score:
snake_case_ = cur_score
snake_case_ = na_probs[qid]
return 1_00.0 * best_score / len(lowercase_ ), best_thresh
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ , snake_case_ = find_best_thresh(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
snake_case_ , snake_case_ = find_best_thresh(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
snake_case_ = best_exact
snake_case_ = exact_thresh
snake_case_ = best_fa
snake_case_ = fa_thresh
def UpperCamelCase( ) -> Union[str, Any]:
'''simple docstring'''
with open(OPTS.data_file ) as f:
snake_case_ = json.load(lowercase_ )
snake_case_ = dataset_json["""data"""]
with open(OPTS.pred_file ) as f:
snake_case_ = json.load(lowercase_ )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
snake_case_ = json.load(lowercase_ )
else:
snake_case_ = {k: 0.0 for k in preds}
snake_case_ = make_qid_to_has_ans(lowercase_ ) # maps qid to True/False
snake_case_ = [k for k, v in qid_to_has_ans.items() if v]
snake_case_ = [k for k, v in qid_to_has_ans.items() if not v]
snake_case_ , snake_case_ = get_raw_scores(lowercase_ , lowercase_ )
snake_case_ = apply_no_ans_threshold(lowercase_ , lowercase_ , lowercase_ , OPTS.na_prob_thresh )
snake_case_ = apply_no_ans_threshold(lowercase_ , lowercase_ , lowercase_ , OPTS.na_prob_thresh )
snake_case_ = make_eval_dict(lowercase_ , lowercase_ )
if has_ans_qids:
snake_case_ = make_eval_dict(lowercase_ , lowercase_ , qid_list=lowercase_ )
merge_eval(lowercase_ , lowercase_ , """HasAns""" )
if no_ans_qids:
snake_case_ = make_eval_dict(lowercase_ , lowercase_ , qid_list=lowercase_ )
merge_eval(lowercase_ , lowercase_ , """NoAns""" )
if OPTS.na_prob_file:
find_all_best_thresh(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , OPTS.out_image_dir )
histogram_na_prob(lowercase_ , lowercase_ , OPTS.out_image_dir , """hasAns""" )
histogram_na_prob(lowercase_ , lowercase_ , OPTS.out_image_dir , """noAns""" )
if OPTS.out_file:
with open(OPTS.out_file , """w""" ) as f:
json.dump(lowercase_ , lowercase_ )
else:
print(json.dumps(lowercase_ , indent=2 ) )
if __name__ == "__main__":
lowerCamelCase_ = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('''Agg''')
import matplotlib.pyplot as plt
main()
| 34 | 1 |
'''simple docstring'''
from math import loga
def a_ ( __snake_case : int ) -> int:
"""simple docstring"""
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(__snake_case , __snake_case ):
raise TypeError('''Input value must be a \'int\' type''' )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 75 |
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
SCREAMING_SNAKE_CASE_: Optional[int] = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError("All input parameters must be positive" )
if any(p > 1 for p in parameters[1:4] ):
raise ValueError("Relative densities cannot be greater than one" )
else:
SCREAMING_SNAKE_CASE_: int = 1 - (matter_density + radiation_density + dark_energy)
SCREAMING_SNAKE_CASE_: Dict = (
radiation_density * (redshift + 1) ** 4
+ matter_density * (redshift + 1) ** 3
+ curvature * (redshift + 1) ** 2
+ dark_energy
)
SCREAMING_SNAKE_CASE_: Any = hubble_constant * e_a ** (1 / 2)
return hubble
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# demo LCDM approximation
lowerCAmelCase : List[Any] = 0.3
print(
hubble_parameter(
hubble_constant=68.3,
radiation_density=1E-4,
matter_density=matter_density,
dark_energy=1 - matter_density,
redshift=0,
)
)
| 13 | 0 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_A = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
' Distillation'
)
)
parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
parser.add_argument('--model_name', default='roberta-large', type=str)
parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
parser.add_argument('--vocab_transform', action='store_true')
_A = parser.parse_args()
if args.model_type == "roberta":
_A = RobertaForMaskedLM.from_pretrained(args.model_name)
_A = 'roberta'
elif args.model_type == "gpt2":
_A = GPTaLMHeadModel.from_pretrained(args.model_name)
_A = 'transformer'
_A = model.state_dict()
_A = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
_A = state_dict[f"""{prefix}.{param_name}"""]
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
_A = f"""{prefix}.embeddings.{w}.weight"""
_A = state_dict[param_name]
for w in ["weight", "bias"]:
_A = f"""{prefix}.embeddings.LayerNorm.{w}"""
_A = state_dict[param_name]
# Transformer Blocks #
_A = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
_A = state_dict[
f"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
]
_A = state_dict[f"""{prefix}.h.{teacher_idx}.attn.bias"""]
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
_A = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
_A = state_dict[f"""{layer}"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
_A = state_dict[f"""lm_head.dense.{w}"""]
_A = state_dict[f"""lm_head.layer_norm.{w}"""]
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
_A = state_dict[f"""{prefix}.ln_f.{w}"""]
_A = state_dict['lm_head.weight']
print(f"""N layers selected for distillation: {std_idx}""")
print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 356 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _UpperCAmelCase ( ):
print('Making key files...' )
make_key_files('rsa' , 10_24 )
print('Key files generation successful.' )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int ):
print('Generating prime p...' )
__UpperCamelCase =rabinMiller.generate_large_prime(SCREAMING_SNAKE_CASE__ )
print('Generating prime q...' )
__UpperCamelCase =rabinMiller.generate_large_prime(SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =p * q
print('Generating e that is relatively prime to (p - 1) * (q - 1)...' )
while True:
__UpperCamelCase =random.randrange(2 ** (key_size - 1) , 2 ** (key_size) )
if cryptoMath.gcd(SCREAMING_SNAKE_CASE__ , (p - 1) * (q - 1) ) == 1:
break
print('Calculating d that is mod inverse of e...' )
__UpperCamelCase =cryptoMath.find_mod_inverse(SCREAMING_SNAKE_CASE__ , (p - 1) * (q - 1) )
__UpperCamelCase =(n, e)
__UpperCamelCase =(n, d)
return (public_key, private_key)
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : int ):
if os.path.exists(F'{name}_pubkey.txt' ) or os.path.exists(F'{name}_privkey.txt' ):
print('\nWARNING:' )
print(
F'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
'Use a different name or delete these files and re-run this program.' )
sys.exit()
__UpperCamelCase , __UpperCamelCase =generate_key(SCREAMING_SNAKE_CASE__ )
print(F'\nWriting public key to file {name}_pubkey.txt...' )
with open(F'{name}_pubkey.txt' , 'w' ) as out_file:
out_file.write(F'{key_size},{public_key[0]},{public_key[1]}' )
print(F'Writing private key to file {name}_privkey.txt...' )
with open(F'{name}_privkey.txt' , 'w' ) as out_file:
out_file.write(F'{key_size},{private_key[0]},{private_key[1]}' )
if __name__ == "__main__":
main()
| 117 | 0 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class a ( unittest.TestCase ):
def __init__( self :Dict ,__lowercase :Dict ,__lowercase :int=1_3 ,__lowercase :Any=7 ,__lowercase :Optional[Any]=True ,__lowercase :Optional[Any]=True ,__lowercase :Dict=True ,__lowercase :Optional[Any]=True ,__lowercase :Dict=9_9 ,__lowercase :str=3_2 ,__lowercase :Optional[Any]=5 ,__lowercase :Any=4 ,__lowercase :Optional[Any]=3_7 ,__lowercase :Tuple="gelu" ,__lowercase :Dict=0.1 ,__lowercase :Dict=0.1 ,__lowercase :Union[str, Any]=5_1_2 ,__lowercase :Tuple=1_6 ,__lowercase :Optional[int]=2 ,__lowercase :Optional[int]=0.02 ,__lowercase :int=4 ,):
snake_case__ : List[Any] = parent
snake_case__ : Optional[Any] = batch_size
snake_case__ : int = seq_length
snake_case__ : str = is_training
snake_case__ : List[str] = use_attention_mask
snake_case__ : int = use_token_type_ids
snake_case__ : Dict = use_labels
snake_case__ : str = vocab_size
snake_case__ : str = hidden_size
snake_case__ : Any = num_hidden_layers
snake_case__ : Union[str, Any] = num_attention_heads
snake_case__ : str = intermediate_size
snake_case__ : str = hidden_act
snake_case__ : Union[str, Any] = hidden_dropout_prob
snake_case__ : Optional[int] = attention_probs_dropout_prob
snake_case__ : Any = max_position_embeddings
snake_case__ : Tuple = type_vocab_size
snake_case__ : Dict = type_sequence_label_size
snake_case__ : Optional[int] = initializer_range
snake_case__ : List[str] = num_choices
def __lowerCamelCase ( self :List[Any] ):
snake_case__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
snake_case__ : int = None
if self.use_attention_mask:
snake_case__ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case__ : List[str] = None
if self.use_token_type_ids:
snake_case__ : Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
snake_case__ : Dict = 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 ,is_decoder=__lowercase ,initializer_range=self.initializer_range ,)
return config, input_ids, token_type_ids, attention_mask
def __lowerCamelCase ( self :int ):
snake_case__ : str = self.prepare_config_and_inputs()
snake_case__ , snake_case__ , snake_case__ , snake_case__ : Union[str, Any] = config_and_inputs
snake_case__ : str = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : List[str] = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __lowerCamelCase ( self :Optional[int] ):
snake_case__ : int = FlaxAlbertModelTester(self )
@slow
def __lowerCamelCase ( self :Tuple ):
for model_class_name in self.all_model_classes:
snake_case__ : Tuple = model_class_name.from_pretrained('''albert-base-v2''' )
snake_case__ : Any = model(np.ones((1, 1) ) )
self.assertIsNotNone(__lowercase )
@require_flax
class a ( unittest.TestCase ):
@slow
def __lowerCamelCase ( self :int ):
snake_case__ : List[str] = FlaxAlbertModel.from_pretrained('''albert-base-v2''' )
snake_case__ : str = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
snake_case__ : int = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
snake_case__ : str = model(__lowercase ,attention_mask=__lowercase )[0]
snake_case__ : Any = (1, 1_1, 7_6_8)
self.assertEqual(output.shape ,__lowercase )
snake_case__ : int = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,__lowercase ,atol=1e-4 ) )
| 230 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
A__ = abspath(join(dirname(dirname(dirname(__file__))), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def _lowerCAmelCase ( __lowerCAmelCase ) -> str:
"""simple docstring"""
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__lowerCAmelCase )
def _lowerCAmelCase ( __lowerCAmelCase ) -> List[Any]:
"""simple docstring"""
from transformers.testing_utils import pytest_terminal_summary_main
snake_case__ : Dict = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(__lowerCAmelCase , id=__lowerCAmelCase )
| 230 | 1 |
'''simple docstring'''
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
BertTokenizer,
ProcessorMixin,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
from test_module.custom_processing import CustomProcessor # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
_lowercase : Any = get_tests_dir("fixtures/dummy_feature_extractor_config.json")
_lowercase : Tuple = get_tests_dir("fixtures/vocab.json")
_lowercase : Optional[int] = get_tests_dir("fixtures")
class __magic_name__ ( unittest.TestCase):
UpperCamelCase__ = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : Union[str, Any] = 0
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
lowercase_ : int = AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h""" )
self.assertIsInstance(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase_ : int = WavaVecaConfig()
lowercase_ : str = AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h""" )
# save in new folder
model_config.save_pretrained(lowercase_ )
processor.save_pretrained(lowercase_ )
lowercase_ : Tuple = AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : int ):
with tempfile.TemporaryDirectory() as tmpdirname:
# copy relevant files
copyfile(lowercase_ , os.path.join(lowercase_ , lowercase_ ) )
copyfile(lowercase_ , os.path.join(lowercase_ , """vocab.json""" ) )
lowercase_ : str = AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase_ : List[str] = WavaVecaFeatureExtractor()
lowercase_ : List[Any] = AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h""" )
lowercase_ : Optional[Any] = WavaVecaProcessor(lowercase_ , lowercase_ )
# save in new folder
processor.save_pretrained(lowercase_ )
# drop `processor_class` in tokenizer
with open(os.path.join(lowercase_ , lowercase_ ) , """r""" ) as f:
lowercase_ : List[Any] = json.load(lowercase_ )
config_dict.pop("""processor_class""" )
with open(os.path.join(lowercase_ , lowercase_ ) , """w""" ) as f:
f.write(json.dumps(lowercase_ ) )
lowercase_ : Optional[int] = AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase_ : Tuple = WavaVecaFeatureExtractor()
lowercase_ : List[Any] = AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h""" )
lowercase_ : Tuple = WavaVecaProcessor(lowercase_ , lowercase_ )
# save in new folder
processor.save_pretrained(lowercase_ )
# drop `processor_class` in feature extractor
with open(os.path.join(lowercase_ , lowercase_ ) , """r""" ) as f:
lowercase_ : Optional[Any] = json.load(lowercase_ )
config_dict.pop("""processor_class""" )
with open(os.path.join(lowercase_ , lowercase_ ) , """w""" ) as f:
f.write(json.dumps(lowercase_ ) )
lowercase_ : Any = AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ):
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase_ : Any = WavaVecaConfig(processor_class="""Wav2Vec2Processor""" )
model_config.save_pretrained(lowercase_ )
# copy relevant files
copyfile(lowercase_ , os.path.join(lowercase_ , """vocab.json""" ) )
# create emtpy sample processor
with open(os.path.join(lowercase_ , lowercase_ ) , """w""" ) as f:
f.write("""{}""" )
lowercase_ : Any = AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : Any ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(lowercase_ ):
lowercase_ : List[Any] = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowercase_ ):
lowercase_ : Optional[Any] = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=lowercase_ )
lowercase_ : Optional[int] = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=lowercase_ )
self.assertTrue(processor.special_attribute_present )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
lowercase_ : Optional[Any] = processor.feature_extractor
self.assertTrue(feature_extractor.special_attribute_present )
self.assertEqual(feature_extractor.__class__.__name__ , """NewFeatureExtractor""" )
lowercase_ : Optional[int] = processor.tokenizer
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizerFast""" )
# Test we can also load the slow version
lowercase_ : Optional[Any] = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=lowercase_ , use_fast=lowercase_ )
lowercase_ : List[Any] = new_processor.tokenizer
self.assertTrue(new_tokenizer.special_attribute_present )
self.assertEqual(new_tokenizer.__class__.__name__ , """NewTokenizer""" )
else:
self.assertEqual(tokenizer.__class__.__name__ , """NewTokenizer""" )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
try:
AutoConfig.register("""custom""" , lowercase_ )
AutoFeatureExtractor.register(lowercase_ , lowercase_ )
AutoTokenizer.register(lowercase_ , slow_tokenizer_class=lowercase_ )
AutoProcessor.register(lowercase_ , lowercase_ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowercase_ ):
AutoProcessor.register(lowercase_ , lowercase_ )
# Now that the config is registered, it can be used as any other config with the auto-API
lowercase_ : str = CustomFeatureExtractor.from_pretrained(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : Optional[int] = os.path.join(lowercase_ , """vocab.txt""" )
with open(lowercase_ , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
lowercase_ : Any = CustomTokenizer(lowercase_ )
lowercase_ : Tuple = CustomProcessor(lowercase_ , lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(lowercase_ )
lowercase_ : Any = AutoProcessor.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ):
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = False
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = False
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = '''AutoFeatureExtractor'''
UpperCamelCase__ = '''AutoTokenizer'''
UpperCamelCase__ = False
try:
AutoConfig.register("""custom""" , lowercase_ )
AutoFeatureExtractor.register(lowercase_ , lowercase_ )
AutoTokenizer.register(lowercase_ , slow_tokenizer_class=lowercase_ )
AutoProcessor.register(lowercase_ , lowercase_ )
# If remote code is not set, the default is to use local classes.
lowercase_ : Optional[Any] = AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor""" )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote code is disabled, we load the local ones.
lowercase_ : List[str] = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=lowercase_ )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub.
lowercase_ : int = AutoProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_processor""" , trust_remote_code=lowercase_ )
self.assertEqual(processor.__class__.__name__ , """NewProcessor""" )
self.assertTrue(processor.special_attribute_present )
self.assertTrue(processor.feature_extractor.special_attribute_present )
self.assertTrue(processor.tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : Tuple = AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
self.assertEqual(processor.__class__.__name__ , """BertTokenizerFast""" )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Optional[int] = AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext""" )
self.assertEqual(processor.__class__.__name__ , """ConvNextImageProcessor""" )
@is_staging_test
class __magic_name__ ( unittest.TestCase):
UpperCamelCase__ = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Union[str, Any] ):
lowercase_ : int = TOKEN
HfFolder.save_token(lowercase_ )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls : Dict ):
try:
delete_repo(token=cls._token , repo_id="""test-processor""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-processor-org""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""test-dynamic-processor""" )
except HTTPError:
pass
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : int = WavaVecaProcessor.from_pretrained(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(lowercase_ , """test-processor""" ) , push_to_hub=lowercase_ , use_auth_token=self._token )
lowercase_ : str = WavaVecaProcessor.from_pretrained(f'''{USER}/test-processor''' )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(lowercase_ , getattr(new_processor.feature_extractor , lowercase_ ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Any = WavaVecaProcessor.from_pretrained(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(lowercase_ , """test-processor-org""" ) , push_to_hub=lowercase_ , use_auth_token=self._token , organization="""valid_org""" , )
lowercase_ : int = WavaVecaProcessor.from_pretrained("""valid_org/test-processor-org""" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(lowercase_ , getattr(new_processor.feature_extractor , lowercase_ ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def SCREAMING_SNAKE_CASE_ ( self : Dict ):
CustomFeatureExtractor.register_for_auto_class()
CustomTokenizer.register_for_auto_class()
CustomProcessor.register_for_auto_class()
lowercase_ : Dict = CustomFeatureExtractor.from_pretrained(lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
lowercase_ : Optional[int] = os.path.join(lowercase_ , """vocab.txt""" )
with open(lowercase_ , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens] ) )
lowercase_ : int = CustomTokenizer(lowercase_ )
lowercase_ : Any = CustomProcessor(lowercase_ , lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dir:
create_repo(f'''{USER}/test-dynamic-processor''' , token=self._token )
lowercase_ : Optional[int] = Repository(lowercase_ , clone_from=f'''{USER}/test-dynamic-processor''' , token=self._token )
processor.save_pretrained(lowercase_ )
# This has added the proper auto_map field to the feature extractor config
self.assertDictEqual(
processor.feature_extractor.auto_map , {
"""AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor""",
"""AutoProcessor""": """custom_processing.CustomProcessor""",
} , )
# This has added the proper auto_map field to the tokenizer config
with open(os.path.join(lowercase_ , """tokenizer_config.json""" ) ) as f:
lowercase_ : Dict = json.load(lowercase_ )
self.assertDictEqual(
tokenizer_config["""auto_map"""] , {
"""AutoTokenizer""": ["""custom_tokenization.CustomTokenizer""", None],
"""AutoProcessor""": """custom_processing.CustomProcessor""",
} , )
# The code has been copied from fixtures
self.assertTrue(os.path.isfile(os.path.join(lowercase_ , """custom_feature_extraction.py""" ) ) )
self.assertTrue(os.path.isfile(os.path.join(lowercase_ , """custom_tokenization.py""" ) ) )
self.assertTrue(os.path.isfile(os.path.join(lowercase_ , """custom_processing.py""" ) ) )
repo.push_to_hub()
lowercase_ : Optional[int] = AutoProcessor.from_pretrained(f'''{USER}/test-dynamic-processor''' , trust_remote_code=lowercase_ )
# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
self.assertEqual(new_processor.__class__.__name__ , """CustomProcessor""" )
| 21 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def lowerCamelCase ( UpperCAmelCase__ : list ) -> int:
if not postfix_notation:
return 0
lowercase_ : Any = {"""+""", """-""", """*""", """/"""}
lowercase_ : list[Any] = []
for token in postfix_notation:
if token in operations:
lowercase_ , lowercase_ : Dict = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(UpperCAmelCase__ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 21 | 1 |
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