code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__)
def UpperCAmelCase ( a_ , a_ , a_ ) -> List[Any]:
"""simple docstring"""
return [
int(1_0_0_0 * (box[0] / width) ),
int(1_0_0_0 * (box[1] / height) ),
int(1_0_0_0 * (box[2] / width) ),
int(1_0_0_0 * (box[3] / height) ),
]
def UpperCAmelCase ( a_ , a_ , a_ ) -> List[str]:
"""simple docstring"""
__A = to_pil_image(a_ )
__A , __A = pil_image.size
__A = pytesseract.image_to_data(a_ , lang=a_ , output_type="dict" , config=a_ )
__A , __A , __A , __A , __A = data["text"], data["left"], data["top"], data["width"], data["height"]
# filter empty words and corresponding coordinates
__A = [idx for idx, word in enumerate(a_ ) if not word.strip()]
__A = [word for idx, word in enumerate(a_ ) if idx not in irrelevant_indices]
__A = [coord for idx, coord in enumerate(a_ ) if idx not in irrelevant_indices]
__A = [coord for idx, coord in enumerate(a_ ) if idx not in irrelevant_indices]
__A = [coord for idx, coord in enumerate(a_ ) if idx not in irrelevant_indices]
__A = [coord for idx, coord in enumerate(a_ ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
__A = []
for x, y, w, h in zip(a_ , a_ , a_ , a_ ):
__A = [x, y, x + w, y + h]
actual_boxes.append(a_ )
# finally, normalize the bounding boxes
__A = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(a_ , a_ , a_ ) )
assert len(a_ ) == len(a_ ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case_ = ["pixel_values"]
def __init__( self : Union[str, Any] ,A : bool = True ,A : Dict[str, int] = None ,A : PILImageResampling = PILImageResampling.BILINEAR ,A : bool = True ,A : float = 1 / 2_55 ,A : bool = True ,A : Union[float, Iterable[float]] = None ,A : Union[float, Iterable[float]] = None ,A : bool = True ,A : Optional[str] = None ,A : Optional[str] = "" ,**A : Optional[Any] ,):
super().__init__(**A )
__A = size if size is not None else {"height": 2_24, "width": 2_24}
__A = get_size_dict(A )
__A = do_resize
__A = size
__A = resample
__A = do_rescale
__A = rescale_value
__A = do_normalize
__A = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__A = image_std if image_std is not None else IMAGENET_STANDARD_STD
__A = apply_ocr
__A = ocr_lang
__A = tesseract_config
def UpperCamelCase_ ( self : Any ,A : np.ndarray ,A : Dict[str, int] ,A : PILImageResampling = PILImageResampling.BILINEAR ,A : Optional[Union[str, ChannelDimension]] = None ,**A : List[str] ,):
__A = get_size_dict(A )
if "height" not in size or "width" not in size:
raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}''' )
__A = (size["height"], size["width"])
return resize(A ,size=A ,resample=A ,data_format=A ,**A )
def UpperCamelCase_ ( self : Tuple ,A : np.ndarray ,A : Union[int, float] ,A : Optional[Union[str, ChannelDimension]] = None ,**A : List[Any] ,):
return rescale(A ,scale=A ,data_format=A ,**A )
def UpperCamelCase_ ( self : Optional[Any] ,A : np.ndarray ,A : Union[float, Iterable[float]] ,A : Union[float, Iterable[float]] ,A : Optional[Union[str, ChannelDimension]] = None ,**A : List[Any] ,):
return normalize(A ,mean=A ,std=A ,data_format=A ,**A )
def UpperCamelCase_ ( self : Any ,A : ImageInput ,A : bool = None ,A : Dict[str, int] = None ,A : List[str]=None ,A : bool = None ,A : float = None ,A : bool = None ,A : Union[float, Iterable[float]] = None ,A : Union[float, Iterable[float]] = None ,A : bool = None ,A : Optional[str] = None ,A : Optional[str] = None ,A : Optional[Union[str, TensorType]] = None ,A : ChannelDimension = ChannelDimension.FIRST ,**A : int ,):
__A = do_resize if do_resize is not None else self.do_resize
__A = size if size is not None else self.size
__A = get_size_dict(A )
__A = resample if resample is not None else self.resample
__A = do_rescale if do_rescale is not None else self.do_rescale
__A = rescale_factor if rescale_factor is not None else self.rescale_factor
__A = do_normalize if do_normalize is not None else self.do_normalize
__A = image_mean if image_mean is not None else self.image_mean
__A = image_std if image_std is not None else self.image_std
__A = apply_ocr if apply_ocr is not None else self.apply_ocr
__A = ocr_lang if ocr_lang is not None else self.ocr_lang
__A = tesseract_config if tesseract_config is not None else self.tesseract_config
__A = make_list_of_images(A )
if not valid_images(A ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_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("If do_normalize is True, image_mean and image_std must be specified." )
# All transformations expect numpy arrays.
__A = [to_numpy_array(A ) for image in images]
# Tesseract OCR to get words + normalized bounding boxes
if apply_ocr:
requires_backends(self ,"pytesseract" )
__A = []
__A = []
for image in images:
__A , __A = apply_tesseract(A ,A ,A )
words_batch.append(A )
boxes_batch.append(A )
if do_resize:
__A = [self.resize(image=A ,size=A ,resample=A ) for image in images]
if do_rescale:
__A = [self.rescale(image=A ,scale=A ) for image in images]
if do_normalize:
__A = [self.normalize(image=A ,mean=A ,std=A ) for image in images]
__A = [to_channel_dimension_format(A ,A ) for image in images]
__A = BatchFeature(data={"pixel_values": images} ,tensor_type=A )
if apply_ocr:
__A = words_batch
__A = boxes_batch
return data
| 15 |
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
SCREAMING_SNAKE_CASE :Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Optional[int] = {'vocab_file': 'sentencepiece.bpe.model'}
SCREAMING_SNAKE_CASE :Tuple = {
'vocab_file': {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
}
}
SCREAMING_SNAKE_CASE :List[Any] = {
'camembert-base': 512,
}
SCREAMING_SNAKE_CASE :List[str] = '▁'
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
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[Any] ,A : List[str] ,A : List[Any]="<s>" ,A : Tuple="</s>" ,A : Any="</s>" ,A : Optional[Any]="<s>" ,A : Tuple="<unk>" ,A : str="<pad>" ,A : int="<mask>" ,A : Optional[int]=["<s>NOTUSED", "</s>NOTUSED"] ,A : Optional[Dict[str, Any]] = None ,**A : Optional[Any] ,):
# Mask token behave like a normal word, i.e. include the space before it
__A = AddedToken(A ,lstrip=A ,rstrip=A ) if isinstance(A ,A ) else mask_token
__A = {} 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 ,additional_special_tokens=A ,sp_model_kwargs=self.sp_model_kwargs ,**A ,)
__A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(A ) )
__A = vocab_file
# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
# sentencepiece vocabulary (this is the case for <s> and </s>
__A = {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
__A = len(self.fairseq_tokens_to_ids )
__A = len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
__A = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def UpperCamelCase_ ( self : int ,A : List[int] ,A : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__A = [self.cls_token_id]
__A = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase_ ( self : Dict ,A : List[int] ,A : Optional[List[int]] = None ,A : bool = 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 UpperCamelCase_ ( self : Union[str, Any] ,A : List[int] ,A : Optional[List[int]] = None ):
__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]
@property
def UpperCamelCase_ ( self : Dict ):
return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )
def UpperCamelCase_ ( self : int ):
__A = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase_ ( self : Any ,A : str ):
return self.sp_model.encode(A ,out_type=A )
def UpperCamelCase_ ( self : List[str] ,A : Dict ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
elif self.sp_model.PieceToId(A ) == 0:
# Convert sentence piece unk token to fairseq unk token index
return self.unk_token_id
return self.fairseq_offset + self.sp_model.PieceToId(A )
def UpperCamelCase_ ( self : Dict ,A : Tuple ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def UpperCamelCase_ ( self : Optional[Any] ,A : Dict ):
__A = []
__A = ""
__A = 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
__A = True
__A = []
else:
current_sub_tokens.append(A )
__A = False
out_string += self.sp_model.decode(A )
return out_string.strip()
def __getstate__( self : Dict ):
__A = self.__dict__.copy()
__A = None
return state
def __setstate__( self : Union[str, Any] ,A : Any ):
__A = d
# for backward compatibility
if not hasattr(self ,"sp_model_kwargs" ):
__A = {}
__A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase_ ( self : Any ,A : str ,A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__A = os.path.join(
A ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,A )
elif not os.path.isfile(self.vocab_file ):
with open(A ,"wb" ) as fi:
__A = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
| 15 | 1 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( snake_case_ : Tuple , snake_case_ : str , snake_case_ : Union[str, Any] ):
# Initialise PyTorch model
snake_case__ : List[str] = MobileBertConfig.from_json_file(snake_case_ )
print(F'''Building PyTorch model from configuration: {config}''' )
snake_case__ : Dict = MobileBertForPreTraining(snake_case_ )
# Load weights from tf checkpoint
snake_case__ : Any = load_tf_weights_in_mobilebert(snake_case_ , snake_case_ , snake_case_ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , snake_case_ )
if __name__ == "__main__":
__lowerCamelCase : List[Any] = 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(
"""--mobilebert_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained MobileBERT 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 : int = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 286 |
import sys
__lowerCamelCase : List[str] = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""66896648950445244523161731856403098711121722383113"""
"""62229893423380308135336276614282806444486645238749"""
"""30358907296290491560440772390713810515859307960866"""
"""70172427121883998797908792274921901699720888093776"""
"""65727333001053367881220235421809751254540594752243"""
"""52584907711670556013604839586446706324415722155397"""
"""53697817977846174064955149290862569321978468622482"""
"""83972241375657056057490261407972968652414535100474"""
"""82166370484403199890008895243450658541227588666881"""
"""16427171479924442928230863465674813919123162824586"""
"""17866458359124566529476545682848912883142607690042"""
"""24219022671055626321111109370544217506941658960408"""
"""07198403850962455444362981230987879927244284909188"""
"""84580156166097919133875499200524063689912560717606"""
"""05886116467109405077541002256983155200055935729725"""
"""71636269561882670428252483600823257530420752963450"""
)
def SCREAMING_SNAKE_CASE ( snake_case_ : str = N ):
snake_case__ : Any = -sys.maxsize - 1
for i in range(len(snake_case_ ) - 12 ):
snake_case__ : Tuple = 1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
snake_case__ : Dict = product
return largest_product
if __name__ == "__main__":
print(f"{solution() = }")
| 286 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__snake_case = {
'''configuration_swiftformer''': [
'''SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SwiftFormerConfig''',
'''SwiftFormerOnnxConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case = [
'''SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''SwiftFormerForImageClassification''',
'''SwiftFormerModel''',
'''SwiftFormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
__snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 310 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A__: Union[str, Any] = input('''Enter image url: ''').strip()
print(F"Downloading image from {url} ...")
A__: Tuple = BeautifulSoup(requests.get(url).content, '''html.parser''')
# The image URL is in the content field of the first meta tag with property og:image
A__: Union[str, Any] = soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
A__: List[Any] = requests.get(image_url).content
A__: List[str] = F"{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"
with open(file_name, '''wb''') as fp:
fp.write(image_data)
print(F"Done. Image saved to disk as {file_name}.")
| 276 | 0 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : int ) -> str:
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
UpperCAmelCase_ = str(bin(__UpperCamelCase ) )
binary_number += "0" * shift_amount
return binary_number
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : int ) -> str:
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
UpperCAmelCase_ = str(bin(__UpperCamelCase ) )[2:]
if shift_amount >= len(__UpperCamelCase ):
return "0b0"
UpperCAmelCase_ = binary_number[: len(__UpperCamelCase ) - shift_amount]
return "0b" + shifted_binary_number
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : int ) -> str:
if number >= 0: # Get binary representation of positive number
UpperCAmelCase_ = '''0''' + str(bin(__UpperCamelCase ) ).strip('''-''' )[2:]
else: # Get binary (2's complement) representation of negative number
UpperCAmelCase_ = len(bin(__UpperCamelCase )[3:] ) # Find 2's complement of number
UpperCAmelCase_ = bin(abs(__UpperCamelCase ) - (1 << binary_number_length) )[3:]
UpperCAmelCase_ = (
'''1''' + '''0''' * (binary_number_length - len(__UpperCamelCase )) + binary_number
)
if shift_amount >= len(__UpperCamelCase ):
return "0b" + binary_number[0] * len(__UpperCamelCase )
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(__UpperCamelCase ) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 177 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_lowerCamelCase = logging.get_logger(__name__)
class a ( _A ):
'''simple docstring'''
lowerCAmelCase : List[Any] = ['pixel_values']
def __init__( self : Any , __snake_case : bool = True , __snake_case : Dict[str, int] = None , __snake_case : PILImageResampling = PILImageResampling.BICUBIC , __snake_case : bool = True , __snake_case : Dict[str, int] = None , __snake_case : bool = True , __snake_case : Union[int, float] = 1 / 2_55 , __snake_case : bool = True , __snake_case : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN , __snake_case : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD , **__snake_case : Optional[Any] , ):
super().__init__(**__snake_case )
UpperCAmelCase_ = size if size is not None else {'''shortest_edge''': 2_24}
UpperCAmelCase_ = get_size_dict(__snake_case , default_to_square=__snake_case )
UpperCAmelCase_ = crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
UpperCAmelCase_ = get_size_dict(__snake_case , param_name='''crop_size''' )
UpperCAmelCase_ = do_resize
UpperCAmelCase_ = size
UpperCAmelCase_ = resample
UpperCAmelCase_ = do_center_crop
UpperCAmelCase_ = crop_size
UpperCAmelCase_ = do_rescale
UpperCAmelCase_ = rescale_factor
UpperCAmelCase_ = do_normalize
UpperCAmelCase_ = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
UpperCAmelCase_ = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def lowerCamelCase_ ( self : int , __snake_case : np.ndarray , __snake_case : Dict[str, int] , __snake_case : PILImageResampling = PILImageResampling.BICUBIC , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Tuple , ):
UpperCAmelCase_ = get_size_dict(__snake_case , default_to_square=__snake_case )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
UpperCAmelCase_ = int((2_56 / 2_24) * size['''shortest_edge'''] )
UpperCAmelCase_ = get_resize_output_image_size(__snake_case , size=__snake_case , default_to_square=__snake_case )
UpperCAmelCase_ = {'''height''': output_size[0], '''width''': output_size[1]}
if "height" not in size_dict or "width" not in size_dict:
raise ValueError(
F'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}' )
return resize(
__snake_case , size=(size_dict['''height'''], size_dict['''width''']) , resample=__snake_case , data_format=__snake_case , **__snake_case )
def lowerCamelCase_ ( self : Tuple , __snake_case : np.ndarray , __snake_case : Dict[str, int] , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Optional[int] , ):
UpperCAmelCase_ = get_size_dict(__snake_case )
if "height" not in size or "width" not in size:
raise ValueError(F'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}' )
return center_crop(__snake_case , size=(size['''height'''], size['''width''']) , data_format=__snake_case , **__snake_case )
def lowerCamelCase_ ( self : Optional[Any] , __snake_case : np.ndarray , __snake_case : Union[int, float] , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : List[Any] , ):
return rescale(__snake_case , scale=__snake_case , data_format=__snake_case , **__snake_case )
def lowerCamelCase_ ( self : int , __snake_case : np.ndarray , __snake_case : Union[float, List[float]] , __snake_case : Union[float, List[float]] , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Union[str, Any] , ):
return normalize(__snake_case , mean=__snake_case , std=__snake_case , data_format=__snake_case , **__snake_case )
def lowerCamelCase_ ( self : int , __snake_case : ImageInput , __snake_case : Optional[bool] = None , __snake_case : Optional[Dict[str, int]] = None , __snake_case : PILImageResampling = None , __snake_case : Optional[bool] = None , __snake_case : Optional[Dict[str, int]] = None , __snake_case : Optional[bool] = None , __snake_case : Optional[float] = None , __snake_case : Optional[bool] = None , __snake_case : Optional[Union[float, Iterable[float]]] = None , __snake_case : Optional[Union[float, Iterable[float]]] = None , __snake_case : Optional[TensorType] = None , __snake_case : ChannelDimension = ChannelDimension.FIRST , **__snake_case : List[str] , ):
UpperCAmelCase_ = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase_ = resample if resample is not None else self.resample
UpperCAmelCase_ = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase_ = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase_ = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase_ = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase_ = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase_ = image_std if image_std is not None else self.image_std
UpperCAmelCase_ = size if size is not None else self.size
UpperCAmelCase_ = get_size_dict(__snake_case , default_to_square=__snake_case )
UpperCAmelCase_ = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase_ = get_size_dict(__snake_case , param_name='''crop_size''' )
UpperCAmelCase_ = make_list_of_images(__snake_case )
if not valid_images(__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.''' )
# All transformations expect numpy arrays.
UpperCAmelCase_ = [to_numpy_array(__snake_case ) for image in images]
if do_resize:
UpperCAmelCase_ = [self.resize(__snake_case , __snake_case , __snake_case ) for image in images]
if do_center_crop:
UpperCAmelCase_ = [self.center_crop(__snake_case , __snake_case ) for image in images]
if do_rescale:
UpperCAmelCase_ = [self.rescale(__snake_case , __snake_case ) for image in images]
if do_normalize:
UpperCAmelCase_ = [self.normalize(__snake_case , __snake_case , __snake_case ) for image in images]
UpperCAmelCase_ = [to_channel_dimension_format(__snake_case , __snake_case ) for image in images]
UpperCAmelCase_ = {'''pixel_values''': images}
return BatchFeature(data=__snake_case , tensor_type=__snake_case )
| 177 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["""DeiTFeatureExtractor"""]
lowercase_ = ["""DeiTImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"""DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""DeiTForImageClassification""",
"""DeiTForImageClassificationWithTeacher""",
"""DeiTForMaskedImageModeling""",
"""DeiTModel""",
"""DeiTPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"""TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFDeiTForImageClassification""",
"""TFDeiTForImageClassificationWithTeacher""",
"""TFDeiTForMaskedImageModeling""",
"""TFDeiTModel""",
"""TFDeiTPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 303 |
import math
import os
import sys
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = ''''''
try:
with open(snake_case , '''rb''' ) as binary_file:
__SCREAMING_SNAKE_CASE : int = binary_file.read()
for dat in data:
__SCREAMING_SNAKE_CASE : Optional[Any] = F'''{dat:08b}'''
result += curr_byte
return result
except OSError:
print('''File not accessible''' )
sys.exit()
def a__ ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
lexicon.pop(snake_case )
__SCREAMING_SNAKE_CASE : List[str] = last_match_id
if math.loga(snake_case ).is_integer():
for curr_key in lexicon:
__SCREAMING_SNAKE_CASE : int = '''0''' + lexicon[curr_key]
__SCREAMING_SNAKE_CASE : List[str] = bin(snake_case )[2:]
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = {'''0''': '''0''', '''1''': '''1'''}
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Any = '''''', ''''''
__SCREAMING_SNAKE_CASE : Optional[Any] = len(snake_case )
for i in range(len(snake_case ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
__SCREAMING_SNAKE_CASE : Any = lexicon[curr_string]
result += last_match_id
add_key_to_lexicon(snake_case , snake_case , snake_case , snake_case )
index += 1
__SCREAMING_SNAKE_CASE : Tuple = ''''''
while curr_string != "" and curr_string not in lexicon:
curr_string += "0"
if curr_string != "":
__SCREAMING_SNAKE_CASE : Dict = lexicon[curr_string]
result += last_match_id
return result
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = os.path.getsize(snake_case )
__SCREAMING_SNAKE_CASE : Union[str, Any] = bin(snake_case )[2:]
__SCREAMING_SNAKE_CASE : int = len(snake_case )
return "0" * (length_length - 1) + file_length_binary + compressed
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = 8
try:
with open(snake_case , '''wb''' ) as opened_file:
__SCREAMING_SNAKE_CASE : Optional[int] = [
to_write[i : i + byte_length]
for i in range(0 , len(snake_case ) , snake_case )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('''10000000''' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array:
opened_file.write(int(snake_case , 2 ).to_bytes(1 , byteorder='''big''' ) )
except OSError:
print('''File not accessible''' )
sys.exit()
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = read_file_binary(snake_case )
__SCREAMING_SNAKE_CASE : Optional[int] = compress_data(snake_case )
__SCREAMING_SNAKE_CASE : Dict = add_file_length(snake_case , snake_case )
write_file_binary(snake_case , snake_case )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 303 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPConfig''',
'''AltCLIPTextConfig''',
'''AltCLIPVisionConfig''',
],
'''processing_altclip''': ['''AltCLIPProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''AltCLIPPreTrainedModel''',
'''AltCLIPModel''',
'''AltCLIPTextModel''',
'''AltCLIPVisionModel''',
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 178 |
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 GLPNImageProcessor
class __A( unittest.TestCase ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=18 , SCREAMING_SNAKE_CASE_=30 , SCREAMING_SNAKE_CASE_=4_00 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=True , ):
UpperCamelCase__ = parent
UpperCamelCase__ = batch_size
UpperCamelCase__ = num_channels
UpperCamelCase__ = image_size
UpperCamelCase__ = min_resolution
UpperCamelCase__ = max_resolution
UpperCamelCase__ = do_resize
UpperCamelCase__ = size_divisor
UpperCamelCase__ = do_rescale
def UpperCAmelCase_ (self ):
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
@require_torch
@require_vision
class __A( __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = GLPNImageProcessor if is_vision_available() else None
def UpperCAmelCase_ (self ):
UpperCamelCase__ = GLPNImageProcessingTester(self )
@property
def UpperCAmelCase_ (self ):
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase_ (self ):
UpperCamelCase__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_resize""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """size_divisor""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """resample""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_rescale""" ) )
def UpperCAmelCase_ (self ):
pass
def UpperCAmelCase_ (self ):
# Initialize image_processing
UpperCamelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , Image.Image )
# Test not batched input (GLPNImageProcessor doesn't support batching)
UpperCamelCase__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def UpperCAmelCase_ (self ):
# Initialize image_processing
UpperCamelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE_ , numpify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , np.ndarray )
# Test not batched input (GLPNImageProcessor doesn't support batching)
UpperCamelCase__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def UpperCAmelCase_ (self ):
# Initialize image_processing
UpperCamelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE_ , torchify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , torch.Tensor )
# Test not batched input (GLPNImageProcessor doesn't support batching)
UpperCamelCase__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
| 178 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
def __lowercase ( lowerCamelCase : Optional[Any] ):
UpperCamelCase_ : List[Any] = 'huggingface/label-files'
UpperCamelCase_ : int = 'imagenet-1k-id2label.json'
UpperCamelCase_ : int = json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase , repo_type='dataset' ) , 'r' ) )
UpperCamelCase_ : str = {int(lowerCamelCase ): v for k, v in idalabel.items()}
UpperCamelCase_ : Optional[int] = {v: k for k, v in idalabel.items()}
UpperCamelCase_ : str = 'std_conv' if 'bit' in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
UpperCamelCase_ : int = BitConfig(
conv_layer=lowerCamelCase , num_labels=1000 , idalabel=lowerCamelCase , labelaid=lowerCamelCase , )
return config
def __lowercase ( lowerCamelCase : int ):
if "stem.conv" in name:
UpperCamelCase_ : str = name.replace('stem.conv' , 'bit.embedder.convolution' )
if "blocks" in name:
UpperCamelCase_ : str = name.replace('blocks' , 'layers' )
if "head.fc" in name:
UpperCamelCase_ : Dict = name.replace('head.fc' , 'classifier.1' )
if name.startswith('norm' ):
UpperCamelCase_ : List[str] = 'bit.' + name
if "bit" not in name and "classifier" not in name:
UpperCamelCase_ : int = 'bit.encoder.' + name
return name
def __lowercase ( ):
UpperCamelCase_ : Tuple = 'http://images.cocodataset.org/val2017/000000039769.jpg'
UpperCamelCase_ : List[Any] = Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase ).raw )
return im
@torch.no_grad()
def __lowercase ( lowerCamelCase : int , lowerCamelCase : str , lowerCamelCase : List[str]=False ):
UpperCamelCase_ : Optional[Any] = get_config(lowerCamelCase )
# load original model from timm
UpperCamelCase_ : Dict = create_model(lowerCamelCase , pretrained=lowerCamelCase )
timm_model.eval()
# load state_dict of original model
UpperCamelCase_ : str = timm_model.state_dict()
for key in state_dict.copy().keys():
UpperCamelCase_ : Tuple = state_dict.pop(lowerCamelCase )
UpperCamelCase_ : str = val.squeeze() if 'head' in key else val
# load HuggingFace model
UpperCamelCase_ : Dict = BitForImageClassification(lowerCamelCase )
model.eval()
model.load_state_dict(lowerCamelCase )
# create image processor
UpperCamelCase_ : int = create_transform(**resolve_data_config({} , model=lowerCamelCase ) )
UpperCamelCase_ : List[Any] = transform.transforms
UpperCamelCase_ : str = {
'bilinear': PILImageResampling.BILINEAR,
'bicubic': PILImageResampling.BICUBIC,
'nearest': PILImageResampling.NEAREST,
}
UpperCamelCase_ : Tuple = BitImageProcessor(
do_resize=lowerCamelCase , size={'shortest_edge': timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=lowerCamelCase , crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]} , do_normalize=lowerCamelCase , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
UpperCamelCase_ : Dict = prepare_img()
UpperCamelCase_ : Any = transform(lowerCamelCase ).unsqueeze(0 )
UpperCamelCase_ : Union[str, Any] = processor(lowerCamelCase , return_tensors='pt' ).pixel_values
# verify pixel values
assert torch.allclose(lowerCamelCase , lowerCamelCase )
# verify logits
with torch.no_grad():
UpperCamelCase_ : Dict = model(lowerCamelCase )
UpperCamelCase_ : Tuple = outputs.logits
print('Logits:' , logits[0, :3] )
print('Predicted class:' , model.config.idalabel[logits.argmax(-1 ).item()] )
UpperCamelCase_ : List[Any] = timm_model(lowerCamelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(lowerCamelCase , outputs.logits , atol=1e-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}" )
model.save_pretrained(lowerCamelCase )
processor.save_pretrained(lowerCamelCase )
if push_to_hub:
print(F"Pushing model {model_name} and processor to the hub" )
model.push_to_hub(F"ybelkada/{model_name}" )
processor.push_to_hub(F"ybelkada/{model_name}" )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='resnetv2_50x1_bitm',
type=str,
help='Name of the BiT timm 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 to push the model to the hub.',
)
a_ = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 175 | import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
def __lowercase ( lowerCamelCase : Dict , lowerCamelCase : Union[str, Any] , lowerCamelCase : Any ):
UpperCamelCase_ : List[Any] = UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase , config=lowerCamelCase )
UpperCamelCase_ : Any = downstream_dict['projector.weight']
UpperCamelCase_ : List[str] = downstream_dict['projector.bias']
UpperCamelCase_ : Tuple = downstream_dict['model.post_net.linear.weight']
UpperCamelCase_ : int = downstream_dict['model.post_net.linear.bias']
return model
def __lowercase ( lowerCamelCase : List[Any] , lowerCamelCase : Tuple , lowerCamelCase : Union[str, Any] ):
UpperCamelCase_ : Any = UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase , config=lowerCamelCase )
UpperCamelCase_ : Tuple = downstream_dict['model.linear.weight']
UpperCamelCase_ : Any = downstream_dict['model.linear.bias']
return model
def __lowercase ( lowerCamelCase : Optional[int] , lowerCamelCase : Optional[int] , lowerCamelCase : List[Any] ):
UpperCamelCase_ : str = UniSpeechSatForXVector.from_pretrained(lowerCamelCase , config=lowerCamelCase )
UpperCamelCase_ : Optional[Any] = downstream_dict['connector.weight']
UpperCamelCase_ : int = downstream_dict['connector.bias']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
UpperCamelCase_ : Union[str, Any] = downstream_dict[
F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
]
UpperCamelCase_ : Tuple = downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]
UpperCamelCase_ : int = downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
UpperCamelCase_ : Optional[int] = downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
UpperCamelCase_ : Dict = downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
UpperCamelCase_ : Optional[int] = downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
UpperCamelCase_ : List[Any] = downstream_dict['objective.W']
return model
@torch.no_grad()
def __lowercase ( lowerCamelCase : Optional[Any] , lowerCamelCase : int , lowerCamelCase : Any , lowerCamelCase : int ):
UpperCamelCase_ : int = torch.load(lowerCamelCase , map_location='cpu' )
UpperCamelCase_ : Any = checkpoint['Downstream']
UpperCamelCase_ : Dict = UniSpeechSatConfig.from_pretrained(lowerCamelCase )
UpperCamelCase_ : str = WavaVecaFeatureExtractor.from_pretrained(
lowerCamelCase , return_attention_mask=lowerCamelCase , do_normalize=lowerCamelCase )
UpperCamelCase_ : int = hf_config.architectures[0]
if arch.endswith('ForSequenceClassification' ):
UpperCamelCase_ : Tuple = convert_classification(lowerCamelCase , lowerCamelCase , lowerCamelCase )
elif arch.endswith('ForAudioFrameClassification' ):
UpperCamelCase_ : List[str] = convert_diarization(lowerCamelCase , lowerCamelCase , lowerCamelCase )
elif arch.endswith('ForXVector' ):
UpperCamelCase_ : Union[str, Any] = convert_xvector(lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}" )
if hf_config.use_weighted_layer_sum:
UpperCamelCase_ : Tuple = checkpoint['Featurizer']['weights']
hf_feature_extractor.save_pretrained(lowerCamelCase )
hf_model.save_pretrained(lowerCamelCase )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
'--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
)
parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
a_ = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 175 | 1 |
from ..utils import DummyObject, requires_backends
class lowercase_ ( metaclass=UpperCamelCase_ ):
'''simple docstring'''
__snake_case = ["""transformers""", """torch""", """note_seq"""]
def __init__( self : List[Any] , *__UpperCAmelCase : int , **__UpperCAmelCase : Union[str, Any] ) ->Union[str, Any]:
"""simple docstring"""
requires_backends(self , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def __lowerCAmelCase ( cls : List[Any] , *__UpperCAmelCase : str , **__UpperCAmelCase : Any ) ->int:
"""simple docstring"""
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def __lowerCAmelCase ( cls : Tuple , *__UpperCAmelCase : List[Any] , **__UpperCAmelCase : int ) ->Optional[Any]:
"""simple docstring"""
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
| 361 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase__ = {
"vocab_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-german-cased": (
"https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
),
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase__ = {
"distilbert-base-uncased": 512,
"distilbert-base-uncased-distilled-squad": 512,
"distilbert-base-cased": 512,
"distilbert-base-cased-distilled-squad": 512,
"distilbert-base-german-cased": 512,
"distilbert-base-multilingual-cased": 512,
}
UpperCAmelCase__ = {
"distilbert-base-uncased": {"do_lower_case": True},
"distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
"distilbert-base-cased": {"do_lower_case": False},
"distilbert-base-cased-distilled-squad": {"do_lower_case": False},
"distilbert-base-german-cased": {"do_lower_case": False},
"distilbert-base-multilingual-cased": {"do_lower_case": False},
}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = VOCAB_FILES_NAMES
__snake_case = PRETRAINED_VOCAB_FILES_MAP
__snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case = PRETRAINED_INIT_CONFIGURATION
__snake_case = ['''input_ids''', '''attention_mask''']
__snake_case = DistilBertTokenizer
def __init__( self : Dict , __UpperCAmelCase : List[Any]=None , __UpperCAmelCase : Optional[int]=None , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : Optional[int]="[UNK]" , __UpperCAmelCase : str="[SEP]" , __UpperCAmelCase : Tuple="[PAD]" , __UpperCAmelCase : Any="[CLS]" , __UpperCAmelCase : int="[MASK]" , __UpperCAmelCase : Optional[int]=True , __UpperCAmelCase : Optional[Any]=None , **__UpperCAmelCase : str , ) ->Optional[int]:
"""simple docstring"""
super().__init__(
__UpperCAmelCase , tokenizer_file=__UpperCAmelCase , do_lower_case=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , tokenize_chinese_chars=__UpperCAmelCase , strip_accents=__UpperCAmelCase , **__UpperCAmelCase , )
a = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , __UpperCAmelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , __UpperCAmelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , __UpperCAmelCase ) != tokenize_chinese_chars
):
a = getattr(__UpperCAmelCase , normalizer_state.pop('''type''' ) )
a = do_lower_case
a = strip_accents
a = tokenize_chinese_chars
a = normalizer_class(**__UpperCAmelCase )
a = do_lower_case
def __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[int]=None ) ->Optional[Any]:
"""simple docstring"""
a = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __lowerCAmelCase ( self : List[Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : 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 ) * [0] + len(token_ids_a + sep ) * [1]
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) ->Tuple[str]:
"""simple docstring"""
a = self._tokenizer.model.save(__UpperCAmelCase , name=__UpperCAmelCase )
return tuple(__UpperCAmelCase )
| 26 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert 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 AlignProcessor, EfficientNetImageProcessor
@require_vision
class lowerCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
def __magic_name__ (self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : Dict = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
SCREAMING_SNAKE_CASE__ : List[Any] = 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] ) )
SCREAMING_SNAKE_CASE__ : List[Any] = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48145466, 0.4578275, 0.40821073],
"""image_std""": [0.26862954, 0.26130258, 0.27577711],
}
SCREAMING_SNAKE_CASE__ : Any = os.path.join(self.tmpdirname , SCREAMING_SNAKE_CASE__ )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , **SCREAMING_SNAKE_CASE__ ) -> Tuple:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , **SCREAMING_SNAKE_CASE__ ) -> Any:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , **SCREAMING_SNAKE_CASE__ ) -> Tuple:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> int:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __magic_name__ (self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE__ : Tuple = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __magic_name__ (self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : int = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : str = AlignProcessor.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 , SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(processor_fast.tokenizer , SCREAMING_SNAKE_CASE__ )
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 , SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(processor_fast.image_processor , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
SCREAMING_SNAKE_CASE__ : Tuple = self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__ , padding_value=1.0 )
SCREAMING_SNAKE_CASE__ : List[Any] = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=SCREAMING_SNAKE_CASE__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , SCREAMING_SNAKE_CASE__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Optional[int] = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : str = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(SCREAMING_SNAKE_CASE__ , return_tensors="""np""" )
SCREAMING_SNAKE_CASE__ : Any = processor(images=SCREAMING_SNAKE_CASE__ , 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 __magic_name__ (self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : str = """lower newer"""
SCREAMING_SNAKE_CASE__ : Optional[int] = processor(text=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Dict = tokenizer(SCREAMING_SNAKE_CASE__ , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Optional[int] = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer"""
SCREAMING_SNAKE_CASE__ : int = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : str = processor(text=SCREAMING_SNAKE_CASE__ , images=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(SCREAMING_SNAKE_CASE__ ):
processor()
def __magic_name__ (self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : List[str] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Optional[int] = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE__ : Dict = processor.batch_decode(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.batch_decode(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[str] = AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__ , image_processor=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : str = """lower newer"""
SCREAMING_SNAKE_CASE__ : Dict = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[str] = processor(text=SCREAMING_SNAKE_CASE__ , images=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 25 |
"""simple docstring"""
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
raise Exception('requires fairseq >= 1.0.0a')
logging.set_verbosity_info()
UpperCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase__ : List[str] = 'Hello world! cécé herlolip'
def lowercase_ ( _snake_case ,_snake_case ,_snake_case ):
SCREAMING_SNAKE_CASE__ : int = FairseqRobertaModel.from_pretrained(_snake_case )
roberta.eval() # disable dropout
SCREAMING_SNAKE_CASE__ : Any = roberta.model.encoder.sentence_encoder
SCREAMING_SNAKE_CASE__ : Any = XLMRobertaConfig(
vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings ,hidden_size=roberta.cfg.model.encoder_embed_dim ,num_hidden_layers=roberta.cfg.model.encoder_layers ,num_attention_heads=roberta.cfg.model.encoder_attention_heads ,intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim ,max_position_embeddings=514 ,type_vocab_size=1 ,layer_norm_eps=1E-5 ,)
if classification_head:
SCREAMING_SNAKE_CASE__ : Dict = roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our RoBERTa config:""" ,_snake_case )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = XLMRobertaXLForSequenceClassification(_snake_case ) if classification_head else XLMRobertaXLForMaskedLM(_snake_case )
model.eval()
# Now let's copy all the weights.
# Embeddings
SCREAMING_SNAKE_CASE__ : Optional[int] = roberta_sent_encoder.embed_tokens.weight
SCREAMING_SNAKE_CASE__ : int = roberta_sent_encoder.embed_positions.weight
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c RoBERTa doesn't use them.
SCREAMING_SNAKE_CASE__ : Optional[int] = roberta_sent_encoder.layer_norm.weight
SCREAMING_SNAKE_CASE__ : Dict = roberta_sent_encoder.layer_norm.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
SCREAMING_SNAKE_CASE__ : BertLayer = model.roberta.encoder.layer[i]
SCREAMING_SNAKE_CASE__ : TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i]
SCREAMING_SNAKE_CASE__ : RobertaAttention = layer.attention
SCREAMING_SNAKE_CASE__ : List[str] = roberta_layer.self_attn_layer_norm.weight
SCREAMING_SNAKE_CASE__ : List[Any] = roberta_layer.self_attn_layer_norm.bias
# self attention
SCREAMING_SNAKE_CASE__ : BertSelfAttention = layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
)
SCREAMING_SNAKE_CASE__ : List[str] = roberta_layer.self_attn.q_proj.weight
SCREAMING_SNAKE_CASE__ : Tuple = roberta_layer.self_attn.q_proj.bias
SCREAMING_SNAKE_CASE__ : Tuple = roberta_layer.self_attn.k_proj.weight
SCREAMING_SNAKE_CASE__ : int = roberta_layer.self_attn.k_proj.bias
SCREAMING_SNAKE_CASE__ : List[str] = roberta_layer.self_attn.v_proj.weight
SCREAMING_SNAKE_CASE__ : Union[str, Any] = roberta_layer.self_attn.v_proj.bias
# self-attention output
SCREAMING_SNAKE_CASE__ : BertSelfOutput = layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
SCREAMING_SNAKE_CASE__ : Union[str, Any] = roberta_layer.self_attn.out_proj.weight
SCREAMING_SNAKE_CASE__ : List[str] = roberta_layer.self_attn.out_proj.bias
# this one is final layer norm
SCREAMING_SNAKE_CASE__ : Tuple = roberta_layer.final_layer_norm.weight
SCREAMING_SNAKE_CASE__ : Optional[int] = roberta_layer.final_layer_norm.bias
# intermediate
SCREAMING_SNAKE_CASE__ : BertIntermediate = layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
SCREAMING_SNAKE_CASE__ : List[Any] = roberta_layer.fca.weight
SCREAMING_SNAKE_CASE__ : Tuple = roberta_layer.fca.bias
# output
SCREAMING_SNAKE_CASE__ : BertOutput = layer.output
assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
SCREAMING_SNAKE_CASE__ : Tuple = roberta_layer.fca.weight
SCREAMING_SNAKE_CASE__ : Optional[int] = roberta_layer.fca.bias
# end of layer
if classification_head:
SCREAMING_SNAKE_CASE__ : List[Any] = roberta.model.classification_heads["""mnli"""].dense.weight
SCREAMING_SNAKE_CASE__ : Optional[Any] = roberta.model.classification_heads["""mnli"""].dense.bias
SCREAMING_SNAKE_CASE__ : Optional[Any] = roberta.model.classification_heads["""mnli"""].out_proj.weight
SCREAMING_SNAKE_CASE__ : Union[str, Any] = roberta.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
SCREAMING_SNAKE_CASE__ : str = roberta.model.encoder.lm_head.dense.weight
SCREAMING_SNAKE_CASE__ : List[Any] = roberta.model.encoder.lm_head.dense.bias
SCREAMING_SNAKE_CASE__ : Union[str, Any] = roberta.model.encoder.lm_head.layer_norm.weight
SCREAMING_SNAKE_CASE__ : Dict = roberta.model.encoder.lm_head.layer_norm.bias
SCREAMING_SNAKE_CASE__ : Optional[int] = roberta.model.encoder.lm_head.weight
SCREAMING_SNAKE_CASE__ : List[str] = roberta.model.encoder.lm_head.bias
# Let's check that we get the same results.
SCREAMING_SNAKE_CASE__ : torch.Tensor = roberta.encode(_snake_case ).unsqueeze(0 ) # batch of size 1
SCREAMING_SNAKE_CASE__ : Tuple = model(_snake_case )[0]
if classification_head:
SCREAMING_SNAKE_CASE__ : Dict = roberta.model.classification_heads["""mnli"""](roberta.extract_features(_snake_case ) )
else:
SCREAMING_SNAKE_CASE__ : Tuple = roberta.model(_snake_case )[0]
print(our_output.shape ,their_output.shape )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.max(torch.abs(our_output - their_output ) ).item()
print(f'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
SCREAMING_SNAKE_CASE__ : Tuple = torch.allclose(_snake_case ,_snake_case ,atol=1E-3 )
print("""Do both models output the same tensors?""" ,"""🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
pathlib.Path(_snake_case ).mkdir(parents=_snake_case ,exist_ok=_snake_case )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(_snake_case )
if __name__ == "__main__":
UpperCAmelCase__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--classification_head', action='store_true', help='Whether to convert a final classification head.'
)
UpperCAmelCase__ : Any = parser.parse_args()
convert_xlm_roberta_xl_checkpoint_to_pytorch(
args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 25 | 1 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_CASE_:Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:List[Any] = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = '''t5'''
__lowerCamelCase : Optional[int] = ['''past_key_values''']
__lowerCamelCase : List[str] = {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self, lowerCamelCase__=3_2128, lowerCamelCase__=512, lowerCamelCase__=64, lowerCamelCase__=2048, lowerCamelCase__=6, lowerCamelCase__=None, lowerCamelCase__=8, lowerCamelCase__=32, lowerCamelCase__=128, lowerCamelCase__=0.1, lowerCamelCase__=1e-6, lowerCamelCase__=1.0, lowerCamelCase__="relu", lowerCamelCase__=True, lowerCamelCase__=True, lowerCamelCase__=0, lowerCamelCase__=1, **lowerCamelCase__, ):
A : List[Any] = vocab_size
A : List[Any] = d_model
A : int = d_kv
A : List[Any] = d_ff
A : List[Any] = num_layers
A : List[Any] = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
A : List[str] = num_heads
A : Any = relative_attention_num_buckets
A : int = relative_attention_max_distance
A : str = dropout_rate
A : Tuple = layer_norm_epsilon
A : Optional[Any] = initializer_factor
A : str = feed_forward_proj
A : Dict = use_cache
A : Dict = self.feed_forward_proj.split("""-""" )
A : Union[str, Any] = act_info[-1]
A : Any = act_info[0] == """gated"""
if len(_snake_case ) > 1 and act_info[0] != "gated" or len(_snake_case ) > 2:
raise ValueError(
f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""\'gated-gelu\' or \'relu\'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
A : Optional[int] = """gelu_new"""
super().__init__(
pad_token_id=_snake_case, eos_token_id=_snake_case, is_encoder_decoder=_snake_case, **_snake_case, )
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
@property
def _lowerCAmelCase ( self ):
A : List[str] = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
A : Tuple = """past_encoder_sequence + sequence"""
A : Optional[Any] = {0: """batch"""}
A : List[str] = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
A : Optional[Any] = {0: """batch""", 1: """decoder_sequence"""}
A : Tuple = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_snake_case, direction="""inputs""" )
return common_inputs
@property
def _lowerCAmelCase ( self ):
return 13
| 357 |
import os
from pathlib import Path
def __UpperCamelCase ( ) -> Any:
"""simple docstring"""
from torch.utils.cpp_extension import load
A : Any = Path(_lowerCAmelCase ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
A : int = [
root / filename
for filename in [
"""vision.cpp""",
os.path.join("""cpu""" , """ms_deform_attn_cpu.cpp""" ),
os.path.join("""cuda""" , """ms_deform_attn_cuda.cu""" ),
]
]
load(
"""MultiScaleDeformableAttention""" , _lowerCAmelCase , with_cuda=_lowerCAmelCase , extra_include_paths=[str(_lowerCAmelCase )] , extra_cflags=["""-DWITH_CUDA=1"""] , extra_cuda_cflags=[
"""-DCUDA_HAS_FP16=1""",
"""-D__CUDA_NO_HALF_OPERATORS__""",
"""-D__CUDA_NO_HALF_CONVERSIONS__""",
"""-D__CUDA_NO_HALF2_OPERATORS__""",
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 115 | 0 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class A_ ( lowercase_ ):
_UpperCAmelCase : int = 42
_UpperCAmelCase : Dict = 42
class A_ ( lowercase_ , lowercase_ ):
_UpperCAmelCase : str = 1
@register_to_config
def __init__( self : Optional[int] ,SCREAMING_SNAKE_CASE__ : Tuple = 2_0_0_0 ,SCREAMING_SNAKE_CASE__ : int = 0.15 ,SCREAMING_SNAKE_CASE__ : Any = 0.01 ,SCREAMING_SNAKE_CASE__ : Dict = 1348.0 ,SCREAMING_SNAKE_CASE__ : List[str] = 1E-5 ,SCREAMING_SNAKE_CASE__ : Tuple = 1 ,):
__lowerCamelCase : Optional[Any] = sigma_max
# setable values
__lowerCamelCase : Union[str, Any] = None
self.set_sigmas(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase)
def lowerCAmelCase ( self : Dict ,SCREAMING_SNAKE_CASE__ : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : Union[str, Any] = None):
return sample
def lowerCAmelCase ( self : List[Any] ,SCREAMING_SNAKE_CASE__ : Any ,SCREAMING_SNAKE_CASE__ : Optional[int] = None ,SCREAMING_SNAKE_CASE__ : List[Any] = None):
__lowerCamelCase : Union[str, Any] = sampling_eps if sampling_eps is not None else self.config.sampling_eps
__lowerCamelCase : Dict = torch.linspace(1 ,__UpperCamelCase ,__UpperCamelCase ,device=__UpperCamelCase)
def lowerCAmelCase ( self : Optional[int] ,SCREAMING_SNAKE_CASE__ : Tuple ,SCREAMING_SNAKE_CASE__ : List[Any] = None ,SCREAMING_SNAKE_CASE__ : Union[str, Any] = None ,SCREAMING_SNAKE_CASE__ : Optional[Any] = None):
__lowerCamelCase : Optional[int] = sigma_min if sigma_min is not None else self.config.sigma_min
__lowerCamelCase : Optional[Any] = sigma_max if sigma_max is not None else self.config.sigma_max
__lowerCamelCase : Any = sampling_eps if sampling_eps is not None else self.config.sampling_eps
if self.timesteps is None:
self.set_timesteps(__UpperCamelCase ,__UpperCamelCase)
__lowerCamelCase : Any = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
__lowerCamelCase : Tuple = torch.exp(torch.linspace(math.log(__UpperCamelCase) ,math.log(__UpperCamelCase) ,__UpperCamelCase))
__lowerCamelCase : Any = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])
def lowerCAmelCase ( self : Optional[Any] ,SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : List[str]):
return torch.where(
timesteps == 0 ,torch.zeros_like(t.to(timesteps.device)) ,self.discrete_sigmas[timesteps - 1].to(timesteps.device) ,)
def lowerCAmelCase ( self : Optional[int] ,SCREAMING_SNAKE_CASE__ : Dict ,SCREAMING_SNAKE_CASE__ : Optional[Any] ,SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : Optional[Any] = None ,SCREAMING_SNAKE_CASE__ : List[Any] = True ,):
if self.timesteps is None:
raise ValueError(
'`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler')
__lowerCamelCase : List[str] = timestep * torch.ones(
sample.shape[0] ,device=sample.device) # torch.repeat_interleave(timestep, sample.shape[0])
__lowerCamelCase : Any = (timestep * (len(self.timesteps) - 1)).long()
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
__lowerCamelCase : str = timesteps.to(self.discrete_sigmas.device)
__lowerCamelCase : Optional[Any] = self.discrete_sigmas[timesteps].to(sample.device)
__lowerCamelCase : int = self.get_adjacent_sigma(__UpperCamelCase ,__UpperCamelCase).to(sample.device)
__lowerCamelCase : List[Any] = torch.zeros_like(__UpperCamelCase)
__lowerCamelCase : str = (sigma**2 - adjacent_sigma**2) ** 0.5
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
__lowerCamelCase : Tuple = diffusion.flatten()
while len(diffusion.shape) < len(sample.shape):
__lowerCamelCase : Any = diffusion.unsqueeze(-1)
__lowerCamelCase : Optional[Any] = drift - diffusion**2 * model_output
# equation 6: sample noise for the diffusion term of
__lowerCamelCase : Optional[Any] = randn_tensor(
sample.shape ,layout=sample.layout ,generator=__UpperCamelCase ,device=sample.device ,dtype=sample.dtype)
__lowerCamelCase : List[Any] = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
__lowerCamelCase : Optional[Any] = prev_sample_mean + diffusion * noise # add impact of diffusion field g
if not return_dict:
return (prev_sample, prev_sample_mean)
return SdeVeOutput(prev_sample=__UpperCamelCase ,prev_sample_mean=__UpperCamelCase)
def lowerCAmelCase ( self : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : Optional[Any] ,SCREAMING_SNAKE_CASE__ : List[str] = None ,SCREAMING_SNAKE_CASE__ : Any = True ,):
if self.timesteps is None:
raise ValueError(
'`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler')
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
# sample noise for correction
__lowerCamelCase : Optional[Any] = randn_tensor(sample.shape ,layout=sample.layout ,generator=__UpperCamelCase).to(sample.device)
# compute step size from the model_output, the noise, and the snr
__lowerCamelCase : Optional[int] = torch.norm(model_output.reshape(model_output.shape[0] ,-1) ,dim=-1).mean()
__lowerCamelCase : Union[str, Any] = torch.norm(noise.reshape(noise.shape[0] ,-1) ,dim=-1).mean()
__lowerCamelCase : Any = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
__lowerCamelCase : Optional[int] = step_size * torch.ones(sample.shape[0]).to(sample.device)
# self.repeat_scalar(step_size, sample.shape[0])
# compute corrected sample: model_output term and noise term
__lowerCamelCase : Tuple = step_size.flatten()
while len(step_size.shape) < len(sample.shape):
__lowerCamelCase : int = step_size.unsqueeze(-1)
__lowerCamelCase : Tuple = sample + step_size * model_output
__lowerCamelCase : Dict = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__UpperCamelCase)
def lowerCAmelCase ( self : Optional[int] ,SCREAMING_SNAKE_CASE__ : List[Any] ,SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : List[str] ,):
__lowerCamelCase : Optional[Any] = timesteps.to(original_samples.device)
__lowerCamelCase : Dict = self.discrete_sigmas.to(original_samples.device)[timesteps]
__lowerCamelCase : List[Any] = (
noise * sigmas[:, None, None, None]
if noise is not None
else torch.randn_like(__UpperCamelCase) * sigmas[:, None, None, None]
)
__lowerCamelCase : List[str] = noise + original_samples
return noisy_samples
def __len__( self : Optional[int]):
return self.config.num_train_timesteps
| 73 | """simple docstring"""
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
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 transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
)
from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class UpperCamelCase ( lowercase_ ):
def _UpperCAmelCase ( self ) -> Any:
'''simple docstring'''
lowercase_ : Any = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__UpperCamelCase ,'hidden_sizes' ) )
self.parent.assertTrue(hasattr(__UpperCamelCase ,'num_attention_heads' ) )
class UpperCamelCase :
def __init__( self ,__UpperCamelCase ,__UpperCamelCase=13 ,__UpperCamelCase=64 ,__UpperCamelCase=3 ,__UpperCamelCase=3 ,__UpperCamelCase=2 ,__UpperCamelCase=1 ,__UpperCamelCase=16 ,__UpperCamelCase=[128, 256, 384] ,__UpperCamelCase=[4, 6, 8] ,__UpperCamelCase=[2, 3, 4] ,__UpperCamelCase=[16, 16, 16] ,__UpperCamelCase=0 ,__UpperCamelCase=[2, 2, 2] ,__UpperCamelCase=[2, 2, 2] ,__UpperCamelCase=0.02 ,__UpperCamelCase=True ,__UpperCamelCase=True ,__UpperCamelCase=2 ,) -> str:
'''simple docstring'''
lowercase_ : Optional[Any] = parent
lowercase_ : Any = batch_size
lowercase_ : str = image_size
lowercase_ : List[Any] = num_channels
lowercase_ : Any = kernel_size
lowercase_ : Any = stride
lowercase_ : List[str] = padding
lowercase_ : Tuple = hidden_sizes
lowercase_ : List[Any] = num_attention_heads
lowercase_ : Optional[int] = depths
lowercase_ : Union[str, Any] = key_dim
lowercase_ : List[str] = drop_path_rate
lowercase_ : Dict = patch_size
lowercase_ : List[Any] = attention_ratio
lowercase_ : Optional[int] = mlp_ratio
lowercase_ : Any = initializer_range
lowercase_ : Optional[Any] = [
['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
lowercase_ : Dict = is_training
lowercase_ : Tuple = use_labels
lowercase_ : Dict = num_labels
lowercase_ : Union[str, Any] = initializer_range
def _UpperCAmelCase ( self ) -> List[str]:
'''simple docstring'''
lowercase_ : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase_ : List[str] = None
if self.use_labels:
lowercase_ : List[Any] = ids_tensor([self.batch_size] ,self.num_labels )
lowercase_ : List[str] = self.get_config()
return config, pixel_values, labels
def _UpperCAmelCase ( self ) -> List[str]:
'''simple docstring'''
return LevitConfig(
image_size=self.image_size ,num_channels=self.num_channels ,kernel_size=self.kernel_size ,stride=self.stride ,padding=self.padding ,patch_size=self.patch_size ,hidden_sizes=self.hidden_sizes ,num_attention_heads=self.num_attention_heads ,depths=self.depths ,key_dim=self.key_dim ,drop_path_rate=self.drop_path_rate ,mlp_ratio=self.mlp_ratio ,attention_ratio=self.attention_ratio ,initializer_range=self.initializer_range ,down_ops=self.down_ops ,)
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Any:
'''simple docstring'''
lowercase_ : Union[str, Any] = LevitModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
lowercase_ : Optional[Any] = model(__UpperCamelCase )
lowercase_ : Union[str, Any] = (self.image_size, self.image_size)
lowercase_ , lowercase_ : Tuple = image_size[0], image_size[1]
for _ in range(4 ):
lowercase_ : Optional[int] = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
lowercase_ : Dict = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1]) ,)
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ : str = self.num_labels
lowercase_ : Optional[Any] = LevitForImageClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
lowercase_ : Union[str, Any] = model(__UpperCamelCase ,labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _UpperCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
lowercase_ : Union[str, Any] = self.prepare_config_and_inputs()
lowercase_ , lowercase_ , lowercase_ : Tuple = config_and_inputs
lowercase_ : int = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class UpperCamelCase ( lowercase_ , lowercase_ , unittest.TestCase ):
lowercase = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
lowercase = (
{
'feature-extraction': LevitModel,
'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
lowercase = False
lowercase = False
lowercase = False
lowercase = False
lowercase = False
def _UpperCAmelCase ( self ) -> List[str]:
'''simple docstring'''
lowercase_ : Tuple = LevitModelTester(self )
lowercase_ : Dict = ConfigTester(self ,config_class=__UpperCamelCase ,has_text_modality=__UpperCamelCase ,hidden_size=37 )
def _UpperCAmelCase ( self ) -> List[str]:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _UpperCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
return
@unittest.skip(reason='Levit does not use inputs_embeds' )
def _UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason='Levit does not support input and output embeddings' )
def _UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
pass
@unittest.skip(reason='Levit does not output attentions' )
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
pass
def _UpperCAmelCase ( self ) -> List[str]:
'''simple docstring'''
lowercase_ , lowercase_ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : Optional[int] = model_class(__UpperCamelCase )
lowercase_ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase_ : Union[str, Any] = [*signature.parameters.keys()]
lowercase_ : Optional[int] = ['pixel_values']
self.assertListEqual(arg_names[:1] ,__UpperCamelCase )
def _UpperCAmelCase ( self ) -> Dict:
'''simple docstring'''
def check_hidden_states_output(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ):
lowercase_ : List[Any] = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
lowercase_ : Optional[int] = model(**self._prepare_for_class(__UpperCamelCase ,__UpperCamelCase ) )
lowercase_ : Dict = outputs.hidden_states
lowercase_ : List[str] = len(self.model_tester.depths ) + 1
self.assertEqual(len(__UpperCamelCase ) ,__UpperCamelCase )
lowercase_ : Optional[int] = (self.model_tester.image_size, self.model_tester.image_size)
lowercase_ , lowercase_ : Union[str, Any] = image_size[0], image_size[1]
for _ in range(4 ):
lowercase_ : Optional[Any] = floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
lowercase_ : Optional[int] = floor(
(
(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) ,[
height * width,
self.model_tester.hidden_sizes[0],
] ,)
lowercase_ , lowercase_ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase_ : Any = True
check_hidden_states_output(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase_ : Optional[int] = True
check_hidden_states_output(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def _UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
pass
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase=False ) -> List[Any]:
'''simple docstring'''
lowercase_ : Optional[Any] = super()._prepare_for_class(__UpperCamelCase ,__UpperCamelCase ,return_labels=__UpperCamelCase )
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def _UpperCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
def _UpperCAmelCase ( self ) -> Optional[int]:
'''simple docstring'''
lowercase_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
if not self.model_tester.is_training:
return
lowercase_ , lowercase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase_ : Optional[Any] = True
for model_class in self.all_model_classes:
# LevitForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(__UpperCamelCase )
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
lowercase_ : Dict = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.train()
lowercase_ : List[Any] = self._prepare_for_class(__UpperCamelCase ,__UpperCamelCase ,return_labels=__UpperCamelCase )
lowercase_ : Union[str, Any] = model(**__UpperCamelCase ).loss
loss.backward()
def _UpperCAmelCase ( self ) -> str:
'''simple docstring'''
lowercase_ , lowercase_ : int = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
lowercase_ : Union[str, Any] = False
lowercase_ : Dict = True
for model_class in self.all_model_classes:
if model_class in get_values(__UpperCamelCase ) or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
lowercase_ : Dict = model_class(__UpperCamelCase )
model.gradient_checkpointing_enable()
model.to(__UpperCamelCase )
model.train()
lowercase_ : Tuple = self._prepare_for_class(__UpperCamelCase ,__UpperCamelCase ,return_labels=__UpperCamelCase )
lowercase_ : Any = model(**__UpperCamelCase ).loss
loss.backward()
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase_ , lowercase_ : Any = self.model_tester.prepare_config_and_inputs_for_common()
lowercase_ : int = [
{'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
{'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
{'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(__UpperCamelCase ),
]
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f'''Testing {model_class} with {problem_type["title"]}''' ):
lowercase_ : Any = problem_type['title']
lowercase_ : Tuple = problem_type['num_labels']
lowercase_ : Tuple = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.train()
lowercase_ : Optional[int] = self._prepare_for_class(__UpperCamelCase ,__UpperCamelCase ,return_labels=__UpperCamelCase )
if problem_type["num_labels"] > 1:
lowercase_ : Any = inputs['labels'].unsqueeze(1 ).repeat(1 ,problem_type['num_labels'] )
lowercase_ : str = inputs['labels'].to(problem_type['dtype'] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=__UpperCamelCase ) as warning_list:
lowercase_ : int = model(**__UpperCamelCase ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f'''Something is going wrong in the regression problem: intercepted {w.message}''' )
loss.backward()
@slow
def _UpperCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase_ : List[Any] = LevitModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def lowercase__( ):
lowercase_ : Any = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class UpperCamelCase ( unittest.TestCase ):
@cached_property
def _UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase_ : Any = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
__UpperCamelCase )
lowercase_ : str = self.default_image_processor
lowercase_ : Any = prepare_img()
lowercase_ : Optional[int] = image_processor(images=__UpperCamelCase ,return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
lowercase_ : Union[str, Any] = model(**__UpperCamelCase )
# verify the logits
lowercase_ : str = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape ,__UpperCamelCase )
lowercase_ : Any = torch.tensor([1.0448, -0.3745, -1.8317] ).to(__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,__UpperCamelCase ,atol=1e-4 ) )
| 213 | 0 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
__lowerCamelCase = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
from run_translation import main # noqa
set_seed(42)
__lowerCamelCase = '''sshleifer/student_marian_en_ro_6_1'''
__lowerCamelCase = '''sshleifer/tiny-mbart'''
@require_torch
class A__ ( _snake_case ):
def snake_case_ ( self , UpperCamelCase__=False , UpperCamelCase__=None , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=True , ) -> Union[str, Any]:
'''simple docstring'''
A_ = self.run_trainer(
eval_steps=1 , max_len=12 , model_name=UpperCamelCase__ , num_train_epochs=1 , distributed=UpperCamelCase__ , extra_args_str=UpperCamelCase__ , predict_with_generate=UpperCamelCase__ , do_train=UpperCamelCase__ , do_eval=UpperCamelCase__ , do_predict=UpperCamelCase__ , )
A_ = TrainerState.load_from_json(os.path.join(UpperCamelCase__ , """trainer_state.json""" ) ).log_history
if not do_eval:
return
A_ = [log for log in logs if """eval_loss""" in log.keys()]
A_ = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
A_ = eval_metrics[-1]
assert isinstance(last_step_stats["""eval_bleu"""] , UpperCamelCase__ )
assert not math.isnan(float(last_step_stats["""eval_loss"""] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def snake_case_ ( self ) -> Optional[Any]:
'''simple docstring'''
self.run_seqaseq_quick()
@require_torch_multi_gpu
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
self.run_seqaseq_quick(distributed=UpperCamelCase__ )
@require_torch_multi_gpu
def snake_case_ ( self ) -> Any:
'''simple docstring'''
self.run_seqaseq_quick(distributed=UpperCamelCase__ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def snake_case_ ( self ) -> Tuple:
'''simple docstring'''
self.run_seqaseq_quick(distributed=UpperCamelCase__ , extra_args_str="""--sharded_ddp simple""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def snake_case_ ( self ) -> Optional[int]:
'''simple docstring'''
self.run_seqaseq_quick(distributed=UpperCamelCase__ , extra_args_str="""--sharded_ddp simple --fp16""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def snake_case_ ( self ) -> Any:
'''simple docstring'''
self.run_seqaseq_quick(distributed=UpperCamelCase__ , extra_args_str="""--sharded_ddp zero_dp_2""" , predict_with_generate=UpperCamelCase__ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
self.run_seqaseq_quick(
distributed=UpperCamelCase__ , extra_args_str="""--sharded_ddp zero_dp_2 --fp16""" , predict_with_generate=UpperCamelCase__ )
@require_apex
@require_torch_gpu
def snake_case_ ( self ) -> List[str]:
'''simple docstring'''
# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
# program and it breaks other tests that run from the same pytest worker, therefore until this is
# sorted out it must be run only in an external program, that is distributed=True in this
# test and only under one or more gpus - if we want cpu will need to make a special test
#
# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
# 2nd main() call it botches the future eval.
#
self.run_seqaseq_quick(distributed=UpperCamelCase__ , extra_args_str="""--fp16 --fp16_backend=apex""" )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=UpperCamelCase__ , extra_args_str="""--fp16 --fp16_backend=apex""" )
@parameterized.expand(["""base""", """low""", """high""", """mixed"""] )
@require_torch_multi_gpu
def snake_case_ ( self , UpperCamelCase__ ) -> int:
'''simple docstring'''
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
A_ = {
# test with the default log_level - should be info and thus log info once
"""base""": {"""extra_args_str""": """""", """n_matches""": 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
"""low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
"""high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
# test with high log_level and log_level_replica - should be quiet on all processes
"""mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
}
A_ = experiments[experiment_id]
A_ = {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
A_ = """Running training"""
with CaptureStderr() as cl:
self.run_seqaseq_quick(**UpperCamelCase__ , extra_args_str=data["""extra_args_str"""] )
A_ = len(re.findall(UpperCamelCase__ , cl.err ) )
self.assertEqual(UpperCamelCase__ , data["""n_matches"""] )
@slow
def snake_case_ ( self ) -> List[str]:
'''simple docstring'''
A_ = self.run_trainer(
eval_steps=2 , max_len=128 , model_name=UpperCamelCase__ , learning_rate=3e-4 , num_train_epochs=10 , distributed=UpperCamelCase__ , )
# Check metrics
A_ = TrainerState.load_from_json(os.path.join(UpperCamelCase__ , """trainer_state.json""" ) ).log_history
A_ = [log for log in logs if """eval_loss""" in log.keys()]
A_ = eval_metrics[0]
A_ = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats["""eval_bleu"""] , UpperCamelCase__ )
# test if do_predict saves generations and metrics
A_ = os.listdir(UpperCamelCase__ )
A_ = {os.path.basename(UpperCamelCase__ ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def snake_case_ ( self ) -> Any:
'''simple docstring'''
from transformers.training_args import OptimizerNames
def train_and_return_metrics(UpperCamelCase__ ) -> Tuple[int, float]:
A_ = """--skip_memory_metrics 0"""
A_ = self.run_trainer(
max_len=128 , model_name=UpperCamelCase__ , learning_rate=3e-4 , num_train_epochs=1 , optim=UpperCamelCase__ , distributed=UpperCamelCase__ , extra_args_str=UpperCamelCase__ , do_eval=UpperCamelCase__ , do_predict=UpperCamelCase__ , n_gpus_to_use=1 , )
# Check metrics
A_ = TrainerState.load_from_json(Path(UpperCamelCase__ , """trainer_state.json""" ) ).log_history
A_ = int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20 )
A_ = int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20 )
A_ = logs[0]["""train_loss"""]
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
A_ , A_ , A_ = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
A_ , A_ , A_ = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
A_ = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
A_ = gpu_peak_mem_orig + gpu_alloc_mem_orig
A_ = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
A_ = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
A_ = 120
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
UpperCamelCase__ , UpperCamelCase__ , """should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
f''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
f''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB''' , )
self.assertGreater(
UpperCamelCase__ , UpperCamelCase__ , """should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
f''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
f''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB''' , )
self.assertEqual(
UpperCamelCase__ , UpperCamelCase__ , f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}''' )
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 3e-3 , UpperCamelCase__ = "adafactor" , UpperCamelCase__ = False , UpperCamelCase__ = None , UpperCamelCase__ = 0 , UpperCamelCase__ = True , UpperCamelCase__ = True , UpperCamelCase__ = True , UpperCamelCase__ = True , UpperCamelCase__ = None , ) -> List[Any]:
'''simple docstring'''
A_ = self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
A_ = self.get_auto_remove_tmp_dir()
A_ = f'''
--model_name_or_path {model_name}
--train_file {data_dir}/train.json
--validation_file {data_dir}/val.json
--test_file {data_dir}/test.json
--output_dir {output_dir}
--overwrite_output_dir
--max_train_samples 8
--max_source_length {max_len}
--max_target_length {max_len}
--do_train
--num_train_epochs {str(UpperCamelCase__ )}
--per_device_train_batch_size 4
--learning_rate {learning_rate}
--warmup_steps 8
--logging_steps 0
--logging_strategy no
--save_steps {str(UpperCamelCase__ )}
--group_by_length
--label_smoothing_factor 0.1
--target_lang ro_RO
--source_lang en_XX
'''.split()
A_ = f'''
--do_eval
--per_device_eval_batch_size 4
--max_eval_samples 8
--val_max_target_length {max_len}
--evaluation_strategy steps
--eval_steps {str(UpperCamelCase__ )}
'''.split()
A_ = """
--do_predict
""".split()
A_ = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += f'''--optim {optim}'''.split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
A_ = get_gpu_count()
A_ = get_torch_dist_unique_port()
A_ = f'''
-m torch.distributed.run
--nproc_per_node={n_gpus_to_use}
--master_port={master_port}
{self.examples_dir_str}/pytorch/translation/run_translation.py
'''.split()
A_ = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(UpperCamelCase__ , env=self.get_env() )
else:
A_ = ["""run_translation.py"""] + args
with patch.object(UpperCamelCase__ , """argv""" , UpperCamelCase__ ):
main()
return output_dir
| 101 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> bool:
A_ = len(UpperCAmelCase__ )
A_ = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
A_ = True
# sum is not zero and set is empty then false
for i in range(1, required_sum + 1 ):
A_ = False
for i in range(1, arr_len + 1 ):
for j in range(1, required_sum + 1 ):
if arr[i - 1] > j:
A_ = subset[i - 1][j]
if arr[i - 1] <= j:
A_ = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 101 | 1 |
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str , __magic_name__ : str ) -> float:
"""simple docstring"""
def get_matched_characters(__magic_name__ : str , __magic_name__ : str ) -> str:
UpperCamelCase :List[Any] = []
UpperCamelCase :List[Any] = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
UpperCamelCase :Union[str, Any] = int(max(0 , i - limit ) )
UpperCamelCase :Optional[int] = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(__magic_name__ )
UpperCamelCase :str = f"""{_stra[0:_stra.index(__magic_name__ )]} {_stra[_stra.index(__magic_name__ ) + 1:]}"""
return "".join(__magic_name__ )
# matching characters
UpperCamelCase :Dict = get_matched_characters(__magic_name__ , __magic_name__ )
UpperCamelCase :List[str] = get_matched_characters(__magic_name__ , __magic_name__ )
UpperCamelCase :Tuple = len(__magic_name__ )
# transposition
UpperCamelCase :Tuple = (
len([(ca, ca) for ca, ca in zip(__magic_name__ , __magic_name__ ) if ca != ca] ) // 2
)
if not match_count:
UpperCamelCase :List[str] = 0.0
else:
UpperCamelCase :Tuple = (
1
/ 3
* (
match_count / len(__magic_name__ )
+ match_count / len(__magic_name__ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
UpperCamelCase :str = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('''hello''', '''world'''))
| 38 |
'''simple docstring'''
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
UpperCAmelCase_ = '\\n@misc{chen2021evaluating,\n title={Evaluating Large Language Models Trained on Code},\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\nand William Saunders and Christopher Hesse and Andrew N. Carr \\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n year={2021},\n eprint={2107.03374},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n'
UpperCAmelCase_ = '\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper "Evaluating Large Language Models Trained on Code"\n(https://arxiv.org/abs/2107.03374).\n'
UpperCAmelCase_ = '\nCalculates how good are predictions given some references, using certain scores\nArgs:\n predictions: list of candidates to evaluate. Each candidates should be a list\n of strings with several code candidates to solve the problem.\n references: a list with a test for each prediction. Each test should evaluate the\n correctness of a code candidate.\n k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\n num_workers: number of workers used to evaluate the canidate programs (Default: 4).\n timeout:\nReturns:\n pass_at_k: dict with pass rates for each k\n results: dict with granular results of each unittest\nExamples:\n >>> code_eval = datasets.load_metric("code_eval")\n >>> test_cases = ["assert add(2,3)==5"]\n >>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]\n >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\n >>> print(pass_at_k)\n {\'pass@1\': 0.5, \'pass@2\': 1.0}\n'
UpperCAmelCase_ = '\n################################################################################\n !!!WARNING!!!\n################################################################################\nThe "code_eval" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper "Evaluating Large\nLanguage Models Trained on Code" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"\n\n################################################################################\\n'
UpperCAmelCase_ = 'The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the "Software"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase_ ( datasets.Metric ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""string""" ) ),
"""references""": datasets.Value("""string""" ),
} ) , homepage="""https://github.com/openai/human-eval""" , codebase_urls=["""https://github.com/openai/human-eval"""] , reference_urls=["""https://github.com/openai/human-eval"""] , license=_LICENSE , )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[str]=[1, 10, 1_00] , _UpperCAmelCase : Optional[Any]=4 , _UpperCAmelCase : Any=3.0 ):
"""simple docstring"""
if os.getenv("""HF_ALLOW_CODE_EVAL""" , 0 ) != "1":
raise ValueError(_WARNING )
if os.name == "nt":
raise NotImplementedError("""This metric is currently not supported on Windows.""" )
with ThreadPoolExecutor(max_workers=_UpperCAmelCase ) as executor:
UpperCAmelCase__ = []
UpperCAmelCase__ = Counter()
UpperCAmelCase__ = 0
UpperCAmelCase__ = defaultdict(_UpperCAmelCase )
for task_id, (candidates, test_case) in enumerate(zip(_UpperCAmelCase , _UpperCAmelCase ) ):
for candidate in candidates:
UpperCAmelCase__ = candidate + """\n""" + test_case
UpperCAmelCase__ = (test_program, timeout, task_id, completion_id[task_id])
UpperCAmelCase__ = executor.submit(_UpperCAmelCase , *_UpperCAmelCase )
futures.append(_UpperCAmelCase )
completion_id[task_id] += 1
n_samples += 1
for future in as_completed(_UpperCAmelCase ):
UpperCAmelCase__ = future.result()
results[result["task_id"]].append((result["""completion_id"""], result) )
UpperCAmelCase__ , UpperCAmelCase__ = [], []
for result in results.values():
result.sort()
UpperCAmelCase__ = [r[1]["""passed"""] for r in result]
total.append(len(_UpperCAmelCase ) )
correct.append(sum(_UpperCAmelCase ) )
UpperCAmelCase__ = np.array(_UpperCAmelCase )
UpperCAmelCase__ = np.array(_UpperCAmelCase )
UpperCAmelCase__ = k
UpperCAmelCase__ = {f'''pass@{k}''': estimate_pass_at_k(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).mean() for k in ks if (total >= k).all()}
return pass_at_k, results
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[Any] ):
'''simple docstring'''
def estimator(SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> float:
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) )
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
UpperCAmelCase__ = itertools.repeat(SCREAMING_SNAKE_CASE__ , len(SCREAMING_SNAKE_CASE__ ) )
else:
assert len(SCREAMING_SNAKE_CASE__ ) == len(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase__ = iter(SCREAMING_SNAKE_CASE__ )
return np.array([estimator(int(SCREAMING_SNAKE_CASE__ ) , int(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ ) for n, c in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )] )
| 346 | 0 |
'''simple docstring'''
def a_ ( __snake_case : int , __snake_case : list[int] , __snake_case : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations(__snake_case : int ) -> 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(__snake_case )
def a_ ( __snake_case : int , __snake_case : list[int] , __snake_case : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations_with_dp_array(
__snake_case : int , __snake_case : list[int] ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
lowerCamelCase_ =sum(
count_of_possible_combinations_with_dp_array(target - item , __snake_case )
for item in array )
lowerCamelCase_ =answer
return answer
lowerCamelCase_ =[-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(__snake_case , __snake_case )
def a_ ( __snake_case : int , __snake_case : list[int] , __snake_case : int ) -> int:
"""simple docstring"""
lowerCamelCase_ =[0] * (target + 1)
lowerCamelCase_ =1
for i in range(1 , target + 1 ):
for j in range(__snake_case ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
a_ : List[str] = 3
a_ : str = 5
a_ : Union[str, Any] = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 6 |
'''simple docstring'''
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def a_ ( ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ ={
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''path''': ['''test_1.py''', '''test_2.py''', '''unit_test.py'''],
'''content''': ['''a ''' * 20, '''a ''' * 30, '''b ''' * 7],
}
lowerCamelCase_ =Dataset.from_dict(__snake_case )
return dataset
class __UpperCamelCase ( lowerCamelCase__ ):
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =get_dataset()
lowerCamelCase_ =make_duplicate_clusters(lowerCAmelCase, 0.8_5 )
self.assertEqual(len(duplicate_clusters[0] ), 2 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =get_dataset()
lowerCamelCase_, lowerCamelCase_ =deduplicate_dataset(lowerCAmelCase )
self.assertEqual(len(lowerCAmelCase ), 2 )
print(lowerCAmelCase )
self.assertEqual(duplicate_clusters[0][0]['''copies'''], 2 )
self.assertEqual(duplicate_clusters[0][0]['''is_extreme'''], lowerCAmelCase )
| 6 | 1 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowercase__ ( ):
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ : List[str] = 9, 14 # noqa: F841
UpperCAmelCase_ : Optional[Any] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
UpperCAmelCase_ : int = defaultdict(__snake_case )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
UpperCAmelCase_ : List[Any] = mst(__snake_case )
UpperCAmelCase_ : Any = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
UpperCAmelCase_ : str = tuple(answer[:2] )
UpperCAmelCase_ : Union[str, Any] = tuple(edge[::-1] )
assert edge in result or reverse in result
| 29 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__UpperCAmelCase = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
__UpperCAmelCase = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(F'A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def lowercase__ ( __snake_case : str ):
'''simple docstring'''
if "://" in dataset_path:
UpperCAmelCase_ : int = dataset_path.split('://' )[1]
return dataset_path
def lowercase__ ( __snake_case : fsspec.AbstractFileSystem ):
'''simple docstring'''
if fs is not None and fs.protocol != "file":
return True
else:
return False
def lowercase__ ( __snake_case : fsspec.AbstractFileSystem , __snake_case : str , __snake_case : str ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = not is_remote_filesystem(__snake_case )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(__snake_case ) , fs._strip_protocol(__snake_case ) )
else:
fs.mv(__snake_case , __snake_case , recursive=__snake_case )
def lowercase__ ( ):
'''simple docstring'''
if hasattr(fsspec.asyn , 'reset_lock' ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
UpperCAmelCase_ : Optional[Any] = None
UpperCAmelCase_ : Union[str, Any] = None
UpperCAmelCase_ : int = threading.Lock()
| 29 | 1 |
'''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 CLIPSegProcessor, ViTImageProcessor
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
def lowercase_ ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : str = tempfile.mkdtemp()
# fmt: off
UpperCAmelCase__ : Union[str, Any] = ['''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
UpperCAmelCase__ : Optional[int] = dict(zip(_A , range(len(_A ) ) ) )
UpperCAmelCase__ : Union[str, Any] = ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
UpperCAmelCase__ : Optional[int] = {'''unk_token''': '''<unk>'''}
UpperCAmelCase__ : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
UpperCAmelCase__ : 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(_A ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(_A ) )
UpperCAmelCase__ : Optional[Any] = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''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],
}
UpperCAmelCase__ : Dict = os.path.join(self.tmpdirname , _A )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_A , _A )
def lowercase_ ( self : List[Any] , **_A : Dict ):
'''simple docstring'''
return CLIPTokenizer.from_pretrained(self.tmpdirname , **_A )
def lowercase_ ( self : Dict , **_A : str ):
'''simple docstring'''
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **_A )
def lowercase_ ( self : List[Any] , **_A : List[Any] ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_A )
def lowercase_ ( self : Any ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
UpperCAmelCase__ : Optional[Any] = [Image.fromarray(np.moveaxis(_A , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = self.get_tokenizer()
UpperCAmelCase__ : Optional[int] = self.get_rust_tokenizer()
UpperCAmelCase__ : str = self.get_image_processor()
UpperCAmelCase__ : str = CLIPSegProcessor(tokenizer=_A , image_processor=_A )
processor_slow.save_pretrained(self.tmpdirname )
UpperCAmelCase__ : Any = CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=_A )
UpperCAmelCase__ : Tuple = CLIPSegProcessor(tokenizer=_A , image_processor=_A )
processor_fast.save_pretrained(self.tmpdirname )
UpperCAmelCase__ : Union[str, Any] = CLIPSegProcessor.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 , _A )
self.assertIsInstance(processor_fast.tokenizer , _A )
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 , _A )
self.assertIsInstance(processor_fast.image_processor , _A )
def lowercase_ ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : str = CLIPSegProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase__ : Optional[Any] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
UpperCAmelCase__ : Optional[Any] = self.get_image_processor(do_normalize=_A , padding_value=1.0 )
UpperCAmelCase__ : Optional[int] = CLIPSegProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_A , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _A )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _A )
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.get_image_processor()
UpperCAmelCase__ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase__ : Tuple = CLIPSegProcessor(tokenizer=_A , image_processor=_A )
UpperCAmelCase__ : int = self.prepare_image_inputs()
UpperCAmelCase__ : int = image_processor(_A , return_tensors='''np''' )
UpperCAmelCase__ : str = processor(images=_A , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.get_image_processor()
UpperCAmelCase__ : List[Any] = self.get_tokenizer()
UpperCAmelCase__ : Dict = CLIPSegProcessor(tokenizer=_A , image_processor=_A )
UpperCAmelCase__ : Tuple = '''lower newer'''
UpperCAmelCase__ : Dict = processor(text=_A )
UpperCAmelCase__ : Any = tokenizer(_A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Any = self.get_image_processor()
UpperCAmelCase__ : Any = self.get_tokenizer()
UpperCAmelCase__ : List[str] = CLIPSegProcessor(tokenizer=_A , image_processor=_A )
UpperCAmelCase__ : Dict = '''lower newer'''
UpperCAmelCase__ : Optional[Any] = self.prepare_image_inputs()
UpperCAmelCase__ : str = processor(text=_A , images=_A )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(_A ):
processor()
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = self.get_image_processor()
UpperCAmelCase__ : Any = self.get_tokenizer()
UpperCAmelCase__ : Optional[int] = CLIPSegProcessor(tokenizer=_A , image_processor=_A )
UpperCAmelCase__ : List[str] = self.prepare_image_inputs()
UpperCAmelCase__ : int = self.prepare_image_inputs()
UpperCAmelCase__ : Optional[int] = processor(images=_A , visual_prompt=_A )
self.assertListEqual(list(inputs.keys() ) , ['''pixel_values''', '''conditional_pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(_A ):
processor()
def lowercase_ ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.get_image_processor()
UpperCAmelCase__ : Tuple = self.get_tokenizer()
UpperCAmelCase__ : List[Any] = CLIPSegProcessor(tokenizer=_A , image_processor=_A )
UpperCAmelCase__ : Tuple = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCAmelCase__ : List[Any] = processor.batch_decode(_A )
UpperCAmelCase__ : Union[str, Any] = tokenizer.batch_decode(_A )
self.assertListEqual(_A , _A )
| 299 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
CTRLTokenizer,
GPTaTokenizer,
GPTaTokenizerFast,
PreTrainedTokenizerFast,
RobertaTokenizer,
RobertaTokenizerFast,
is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
TOKENIZER_MAPPING,
get_tokenizer_config,
tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
DUMMY_DIFF_TOKENIZER_IDENTIFIER,
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tokenizers,
slow,
)
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class lowerCamelCase_ ( unittest.TestCase ):
def lowercase_ ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = 0
@slow
def lowercase_ ( self : Dict ):
'''simple docstring'''
for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
UpperCAmelCase__ : int = AutoTokenizer.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , (BertTokenizer, BertTokenizerFast) )
self.assertGreater(len(_A ) , 0 )
for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
UpperCAmelCase__ : Tuple = AutoTokenizer.from_pretrained(_A )
self.assertIsNotNone(_A )
self.assertIsInstance(_A , (GPTaTokenizer, GPTaTokenizerFast) )
self.assertGreater(len(_A ) , 0 )
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : int = AutoTokenizer.from_pretrained(_A )
self.assertIsInstance(_A , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 12 )
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : int = AutoTokenizer.from_pretrained(_A )
self.assertIsInstance(_A , (RobertaTokenizer, RobertaTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 20 )
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = AutoConfig.from_pretrained(_A )
self.assertIsInstance(_A , _A )
# Check that tokenizer_type ≠ model_type
UpperCAmelCase__ : Dict = AutoTokenizer.from_pretrained(_A , config=_A )
self.assertIsInstance(_A , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(tokenizer.vocab_size , 12 )
def lowercase_ ( self : str ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('''./tests/fixtures/vocab.txt''' , os.path.join(_A , '''vocab.txt''' ) )
UpperCAmelCase__ : Dict = AutoTokenizer.from_pretrained(_A , tokenizer_type='''bert''' , use_fast=_A )
self.assertIsInstance(_A , _A )
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('''./tests/fixtures/vocab.json''' , os.path.join(_A , '''vocab.json''' ) )
shutil.copy('''./tests/fixtures/merges.txt''' , os.path.join(_A , '''merges.txt''' ) )
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained(_A , tokenizer_type='''gpt2''' , use_fast=_A )
self.assertIsInstance(_A , _A )
@require_tokenizers
def lowercase_ ( self : str ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('''./tests/fixtures/vocab.txt''' , os.path.join(_A , '''vocab.txt''' ) )
UpperCAmelCase__ : str = AutoTokenizer.from_pretrained(_A , tokenizer_type='''bert''' )
self.assertIsInstance(_A , _A )
with tempfile.TemporaryDirectory() as tmp_dir:
shutil.copy('''./tests/fixtures/vocab.json''' , os.path.join(_A , '''vocab.json''' ) )
shutil.copy('''./tests/fixtures/merges.txt''' , os.path.join(_A , '''merges.txt''' ) )
UpperCAmelCase__ : Any = AutoTokenizer.from_pretrained(_A , tokenizer_type='''gpt2''' )
self.assertIsInstance(_A , _A )
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
with pytest.raises(_A ):
AutoTokenizer.from_pretrained('''./''' , tokenizer_type='''xxx''' )
@require_tokenizers
def lowercase_ ( self : int ):
'''simple docstring'''
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
UpperCAmelCase__ : Optional[int] = tokenizer_class.from_pretrained('''wietsedv/bert-base-dutch-cased''' )
self.assertIsInstance(_A , (BertTokenizer, BertTokenizerFast) )
if isinstance(_A , _A ):
self.assertEqual(tokenizer.basic_tokenizer.do_lower_case , _A )
else:
self.assertEqual(tokenizer.do_lower_case , _A )
self.assertEqual(tokenizer.model_max_length , 512 )
@require_tokenizers
def lowercase_ ( self : List[str] ):
'''simple docstring'''
for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
with self.assertRaisesRegex(
_A , '''julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier''' , ):
UpperCAmelCase__ : Dict = tokenizer_class.from_pretrained('''julien-c/herlolip-not-exists''' )
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = TOKENIZER_MAPPING.values()
UpperCAmelCase__ : Any = []
for slow_tok, fast_tok in tokenizers:
if slow_tok is not None:
tokenizer_names.append(slow_tok.__name__ )
if fast_tok is not None:
tokenizer_names.append(fast_tok.__name__ )
for tokenizer_name in tokenizer_names:
# must find the right class
tokenizer_class_from_name(_A )
@require_tokenizers
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased''' , use_fast=_A ) , _A )
self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased''' ) , _A )
@require_tokenizers
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = AutoTokenizer.from_pretrained('''distilbert-base-uncased''' , do_lower_case=_A )
UpperCAmelCase__ : Any = '''Hello, world. How are you?'''
UpperCAmelCase__ : Dict = tokenizer.tokenize(_A )
self.assertEqual('''[UNK]''' , tokens[0] )
UpperCAmelCase__ : Union[str, Any] = AutoTokenizer.from_pretrained('''microsoft/mpnet-base''' , do_lower_case=_A )
UpperCAmelCase__ : Union[str, Any] = tokenizer.tokenize(_A )
self.assertEqual('''[UNK]''' , tokens[0] )
@require_tokenizers
def lowercase_ ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = AutoTokenizer.from_pretrained('''robot-test/dummy-tokenizer-fast-with-model-config''' )
self.assertEqual(type(_A ) , _A )
self.assertEqual(tokenizer.model_max_length , 512 )
self.assertEqual(tokenizer.vocab_size , 30_000 )
self.assertEqual(tokenizer.unk_token , '''[UNK]''' )
self.assertEqual(tokenizer.padding_side , '''right''' )
self.assertEqual(tokenizer.truncation_side , '''right''' )
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained(_A )
self.assertIsInstance(_A , (BertTokenizer, BertTokenizerFast) )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_A )
UpperCAmelCase__ : int = AutoTokenizer.from_pretrained(_A )
self.assertIsInstance(_A , tokenizer.__class__ )
self.assertEqual(tokenizera.vocab_size , 12 )
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = AutoTokenizer.from_pretrained('''ctrl''' )
# There is no fast CTRL so this always gives us a slow tokenizer.
self.assertIsInstance(_A , _A )
def lowercase_ ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : str = get_tokenizer_config('''bert-base-cased''' )
UpperCAmelCase__ : Optional[int] = config.pop('''_commit_hash''' , _A )
# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
self.assertEqual(_A , {'''do_lower_case''': False} )
# This model does not have a tokenizer_config so we get back an empty dict.
UpperCAmelCase__ : Tuple = get_tokenizer_config(_A )
self.assertDictEqual(_A , {} )
# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained(_A )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_A )
UpperCAmelCase__ : List[Any] = get_tokenizer_config(_A )
# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
self.assertEqual(config['''tokenizer_class'''] , '''BertTokenizer''' )
def lowercase_ ( self : Dict ):
'''simple docstring'''
try:
AutoConfig.register('''custom''' , _A )
AutoTokenizer.register(_A , slow_tokenizer_class=_A )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_A ):
AutoTokenizer.register(_A , slow_tokenizer_class=_A )
UpperCAmelCase__ : Optional[int] = CustomTokenizer.from_pretrained(_A )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_A )
UpperCAmelCase__ : List[Any] = AutoTokenizer.from_pretrained(_A )
self.assertIsInstance(_A , _A )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
@require_tokenizers
def lowercase_ ( self : Any ):
'''simple docstring'''
try:
AutoConfig.register('''custom''' , _A )
# Can register in two steps
AutoTokenizer.register(_A , slow_tokenizer_class=_A )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, None) )
AutoTokenizer.register(_A , fast_tokenizer_class=_A )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast) )
del TOKENIZER_MAPPING._extra_content[CustomConfig]
# Can register in one step
AutoTokenizer.register(
_A , slow_tokenizer_class=_A , fast_tokenizer_class=_A )
self.assertEqual(TOKENIZER_MAPPING[CustomConfig] , (CustomTokenizer, CustomTokenizerFast) )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_A ):
AutoTokenizer.register(_A , fast_tokenizer_class=_A )
# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
# and that model does not have a tokenizer.json
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase__ : Any = BertTokenizerFast.from_pretrained(_A )
bert_tokenizer.save_pretrained(_A )
UpperCAmelCase__ : Optional[int] = CustomTokenizerFast.from_pretrained(_A )
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_A )
UpperCAmelCase__ : List[Any] = AutoTokenizer.from_pretrained(_A )
self.assertIsInstance(_A , _A )
UpperCAmelCase__ : Union[str, Any] = AutoTokenizer.from_pretrained(_A , use_fast=_A )
self.assertIsInstance(_A , _A )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
with self.assertRaises(_A ):
UpperCAmelCase__ : Tuple = AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_A ):
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=_A )
UpperCAmelCase__ : Dict = AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=_A )
self.assertTrue(tokenizer.special_attribute_present )
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_A )
UpperCAmelCase__ : List[Any] = AutoTokenizer.from_pretrained(_A , trust_remote_code=_A )
self.assertTrue(reloaded_tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
# Test we can also load the slow version
UpperCAmelCase__ : Dict = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=_A , use_fast=_A )
self.assertTrue(tokenizer.special_attribute_present )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
# Test tokenizer can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(_A )
UpperCAmelCase__ : Any = AutoTokenizer.from_pretrained(_A , trust_remote_code=_A , use_fast=_A )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , '''NewTokenizer''' )
self.assertTrue(reloaded_tokenizer.special_attribute_present )
else:
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
self.assertEqual(reloaded_tokenizer.__class__.__name__ , '''NewTokenizer''' )
@require_tokenizers
def lowercase_ ( self : int ):
'''simple docstring'''
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = False
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = NewTokenizer
lowerCAmelCase__ = False
try:
AutoConfig.register('''custom''' , _A )
AutoTokenizer.register(_A , slow_tokenizer_class=_A )
AutoTokenizer.register(_A , fast_tokenizer_class=_A )
# If remote code is not set, the default is to use local
UpperCAmelCase__ : Dict = AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''' )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
self.assertFalse(tokenizer.special_attribute_present )
UpperCAmelCase__ : List[Any] = AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''' , use_fast=_A )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
self.assertFalse(tokenizer.special_attribute_present )
# If remote code is disabled, we load the local one.
UpperCAmelCase__ : Tuple = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=_A )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
self.assertFalse(tokenizer.special_attribute_present )
UpperCAmelCase__ : str = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=_A , use_fast=_A )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
self.assertFalse(tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub
UpperCAmelCase__ : Dict = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=_A )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
self.assertTrue(tokenizer.special_attribute_present )
UpperCAmelCase__ : Any = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer''' , trust_remote_code=_A , use_fast=_A )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
self.assertTrue(tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
def lowercase_ ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer_legacy''' , trust_remote_code=_A )
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizerFast''' )
# Test we can also load the slow version
UpperCAmelCase__ : Optional[Any] = AutoTokenizer.from_pretrained(
'''hf-internal-testing/test_dynamic_tokenizer_legacy''' , trust_remote_code=_A , use_fast=_A )
self.assertTrue(tokenizer.special_attribute_present )
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
else:
self.assertEqual(tokenizer.__class__.__name__ , '''NewTokenizer''' )
def lowercase_ ( self : Tuple ):
'''simple docstring'''
with self.assertRaisesRegex(
_A , '''bert-base is not a local folder and is not a valid model identifier''' ):
UpperCAmelCase__ : Tuple = AutoTokenizer.from_pretrained('''bert-base''' )
def lowercase_ ( self : Dict ):
'''simple docstring'''
with self.assertRaisesRegex(
_A , R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained(_A , revision='''aaaaaa''' )
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
with RequestCounter() as counter:
UpperCAmelCase__ : Optional[int] = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
self.assertEqual(counter.get_request_count , 0 )
self.assertEqual(counter.head_request_count , 1 )
self.assertEqual(counter.other_request_count , 0 )
| 299 | 1 |
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 91 |
"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def _A (__a , __a ) -> Tuple:
"""simple docstring"""
try:
with open(__a , '''rb''' ) as flax_state_f:
SCREAMING_SNAKE_CASE_ : Optional[int] = from_bytes(__a , flax_state_f.read() )
except UnpicklingError as e:
try:
with open(__a ) as f:
if f.read().startswith('''version''' ):
raise OSError(
'''You seem to have cloned a repository without having git-lfs installed. Please'''
''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
''' folder you cloned.''' )
else:
raise ValueError from e
except (UnicodeDecodeError, ValueError):
raise EnvironmentError(f'Unable to convert {model_file} to Flax deserializable object. ' )
return load_flax_weights_in_pytorch_model(__a , __a )
def _A (__a , __a ) -> Tuple:
"""simple docstring"""
try:
import torch # noqa: F401
except ImportError:
logger.error(
'''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
''' instructions.''' )
raise
# check if we have bf16 weights
SCREAMING_SNAKE_CASE_ : Optional[int] = flatten_dict(jax.tree_util.tree_map(lambda __a : x.dtype == jnp.bfloataa , __a ) ).values()
if any(__a ):
# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
'''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
'''before loading those in PyTorch model.''' )
SCREAMING_SNAKE_CASE_ : Optional[Any] = jax.tree_util.tree_map(
lambda __a : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , __a )
SCREAMING_SNAKE_CASE_ : int = ''''''
SCREAMING_SNAKE_CASE_ : str = flatten_dict(__a , sep='''.''' )
SCREAMING_SNAKE_CASE_ : List[Any] = pt_model.state_dict()
# keep track of unexpected & missing keys
SCREAMING_SNAKE_CASE_ : str = []
SCREAMING_SNAKE_CASE_ : Any = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
SCREAMING_SNAKE_CASE_ : Any = flax_key_tuple.split('''.''' )
if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
SCREAMING_SNAKE_CASE_ : Any = flax_key_tuple_array[:-1] + ['''weight''']
SCREAMING_SNAKE_CASE_ : Optional[Any] = jnp.transpose(__a , (3, 2, 0, 1) )
elif flax_key_tuple_array[-1] == "kernel":
SCREAMING_SNAKE_CASE_ : Tuple = flax_key_tuple_array[:-1] + ['''weight''']
SCREAMING_SNAKE_CASE_ : Optional[int] = flax_tensor.T
elif flax_key_tuple_array[-1] == "scale":
SCREAMING_SNAKE_CASE_ : Optional[int] = flax_key_tuple_array[:-1] + ['''weight''']
if "time_embedding" not in flax_key_tuple_array:
for i, flax_key_tuple_string in enumerate(__a ):
SCREAMING_SNAKE_CASE_ : List[str] = (
flax_key_tuple_string.replace('''_0''' , '''.0''' )
.replace('''_1''' , '''.1''' )
.replace('''_2''' , '''.2''' )
.replace('''_3''' , '''.3''' )
.replace('''_4''' , '''.4''' )
.replace('''_5''' , '''.5''' )
.replace('''_6''' , '''.6''' )
.replace('''_7''' , '''.7''' )
.replace('''_8''' , '''.8''' )
.replace('''_9''' , '''.9''' )
)
SCREAMING_SNAKE_CASE_ : Optional[Any] = '''.'''.join(__a )
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
else:
# add weight to pytorch dict
SCREAMING_SNAKE_CASE_ : Optional[int] = np.asarray(__a ) if not isinstance(__a , np.ndarray ) else flax_tensor
SCREAMING_SNAKE_CASE_ : Union[str, Any] = torch.from_numpy(__a )
# remove from missing keys
missing_keys.remove(__a )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(__a )
pt_model.load_state_dict(__a )
# re-transform missing_keys to list
SCREAMING_SNAKE_CASE_ : int = list(__a )
if len(__a ) > 0:
logger.warning(
'''Some weights of the Flax model were not used when initializing the PyTorch model'''
f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
''' FlaxBertForSequenceClassification model).''' )
if len(__a ) > 0:
logger.warning(
f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
''' use it for predictions and inference.''' )
return pt_model
| 91 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class UpperCamelCase_ ( _lowerCamelCase ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ ) -> Union[str, Any]:
_snake_case = params
_snake_case = np.array(lowerCAmelCase_ )
_snake_case = np.array([len(lowerCAmelCase_ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self , lowerCAmelCase_ ) -> Tuple:
return (self.token_ids[index], self.lengths[index])
def __len__( self ) -> Optional[Any]:
return len(self.lengths )
def lowerCAmelCase ( self ) -> Tuple:
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def lowerCAmelCase ( self ) -> Tuple:
_snake_case = self.params.max_model_input_size
_snake_case = self.lengths > max_len
logger.info(F'''Splitting {sum(lowerCAmelCase_ )} too long sequences.''' )
def divide_chunks(lowerCAmelCase_ , lowerCAmelCase_ ):
return [l[i : i + n] for i in range(0 , len(lowerCAmelCase_ ) , lowerCAmelCase_ )]
_snake_case = []
_snake_case = []
if self.params.mlm:
_snake_case , _snake_case = self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
else:
_snake_case , _snake_case = self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
_snake_case = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
_snake_case = np.insert(lowerCAmelCase_ , 0 , lowerCAmelCase_ )
if sub_s[-1] != sep_id:
_snake_case = np.insert(lowerCAmelCase_ , len(lowerCAmelCase_ ) , lowerCAmelCase_ )
assert len(lowerCAmelCase_ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(lowerCAmelCase_ )
new_tok_ids.extend(lowerCAmelCase_ )
new_lengths.extend([len(lowerCAmelCase_ ) for l in sub_seqs] )
_snake_case = np.array(lowerCAmelCase_ )
_snake_case = np.array(lowerCAmelCase_ )
def lowerCAmelCase ( self ) -> Any:
_snake_case = len(self )
_snake_case = self.lengths > 11
_snake_case = self.token_ids[indices]
_snake_case = self.lengths[indices]
_snake_case = len(self )
logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.''' )
def lowerCAmelCase ( self ) -> Optional[Any]:
if "unk_token" not in self.params.special_tok_ids:
return
else:
_snake_case = self.params.special_tok_ids['unk_token']
_snake_case = len(self )
_snake_case = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
_snake_case = (unk_occs / self.lengths) < 0.5
_snake_case = self.token_ids[indices]
_snake_case = self.lengths[indices]
_snake_case = len(self )
logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).''' )
def lowerCAmelCase ( self ) -> List[Any]:
if not self.params.is_master:
return
logger.info(F'''{len(self )} sequences''' )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def lowerCAmelCase ( self , lowerCAmelCase_ ) -> Tuple:
_snake_case = [t[0] for t in batch]
_snake_case = [t[1] for t in batch]
assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ )
# Max for paddings
_snake_case = max(lowerCAmelCase_ )
# Pad token ids
if self.params.mlm:
_snake_case = self.params.special_tok_ids['pad_token']
else:
_snake_case = self.params.special_tok_ids['unk_token']
_snake_case = [list(t.astype(lowerCAmelCase_ ) ) + [pad_idx] * (max_seq_len_ - len(lowerCAmelCase_ )) for t in token_ids]
assert len(tk_ ) == len(lowerCAmelCase_ )
assert all(len(lowerCAmelCase_ ) == max_seq_len_ for t in tk_ )
_snake_case = torch.tensor(tk_ ) # (bs, max_seq_len_)
_snake_case = torch.tensor(lowerCAmelCase_ ) # (bs)
return tk_t, lg_t
| 295 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class UpperCamelCase_ :
@property
def lowerCAmelCase ( self ) -> int:
return self.get_dummy_input()
@property
def lowerCAmelCase ( self ) -> Optional[Any]:
if self.block_type == "down":
return (4, 32, 16, 16)
elif self.block_type == "mid":
return (4, 32, 32, 32)
elif self.block_type == "up":
return (4, 32, 64, 64)
raise ValueError(F'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.''' )
def lowerCAmelCase ( self , lowerCAmelCase_=True , lowerCAmelCase_=False , lowerCAmelCase_=False , lowerCAmelCase_=False , ) -> List[str]:
_snake_case = 4
_snake_case = 32
_snake_case = (32, 32)
_snake_case = torch.manual_seed(0 )
_snake_case = torch.device(lowerCAmelCase_ )
_snake_case = (batch_size, num_channels) + sizes
_snake_case = randn_tensor(lowerCAmelCase_ , generator=lowerCAmelCase_ , device=lowerCAmelCase_ )
_snake_case = {'hidden_states': hidden_states}
if include_temb:
_snake_case = 128
_snake_case = randn_tensor((batch_size, temb_channels) , generator=lowerCAmelCase_ , device=lowerCAmelCase_ )
if include_res_hidden_states_tuple:
_snake_case = torch.manual_seed(1 )
_snake_case = (randn_tensor(lowerCAmelCase_ , generator=lowerCAmelCase_ , device=lowerCAmelCase_ ),)
if include_encoder_hidden_states:
_snake_case = floats_tensor((batch_size, 32, 32) ).to(lowerCAmelCase_ )
if include_skip_sample:
_snake_case = randn_tensor(((batch_size, 3) + sizes) , generator=lowerCAmelCase_ , device=lowerCAmelCase_ )
return dummy_input
def lowerCAmelCase ( self ) -> Tuple:
_snake_case = {
'in_channels': 32,
'out_channels': 32,
'temb_channels': 128,
}
if self.block_type == "up":
_snake_case = 32
if self.block_type == "mid":
init_dict.pop('out_channels' )
_snake_case = self.dummy_input
return init_dict, inputs_dict
def lowerCAmelCase ( self , lowerCAmelCase_ ) -> Optional[int]:
_snake_case , _snake_case = self.prepare_init_args_and_inputs_for_common()
_snake_case = self.block_class(**lowerCAmelCase_ )
unet_block.to(lowerCAmelCase_ )
unet_block.eval()
with torch.no_grad():
_snake_case = unet_block(**lowerCAmelCase_ )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_snake_case = output[0]
self.assertEqual(output.shape , self.output_shape )
_snake_case = output[0, -1, -3:, -3:]
_snake_case = torch.tensor(lowerCAmelCase_ ).to(lowerCAmelCase_ )
assert torch_all_close(output_slice.flatten() , lowerCAmelCase_ , atol=5E-3 )
@unittest.skipIf(torch_device == 'mps' , 'Training is not supported in mps' )
def lowerCAmelCase ( self ) -> Tuple:
_snake_case , _snake_case = self.prepare_init_args_and_inputs_for_common()
_snake_case = self.block_class(**lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.train()
_snake_case = model(**lowerCAmelCase_ )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_snake_case = output[0]
_snake_case = torch.device(lowerCAmelCase_ )
_snake_case = randn_tensor(output.shape , device=lowerCAmelCase_ )
_snake_case = torch.nn.functional.mse_loss(lowerCAmelCase_ , lowerCAmelCase_ )
loss.backward()
| 295 | 1 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict" , [
SplitDict(),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name="my_dataset" )} ),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1_3_3_7 , num_examples=4_2 )} ),
SplitDict({"train": SplitInfo()} ),
] , )
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = split_dict._to_yaml_list()
assert len(lowerCamelCase__ ) == len(lowerCamelCase__ )
lowerCamelCase_ = SplitDict._from_yaml_list(lowerCamelCase__ )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
lowerCamelCase_ = None
# the split name of split_dict takes over the name of the split info object
lowerCamelCase_ = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
"split_info" , [SplitInfo(), SplitInfo(dataset_name=lowerCamelCase__ ), SplitInfo(dataset_name="my_dataset" )] )
def lowerCamelCase_ ( lowerCamelCase__ ):
# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
lowerCamelCase_ = asdict(SplitDict({"train": split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 19 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class snake_case :
"""simple docstring"""
@staticmethod
def snake_case ( *UpperCamelCase , **UpperCamelCase ):
"""simple docstring"""
pass
def __snake_case ( UpperCAmelCase_ : List[Any] ):
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
a_ : Dict = (
"""https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png"""
)
@is_pipeline_test
@require_torch
@require_vision
class snake_case ( unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def snake_case ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = pipeline(
"document-question-answering" , model=UpperCamelCase , tokenizer=UpperCamelCase , image_processor=UpperCamelCase )
lowerCamelCase_ = INVOICE_URL
lowerCamelCase_ = list(zip(*apply_tesseract(load_image(UpperCamelCase ) , UpperCamelCase , "" ) ) )
lowerCamelCase_ = "What is the placebo?"
lowerCamelCase_ = [
{
"image": load_image(UpperCamelCase ),
"question": question,
},
{
"image": image,
"question": question,
},
{
"image": image,
"question": question,
"word_boxes": word_boxes,
},
]
return dqa_pipeline, examples
def snake_case ( self , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCamelCase_ = dqa_pipeline(UpperCamelCase , top_k=2 )
self.assertEqual(
UpperCamelCase , [
[
{"score": ANY(UpperCamelCase ), "answer": ANY(UpperCamelCase ), "start": ANY(UpperCamelCase ), "end": ANY(UpperCamelCase )},
{"score": ANY(UpperCamelCase ), "answer": ANY(UpperCamelCase ), "start": ANY(UpperCamelCase ), "end": ANY(UpperCamelCase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = pipeline("document-question-answering" , model="hf-internal-testing/tiny-random-layoutlmv2" )
lowerCamelCase_ = INVOICE_URL
lowerCamelCase_ = "How many cats are there?"
lowerCamelCase_ = [
{"score": 0.0_001, "answer": "oy 2312/2019", "start": 38, "end": 39},
{"score": 0.0_001, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
]
lowerCamelCase_ = dqa_pipeline(image=UpperCamelCase , question=UpperCamelCase , top_k=2 )
self.assertEqual(nested_simplify(UpperCamelCase , decimals=4 ) , UpperCamelCase )
lowerCamelCase_ = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(nested_simplify(UpperCamelCase , decimals=4 ) , UpperCamelCase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
lowerCamelCase_ = "./tests/fixtures/tests_samples/COCO/000000039769.png"
lowerCamelCase_ = dqa_pipeline(image=UpperCamelCase , question=UpperCamelCase , top_k=2 )
self.assertEqual(UpperCamelCase , [] )
# We can optionnally pass directly the words and bounding boxes
lowerCamelCase_ = "./tests/fixtures/tests_samples/COCO/000000039769.png"
lowerCamelCase_ = []
lowerCamelCase_ = []
lowerCamelCase_ = dqa_pipeline(image=UpperCamelCase , question=UpperCamelCase , words=UpperCamelCase , boxes=UpperCamelCase , top_k=2 )
self.assertEqual(UpperCamelCase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , )
lowerCamelCase_ = INVOICE_URL
lowerCamelCase_ = "What is the invoice number?"
lowerCamelCase_ = dqa_pipeline(image=UpperCamelCase , question=UpperCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
] , )
lowerCamelCase_ = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
] , )
lowerCamelCase_ = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
[
{"score": 0.9_944, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_009, "answer": "us-001", "start": 16, "end": 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = pipeline(
"document-question-answering" , model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa" , revision="9977165" , max_seq_len=50 , )
lowerCamelCase_ = INVOICE_URL
lowerCamelCase_ = "What is the invoice number?"
lowerCamelCase_ = dqa_pipeline(image=UpperCamelCase , question=UpperCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
] , )
lowerCamelCase_ = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
] , )
lowerCamelCase_ = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
[
{"score": 0.9_974, "answer": "1110212019", "start": 23, "end": 23},
{"score": 0.9_948, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=UpperCamelCase )
lowerCamelCase_ = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=UpperCamelCase , revision="3dc6de3" , )
lowerCamelCase_ = INVOICE_URL
lowerCamelCase_ = "What is the invoice number?"
lowerCamelCase_ = dqa_pipeline(image=UpperCamelCase , question=UpperCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
lowerCamelCase_ = dqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
lowerCamelCase_ = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
[
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
]
]
* 2 , )
lowerCamelCase_ = list(zip(*apply_tesseract(load_image(UpperCamelCase ) , UpperCamelCase , "" ) ) )
# This model should also work if `image` is set to None
lowerCamelCase_ = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
{"score": 0.4_251, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.0_819, "answer": "1110212019", "start": 23, "end": 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = AutoTokenizer.from_pretrained(
"impira/layoutlm-document-qa" , revision="3dc6de3" , add_prefix_space=UpperCamelCase )
lowerCamelCase_ = pipeline(
"document-question-answering" , model="impira/layoutlm-document-qa" , tokenizer=UpperCamelCase , revision="3dc6de3" , max_seq_len=50 , )
lowerCamelCase_ = INVOICE_URL
lowerCamelCase_ = "What is the invoice number?"
lowerCamelCase_ = dqa_pipeline(image=UpperCamelCase , question=UpperCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
] , )
lowerCamelCase_ = dqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
[
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
]
]
* 2 , )
lowerCamelCase_ = list(zip(*apply_tesseract(load_image(UpperCamelCase ) , UpperCamelCase , "" ) ) )
# This model should also work if `image` is set to None
lowerCamelCase_ = dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(UpperCamelCase , decimals=4 ) , [
{"score": 0.9_999, "answer": "us-001", "start": 16, "end": 16},
{"score": 0.9_998, "answer": "us-001", "start": 16, "end": 16},
] , )
@slow
@require_torch
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = pipeline(
"document-question-answering" , model="naver-clova-ix/donut-base-finetuned-docvqa" , tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa" ) , feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa" , )
lowerCamelCase_ = INVOICE_URL
lowerCamelCase_ = "What is the invoice number?"
lowerCamelCase_ = dqa_pipeline(image=UpperCamelCase , question=UpperCamelCase , top_k=2 )
self.assertEqual(nested_simplify(UpperCamelCase , decimals=4 ) , [{"answer": "us-001"}] )
@require_tf
@unittest.skip("Document question answering not implemented in TF" )
def snake_case ( self ):
"""simple docstring"""
pass
| 55 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : Optional[int] = {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : List[str] = ['''ReformerTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Union[str, Any] = ['''ReformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ : Any = [
'''REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ReformerAttention''',
'''ReformerForMaskedLM''',
'''ReformerForQuestionAnswering''',
'''ReformerForSequenceClassification''',
'''ReformerLayer''',
'''ReformerModel''',
'''ReformerModelWithLMHead''',
'''ReformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer import ReformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_reformer_fast import ReformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_reformer import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerAttention,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerLayer,
ReformerModel,
ReformerModelWithLMHead,
ReformerPreTrainedModel,
)
else:
import sys
snake_case__ : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 355 | '''simple docstring'''
from manim import *
class __SCREAMING_SNAKE_CASE ( lowerCamelCase_ ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
UpperCAmelCase_ : Dict = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase_ : Any = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
UpperCAmelCase_ : List[str] = Rectangle(height=0.25 , width=0.25 )
UpperCAmelCase_ : Any = [mem.copy() for i in range(6 )]
UpperCAmelCase_ : Union[str, Any] = [mem.copy() for i in range(6 )]
UpperCAmelCase_ : Optional[int] = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
UpperCAmelCase_ : Any = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
UpperCAmelCase_ : str = VGroup(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0 )
UpperCAmelCase_ : Any = Text('CPU' , font_size=2_4 )
UpperCAmelCase_ : Tuple = Group(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0.5 , aligned_edge=snake_case_ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(snake_case_ )
UpperCAmelCase_ : str = [mem.copy() for i in range(4 )]
UpperCAmelCase_ : Dict = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
UpperCAmelCase_ : List[str] = Text('GPU' , font_size=2_4 )
UpperCAmelCase_ : Optional[int] = Group(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0.5 , aligned_edge=snake_case_ )
gpu.move_to([-1, -1, 0] )
self.add(snake_case_ )
UpperCAmelCase_ : str = [mem.copy() for i in range(6 )]
UpperCAmelCase_ : Tuple = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
UpperCAmelCase_ : str = Text('Model' , font_size=2_4 )
UpperCAmelCase_ : Optional[int] = Group(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0.5 , aligned_edge=snake_case_ )
model.move_to([3, -1.0, 0] )
self.add(snake_case_ )
UpperCAmelCase_ : str = []
UpperCAmelCase_ : Optional[Any] = []
for i, rect in enumerate(snake_case_ ):
UpperCAmelCase_ : str = fill.copy().set_fill(snake_case_ , opacity=0.8 )
target.move_to(snake_case_ )
model_arr.append(snake_case_ )
UpperCAmelCase_ : int = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(snake_case_ , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(snake_case_ )
self.add(*snake_case_ , *snake_case_ )
UpperCAmelCase_ : List[Any] = [meta_mem.copy() for i in range(6 )]
UpperCAmelCase_ : List[str] = [meta_mem.copy() for i in range(6 )]
UpperCAmelCase_ : Tuple = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
UpperCAmelCase_ : Dict = VGroup(*snake_case_ ).arrange(snake_case_ , buff=0 )
UpperCAmelCase_ : Optional[Any] = VGroup(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0 )
UpperCAmelCase_ : Tuple = Text('Disk' , font_size=2_4 )
UpperCAmelCase_ : Union[str, Any] = Group(snake_case_ , snake_case_ ).arrange(snake_case_ , buff=0.5 , aligned_edge=snake_case_ )
disk.move_to([-4, -1.25, 0] )
self.add(snake_case_ , snake_case_ )
UpperCAmelCase_ : List[Any] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
UpperCAmelCase_ : Any = MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=1_8 , )
key_text.move_to([-5, 2.4, 0] )
self.add(snake_case_ , snake_case_ )
UpperCAmelCase_ : Dict = MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=1_8 , )
blue_text.next_to(snake_case_ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(snake_case_ )
UpperCAmelCase_ : Optional[Any] = MarkupText(
F'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.''' , font_size=2_4 , )
step_a.move_to([2, 2, 0] )
self.play(Write(snake_case_ ) )
UpperCAmelCase_ : Tuple = Square(0.3 )
input.set_fill(snake_case_ , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , snake_case_ , buff=0.5 )
self.play(Write(snake_case_ ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=snake_case_ , buff=0.02 )
self.play(MoveToTarget(snake_case_ ) )
self.play(FadeOut(snake_case_ ) )
UpperCAmelCase_ : Any = Arrow(start=snake_case_ , end=snake_case_ , color=snake_case_ , buff=0.5 )
a.next_to(model_arr[0].get_left() , snake_case_ , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
UpperCAmelCase_ : List[str] = MarkupText(
F'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.''' , font_size=2_4 , )
step_a.move_to([2, 2, 0] )
self.play(Write(snake_case_ , run_time=3 ) )
UpperCAmelCase_ : List[Any] = {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}
self.play(
Write(snake_case_ ) , Circumscribe(model_arr[0] , color=snake_case_ , **snake_case_ ) , Circumscribe(model_cpu_arr[0] , color=snake_case_ , **snake_case_ ) , Circumscribe(gpu_rect[0] , color=snake_case_ , **snake_case_ ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
UpperCAmelCase_ : Union[str, Any] = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.02 , snake_case_ , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02 )
UpperCAmelCase_ : Tuple = AnimationGroup(
FadeOut(snake_case_ , run_time=0.5 ) , MoveToTarget(snake_case_ , run_time=0.5 ) , FadeIn(snake_case_ , run_time=0.5 ) , lag_ratio=0.2 )
self.play(snake_case_ )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
UpperCAmelCase_ : Any = 0.7
self.play(
Circumscribe(model_arr[i] , **snake_case_ ) , Circumscribe(cpu_left_col_base[i] , **snake_case_ ) , Circumscribe(cpu_left_col_base[i + 1] , color=snake_case_ , **snake_case_ ) , Circumscribe(gpu_rect[0] , color=snake_case_ , **snake_case_ ) , Circumscribe(model_arr[i + 1] , color=snake_case_ , **snake_case_ ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=snake_case_ , **snake_case_ ) , Circumscribe(cpu_left_col_base[-1] , color=snake_case_ , **snake_case_ ) , Circumscribe(gpu_rect[0] , color=snake_case_ , **snake_case_ ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
UpperCAmelCase_ : Any = a_c
UpperCAmelCase_ : int = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5 )
self.play(
FadeOut(snake_case_ ) , FadeOut(snake_case_ , run_time=0.5 ) , )
UpperCAmelCase_ : Optional[Any] = MarkupText(F'''Inference on a model too large for GPU memory\nis successfully completed.''' , font_size=2_4 )
step_a.move_to([2, 2, 0] )
self.play(Write(snake_case_ , run_time=3 ) , MoveToTarget(snake_case_ ) )
self.wait()
| 274 | 0 |
"""simple docstring"""
lowerCamelCase_ : Any = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
lowerCamelCase_ : Optional[int] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _A ( lowercase , lowercase , lowercase ):
"""simple docstring"""
a =True
a =[]
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(lowercase , lowercase , lowercase )
order.append(lowercase )
return order
def _A ( lowercase , lowercase , lowercase ):
"""simple docstring"""
a =True
a =[vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(lowercase , lowercase , lowercase )
return component
def _A ( lowercase ):
"""simple docstring"""
a =len(lowercase ) * [False]
a ={vert: [] for vert in range(len(lowercase ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(lowercase )
a =[]
for i, was_visited in enumerate(lowercase ):
if not was_visited:
order += topology_sort(lowercase , lowercase , lowercase )
a =[]
a =len(lowercase ) * [False]
for i in range(len(lowercase ) ):
a =order[len(lowercase ) - i - 1]
if not visited[vert]:
a =find_components(lowercase , lowercase , lowercase )
components_list.append(lowercase )
return components_list | 81 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCamelCase_( _snake_case : Dict , _snake_case : Optional[int] , _snake_case : str ):
"""simple docstring"""
if openai_config_file == "":
__a =OpenAIGPTConfig()
else:
__a =OpenAIGPTConfig.from_json_file(_snake_case )
__a =OpenAIGPTModel(_snake_case )
# Load weights from numpy
load_tf_weights_in_openai_gpt(_snake_case , _snake_case , _snake_case )
# Save pytorch-model
__a =pytorch_dump_folder_path + '/' + WEIGHTS_NAME
__a =pytorch_dump_folder_path + '/' + CONFIG_NAME
print(F'Save PyTorch model to {pytorch_weights_dump_path}' )
torch.save(model.state_dict() , _snake_case )
print(F'Save configuration file to {pytorch_config_dump_path}' )
with open(_snake_case , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
_lowerCAmelCase : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--openai_checkpoint_folder_path",
default=None,
type=str,
required=True,
help="Path to the TensorFlow checkpoint path.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--openai_config_file",
default="",
type=str,
help=(
"An optional config json file corresponding to the pre-trained OpenAI model. \n"
"This specifies the model architecture."
),
)
_lowerCAmelCase : int = parser.parse_args()
convert_openai_checkpoint_to_pytorch(
args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
)
| 218 | 0 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
__snake_case ="""\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n"""
__snake_case ="""\nArgs:\n predictions (`List[ndarray]`):\n List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n references (`List[ndarray]`):\n List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n num_labels (`int`):\n Number of classes (categories).\n ignore_index (`int`):\n Index that will be ignored during evaluation.\n nan_to_num (`int`, *optional*):\n If specified, NaN values will be replaced by the number defined by the user.\n label_map (`dict`, *optional*):\n If specified, dictionary mapping old label indices to new label indices.\n reduce_labels (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n `Dict[str, float | ndarray]` comprising various elements:\n - *mean_iou* (`float`):\n Mean Intersection-over-Union (IoU averaged over all categories).\n - *mean_accuracy* (`float`):\n Mean accuracy (averaged over all categories).\n - *overall_accuracy* (`float`):\n Overall accuracy on all images.\n - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n Per category accuracy.\n - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n Per category IoU.\n\nExamples:\n\n >>> import numpy as np\n\n >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n >>> # suppose one has 3 different segmentation maps predicted\n >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n >>> predicted = [predicted_1, predicted_2, predicted_3]\n >>> ground_truth = [actual_1, actual_2, actual_3]\n\n >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), 'per_category_accuracy': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}\n"""
__snake_case ="""\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"""
def a_ ( lowerCamelCase : Union[str, Any] , lowerCamelCase : Union[str, Any] , lowerCamelCase : List[str] , lowerCamelCase : bool , lowerCamelCase : Optional[Dict[int, int]] = None , lowerCamelCase : bool = False , ):
if label_map is not None:
for old_id, new_id in label_map.items():
lowerCAmelCase = new_id
# turn into Numpy arrays
lowerCAmelCase = np.array(lowerCAmelCase__ )
lowerCAmelCase = np.array(lowerCAmelCase__ )
if reduce_labels:
lowerCAmelCase = 255
lowerCAmelCase = label - 1
lowerCAmelCase = 255
lowerCAmelCase = label != ignore_index
lowerCAmelCase = np.not_equal(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase = pred_label[mask]
lowerCAmelCase = np.array(lowerCAmelCase__ )[mask]
lowerCAmelCase = pred_label[pred_label == label]
lowerCAmelCase = np.histogram(lowerCAmelCase__ , bins=lowerCAmelCase__ , range=(0, num_labels - 1) )[0]
lowerCAmelCase = np.histogram(lowerCAmelCase__ , bins=lowerCAmelCase__ , range=(0, num_labels - 1) )[0]
lowerCAmelCase = np.histogram(lowerCAmelCase__ , bins=lowerCAmelCase__ , range=(0, num_labels - 1) )[0]
lowerCAmelCase = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def a_ ( lowerCamelCase : Any , lowerCamelCase : Any , lowerCamelCase : int , lowerCamelCase : bool , lowerCamelCase : Optional[Dict[int, int]] = None , lowerCamelCase : bool = False , ):
lowerCAmelCase = np.zeros((num_labels,) , dtype=np.floataa )
lowerCAmelCase = np.zeros((num_labels,) , dtype=np.floataa )
lowerCAmelCase = np.zeros((num_labels,) , dtype=np.floataa )
lowerCAmelCase = np.zeros((num_labels,) , dtype=np.floataa )
for result, gt_seg_map in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = intersect_and_union(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def a_ ( lowerCamelCase : Any , lowerCamelCase : Tuple , lowerCamelCase : Optional[Any] , lowerCamelCase : bool , lowerCamelCase : Optional[int] = None , lowerCamelCase : Optional[Dict[int, int]] = None , lowerCamelCase : bool = False , ):
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = total_intersect_and_union(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# compute metrics
lowerCAmelCase = {}
lowerCAmelCase = total_area_intersect.sum() / total_area_label.sum()
lowerCAmelCase = total_area_intersect / total_area_union
lowerCAmelCase = total_area_intersect / total_area_label
lowerCAmelCase = np.nanmean(lowerCAmelCase__ )
lowerCAmelCase = np.nanmean(lowerCAmelCase__ )
lowerCAmelCase = all_acc
lowerCAmelCase = iou
lowerCAmelCase = acc
if nan_to_num is not None:
lowerCAmelCase = {metric: np.nan_to_num(lowerCAmelCase__ , nan=lowerCAmelCase__ ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase_ ( datasets.Metric ):
def __UpperCAmelCase ( self : int ) -> str:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
'predictions': datasets.Sequence(datasets.Sequence(datasets.Value('uint16' ) ) ),
'references': datasets.Sequence(datasets.Sequence(datasets.Value('uint16' ) ) ),
} ) , reference_urls=[
'https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'
] , )
def __UpperCAmelCase ( self : Optional[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : Any , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Union[str, Any] = None , UpperCAmelCase__ : Any = None , UpperCAmelCase__ : Optional[Any] = False , ) -> int:
lowerCAmelCase = mean_iou(
results=_a , gt_seg_maps=_a , num_labels=_a , ignore_index=_a , nan_to_num=_a , label_map=_a , reduce_labels=_a , )
return iou_result
| 361 |
'''simple docstring'''
from __future__ import annotations
def a_ ( lowerCamelCase : list[float] , lowerCamelCase : list[float] ):
lowerCAmelCase = sorted(numsa + numsa )
lowerCAmelCase , lowerCAmelCase = divmod(len(lowerCamelCase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
__snake_case =[float(x) for x in input("""Enter the elements of first array: """).split()]
__snake_case =[float(x) for x in input("""Enter the elements of second array: """).split()]
print(F'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')
| 55 | 0 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError
from .utils import require_pil
class __snake_case ( __lowerCamelCase ):
'''simple docstring'''
def UpperCAmelCase__ ( self : Union[str, Any] ):
__snake_case: List[Any] = pa.array(TypedSequence([1, 2, 3] ) )
self.assertEqual(arr.type , pa.intaa() )
def UpperCAmelCase__ ( self : Optional[int] ):
with self.assertRaises(A ):
__snake_case: Union[str, Any] = pa.array(TypedSequence([1, 2, 3] ) , type=pa.intaa() )
def UpperCAmelCase__ ( self : int ):
with self.assertRaises(A ):
__snake_case: List[Any] = pa.array(TypedSequence([1, 2, 3] , try_type=Value("""bool""" ) , type=Value("""int64""" ) ) )
def UpperCAmelCase__ ( self : Union[str, Any] ):
__snake_case: int = pa.array(TypedSequence([1, 2, 3] , type=Value("""int32""" ) ) )
self.assertEqual(arr.type , pa.intaa() )
def UpperCAmelCase__ ( self : Optional[Any] ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
__snake_case: str = pa.array(TypedSequence(["""foo""", """bar"""] , type=Value("""int64""" ) ) )
def UpperCAmelCase__ ( self : Dict ):
__snake_case: Any = pa.array(TypedSequence([1, 2, 3] , try_type=Value("""int32""" ) ) )
self.assertEqual(arr.type , pa.intaa() )
def UpperCAmelCase__ ( self : Optional[Any] ):
__snake_case: Tuple = pa.array(TypedSequence(["""foo""", """bar"""] , try_type=Value("""int64""" ) ) )
self.assertEqual(arr.type , pa.string() )
def UpperCAmelCase__ ( self : Dict ):
__snake_case: str = pa.array(TypedSequence([[[1, 2, 3]]] , type=ArrayaD((1, 3) , """int64""" ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , """int64""" ) )
def UpperCAmelCase__ ( self : Dict ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
__snake_case: List[Any] = pa.array(TypedSequence(["""foo""", """bar"""] , type=ArrayaD((1, 3) , """int64""" ) ) )
def UpperCAmelCase__ ( self : Union[str, Any] ):
__snake_case: List[Any] = pa.array(TypedSequence([[[1, 2, 3]]] , try_type=ArrayaD((1, 3) , """int64""" ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , """int64""" ) )
def UpperCAmelCase__ ( self : Optional[Any] ):
__snake_case: Tuple = pa.array(TypedSequence(["""foo""", """bar"""] , try_type=ArrayaD((1, 3) , """int64""" ) ) )
self.assertEqual(arr.type , pa.string() )
@require_pil
def UpperCAmelCase__ ( self : str ):
import PIL.Image
__snake_case: Union[str, Any] = PIL.Image.fromarray(np.arange(10 , dtype=np.uinta ).reshape(2 , 5 ) )
with patch(
"""datasets.arrow_writer.cast_to_python_objects""" , side_effect=A ) as mock_cast_to_python_objects:
__snake_case: List[Any] = pa.array(TypedSequence([{"""path""": None, """bytes""": b"""image_bytes"""}, pil_image] , type=Image() ) )
__snake_case , __snake_case: List[Any] = mock_cast_to_python_objects.call_args_list[-1]
self.assertIn("""optimize_list_casting""" , A )
self.assertFalse(kwargs["""optimize_list_casting"""] )
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> Tuple:
__snake_case: List[Any] = pa.BufferReader(SCREAMING_SNAKE_CASE__) if isinstance(SCREAMING_SNAKE_CASE__ , pa.Buffer) else pa.memory_map(SCREAMING_SNAKE_CASE__)
__snake_case: Tuple = pa.ipc.open_stream(SCREAMING_SNAKE_CASE__)
__snake_case: pa.Table = f.read_all()
assert len(pa_table.to_batches()) == expected_num_chunks
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
del pa_table
@pytest.mark.parametrize("""writer_batch_size""" , [None, 1, 10])
@pytest.mark.parametrize(
"""fields""" , [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}])
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> int:
__snake_case: List[str] = pa.BufferOutputStream()
__snake_case: Union[str, Any] = pa.schema(SCREAMING_SNAKE_CASE__) if fields else None
with ArrowWriter(stream=SCREAMING_SNAKE_CASE__ , schema=SCREAMING_SNAKE_CASE__ , writer_batch_size=SCREAMING_SNAKE_CASE__) as writer:
writer.write({"""col_1""": """foo""", """col_2""": 1})
writer.write({"""col_1""": """bar""", """col_2""": 2})
__snake_case , __snake_case: Union[str, Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
__snake_case: Dict = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE__ , metadata=writer._schema.metadata)
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1)
def A__ ( ) -> Union[str, Any]:
__snake_case: Union[str, Any] = pa.BufferOutputStream()
__snake_case: Tuple = Features({"""labels""": ClassLabel(names=["""neg""", """pos"""])})
with ArrowWriter(stream=SCREAMING_SNAKE_CASE__ , features=SCREAMING_SNAKE_CASE__) as writer:
writer.write({"""labels""": 0})
writer.write({"""labels""": 1})
__snake_case , __snake_case: List[str] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == features.arrow_schema
assert writer._schema.metadata == features.arrow_schema.metadata
__snake_case: Optional[Any] = pa.BufferReader(output.getvalue())
__snake_case: Optional[int] = pa.ipc.open_stream(SCREAMING_SNAKE_CASE__)
__snake_case: pa.Table = f.read_all()
__snake_case: Tuple = pa_table.schema
assert pa_table.num_rows == 2
assert schema == features.arrow_schema
assert schema.metadata == features.arrow_schema.metadata
assert features == Features.from_arrow_schema(SCREAMING_SNAKE_CASE__)
@pytest.mark.parametrize("""writer_batch_size""" , [None, 1, 10])
def A__ ( SCREAMING_SNAKE_CASE__) -> Tuple:
__snake_case: List[str] = pa.BufferOutputStream()
with ArrowWriter(
stream=SCREAMING_SNAKE_CASE__ , writer_batch_size=SCREAMING_SNAKE_CASE__ , hash_salt="""split_name""" , check_duplicates=SCREAMING_SNAKE_CASE__ , ) as writer:
with pytest.raises(SCREAMING_SNAKE_CASE__):
writer.write({"""col_1""": """foo""", """col_2""": 1} , key=[1, 2])
__snake_case , __snake_case: List[str] = writer.finalize()
@pytest.mark.parametrize("""writer_batch_size""" , [None, 2, 10])
def A__ ( SCREAMING_SNAKE_CASE__) -> Any:
__snake_case: Union[str, Any] = pa.BufferOutputStream()
with ArrowWriter(
stream=SCREAMING_SNAKE_CASE__ , writer_batch_size=SCREAMING_SNAKE_CASE__ , hash_salt="""split_name""" , check_duplicates=SCREAMING_SNAKE_CASE__ , ) as writer:
with pytest.raises(SCREAMING_SNAKE_CASE__):
writer.write({"""col_1""": """foo""", """col_2""": 1} , key=10)
writer.write({"""col_1""": """bar""", """col_2""": 2} , key=10)
__snake_case , __snake_case: Tuple = writer.finalize()
@pytest.mark.parametrize("""writer_batch_size""" , [None, 2, 10])
def A__ ( SCREAMING_SNAKE_CASE__) -> Optional[int]:
__snake_case: str = pa.BufferOutputStream()
with ArrowWriter(
stream=SCREAMING_SNAKE_CASE__ , writer_batch_size=SCREAMING_SNAKE_CASE__ , hash_salt="""split_name""" , check_duplicates=SCREAMING_SNAKE_CASE__ , ) as writer:
writer.write({"""col_1""": """foo""", """col_2""": 1} , key=1)
writer.write({"""col_1""": """bar""", """col_2""": 2} , key=2)
__snake_case , __snake_case: Any = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1)
@pytest.mark.parametrize("""writer_batch_size""" , [None, 1, 10])
@pytest.mark.parametrize(
"""fields""" , [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}])
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> Optional[int]:
__snake_case: Tuple = pa.BufferOutputStream()
__snake_case: List[str] = pa.schema(SCREAMING_SNAKE_CASE__) if fields else None
with ArrowWriter(stream=SCREAMING_SNAKE_CASE__ , schema=SCREAMING_SNAKE_CASE__ , writer_batch_size=SCREAMING_SNAKE_CASE__) as writer:
writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]})
writer.write_batch({"""col_1""": [], """col_2""": []})
__snake_case , __snake_case: Optional[Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
__snake_case: Optional[int] = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE__ , metadata=writer._schema.metadata)
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1)
@pytest.mark.parametrize("""writer_batch_size""" , [None, 1, 10])
@pytest.mark.parametrize(
"""fields""" , [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}])
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> List[str]:
__snake_case: Tuple = pa.BufferOutputStream()
__snake_case: Optional[int] = pa.schema(SCREAMING_SNAKE_CASE__) if fields else None
with ArrowWriter(stream=SCREAMING_SNAKE_CASE__ , schema=SCREAMING_SNAKE_CASE__ , writer_batch_size=SCREAMING_SNAKE_CASE__) as writer:
writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}))
__snake_case , __snake_case: str = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
__snake_case: Any = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE__ , metadata=writer._schema.metadata)
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1)
@pytest.mark.parametrize("""writer_batch_size""" , [None, 1, 10])
@pytest.mark.parametrize(
"""fields""" , [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}])
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> Union[str, Any]:
__snake_case: int = pa.BufferOutputStream()
__snake_case: Optional[int] = pa.schema(SCREAMING_SNAKE_CASE__) if fields else None
with ArrowWriter(stream=SCREAMING_SNAKE_CASE__ , schema=SCREAMING_SNAKE_CASE__ , writer_batch_size=SCREAMING_SNAKE_CASE__) as writer:
writer.write_row(pa.Table.from_pydict({"""col_1""": ["""foo"""], """col_2""": [1]}))
writer.write_row(pa.Table.from_pydict({"""col_1""": ["""bar"""], """col_2""": [2]}))
__snake_case , __snake_case: Union[str, Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
__snake_case: Tuple = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE__ , metadata=writer._schema.metadata)
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1)
def A__ ( ) -> str:
with tempfile.TemporaryDirectory() as tmp_dir:
__snake_case: Optional[int] = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
__snake_case: Any = os.path.join(SCREAMING_SNAKE_CASE__ , """test.arrow""")
with ArrowWriter(path=SCREAMING_SNAKE_CASE__ , schema=pa.schema(SCREAMING_SNAKE_CASE__)) as writer:
writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]})
__snake_case , __snake_case: Any = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == pa.schema(SCREAMING_SNAKE_CASE__ , metadata=writer._schema.metadata)
_check_output(SCREAMING_SNAKE_CASE__ , 1)
def A__ ( SCREAMING_SNAKE_CASE__) -> Union[str, Any]:
if pa.types.is_list(SCREAMING_SNAKE_CASE__):
return get_base_dtype(arr_type.value_type)
else:
return arr_type
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> int:
if isinstance(lst[0] , SCREAMING_SNAKE_CASE__):
change_first_primitive_element_in_list(lst[0] , SCREAMING_SNAKE_CASE__)
else:
__snake_case: Optional[Any] = value
@pytest.mark.parametrize("""optimized_int_type, expected_dtype""" , [(None, pa.intaa()), (Value("""int32"""), pa.intaa())])
@pytest.mark.parametrize("""sequence""" , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]])
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> str:
__snake_case: str = pa.array(TypedSequence(SCREAMING_SNAKE_CASE__ , optimized_int_type=SCREAMING_SNAKE_CASE__))
assert get_base_dtype(arr.type) == expected_dtype
@pytest.mark.parametrize(
"""col, expected_dtype""" , [
("""attention_mask""", pa.inta()),
("""special_tokens_mask""", pa.inta()),
("""token_type_ids""", pa.inta()),
("""input_ids""", pa.intaa()),
("""other""", pa.intaa()),
] , )
@pytest.mark.parametrize("""sequence""" , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]])
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> Optional[Any]:
# in range
__snake_case: Optional[int] = pa.array(OptimizedTypedSequence(SCREAMING_SNAKE_CASE__ , col=SCREAMING_SNAKE_CASE__))
assert get_base_dtype(arr.type) == expected_dtype
# not in range
if col != "other":
# avoids errors due to in-place modifications
__snake_case: Optional[int] = copy.deepcopy(SCREAMING_SNAKE_CASE__)
__snake_case: Optional[int] = np.iinfo(expected_dtype.to_pandas_dtype()).max + 1
change_first_primitive_element_in_list(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
__snake_case: Union[str, Any] = pa.array(OptimizedTypedSequence(SCREAMING_SNAKE_CASE__ , col=SCREAMING_SNAKE_CASE__))
assert get_base_dtype(arr.type) == pa.intaa()
@pytest.mark.parametrize("""raise_exception""" , [False, True])
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> List[str]:
__snake_case: int = str(tmp_path / """dataset-train.arrow""")
try:
with ArrowWriter(path=SCREAMING_SNAKE_CASE__) as writer:
if raise_exception:
raise pa.lib.ArrowInvalid()
else:
writer.stream.close()
except pa.lib.ArrowInvalid:
pass
finally:
assert writer.stream.closed
def A__ ( SCREAMING_SNAKE_CASE__) -> List[Any]:
__snake_case: Any = """mock://dataset-train.arrow"""
with ArrowWriter(path=SCREAMING_SNAKE_CASE__ , storage_options=mockfs.storage_options) as writer:
assert isinstance(writer._fs , type(SCREAMING_SNAKE_CASE__))
assert writer._fs.storage_options == mockfs.storage_options
writer.write({"""col_1""": """foo""", """col_2""": 1})
writer.write({"""col_1""": """bar""", """col_2""": 2})
__snake_case , __snake_case: Dict = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert mockfs.exists(SCREAMING_SNAKE_CASE__)
def A__ ( ) -> Optional[int]:
__snake_case: List[str] = pa.BufferOutputStream()
with ParquetWriter(stream=SCREAMING_SNAKE_CASE__) as writer:
writer.write({"""col_1""": """foo""", """col_2""": 1})
writer.write({"""col_1""": """bar""", """col_2""": 2})
__snake_case , __snake_case: str = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
__snake_case: List[Any] = pa.BufferReader(output.getvalue())
__snake_case: pa.Table = pq.read_table(SCREAMING_SNAKE_CASE__)
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize("""embed_local_files""" , [False, True])
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> str:
import PIL.Image
__snake_case: Any = str(tmp_path / """test_image_rgb.jpg""")
PIL.Image.fromarray(np.zeros((5, 5) , dtype=np.uinta)).save(SCREAMING_SNAKE_CASE__ , format="""png""")
__snake_case: List[str] = pa.BufferOutputStream()
with ParquetWriter(
stream=SCREAMING_SNAKE_CASE__ , features=Features({"""image""": Image()}) , embed_local_files=SCREAMING_SNAKE_CASE__) as writer:
writer.write({"""image""": image_path})
writer.finalize()
__snake_case: Tuple = pa.BufferReader(output.getvalue())
__snake_case: pa.Table = pq.read_table(SCREAMING_SNAKE_CASE__)
__snake_case: Tuple = pa_table.to_pydict()
if embed_local_files:
assert isinstance(out["""image"""][0]["""path"""] , SCREAMING_SNAKE_CASE__)
with open(SCREAMING_SNAKE_CASE__ , """rb""") as f:
assert out["image"][0]["bytes"] == f.read()
else:
assert out["image"][0]["path"] == image_path
assert out["image"][0]["bytes"] is None
def A__ ( ) -> Union[str, Any]:
__snake_case: Optional[int] = pa.schema([pa.field("""col_1""" , pa.string() , nullable=SCREAMING_SNAKE_CASE__)])
__snake_case: Tuple = pa.BufferOutputStream()
with ArrowWriter(stream=SCREAMING_SNAKE_CASE__) as writer:
writer._build_writer(inferred_schema=SCREAMING_SNAKE_CASE__)
assert writer._schema == pa.schema([pa.field("""col_1""" , pa.string())])
| 111 |
import pytest
__UpperCAmelCase : Optional[Any] = "__dummy_dataset1__"
__UpperCAmelCase : List[str] = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n def _info(self):\n features = datasets.Features(\n {\n \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n \"ner_tags\": datasets.Sequence(\n datasets.features.ClassLabel(\n names=[\n \"O\",\n \"B-PER\",\n \"I-PER\",\n \"B-ORG\",\n \"I-ORG\",\n \"B-LOC\",\n \"I-LOC\",\n ]\n )\n ),\n \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n }\n )\n return datasets.DatasetInfo(features=features)\n\n def _split_generators(self, dl_manager):\n dl_path = dl_manager.download(URLS)\n return [\n datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n ]\n\n def _generate_examples(self, filepath):\n with open(filepath, \"r\", encoding=\"utf-8\") as f:\n for i, line in enumerate(f):\n yield i, json.loads(line)\n"
@pytest.fixture
def A__ ( ) -> Optional[int]:
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def A__ ( ) -> Tuple:
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> Tuple:
__snake_case: List[Any] = dataset_loading_script_name
__snake_case: Any = tmp_path / """datasets""" / script_name
script_dir.mkdir(parents=SCREAMING_SNAKE_CASE__)
__snake_case: int = script_dir / F'''{script_name}.py'''
with open(SCREAMING_SNAKE_CASE__ , """w""") as f:
f.write(SCREAMING_SNAKE_CASE__)
return str(SCREAMING_SNAKE_CASE__)
| 111 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
UpperCAmelCase_ = (720, 1280) # Height, Width
UpperCAmelCase_ = (0.4, 0.6) # if height or width lower than this scale, drop it.
UpperCAmelCase_ = 1 / 100
UpperCAmelCase_ = ''
UpperCAmelCase_ = ''
UpperCAmelCase_ = ''
UpperCAmelCase_ = 250
def lowerCAmelCase_ ( ) -> None:
UpperCamelCase__ : int = get_dataset(__UpperCAmelCase , __UpperCAmelCase )
for index in range(__UpperCAmelCase ):
UpperCamelCase__ : List[str] = random.sample(range(len(__UpperCAmelCase ) ) , 4 )
UpperCamelCase__ : Any = update_image_and_anno(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , filter_scale=__UpperCAmelCase , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
UpperCamelCase__ : List[Any] = random_chars(32 )
UpperCamelCase__ : Dict = path.split(os.sep )[-1].rsplit('''.''' , 1 )[0]
UpperCamelCase__ : Any = f"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
cva.imwrite(f"{file_root}.jpg" , __UpperCAmelCase , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}" )
UpperCamelCase__ : Any = []
for anno in new_annos:
UpperCamelCase__ : Tuple = anno[3] - anno[1]
UpperCamelCase__ : List[str] = anno[4] - anno[2]
UpperCamelCase__ : List[str] = anno[1] + width / 2
UpperCamelCase__ : Tuple = anno[2] + height / 2
UpperCamelCase__ : Tuple = f"{anno[0]} {x_center} {y_center} {width} {height}"
annos_list.append(__UpperCAmelCase )
with open(f"{file_root}.txt" , '''w''' ) as outfile:
outfile.write('''\n'''.join(line for line in annos_list ) )
def lowerCAmelCase_ ( __UpperCAmelCase: str , __UpperCAmelCase: str ) -> tuple[list, list]:
UpperCamelCase__ : Union[str, Any] = []
UpperCamelCase__ : Optional[int] = []
for label_file in glob.glob(os.path.join(__UpperCAmelCase , '''*.txt''' ) ):
UpperCamelCase__ : Dict = label_file.split(os.sep )[-1].rsplit('''.''' , 1 )[0]
with open(__UpperCAmelCase ) as in_file:
UpperCamelCase__ : Dict = in_file.readlines()
UpperCamelCase__ : str = os.path.join(__UpperCAmelCase , f"{label_name}.jpg" )
UpperCamelCase__ : int = []
for obj_list in obj_lists:
UpperCamelCase__ : Optional[int] = obj_list.rstrip('''\n''' ).split(''' ''' )
UpperCamelCase__ : Tuple = float(obj[1] ) - float(obj[3] ) / 2
UpperCamelCase__ : int = float(obj[2] ) - float(obj[4] ) / 2
UpperCamelCase__ : List[Any] = float(obj[1] ) + float(obj[3] ) / 2
UpperCamelCase__ : Tuple = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(__UpperCAmelCase )
labels.append(__UpperCAmelCase )
return img_paths, labels
def lowerCAmelCase_ ( __UpperCAmelCase: list , __UpperCAmelCase: list , __UpperCAmelCase: list[int] , __UpperCAmelCase: tuple[int, int] , __UpperCAmelCase: tuple[float, float] , __UpperCAmelCase: float = 0.0 , ) -> tuple[list, list, str]:
UpperCamelCase__ : List[Any] = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
UpperCamelCase__ : List[str] = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
UpperCamelCase__ : Tuple = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
UpperCamelCase__ : Optional[Any] = int(scale_x * output_size[1] )
UpperCamelCase__ : Union[str, Any] = int(scale_y * output_size[0] )
UpperCamelCase__ : Union[str, Any] = []
UpperCamelCase__ : Any = []
for i, index in enumerate(__UpperCAmelCase ):
UpperCamelCase__ : int = all_img_list[index]
path_list.append(__UpperCAmelCase )
UpperCamelCase__ : List[Any] = all_annos[index]
UpperCamelCase__ : Dict = cva.imread(__UpperCAmelCase )
if i == 0: # top-left
UpperCamelCase__ : Optional[Any] = cva.resize(__UpperCAmelCase , (divid_point_x, divid_point_y) )
UpperCamelCase__ : List[Any] = img
for bbox in img_annos:
UpperCamelCase__ : str = bbox[1] * scale_x
UpperCamelCase__ : Tuple = bbox[2] * scale_y
UpperCamelCase__ : Dict = bbox[3] * scale_x
UpperCamelCase__ : Optional[Any] = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
UpperCamelCase__ : str = cva.resize(__UpperCAmelCase , (output_size[1] - divid_point_x, divid_point_y) )
UpperCamelCase__ : Optional[Any] = img
for bbox in img_annos:
UpperCamelCase__ : Tuple = scale_x + bbox[1] * (1 - scale_x)
UpperCamelCase__ : List[str] = bbox[2] * scale_y
UpperCamelCase__ : Optional[Any] = scale_x + bbox[3] * (1 - scale_x)
UpperCamelCase__ : Any = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
UpperCamelCase__ : List[Any] = cva.resize(__UpperCAmelCase , (divid_point_x, output_size[0] - divid_point_y) )
UpperCamelCase__ : List[str] = img
for bbox in img_annos:
UpperCamelCase__ : Dict = bbox[1] * scale_x
UpperCamelCase__ : List[Any] = scale_y + bbox[2] * (1 - scale_y)
UpperCamelCase__ : int = bbox[3] * scale_x
UpperCamelCase__ : Any = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
UpperCamelCase__ : List[Any] = cva.resize(
__UpperCAmelCase , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
UpperCamelCase__ : Optional[Any] = img
for bbox in img_annos:
UpperCamelCase__ : Dict = scale_x + bbox[1] * (1 - scale_x)
UpperCamelCase__ : int = scale_y + bbox[2] * (1 - scale_y)
UpperCamelCase__ : Dict = scale_x + bbox[3] * (1 - scale_x)
UpperCamelCase__ : Dict = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
UpperCamelCase__ : int = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def lowerCAmelCase_ ( __UpperCAmelCase: int ) -> str:
assert number_char > 1, "The number of character should greater than 1"
UpperCamelCase__ : Optional[int] = ascii_lowercase + digits
return "".join(random.choice(__UpperCAmelCase ) for _ in range(__UpperCAmelCase ) )
if __name__ == "__main__":
main()
print('DONE ✅')
| 354 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ = logging.get_logger()
def lowerCAmelCase_ ( __UpperCAmelCase: int , __UpperCAmelCase: str , __UpperCAmelCase: LevitConfig , __UpperCAmelCase: Path , __UpperCAmelCase: bool = True ) -> int:
print(f"Converting {name}..." )
with torch.no_grad():
if hidden_sizes == 128:
if name[-1] == "S":
UpperCamelCase__ : List[Any] = timm.create_model('''levit_128s''' , pretrained=__UpperCAmelCase )
else:
UpperCamelCase__ : Tuple = timm.create_model('''levit_128''' , pretrained=__UpperCAmelCase )
if hidden_sizes == 192:
UpperCamelCase__ : str = timm.create_model('''levit_192''' , pretrained=__UpperCAmelCase )
if hidden_sizes == 256:
UpperCamelCase__ : Any = timm.create_model('''levit_256''' , pretrained=__UpperCAmelCase )
if hidden_sizes == 384:
UpperCamelCase__ : int = timm.create_model('''levit_384''' , pretrained=__UpperCAmelCase )
from_model.eval()
UpperCamelCase__ : int = LevitForImageClassificationWithTeacher(__UpperCAmelCase ).eval()
UpperCamelCase__ : str = OrderedDict()
UpperCamelCase__ : Any = from_model.state_dict()
UpperCamelCase__ : Dict = list(from_model.state_dict().keys() )
UpperCamelCase__ : Tuple = list(our_model.state_dict().keys() )
print(len(__UpperCAmelCase ) , len(__UpperCAmelCase ) )
for i in range(len(__UpperCAmelCase ) ):
UpperCamelCase__ : int = weights[og_keys[i]]
our_model.load_state_dict(__UpperCAmelCase )
UpperCamelCase__ : Optional[int] = torch.randn((2, 3, 224, 224) )
UpperCamelCase__ : Any = from_model(__UpperCAmelCase )
UpperCamelCase__ : Any = our_model(__UpperCAmelCase ).logits
assert torch.allclose(__UpperCAmelCase , __UpperCAmelCase ), "The model logits don't match the original one."
UpperCamelCase__ : List[Any] = name
print(__UpperCAmelCase )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
UpperCamelCase__ : Union[str, Any] = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(f"Pushed {checkpoint_name}" )
def lowerCAmelCase_ ( __UpperCAmelCase: Path , __UpperCAmelCase: str = None , __UpperCAmelCase: bool = True ) -> List[str]:
UpperCamelCase__ : Any = '''imagenet-1k-id2label.json'''
UpperCamelCase__ : str = 1000
UpperCamelCase__ : List[str] = (1, num_labels)
UpperCamelCase__ : str = '''huggingface/label-files'''
UpperCamelCase__ : str = num_labels
UpperCamelCase__ : Dict = json.load(open(hf_hub_download(__UpperCAmelCase , __UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCamelCase__ : Optional[Any] = {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCamelCase__ : List[Any] = idalabel
UpperCamelCase__ : Dict = {v: k for k, v in idalabel.items()}
UpperCamelCase__ : Tuple = partial(__UpperCAmelCase , num_labels=__UpperCAmelCase , idalabel=__UpperCAmelCase , labelaid=__UpperCAmelCase )
UpperCamelCase__ : Optional[Any] = {
'''levit-128S''': 128,
'''levit-128''': 128,
'''levit-192''': 192,
'''levit-256''': 256,
'''levit-384''': 384,
}
UpperCamelCase__ : Optional[Any] = {
'''levit-128S''': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 6, 8] , depths=[2, 3, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'''levit-128''': ImageNetPreTrainedConfig(
hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 8, 12] , depths=[4, 4, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ),
'''levit-192''': ImageNetPreTrainedConfig(
hidden_sizes=[192, 288, 384] , num_attention_heads=[3, 5, 6] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'''levit-256''': ImageNetPreTrainedConfig(
hidden_sizes=[256, 384, 512] , num_attention_heads=[4, 6, 8] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ),
'''levit-384''': ImageNetPreTrainedConfig(
hidden_sizes=[384, 512, 768] , num_attention_heads=[6, 9, 12] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0.1 , ),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name] , __UpperCAmelCase , names_to_config[model_name] , __UpperCAmelCase , __UpperCAmelCase )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name] , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
return config, expected_shape
if __name__ == "__main__":
UpperCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default=None,
type=str,
help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='levit-dump-folder/',
type=Path,
required=False,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
parser.add_argument(
'--no-push_to_hub',
dest='push_to_hub',
action='store_false',
help='Do not push model and image processor to the hub',
)
UpperCAmelCase_ = parser.parse_args()
UpperCAmelCase_ = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 247 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase = {
"configuration_distilbert": [
"DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"DistilBertConfig",
"DistilBertOnnxConfig",
],
"tokenization_distilbert": ["DistilBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ["DistilBertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
"DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"DistilBertForMaskedLM",
"DistilBertForMultipleChoice",
"DistilBertForQuestionAnswering",
"DistilBertForSequenceClassification",
"DistilBertForTokenClassification",
"DistilBertModel",
"DistilBertPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
"TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDistilBertForMaskedLM",
"TFDistilBertForMultipleChoice",
"TFDistilBertForQuestionAnswering",
"TFDistilBertForSequenceClassification",
"TFDistilBertForTokenClassification",
"TFDistilBertMainLayer",
"TFDistilBertModel",
"TFDistilBertPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
"FlaxDistilBertForMaskedLM",
"FlaxDistilBertForMultipleChoice",
"FlaxDistilBertForQuestionAnswering",
"FlaxDistilBertForSequenceClassification",
"FlaxDistilBertForTokenClassification",
"FlaxDistilBertModel",
"FlaxDistilBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 178 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowercase = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ["GPTNeoXTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
"GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
"GPTNeoXForCausalLM",
"GPTNeoXForQuestionAnswering",
"GPTNeoXForSequenceClassification",
"GPTNeoXForTokenClassification",
"GPTNeoXLayer",
"GPTNeoXModel",
"GPTNeoXPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 178 | 1 |
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path import join as renamed_join # noqa: this is just for tests
snake_case__ = open # noqa: we just need to have a builtin inside this module to test it properly
| 4 |
'''simple docstring'''
# 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 numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = (
'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
)
_lowerCAmelCase = 'CIDAS/clipseg-rd64-refined'
_lowerCAmelCase = 'image_segmenter'
_lowerCAmelCase = CLIPSegForImageSegmentation
_lowerCAmelCase = ['image', 'text']
_lowerCAmelCase = ['image']
def __init__( self : Optional[int] , *_lowerCamelCase : Optional[int] , **_lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
requires_backends(self , ['''vision'''] )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def _a ( self : List[str] , _lowerCamelCase : "Image" , _lowerCamelCase : str ):
"""simple docstring"""
return self.pre_processor(text=[label] , images=[image] , padding=_lowerCamelCase , return_tensors='''pt''' )
def _a ( self : Union[str, Any] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
with torch.no_grad():
A_ : Optional[int] = self.model(**_lowerCamelCase ).logits
return logits
def _a ( self : List[str] , _lowerCamelCase : Optional[int] ):
"""simple docstring"""
A_ : int = outputs.cpu().detach().numpy()
A_ : Tuple = 0
A_ : List[str] = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 4 | 1 |
def SCREAMING_SNAKE_CASE_ ( __A : int ) -> bool:
"""simple docstring"""
a_ : Dict = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 32 |
"""simple docstring"""
from functools import lru_cache
@lru_cache
def UpperCamelCase__ ( lowercase__ : int ):
if num < 0:
raise ValueError("Number should not be negative." )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 148 | 0 |
"""simple docstring"""
import itertools
import math
def A ( snake_case :int ) -> bool:
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(snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def A ( ) -> Union[str, Any]:
__UpperCamelCase = 2
while True:
if is_prime(snake_case ):
yield num
num += 1
def A ( snake_case :int = 1_0_0_0_1 ) -> int:
return next(itertools.islice(prime_generator() , nth - 1 , snake_case ) )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 354 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
UpperCamelCase : Optional[int] = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
'''simple docstring'''
warnings.warn(
'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use DonutImageProcessor instead.' , __UpperCAmelCase , )
super().__init__(*__UpperCAmelCase , **__UpperCAmelCase )
| 263 | 0 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__A : Any = 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 : Union[str, Any] = parser.parse_args()
if args.model_type == "roberta":
__A : int = RobertaForMaskedLM.from_pretrained(args.model_name)
__A : List[Any] = '''roberta'''
elif args.model_type == "gpt2":
__A : Any = GPTaLMHeadModel.from_pretrained(args.model_name)
__A : List[str] = '''transformer'''
__A : Optional[int] = model.state_dict()
__A : int = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
__A : Tuple = state_dict[F'{prefix}.{param_name}']
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
__A : Dict = F'{prefix}.embeddings.{w}.weight'
__A : Any = state_dict[param_name]
for w in ["weight", "bias"]:
__A : Union[str, Any] = F'{prefix}.embeddings.LayerNorm.{w}'
__A : Tuple = state_dict[param_name]
# Transformer Blocks #
__A : Optional[int] = 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 : str = state_dict[
F'{prefix}.h.{teacher_idx}.{layer}.{w}'
]
__A : Optional[int] = 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 : Union[str, Any] = 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 : Tuple = state_dict[F'{layer}']
if args.vocab_transform:
for w in ["weight", "bias"]:
__A : Tuple = state_dict[F'lm_head.dense.{w}']
__A : Optional[int] = state_dict[F'lm_head.layer_norm.{w}']
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
__A : Optional[Any] = state_dict[F'{prefix}.ln_f.{w}']
__A : List[str] = 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)
| 138 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
__A : Tuple = logging.get_logger(__name__)
__A : List[Any] = R'''
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs (`Dict[str, Any]`, *optional*):
Additional stopping criteria specific kwargs.
Return:
`bool`. `False` indicates we should continue, `True` indicates we should stop.
'''
class __A ( lowerCAmelCase ):
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : Any , UpperCAmelCase_ : torch.LongTensor , UpperCAmelCase_ : torch.FloatTensor , **UpperCAmelCase_ : str ):
raise NotImplementedError('StoppingCriteria needs to be subclassed' )
class __A ( lowerCAmelCase ):
def __init__( self : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] = None ):
lowerCAmelCase : str = max_length
lowerCAmelCase : Any = max_position_embeddings
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : Any , UpperCAmelCase_ : torch.LongTensor , UpperCAmelCase_ : torch.FloatTensor , **UpperCAmelCase_ : Optional[Any] ):
lowerCAmelCase : List[Any] = input_ids.shape[-1]
lowerCAmelCase : List[Any] = cur_len >= self.max_length
if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
logger.warning_once(
'This is a friendly reminder - the current text generation call will exceed the model\'s predefined '
f"maximum length ({self.max_position_embeddings}). Depending on the model, you may observe "
'exceptions, performance degradation, or nothing at all.' )
return is_done
class __A ( lowerCAmelCase ):
def __init__( self : Tuple , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
warnings.warn(
'The class `MaxNewTokensCriteria` is deprecated. '
f"Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` "
'with `max_length = start_length + max_new_tokens` instead.' , UpperCAmelCase_ , )
lowerCAmelCase : Optional[Any] = start_length
lowerCAmelCase : List[Any] = max_new_tokens
lowerCAmelCase : Union[str, Any] = start_length + max_new_tokens
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : Dict , UpperCAmelCase_ : torch.LongTensor , UpperCAmelCase_ : torch.FloatTensor , **UpperCAmelCase_ : Optional[int] ):
return input_ids.shape[-1] >= self.max_length
class __A ( lowerCAmelCase ):
def __init__( self : List[Any] , UpperCAmelCase_ : float , UpperCAmelCase_ : Optional[float] = None ):
lowerCAmelCase : List[str] = max_time
lowerCAmelCase : Optional[Any] = time.time() if initial_timestamp is None else initial_timestamp
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : List[Any] , UpperCAmelCase_ : torch.LongTensor , UpperCAmelCase_ : torch.FloatTensor , **UpperCAmelCase_ : List[Any] ):
return time.time() - self.initial_timestamp > self.max_time
class __A ( lowerCAmelCase ):
@add_start_docstrings(UpperCAmelCase_ )
def __call__( self : Optional[Any] , UpperCAmelCase_ : torch.LongTensor , UpperCAmelCase_ : torch.FloatTensor , **UpperCAmelCase_ : Optional[Any] ):
return any(criteria(UpperCAmelCase_ , UpperCAmelCase_ ) for criteria in self )
@property
def lowercase__ ( self : Tuple ):
for stopping_criterium in self:
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
return stopping_criterium.max_length
elif isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
return stopping_criterium.max_length
return None
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> StoppingCriteriaList:
'''simple docstring'''
lowerCAmelCase : Dict = stopping_criteria.max_length
lowerCAmelCase : Dict = deepcopy(_UpperCAmelCase )
if stopping_max_length is not None and stopping_max_length != max_length:
warnings.warn('You set different `max_length` for stopping criteria and `max_length` parameter', _UpperCAmelCase )
elif stopping_max_length is None:
new_stopping_criteria.append(MaxLengthCriteria(max_length=_UpperCAmelCase ) )
return new_stopping_criteria
| 138 | 1 |
from collections import deque
def lowerCAmelCase_ ( __UpperCAmelCase: int ) -> str:
UpperCamelCase__ : Optional[int] = len(__UpperCAmelCase )
UpperCamelCase__ : str = deque()
UpperCamelCase__ : int = [False for _ in range(__UpperCAmelCase )]
UpperCamelCase__ : Optional[int] = [-1 for _ in range(__UpperCAmelCase )]
UpperCamelCase__ : str = index_of[:]
def strong_connect(__UpperCAmelCase: Optional[int] , __UpperCAmelCase: Union[str, Any] , __UpperCAmelCase: Union[str, Any] ):
UpperCamelCase__ : str = index # the number when this node is seen
UpperCamelCase__ : Any = index # lowest rank node reachable from here
index += 1
stack.append(__UpperCAmelCase )
UpperCamelCase__ : Optional[Any] = True
for w in g[v]:
if index_of[w] == -1:
UpperCamelCase__ : str = strong_connect(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
UpperCamelCase__ : List[str] = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
elif on_stack[w]:
UpperCamelCase__ : Dict = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
if lowlink_of[v] == index_of[v]:
UpperCamelCase__ : Tuple = []
UpperCamelCase__ : str = stack.pop()
UpperCamelCase__ : int = False
component.append(__UpperCAmelCase )
while w != v:
UpperCamelCase__ : int = stack.pop()
UpperCamelCase__ : Optional[Any] = False
component.append(__UpperCAmelCase )
components.append(__UpperCAmelCase )
return index
UpperCamelCase__ : Optional[Any] = []
for v in range(__UpperCAmelCase ):
if index_of[v] == -1:
strong_connect(__UpperCAmelCase , 0 , __UpperCAmelCase )
return components
def lowerCAmelCase_ ( __UpperCAmelCase: Union[str, Any] , __UpperCAmelCase: List[Any] ) -> str:
UpperCamelCase__ : Dict = [[] for _ in range(__UpperCAmelCase )]
for u, v in edges:
g[u].append(__UpperCAmelCase )
return g
if __name__ == "__main__":
# Test
UpperCAmelCase_ = 7
UpperCAmelCase_ = [0, 0, 1, 2, 3, 3, 4, 4, 6]
UpperCAmelCase_ = [1, 3, 2, 0, 1, 4, 5, 6, 5]
UpperCAmelCase_ = [(u, v) for u, v in zip(source, target)]
UpperCAmelCase_ = create_graph(n_vertices, edges)
assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
| 247 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForPreTraining,
is_apex_available,
trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
UpperCAmelCase_ = True
from torch.cuda.amp import autocast
UpperCAmelCase_ = logging.getLogger(__name__)
@dataclass
class lowercase__ :
'''simple docstring'''
a : str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
a : Optional[str] = field(
default=__lowerCamelCase , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
a : Optional[bool] = field(
default=__lowerCamelCase , metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
a : Optional[bool] = field(
default=__lowerCamelCase , metadata={"help": "Whether to log verbose messages or not."} , )
a : Optional[float] = field(
default=2.0 , metadata={"help": "Maximum temperature for gumbel softmax."} )
a : Optional[float] = field(
default=0.5 , metadata={"help": "Minimum temperature for gumbel softmax."} )
a : Optional[float] = field(
default=0.9_9_9_9_9_5 , metadata={"help": "Decay of gumbel temperature during training."} )
def lowerCAmelCase_ ( __UpperCAmelCase: ModelArguments , __UpperCAmelCase: TrainingArguments ) -> Any:
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
UpperCamelCase__ : Tuple = logging.WARNING
if model_args.verbose_logging:
UpperCamelCase__ : List[Any] = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank ):
UpperCamelCase__ : Dict = logging.INFO
logger.setLevel(__UpperCAmelCase )
@dataclass
class lowercase__ :
'''simple docstring'''
a : str = field(
default=__lowerCamelCase , metadata={"help": "The name of the dataset to use (via the datasets library)."} )
a : Optional[str] = field(
default=__lowerCamelCase , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
a : Optional[str] = field(
default="train" , metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
} , )
a : Optional[str] = field(
default="validation" , metadata={
"help": (
"The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
)
} , )
a : Optional[str] = field(
default="file" , metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"} , )
a : bool = field(
default=__lowerCamelCase , metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
a : Optional[int] = field(
default=1 , metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
} , )
a : Optional[int] = field(
default=__lowerCamelCase , metadata={"help": "The number of processes to use for the preprocessing."} , )
a : Optional[float] = field(
default=2_0.0 , metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"} )
@dataclass
class lowercase__ :
'''simple docstring'''
a : WavaVecaForPreTraining
a : WavaVecaFeatureExtractor
a : Union[bool, str] = "longest"
a : Optional[int] = None
a : Optional[int] = None
def __call__( self, __magic_name__ ) -> Dict[str, torch.Tensor]:
"""simple docstring"""
# reformat list to dict and set to pytorch format
UpperCamelCase__ : List[Any] = self.feature_extractor.pad(
__magic_name__, max_length=self.max_length, padding=self.padding, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors='''pt''', )
UpperCamelCase__ : Dict = self.model._get_feat_extract_output_lengths(batch['''input_values'''].shape[-1] )
UpperCamelCase__ : Union[str, Any] = batch['''input_values'''].shape[0]
# make sure that no loss is computed on padded inputs
if batch["attention_mask"] is not None:
# compute real output lengths according to convolution formula
UpperCamelCase__ : List[str] = self.model._get_feat_extract_output_lengths(batch['''attention_mask'''].sum(-1 ) ).to(
torch.long )
UpperCamelCase__ : Dict = torch.zeros(
(batch_size, mask_indices_seq_length), dtype=torch.long, device=batch['''input_values'''].device )
# these two operations makes sure that all values
# before the output lengths indices are attended to
UpperCamelCase__ : str = 1
UpperCamelCase__ : Union[str, Any] = attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()
# sample randomly masked indices
UpperCamelCase__ : Dict = _compute_mask_indices(
(batch_size, mask_indices_seq_length), self.model.config.mask_time_prob, self.model.config.mask_time_length, attention_mask=__magic_name__, min_masks=2, )
return batch
class lowercase__ ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self, *__magic_name__, __magic_name__=1, __magic_name__=0, __magic_name__=1.0, **__magic_name__ ) -> Dict:
"""simple docstring"""
super().__init__(*__magic_name__, **__magic_name__ )
UpperCamelCase__ : Any = 0
UpperCamelCase__ : List[Any] = max_gumbel_temp
UpperCamelCase__ : List[str] = min_gumbel_temp
UpperCamelCase__ : Any = gumbel_temp_decay
def UpperCamelCase__ ( self, __magic_name__, __magic_name__ ) -> torch.Tensor:
"""simple docstring"""
model.train()
UpperCamelCase__ : str = self._prepare_inputs(__magic_name__ )
if self.use_amp:
with autocast():
UpperCamelCase__ : Optional[Any] = self.compute_loss(__magic_name__, __magic_name__ )
else:
UpperCamelCase__ : Tuple = self.compute_loss(__magic_name__, __magic_name__ )
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
UpperCamelCase__ : Any = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
UpperCamelCase__ : List[str] = loss.sum() / (inputs['''mask_time_indices''']).sum()
else:
raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']" )
if self.args.gradient_accumulation_steps > 1:
UpperCamelCase__ : Tuple = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(__magic_name__ ).backward()
elif self.use_apex:
with amp.scale_loss(__magic_name__, self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(__magic_name__ )
else:
loss.backward()
self.num_update_step += 1
# make sure gumbel softmax temperature is decayed
if self.args.n_gpu > 1 or self.deepspeed:
model.module.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step, self.min_gumbel_temp ) )
else:
model.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step, self.min_gumbel_temp ) )
return loss.detach()
def lowerCAmelCase_ ( ) -> str:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase__ : List[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : str = parser.parse_args_into_dataclasses()
configure_logger(__UpperCAmelCase , __UpperCAmelCase )
# Downloading and loading a dataset from the hub.
UpperCamelCase__ : str = load_dataset(data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
if "validation" not in datasets.keys():
# make sure only "validation" and "train" keys remain"
UpperCamelCase__ : Any = DatasetDict()
UpperCamelCase__ : List[Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f"{data_args.train_split_name}[:{data_args.validation_split_percentage}%]" , cache_dir=model_args.cache_dir , )
UpperCamelCase__ : Any = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f"{data_args.train_split_name}[{data_args.validation_split_percentage}%:]" , cache_dir=model_args.cache_dir , )
else:
# make sure only "validation" and "train" keys remain"
UpperCamelCase__ : int = DatasetDict()
UpperCamelCase__ : str = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split='''validation''' , cache_dir=model_args.cache_dir , )
UpperCamelCase__ : List[Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f"{data_args.train_split_name}" , cache_dir=model_args.cache_dir , )
# only normalized-inputs-training is supported
UpperCamelCase__ : str = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=__UpperCAmelCase )
def prepare_dataset(__UpperCAmelCase: Union[str, Any] ):
# check that all files have the correct sampling rate
UpperCamelCase__ ,UpperCamelCase__ : List[str] = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate )
return batch
# load audio files into numpy arrays
UpperCamelCase__ : Any = datasets.map(
__UpperCAmelCase , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets['''train'''].column_names )
# filter audio files that are too long
UpperCamelCase__ : Tuple = vectorized_datasets.filter(
lambda __UpperCAmelCase : len(data['''speech'''] ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate ) )
def normalize(__UpperCAmelCase: Optional[int] ):
return feature_extractor(batch['''speech'''] , sampling_rate=feature_extractor.sampling_rate )
# normalize and transform to `BatchFeatures`
UpperCamelCase__ : Any = vectorized_datasets.map(
__UpperCAmelCase , batched=__UpperCAmelCase , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , remove_columns=vectorized_datasets['''train'''].column_names , )
# pretraining is only supported for "newer" stable layer norm architecture
# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
UpperCamelCase__ : int = WavaVecaConfig.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , gradient_checkpointing=training_args.gradient_checkpointing , )
if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
raise ValueError(
'''PreTraining is only supported for ``config.do_stable_layer_norm=True`` and'''
''' ``config.feat_extract_norm=\'layer\'''' )
UpperCamelCase__ : Optional[int] = WavaVecaForPreTraining(__UpperCAmelCase )
UpperCamelCase__ : List[str] = DataCollatorForWavaVecaPretraining(model=__UpperCAmelCase , feature_extractor=__UpperCAmelCase )
UpperCamelCase__ : List[Any] = WavaVecaPreTrainer(
model=__UpperCAmelCase , data_collator=__UpperCAmelCase , args=__UpperCAmelCase , train_dataset=vectorized_datasets['''train'''] , eval_dataset=vectorized_datasets['''validation'''] , tokenizer=__UpperCAmelCase , max_gumbel_temp=model_args.max_gumbel_temperature , min_gumbel_temp=model_args.min_gumbel_temperature , gumbel_temp_decay=model_args.gumbel_temperature_decay , )
trainer.train()
if __name__ == "__main__":
main()
| 247 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase ( lowercase_ , lowercase_ , lowercase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case = StableDiffusionInpaintPipeline
__snake_case = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
__snake_case = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__snake_case = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
__snake_case = frozenset([] )
def lowercase__ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
A__ : Optional[Any] =UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=9 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=lowerCAmelCase_ , )
A__ : Dict =PNDMScheduler(skip_prk_steps=lowerCAmelCase_ )
torch.manual_seed(0 )
A__ : 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 , sample_size=1_28 , )
torch.manual_seed(0 )
A__ : str =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=10_00 , hidden_act="""gelu""" , projection_dim=5_12 , )
A__ : Optional[int] =CLIPTextModel(lowerCAmelCase_ )
A__ : Dict =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
A__ : str ={
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowercase__ ( self : Dict , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Union[str, Any]=0 ) -> List[str]:
'''simple docstring'''
# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
A__ : List[str] =floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase_ ) ).to(lowerCAmelCase_ )
A__ : List[str] =image.cpu().permute(0 , 2 , 3 , 1 )[0]
A__ : List[str] =Image.fromarray(np.uinta(lowerCAmelCase_ ) ).convert("""RGB""" ).resize((64, 64) )
A__ : int =Image.fromarray(np.uinta(image + 4 ) ).convert("""RGB""" ).resize((64, 64) )
if str(lowerCAmelCase_ ).startswith("""mps""" ):
A__ : str =torch.manual_seed(lowerCAmelCase_ )
else:
A__ : Tuple =torch.Generator(device=lowerCAmelCase_ ).manual_seed(lowerCAmelCase_ )
A__ : Optional[Any] ={
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": init_image,
"""mask_image""": mask_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def lowercase__ ( self : Any ) -> Tuple:
'''simple docstring'''
A__ : str ="""cpu""" # ensure determinism for the device-dependent torch.Generator
A__ : Tuple =self.get_dummy_components()
A__ : str =StableDiffusionInpaintPipeline(**lowerCAmelCase_ )
A__ : Any =sd_pipe.to(lowerCAmelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
A__ : Optional[Any] =self.get_dummy_inputs(lowerCAmelCase_ )
A__ : Dict =sd_pipe(**lowerCAmelCase_ ).images
A__ : Union[str, Any] =image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
A__ : Optional[Any] =np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowercase__ ( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowercase__ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self : Any ) -> Tuple:
'''simple docstring'''
A__ : int =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
A__ : int =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
A__ : Union[str, Any] =load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
"""/yellow_cat_sitting_on_a_park_bench.npy""" )
A__ : Optional[Any] ="""stabilityai/stable-diffusion-2-inpainting"""
A__ : int =StableDiffusionInpaintPipeline.from_pretrained(lowerCAmelCase_ , safety_checker=lowerCAmelCase_ )
pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
pipe.enable_attention_slicing()
A__ : Dict ="""Face of a yellow cat, high resolution, sitting on a park bench"""
A__ : str =torch.manual_seed(0 )
A__ : Dict =pipe(
prompt=lowerCAmelCase_ , image=lowerCAmelCase_ , mask_image=lowerCAmelCase_ , generator=lowerCAmelCase_ , output_type="""np""" , )
A__ : Tuple =output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert np.abs(expected_image - image ).max() < 9e-3
def lowercase__ ( self : str ) -> int:
'''simple docstring'''
A__ : int =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
A__ : List[Any] =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
A__ : List[Any] =load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
"""/yellow_cat_sitting_on_a_park_bench_fp16.npy""" )
A__ : int ="""stabilityai/stable-diffusion-2-inpainting"""
A__ : List[Any] =StableDiffusionInpaintPipeline.from_pretrained(
lowerCAmelCase_ , torch_dtype=torch.floataa , safety_checker=lowerCAmelCase_ , )
pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
pipe.enable_attention_slicing()
A__ : Union[str, Any] ="""Face of a yellow cat, high resolution, sitting on a park bench"""
A__ : Union[str, Any] =torch.manual_seed(0 )
A__ : Dict =pipe(
prompt=lowerCAmelCase_ , image=lowerCAmelCase_ , mask_image=lowerCAmelCase_ , generator=lowerCAmelCase_ , output_type="""np""" , )
A__ : str =output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert np.abs(expected_image - image ).max() < 5e-1
def lowercase__ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
A__ : Union[str, Any] =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
A__ : Optional[Any] =load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
A__ : List[str] ="""stabilityai/stable-diffusion-2-inpainting"""
A__ : Any =PNDMScheduler.from_pretrained(lowerCAmelCase_ , subfolder="""scheduler""" )
A__ : Optional[int] =StableDiffusionInpaintPipeline.from_pretrained(
lowerCAmelCase_ , safety_checker=lowerCAmelCase_ , scheduler=lowerCAmelCase_ , torch_dtype=torch.floataa , )
pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
A__ : Dict ="""Face of a yellow cat, high resolution, sitting on a park bench"""
A__ : Any =torch.manual_seed(0 )
A__ : Tuple =pipe(
prompt=lowerCAmelCase_ , image=lowerCAmelCase_ , mask_image=lowerCAmelCase_ , generator=lowerCAmelCase_ , num_inference_steps=2 , output_type="""np""" , )
A__ : Dict =torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.65 * 10**9
| 134 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case : Union[str, Any] = logging.get_logger(__name__)
__snake_case : List[str] = {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
'umberto-commoncrawl-cased-v1': (
'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
),
'umberto-wikipedia-uncased-v1': (
'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
),
}
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
__snake_case = 'camembert'
def __init__( self : int , lowerCAmelCase_ : Tuple=3_05_22 , lowerCAmelCase_ : Tuple=7_68 , lowerCAmelCase_ : List[str]=12 , lowerCAmelCase_ : Any=12 , lowerCAmelCase_ : Tuple=30_72 , lowerCAmelCase_ : Dict="gelu" , lowerCAmelCase_ : Dict=0.1 , lowerCAmelCase_ : Tuple=0.1 , lowerCAmelCase_ : Tuple=5_12 , lowerCAmelCase_ : Optional[Any]=2 , lowerCAmelCase_ : Tuple=0.02 , lowerCAmelCase_ : Tuple=1e-12 , lowerCAmelCase_ : int=1 , lowerCAmelCase_ : Optional[Any]=0 , lowerCAmelCase_ : List[str]=2 , lowerCAmelCase_ : Dict="absolute" , lowerCAmelCase_ : List[str]=True , lowerCAmelCase_ : Union[str, Any]=None , **lowerCAmelCase_ : Union[str, Any] , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(pad_token_id=lowerCAmelCase_ , bos_token_id=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ , **lowerCAmelCase_ )
A__ : Optional[int] =vocab_size
A__ : Any =hidden_size
A__ : Optional[Any] =num_hidden_layers
A__ : Optional[int] =num_attention_heads
A__ : Optional[int] =hidden_act
A__ : Optional[Any] =intermediate_size
A__ : Tuple =hidden_dropout_prob
A__ : Union[str, Any] =attention_probs_dropout_prob
A__ : List[str] =max_position_embeddings
A__ : int =type_vocab_size
A__ : int =initializer_range
A__ : Tuple =layer_norm_eps
A__ : Dict =position_embedding_type
A__ : List[str] =use_cache
A__ : Dict =classifier_dropout
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
@property
def lowercase__ ( self : Optional[Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
A__ : Optional[int] ={0: """batch""", 1: """choice""", 2: """sequence"""}
else:
A__ : List[str] ={0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 134 | 1 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import BertGenerationDecoder, BertGenerationEncoder
class _a :
'''simple docstring'''
def __init__( self , A__ , A__=13 , A__=7 , A__=True , A__=True , A__=99 , A__=32 , A__=5 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=50 , A__=0.0_2 , A__=True , A__=None , ):
A__ : Optional[int] = parent
A__ : Optional[Any] = batch_size
A__ : Any = seq_length
A__ : Union[str, Any] = is_training
A__ : int = use_input_mask
A__ : Tuple = vocab_size
A__ : Any = hidden_size
A__ : List[Any] = num_hidden_layers
A__ : List[str] = num_attention_heads
A__ : Union[str, Any] = intermediate_size
A__ : Tuple = hidden_act
A__ : int = hidden_dropout_prob
A__ : Dict = attention_probs_dropout_prob
A__ : int = max_position_embeddings
A__ : Any = initializer_range
A__ : Tuple = use_labels
A__ : Union[str, Any] = scope
def __A ( self ):
A__ : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ : Dict = None
if self.use_input_mask:
A__ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
if self.use_labels:
A__ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ : Optional[Any] = self.get_config()
return config, input_ids, input_mask, token_labels
def __A ( self ):
return BertGenerationConfig(
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 , is_decoder=A__ , initializer_range=self.initializer_range , )
def __A ( self ):
(
(
A__
) , (
A__
) , (
A__
) , (
A__
) ,
) : Any = self.prepare_config_and_inputs()
A__ : Dict = True
A__ : Dict = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
A__ : Dict = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def __A ( self , A__ , A__ , A__ , A__ , **A__ , ):
A__ : List[str] = BertGenerationEncoder(config=A__ )
model.to(A__ )
model.eval()
A__ : int = model(A__ , attention_mask=A__ )
A__ : Optional[int] = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __A ( self , A__ , A__ , A__ , A__ , A__ , A__ , **A__ , ):
A__ : Any = True
A__ : List[str] = BertGenerationEncoder(config=A__ )
model.to(A__ )
model.eval()
A__ : Dict = model(
A__ , attention_mask=A__ , encoder_hidden_states=A__ , encoder_attention_mask=A__ , )
A__ : int = model(
A__ , attention_mask=A__ , encoder_hidden_states=A__ , )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __A ( self , A__ , A__ , A__ , A__ , A__ , A__ , **A__ , ):
A__ : Union[str, Any] = True
A__ : List[Any] = True
A__ : Union[str, Any] = BertGenerationDecoder(config=A__ ).to(A__ ).eval()
# first forward pass
A__ : Optional[int] = model(
A__ , attention_mask=A__ , encoder_hidden_states=A__ , encoder_attention_mask=A__ , use_cache=A__ , )
A__ : Union[str, Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
A__ : List[Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
A__ : Union[str, Any] = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
A__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
A__ : Any = torch.cat([input_mask, next_mask] , dim=-1 )
A__ : str = model(
A__ , attention_mask=A__ , encoder_hidden_states=A__ , encoder_attention_mask=A__ , output_hidden_states=A__ , )["""hidden_states"""][0]
A__ : Optional[int] = model(
A__ , attention_mask=A__ , encoder_hidden_states=A__ , encoder_attention_mask=A__ , past_key_values=A__ , output_hidden_states=A__ , )["""hidden_states"""][0]
# select random slice
A__ : Optional[int] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
A__ : Tuple = output_from_no_past[:, -3:, random_slice_idx].detach()
A__ : Dict = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A__ , A__ , atol=1e-3 ) )
def __A ( self , A__ , A__ , A__ , A__ , *A__ , ):
A__ : str = BertGenerationDecoder(A__ )
model.to(A__ )
model.eval()
A__ : List[Any] = model(A__ , attention_mask=A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __A ( self ):
A__ , A__ , A__ , A__ : List[str] = self.prepare_config_and_inputs()
A__ : int = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class _a (__magic_name__ , __magic_name__ , __magic_name__ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__: List[str] = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
UpperCAmelCase__: int = (BertGenerationDecoder,) if is_torch_available() else ()
UpperCAmelCase__: str = (
{'''feature-extraction''': BertGenerationEncoder, '''text-generation''': BertGenerationDecoder}
if is_torch_available()
else {}
)
def __A ( self ):
A__ : Any = BertGenerationEncoderTester(self )
A__ : List[Any] = ConfigTester(self , config_class=A__ , hidden_size=37 )
def __A ( self ):
self.config_tester.run_common_tests()
def __A ( self ):
A__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def __A ( self ):
A__ , A__ , A__ , A__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
A__ : List[Any] = """bert"""
self.model_tester.create_and_check_model(A__ , A__ , A__ , A__ )
def __A ( self ):
A__ : Dict = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*A__ )
def __A ( self ):
A__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*A__ )
def __A ( self ):
# This regression test was failing with PyTorch < 1.3
(
(
A__
) , (
A__
) , (
A__
) , (
A__
) , (
A__
) , (
A__
) ,
) : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
A__ : str = None
self.model_tester.create_and_check_model_as_decoder(
A__ , A__ , A__ , A__ , A__ , A__ , )
def __A ( self ):
A__ : Any = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*A__ )
@slow
def __A ( self ):
A__ : Dict = BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""" )
self.assertIsNotNone(A__ )
@require_torch
class _a (unittest.TestCase ):
'''simple docstring'''
@slow
def __A ( self ):
A__ : List[str] = BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""" )
A__ : Optional[Any] = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]] )
with torch.no_grad():
A__ : Union[str, Any] = model(A__ )[0]
A__ : Dict = torch.Size([1, 8, 1024] )
self.assertEqual(output.shape , A__ )
A__ : str = torch.tensor(
[[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A__ , atol=1e-4 ) )
@require_torch
class _a (unittest.TestCase ):
'''simple docstring'''
@slow
def __A ( self ):
A__ : int = BertGenerationDecoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""" )
A__ : Tuple = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]] )
with torch.no_grad():
A__ : Optional[int] = model(A__ )[0]
A__ : str = torch.Size([1, 8, 5_0358] )
self.assertEqual(output.shape , A__ )
A__ : int = torch.tensor(
[[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A__ , atol=1e-4 ) )
| 141 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
A_ : Optional[Any] = 16
A_ : Optional[int] = 32
def UpperCamelCase (lowercase_: Accelerator , lowercase_: int = 16 , lowercase_: str = "bert-base-cased" ) -> List[str]:
A__ : int = AutoTokenizer.from_pretrained(lowercase_ )
A__ : Union[str, Any] = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowercase_: Tuple ):
# max_length=None => use the model max length (it's actually the default)
A__ : Optional[Any] = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowercase_ , max_length=lowercase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
A__ : int = datasets.map(
lowercase_ , batched=lowercase_ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=lowercase_ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
A__ : int = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowercase_: Optional[int] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowercase_ , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return tokenizer.pad(lowercase_ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
A__ : int = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=lowercase_ )
A__ : Optional[Any] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=lowercase_ )
return train_dataloader, eval_dataloader
def UpperCamelCase (lowercase_: Dict , lowercase_: Dict , lowercase_: Tuple , lowercase_: Optional[int] ) -> int:
model.eval()
A__ : str = 0
for step, batch in enumerate(lowercase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
A__ : Any = model(**lowercase_ )
A__ : List[Any] = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
A__ , A__ : str = accelerator.gather(
(predictions, batch["""labels"""]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(lowercase_ ) - 1:
A__ : List[str] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
A__ : Dict = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=lowercase_ , references=lowercase_ , )
A__ : int = metric.compute()
return eval_metric["accuracy"]
def UpperCamelCase (lowercase_: List[Any] , lowercase_: str ) -> List[str]:
# Initialize accelerator
A__ : str = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
A__ : List[Any] = config["""lr"""]
A__ : Union[str, Any] = int(config["""num_epochs"""] )
A__ : List[Any] = int(config["""seed"""] )
A__ : Optional[Any] = int(config["""batch_size"""] )
A__ : Tuple = args.model_name_or_path
set_seed(lowercase_ )
A__ , A__ : Optional[Any] = get_dataloaders(lowercase_ , lowercase_ , lowercase_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
A__ : Tuple = AutoModelForSequenceClassification.from_pretrained(lowercase_ , return_dict=lowercase_ )
# Instantiate optimizer
A__ : Dict = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
A__ : Union[str, Any] = optimizer_cls(params=model.parameters() , lr=lowercase_ )
if accelerator.state.deepspeed_plugin is not None:
A__ : List[Any] = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
A__ : Optional[int] = 1
A__ : Optional[int] = (len(lowercase_ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
A__ : Optional[int] = get_linear_schedule_with_warmup(
optimizer=lowercase_ , num_warmup_steps=0 , num_training_steps=lowercase_ , )
else:
A__ : int = DummyScheduler(lowercase_ , total_num_steps=lowercase_ , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
A__ , A__ , A__ , A__ , A__ : str = accelerator.prepare(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
# We need to keep track of how many total steps we have iterated over
A__ : Dict = 0
# We also need to keep track of the stating epoch so files are named properly
A__ : Any = 0
A__ : Optional[Any] = evaluate.load("""glue""" , """mrpc""" )
A__ : Optional[Any] = num_epochs
if args.partial_train_epoch is not None:
A__ : Tuple = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
A__ : Dict = args.resume_from_checkpoint.split("""epoch_""" )[1]
A__ : int = """"""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
A__ : Any = int(lowercase_ ) + 1
A__ : Any = evaluation_loop(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
accelerator.print("""resumed checkpoint performance:""" , lowercase_ )
accelerator.print("""resumed checkpoint's scheduler's lr:""" , lr_scheduler.get_lr()[0] )
accelerator.print("""resumed optimizers's lr:""" , optimizer.param_groups[0]["""lr"""] )
with open(os.path.join(args.output_dir , f"""state_{starting_epoch-1}.json""" ) , """r""" ) as f:
A__ : int = json.load(lowercase_ )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
A__ : Optional[Any] = {}
for epoch in range(lowercase_ , lowercase_ ):
model.train()
for step, batch in enumerate(lowercase_ ):
A__ : int = model(**lowercase_ )
A__ : int = outputs.loss
A__ : int = loss / gradient_accumulation_steps
accelerator.backward(lowercase_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
A__ : Any = f"""epoch_{epoch}"""
A__ : int = os.path.join(args.output_dir , lowercase_ )
accelerator.save_state(lowercase_ )
A__ : List[Any] = evaluation_loop(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
A__ : Tuple = accuracy
A__ : Optional[Any] = lr_scheduler.get_lr()[0]
A__ : Tuple = optimizer.param_groups[0]["""lr"""]
A__ : int = epoch
A__ : int = overall_step
accelerator.print(f"""epoch {epoch}:""" , lowercase_ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , f"""state_{epoch}.json""" ) , """w""" ) as f:
json.dump(lowercase_ , lowercase_ )
def UpperCamelCase () -> int:
A__ : Optional[int] = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=lowercase_ , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=lowercase_ , )
parser.add_argument(
"""--output_dir""" , type=lowercase_ , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--resume_from_checkpoint""" , type=lowercase_ , default=lowercase_ , help="""If the training should continue from a checkpoint folder.""" , )
parser.add_argument(
"""--partial_train_epoch""" , type=lowercase_ , default=lowercase_ , help="""If passed, the training will stop after this number of epochs.""" , )
parser.add_argument(
"""--num_epochs""" , type=lowercase_ , default=2 , help="""Number of train epochs.""" , )
A__ : List[str] = parser.parse_args()
A__ : List[str] = {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(lowercase_ , lowercase_ )
if __name__ == "__main__":
main()
| 141 | 1 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
SCREAMING_SNAKE_CASE : int = logging.getLogger(__name__)
@dataclass
class _lowerCamelCase:
lowercase_ : Optional[str] = field(
default="""tab_fact""", metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
lowercase_ : Optional[str] = field(
default="""tab_fact""", metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}, )
lowercase_ : int = field(
default=10_24, metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
}, )
lowercase_ : bool = field(
default=_a, metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
lowercase_ : bool = field(
default=_a, metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
}, )
lowercase_ : Optional[int] = field(
default=_a, metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
}, )
lowercase_ : Optional[int] = field(
default=_a, metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
}, )
lowercase_ : Optional[int] = field(
default=_a, metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
}, )
lowercase_ : Optional[str] = field(
default=_a, metadata={"""help""": """A csv or a json file containing the training data."""} )
lowercase_ : Optional[str] = field(
default=_a, metadata={"""help""": """A csv or a json file containing the validation data."""} )
lowercase_ : Optional[str] = field(default=_a, metadata={"""help""": """A csv or a json file containing the test data."""} )
def UpperCamelCase ( self) -> Optional[Any]:
"""simple docstring"""
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.')
else:
_lowercase : int = self.train_file.split('.')[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
_lowercase : Optional[int] = self.validation_file.split('.')[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class _lowerCamelCase:
lowercase_ : str = field(
default=_a, metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
lowercase_ : Optional[str] = field(
default=_a, metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
lowercase_ : Optional[str] = field(
default=_a, metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
lowercase_ : Optional[str] = field(
default=_a, metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}, )
lowercase_ : bool = field(
default=_a, metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}, )
lowercase_ : str = field(
default="""main""", metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}, )
lowercase_ : bool = field(
default=_a, metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
}, )
def UpperCamelCase_( ) -> List[str]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_lowercase : List[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_lowercase , _lowercase , _lowercase : Union[str, Any] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowercase , _lowercase , _lowercase : Optional[int] = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
_lowercase : Tuple = training_args.get_process_log_level()
logger.setLevel(lowerCamelCase_ )
datasets.utils.logging.set_verbosity(lowerCamelCase_ )
transformers.utils.logging.set_verbosity(lowerCamelCase_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
_lowercase : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_lowercase : List[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_lowercase : Any = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
_lowercase : Tuple = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
_lowercase : Dict = data_args.train_file.split('.' )[-1]
_lowercase : List[str] = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
_lowercase : Tuple = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(F'''load a local file for {key}: {data_files[key]}''' )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
_lowercase : Optional[int] = load_dataset('csv' , data_files=lowerCamelCase_ , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
_lowercase : List[str] = load_dataset('json' , data_files=lowerCamelCase_ , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
_lowercase : Optional[Any] = raw_datasets['train'].features['label'].names
_lowercase : int = len(lowerCamelCase_ )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowercase : Union[str, Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowerCamelCase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
_lowercase : int = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=lowerCamelCase_ , )
_lowercase : List[str] = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=lowerCamelCase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
_lowercase : Union[str, Any] = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
_lowercase : Dict = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
_lowercase : List[str] = {'Refused': 0, 'Entailed': 1}
_lowercase : Any = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.''' )
_lowercase : Optional[Any] = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(lowerCamelCase_ ):
# Tokenize the texts
def _convert_table_text_to_pandas(lowerCamelCase_ ):
_lowercase : Optional[Any] = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
_lowercase : Any = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
_lowercase : int = examples['statement']
_lowercase : Any = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
_lowercase : Dict = tokenizer(lowerCamelCase_ , lowerCamelCase_ , padding=lowerCamelCase_ , max_length=lowerCamelCase_ , truncation=lowerCamelCase_ )
_lowercase : List[Any] = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
_lowercase : Tuple = raw_datasets.map(
lowerCamelCase_ , batched=lowerCamelCase_ , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
_lowercase : List[str] = raw_datasets['train']
if data_args.max_train_samples is not None:
_lowercase : str = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
_lowercase : List[Any] = raw_datasets['validation']
if data_args.max_eval_samples is not None:
_lowercase : int = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
_lowercase : Dict = raw_datasets['test']
if data_args.max_predict_samples is not None:
_lowercase : Optional[int] = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(lowerCamelCase_ ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(lowerCamelCase_ ):
_lowercase : List[Any] = p.predictions[0] if isinstance(p.predictions , lowerCamelCase_ ) else p.predictions
_lowercase : Dict = np.argmax(lowerCamelCase_ , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
_lowercase : Union[str, Any] = default_data_collator
elif training_args.fpaa:
_lowercase : Dict = DataCollatorWithPadding(lowerCamelCase_ , pad_to_multiple_of=8 )
else:
_lowercase : Optional[int] = None
# Initialize our Trainer
_lowercase : str = Trainer(
model=lowerCamelCase_ , args=lowerCamelCase_ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=lowerCamelCase_ , tokenizer=lowerCamelCase_ , data_collator=lowerCamelCase_ , )
# Training
if training_args.do_train:
_lowercase : Dict = None
if training_args.resume_from_checkpoint is not None:
_lowercase : Any = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_lowercase : Union[str, Any] = last_checkpoint
_lowercase : List[str] = trainer.train(resume_from_checkpoint=lowerCamelCase_ )
_lowercase : str = train_result.metrics
_lowercase : Tuple = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase_ )
)
_lowercase : Optional[Any] = min(lowerCamelCase_ , len(lowerCamelCase_ ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , lowerCamelCase_ )
trainer.save_metrics('train' , lowerCamelCase_ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
_lowercase : Tuple = trainer.evaluate(eval_dataset=lowerCamelCase_ )
_lowercase : Union[str, Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase_ )
_lowercase : List[Any] = min(lowerCamelCase_ , len(lowerCamelCase_ ) )
trainer.log_metrics('eval' , lowerCamelCase_ )
trainer.save_metrics('eval' , lowerCamelCase_ )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
_lowercase : Tuple = predict_dataset.remove_columns('label' )
_lowercase : Any = trainer.predict(lowerCamelCase_ , metric_key_prefix='predict' ).predictions
_lowercase : Union[str, Any] = np.argmax(lowerCamelCase_ , axis=1 )
_lowercase : int = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(lowerCamelCase_ , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(lowerCamelCase_ ):
_lowercase : List[str] = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
_lowercase : str = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**lowerCamelCase_ )
else:
trainer.create_model_card(**lowerCamelCase_ )
def UpperCamelCase_( lowerCamelCase_ ) -> Union[str, Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 21 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowercase__ ( _UpperCAmelCase ):
A__ : Union[str, Any] ="""Wav2Vec2FeatureExtractor"""
A__ : Any ="""AutoTokenizer"""
def __init__( self : List[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : str ):
super().__init__(UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = self.feature_extractor
SCREAMING_SNAKE_CASE__ = False
@classmethod
def A_ ( cls : Union[str, Any] , UpperCAmelCase_ : Any , **UpperCAmelCase_ : Optional[Any] ):
try:
return super().from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
except OSError:
warnings.warn(
F'Loading a tokenizer inside {cls.__name__} from a config that does not'
' include a `tokenizer_class` attribute is deprecated and will be '
'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
' attribute to either your `config.json` or `tokenizer_config.json` '
'file to suppress this warning: ' , UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE__ = WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = WavaVecaCTCTokenizer.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
return cls(feature_extractor=UpperCAmelCase_ , tokenizer=UpperCAmelCase_ )
def __call__( self : Any , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : Any ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*UpperCAmelCase_ , **UpperCAmelCase_ )
if "raw_speech" in kwargs:
warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.' )
SCREAMING_SNAKE_CASE__ = kwargs.pop('raw_speech' )
else:
SCREAMING_SNAKE_CASE__ = kwargs.pop('audio' , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = kwargs.pop('sampling_rate' , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = kwargs.pop('text' , UpperCAmelCase_ )
if len(UpperCAmelCase_ ) > 0:
SCREAMING_SNAKE_CASE__ = args[0]
SCREAMING_SNAKE_CASE__ = args[1:]
if audio is None and text is None:
raise ValueError('You need to specify either an `audio` or `text` input to process.' )
if audio is not None:
SCREAMING_SNAKE_CASE__ = self.feature_extractor(UpperCAmelCase_ , *UpperCAmelCase_ , sampling_rate=UpperCAmelCase_ , **UpperCAmelCase_ )
if text is not None:
SCREAMING_SNAKE_CASE__ = self.tokenizer(UpperCAmelCase_ , **UpperCAmelCase_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
SCREAMING_SNAKE_CASE__ = encodings['input_ids']
return inputs
def A_ ( self : List[str] , *UpperCAmelCase_ : List[Any] , **UpperCAmelCase_ : Optional[int] ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*UpperCAmelCase_ , **UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = kwargs.pop('input_features' , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = kwargs.pop('labels' , UpperCAmelCase_ )
if len(UpperCAmelCase_ ) > 0:
SCREAMING_SNAKE_CASE__ = args[0]
SCREAMING_SNAKE_CASE__ = args[1:]
if input_features is not None:
SCREAMING_SNAKE_CASE__ = self.feature_extractor.pad(UpperCAmelCase_ , *UpperCAmelCase_ , **UpperCAmelCase_ )
if labels is not None:
SCREAMING_SNAKE_CASE__ = self.tokenizer.pad(UpperCAmelCase_ , **UpperCAmelCase_ )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
SCREAMING_SNAKE_CASE__ = labels['input_ids']
return input_features
def A_ ( self : List[Any] , *UpperCAmelCase_ : List[str] , **UpperCAmelCase_ : List[str] ):
return self.tokenizer.batch_decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
def A_ ( self : Tuple , *UpperCAmelCase_ : int , **UpperCAmelCase_ : List[str] ):
return self.tokenizer.decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
@contextmanager
def A_ ( self : str ):
warnings.warn(
'`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
'your audio inputs, or in a separate call.' )
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = self.tokenizer
yield
SCREAMING_SNAKE_CASE__ = self.feature_extractor
SCREAMING_SNAKE_CASE__ = False
| 176 | 0 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class __lowerCAmelCase ( __a ):
def __get__(self , lowerCAmelCase__ , lowerCAmelCase__=None ):
# See docs.python.org/3/howto/descriptor.html#properties
if obj is None:
return self
if self.fget is None:
raise AttributeError("""unreadable attribute""" )
_UpperCAmelCase : Optional[int] = """__cached_""" + self.fget.__name__
_UpperCAmelCase : int = getattr(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
if cached is None:
_UpperCAmelCase : Tuple = self.fget(lowerCAmelCase__ )
setattr(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
return cached
def __A ( lowerCAmelCase_ ):
_UpperCAmelCase : Optional[Any] = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(f"invalid truth value {val!r}" )
def __A ( lowerCAmelCase_ ):
if is_torch_fx_proxy(lowerCAmelCase_ ):
return True
if is_torch_available():
import torch
if isinstance(lowerCAmelCase_ , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(lowerCAmelCase_ , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(lowerCAmelCase_ , (jnp.ndarray, Tracer) ):
return True
return isinstance(lowerCAmelCase_ , np.ndarray )
def __A ( lowerCAmelCase_ ):
return isinstance(lowerCAmelCase_ , np.ndarray )
def __A ( lowerCAmelCase_ ):
return _is_numpy(lowerCAmelCase_ )
def __A ( lowerCAmelCase_ ):
import torch
return isinstance(lowerCAmelCase_ , torch.Tensor )
def __A ( lowerCAmelCase_ ):
return False if not is_torch_available() else _is_torch(lowerCAmelCase_ )
def __A ( lowerCAmelCase_ ):
import torch
return isinstance(lowerCAmelCase_ , torch.device )
def __A ( lowerCAmelCase_ ):
return False if not is_torch_available() else _is_torch_device(lowerCAmelCase_ )
def __A ( lowerCAmelCase_ ):
import torch
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
if hasattr(lowerCAmelCase_ , lowerCAmelCase_ ):
_UpperCAmelCase : str = getattr(lowerCAmelCase_ , lowerCAmelCase_ )
else:
return False
return isinstance(lowerCAmelCase_ , torch.dtype )
def __A ( lowerCAmelCase_ ):
return False if not is_torch_available() else _is_torch_dtype(lowerCAmelCase_ )
def __A ( lowerCAmelCase_ ):
import tensorflow as tf
return isinstance(lowerCAmelCase_ , tf.Tensor )
def __A ( lowerCAmelCase_ ):
return False if not is_tf_available() else _is_tensorflow(lowerCAmelCase_ )
def __A ( lowerCAmelCase_ ):
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(lowerCAmelCase_ , """is_symbolic_tensor""" ):
return tf.is_symbolic_tensor(lowerCAmelCase_ )
return type(lowerCAmelCase_ ) == tf.Tensor
def __A ( lowerCAmelCase_ ):
return False if not is_tf_available() else _is_tf_symbolic_tensor(lowerCAmelCase_ )
def __A ( lowerCAmelCase_ ):
import jax.numpy as jnp # noqa: F811
return isinstance(lowerCAmelCase_ , jnp.ndarray )
def __A ( lowerCAmelCase_ ):
return False if not is_flax_available() else _is_jax(lowerCAmelCase_ )
def __A ( lowerCAmelCase_ ):
if isinstance(lowerCAmelCase_ , (dict, UserDict) ):
return {k: to_py_obj(lowerCAmelCase_ ) for k, v in obj.items()}
elif isinstance(lowerCAmelCase_ , (list, tuple) ):
return [to_py_obj(lowerCAmelCase_ ) for o in obj]
elif is_tf_tensor(lowerCAmelCase_ ):
return obj.numpy().tolist()
elif is_torch_tensor(lowerCAmelCase_ ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(lowerCAmelCase_ ):
return np.asarray(lowerCAmelCase_ ).tolist()
elif isinstance(lowerCAmelCase_ , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def __A ( lowerCAmelCase_ ):
if isinstance(lowerCAmelCase_ , (dict, UserDict) ):
return {k: to_numpy(lowerCAmelCase_ ) for k, v in obj.items()}
elif isinstance(lowerCAmelCase_ , (list, tuple) ):
return np.array(lowerCAmelCase_ )
elif is_tf_tensor(lowerCAmelCase_ ):
return obj.numpy()
elif is_torch_tensor(lowerCAmelCase_ ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(lowerCAmelCase_ ):
return np.asarray(lowerCAmelCase_ )
else:
return obj
class __lowerCAmelCase ( __a ):
def snake_case_ (self ):
_UpperCAmelCase : Optional[int] = fields(self )
# Safety and consistency checks
if not len(lowerCAmelCase__ ):
raise ValueError(F"{self.__class__.__name__} has no fields." )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(F"{self.__class__.__name__} should not have more than one required field." )
_UpperCAmelCase : List[Any] = getattr(self , class_fields[0].name )
_UpperCAmelCase : str = all(getattr(self , field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(lowerCAmelCase__ ):
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : Any = first_field.items()
_UpperCAmelCase : Optional[Any] = True
else:
try:
_UpperCAmelCase : Dict = iter(lowerCAmelCase__ )
_UpperCAmelCase : Optional[int] = True
except TypeError:
_UpperCAmelCase : Dict = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(lowerCAmelCase__ ):
if (
not isinstance(lowerCAmelCase__ , (list, tuple) )
or not len(lowerCAmelCase__ ) == 2
or not isinstance(element[0] , lowerCAmelCase__ )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
_UpperCAmelCase : List[str] = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
F"Cannot set key/value for {element}. It needs to be a tuple (key, value)." )
break
setattr(self , element[0] , element[1] )
if element[1] is not None:
_UpperCAmelCase : int = element[1]
elif first_field is not None:
_UpperCAmelCase : List[Any] = first_field
else:
for field in class_fields:
_UpperCAmelCase : Union[str, Any] = getattr(self , field.name )
if v is not None:
_UpperCAmelCase : Union[str, Any] = v
def __delitem__(self , *lowerCAmelCase__ , **lowerCAmelCase__ ):
raise Exception(F"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance." )
def snake_case_ (self , *lowerCAmelCase__ , **lowerCAmelCase__ ):
raise Exception(F"You cannot use ``setdefault`` on a {self.__class__.__name__} instance." )
def snake_case_ (self , *lowerCAmelCase__ , **lowerCAmelCase__ ):
raise Exception(F"You cannot use ``pop`` on a {self.__class__.__name__} instance." )
def snake_case_ (self , *lowerCAmelCase__ , **lowerCAmelCase__ ):
raise Exception(F"You cannot use ``update`` on a {self.__class__.__name__} instance." )
def __getitem__(self , lowerCAmelCase__ ):
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : Optional[Any] = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__(self , lowerCAmelCase__ , lowerCAmelCase__ ):
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(lowerCAmelCase__ , lowerCAmelCase__ )
super().__setattr__(lowerCAmelCase__ , lowerCAmelCase__ )
def __setitem__(self , lowerCAmelCase__ , lowerCAmelCase__ ):
# Will raise a KeyException if needed
super().__setitem__(lowerCAmelCase__ , lowerCAmelCase__ )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(lowerCAmelCase__ , lowerCAmelCase__ )
def snake_case_ (self ):
return tuple(self[k] for k in self.keys() )
class __lowerCAmelCase ( __a , __a ):
@classmethod
def snake_case_ (cls , lowerCAmelCase__ ):
raise ValueError(
F"{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}" )
class __lowerCAmelCase ( __a ):
snake_case : List[str] = """longest"""
snake_case : str = """max_length"""
snake_case : str = """do_not_pad"""
class __lowerCAmelCase ( __a ):
snake_case : List[Any] = """pt"""
snake_case : Optional[int] = """tf"""
snake_case : Optional[Any] = """np"""
snake_case : str = """jax"""
class __lowerCAmelCase :
def __init__(self , lowerCAmelCase__ ):
_UpperCAmelCase : str = context_managers
_UpperCAmelCase : List[Any] = ExitStack()
def __enter__(self ):
for context_manager in self.context_managers:
self.stack.enter_context(lowerCAmelCase__ )
def __exit__(self , *lowerCAmelCase__ , **lowerCAmelCase__ ):
self.stack.__exit__(*lowerCAmelCase__ , **lowerCAmelCase__ )
def __A ( lowerCAmelCase_ ):
_UpperCAmelCase : int = infer_framework(lowerCAmelCase_ )
if framework == "tf":
_UpperCAmelCase : Union[str, Any] = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_UpperCAmelCase : List[Any] = inspect.signature(model_class.forward ) # PyTorch models
else:
_UpperCAmelCase : Optional[Any] = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def __A ( lowerCAmelCase_ ):
_UpperCAmelCase : List[str] = model_class.__name__
_UpperCAmelCase : Tuple = infer_framework(lowerCAmelCase_ )
if framework == "tf":
_UpperCAmelCase : str = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_UpperCAmelCase : Optional[Any] = inspect.signature(model_class.forward ) # PyTorch models
else:
_UpperCAmelCase : int = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def __A ( lowerCAmelCase_ , lowerCAmelCase_ = "" , lowerCAmelCase_ = "." ):
def _flatten_dict(lowerCAmelCase_ , lowerCAmelCase_="" , lowerCAmelCase_="." ):
for k, v in d.items():
_UpperCAmelCase : str = str(lowerCAmelCase_ ) + delimiter + str(lowerCAmelCase_ ) if parent_key else k
if v and isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
yield from flatten_dict(lowerCAmelCase_ , lowerCAmelCase_ , delimiter=lowerCAmelCase_ ).items()
else:
yield key, v
return dict(_flatten_dict(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) )
@contextmanager
def __A ( lowerCAmelCase_ , lowerCAmelCase_ = False ):
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def __A ( lowerCAmelCase_ , lowerCAmelCase_=None ):
if is_numpy_array(lowerCAmelCase_ ):
return np.transpose(lowerCAmelCase_ , axes=lowerCAmelCase_ )
elif is_torch_tensor(lowerCAmelCase_ ):
return array.T if axes is None else array.permute(*lowerCAmelCase_ )
elif is_tf_tensor(lowerCAmelCase_ ):
import tensorflow as tf
return tf.transpose(lowerCAmelCase_ , perm=lowerCAmelCase_ )
elif is_jax_tensor(lowerCAmelCase_ ):
return jnp.transpose(lowerCAmelCase_ , axes=lowerCAmelCase_ )
else:
raise ValueError(f"Type not supported for transpose: {type(lowerCAmelCase_ )}." )
def __A ( lowerCAmelCase_ , lowerCAmelCase_ ):
if is_numpy_array(lowerCAmelCase_ ):
return np.reshape(lowerCAmelCase_ , lowerCAmelCase_ )
elif is_torch_tensor(lowerCAmelCase_ ):
return array.reshape(*lowerCAmelCase_ )
elif is_tf_tensor(lowerCAmelCase_ ):
import tensorflow as tf
return tf.reshape(lowerCAmelCase_ , lowerCAmelCase_ )
elif is_jax_tensor(lowerCAmelCase_ ):
return jnp.reshape(lowerCAmelCase_ , lowerCAmelCase_ )
else:
raise ValueError(f"Type not supported for reshape: {type(lowerCAmelCase_ )}." )
def __A ( lowerCAmelCase_ , lowerCAmelCase_=None ):
if is_numpy_array(lowerCAmelCase_ ):
return np.squeeze(lowerCAmelCase_ , axis=lowerCAmelCase_ )
elif is_torch_tensor(lowerCAmelCase_ ):
return array.squeeze() if axis is None else array.squeeze(dim=lowerCAmelCase_ )
elif is_tf_tensor(lowerCAmelCase_ ):
import tensorflow as tf
return tf.squeeze(lowerCAmelCase_ , axis=lowerCAmelCase_ )
elif is_jax_tensor(lowerCAmelCase_ ):
return jnp.squeeze(lowerCAmelCase_ , axis=lowerCAmelCase_ )
else:
raise ValueError(f"Type not supported for squeeze: {type(lowerCAmelCase_ )}." )
def __A ( lowerCAmelCase_ , lowerCAmelCase_ ):
if is_numpy_array(lowerCAmelCase_ ):
return np.expand_dims(lowerCAmelCase_ , lowerCAmelCase_ )
elif is_torch_tensor(lowerCAmelCase_ ):
return array.unsqueeze(dim=lowerCAmelCase_ )
elif is_tf_tensor(lowerCAmelCase_ ):
import tensorflow as tf
return tf.expand_dims(lowerCAmelCase_ , axis=lowerCAmelCase_ )
elif is_jax_tensor(lowerCAmelCase_ ):
return jnp.expand_dims(lowerCAmelCase_ , axis=lowerCAmelCase_ )
else:
raise ValueError(f"Type not supported for expand_dims: {type(lowerCAmelCase_ )}." )
def __A ( lowerCAmelCase_ ):
if is_numpy_array(lowerCAmelCase_ ):
return np.size(lowerCAmelCase_ )
elif is_torch_tensor(lowerCAmelCase_ ):
return array.numel()
elif is_tf_tensor(lowerCAmelCase_ ):
import tensorflow as tf
return tf.size(lowerCAmelCase_ )
elif is_jax_tensor(lowerCAmelCase_ ):
return array.size
else:
raise ValueError(f"Type not supported for expand_dims: {type(lowerCAmelCase_ )}." )
def __A ( lowerCAmelCase_ , lowerCAmelCase_ ):
for key, value in auto_map.items():
if isinstance(lowerCAmelCase_ , (tuple, list) ):
_UpperCAmelCase : Optional[int] = [f"{repo_id}--{v}" if (v is not None and """--""" not in v) else v for v in value]
elif value is not None and "--" not in value:
_UpperCAmelCase : Optional[Any] = f"{repo_id}--{value}"
return auto_map
def __A ( lowerCAmelCase_ ):
for base_class in inspect.getmro(lowerCAmelCase_ ):
_UpperCAmelCase : Dict = base_class.__module__
_UpperCAmelCase : Optional[Any] = base_class.__name__
if module.startswith("""tensorflow""" ) or module.startswith("""keras""" ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith("""torch""" ) or name == "PreTrainedModel":
return "pt"
elif module.startswith("""flax""" ) or module.startswith("""jax""" ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(f"Could not infer framework from class {model_class}." )
| 363 |
'''simple docstring'''
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 __lowerCAmelCase ( tf.keras.optimizers.schedules.LearningRateSchedule ):
def __init__(self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = 1.0 , lowerCAmelCase__ = None , ):
super().__init__()
_UpperCAmelCase : str = initial_learning_rate
_UpperCAmelCase : str = warmup_steps
_UpperCAmelCase : str = power
_UpperCAmelCase : Any = decay_schedule_fn
_UpperCAmelCase : List[Any] = 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`.
_UpperCAmelCase : List[Any] = tf.cast(lowerCAmelCase__ , tf.floataa )
_UpperCAmelCase : List[Any] = tf.cast(self.warmup_steps , tf.floataa )
_UpperCAmelCase : Union[str, Any] = global_step_float / warmup_steps_float
_UpperCAmelCase : List[Any] = 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 snake_case_ (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 __A ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 0.0 , lowerCAmelCase_ = 0.9 , lowerCAmelCase_ = 0.999 , lowerCAmelCase_ = 1e-8 , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = 0.0 , lowerCAmelCase_ = 1.0 , lowerCAmelCase_ = None , ):
_UpperCAmelCase : Dict = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=lowerCAmelCase_ , decay_steps=num_train_steps - num_warmup_steps , end_learning_rate=init_lr * min_lr_ratio , power=lowerCAmelCase_ , )
if num_warmup_steps:
_UpperCAmelCase : Optional[int] = WarmUp(
initial_learning_rate=lowerCAmelCase_ , decay_schedule_fn=lowerCAmelCase_ , warmup_steps=lowerCAmelCase_ , )
if weight_decay_rate > 0.0:
_UpperCAmelCase : Any = AdamWeightDecay(
learning_rate=lowerCAmelCase_ , weight_decay_rate=lowerCAmelCase_ , beta_a=lowerCAmelCase_ , beta_a=lowerCAmelCase_ , epsilon=lowerCAmelCase_ , clipnorm=lowerCAmelCase_ , global_clipnorm=lowerCAmelCase_ , exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""] , include_in_weight_decay=lowerCAmelCase_ , )
else:
_UpperCAmelCase : str = tf.keras.optimizers.Adam(
learning_rate=lowerCAmelCase_ , beta_a=lowerCAmelCase_ , beta_a=lowerCAmelCase_ , epsilon=lowerCAmelCase_ , clipnorm=lowerCAmelCase_ , global_clipnorm=lowerCAmelCase_ , )
# 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 __lowerCAmelCase ( __a ):
def __init__(self , lowerCAmelCase__ = 0.0_0_1 , lowerCAmelCase__ = 0.9 , lowerCAmelCase__ = 0.9_9_9 , lowerCAmelCase__ = 1e-7 , lowerCAmelCase__ = False , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = "AdamWeightDecay" , **lowerCAmelCase__ , ):
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = weight_decay_rate
_UpperCAmelCase : Tuple = include_in_weight_decay
_UpperCAmelCase : List[Any] = exclude_from_weight_decay
@classmethod
def snake_case_ (cls , lowerCAmelCase__ ):
_UpperCAmelCase : str = {"""WarmUp""": WarmUp}
return super(lowerCAmelCase__ , cls ).from_config(lowerCAmelCase__ , custom_objects=lowerCAmelCase__ )
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
super(lowerCAmelCase__ , self )._prepare_local(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Tuple = tf.constant(
self.weight_decay_rate , name="""adam_weight_decay_rate""" )
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : List[Any] = 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 snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__=None , **lowerCAmelCase__ ):
_UpperCAmelCase , _UpperCAmelCase : Dict = list(zip(*lowerCAmelCase__ ) )
return super(lowerCAmelCase__ , self ).apply_gradients(zip(lowerCAmelCase__ , lowerCAmelCase__ ) , name=lowerCAmelCase__ , **lowerCAmelCase__ )
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
_UpperCAmelCase : List[Any] = apply_state or {}
_UpperCAmelCase : Optional[int] = apply_state.get((var_device, var_dtype) )
if coefficients is None:
_UpperCAmelCase : Dict = self._fallback_apply_state(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Any = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=None ):
_UpperCAmelCase , _UpperCAmelCase : Tuple = self._get_lr(var.device , var.dtype.base_dtype , lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = self._decay_weights_op(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
with tf.control_dependencies([decay] ):
return super(lowerCAmelCase__ , self )._resource_apply_dense(lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ )
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=None ):
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = self._get_lr(var.device , var.dtype.base_dtype , lowerCAmelCase__ )
_UpperCAmelCase : List[str] = self._decay_weights_op(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
with tf.control_dependencies([decay] ):
return super(lowerCAmelCase__ , self )._resource_apply_sparse(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ )
def snake_case_ (self ):
_UpperCAmelCase : Optional[Any] = super().get_config()
config.update({"""weight_decay_rate""": self.weight_decay_rate} )
return config
def snake_case_ (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 __lowerCAmelCase ( __a ):
def __init__(self ):
_UpperCAmelCase : Optional[int] = []
_UpperCAmelCase : List[Any] = None
@property
def snake_case_ (self ):
if self._accum_steps is None:
_UpperCAmelCase : str = 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 snake_case_ (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:
_UpperCAmelCase : Optional[int] = 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 snake_case_ (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__ ) )
| 170 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCamelCase__ : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Optional[Any] = ['BartphoTokenizer']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
UpperCamelCase__ : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 344 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
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 torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase=13 , _lowerCamelCase=30 , _lowerCamelCase=2 , _lowerCamelCase=3 , _lowerCamelCase=True , _lowerCamelCase=True , _lowerCamelCase=32 , _lowerCamelCase=5 , _lowerCamelCase=4 , _lowerCamelCase=37 , _lowerCamelCase="gelu" , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase=10 , _lowerCamelCase=0.02 , _lowerCamelCase=3 , _lowerCamelCase=None , _lowerCamelCase=2 , ) -> str:
A_ : Optional[int] = parent
A_ : Dict = batch_size
A_ : List[Any] = image_size
A_ : Optional[int] = patch_size
A_ : List[str] = num_channels
A_ : List[Any] = is_training
A_ : Union[str, Any] = use_labels
A_ : Union[str, Any] = hidden_size
A_ : str = num_hidden_layers
A_ : List[str] = num_attention_heads
A_ : Union[str, Any] = intermediate_size
A_ : Any = hidden_act
A_ : Optional[Any] = hidden_dropout_prob
A_ : List[Any] = attention_probs_dropout_prob
A_ : Dict = type_sequence_label_size
A_ : Optional[int] = initializer_range
A_ : str = scope
A_ : Optional[Any] = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
A_ : Tuple = (image_size // patch_size) ** 2
A_ : Union[str, Any] = num_patches + 2
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
A_ : Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : Dict = None
if self.use_labels:
A_ : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ : Optional[Any] = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase_ ( self ) -> int:
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_lowerCamelCase , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
A_ : List[str] = DeiTModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Dict = model(_lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
A_ : int = DeiTForMaskedImageModeling(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : int = model(_lowerCamelCase )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
A_ : Dict = 1
A_ : Optional[int] = DeiTForMaskedImageModeling(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Optional[int] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A_ : int = model(_lowerCamelCase )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> Union[str, Any]:
A_ : Tuple = self.type_sequence_label_size
A_ : Tuple = DeiTForImageClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : int = model(_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
A_ : Dict = 1
A_ : Any = DeiTForImageClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A_ : List[str] = model(_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCAmelCase_ ( self ) -> List[str]:
A_ : List[Any] = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) ,
) : Union[str, Any] = config_and_inputs
A_ : Tuple = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( __A, __A, unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
lowerCamelCase = (
{
'''feature-extraction''': DeiTModel,
'''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = False
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
A_ : int = DeiTModelTester(self )
A_ : str = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase , hidden_size=37 )
def UpperCAmelCase_ ( self ) -> List[str]:
self.config_tester.run_common_tests()
@unittest.skip(reason="""DeiT does not use inputs_embeds""" )
def UpperCAmelCase_ ( self ) -> Optional[int]:
pass
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
A_ , A_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : List[Any] = model_class(_lowerCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A_ : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCamelCase , nn.Linear ) )
def UpperCAmelCase_ ( self ) -> Optional[Any]:
A_ , A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : List[str] = model_class(_lowerCamelCase )
A_ : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : Union[str, Any] = [*signature.parameters.keys()]
A_ : List[str] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def UpperCAmelCase_ ( self ) -> List[str]:
A_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCamelCase )
def UpperCAmelCase_ ( self ) -> Optional[Any]:
A_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=False ) -> Union[str, Any]:
A_ : int = super()._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def UpperCAmelCase_ ( self ) -> Optional[Any]:
if not self.model_tester.is_training:
return
A_ , A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Optional[Any] = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(_lowerCamelCase )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
A_ : List[str] = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
A_ : List[str] = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
A_ : List[str] = model(**_lowerCamelCase ).loss
loss.backward()
def UpperCAmelCase_ ( self ) -> int:
A_ , A_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
A_ : Any = False
A_ : Union[str, Any] = True
for model_class in self.all_model_classes:
if model_class in get_values(_lowerCamelCase ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
A_ : List[Any] = model_class(_lowerCamelCase )
model.gradient_checkpointing_enable()
model.to(_lowerCamelCase )
model.train()
A_ : str = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
A_ : Union[str, Any] = model(**_lowerCamelCase ).loss
loss.backward()
def UpperCAmelCase_ ( self ) -> Tuple:
A_ , A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Optional[Any] = [
{"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
{"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
{"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(_lowerCamelCase ),
*get_values(_lowerCamelCase ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}" ):
A_ : Dict = problem_type["""title"""]
A_ : List[Any] = problem_type["""num_labels"""]
A_ : List[str] = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
A_ : List[Any] = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
if problem_type["num_labels"] > 1:
A_ : Tuple = inputs["""labels"""].unsqueeze(1 ).repeat(1 , problem_type["""num_labels"""] )
A_ : Union[str, Any] = inputs["""labels"""].to(problem_type["""dtype"""] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=_lowerCamelCase ) as warning_list:
A_ : List[str] = model(**_lowerCamelCase ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
F"Something is going wrong in the regression problem: intercepted {w.message}" )
loss.backward()
@slow
def UpperCAmelCase_ ( self ) -> Tuple:
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : int = DeiTModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def UpperCAmelCase ( ) -> Tuple:
"""simple docstring"""
A_ : Optional[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCAmelCase_ ( self ) -> Optional[Any]:
return (
DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224""" )
if is_vision_available()
else None
)
@slow
def UpperCAmelCase_ ( self ) -> Tuple:
A_ : Any = DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224""" ).to(
_lowerCamelCase )
A_ : Optional[int] = self.default_image_processor
A_ : str = prepare_img()
A_ : Any = image_processor(images=_lowerCamelCase , return_tensors="""pt""" ).to(_lowerCamelCase )
# forward pass
with torch.no_grad():
A_ : Any = model(**_lowerCamelCase )
# verify the logits
A_ : Tuple = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
A_ : List[Any] = torch.tensor([-1.0266, 0.1912, -1.2861] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCamelCase , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def UpperCAmelCase_ ( self ) -> Tuple:
A_ : Optional[Any] = DeiTModel.from_pretrained(
"""facebook/deit-base-distilled-patch16-224""" , torch_dtype=torch.floataa , device_map="""auto""" )
A_ : int = self.default_image_processor
A_ : List[str] = prepare_img()
A_ : List[Any] = image_processor(images=_lowerCamelCase , return_tensors="""pt""" )
A_ : Union[str, Any] = inputs.pixel_values.to(_lowerCamelCase )
# forward pass to make sure inference works in fp16
with torch.no_grad():
A_ : List[Any] = model(_lowerCamelCase )
| 344 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : Dict ={'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A : Dict =[
'''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''SEWForCTC''',
'''SEWForSequenceClassification''',
'''SEWModel''',
'''SEWPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_sew import (
SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
SEWForCTC,
SEWForSequenceClassification,
SEWModel,
SEWPreTrainedModel,
)
else:
import sys
_A : Dict =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 364 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_A : Optional[Any] =typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_A : Optional[int] =typing.Union[np.floataa, int, float] # noqa: UP007
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> VectorOut:
return np.sqrt(np.sum((np.asarray(UpperCamelCase ) - np.asarray(UpperCamelCase )) ** 2 ) )
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> VectorOut:
return sum((va - va) ** 2 for va, va in zip(UpperCamelCase , UpperCamelCase ) ) ** (1 / 2)
if __name__ == "__main__":
def SCREAMING_SNAKE_CASE_ () -> None:
from timeit import timeit
print("""Without Numpy""" )
print(
timeit(
"""euclidean_distance_no_np([1, 2, 3], [4, 5, 6])""" , number=10000 , globals=globals() , ) )
print("""With Numpy""" )
print(
timeit(
"""euclidean_distance([1, 2, 3], [4, 5, 6])""" , number=10000 , globals=globals() , ) )
benchmark()
| 129 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
# See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class A_ ( _snake_case ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = """pegasus"""
UpperCAmelCase_ : int = ["""past_key_values"""]
UpperCAmelCase_ : Dict = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : List[str] , lowercase_ : Any=50_265 , lowercase_ : List[str]=1_024 , lowercase_ : List[str]=12 , lowercase_ : Tuple=4_096 , lowercase_ : Optional[int]=16 , lowercase_ : Any=12 , lowercase_ : Any=4_096 , lowercase_ : int=16 , lowercase_ : List[Any]=0.0 , lowercase_ : Tuple=0.0 , lowercase_ : Union[str, Any]=True , lowercase_ : str=True , lowercase_ : Union[str, Any]="gelu" , lowercase_ : Any=1_024 , lowercase_ : Optional[int]=0.1 , lowercase_ : Tuple=0.0 , lowercase_ : Union[str, Any]=0.0 , lowercase_ : Dict=0.02 , lowercase_ : Any=0 , lowercase_ : Any=False , lowercase_ : Union[str, Any]=0 , lowercase_ : Union[str, Any]=1 , lowercase_ : List[Any]=1 , **lowercase_ : Tuple , ) -> str:
UpperCAmelCase : List[Any] = vocab_size
UpperCAmelCase : Optional[int] = max_position_embeddings
UpperCAmelCase : Any = d_model
UpperCAmelCase : List[Any] = encoder_ffn_dim
UpperCAmelCase : Union[str, Any] = encoder_layers
UpperCAmelCase : Optional[int] = encoder_attention_heads
UpperCAmelCase : List[str] = decoder_ffn_dim
UpperCAmelCase : Optional[Any] = decoder_layers
UpperCAmelCase : Optional[Any] = decoder_attention_heads
UpperCAmelCase : Optional[Any] = dropout
UpperCAmelCase : Dict = attention_dropout
UpperCAmelCase : Dict = activation_dropout
UpperCAmelCase : List[str] = activation_function
UpperCAmelCase : Union[str, Any] = init_std
UpperCAmelCase : List[str] = encoder_layerdrop
UpperCAmelCase : Dict = decoder_layerdrop
UpperCAmelCase : List[Any] = use_cache
UpperCAmelCase : Dict = encoder_layers
UpperCAmelCase : List[Any] = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=lowercase_ , eos_token_id=lowercase_ , is_encoder_decoder=lowercase_ , decoder_start_token_id=lowercase_ , forced_eos_token_id=lowercase_ , **lowercase_ , )
@property
def UpperCAmelCase_ ( self : Union[str, Any] ) -> int:
return self.encoder_attention_heads
@property
def UpperCAmelCase_ ( self : List[str] ) -> int:
return self.d_model
| 151 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
if partitions <= 0:
raise ValueError('partitions must be a positive number!' )
if partitions > number_of_bytes:
raise ValueError('partitions can not > number_of_bytes!' )
UpperCAmelCase : int = number_of_bytes // partitions
UpperCAmelCase : List[str] = []
for i in range(UpperCAmelCase_ ):
UpperCAmelCase : List[Any] = i * bytes_per_partition + 1
UpperCAmelCase : str = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(F"""{start_bytes}-{end_bytes}""" )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 151 | 1 |
"""simple docstring"""
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
from .ta_film_transformer import TaFilmDecoder
from .transformer_ad import TransformeraDModel
from .unet_ad import UNetaDModel
from .unet_ad import UNetaDModel
from .unet_ad_condition import UNetaDConditionModel
from .unet_ad_condition import UNetaDConditionModel
from .vq_model import VQModel
if is_flax_available():
from .controlnet_flax import FlaxControlNetModel
from .unet_ad_condition_flax import FlaxUNetaDConditionModel
from .vae_flax import FlaxAutoencoderKL
| 350 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
lowerCAmelCase_ = logging.getLogger(__name__)
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Pretrained config name or path if not the same as model_name"} )
lowerCAmelCase : Optional[str] = field(
default="NER" ,metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} )
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
lowerCAmelCase : bool = field(default=A_ ,metadata={"help": "Set this flag to use fast tokenization."} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,)
@dataclass
class __A :
'''simple docstring'''
lowerCAmelCase : str = field(
metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
lowerCAmelCase : Optional[str] = field(
default=A_ ,metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."} ,)
lowerCAmelCase : int = field(
default=1_2_8 ,metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
} ,)
lowerCAmelCase : bool = field(
default=A_ ,metadata={"help": "Overwrite the cached training and evaluation sets"} )
def __UpperCAmelCase ( ) -> Optional[int]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowercase__ : List[str] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
lowercase__ , lowercase__ , lowercase__ : List[str] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase__ , lowercase__ , lowercase__ : List[str] = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
''' --overwrite_output_dir to overcome.''' )
lowercase__ : str = import_module('''tasks''' )
try:
lowercase__ : List[str] = getattr(__lowerCamelCase , model_args.task_type )
lowercase__ : TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , __lowerCamelCase )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
lowercase__ : Union[str, Any] = token_classification_task.get_labels(data_args.labels )
lowercase__ : Dict[int, str] = dict(enumerate(__lowerCamelCase ) )
lowercase__ : Optional[int] = len(__lowerCamelCase )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase__ : List[Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid={label: i for i, label in enumerate(__lowerCamelCase )} , cache_dir=model_args.cache_dir , )
lowercase__ : Any = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , )
lowercase__ : str = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=__lowerCamelCase , cache_dir=model_args.cache_dir , )
# Get datasets
lowercase__ : str = (
TokenClassificationDataset(
token_classification_task=__lowerCamelCase , data_dir=data_args.data_dir , tokenizer=__lowerCamelCase , labels=__lowerCamelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
lowercase__ : str = (
TokenClassificationDataset(
token_classification_task=__lowerCamelCase , data_dir=data_args.data_dir , tokenizer=__lowerCamelCase , labels=__lowerCamelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(__lowerCamelCase , __lowerCamelCase ) -> Tuple[List[int], List[int]]:
lowercase__ : Tuple = np.argmax(__lowerCamelCase , axis=2 )
lowercase__ , lowercase__ : Tuple = preds.shape
lowercase__ : List[str] = [[] for _ in range(__lowerCamelCase )]
lowercase__ : Tuple = [[] for _ in range(__lowerCamelCase )]
for i in range(__lowerCamelCase ):
for j in range(__lowerCamelCase ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(__lowerCamelCase ) -> Dict:
lowercase__ , lowercase__ : List[Any] = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(__lowerCamelCase , __lowerCamelCase ),
"precision": precision_score(__lowerCamelCase , __lowerCamelCase ),
"recall": recall_score(__lowerCamelCase , __lowerCamelCase ),
"f1": fa_score(__lowerCamelCase , __lowerCamelCase ),
}
# Data collator
lowercase__ : Tuple = DataCollatorWithPadding(__lowerCamelCase , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
lowercase__ : str = Trainer(
model=__lowerCamelCase , args=__lowerCamelCase , train_dataset=__lowerCamelCase , eval_dataset=__lowerCamelCase , compute_metrics=__lowerCamelCase , data_collator=__lowerCamelCase , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase__ : int = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowercase__ : Optional[int] = trainer.evaluate()
lowercase__ : Union[str, Any] = os.path.join(training_args.output_dir , '''eval_results.txt''' )
if trainer.is_world_process_zero():
with open(__lowerCamelCase , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key, value in result.items():
logger.info(''' %s = %s''' , __lowerCamelCase , __lowerCamelCase )
writer.write('''%s = %s\n''' % (key, value) )
results.update(__lowerCamelCase )
# Predict
if training_args.do_predict:
lowercase__ : Optional[int] = TokenClassificationDataset(
token_classification_task=__lowerCamelCase , data_dir=data_args.data_dir , tokenizer=__lowerCamelCase , labels=__lowerCamelCase , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
lowercase__ , lowercase__ , lowercase__ : Union[str, Any] = trainer.predict(__lowerCamelCase )
lowercase__ , lowercase__ : Tuple = align_predictions(__lowerCamelCase , __lowerCamelCase )
lowercase__ : Dict = os.path.join(training_args.output_dir , '''test_results.txt''' )
if trainer.is_world_process_zero():
with open(__lowerCamelCase , '''w''' ) as writer:
for key, value in metrics.items():
logger.info(''' %s = %s''' , __lowerCamelCase , __lowerCamelCase )
writer.write('''%s = %s\n''' % (key, value) )
# Save predictions
lowercase__ : Dict = os.path.join(training_args.output_dir , '''test_predictions.txt''' )
if trainer.is_world_process_zero():
with open(__lowerCamelCase , '''w''' ) as writer:
with open(os.path.join(data_args.data_dir , '''test.txt''' ) , '''r''' ) as f:
token_classification_task.write_predictions_to_file(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return results
def __UpperCAmelCase ( __lowerCamelCase ) -> List[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 302 | 0 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range(10 )]),
({"num_shards": 10, "max_num_jobs": 10}, [range(UpperCamelCase__ , i + 1 ) for i in range(10 )]),
({"num_shards": 1, "max_num_jobs": 10}, [range(1 )]),
({"num_shards": 10, "max_num_jobs": 3}, [range(0 , 4 ), range(4 , 7 ), range(7 , 10 )]),
({"num_shards": 3, "max_num_jobs": 10}, [range(0 , 1 ), range(1 , 2 ), range(2 , 3 )]),
] , )
def __lowerCAmelCase ( snake_case__ , snake_case__ ):
__UpperCamelCase : List[str] = _distribute_shards(**UpperCamelCase__ )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, max_num_jobs, expected" , [
({"foo": 0}, 10, [{"foo": 0}]),
({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
] , )
def __lowerCAmelCase ( snake_case__ , snake_case__ , snake_case__ ):
__UpperCamelCase : Union[str, Any] = _split_gen_kwargs(UpperCamelCase__ , UpperCamelCase__ )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, expected" , [
({"foo": 0}, 1),
({"shards": [0]}, 1),
({"shards": [0, 1, 2, 3]}, 4),
({"shards": [0, 1, 2, 3], "foo": 0}, 4),
({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
] , )
def __lowerCAmelCase ( snake_case__ , snake_case__ ):
if expected is RuntimeError:
with pytest.raises(UpperCamelCase__ ):
_number_of_shards_in_gen_kwargs(UpperCamelCase__ )
else:
__UpperCamelCase : Dict = _number_of_shards_in_gen_kwargs(UpperCamelCase__ )
assert out == expected
| 298 |
from datetime import datetime
import requests
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> bytes:
'''simple docstring'''
UpperCAmelCase = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase = requests.get(base_url + url ).json()[0]['''urls'''][0]['''src''']
return requests.get(UpperCamelCase__ ).content
if __name__ == "__main__":
__A : Union[str, Any] = input("Enter Video/IGTV url: ").strip()
__A : Tuple = F'{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4'
with open(file_name, "wb") as fp:
fp.write(download_video(url))
print(F'Done. Video saved to disk as {file_name}.')
| 273 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
lowerCAmelCase__ : List[Any] =False
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _A ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def _A ( self ):
'''simple docstring'''
return 12
@property
def _A ( self ):
'''simple docstring'''
return 12
@property
def _A ( self ):
'''simple docstring'''
return 32
@property
def _A ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def _A ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModel(_A )
@property
def _A ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE = 12
__SCREAMING_SNAKE_CASE = 12
__SCREAMING_SNAKE_CASE = {
'attention_bias': True,
'cross_attention_dim': 32,
'attention_head_dim': height * width,
'num_attention_heads': 1,
'num_vector_embeds': self.num_embed,
'num_embeds_ada_norm': self.num_embeds_ada_norm,
'norm_num_groups': 32,
'sample_size': width,
'activation_fn': 'geglu-approximate',
}
__SCREAMING_SNAKE_CASE = TransformeraDModel(**_A )
return model
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 'cpu'
__SCREAMING_SNAKE_CASE = self.dummy_vqvae
__SCREAMING_SNAKE_CASE = self.dummy_text_encoder
__SCREAMING_SNAKE_CASE = self.dummy_tokenizer
__SCREAMING_SNAKE_CASE = self.dummy_transformer
__SCREAMING_SNAKE_CASE = VQDiffusionScheduler(self.num_embed )
__SCREAMING_SNAKE_CASE = LearnedClassifierFreeSamplingEmbeddings(learnable=_A )
__SCREAMING_SNAKE_CASE = VQDiffusionPipeline(
vqvae=_A , text_encoder=_A , tokenizer=_A , transformer=_A , scheduler=_A , learned_classifier_free_sampling_embeddings=_A , )
__SCREAMING_SNAKE_CASE = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
__SCREAMING_SNAKE_CASE = 'teddy bear playing in the pool'
__SCREAMING_SNAKE_CASE = torch.Generator(device=_A ).manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe([prompt] , generator=_A , num_inference_steps=2 , output_type='np' )
__SCREAMING_SNAKE_CASE = output.images
__SCREAMING_SNAKE_CASE = torch.Generator(device=_A ).manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(
[prompt] , generator=_A , output_type='np' , return_dict=_A , num_inference_steps=2 )[0]
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
__SCREAMING_SNAKE_CASE = np.array([0.6_5_5_1, 0.6_1_6_8, 0.5_0_0_8, 0.5_6_7_6, 0.5_6_5_9, 0.4_2_9_5, 0.6_0_7_3, 0.5_5_9_9, 0.4_9_9_2] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 'cpu'
__SCREAMING_SNAKE_CASE = self.dummy_vqvae
__SCREAMING_SNAKE_CASE = self.dummy_text_encoder
__SCREAMING_SNAKE_CASE = self.dummy_tokenizer
__SCREAMING_SNAKE_CASE = self.dummy_transformer
__SCREAMING_SNAKE_CASE = VQDiffusionScheduler(self.num_embed )
__SCREAMING_SNAKE_CASE = LearnedClassifierFreeSamplingEmbeddings(
learnable=_A , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
__SCREAMING_SNAKE_CASE = VQDiffusionPipeline(
vqvae=_A , text_encoder=_A , tokenizer=_A , transformer=_A , scheduler=_A , learned_classifier_free_sampling_embeddings=_A , )
__SCREAMING_SNAKE_CASE = pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
__SCREAMING_SNAKE_CASE = 'teddy bear playing in the pool'
__SCREAMING_SNAKE_CASE = torch.Generator(device=_A ).manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe([prompt] , generator=_A , num_inference_steps=2 , output_type='np' )
__SCREAMING_SNAKE_CASE = output.images
__SCREAMING_SNAKE_CASE = torch.Generator(device=_A ).manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipe(
[prompt] , generator=_A , output_type='np' , return_dict=_A , num_inference_steps=2 )[0]
__SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
__SCREAMING_SNAKE_CASE = np.array([0.6_6_9_3, 0.6_0_7_5, 0.4_9_5_9, 0.5_7_0_1, 0.5_5_8_3, 0.4_3_3_3, 0.6_1_7_1, 0.5_6_8_4, 0.4_9_8_8] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _A ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy' )
__SCREAMING_SNAKE_CASE = VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq' )
__SCREAMING_SNAKE_CASE = pipeline.to(_A )
pipeline.set_progress_bar_config(disable=_A )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
__SCREAMING_SNAKE_CASE = torch.Generator(device=_A ).manual_seed(0 )
__SCREAMING_SNAKE_CASE = pipeline(
'teddy bear playing in the pool' , num_images_per_prompt=1 , generator=_A , output_type='np' , )
__SCREAMING_SNAKE_CASE = output.images[0]
assert image.shape == (256, 256, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 118 |
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 UpperCAmelCase_ ( UpperCamelCase_ , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = LayoutLMTokenizer
UpperCamelCase__ : Any = LayoutLMTokenizerFast
UpperCamelCase__ : Optional[int] = True
UpperCamelCase__ : int = True
def _A ( self ):
'''simple docstring'''
super().setUp()
__SCREAMING_SNAKE_CASE = [
'[UNK]',
'[CLS]',
'[SEP]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
__SCREAMING_SNAKE_CASE = 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 _A ( self , **_A ):
'''simple docstring'''
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **_A )
def _A ( self , _A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 'UNwant\u00E9d,running'
__SCREAMING_SNAKE_CASE = 'unwanted, running'
return input_text, output_text
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = self.tokenizer_class(self.vocab_file )
__SCREAMING_SNAKE_CASE = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(_A , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_A ) , [7, 4, 5, 10, 8, 9] )
def _A ( self ):
'''simple docstring'''
pass
| 118 | 1 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
snake_case : Dict = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
class _snake_case ( lowercase__ , unittest.TestCase ):
SCREAMING_SNAKE_CASE__ = XLMProphetNetTokenizer
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = True
def SCREAMING_SNAKE_CASE__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
a :Optional[Any] = XLMProphetNetTokenizer(_A , keep_accents=_A )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self ):
a :Optional[Any] = '[PAD]'
a :Optional[Any] = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A ) , _A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A ) , _A )
def SCREAMING_SNAKE_CASE__ ( self ):
a :Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''[PAD]''' )
self.assertEqual(vocab_keys[1] , '''[CLS]''' )
self.assertEqual(vocab_keys[-1] , '''j''' )
self.assertEqual(len(_A ) , 1012 )
def SCREAMING_SNAKE_CASE__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1012 )
def SCREAMING_SNAKE_CASE__ ( self ):
a :Any = XLMProphetNetTokenizer(_A , keep_accents=_A )
a :List[str] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_A , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_A ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
a :List[Any] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_A , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
a :int = tokenizer.convert_tokens_to_ids(_A )
self.assertListEqual(
_A , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
] , )
a :Optional[int] = tokenizer.convert_ids_to_tokens(_A )
self.assertListEqual(
_A , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''[UNK]''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''[UNK]''',
'''.''',
] , )
@cached_property
def SCREAMING_SNAKE_CASE__ ( self ):
return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased''' )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
a :Optional[Any] = 'Hello World!'
a :List[Any] = [3_5389, 6672, 49, 2]
self.assertListEqual(_A , self.big_tokenizer.encode(_A ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
# fmt: off
a :Dict = {'input_ids': [[1_1073, 8_2783, 18, 26, 8_2783, 549, 5_1540, 248, 1_7209, 1301, 217, 20, 21_5186, 1325, 147, 1_7209, 1301, 217, 20, 5_6370, 53, 12_2020, 20, 1_6477, 27, 8_7355, 4548, 20, 4728, 7_8392, 17, 15_9969, 18, 26, 2_4491, 629, 15, 538, 2_2704, 5439, 15, 2788, 2_4491, 9885, 15, 4_3534, 605, 15, 814, 1_8403, 3_3200, 29, 15, 4_3534, 2_4458, 1_2410, 111, 2_4966, 8_3669, 9637, 14_4068, 26, 850, 2_2346, 27, 147, 2_4966, 8_3669, 8_3490, 26, 3_9113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 12_2020, 11_5785, 34, 816, 1339, 4_6887, 18, 147, 5_3905, 1951, 4_2238, 4_1170, 1_7732, 834, 436, 15, 2_7523, 9_8733, 217, 147, 5542, 4981, 930, 1_7347, 16, 2], [2_0091, 629, 94, 8_2786, 58, 490, 20, 1528, 84, 5_3905, 344, 8_0592, 11_0128, 1_8822, 5267, 1306, 62, 15_2537, 308, 7997, 401, 12_4427, 549, 3_5442, 225, 109, 1_5055, 2_5748, 147, 7119, 4_3712, 34, 767, 13_5366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 6_3784, 11_9466, 17, 14_7808, 8_8214, 18, 656, 81, 32, 3296, 1_0280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_A , model_name='''microsoft/xprophetnet-large-wiki100-cased''' , revision='''1acad1643ddd54a44df6a1b797ada8373685d90e''' , )
| 94 |
'''simple docstring'''
class _lowerCamelCase : # Public class to implement a graph
'''simple docstring'''
def __init__( self : List[Any] , _A : int , _A : int , _A : list[list[bool]] ) -> None:
__magic_name__ : Tuple = row
__magic_name__ : str = col
__magic_name__ : Optional[Any] = graph
def __lowerCAmelCase ( self : Any , _A : int , _A : int , _A : list[list[bool]] ) -> bool:
return (
0 <= i < self.ROW
and 0 <= j < self.COL
and not visited[i][j]
and self.graph[i][j]
)
def __lowerCAmelCase ( self : List[Any] , _A : int , _A : int , _A : list[list[bool]] ) -> None:
# Checking all 8 elements surrounding nth element
__magic_name__ : List[str] = [-1, -1, -1, 0, 0, 1, 1, 1] # Coordinate order
__magic_name__ : List[str] = [-1, 0, 1, -1, 1, -1, 0, 1]
__magic_name__ : Optional[int] = True # Make those cells visited
for k in range(8 ):
if self.is_safe(i + row_nbr[k] , j + col_nbr[k] , _A ):
self.diffs(i + row_nbr[k] , j + col_nbr[k] , _A )
def __lowerCAmelCase ( self : int ) -> int: # And finally, count all islands.
__magic_name__ : List[str] = [[False for j in range(self.COL )] for i in range(self.ROW )]
__magic_name__ : Any = 0
for i in range(self.ROW ):
for j in range(self.COL ):
if visited[i][j] is False and self.graph[i][j] == 1:
self.diffs(_A , _A , _A )
count += 1
return count | 331 | 0 |
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def __A ( __lowerCamelCase ) -> Optional[Any]:
return EnvironmentCommand()
class __lowerCAmelCase ( __magic_name__ ):
@staticmethod
def lowerCamelCase__ ( __magic_name__ :ArgumentParser ):
'''simple docstring'''
a = parser.add_parser("""env""" )
download_parser.set_defaults(func=__magic_name__ )
def lowerCamelCase__ ( self :List[str] ):
'''simple docstring'''
a = huggingface_hub.__version__
a = """not installed"""
a = """NA"""
if is_torch_available():
import torch
a = torch.__version__
a = torch.cuda.is_available()
a = """not installed"""
if is_transformers_available():
import transformers
a = transformers.__version__
a = """not installed"""
if is_accelerate_available():
import accelerate
a = accelerate.__version__
a = """not installed"""
if is_xformers_available():
import xformers
a = xformers.__version__
a = {
"""`diffusers` version""": version,
"""Platform""": platform.platform(),
"""Python version""": platform.python_version(),
"""PyTorch version (GPU?)""": F'{pt_version} ({pt_cuda_available})',
"""Huggingface_hub version""": hub_version,
"""Transformers version""": transformers_version,
"""Accelerate version""": accelerate_version,
"""xFormers version""": xformers_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(__magic_name__ ) )
return info
@staticmethod
def lowerCamelCase__ ( __magic_name__ :Union[str, Any] ):
'''simple docstring'''
return "\n".join([F'- {prop}: {val}' for prop, val in d.items()] ) + "\n"
| 347 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( __magic_name__ , unittest.TestCase ):
UpperCamelCase__ = CanineTokenizer
UpperCamelCase__ = False
def lowerCamelCase__ ( self :Tuple ):
'''simple docstring'''
super().setUp()
a = CanineTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
return CanineTokenizer.from_pretrained("""google/canine-s""" )
def lowerCamelCase__ ( self :Tuple , **__magic_name__ :Dict ):
'''simple docstring'''
a = self.tokenizer_class.from_pretrained(self.tmpdirname , **__magic_name__ )
a = 1024
return tokenizer
@require_torch
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
a = self.canine_tokenizer
a = ["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
# fmt: off
a = [5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
# fmt: on
a = tokenizer(__magic_name__ , padding=__magic_name__ , return_tensors="""pt""" )
self.assertIsInstance(__magic_name__ , __magic_name__ )
a = list(batch.input_ids.numpy()[0] )
self.assertListEqual(__magic_name__ , __magic_name__ )
self.assertEqual((2, 39) , batch.input_ids.shape )
self.assertEqual((2, 39) , batch.attention_mask.shape )
@require_torch
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
a = self.canine_tokenizer
a = ["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
a = tokenizer(__magic_name__ , padding=__magic_name__ , return_tensors="""pt""" )
# check if input_ids, attention_mask and token_type_ids are returned
self.assertIn("""input_ids""" , __magic_name__ )
self.assertIn("""attention_mask""" , __magic_name__ )
self.assertIn("""token_type_ids""" , __magic_name__ )
@require_torch
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
a = self.canine_tokenizer
a = [
"""What's the weater?""",
"""It's about 25 degrees.""",
]
a = tokenizer(
text_target=__magic_name__ , max_length=32 , padding="""max_length""" , truncation=__magic_name__ , return_tensors="""pt""" )
self.assertEqual(32 , targets["""input_ids"""].shape[1] )
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
a = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
a = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
# Isolate this from the other tests because we save additional tokens/etc
a = tempfile.mkdtemp()
a = """ He is very happy, UNwant\u00E9d,running"""
a = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
tokenizer.save_pretrained(__magic_name__ )
a = tokenizer.__class__.from_pretrained(__magic_name__ )
a = after_tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
shutil.rmtree(__magic_name__ )
a = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
# Isolate this from the other tests because we save additional tokens/etc
a = tempfile.mkdtemp()
a = """ He is very happy, UNwant\u00E9d,running"""
a = tokenizer.additional_special_tokens
# We can add a new special token for Canine as follows:
a = chr(0Xe_0_0_7 )
additional_special_tokens.append(__magic_name__ )
tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens} )
a = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
tokenizer.save_pretrained(__magic_name__ )
a = tokenizer.__class__.from_pretrained(__magic_name__ )
a = after_tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
self.assertListEqual(__magic_name__ , __magic_name__ )
self.assertIn(__magic_name__ , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
a = tokenizer.__class__.from_pretrained(__magic_name__ , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(__magic_name__ )
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
a = self.get_tokenizers(do_lower_case=__magic_name__ )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
a , a = self.get_clean_sequence(__magic_name__ )
# a special token for Canine can be defined as follows:
a = 0Xe_0_0_5
a = chr(__magic_name__ )
tokenizer.add_special_tokens({"""cls_token""": special_token} )
a = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
self.assertEqual(len(__magic_name__ ) , 1 )
a = tokenizer.decode(ids + encoded_special_token , clean_up_tokenization_spaces=__magic_name__ )
a = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
a = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
a = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
self.assertEqual(__magic_name__ , input_encoded + special_token_id )
a = tokenizer.decode(__magic_name__ , skip_special_tokens=__magic_name__ )
self.assertTrue(special_token not in decoded )
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
a = self.get_tokenizers(do_lower_case=__magic_name__ )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
a = chr(0Xe_0_0_5 )
a = chr(0Xe_0_0_6 )
# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
tokenizer.add_tokens([SPECIAL_TOKEN_1] , special_tokens=__magic_name__ )
# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
tokenizer.add_special_tokens({"""additional_special_tokens""": [SPECIAL_TOKEN_2]} )
a = tokenizer.tokenize(__magic_name__ )
a = tokenizer.tokenize(__magic_name__ )
self.assertEqual(len(__magic_name__ ) , 1 )
self.assertEqual(len(__magic_name__ ) , 1 )
self.assertEqual(token_a[0] , __magic_name__ )
self.assertEqual(token_a[0] , __magic_name__ )
@require_tokenizers
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
a = self.get_tokenizers(do_lower_case=__magic_name__ )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
# a special token for Canine can be defined as follows:
a = 0Xe_0_0_6
a = chr(__magic_name__ )
a = AddedToken(__magic_name__ , lstrip=__magic_name__ )
tokenizer.add_special_tokens({"""additional_special_tokens""": [new_token]} )
with tempfile.TemporaryDirectory() as tmp_dir_name:
tokenizer.save_pretrained(__magic_name__ )
tokenizer.from_pretrained(__magic_name__ )
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
a = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(__magic_name__ )
with open(os.path.join(__magic_name__ , """special_tokens_map.json""" ) , encoding="""utf-8""" ) as json_file:
a = json.load(__magic_name__ )
with open(os.path.join(__magic_name__ , """tokenizer_config.json""" ) , encoding="""utf-8""" ) as json_file:
a = json.load(__magic_name__ )
# a special token for Canine can be defined as follows:
a = 0Xe_0_0_6
a = chr(__magic_name__ )
a = [new_token_a]
a = [new_token_a]
with open(os.path.join(__magic_name__ , """special_tokens_map.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(__magic_name__ , __magic_name__ )
with open(os.path.join(__magic_name__ , """tokenizer_config.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(__magic_name__ , __magic_name__ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
a = tokenizer_class.from_pretrained(__magic_name__ , extra_ids=0 )
self.assertIn(__magic_name__ , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a] ) ) , )
a = 0Xe_0_0_7
a = chr(__magic_name__ )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
a = [AddedToken(__magic_name__ , lstrip=__magic_name__ )]
a = tokenizer_class.from_pretrained(
__magic_name__ , additional_special_tokens=__magic_name__ , extra_ids=0 )
self.assertIn(__magic_name__ , tokenizer.additional_special_tokens )
# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a] ) ) )
@require_tokenizers
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
a = self.get_tokenizers(do_lower_case=__magic_name__ )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
a = """hello world"""
if self.space_between_special_tokens:
a = """[CLS] hello world [SEP]"""
else:
a = input
a = tokenizer.encode(__magic_name__ , add_special_tokens=__magic_name__ )
a = tokenizer.decode(__magic_name__ , spaces_between_special_tokens=self.space_between_special_tokens )
self.assertIn(__magic_name__ , [output, output.lower()] )
def lowerCamelCase__ ( self :List[str] ):
'''simple docstring'''
a = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
a = [
"""bos_token""",
"""eos_token""",
"""unk_token""",
"""sep_token""",
"""pad_token""",
"""cls_token""",
"""mask_token""",
]
a = """a"""
a = ord(__magic_name__ )
for attr in attributes_list:
setattr(__magic_name__ , attr + """_id""" , __magic_name__ )
self.assertEqual(getattr(__magic_name__ , __magic_name__ ) , __magic_name__ )
self.assertEqual(getattr(__magic_name__ , attr + """_id""" ) , __magic_name__ )
setattr(__magic_name__ , attr + """_id""" , __magic_name__ )
self.assertEqual(getattr(__magic_name__ , __magic_name__ ) , __magic_name__ )
self.assertEqual(getattr(__magic_name__ , attr + """_id""" ) , __magic_name__ )
setattr(__magic_name__ , """additional_special_tokens_ids""" , [] )
self.assertListEqual(getattr(__magic_name__ , """additional_special_tokens""" ) , [] )
self.assertListEqual(getattr(__magic_name__ , """additional_special_tokens_ids""" ) , [] )
a = 0Xe_0_0_6
a = chr(__magic_name__ )
setattr(__magic_name__ , """additional_special_tokens_ids""" , [additional_special_token_id] )
self.assertListEqual(getattr(__magic_name__ , """additional_special_tokens""" ) , [additional_special_token] )
self.assertListEqual(getattr(__magic_name__ , """additional_special_tokens_ids""" ) , [additional_special_token_id] )
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :str ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :Optional[int] ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :List[Any] ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :List[str] ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :List[str] ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :List[str] ):
'''simple docstring'''
pass
| 347 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _UpperCamelCase ( lowerCAmelCase ):
UpperCAmelCase_ = """"""
UpperCAmelCase_ = """hf-legacy""" # "hf://"" is reserved for hffs
def __init__( self :str , lowerCamelCase :Optional[DatasetInfo] = None , lowerCamelCase :Optional[str] = None , **lowerCamelCase :Dict , ) -> Optional[Any]:
super().__init__(self , **lowerCamelCase )
UpperCAmelCase__ = repo_info
UpperCAmelCase__ = token
UpperCAmelCase__ = None
def UpperCAmelCase_ ( self :Tuple ) -> Optional[Any]:
if self.dir_cache is None:
UpperCAmelCase__ = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
UpperCAmelCase__ = {
"name": hf_file.rfilename,
"size": None,
"type": "file",
}
self.dir_cache.update(
{
str(lowerCamelCase ): {"name": str(lowerCamelCase ), "size": None, "type": "directory"}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def UpperCAmelCase_ ( self :Dict , lowerCamelCase :str , lowerCamelCase :str = "rb" , **lowerCamelCase :Optional[int] , ) -> Optional[Any]:
if not isinstance(self.repo_info , lowerCamelCase ):
raise NotImplementedError(f'''Open is only implemented for dataset repositories, but got {self.repo_info}''' )
UpperCAmelCase__ = hf_hub_url(self.repo_info.id , lowerCamelCase , revision=self.repo_info.sha )
return fsspec.open(
lowerCamelCase , mode=lowerCamelCase , headers=get_authentication_headers_for_url(lowerCamelCase , use_auth_token=self.token ) , client_kwargs={"trust_env": True} , ).open()
def UpperCAmelCase_ ( self :str , lowerCamelCase :Any , **lowerCamelCase :str ) -> Optional[Any]:
self._get_dirs()
UpperCAmelCase__ = self._strip_protocol(lowerCamelCase )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(lowerCamelCase )
def UpperCAmelCase_ ( self :Any , lowerCamelCase :Optional[Any] , lowerCamelCase :Tuple=False , **lowerCamelCase :int ) -> Optional[int]:
self._get_dirs()
UpperCAmelCase__ = PurePosixPath(path.strip("/" ) )
UpperCAmelCase__ = {}
for p, f in self.dir_cache.items():
UpperCAmelCase__ = PurePosixPath(p.strip("/" ) )
UpperCAmelCase__ = p.parent
if root == path:
UpperCAmelCase__ = f
UpperCAmelCase__ = list(paths.values() )
if detail:
return out
else:
return sorted(f["name"] for f in out )
| 169 |
def lowerCAmelCase ( _lowerCAmelCase : str ):
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(_lowerCAmelCase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("doctest").testmod()
| 169 | 1 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class lowercase_ ( _UpperCAmelCase ):
'''simple docstring'''
UpperCAmelCase : List[Any] = 'umt5'
UpperCAmelCase : Union[str, Any] = ['past_key_values']
def __init__( self : Optional[int] , _UpperCAmelCase : Dict=250_112 , _UpperCAmelCase : Any=512 , _UpperCAmelCase : Optional[int]=64 , _UpperCAmelCase : Dict=1_024 , _UpperCAmelCase : int=8 , _UpperCAmelCase : Union[str, Any]=None , _UpperCAmelCase : str=6 , _UpperCAmelCase : List[Any]=32 , _UpperCAmelCase : Optional[int]=128 , _UpperCAmelCase : int=0.1 , _UpperCAmelCase : int=1E-6 , _UpperCAmelCase : str=1.0 , _UpperCAmelCase : Optional[int]="gated-gelu" , _UpperCAmelCase : Dict=True , _UpperCAmelCase : int=True , _UpperCAmelCase : Tuple="T5Tokenizer" , _UpperCAmelCase : List[str]=True , _UpperCAmelCase : Union[str, Any]=0 , _UpperCAmelCase : Optional[Any]=1 , _UpperCAmelCase : List[str]=0 , **_UpperCAmelCase : str , ):
super().__init__(
is_encoder_decoder=SCREAMING_SNAKE_CASE_ , tokenizer_class=SCREAMING_SNAKE_CASE_ , tie_word_embeddings=SCREAMING_SNAKE_CASE_ , pad_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
_A = vocab_size
_A = d_model
_A = d_kv
_A = d_ff
_A = num_layers
_A = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
_A = num_heads
_A = relative_attention_num_buckets
_A = relative_attention_max_distance
_A = dropout_rate
_A = layer_norm_epsilon
_A = initializer_factor
_A = feed_forward_proj
_A = use_cache
_A = self.feed_forward_proj.split('-' )
_A = act_info[-1]
_A = act_info[0] == """gated"""
if len(SCREAMING_SNAKE_CASE_ ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE_ ) > 2:
raise ValueError(
F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
'\'gated-gelu\' or \'relu\'' )
if feed_forward_proj == "gated-gelu":
_A = """gelu_new"""
@property
def lowerCAmelCase_ ( self : Tuple ):
return self.d_model
@property
def lowerCAmelCase_ ( self : str ):
return self.num_heads
@property
def lowerCAmelCase_ ( self : List[Any] ):
return self.num_layers
class lowercase_ ( _UpperCAmelCase ):
'''simple docstring'''
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def lowerCAmelCase_ ( self : Dict ):
_A = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
_A = """past_encoder_sequence + sequence"""
_A = {0: """batch"""}
_A = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
_A = {0: """batch""", 1: """decoder_sequence"""}
_A = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_ , direction='inputs' )
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def lowerCAmelCase_ ( self : List[str] ):
return 13
@property
def lowerCAmelCase_ ( self : List[Any] ):
return 5E-4
| 355 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
a = {
'''cola''': 2,
'''mnli''': 3,
'''mrpc''': 2,
'''sst-2''': 2,
'''sts-b''': 1,
'''qqp''': 2,
'''qnli''': 2,
'''rte''': 2,
'''wnli''': 2,
}
logging.set_verbosity_info()
def _snake_case ( _snake_case : str , _snake_case : Optional[int] , _snake_case : List[Any] , _snake_case : Tuple=None ) -> List[str]:
'''simple docstring'''
_A = XLNetConfig.from_json_file(_snake_case )
_A = finetuning_task.lower() if finetuning_task is not None else ''
if finetuning_task in GLUE_TASKS_NUM_LABELS:
print(F'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}''' )
_A = finetuning_task
_A = GLUE_TASKS_NUM_LABELS[finetuning_task]
_A = XLNetForSequenceClassification(_snake_case )
elif "squad" in finetuning_task:
_A = finetuning_task
_A = XLNetForQuestionAnswering(_snake_case )
else:
_A = XLNetLMHeadModel(_snake_case )
# Load weights from tf checkpoint
load_tf_weights_in_xlnet(_snake_case , _snake_case , _snake_case )
# Save pytorch-model
_A = os.path.join(_snake_case , _snake_case )
_A = os.path.join(_snake_case , _snake_case )
print(F'''Save PyTorch model to {os.path.abspath(_snake_case )}''' )
torch.save(model.state_dict() , _snake_case )
print(F'''Save configuration file to {os.path.abspath(_snake_case )}''' )
with open(_snake_case , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
a = 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(
'''--xlnet_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained XLNet model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
)
parser.add_argument(
'''--finetuning_task''',
default=None,
type=str,
help='''Name of a task on which the XLNet TensorFlow model was fine-tuned''',
)
a = parser.parse_args()
print(args)
convert_xlnet_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
)
| 271 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : Any = {
'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 __SCREAMING_SNAKE_CASE (__a ):
"""simple docstring"""
__a ="""xlm-roberta"""
def __init__( self : List[str] , __a : Any=3_05_22 , __a : Optional[Any]=7_68 , __a : Optional[int]=12 , __a : List[str]=12 , __a : Optional[Any]=30_72 , __a : Any="gelu" , __a : Union[str, Any]=0.1 , __a : List[str]=0.1 , __a : Union[str, Any]=5_12 , __a : List[str]=2 , __a : Dict=0.02 , __a : Union[str, Any]=1e-1_2 , __a : Tuple=1 , __a : int=0 , __a : Dict=2 , __a : List[Any]="absolute" , __a : List[str]=True , __a : Dict=None , **__a : str , ):
super().__init__(pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a )
_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
_a = classifier_dropout
class __SCREAMING_SNAKE_CASE (__a ):
"""simple docstring"""
@property
def UpperCamelCase__ ( self : Optional[Any] ):
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),
] )
| 63 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( __a ):
__a : int = ["""image_processor""", """tokenizer"""]
__a : Union[str, Any] = """ChineseCLIPImageProcessor"""
__a : List[Any] = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : Dict , lowercase : Union[str, Any]=None , lowercase : Dict=None , **lowercase : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , lowercase , )
UpperCAmelCase = kwargs.pop('''feature_extractor''' )
UpperCAmelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
super().__init__(lowercase , lowercase )
UpperCAmelCase = self.image_processor
def __call__( self : Tuple , lowercase : Optional[Any]=None , lowercase : Union[str, Any]=None , lowercase : int=None , **lowercase : Dict ):
'''simple docstring'''
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
UpperCAmelCase = self.tokenizer(lowercase , return_tensors=lowercase , **lowercase )
if images is not None:
UpperCAmelCase = self.image_processor(lowercase , return_tensors=lowercase , **lowercase )
if text is not None and images is not None:
UpperCAmelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowercase ) , tensor_type=lowercase )
def A ( self : int , *lowercase : Tuple , **lowercase : List[str] ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def A ( self : Optional[Any] , *lowercase : int , **lowercase : Optional[int] ):
'''simple docstring'''
return self.tokenizer.decode(*lowercase , **lowercase )
@property
def A ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase = self.tokenizer.model_input_names
UpperCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def A ( self : List[Any] ):
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , lowercase , )
return self.image_processor_class
| 34 | 0 |
"""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 UpperCamelCase_ (unittest.TestCase ):
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Optional[Any]:
UpperCAmelCase_ : Tuple = tempfile.mkdtemp()
# fmt: off
UpperCAmelCase_ : Union[str, Any] = ["", "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
UpperCAmelCase_ : Dict = dict(zip(lowercase__ , range(len(lowercase__ ) ) ) )
UpperCAmelCase_ : Any = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
UpperCAmelCase_ : Any = {"unk_token": "<unk>"}
UpperCAmelCase_ : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase_ : Optional[Any] = 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__ ) )
UpperCAmelCase_ : Dict = {
"do_resize": True,
"size": 20,
"do_center_crop": True,
"crop_size": 18,
"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],
}
UpperCAmelCase_ : Union[str, Any] = 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 , **lowerCAmelCase_ : Optional[int] ) -> List[Any]:
return CLIPTokenizer.from_pretrained(self.tmpdirname , pad_token="!" , **lowercase__ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , **lowerCAmelCase_ : Union[str, Any] ) -> Any:
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , pad_token="!" , **lowercase__ )
def _SCREAMING_SNAKE_CASE ( self : int , **lowerCAmelCase_ : int ) -> str:
return OwlViTImageProcessor.from_pretrained(self.tmpdirname , **lowercase__ )
def _SCREAMING_SNAKE_CASE ( self : int ) -> Union[str, Any]:
shutil.rmtree(self.tmpdirname )
def _SCREAMING_SNAKE_CASE ( self : int ) -> List[Any]:
UpperCAmelCase_ : Union[str, Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
UpperCAmelCase_ : str = [Image.fromarray(np.moveaxis(lowercase__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _SCREAMING_SNAKE_CASE ( self : str ) -> List[str]:
UpperCAmelCase_ : int = self.get_tokenizer()
UpperCAmelCase_ : Any = self.get_rust_tokenizer()
UpperCAmelCase_ : Optional[int] = self.get_image_processor()
UpperCAmelCase_ : Tuple = OwlViTProcessor(tokenizer=lowercase__ , image_processor=lowercase__ )
processor_slow.save_pretrained(self.tmpdirname )
UpperCAmelCase_ : int = OwlViTProcessor.from_pretrained(self.tmpdirname , use_fast=lowercase__ )
UpperCAmelCase_ : List[Any] = OwlViTProcessor(tokenizer=lowercase__ , image_processor=lowercase__ )
processor_fast.save_pretrained(self.tmpdirname )
UpperCAmelCase_ : Union[str, Any] = 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] ) -> Optional[Any]:
UpperCAmelCase_ : Optional[int] = OwlViTProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCAmelCase_ : Union[str, Any] = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
UpperCAmelCase_ : Union[str, Any] = self.get_image_processor(do_normalize=lowercase__ )
UpperCAmelCase_ : Optional[Any] = 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 ) -> int:
UpperCAmelCase_ : str = self.get_image_processor()
UpperCAmelCase_ : Any = self.get_tokenizer()
UpperCAmelCase_ : int = OwlViTProcessor(tokenizer=lowercase__ , image_processor=lowercase__ )
UpperCAmelCase_ : Union[str, Any] = self.prepare_image_inputs()
UpperCAmelCase_ : Optional[Any] = image_processor(lowercase__ , return_tensors="np" )
UpperCAmelCase_ : List[str] = 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] ) -> int:
UpperCAmelCase_ : str = self.get_image_processor()
UpperCAmelCase_ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase_ : List[Any] = OwlViTProcessor(tokenizer=lowercase__ , image_processor=lowercase__ )
UpperCAmelCase_ : List[str] = "lower newer"
UpperCAmelCase_ : Optional[int] = processor(text=lowercase__ , return_tensors="np" )
UpperCAmelCase_ : Optional[Any] = 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 ) -> str:
UpperCAmelCase_ : Optional[Any] = self.get_image_processor()
UpperCAmelCase_ : Union[str, Any] = self.get_tokenizer()
UpperCAmelCase_ : Union[str, Any] = OwlViTProcessor(tokenizer=lowercase__ , image_processor=lowercase__ )
UpperCAmelCase_ : int = "lower newer"
UpperCAmelCase_ : Dict = self.prepare_image_inputs()
UpperCAmelCase_ : Optional[Any] = 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] ) -> Any:
UpperCAmelCase_ : Optional[Any] = "google/owlvit-base-patch32"
UpperCAmelCase_ : Union[str, Any] = OwlViTProcessor.from_pretrained(lowercase__ )
UpperCAmelCase_ : Dict = ["cat", "nasa badge"]
UpperCAmelCase_ : Any = processor(text=lowercase__ )
UpperCAmelCase_ : Optional[Any] = 16
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] ) -> List[str]:
UpperCAmelCase_ : Tuple = "google/owlvit-base-patch32"
UpperCAmelCase_ : List[str] = OwlViTProcessor.from_pretrained(lowercase__ )
UpperCAmelCase_ : Optional[int] = [["cat", "nasa badge"], ["person"]]
UpperCAmelCase_ : List[str] = processor(text=lowercase__ )
UpperCAmelCase_ : Optional[Any] = 16
UpperCAmelCase_ : Optional[Any] = len(lowercase__ )
UpperCAmelCase_ : Optional[Any] = 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] ) -> Union[str, Any]:
UpperCAmelCase_ : Optional[int] = "google/owlvit-base-patch32"
UpperCAmelCase_ : Dict = OwlViTProcessor.from_pretrained(lowercase__ )
UpperCAmelCase_ : int = ["cat", "nasa badge"]
UpperCAmelCase_ : str = processor(text=lowercase__ )
UpperCAmelCase_ : Optional[int] = 16
UpperCAmelCase_ : Tuple = inputs["input_ids"]
UpperCAmelCase_ : Union[str, Any] = [
[49_406, 2_368, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[49_406, 6_841, 11_301, 49_407, 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] ) -> Dict:
UpperCAmelCase_ : List[str] = self.get_image_processor()
UpperCAmelCase_ : Dict = self.get_tokenizer()
UpperCAmelCase_ : str = OwlViTProcessor(tokenizer=lowercase__ , image_processor=lowercase__ )
UpperCAmelCase_ : Optional[int] = self.prepare_image_inputs()
UpperCAmelCase_ : str = self.prepare_image_inputs()
UpperCAmelCase_ : Optional[Any] = 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] ) -> str:
UpperCAmelCase_ : Tuple = self.get_image_processor()
UpperCAmelCase_ : Optional[Any] = self.get_tokenizer()
UpperCAmelCase_ : Optional[int] = OwlViTProcessor(tokenizer=lowercase__ , image_processor=lowercase__ )
UpperCAmelCase_ : Dict = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCAmelCase_ : Tuple = processor.batch_decode(lowercase__ )
UpperCAmelCase_ : List[str] = tokenizer.batch_decode(lowercase__ )
self.assertListEqual(lowercase__ , lowercase__ ) | 358 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
lowerCamelCase_ = tuple[int, int]
class UpperCamelCase_ :
def __init__( self : List[Any] , lowerCAmelCase_ : set[int] , lowerCAmelCase_ : Mapping[EdgeT, int] ) -> None:
UpperCAmelCase_ : set[int] = vertices
UpperCAmelCase_ : dict[EdgeT, int] = {
(min(lowerCAmelCase_ ), max(lowerCAmelCase_ )): weight for edge, weight in edges.items()
}
def _SCREAMING_SNAKE_CASE ( self : Tuple , lowerCAmelCase_ : EdgeT , lowerCAmelCase_ : int ) -> None:
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
UpperCAmelCase_ : Tuple = weight
def _SCREAMING_SNAKE_CASE ( self : str ) -> Graph:
UpperCAmelCase_ : Graph = Graph({min(self.vertices )} , {} )
UpperCAmelCase_ : EdgeT
UpperCAmelCase_ : int
UpperCAmelCase_ : EdgeT
UpperCAmelCase_ : int
while len(subgraph.vertices ) < len(self.vertices ):
UpperCAmelCase_ : int = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
UpperCAmelCase_ : Tuple = edge
UpperCAmelCase_ : Dict = weight
subgraph.add_edge(lowerCAmelCase_ , lowerCAmelCase_ )
return subgraph
def snake_case ( A__ = "p107_network.txt" ):
UpperCAmelCase_ : str = os.path.abspath(os.path.dirname(A__ ) )
UpperCAmelCase_ : str = os.path.join(A__ ,A__ )
UpperCAmelCase_ : dict[EdgeT, int] = {}
UpperCAmelCase_ : list[str]
UpperCAmelCase_ : int
UpperCAmelCase_ : int
with open(A__ ) as f:
UpperCAmelCase_ : Dict = f.read().strip().split("\n" )
UpperCAmelCase_ : str = [line.split("," ) for line in data]
for edgea in range(1 ,len(A__ ) ):
for edgea in range(A__ ):
if adjaceny_matrix[edgea][edgea] != "-":
UpperCAmelCase_ : Union[str, Any] = int(adjaceny_matrix[edgea][edgea] )
UpperCAmelCase_ : Graph = Graph(set(range(len(A__ ) ) ) ,A__ )
UpperCAmelCase_ : Graph = graph.prims_algorithm()
UpperCAmelCase_ : int = sum(graph.edges.values() )
UpperCAmelCase_ : int = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(f'{solution() = }')
| 253 | 0 |
'''simple docstring'''
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : BigBirdConfig
SCREAMING_SNAKE_CASE : jnp.dtype = jnp.floataa
SCREAMING_SNAKE_CASE : bool = True
def SCREAMING_SNAKE_CASE ( self : Tuple ):
super().setup()
__lowercase = nn.Dense(5 ,dtype=self.dtype )
def __call__( self : List[str] ,*lowercase__ : int ,**lowercase__ : int ):
__lowercase = super().__call__(*lowercase__ ,**lowercase__ )
__lowercase = self.cls(outputs[2] )
return outputs[:2] + (cls_out,)
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = FlaxBigBirdForNaturalQuestionsModule
def _A ( A__ , A__ , A__ , A__ , A__ , A__ ):
"""simple docstring"""
def cross_entropy(A__ , A__ , A__=None ):
__lowercase = logits.shape[-1]
__lowercase = (labels[..., None] == jnp.arange(A__ )[None]).astype('''f4''' )
__lowercase = jax.nn.log_softmax(A__ , axis=-1 )
__lowercase = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
__lowercase = reduction(A__ )
return loss
__lowercase = partial(A__ , reduction=jnp.mean )
__lowercase = cross_entropy(A__ , A__ )
__lowercase = cross_entropy(A__ , A__ )
__lowercase = cross_entropy(A__ , A__ )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class lowercase_ :
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = "google/bigbird-roberta-base"
SCREAMING_SNAKE_CASE : int = 3_0_0_0
SCREAMING_SNAKE_CASE : int = 1_0_5_0_0
SCREAMING_SNAKE_CASE : int = 1_2_8
SCREAMING_SNAKE_CASE : int = 3
SCREAMING_SNAKE_CASE : int = 1
SCREAMING_SNAKE_CASE : int = 5
# tx_args
SCREAMING_SNAKE_CASE : float = 3e-5
SCREAMING_SNAKE_CASE : float = 0.0
SCREAMING_SNAKE_CASE : int = 2_0_0_0_0
SCREAMING_SNAKE_CASE : float = 0.0095
SCREAMING_SNAKE_CASE : str = "bigbird-roberta-natural-questions"
SCREAMING_SNAKE_CASE : str = "training-expt"
SCREAMING_SNAKE_CASE : str = "data/nq-training.jsonl"
SCREAMING_SNAKE_CASE : str = "data/nq-validation.jsonl"
def SCREAMING_SNAKE_CASE ( self : List[str] ):
os.makedirs(self.base_dir ,exist_ok=lowercase__ )
__lowercase = os.path.join(self.base_dir ,self.save_dir )
__lowercase = self.batch_size_per_device * jax.device_count()
@dataclass
class lowercase_ :
"""simple docstring"""
SCREAMING_SNAKE_CASE : int
SCREAMING_SNAKE_CASE : int = 4_0_9_6 # no dynamic padding on TPUs
def __call__( self : Dict ,lowercase__ : int ):
__lowercase = self.collate_fn(lowercase__ )
__lowercase = jax.tree_util.tree_map(lowercase__ ,lowercase__ )
return batch
def SCREAMING_SNAKE_CASE ( self : int ,lowercase__ : Tuple ):
__lowercase , __lowercase = self.fetch_inputs(features['''input_ids'''] )
__lowercase = {
'''input_ids''': jnp.array(lowercase__ ,dtype=jnp.intaa ),
'''attention_mask''': jnp.array(lowercase__ ,dtype=jnp.intaa ),
'''start_labels''': jnp.array(features['''start_token'''] ,dtype=jnp.intaa ),
'''end_labels''': jnp.array(features['''end_token'''] ,dtype=jnp.intaa ),
'''pooled_labels''': jnp.array(features['''category'''] ,dtype=jnp.intaa ),
}
return batch
def SCREAMING_SNAKE_CASE ( self : Tuple ,lowercase__ : list ):
__lowercase = [self._fetch_inputs(lowercase__ ) for ids in input_ids]
return zip(*lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Any ,lowercase__ : list ):
__lowercase = [1 for _ in range(len(lowercase__ ) )]
while len(lowercase__ ) < self.max_length:
input_ids.append(self.pad_id )
attention_mask.append(0 )
return input_ids, attention_mask
def _A ( A__ , A__ , A__=None ):
"""simple docstring"""
if seed is not None:
__lowercase = dataset.shuffle(seed=A__ )
for i in range(len(A__ ) // batch_size ):
__lowercase = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(A__ )
@partial(jax.pmap , axis_name='''batch''' )
def _A ( A__ , A__ , **A__ ):
"""simple docstring"""
def loss_fn(A__ ):
__lowercase = model_inputs.pop('''start_labels''' )
__lowercase = model_inputs.pop('''end_labels''' )
__lowercase = model_inputs.pop('''pooled_labels''' )
__lowercase = state.apply_fn(**A__ , params=A__ , dropout_rng=A__ , train=A__ )
__lowercase , __lowercase , __lowercase = outputs
return state.loss_fn(
A__ , A__ , A__ , A__ , A__ , A__ , )
__lowercase , __lowercase = jax.random.split(A__ )
__lowercase = jax.value_and_grad(A__ )
__lowercase , __lowercase = grad_fn(state.params )
__lowercase = jax.lax.pmean({'''loss''': loss} , axis_name='''batch''' )
__lowercase = jax.lax.pmean(A__ , '''batch''' )
__lowercase = state.apply_gradients(grads=A__ )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name='''batch''' )
def _A ( A__ , **A__ ):
"""simple docstring"""
__lowercase = model_inputs.pop('''start_labels''' )
__lowercase = model_inputs.pop('''end_labels''' )
__lowercase = model_inputs.pop('''pooled_labels''' )
__lowercase = state.apply_fn(**A__ , params=state.params , train=A__ )
__lowercase , __lowercase , __lowercase = outputs
__lowercase = state.loss_fn(A__ , A__ , A__ , A__ , A__ , A__ )
__lowercase = jax.lax.pmean({'''loss''': loss} , axis_name='''batch''' )
return metrics
class lowercase_ (train_state.TrainState ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Callable = struct.field(pytree_node=lowerCamelCase__ )
@dataclass
class lowercase_ :
"""simple docstring"""
SCREAMING_SNAKE_CASE : Args
SCREAMING_SNAKE_CASE : Callable
SCREAMING_SNAKE_CASE : Callable
SCREAMING_SNAKE_CASE : Callable
SCREAMING_SNAKE_CASE : Callable
SCREAMING_SNAKE_CASE : wandb
SCREAMING_SNAKE_CASE : Callable = None
def SCREAMING_SNAKE_CASE ( self : Dict ,lowercase__ : Union[str, Any] ,lowercase__ : Optional[int] ,lowercase__ : List[str] ,lowercase__ : Union[str, Any]=None ):
__lowercase = model.params
__lowercase = TrainState.create(
apply_fn=model.__call__ ,params=lowercase__ ,tx=lowercase__ ,loss_fn=lowercase__ ,)
if ckpt_dir is not None:
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase = restore_checkpoint(lowercase__ ,lowercase__ )
__lowercase = {
'''lr''': args.lr,
'''init_lr''': args.init_lr,
'''warmup_steps''': args.warmup_steps,
'''num_train_steps''': num_train_steps,
'''weight_decay''': args.weight_decay,
}
__lowercase , __lowercase = build_tx(**lowercase__ )
__lowercase = train_state.TrainState(
step=lowercase__ ,apply_fn=model.__call__ ,params=lowercase__ ,tx=lowercase__ ,opt_state=lowercase__ ,)
__lowercase = args
__lowercase = data_collator
__lowercase = lr
__lowercase = params
__lowercase = jax_utils.replicate(lowercase__ )
return state
def SCREAMING_SNAKE_CASE ( self : List[Any] ,lowercase__ : List[Any] ,lowercase__ : Tuple ,lowercase__ : Dict ):
__lowercase = self.args
__lowercase = len(lowercase__ ) // args.batch_size
__lowercase = jax.random.PRNGKey(0 )
__lowercase = jax.random.split(lowercase__ ,jax.device_count() )
for epoch in range(args.max_epochs ):
__lowercase = jnp.array(0 ,dtype=jnp.floataa )
__lowercase = get_batched_dataset(lowercase__ ,args.batch_size ,seed=lowercase__ )
__lowercase = 0
for batch in tqdm(lowercase__ ,total=lowercase__ ,desc=F"Running EPOCH-{epoch}" ):
__lowercase = self.data_collator(lowercase__ )
__lowercase , __lowercase , __lowercase = self.train_step_fn(lowercase__ ,lowercase__ ,**lowercase__ )
running_loss += jax_utils.unreplicate(metrics['''loss'''] )
i += 1
if i % args.logging_steps == 0:
__lowercase = jax_utils.unreplicate(state.step )
__lowercase = running_loss.item() / i
__lowercase = self.scheduler_fn(state_step - 1 )
__lowercase = self.evaluate(lowercase__ ,lowercase__ )
__lowercase = {
'''step''': state_step.item(),
'''eval_loss''': eval_loss.item(),
'''tr_loss''': tr_loss,
'''lr''': lr.item(),
}
tqdm.write(str(lowercase__ ) )
self.logger.log(lowercase__ ,commit=lowercase__ )
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + F"-e{epoch}-s{i}" ,state=lowercase__ )
def SCREAMING_SNAKE_CASE ( self : List[Any] ,lowercase__ : Dict ,lowercase__ : Optional[Any] ):
__lowercase = get_batched_dataset(lowercase__ ,self.args.batch_size )
__lowercase = len(lowercase__ ) // self.args.batch_size
__lowercase = jnp.array(0 ,dtype=jnp.floataa )
__lowercase = 0
for batch in tqdm(lowercase__ ,total=lowercase__ ,desc='''Evaluating ... ''' ):
__lowercase = self.data_collator(lowercase__ )
__lowercase = self.val_step_fn(lowercase__ ,**lowercase__ )
running_loss += jax_utils.unreplicate(metrics['''loss'''] )
i += 1
return running_loss / i
def SCREAMING_SNAKE_CASE ( self : Optional[int] ,lowercase__ : int ,lowercase__ : Any ):
__lowercase = jax_utils.unreplicate(lowercase__ )
print(F"SAVING CHECKPOINT IN {save_dir}" ,end=''' ... ''' )
self.model_save_fn(lowercase__ ,params=state.params )
with open(os.path.join(lowercase__ ,'''opt_state.msgpack''' ) ,'''wb''' ) as f:
f.write(to_bytes(state.opt_state ) )
joblib.dump(self.args ,os.path.join(lowercase__ ,'''args.joblib''' ) )
joblib.dump(self.data_collator ,os.path.join(lowercase__ ,'''data_collator.joblib''' ) )
with open(os.path.join(lowercase__ ,'''training_state.json''' ) ,'''w''' ) as f:
json.dump({'''step''': state.step.item()} ,lowercase__ )
print('''DONE''' )
def _A ( A__ , A__ ):
"""simple docstring"""
print(F"RESTORING CHECKPOINT FROM {save_dir}" , end=''' ... ''' )
with open(os.path.join(A__ , '''flax_model.msgpack''' ) , '''rb''' ) as f:
__lowercase = from_bytes(state.params , f.read() )
with open(os.path.join(A__ , '''opt_state.msgpack''' ) , '''rb''' ) as f:
__lowercase = from_bytes(state.opt_state , f.read() )
__lowercase = joblib.load(os.path.join(A__ , '''args.joblib''' ) )
__lowercase = joblib.load(os.path.join(A__ , '''data_collator.joblib''' ) )
with open(os.path.join(A__ , '''training_state.json''' ) , '''r''' ) as f:
__lowercase = json.load(A__ )
__lowercase = training_state['''step''']
print('''DONE''' )
return params, opt_state, step, args, data_collator
def _A ( A__ , A__ , A__ , A__ ):
"""simple docstring"""
__lowercase = num_train_steps - warmup_steps
__lowercase = optax.linear_schedule(init_value=A__ , end_value=A__ , transition_steps=A__ )
__lowercase = optax.linear_schedule(init_value=A__ , end_value=1e-7 , transition_steps=A__ )
__lowercase = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def _A ( A__ , A__ , A__ , A__ , A__ ):
"""simple docstring"""
def weight_decay_mask(A__ ):
__lowercase = traverse_util.flatten_dict(A__ )
__lowercase = {k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
return traverse_util.unflatten_dict(A__ )
__lowercase = scheduler_fn(A__ , A__ , A__ , A__ )
__lowercase = optax.adamw(learning_rate=A__ , weight_decay=A__ , mask=A__ )
return tx, lr
| 104 |
'''simple docstring'''
def _A ( A__ = 10 , A__ = 22 ):
"""simple docstring"""
__lowercase = range(1 , A__ )
__lowercase = range(1 , A__ )
return sum(
1 for power in powers for base in bases if len(str(base**power ) ) == power )
if __name__ == "__main__":
print(f'{solution(10, 22) = }')
| 104 | 1 |
import os
import string
import sys
_lowerCamelCase = 1 << 8
_lowerCamelCase = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'left': 68 + ARROW_KEY_FLAG,
'mod_int': 91,
'undefined': sys.maxsize,
'interrupt': 3,
'insert': 50,
'delete': 51,
'pg_up': 53,
'pg_down': 54,
}
_lowerCamelCase = KEYMAP['up']
_lowerCamelCase = KEYMAP['left']
if sys.platform == "win32":
_lowerCamelCase = []
_lowerCamelCase = {
B'\xe0H': KEYMAP['up'] - ARROW_KEY_FLAG,
B'\x00H': KEYMAP['up'] - ARROW_KEY_FLAG,
B'\xe0P': KEYMAP['down'] - ARROW_KEY_FLAG,
B'\x00P': KEYMAP['down'] - ARROW_KEY_FLAG,
B'\xe0M': KEYMAP['right'] - ARROW_KEY_FLAG,
B'\x00M': KEYMAP['right'] - ARROW_KEY_FLAG,
B'\xe0K': KEYMAP['left'] - ARROW_KEY_FLAG,
B'\x00K': KEYMAP['left'] - ARROW_KEY_FLAG,
}
for i in range(10):
_lowerCamelCase = ord(str(i))
def SCREAMING_SNAKE_CASE ( ) -> List[Any]:
if os.name == "nt":
import msvcrt
UpperCAmelCase_ = '''mbcs'''
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(__UpperCamelCase ) == 0:
# Read the keystroke
UpperCAmelCase_ = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
UpperCAmelCase_ = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
UpperCAmelCase_ = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP['''mod_int'''] ) )
WIN_CH_BUFFER.append(__UpperCamelCase )
if ord(__UpperCamelCase ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(126 ) )
UpperCAmelCase_ = chr(KEYMAP['''esc'''] )
except KeyError:
UpperCAmelCase_ = cha[1]
else:
UpperCAmelCase_ = ch.decode(__UpperCamelCase )
else:
UpperCAmelCase_ = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
UpperCAmelCase_ = sys.stdin.fileno()
UpperCAmelCase_ = termios.tcgetattr(__UpperCamelCase )
try:
tty.setraw(__UpperCamelCase )
UpperCAmelCase_ = sys.stdin.read(1 )
finally:
termios.tcsetattr(__UpperCamelCase , termios.TCSADRAIN , __UpperCamelCase )
return ch
def SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
UpperCAmelCase_ = get_raw_chars()
if ord(__UpperCamelCase ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(__UpperCamelCase ) == KEYMAP["esc"]:
UpperCAmelCase_ = get_raw_chars()
if ord(__UpperCamelCase ) == KEYMAP["mod_int"]:
UpperCAmelCase_ = get_raw_chars()
if ord(__UpperCamelCase ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(__UpperCamelCase ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(__UpperCamelCase ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 177 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
_lowerCamelCase = logging.get_logger(__name__)
class a ( _A ):
'''simple docstring'''
lowerCAmelCase : List[Any] = ['pixel_values']
def __init__( self : Any , __snake_case : bool = True , __snake_case : Dict[str, int] = None , __snake_case : PILImageResampling = PILImageResampling.BICUBIC , __snake_case : bool = True , __snake_case : Dict[str, int] = None , __snake_case : bool = True , __snake_case : Union[int, float] = 1 / 2_55 , __snake_case : bool = True , __snake_case : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN , __snake_case : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD , **__snake_case : Optional[Any] , ):
super().__init__(**__snake_case )
UpperCAmelCase_ = size if size is not None else {'''shortest_edge''': 2_24}
UpperCAmelCase_ = get_size_dict(__snake_case , default_to_square=__snake_case )
UpperCAmelCase_ = crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
UpperCAmelCase_ = get_size_dict(__snake_case , param_name='''crop_size''' )
UpperCAmelCase_ = do_resize
UpperCAmelCase_ = size
UpperCAmelCase_ = resample
UpperCAmelCase_ = do_center_crop
UpperCAmelCase_ = crop_size
UpperCAmelCase_ = do_rescale
UpperCAmelCase_ = rescale_factor
UpperCAmelCase_ = do_normalize
UpperCAmelCase_ = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
UpperCAmelCase_ = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def lowerCamelCase_ ( self : int , __snake_case : np.ndarray , __snake_case : Dict[str, int] , __snake_case : PILImageResampling = PILImageResampling.BICUBIC , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Tuple , ):
UpperCAmelCase_ = get_size_dict(__snake_case , default_to_square=__snake_case )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
UpperCAmelCase_ = int((2_56 / 2_24) * size['''shortest_edge'''] )
UpperCAmelCase_ = get_resize_output_image_size(__snake_case , size=__snake_case , default_to_square=__snake_case )
UpperCAmelCase_ = {'''height''': output_size[0], '''width''': output_size[1]}
if "height" not in size_dict or "width" not in size_dict:
raise ValueError(
F'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}' )
return resize(
__snake_case , size=(size_dict['''height'''], size_dict['''width''']) , resample=__snake_case , data_format=__snake_case , **__snake_case )
def lowerCamelCase_ ( self : Tuple , __snake_case : np.ndarray , __snake_case : Dict[str, int] , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Optional[int] , ):
UpperCAmelCase_ = get_size_dict(__snake_case )
if "height" not in size or "width" not in size:
raise ValueError(F'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}' )
return center_crop(__snake_case , size=(size['''height'''], size['''width''']) , data_format=__snake_case , **__snake_case )
def lowerCamelCase_ ( self : Optional[Any] , __snake_case : np.ndarray , __snake_case : Union[int, float] , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : List[Any] , ):
return rescale(__snake_case , scale=__snake_case , data_format=__snake_case , **__snake_case )
def lowerCamelCase_ ( self : int , __snake_case : np.ndarray , __snake_case : Union[float, List[float]] , __snake_case : Union[float, List[float]] , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Union[str, Any] , ):
return normalize(__snake_case , mean=__snake_case , std=__snake_case , data_format=__snake_case , **__snake_case )
def lowerCamelCase_ ( self : int , __snake_case : ImageInput , __snake_case : Optional[bool] = None , __snake_case : Optional[Dict[str, int]] = None , __snake_case : PILImageResampling = None , __snake_case : Optional[bool] = None , __snake_case : Optional[Dict[str, int]] = None , __snake_case : Optional[bool] = None , __snake_case : Optional[float] = None , __snake_case : Optional[bool] = None , __snake_case : Optional[Union[float, Iterable[float]]] = None , __snake_case : Optional[Union[float, Iterable[float]]] = None , __snake_case : Optional[TensorType] = None , __snake_case : ChannelDimension = ChannelDimension.FIRST , **__snake_case : List[str] , ):
UpperCAmelCase_ = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase_ = resample if resample is not None else self.resample
UpperCAmelCase_ = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase_ = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase_ = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase_ = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase_ = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase_ = image_std if image_std is not None else self.image_std
UpperCAmelCase_ = size if size is not None else self.size
UpperCAmelCase_ = get_size_dict(__snake_case , default_to_square=__snake_case )
UpperCAmelCase_ = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase_ = get_size_dict(__snake_case , param_name='''crop_size''' )
UpperCAmelCase_ = make_list_of_images(__snake_case )
if not valid_images(__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.''' )
# All transformations expect numpy arrays.
UpperCAmelCase_ = [to_numpy_array(__snake_case ) for image in images]
if do_resize:
UpperCAmelCase_ = [self.resize(__snake_case , __snake_case , __snake_case ) for image in images]
if do_center_crop:
UpperCAmelCase_ = [self.center_crop(__snake_case , __snake_case ) for image in images]
if do_rescale:
UpperCAmelCase_ = [self.rescale(__snake_case , __snake_case ) for image in images]
if do_normalize:
UpperCAmelCase_ = [self.normalize(__snake_case , __snake_case , __snake_case ) for image in images]
UpperCAmelCase_ = [to_channel_dimension_format(__snake_case , __snake_case ) for image in images]
UpperCAmelCase_ = {'''pixel_values''': images}
return BatchFeature(data=__snake_case , tensor_type=__snake_case )
| 177 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ['''XLNetTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ['''XLNetTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLNetForMultipleChoice''',
'''XLNetForQuestionAnswering''',
'''XLNetForQuestionAnsweringSimple''',
'''XLNetForSequenceClassification''',
'''XLNetForTokenClassification''',
'''XLNetLMHeadModel''',
'''XLNetModel''',
'''XLNetPreTrainedModel''',
'''load_tf_weights_in_xlnet''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLNetForMultipleChoice''',
'''TFXLNetForQuestionAnsweringSimple''',
'''TFXLNetForSequenceClassification''',
'''TFXLNetForTokenClassification''',
'''TFXLNetLMHeadModel''',
'''TFXLNetMainLayer''',
'''TFXLNetModel''',
'''TFXLNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet import XLNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet_fast import XLNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlnet import (
XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetLMHeadModel,
XLNetModel,
XLNetPreTrainedModel,
load_tf_weights_in_xlnet,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlnet import (
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLNetForMultipleChoice,
TFXLNetForQuestionAnsweringSimple,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetLMHeadModel,
TFXLNetMainLayer,
TFXLNetModel,
TFXLNetPreTrainedModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 195 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_A = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_A = " \"\"\"\n Output class for the scheduler's step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n"
class _lowerCAmelCase ( unittest.TestCase ):
def __a ( self ) -> str:
lowerCAmelCase_ = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , "schedulers/" ) )
lowerCAmelCase_ = self.diffusers_dir
shutil.copy(
os.path.join(_UpperCamelCase , "src/diffusers/schedulers/scheduling_ddpm.py" ) , os.path.join(self.diffusers_dir , "schedulers/scheduling_ddpm.py" ) , )
def __a ( self ) -> Optional[int]:
lowerCAmelCase_ = "src/diffusers"
shutil.rmtree(self.diffusers_dir )
def __a ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=None ) -> Optional[Any]:
lowerCAmelCase_ = comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
lowerCAmelCase_ = comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
lowerCAmelCase_ = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 )
lowerCAmelCase_ = black.format_str(_UpperCamelCase , mode=_UpperCamelCase )
lowerCAmelCase_ = os.path.join(self.diffusers_dir , "new_code.py" )
with open(_UpperCamelCase , "w" , newline="\n" ) as f:
f.write(_UpperCamelCase )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(_UpperCamelCase ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=_UpperCamelCase )
with open(_UpperCamelCase , "r" ) as f:
self.assertTrue(f.read() , _UpperCamelCase )
def __a ( self ) -> Optional[Any]:
lowerCAmelCase_ = check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput" )
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
def __a ( self ) -> Tuple:
# Base copy consistency
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , REFERENCE_CODE + "\n" , )
# With no empty line at the end
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput" , "DDPMSchedulerOutput" , _UpperCamelCase , )
# Copy consistency with rename
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , re.sub("DDPM" , "Test" , _UpperCamelCase ) , )
# Copy consistency with a really long name
lowerCAmelCase_ = "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
self.check_copy_consistency(
f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , f"""{long_class_name}SchedulerOutput""" , re.sub("Bert" , _UpperCamelCase , _UpperCamelCase ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test" , "TestSchedulerOutput" , _UpperCamelCase , overwrite_result=re.sub("DDPM" , "Test" , _UpperCamelCase ) , )
| 231 | 0 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""nielsr/canine-s""": 2_048,
}
# Unicode defines 1,114,112 total “codepoints”
__UpperCAmelCase = 1_114_112
# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
__UpperCAmelCase = 0
__UpperCAmelCase = 0XE_0_0_0
__UpperCAmelCase = 0XE_0_0_1
__UpperCAmelCase = 0XE_0_0_2
__UpperCAmelCase = 0XE_0_0_3
__UpperCAmelCase = 0XE_0_0_4
# Maps special codepoints to human-readable names.
__UpperCAmelCase = {
# Special symbols are represented using codepoints values that are valid,
# but designated as "Private Use", meaning that they will never be assigned
# characters by the Unicode Consortium, and are thus safe for use here.
#
# NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
# excluded and should fail with a hard error.
CLS: """[CLS]""",
SEP: """[SEP]""",
BOS: """[BOS]""",
MASK: """[MASK]""",
PAD: """[PAD]""",
RESERVED: """[RESERVED]""",
}
# Maps special codepoint human-readable names to their codepoint values.
__UpperCAmelCase = {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
class SCREAMING_SNAKE_CASE ( __UpperCamelCase ):
"""simple docstring"""
lowerCamelCase : List[Any] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Optional[Any] , lowerCAmelCase : Union[str, Any]=chr(lowerCAmelCase ) , lowerCAmelCase : Any=chr(lowerCAmelCase ) , lowerCAmelCase : int=chr(lowerCAmelCase ) , lowerCAmelCase : Optional[int]=chr(lowerCAmelCase ) , lowerCAmelCase : Union[str, Any]=chr(lowerCAmelCase ) , lowerCAmelCase : List[str]=chr(lowerCAmelCase ) , lowerCAmelCase : Any=False , lowerCAmelCase : Optional[Any]=20_48 , **lowerCAmelCase : Any , ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = AddedToken(lowerCAmelCase , lstrip=lowerCAmelCase , rstrip=lowerCAmelCase ) if isinstance(lowerCAmelCase , lowerCAmelCase ) else bos_token
__lowerCAmelCase : List[Any] = AddedToken(lowerCAmelCase , lstrip=lowerCAmelCase , rstrip=lowerCAmelCase ) if isinstance(lowerCAmelCase , lowerCAmelCase ) else eos_token
__lowerCAmelCase : int = AddedToken(lowerCAmelCase , lstrip=lowerCAmelCase , rstrip=lowerCAmelCase ) if isinstance(lowerCAmelCase , lowerCAmelCase ) else sep_token
__lowerCAmelCase : Any = AddedToken(lowerCAmelCase , lstrip=lowerCAmelCase , rstrip=lowerCAmelCase ) if isinstance(lowerCAmelCase , lowerCAmelCase ) else cls_token
__lowerCAmelCase : Optional[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
__lowerCAmelCase : int = AddedToken(lowerCAmelCase , lstrip=lowerCAmelCase , rstrip=lowerCAmelCase ) if isinstance(lowerCAmelCase , lowerCAmelCase ) else mask_token
super().__init__(
bos_token=lowerCAmelCase , eos_token=lowerCAmelCase , sep_token=lowerCAmelCase , cls_token=lowerCAmelCase , pad_token=lowerCAmelCase , mask_token=lowerCAmelCase , add_prefix_space=lowerCAmelCase , model_max_length=lowerCAmelCase , **lowerCAmelCase , )
# Creates a mapping for looking up the IDs of special symbols.
__lowerCAmelCase : Dict[str, int] = {}
for codepoint, name in SPECIAL_CODEPOINTS.items():
__lowerCAmelCase : Any = codepoint
# Creates a mapping for looking up the string forms of special symbol IDs.
__lowerCAmelCase : Dict[int, str] = {
codepoint: name for name, codepoint in self._special_codepoints.items()
}
__lowerCAmelCase : List[Any] = UNICODE_VOCAB_SIZE
__lowerCAmelCase : Tuple = len(self._special_codepoints )
@property
def SCREAMING_SNAKE_CASE ( self : int ) -> Tuple:
"""simple docstring"""
return self._unicode_vocab_size
def SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase : str ) -> Dict:
"""simple docstring"""
return list(lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase : str ) -> int:
"""simple docstring"""
try:
return ord(lowerCAmelCase )
except TypeError:
raise ValueError(f'''invalid token: \'{token}\'''' )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase : int ) -> List[str]:
"""simple docstring"""
try:
if index in SPECIAL_CODEPOINTS:
return SPECIAL_CODEPOINTS[index]
return chr(lowerCAmelCase )
except TypeError:
raise ValueError(f'''invalid id: {index}''' )
def SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
return "".join(lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase : List[int] , lowerCAmelCase : Optional[List[int]] = None ) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Dict = [self.sep_token_id]
__lowerCAmelCase : Any = [self.cls_token_id]
__lowerCAmelCase : List[str] = cls + token_ids_a + sep
if token_ids_a is not None:
result += token_ids_a + sep
return result
def SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase : List[int] , lowerCAmelCase : Optional[List[int]] = None , lowerCAmelCase : bool = False ) -> Tuple:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase , token_ids_a=lowerCAmelCase , already_has_special_tokens=lowerCAmelCase )
__lowerCAmelCase : Tuple = [1] + ([0] * len(lowerCAmelCase )) + [1]
if token_ids_a is not None:
result += ([0] * len(lowerCAmelCase )) + [1]
return result
def SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase : List[int] , lowerCAmelCase : Optional[List[int]] = None ) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : str = [self.sep_token_id]
__lowerCAmelCase : List[str] = [self.cls_token_id]
__lowerCAmelCase : str = len(cls + token_ids_a + sep ) * [0]
if token_ids_a is not None:
result += len(token_ids_a + sep ) * [1]
return result
def SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase : str , lowerCAmelCase : Optional[str] = None ) -> Union[str, Any]:
"""simple docstring"""
return ()
| 356 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__UpperCAmelCase = logging.get_logger(__name__)
def snake_case_ (__A : List[str] , __A : str ) -> int:
__lowerCAmelCase : str = set()
__lowerCAmelCase : int = []
def parse_line(__A : List[Any] ):
for line in fp:
if isinstance(__A , __A ):
__lowerCAmelCase : str = line.decode("""UTF-8""" )
if "warnings summary (final)" in line:
continue
# This means we are outside the body of a warning
elif not line.startswith(""" """ ):
# process a single warning and move it to `selected_warnings`.
if len(__A ) > 0:
__lowerCAmelCase : Tuple = """\n""".join(__A )
# Only keep the warnings specified in `targets`
if any(f''': {x}: ''' in warning for x in targets ):
selected_warnings.add(__A )
buffer.clear()
continue
else:
__lowerCAmelCase : Optional[int] = line.strip()
buffer.append(__A )
if from_gh:
for filename in os.listdir(__A ):
__lowerCAmelCase : Optional[Any] = os.path.join(__A , __A )
if not os.path.isdir(__A ):
# read the file
if filename != "warnings.txt":
continue
with open(__A ) as fp:
parse_line(__A )
else:
try:
with zipfile.ZipFile(__A ) as z:
for filename in z.namelist():
if not os.path.isdir(__A ):
# read the file
if filename != "warnings.txt":
continue
with z.open(__A ) as fp:
parse_line(__A )
except Exception:
logger.warning(
f'''{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.''' )
return selected_warnings
def snake_case_ (__A : Dict , __A : Union[str, Any] ) -> Dict:
__lowerCAmelCase : Any = set()
__lowerCAmelCase : Optional[int] = [os.path.join(__A , __A ) for p in os.listdir(__A ) if (p.endswith(""".zip""" ) or from_gh)]
for p in paths:
selected_warnings.update(extract_warnings_from_single_artifact(__A , __A ) )
return selected_warnings
if __name__ == "__main__":
def snake_case_ (__A : int ) -> Tuple:
return values.split(""",""" )
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
parser.add_argument(
"""--output_dir""",
type=str,
required=True,
help="""Where to store the downloaded artifacts and other result files.""",
)
parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
# optional parameters
parser.add_argument(
"""--targets""",
default="""DeprecationWarning,UserWarning,FutureWarning""",
type=list_str,
help="""Comma-separated list of target warning(s) which we want to extract.""",
)
parser.add_argument(
"""--from_gh""",
action="""store_true""",
help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
)
__UpperCAmelCase = parser.parse_args()
__UpperCAmelCase = args.from_gh
if from_gh:
# The artifacts have to be downloaded using `actions/download-artifact@v3`
pass
else:
os.makedirs(args.output_dir, exist_ok=True)
# get download links
__UpperCAmelCase = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
# download artifacts
for idx, (name, url) in enumerate(artifacts.items()):
print(name)
print(url)
print("""=""" * 80)
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
# extract warnings from artifacts
__UpperCAmelCase = extract_warnings(args.output_dir, args.targets)
__UpperCAmelCase = sorted(selected_warnings)
with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)
| 139 | 0 |
'''simple docstring'''
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.basicConfig(level=logging.DEBUG)
_UpperCAmelCase : Union[str, Any] = logging.getLogger()
_UpperCAmelCase : List[Any] = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class a__ ( __A ):
"""simple docstring"""
def _snake_case (self , __lowercase ):
os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase )
__lowerCAmelCase = {'''source''': '''What is love ?''', '''target''': '''life'''}
__lowerCAmelCase = {'''train''': 12, '''val''': 2, '''test''': 2}
for split in ["train", "test", "val"]:
for field in ["source", "target"]:
__lowerCAmelCase = '''\n'''.join([contents[field]] * n_lines[split] )
with open(os.path.join(_lowerCAmelCase , F"""{split}.{field}""" ) , '''w''' ) as f:
f.write(_lowerCAmelCase )
def _snake_case (self , __lowercase , __lowercase = "pytorch" ):
__lowerCAmelCase = self.get_auto_remove_tmp_dir()
__lowerCAmelCase = os.path.join(_lowerCAmelCase , '''output''' )
__lowerCAmelCase = os.path.join(_lowerCAmelCase , '''data''' )
self._create_dummy_data(data_dir=_lowerCAmelCase )
__lowerCAmelCase = F"""
--data_dir {data_dir} \
--output_dir {output_dir} \
--model_name_or_path facebook/rag-sequence-base \
--model_type rag_sequence \
--do_train \
--do_predict \
--n_val -1 \
--val_check_interval 1.0 \
--train_batch_size 2 \
--eval_batch_size 1 \
--max_source_length 25 \
--max_target_length 25 \
--val_max_target_length 25 \
--test_max_target_length 25 \
--label_smoothing 0.1 \
--dropout 0.1 \
--attention_dropout 0.1 \
--weight_decay 0.001 \
--adam_epsilon 1e-08 \
--max_grad_norm 0.1 \
--lr_scheduler polynomial \
--learning_rate 3e-04 \
--num_train_epochs 1 \
--warmup_steps 4 \
--gradient_accumulation_steps 1 \
--distributed-port 8787 \
--use_dummy_dataset 1 \
--distributed_retriever {distributed_retriever} \
""".split()
if gpus > 0:
testargs.append(F"""--gpus={gpus}""" )
if is_apex_available():
testargs.append('''--fp16''' )
else:
testargs.append('''--gpus=0''' )
testargs.append('''--distributed_backend=ddp_cpu''' )
testargs.append('''--num_processes=2''' )
__lowerCAmelCase = [sys.executable, str(Path(finetune_rag.__file__ ).resolve() )] + testargs
execute_subprocess_async(_lowerCAmelCase , env=self.get_env() )
__lowerCAmelCase = os.path.join(_lowerCAmelCase , '''metrics.json''' )
with open(_lowerCAmelCase ) as f:
__lowerCAmelCase = json.load(_lowerCAmelCase )
return result
@require_torch_gpu
def _snake_case (self ):
__lowerCAmelCase = self._run_finetune(gpus=1 )
self.assertGreaterEqual(result['''test'''][0]['''test_avg_em'''] , 0.2 )
@require_torch_multi_gpu
def _snake_case (self ):
__lowerCAmelCase = self._run_finetune(gpus=2 )
self.assertGreaterEqual(result['''test'''][0]['''test_avg_em'''] , 0.2 )
@require_torch_gpu
@require_ray
def _snake_case (self ):
__lowerCAmelCase = self._run_finetune(gpus=1 , distributed_retriever='''ray''' )
self.assertGreaterEqual(result['''test'''][0]['''test_avg_em'''] , 0.2 )
@require_torch_multi_gpu
@require_ray
def _snake_case (self ):
__lowerCAmelCase = self._run_finetune(gpus=1 , distributed_retriever='''ray''' )
self.assertGreaterEqual(result['''test'''][0]['''test_avg_em'''] , 0.2 )
| 174 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_SCREAMING_SNAKE_CASE = datasets.utils.logging.get_logger(__name__)
class lowerCAmelCase_ ( folder_based_builder.FolderBasedBuilderConfig ):
__lowerCamelCase : bool = None
__lowerCamelCase : bool = None
class lowerCAmelCase_ ( folder_based_builder.FolderBasedBuilder ):
__lowerCamelCase : Tuple = datasets.Audio()
__lowerCamelCase : List[str] = "audio"
__lowerCamelCase : Optional[int] = AudioFolderConfig
__lowerCamelCase : List[str] # definition at the bottom of the script
__lowerCamelCase : Optional[int] = AudioClassification(audio_column="audio" ,label_column="label" )
_SCREAMING_SNAKE_CASE = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
_SCREAMING_SNAKE_CASE = AUDIO_EXTENSIONS
| 158 | 0 |
from math import sqrt
def __lowerCamelCase ( snake_case__ = 1_00_00_00 ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 ,2 * max_cuboid_size + 1 ):
if sqrt(sum_shortest_sides**2 + max_cuboid_size**2 ).is_integer():
num_cuboids += (
min(snake_case__ ,sum_shortest_sides // 2 )
- max(1 ,sum_shortest_sides - max_cuboid_size )
+ 1
)
return max_cuboid_size
if __name__ == "__main__":
print(f"{solution() = }")
| 365 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase = logging.get_logger(__name__)
def __lowerCamelCase ( snake_case__ ,snake_case__ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
for i in range(encoder_config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'encoder.deit.blocks.{i}.norm1.weight', F'encoder.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((F'encoder.deit.blocks.{i}.norm1.bias', F'encoder.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.attn.proj.weight', F'encoder.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.attn.proj.bias', F'encoder.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.norm2.weight', F'encoder.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((F'encoder.deit.blocks.{i}.norm2.bias', F'encoder.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.mlp.fc1.weight', F'encoder.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.mlp.fc1.bias', F'encoder.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.mlp.fc2.weight', F'encoder.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((F'encoder.deit.blocks.{i}.mlp.fc2.bias', F'encoder.encoder.layer.{i}.output.dense.bias') )
# cls token, position embeddings and patch embeddings of encoder
rename_keys.extend(
[
("""encoder.deit.cls_token""", """encoder.embeddings.cls_token"""),
("""encoder.deit.pos_embed""", """encoder.embeddings.position_embeddings"""),
("""encoder.deit.patch_embed.proj.weight""", """encoder.embeddings.patch_embeddings.projection.weight"""),
("""encoder.deit.patch_embed.proj.bias""", """encoder.embeddings.patch_embeddings.projection.bias"""),
("""encoder.deit.norm.weight""", """encoder.layernorm.weight"""),
("""encoder.deit.norm.bias""", """encoder.layernorm.bias"""),
] )
return rename_keys
def __lowerCamelCase ( snake_case__ ,snake_case__ ) -> Dict:
"""simple docstring"""
for i in range(encoder_config.num_hidden_layers ):
# queries, keys and values (only weights, no biases)
_SCREAMING_SNAKE_CASE = state_dict.pop(F'encoder.deit.blocks.{i}.attn.qkv.weight' )
_SCREAMING_SNAKE_CASE = in_proj_weight[
: encoder_config.hidden_size, :
]
_SCREAMING_SNAKE_CASE = in_proj_weight[
encoder_config.hidden_size : encoder_config.hidden_size * 2, :
]
_SCREAMING_SNAKE_CASE = in_proj_weight[
-encoder_config.hidden_size :, :
]
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = dct.pop(snake_case__ )
_SCREAMING_SNAKE_CASE = val
def __lowerCamelCase ( snake_case__ ) -> Union[str, Any]:
"""simple docstring"""
if "handwritten" in checkpoint_url:
_SCREAMING_SNAKE_CASE = """https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg""" # industry
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" #
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
_SCREAMING_SNAKE_CASE = """https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
_SCREAMING_SNAKE_CASE = Image.open(requests.get(snake_case__ ,stream=snake_case__ ).raw ).convert("""RGB""" )
return im
@torch.no_grad()
def __lowerCamelCase ( snake_case__ ,snake_case__ ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ViTConfig(image_size=3_84 ,qkv_bias=snake_case__ )
_SCREAMING_SNAKE_CASE = TrOCRConfig()
# size of the architecture
if "base" in checkpoint_url:
_SCREAMING_SNAKE_CASE = 7_68
elif "large" in checkpoint_url:
# use ViT-large encoder
_SCREAMING_SNAKE_CASE = 10_24
_SCREAMING_SNAKE_CASE = 40_96
_SCREAMING_SNAKE_CASE = 24
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 10_24
else:
raise ValueError("""Should either find 'base' or 'large' in checkpoint URL""" )
# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = """relu"""
_SCREAMING_SNAKE_CASE = 10_24
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
# load HuggingFace model
_SCREAMING_SNAKE_CASE = ViTModel(snake_case__ ,add_pooling_layer=snake_case__ )
_SCREAMING_SNAKE_CASE = TrOCRForCausalLM(snake_case__ )
_SCREAMING_SNAKE_CASE = VisionEncoderDecoderModel(encoder=snake_case__ ,decoder=snake_case__ )
model.eval()
# load state_dict of original model, rename some keys
_SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(snake_case__ ,map_location="""cpu""" ,check_hash=snake_case__ )["""model"""]
_SCREAMING_SNAKE_CASE = create_rename_keys(snake_case__ ,snake_case__ )
for src, dest in rename_keys:
rename_key(snake_case__ ,snake_case__ ,snake_case__ )
read_in_q_k_v(snake_case__ ,snake_case__ )
# remove parameters we don't need
del state_dict["encoder.deit.head.weight"]
del state_dict["encoder.deit.head.bias"]
del state_dict["decoder.version"]
# add prefix to decoder keys
for key, val in state_dict.copy().items():
_SCREAMING_SNAKE_CASE = state_dict.pop(snake_case__ )
if key.startswith("""decoder""" ) and "output_projection" not in key:
_SCREAMING_SNAKE_CASE = val
else:
_SCREAMING_SNAKE_CASE = val
# load state dict
model.load_state_dict(snake_case__ )
# Check outputs on an image
_SCREAMING_SNAKE_CASE = ViTImageProcessor(size=encoder_config.image_size )
_SCREAMING_SNAKE_CASE = RobertaTokenizer.from_pretrained("""roberta-large""" )
_SCREAMING_SNAKE_CASE = TrOCRProcessor(snake_case__ ,snake_case__ )
_SCREAMING_SNAKE_CASE = processor(images=prepare_img(snake_case__ ) ,return_tensors="""pt""" ).pixel_values
# verify logits
_SCREAMING_SNAKE_CASE = torch.tensor([[model.config.decoder.decoder_start_token_id]] )
_SCREAMING_SNAKE_CASE = model(pixel_values=snake_case__ ,decoder_input_ids=snake_case__ )
_SCREAMING_SNAKE_CASE = outputs.logits
_SCREAMING_SNAKE_CASE = torch.Size([1, 1, 5_02_65] )
if "trocr-base-handwritten" in checkpoint_url:
_SCREAMING_SNAKE_CASE = torch.tensor(
[-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311] )
elif "trocr-large-handwritten" in checkpoint_url:
_SCREAMING_SNAKE_CASE = torch.tensor(
[-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170] )
elif "trocr-base-printed" in checkpoint_url:
_SCREAMING_SNAKE_CASE = torch.tensor(
[-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210] )
elif "trocr-large-printed" in checkpoint_url:
_SCREAMING_SNAKE_CASE = torch.tensor(
[-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535] )
if "stage1" not in checkpoint_url:
assert logits.shape == expected_shape, "Shape of logits not as expected"
assert torch.allclose(logits[0, 0, :10] ,snake_case__ ,atol=1e-3 ), "First elements of logits not as expected"
Path(snake_case__ ).mkdir(exist_ok=snake_case__ )
print(F'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(snake_case__ )
print(F'Saving processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(snake_case__ )
if __name__ == "__main__":
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_url''',
default='''https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt''',
type=str,
help='''URL to the original PyTorch checkpoint (.pth file).''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
UpperCamelCase = parser.parse_args()
convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 125 | 0 |
from itertools import count
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 5_0 ) -> int:
'''simple docstring'''
A__ = [1] * min_block_length
for n in count(SCREAMING_SNAKE_CASE_ ):
fill_count_functions.append(1 )
for block_length in range(SCREAMING_SNAKE_CASE_ , n + 1 ):
for block_start in range(n - block_length ):
fill_count_functions[n] += fill_count_functions[
n - block_start - block_length - 1
]
fill_count_functions[n] += 1
if fill_count_functions[n] > 1_0_0_0_0_0_0:
break
return n
if __name__ == "__main__":
print(f"""{solution() = }""")
| 68 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a__ : List[Any] = {
"configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
"processing_vision_text_dual_encoder": ["VisionTextDualEncoderProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Tuple = ["VisionTextDualEncoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Union[str, Any] = ["FlaxVisionTextDualEncoderModel"]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Optional[int] = ["TFVisionTextDualEncoderModel"]
if TYPE_CHECKING:
from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel
else:
import sys
a__ : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 161 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
lowercase__ : Dict = logging.get_logger(__name__)
lowercase__ : Tuple = {
"""Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """dpt"""
def __init__( self : str , SCREAMING_SNAKE_CASE_ : int=7_6_8 , SCREAMING_SNAKE_CASE_ : Optional[int]=1_2 , SCREAMING_SNAKE_CASE_ : Tuple=1_2 , SCREAMING_SNAKE_CASE_ : Optional[int]=3_0_7_2 , SCREAMING_SNAKE_CASE_ : List[str]="gelu" , SCREAMING_SNAKE_CASE_ : Optional[Any]=0.0 , SCREAMING_SNAKE_CASE_ : str=0.0 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=0.02 , SCREAMING_SNAKE_CASE_ : Tuple=1E-12 , SCREAMING_SNAKE_CASE_ : List[str]=3_8_4 , SCREAMING_SNAKE_CASE_ : List[Any]=1_6 , SCREAMING_SNAKE_CASE_ : Optional[int]=3 , SCREAMING_SNAKE_CASE_ : int=False , SCREAMING_SNAKE_CASE_ : Union[str, Any]=True , SCREAMING_SNAKE_CASE_ : str=[2, 5, 8, 1_1] , SCREAMING_SNAKE_CASE_ : int="project" , SCREAMING_SNAKE_CASE_ : Tuple=[4, 2, 1, 0.5] , SCREAMING_SNAKE_CASE_ : int=[9_6, 1_9_2, 3_8_4, 7_6_8] , SCREAMING_SNAKE_CASE_ : Optional[int]=2_5_6 , SCREAMING_SNAKE_CASE_ : List[str]=-1 , SCREAMING_SNAKE_CASE_ : Optional[int]=False , SCREAMING_SNAKE_CASE_ : Optional[Any]=True , SCREAMING_SNAKE_CASE_ : Optional[int]=0.4 , SCREAMING_SNAKE_CASE_ : Optional[Any]=2_5_5 , SCREAMING_SNAKE_CASE_ : Any=0.1 , SCREAMING_SNAKE_CASE_ : Optional[int]=[1, 1_0_2_4, 2_4, 2_4] , SCREAMING_SNAKE_CASE_ : List[str]=[0, 1] , SCREAMING_SNAKE_CASE_ : Optional[Any]=None , **SCREAMING_SNAKE_CASE_ : List[str] , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Dict = hidden_size
lowerCAmelCase_ : List[str] = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info('Initializing the config with a `BiT` backbone.' )
lowerCAmelCase_ : int = {
'global_padding': 'same',
'layer_type': 'bottleneck',
'depths': [3, 4, 9],
'out_features': ['stage1', 'stage2', 'stage3'],
'embedding_dynamic_padding': True,
}
lowerCAmelCase_ : str = BitConfig(**SCREAMING_SNAKE_CASE_ )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
logger.info('Initializing the config with a `BiT` backbone.' )
lowerCAmelCase_ : Tuple = BitConfig(**SCREAMING_SNAKE_CASE_ )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase_ : Tuple = backbone_config
else:
raise ValueError(
F"backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}." )
lowerCAmelCase_ : Union[str, Any] = backbone_featmap_shape
lowerCAmelCase_ : Union[str, Any] = neck_ignore_stages
if readout_type != "project":
raise ValueError('Readout type must be \'project\' when using `DPT-hybrid` mode.' )
else:
lowerCAmelCase_ : Optional[Any] = None
lowerCAmelCase_ : Dict = None
lowerCAmelCase_ : Optional[int] = []
lowerCAmelCase_ : Union[str, Any] = num_hidden_layers
lowerCAmelCase_ : Optional[int] = num_attention_heads
lowerCAmelCase_ : Union[str, Any] = intermediate_size
lowerCAmelCase_ : List[Any] = hidden_act
lowerCAmelCase_ : int = hidden_dropout_prob
lowerCAmelCase_ : Any = attention_probs_dropout_prob
lowerCAmelCase_ : int = initializer_range
lowerCAmelCase_ : List[str] = layer_norm_eps
lowerCAmelCase_ : List[str] = image_size
lowerCAmelCase_ : Optional[int] = patch_size
lowerCAmelCase_ : List[str] = num_channels
lowerCAmelCase_ : str = qkv_bias
lowerCAmelCase_ : Optional[int] = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError('Readout_type must be one of [\'ignore\', \'add\', \'project\']' )
lowerCAmelCase_ : Optional[int] = readout_type
lowerCAmelCase_ : Union[str, Any] = reassemble_factors
lowerCAmelCase_ : Union[str, Any] = neck_hidden_sizes
lowerCAmelCase_ : Dict = fusion_hidden_size
lowerCAmelCase_ : Any = head_in_index
lowerCAmelCase_ : Optional[Any] = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
lowerCAmelCase_ : Union[str, Any] = use_auxiliary_head
lowerCAmelCase_ : Dict = auxiliary_loss_weight
lowerCAmelCase_ : Optional[Any] = semantic_loss_ignore_index
lowerCAmelCase_ : int = semantic_classifier_dropout
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
lowerCAmelCase_ : Dict = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
lowerCAmelCase_ : List[str] = self.backbone_config.to_dict()
lowerCAmelCase_ : Optional[Any] = self.__class__.model_type
return output
| 289 |
"""simple docstring"""
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowercase__ : Optional[int] = 1_6
lowercase__ : List[str] = 3_2
def UpperCamelCase_ ( lowerCAmelCase__ : Accelerator , lowerCAmelCase__ : int = 16 ) -> Dict:
"""simple docstring"""
lowerCAmelCase_ : Union[str, Any] = AutoTokenizer.from_pretrained('bert-base-cased' )
lowerCAmelCase_ : Union[str, Any] = load_dataset('glue' , 'mrpc' )
def tokenize_function(lowerCAmelCase__ : Tuple ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase_ : Dict = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=lowerCAmelCase__ , max_length=lowerCAmelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowerCAmelCase_ : Dict = datasets.map(
lowerCAmelCase__ , batched=lowerCAmelCase__ , remove_columns=['idx', 'sentence1', 'sentence2'] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowerCAmelCase_ : int = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(lowerCAmelCase__ : List[str] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowerCAmelCase_ : Any = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowerCAmelCase_ : Dict = 16
elif accelerator.mixed_precision != "no":
lowerCAmelCase_ : str = 8
else:
lowerCAmelCase_ : str = None
return tokenizer.pad(
lowerCAmelCase__ , padding='longest' , max_length=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_tensors='pt' , )
# Instantiate dataloaders.
lowerCAmelCase_ : List[Any] = DataLoader(
tokenized_datasets['train'] , shuffle=lowerCAmelCase__ , collate_fn=lowerCAmelCase__ , batch_size=lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = DataLoader(
tokenized_datasets['validation'] , shuffle=lowerCAmelCase__ , collate_fn=lowerCAmelCase__ , batch_size=lowerCAmelCase__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
lowercase__ : int = mocked_dataloaders # noqa: F811
def UpperCamelCase_ ( lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
if os.environ.get('TESTING_MOCKED_DATALOADERS' , lowerCAmelCase__ ) == "1":
lowerCAmelCase_ : Optional[int] = 2
# Initialize accelerator
lowerCAmelCase_ : str = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCAmelCase_ : Optional[int] = config['lr']
lowerCAmelCase_ : Tuple = int(config['num_epochs'] )
lowerCAmelCase_ : int = int(config['seed'] )
lowerCAmelCase_ : str = int(config['batch_size'] )
lowerCAmelCase_ : str = evaluate.load('glue' , 'mrpc' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=lowerCAmelCase__ )
def inner_training_loop(lowerCAmelCase__ : Optional[int] ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(lowerCAmelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCAmelCase_ : str = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=lowerCAmelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowerCAmelCase_ : int = model.to(accelerator.device )
# Instantiate optimizer
lowerCAmelCase_ : List[Any] = AdamW(params=model.parameters() , lr=lowerCAmelCase__ )
lowerCAmelCase_ ,lowerCAmelCase_ : Any = get_dataloaders(lowerCAmelCase__ , lowerCAmelCase__ )
# Instantiate scheduler
lowerCAmelCase_ : Dict = get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase__ , num_warmup_steps=100 , num_training_steps=(len(lowerCAmelCase__ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ ,lowerCAmelCase_ : List[Any] = accelerator.prepare(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# Now we train the model
for epoch in range(lowerCAmelCase__ ):
model.train()
for step, batch in enumerate(lowerCAmelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowerCAmelCase_ : List[str] = model(**lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = outputs.loss
accelerator.backward(lowerCAmelCase__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowerCAmelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCAmelCase_ : Union[str, Any] = model(**lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = outputs.logits.argmax(dim=-1 )
lowerCAmelCase_ ,lowerCAmelCase_ : Optional[Any] = accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=lowerCAmelCase__ , references=lowerCAmelCase__ , )
lowerCAmelCase_ : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"epoch {epoch}:" , lowerCAmelCase__ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def UpperCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase_ : int = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=lowerCAmelCase__ , default=lowerCAmelCase__ , choices=['no', 'fp16', 'bf16', 'fp8'] , help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
lowerCAmelCase_ : str = parser.parse_args()
lowerCAmelCase_ : Union[str, Any] = {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(lowerCAmelCase__ , lowerCAmelCase__ )
if __name__ == "__main__":
main()
| 289 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
UpperCamelCase_ : str = """timm_backbone"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : str=None , SCREAMING_SNAKE_CASE_ : List[str]=3 , SCREAMING_SNAKE_CASE_ : Optional[Any]=True , SCREAMING_SNAKE_CASE_ : Optional[Any]=True , SCREAMING_SNAKE_CASE_ : Optional[int]=None , **SCREAMING_SNAKE_CASE_ : Dict , ) -> List[str]:
'''simple docstring'''
super().__init__(**SCREAMING_SNAKE_CASE_ )
A: str = backbone
A: Optional[int] = num_channels
A: List[Any] = features_only
A: int = use_pretrained_backbone
A: Union[str, Any] = True
A: Any = out_indices if out_indices is not None else (-1,)
| 319 |
'''simple docstring'''
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> list:
if len(__lowercase ) != 2 or len(a[0] ) != 2 or len(__lowercase ) != 2 or len(b[0] ) != 2:
raise Exception('''Matrices are not 2x2''' )
A: str = [
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
[a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
]
return new_matrix
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> Union[str, Any]:
return [
[matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(__lowercase ) )
]
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> Union[str, Any]:
return [
[matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(__lowercase ) )
]
def SCREAMING_SNAKE_CASE( __lowercase ) -> tuple[list, list, list, list]:
if len(__lowercase ) % 2 != 0 or len(a[0] ) % 2 != 0:
raise Exception('''Odd matrices are not supported!''' )
A: Union[str, Any] = len(__lowercase )
A: str = matrix_length // 2
A: Optional[int] = [[a[i][j] for j in range(__lowercase , __lowercase )] for i in range(__lowercase )]
A: Optional[Any] = [
[a[i][j] for j in range(__lowercase , __lowercase )] for i in range(__lowercase , __lowercase )
]
A: Union[str, Any] = [[a[i][j] for j in range(__lowercase )] for i in range(__lowercase )]
A: int = [[a[i][j] for j in range(__lowercase )] for i in range(__lowercase , __lowercase )]
return top_left, top_right, bot_left, bot_right
def SCREAMING_SNAKE_CASE( __lowercase ) -> tuple[int, int]:
return len(__lowercase ), len(matrix[0] )
def SCREAMING_SNAKE_CASE( __lowercase ) -> None:
print('''\n'''.join(str(__lowercase ) for line in matrix ) )
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> list:
if matrix_dimensions(__lowercase ) == (2, 2):
return default_matrix_multiplication(__lowercase , __lowercase )
A , A , A , A: Union[str, Any] = split_matrix(__lowercase )
A , A , A , A: List[Any] = split_matrix(__lowercase )
A: Optional[int] = actual_strassen(__lowercase , matrix_subtraction(__lowercase , __lowercase ) )
A: Any = actual_strassen(matrix_addition(__lowercase , __lowercase ) , __lowercase )
A: Tuple = actual_strassen(matrix_addition(__lowercase , __lowercase ) , __lowercase )
A: Optional[int] = actual_strassen(__lowercase , matrix_subtraction(__lowercase , __lowercase ) )
A: Tuple = actual_strassen(matrix_addition(__lowercase , __lowercase ) , matrix_addition(__lowercase , __lowercase ) )
A: Union[str, Any] = actual_strassen(matrix_subtraction(__lowercase , __lowercase ) , matrix_addition(__lowercase , __lowercase ) )
A: List[str] = actual_strassen(matrix_subtraction(__lowercase , __lowercase ) , matrix_addition(__lowercase , __lowercase ) )
A: int = matrix_addition(matrix_subtraction(matrix_addition(__lowercase , __lowercase ) , __lowercase ) , __lowercase )
A: Any = matrix_addition(__lowercase , __lowercase )
A: List[Any] = matrix_addition(__lowercase , __lowercase )
A: List[str] = matrix_subtraction(matrix_subtraction(matrix_addition(__lowercase , __lowercase ) , __lowercase ) , __lowercase )
# construct the new matrix from our 4 quadrants
A: Union[str, Any] = []
for i in range(len(__lowercase ) ):
new_matrix.append(top_left[i] + top_right[i] )
for i in range(len(__lowercase ) ):
new_matrix.append(bot_left[i] + bot_right[i] )
return new_matrix
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase ) -> list:
if matrix_dimensions(__lowercase )[1] != matrix_dimensions(__lowercase )[0]:
A: int = (
'''Unable to multiply these matrices, please check the dimensions.\n'''
F"""Matrix A: {matrixa}\n"""
F"""Matrix B: {matrixa}"""
)
raise Exception(__lowercase )
A: str = matrix_dimensions(__lowercase )
A: str = matrix_dimensions(__lowercase )
if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
return [matrixa, matrixa]
A: Union[str, Any] = max(*__lowercase , *__lowercase )
A: Optional[int] = int(math.pow(2 , math.ceil(math.loga(__lowercase ) ) ) )
A: List[Any] = matrixa
A: Tuple = matrixa
# Adding zeros to the matrices so that the arrays dimensions are the same and also
# power of 2
for i in range(0 , __lowercase ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __lowercase ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __lowercase ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
A: Any = actual_strassen(__lowercase , __lowercase )
# Removing the additional zeros
for i in range(0 , __lowercase ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , __lowercase ):
final_matrix[i].pop()
else:
final_matrix.pop()
return final_matrix
if __name__ == "__main__":
UpperCamelCase = [
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 2, 3, 1],
]
UpperCamelCase = [[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
print(strassen(matrixa, matrixa))
| 319 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
__snake_case : Union[str, Any] = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class __UpperCAmelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowercase : Optional[int] = 'mgp-str'
def __init__( self , _SCREAMING_SNAKE_CASE=[32, 128] , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=27 , _SCREAMING_SNAKE_CASE=38 , _SCREAMING_SNAKE_CASE=5_0257 , _SCREAMING_SNAKE_CASE=3_0522 , _SCREAMING_SNAKE_CASE=768 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=4.0 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=1E-5 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=0.02 , **_SCREAMING_SNAKE_CASE , ) -> List[Any]:
super().__init__(**_SCREAMING_SNAKE_CASE )
A_ = image_size
A_ = patch_size
A_ = num_channels
A_ = max_token_length
A_ = num_character_labels
A_ = num_bpe_labels
A_ = num_wordpiece_labels
A_ = hidden_size
A_ = num_hidden_layers
A_ = num_attention_heads
A_ = mlp_ratio
A_ = distilled
A_ = layer_norm_eps
A_ = drop_rate
A_ = qkv_bias
A_ = attn_drop_rate
A_ = drop_path_rate
A_ = output_aa_attentions
A_ = initializer_range
| 18 | '''simple docstring'''
from __future__ import annotations
def _UpperCAmelCase ( _UpperCamelCase : int | str ) -> bool:
A_ = str(_UpperCamelCase )
return n == n[::-1]
def _UpperCAmelCase ( _UpperCamelCase : int = 1_00_00_00 ) -> Any:
A_ = 0
for i in range(1, _UpperCamelCase ):
if is_palindrome(_UpperCamelCase ) and is_palindrome(bin(_UpperCamelCase ).split('''b''' )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 18 | 1 |
from __future__ import annotations
import math
lowerCamelCase : List[Any] = '''2020.9.26'''
lowerCamelCase : str = '''xcodz-dot, cclaus, dhruvmanila'''
def snake_case_ ( lowerCAmelCase_ : float , lowerCAmelCase_ : float , lowerCAmelCase_ : float , lowerCAmelCase_ : float , lowerCAmelCase_ : float ):
if not all(isinstance(lowerCAmelCase_ , (float, int) ) for val in locals().values() ):
__lowercase : str = F"Input values must either be float or int: {list(locals().values() )}"
raise TypeError(lowerCAmelCase_ )
__lowercase : List[Any] = ((x * distance) / (z + distance)) * scale
__lowercase : Tuple = ((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def snake_case_ ( lowerCAmelCase_ : float , lowerCAmelCase_ : float , lowerCAmelCase_ : float , lowerCAmelCase_ : str , lowerCAmelCase_ : float ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("""Axis must be a str""" )
__lowercase : Optional[int] = locals()
del input_variables["axis"]
if not all(isinstance(lowerCAmelCase_ , (float, int) ) for val in input_variables.values() ):
__lowercase : List[str] = (
"""Input values except axis must either be float or int: """
F"{list(input_variables.values() )}"
)
raise TypeError(lowerCAmelCase_ )
__lowercase : Tuple = (angle % 360) / 450 * 180 / math.pi
if axis == "z":
__lowercase : int = x * math.cos(lowerCAmelCase_ ) - y * math.sin(lowerCAmelCase_ )
__lowercase : Tuple = y * math.cos(lowerCAmelCase_ ) + x * math.sin(lowerCAmelCase_ )
__lowercase : Union[str, Any] = z
elif axis == "x":
__lowercase : str = y * math.cos(lowerCAmelCase_ ) - z * math.sin(lowerCAmelCase_ )
__lowercase : Dict = z * math.cos(lowerCAmelCase_ ) + y * math.sin(lowerCAmelCase_ )
__lowercase : List[str] = x
elif axis == "y":
__lowercase : List[str] = x * math.cos(lowerCAmelCase_ ) - z * math.sin(lowerCAmelCase_ )
__lowercase : List[str] = z * math.cos(lowerCAmelCase_ ) + x * math.sin(lowerCAmelCase_ )
__lowercase : List[Any] = y
else:
raise ValueError("""not a valid axis, choose one of 'x', 'y', 'z'""" )
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'''{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }''')
print(f'''{rotate(1.0, 2.0, 3.0, "y", 90.0) = }''') | 233 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case_ ( lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Any , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : int ):
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
__lowercase : Optional[int] = TapasConfig.from_json_file(lowerCAmelCase_ )
# set absolute/relative position embeddings parameter
__lowercase : Optional[Any] = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
__lowercase : Union[str, Any] = TapasForQuestionAnswering(config=lowerCAmelCase_ )
elif task == "WTQ":
# run_task_main.py hparams
__lowercase : List[Any] = 4
__lowercase : Union[str, Any] = True
# hparam_utils.py hparams
__lowercase : Any = 0.664_694
__lowercase : Tuple = 0.207_951
__lowercase : Dict = 0.121_194
__lowercase : List[str] = True
__lowercase : str = True
__lowercase : Dict = False
__lowercase : Tuple = 0.0_352_513
__lowercase : List[Any] = TapasForQuestionAnswering(config=lowerCAmelCase_ )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
__lowercase : Optional[int] = 4
__lowercase : int = False
# hparam_utils.py hparams
__lowercase : Tuple = 36.4_519
__lowercase : str = 0.903_421
__lowercase : List[Any] = 222.088
__lowercase : Union[str, Any] = True
__lowercase : Tuple = True
__lowercase : Union[str, Any] = True
__lowercase : Optional[Any] = 0.763_141
__lowercase : str = TapasForQuestionAnswering(config=lowerCAmelCase_ )
elif task == "TABFACT":
__lowercase : List[Any] = TapasForSequenceClassification(config=lowerCAmelCase_ )
elif task == "MLM":
__lowercase : Optional[int] = TapasForMaskedLM(config=lowerCAmelCase_ )
elif task == "INTERMEDIATE_PRETRAINING":
__lowercase : Dict = TapasModel(config=lowerCAmelCase_ )
else:
raise ValueError(F"Task {task} not supported." )
print(F"Building PyTorch model from configuration: {config}" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# Save pytorch-model (weights and configuration)
print(F"Save PyTorch model to {pytorch_dump_path}" )
model.save_pretrained(lowerCAmelCase_ )
# Save tokenizer files
print(F"Save tokenizer files to {pytorch_dump_path}" )
__lowercase : Any = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt""" , model_max_length=512 )
tokenizer.save_pretrained(lowerCAmelCase_ )
print("""Used relative position embeddings:""" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
lowerCamelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
)
parser.add_argument(
'''--reset_position_index_per_cell''',
default=False,
action='''store_true''',
help='''Whether to use relative position embeddings or not. Defaults to True.''',
)
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--tapas_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained TAPAS 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 : int = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
) | 233 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : str = {
'configuration_blenderbot_small': [
'BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlenderbotSmallConfig',
'BlenderbotSmallOnnxConfig',
],
'tokenization_blenderbot_small': ['BlenderbotSmallTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ['BlenderbotSmallTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = [
'BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST',
'BlenderbotSmallForCausalLM',
'BlenderbotSmallForConditionalGeneration',
'BlenderbotSmallModel',
'BlenderbotSmallPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[Any] = [
'TFBlenderbotSmallForConditionalGeneration',
'TFBlenderbotSmallModel',
'TFBlenderbotSmallPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = [
'FlaxBlenderbotSmallForConditionalGeneration',
'FlaxBlenderbotSmallModel',
'FlaxBlenderbotSmallPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotSmallConfig,
BlenderbotSmallOnnxConfig,
)
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotSmallForCausalLM,
BlenderbotSmallForConditionalGeneration,
BlenderbotSmallModel,
BlenderbotSmallPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot_small import (
TFBlenderbotSmallForConditionalGeneration,
TFBlenderbotSmallModel,
TFBlenderbotSmallPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallPreTrainedModel,
)
else:
import sys
__A : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 57 |
"""simple docstring"""
__A : Dict = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
__A : List[Any] = [{'type': 'code', 'content': INSTALL_CONTENT}]
__A : List[Any] = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 57 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Any = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Optional[int] = ['''LlamaTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Optional[Any] = ['''LlamaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : List[Any] = [
'''LlamaForCausalLM''',
'''LlamaModel''',
'''LlamaPreTrainedModel''',
'''LlamaForSequenceClassification''',
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
a__ : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 313 |
from maths.prime_check import is_prime
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if not isinstance(a__ , a__ ):
SCREAMING_SNAKE_CASE : List[Any] = F"""Input value of [number={number}] must be an integer"""
raise TypeError(a__ )
if is_prime(a__ ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 313 | 1 |
'''simple docstring'''
from __future__ import annotations
import queue
class UpperCamelCase__:
def __init__( self : Any , lowerCAmelCase : Optional[int] )-> List[str]:
"""simple docstring"""
UpperCAmelCase = data
UpperCAmelCase = None
UpperCAmelCase = None
def lowerCamelCase__ ( ):
'''simple docstring'''
print('''\n********Press N to stop entering at any point of time********\n''' )
UpperCAmelCase = input('''Enter the value of the root node: ''' ).strip().lower()
UpperCAmelCase = queue.Queue()
UpperCAmelCase = TreeNode(int(__lowerCAmelCase ) )
q.put(__lowerCAmelCase )
while not q.empty():
UpperCAmelCase = q.get()
UpperCAmelCase = f"""Enter the left node of {node_found.data}: """
UpperCAmelCase = input(__lowerCAmelCase ).strip().lower() or '''n'''
if check == "n":
return tree_node
UpperCAmelCase = TreeNode(int(__lowerCAmelCase ) )
UpperCAmelCase = left_node
q.put(__lowerCAmelCase )
UpperCAmelCase = f"""Enter the right node of {node_found.data}: """
UpperCAmelCase = input(__lowerCAmelCase ).strip().lower() or '''n'''
if check == "n":
return tree_node
UpperCAmelCase = TreeNode(int(__lowerCAmelCase ) )
UpperCAmelCase = right_node
q.put(__lowerCAmelCase )
raise
def lowerCamelCase__ ( A : TreeNode ):
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not node:
return
print(node.data , end=''',''' )
pre_order(node.left )
pre_order(node.right )
def lowerCamelCase__ ( A : TreeNode ):
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not node:
return
in_order(node.left )
print(node.data , end=''',''' )
in_order(node.right )
def lowerCamelCase__ ( A : TreeNode ):
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data , end=''',''' )
def lowerCamelCase__ ( A : TreeNode ):
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not node:
return
UpperCAmelCase = queue.Queue()
q.put(__lowerCAmelCase )
while not q.empty():
UpperCAmelCase = q.get()
print(node_dequeued.data , end=''',''' )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def lowerCamelCase__ ( A : TreeNode ):
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not node:
return
UpperCAmelCase = queue.Queue()
q.put(__lowerCAmelCase )
while not q.empty():
UpperCAmelCase = []
while not q.empty():
UpperCAmelCase = q.get()
print(node_dequeued.data , end=''',''' )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(__lowerCAmelCase )
def lowerCamelCase__ ( A : TreeNode ):
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not node:
return
UpperCAmelCase = []
UpperCAmelCase = node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end=''',''' )
stack.append(__lowerCAmelCase )
UpperCAmelCase = n.left
# end of while means current node doesn't have left child
UpperCAmelCase = stack.pop()
# start to traverse its right child
UpperCAmelCase = n.right
def lowerCamelCase__ ( A : TreeNode ):
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not node:
return
UpperCAmelCase = []
UpperCAmelCase = node
while n or stack:
while n:
stack.append(__lowerCAmelCase )
UpperCAmelCase = n.left
UpperCAmelCase = stack.pop()
print(n.data , end=''',''' )
UpperCAmelCase = n.right
def lowerCamelCase__ ( A : TreeNode ):
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not node:
return
UpperCAmelCase , UpperCAmelCase = [], []
UpperCAmelCase = node
stacka.append(__lowerCAmelCase )
while stacka: # to find the reversed order of post order, store it in stack2
UpperCAmelCase = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(__lowerCAmelCase )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end=''',''' )
def lowerCamelCase__ ( A : str = "" , A : str=50 , A : Dict="*" ):
'''simple docstring'''
if not s:
return "\n" + width * char
UpperCAmelCase , UpperCAmelCase = divmod(width - len(__lowerCAmelCase ) - 2 , 2 )
return f"""{left * char} {s} {(left + extra) * char}"""
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt("""Binary Tree Traversals"""))
_lowercase : TreeNode = build_tree()
print(prompt("""Pre Order Traversal"""))
pre_order(node)
print(prompt() + """\n""")
print(prompt("""In Order Traversal"""))
in_order(node)
print(prompt() + """\n""")
print(prompt("""Post Order Traversal"""))
post_order(node)
print(prompt() + """\n""")
print(prompt("""Level Order Traversal"""))
level_order(node)
print(prompt() + """\n""")
print(prompt("""Actual Level Order Traversal"""))
level_order_actual(node)
print("""*""" * 50 + """\n""")
print(prompt("""Pre Order Traversal - Iteration Version"""))
pre_order_iter(node)
print(prompt() + """\n""")
print(prompt("""In Order Traversal - Iteration Version"""))
in_order_iter(node)
print(prompt() + """\n""")
print(prompt("""Post Order Traversal - Iteration Version"""))
post_order_iter(node)
print(prompt())
| 370 |
'''simple docstring'''
_lowercase : Any = range(2, 20 + 1)
_lowercase : str = [10**k for k in range(ks[-1] + 1)]
_lowercase : dict[int, dict[int, list[list[int]]]] = {}
def lowerCamelCase__ ( A : int , A : str , A : List[Any] , A : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = sum(a_i[j] for j in range(A , len(A ) ) )
UpperCAmelCase = sum(a_i[j] * base[j] for j in range(min(len(A ) , A ) ) )
UpperCAmelCase , UpperCAmelCase = 0, 0
UpperCAmelCase = n - i
UpperCAmelCase = memo.get(A )
if sub_memo is not None:
UpperCAmelCase = sub_memo.get(A )
if jumps is not None and len(A ) > 0:
# find and make the largest jump without going over
UpperCAmelCase = -1
for _k in range(len(A ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
UpperCAmelCase = _k
break
if max_jump >= 0:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = jumps[max_jump]
# since the difference between jumps is cached, add c
UpperCAmelCase = diff + c
for j in range(min(A , len(A ) ) ):
UpperCAmelCase , UpperCAmelCase = divmod(A , 10 )
if new_c > 0:
add(A , A , A )
else:
UpperCAmelCase = []
else:
UpperCAmelCase = {c: []}
UpperCAmelCase = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
UpperCAmelCase , UpperCAmelCase = next_term(A , k - 1 , i + dn , A )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
UpperCAmelCase , UpperCAmelCase = compute(A , A , i + dn , A )
diff += _diff
dn += terms_jumped
UpperCAmelCase = sub_memo[c]
# keep jumps sorted by # of terms skipped
UpperCAmelCase = 0
while j < len(A ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(A , (diff, dn, k) )
return (diff, dn)
def lowerCamelCase__ ( A : Dict , A : Optional[int] , A : List[Any] , A : int ):
'''simple docstring'''
if i >= n:
return 0, i
if k > len(A ):
a_i.extend([0 for _ in range(k - len(A ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
UpperCAmelCase = i
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 0, 0, 0
for j in range(len(A ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
UpperCAmelCase = ds_c + ds_b
diff += addend
UpperCAmelCase = 0
for j in range(A ):
UpperCAmelCase = a_i[j] + addend
UpperCAmelCase , UpperCAmelCase = divmod(A , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(A , A , A )
return diff, i - start_i
def lowerCamelCase__ ( A : List[str] , A : Optional[int] , A : Optional[Any] ):
'''simple docstring'''
for j in range(A , len(A ) ):
UpperCAmelCase = digits[j] + addend
if s >= 10:
UpperCAmelCase , UpperCAmelCase = divmod(A , 10 )
UpperCAmelCase = addend // 10 + quotient
else:
UpperCAmelCase = s
UpperCAmelCase = addend // 10
if addend == 0:
break
while addend > 0:
UpperCAmelCase , UpperCAmelCase = divmod(A , 10 )
digits.append(A )
def lowerCamelCase__ ( A : int = 10**15 ):
'''simple docstring'''
UpperCAmelCase = [1]
UpperCAmelCase = 1
UpperCAmelCase = 0
while True:
UpperCAmelCase , UpperCAmelCase = next_term(A , 20 , i + dn , A )
dn += terms_jumped
if dn == n - i:
break
UpperCAmelCase = 0
for j in range(len(A ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(F"""{solution() = }""")
| 91 | 0 |
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info
def UpperCAmelCase_ (__a : Dict ):
"""simple docstring"""
return {key.lstrip('-' ): value for key, value in zip(unknown_args[::2] , unknown_args[1::2] )}
def UpperCAmelCase_ ():
"""simple docstring"""
_a : Optional[Any] = ArgumentParser(
'HuggingFace Datasets CLI tool' , usage='datasets-cli <command> [<args>]' , allow_abbrev=A__ )
_a : str = parser.add_subparsers(help='datasets-cli command helpers' )
set_verbosity_info()
# Register commands
ConvertCommand.register_subcommand(A__ )
EnvironmentCommand.register_subcommand(A__ )
TestCommand.register_subcommand(A__ )
RunBeamCommand.register_subcommand(A__ )
DummyDataCommand.register_subcommand(A__ )
# Parse args
_a : int = parser.parse_known_args()
if not hasattr(A__ , 'func' ):
parser.print_help()
exit(1 )
_a : Tuple = parse_unknown_args(A__ )
# Run
_a : Union[str, Any] = args.func(A__ , **A__ )
service.run()
if __name__ == "__main__":
main()
| 271 |
from math import loga
def A_ ( A__ ) -> int:
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(A__ , A__ ):
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()
| 99 | 0 |
"""simple docstring"""
from collections import defaultdict
def __lowerCamelCase ( a_ : str , a_ : str ) -> bool:
__SCREAMING_SNAKE_CASE :Optional[int] = first_str.lower().strip()
__SCREAMING_SNAKE_CASE :Any = second_str.lower().strip()
# Remove whitespace
__SCREAMING_SNAKE_CASE :Any = first_str.replace(''' ''' , '''''' )
__SCREAMING_SNAKE_CASE :Any = second_str.replace(''' ''' , '''''' )
# Strings of different lengths are not anagrams
if len(a_ ) != len(a_ ):
return False
# Default values for count should be 0
__SCREAMING_SNAKE_CASE :defaultdict[str, int] = defaultdict(a_ )
# For each character in input strings,
# increment count in the corresponding
for i in range(len(a_ ) ):
count[first_str[i]] += 1
count[second_str[i]] -= 1
return all(_count == 0 for _count in count.values() )
if __name__ == "__main__":
from doctest import testmod
testmod()
lowerCamelCase_ = input("Enter the first string ").strip()
lowerCamelCase_ = input("Enter the second string ").strip()
lowerCamelCase_ = check_anagrams(input_a, input_b)
print(f'{input_a} and {input_b} are {"" if status else "not "}anagrams.') | 239 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class _SCREAMING_SNAKE_CASE:
def __init__( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__=13 ,SCREAMING_SNAKE_CASE__=7 ,SCREAMING_SNAKE_CASE__=True ,SCREAMING_SNAKE_CASE__=True ,SCREAMING_SNAKE_CASE__=True ,SCREAMING_SNAKE_CASE__=True ,SCREAMING_SNAKE_CASE__=99 ,SCREAMING_SNAKE_CASE__=32 ,SCREAMING_SNAKE_CASE__=5 ,SCREAMING_SNAKE_CASE__=4 ,SCREAMING_SNAKE_CASE__=37 ,SCREAMING_SNAKE_CASE__="gelu" ,SCREAMING_SNAKE_CASE__=0.1 ,SCREAMING_SNAKE_CASE__=0.1 ,SCREAMING_SNAKE_CASE__=5_12 ,SCREAMING_SNAKE_CASE__=16 ,SCREAMING_SNAKE_CASE__=2 ,SCREAMING_SNAKE_CASE__=0.0_2 ,SCREAMING_SNAKE_CASE__=3 ,SCREAMING_SNAKE_CASE__=4 ,SCREAMING_SNAKE_CASE__=None ,) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Any = parent
__SCREAMING_SNAKE_CASE :Tuple = batch_size
__SCREAMING_SNAKE_CASE :Tuple = seq_length
__SCREAMING_SNAKE_CASE :Any = is_training
__SCREAMING_SNAKE_CASE :Tuple = use_input_mask
__SCREAMING_SNAKE_CASE :List[Any] = use_token_type_ids
__SCREAMING_SNAKE_CASE :int = use_labels
__SCREAMING_SNAKE_CASE :Dict = vocab_size
__SCREAMING_SNAKE_CASE :int = hidden_size
__SCREAMING_SNAKE_CASE :int = num_hidden_layers
__SCREAMING_SNAKE_CASE :Union[str, Any] = num_attention_heads
__SCREAMING_SNAKE_CASE :Any = intermediate_size
__SCREAMING_SNAKE_CASE :Any = hidden_act
__SCREAMING_SNAKE_CASE :str = hidden_dropout_prob
__SCREAMING_SNAKE_CASE :List[str] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE :int = max_position_embeddings
__SCREAMING_SNAKE_CASE :Any = type_vocab_size
__SCREAMING_SNAKE_CASE :Optional[Any] = type_sequence_label_size
__SCREAMING_SNAKE_CASE :Optional[int] = initializer_range
__SCREAMING_SNAKE_CASE :Union[str, Any] = num_labels
__SCREAMING_SNAKE_CASE :Union[str, Any] = num_choices
__SCREAMING_SNAKE_CASE :str = scope
def _UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
__SCREAMING_SNAKE_CASE :Union[str, Any] = None
if self.use_input_mask:
__SCREAMING_SNAKE_CASE :List[str] = random_attention_mask([self.batch_size, self.seq_length] )
__SCREAMING_SNAKE_CASE :Union[str, Any] = None
if self.use_token_type_ids:
__SCREAMING_SNAKE_CASE :Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
__SCREAMING_SNAKE_CASE :Dict = None
__SCREAMING_SNAKE_CASE :Dict = None
__SCREAMING_SNAKE_CASE :Dict = None
if self.use_labels:
__SCREAMING_SNAKE_CASE :List[Any] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
__SCREAMING_SNAKE_CASE :Optional[int] = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
__SCREAMING_SNAKE_CASE :Dict = ids_tensor([self.batch_size] ,self.num_choices )
__SCREAMING_SNAKE_CASE :Tuple = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
return NystromformerConfig(
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=SCREAMING_SNAKE_CASE__ ,initializer_range=self.initializer_range ,)
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :List[Any] = NystromformerModel(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__SCREAMING_SNAKE_CASE :Any = model(SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :List[str] = model(SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :int = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Dict = NystromformerForMaskedLM(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__SCREAMING_SNAKE_CASE :Tuple = model(SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ ,labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :List[str] = NystromformerForQuestionAnswering(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__SCREAMING_SNAKE_CASE :Optional[Any] = model(
SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ ,start_positions=SCREAMING_SNAKE_CASE__ ,end_positions=SCREAMING_SNAKE_CASE__ ,)
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 _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :int = self.num_labels
__SCREAMING_SNAKE_CASE :Any = NystromformerForSequenceClassification(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__SCREAMING_SNAKE_CASE :Dict = model(SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ ,labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Optional[Any] = self.num_labels
__SCREAMING_SNAKE_CASE :Tuple = NystromformerForTokenClassification(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__SCREAMING_SNAKE_CASE :Any = model(SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ ,labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Optional[Any] = self.num_choices
__SCREAMING_SNAKE_CASE :Dict = NystromformerForMultipleChoice(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__SCREAMING_SNAKE_CASE :List[str] = input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
__SCREAMING_SNAKE_CASE :Optional[int] = token_type_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
__SCREAMING_SNAKE_CASE :List[Any] = input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
__SCREAMING_SNAKE_CASE :Dict = model(
SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__ ,token_type_ids=SCREAMING_SNAKE_CASE__ ,labels=SCREAMING_SNAKE_CASE__ ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def _UpperCamelCase ( self ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :List[Any] = self.prepare_config_and_inputs()
(
(
__SCREAMING_SNAKE_CASE
) ,(
__SCREAMING_SNAKE_CASE
) ,(
__SCREAMING_SNAKE_CASE
) ,(
__SCREAMING_SNAKE_CASE
) ,(
__SCREAMING_SNAKE_CASE
) ,(
__SCREAMING_SNAKE_CASE
) ,(
__SCREAMING_SNAKE_CASE
) ,
) :Dict = config_and_inputs
__SCREAMING_SNAKE_CASE :str = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _SCREAMING_SNAKE_CASE( A , A , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ : int = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = (
{
'''feature-extraction''': NystromformerModel,
'''fill-mask''': NystromformerForMaskedLM,
'''question-answering''': NystromformerForQuestionAnswering,
'''text-classification''': NystromformerForSequenceClassification,
'''token-classification''': NystromformerForTokenClassification,
'''zero-shot''': NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_ : str = False
SCREAMING_SNAKE_CASE_ : Tuple = False
def _UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Any = NystromformerModelTester(self )
__SCREAMING_SNAKE_CASE :Optional[Any] = ConfigTester(self ,config_class=SCREAMING_SNAKE_CASE__ ,hidden_size=37 )
def _UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Any = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__SCREAMING_SNAKE_CASE :Any = type
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__ )
@slow
def _UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE :Tuple = NystromformerModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
@require_torch
class _SCREAMING_SNAKE_CASE( unittest.TestCase ):
@slow
def _UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :int = NystromformerModel.from_pretrained('''uw-madison/nystromformer-512''' )
__SCREAMING_SNAKE_CASE :Dict = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
__SCREAMING_SNAKE_CASE :str = model(SCREAMING_SNAKE_CASE__ )[0]
__SCREAMING_SNAKE_CASE :Optional[int] = torch.Size((1, 6, 7_68) )
self.assertEqual(output.shape ,SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :int = torch.tensor(
[[[-0.4_5_3_2, -0.0_9_3_6, 0.5_1_3_7], [-0.2_6_7_6, 0.0_6_2_8, 0.6_1_8_6], [-0.3_6_2_9, -0.1_7_2_6, 0.4_7_1_6]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,SCREAMING_SNAKE_CASE__ ,atol=1E-4 ) )
@slow
def _UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Optional[Any] = '''the [MASK] of Belgium is Brussels'''
__SCREAMING_SNAKE_CASE :Optional[Any] = AutoTokenizer.from_pretrained('''uw-madison/nystromformer-512''' )
__SCREAMING_SNAKE_CASE :str = NystromformerForMaskedLM.from_pretrained('''uw-madison/nystromformer-512''' )
__SCREAMING_SNAKE_CASE :Union[str, Any] = tokenizer(SCREAMING_SNAKE_CASE__ ,return_tensors='''pt''' )
with torch.no_grad():
__SCREAMING_SNAKE_CASE :Union[str, Any] = model(encoding.input_ids ).logits
__SCREAMING_SNAKE_CASE :List[str] = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE__ ) ,'''capital''' ) | 239 | 1 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
lowerCamelCase__ : int = logging.getLogger(__name__)
@dataclass
class _UpperCAmelCase :
__a : str
__a : List[str]
__a : Optional[List[str]]
@dataclass
class _UpperCAmelCase :
__a : List[int]
__a : List[int]
__a : Optional[List[int]] = None
__a : Optional[List[int]] = None
class _UpperCAmelCase ( _snake_case):
__a : str = """train"""
__a : Any = """dev"""
__a : List[Any] = """test"""
class _UpperCAmelCase :
@staticmethod
def __snake_case ( _A , _A ) -> List[InputExample]:
'''simple docstring'''
raise NotImplementedError
@staticmethod
def __snake_case ( _A ) -> List[str]:
'''simple docstring'''
raise NotImplementedError
@staticmethod
def __snake_case ( _A , _A , _A , _A , _A=False , _A="[CLS]" , _A=1 , _A="[SEP]" , _A=False , _A=False , _A=0 , _A=0 , _A=-1_00 , _A=0 , _A=True , ) -> List[InputFeatures]:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = {label: i for i, label in enumerate(lowercase_ )}
_UpperCAmelCase : Optional[int] = []
for ex_index, example in enumerate(lowercase_ ):
if ex_index % 1_00_00 == 0:
logger.info("""Writing example %d of %d""" , lowercase_ , len(lowercase_ ) )
_UpperCAmelCase : Tuple = []
_UpperCAmelCase : Any = []
for word, label in zip(example.words , example.labels ):
_UpperCAmelCase : Optional[int] = tokenizer.tokenize(lowercase_ )
# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
if len(lowercase_ ) > 0:
tokens.extend(lowercase_ )
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(lowercase_ ) - 1) )
# Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
_UpperCAmelCase : List[str] = tokenizer.num_special_tokens_to_add()
if len(lowercase_ ) > max_seq_length - special_tokens_count:
_UpperCAmelCase : Optional[Any] = tokens[: (max_seq_length - special_tokens_count)]
_UpperCAmelCase : Any = label_ids[: (max_seq_length - special_tokens_count)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambiguously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens += [sep_token]
label_ids += [pad_token_label_id]
if sep_token_extra:
# roberta uses an extra separator b/w pairs of sentences
tokens += [sep_token]
label_ids += [pad_token_label_id]
_UpperCAmelCase : Optional[int] = [sequence_a_segment_id] * len(lowercase_ )
if cls_token_at_end:
tokens += [cls_token]
label_ids += [pad_token_label_id]
segment_ids += [cls_token_segment_id]
else:
_UpperCAmelCase : Tuple = [cls_token] + tokens
_UpperCAmelCase : Any = [pad_token_label_id] + label_ids
_UpperCAmelCase : Union[str, Any] = [cls_token_segment_id] + segment_ids
_UpperCAmelCase : int = tokenizer.convert_tokens_to_ids(lowercase_ )
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
_UpperCAmelCase : int = [1 if mask_padding_with_zero else 0] * len(lowercase_ )
# Zero-pad up to the sequence length.
_UpperCAmelCase : Optional[int] = max_seq_length - len(lowercase_ )
if pad_on_left:
_UpperCAmelCase : str = ([pad_token] * padding_length) + input_ids
_UpperCAmelCase : Union[str, Any] = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
_UpperCAmelCase : Any = ([pad_token_segment_id] * padding_length) + segment_ids
_UpperCAmelCase : Optional[int] = ([pad_token_label_id] * padding_length) + label_ids
else:
input_ids += [pad_token] * padding_length
input_mask += [0 if mask_padding_with_zero else 1] * padding_length
segment_ids += [pad_token_segment_id] * padding_length
label_ids += [pad_token_label_id] * padding_length
assert len(lowercase_ ) == max_seq_length
assert len(lowercase_ ) == max_seq_length
assert len(lowercase_ ) == max_seq_length
assert len(lowercase_ ) == max_seq_length
if ex_index < 5:
logger.info("""*** Example ***""" )
logger.info("""guid: %s""" , example.guid )
logger.info("""tokens: %s""" , """ """.join([str(lowercase_ ) for x in tokens] ) )
logger.info("""input_ids: %s""" , """ """.join([str(lowercase_ ) for x in input_ids] ) )
logger.info("""input_mask: %s""" , """ """.join([str(lowercase_ ) for x in input_mask] ) )
logger.info("""segment_ids: %s""" , """ """.join([str(lowercase_ ) for x in segment_ids] ) )
logger.info("""label_ids: %s""" , """ """.join([str(lowercase_ ) for x in label_ids] ) )
if "token_type_ids" not in tokenizer.model_input_names:
_UpperCAmelCase : List[str] = None
features.append(
InputFeatures(
input_ids=lowercase_ , attention_mask=lowercase_ , token_type_ids=lowercase_ , label_ids=lowercase_ ) )
return features
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
class _UpperCAmelCase ( _snake_case):
__a : List[InputFeatures]
__a : int = nn.CrossEntropyLoss().ignore_index
def __init__( self , _A , _A , _A , _A , _A , _A = None , _A=False , _A = Split.train , ) -> Any:
'''simple docstring'''
_UpperCAmelCase : Any = os.path.join(
lowercase_ , """cached_{}_{}_{}""".format(mode.value , tokenizer.__class__.__name__ , str(lowercase_ ) ) , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
_UpperCAmelCase : str = cached_features_file + '.lock'
with FileLock(lowercase_ ):
if os.path.exists(lowercase_ ) and not overwrite_cache:
logger.info(f'''Loading features from cached file {cached_features_file}''' )
_UpperCAmelCase : Dict = torch.load(lowercase_ )
else:
logger.info(f'''Creating features from dataset file at {data_dir}''' )
_UpperCAmelCase : int = token_classification_task.read_examples_from_file(lowercase_ , lowercase_ )
# TODO clean up all this to leverage built-in features of tokenizers
_UpperCAmelCase : Tuple = token_classification_task.convert_examples_to_features(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , cls_token_at_end=bool(model_type in ["""xlnet"""] ) , cls_token=tokenizer.cls_token , cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 , sep_token=tokenizer.sep_token , sep_token_extra=lowercase_ , pad_on_left=bool(tokenizer.padding_side == """left""" ) , pad_token=tokenizer.pad_token_id , pad_token_segment_id=tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info(f'''Saving features into cached file {cached_features_file}''' )
torch.save(self.features , lowercase_ )
def __len__( self ) -> Optional[Any]:
'''simple docstring'''
return len(self.features )
def __getitem__( self , _A ) -> InputFeatures:
'''simple docstring'''
return self.features[i]
if is_tf_available():
import tensorflow as tf
class _UpperCAmelCase :
__a : List[InputFeatures]
__a : int = -1_0_0
def __init__( self , _A , _A , _A , _A , _A , _A = None , _A=False , _A = Split.train , ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = token_classification_task.read_examples_from_file(lowercase_ , lowercase_ )
# TODO clean up all this to leverage built-in features of tokenizers
_UpperCAmelCase : Optional[Any] = token_classification_task.convert_examples_to_features(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , cls_token_at_end=bool(model_type in ["""xlnet"""] ) , cls_token=tokenizer.cls_token , cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 , sep_token=tokenizer.sep_token , sep_token_extra=lowercase_ , pad_on_left=bool(tokenizer.padding_side == """left""" ) , pad_token=tokenizer.pad_token_id , pad_token_segment_id=tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
def gen():
for ex in self.features:
if ex.token_type_ids is None:
yield (
{"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
ex.label_ids,
)
else:
yield (
{
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label_ids,
)
if "token_type_ids" not in tokenizer.model_input_names:
_UpperCAmelCase : Optional[int] = tf.data.Dataset.from_generator(
lowercase_ , ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa) , (
{"""input_ids""": tf.TensorShape([None] ), """attention_mask""": tf.TensorShape([None] )},
tf.TensorShape([None] ),
) , )
else:
_UpperCAmelCase : Optional[Any] = tf.data.Dataset.from_generator(
lowercase_ , ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa) , (
{
"""input_ids""": tf.TensorShape([None] ),
"""attention_mask""": tf.TensorShape([None] ),
"""token_type_ids""": tf.TensorShape([None] ),
},
tf.TensorShape([None] ),
) , )
def __snake_case ( self ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Tuple = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )
return self.dataset
def __len__( self ) -> List[str]:
'''simple docstring'''
return len(self.features )
def __getitem__( self , _A ) -> InputFeatures:
'''simple docstring'''
return self.features[i]
| 246 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase__ = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = ["YolosFeatureExtractor"]
lowercase__ = ["YolosImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = [
"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
lowercase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 151 | 0 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
snake_case = logging.get_logger(__name__)
snake_case = Dict[str, Any]
snake_case = List[Prediction]
@add_end_docstrings(lowerCAmelCase )
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Tuple , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : int ):
super().__init__(*UpperCAmelCase_ , **UpperCAmelCase_ )
if self.framework == "tf":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
requires_backends(self , "vision" )
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )
def _A ( self : str , **UpperCAmelCase_ : Dict ):
SCREAMING_SNAKE_CASE : Dict = {}
if "threshold" in kwargs:
SCREAMING_SNAKE_CASE : str = kwargs["threshold"]
return {}, {}, postprocess_kwargs
def __call__( self : List[Any] , *UpperCAmelCase_ : Union[str, Any] , **UpperCAmelCase_ : int ):
return super().__call__(*UpperCAmelCase_ , **UpperCAmelCase_ )
def _A ( self : Any , UpperCAmelCase_ : Any ):
SCREAMING_SNAKE_CASE : List[str] = load_image(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : int = torch.IntTensor([[image.height, image.width]] )
SCREAMING_SNAKE_CASE : List[str] = self.image_processor(images=[image] , return_tensors="pt" )
if self.tokenizer is not None:
SCREAMING_SNAKE_CASE : Tuple = self.tokenizer(text=inputs["words"] , boxes=inputs["boxes"] , return_tensors="pt" )
SCREAMING_SNAKE_CASE : Union[str, Any] = target_size
return inputs
def _A ( self : str , UpperCAmelCase_ : Tuple ):
SCREAMING_SNAKE_CASE : str = model_inputs.pop("target_size" )
SCREAMING_SNAKE_CASE : Any = self.model(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = outputs.__class__({"target_size": target_size, **outputs} )
if self.tokenizer is not None:
SCREAMING_SNAKE_CASE : int = model_inputs["bbox"]
return model_outputs
def _A ( self : Union[str, Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : List[str]=0.9 ):
SCREAMING_SNAKE_CASE : Dict = model_outputs["target_size"]
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
SCREAMING_SNAKE_CASE : Any = target_size[0].tolist()
def unnormalize(UpperCAmelCase_ : List[Any] ):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 1000),
(height * bbox[1] / 1000),
(width * bbox[2] / 1000),
(height * bbox[3] / 1000),
] ) )
SCREAMING_SNAKE_CASE : Dict = model_outputs["logits"].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
SCREAMING_SNAKE_CASE : Dict = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
SCREAMING_SNAKE_CASE : List[Any] = [unnormalize(UpperCAmelCase_ ) for bbox in model_outputs["bbox"].squeeze(0 )]
SCREAMING_SNAKE_CASE : List[Any] = ["score", "label", "box"]
SCREAMING_SNAKE_CASE : Union[str, Any] = [dict(zip(UpperCAmelCase_ , UpperCAmelCase_ ) ) for vals in zip(scores.tolist() , UpperCAmelCase_ , UpperCAmelCase_ ) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
SCREAMING_SNAKE_CASE : List[Any] = self.image_processor.post_process_object_detection(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = raw_annotations[0]
SCREAMING_SNAKE_CASE : List[str] = raw_annotation["scores"]
SCREAMING_SNAKE_CASE : Dict = raw_annotation["labels"]
SCREAMING_SNAKE_CASE : Dict = raw_annotation["boxes"]
SCREAMING_SNAKE_CASE : List[Any] = scores.tolist()
SCREAMING_SNAKE_CASE : str = [self.model.config.idalabel[label.item()] for label in labels]
SCREAMING_SNAKE_CASE : Dict = [self._get_bounding_box(UpperCAmelCase_ ) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
SCREAMING_SNAKE_CASE : Any = ["score", "label", "box"]
SCREAMING_SNAKE_CASE : str = [
dict(zip(UpperCAmelCase_ , UpperCAmelCase_ ) )
for vals in zip(raw_annotation["scores"] , raw_annotation["labels"] , raw_annotation["boxes"] )
]
return annotation
def _A ( self : Optional[Any] , UpperCAmelCase_ : "torch.Tensor" ):
if self.framework != "pt":
raise ValueError("The ObjectDetectionPipeline is only available in PyTorch." )
SCREAMING_SNAKE_CASE : int = box.int().tolist()
SCREAMING_SNAKE_CASE : List[Any] = {
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
}
return bbox
| 360 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"""pipelines_utils""",
"""0.22.0""",
"""Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
standard_warn=False,
stacklevel=3,
)
| 319 | 0 |
from math import factorial
snake_case_ = {str(d): factorial(d) for d in range(10)}
def lowerCamelCase__ ( snake_case_ : int ) -> int:
return sum(DIGIT_FACTORIAL[d] for d in str(snake_case_ ) )
def lowerCamelCase__ ( ) -> int:
__snake_case = 7 * factorial(9 ) + 1
return sum(i for i in range(3 , snake_case_ ) if sum_of_digit_factorial(snake_case_ ) == i )
if __name__ == "__main__":
print(F'{solution() = }')
| 24 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class SCREAMING_SNAKE_CASE__ :
@staticmethod
def a (*a__ : List[str] , **a__ : List[str] ):
"""simple docstring"""
pass
def lowerCamelCase__ ( snake_case_ : int ) -> Optional[int]:
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
snake_case_ = (
'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)
@is_pipeline_test
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
A_ : Optional[Any] = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def a (self : List[Any] , a__ : Tuple , a__ : Union[str, Any] , a__ : Any ):
"""simple docstring"""
__snake_case = pipeline(
'''document-question-answering''' , model=a__ , tokenizer=a__ , image_processor=a__ )
__snake_case = INVOICE_URL
__snake_case = list(zip(*apply_tesseract(load_image(a__ ) , a__ , '''''' ) ) )
__snake_case = '''What is the placebo?'''
__snake_case = [
{
'''image''': load_image(a__ ),
'''question''': question,
},
{
'''image''': image,
'''question''': question,
},
{
'''image''': image,
'''question''': question,
'''word_boxes''': word_boxes,
},
]
return dqa_pipeline, examples
def a (self : Union[str, Any] , a__ : Optional[int] , a__ : Dict ):
"""simple docstring"""
__snake_case = dqa_pipeline(a__ , top_k=2 )
self.assertEqual(
a__ , [
[
{'''score''': ANY(a__ ), '''answer''': ANY(a__ ), '''start''': ANY(a__ ), '''end''': ANY(a__ )},
{'''score''': ANY(a__ ), '''answer''': ANY(a__ ), '''start''': ANY(a__ ), '''end''': ANY(a__ )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def a (self : Dict ):
"""simple docstring"""
__snake_case = pipeline('''document-question-answering''' , model='''hf-internal-testing/tiny-random-layoutlmv2''' )
__snake_case = INVOICE_URL
__snake_case = '''How many cats are there?'''
__snake_case = [
{'''score''': 0.0_0_0_1, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
{'''score''': 0.0_0_0_1, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
]
__snake_case = dqa_pipeline(image=a__ , question=a__ , top_k=2 )
self.assertEqual(nested_simplify(a__ , decimals=4 ) , a__ )
__snake_case = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(nested_simplify(a__ , decimals=4 ) , a__ )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
__snake_case = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
__snake_case = dqa_pipeline(image=a__ , question=a__ , top_k=2 )
self.assertEqual(a__ , [] )
# We can optionnally pass directly the words and bounding boxes
__snake_case = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
__snake_case = []
__snake_case = []
__snake_case = dqa_pipeline(image=a__ , question=a__ , words=a__ , boxes=a__ , top_k=2 )
self.assertEqual(a__ , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def a (self : str ):
"""simple docstring"""
__snake_case = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , )
__snake_case = INVOICE_URL
__snake_case = '''What is the invoice number?'''
__snake_case = dqa_pipeline(image=a__ , question=a__ , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{'''score''': 0.9_9_4_4, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_0_0_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__snake_case = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{'''score''': 0.9_9_4_4, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_0_0_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__snake_case = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
[
{'''score''': 0.9_9_4_4, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_0_0_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def a (self : List[Any] ):
"""simple docstring"""
__snake_case = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , max_seq_len=50 , )
__snake_case = INVOICE_URL
__snake_case = '''What is the invoice number?'''
__snake_case = dqa_pipeline(image=a__ , question=a__ , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{'''score''': 0.9_9_7_4, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_9_4_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__snake_case = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{'''score''': 0.9_9_7_4, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_9_4_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__snake_case = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
[
{'''score''': 0.9_9_7_4, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.9_9_4_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def a (self : Tuple ):
"""simple docstring"""
__snake_case = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=a__ )
__snake_case = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=a__ , revision='''3dc6de3''' , )
__snake_case = INVOICE_URL
__snake_case = '''What is the invoice number?'''
__snake_case = dqa_pipeline(image=a__ , question=a__ , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
__snake_case = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
__snake_case = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
[
{'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
]
]
* 2 , )
__snake_case = list(zip(*apply_tesseract(load_image(a__ ) , a__ , '''''' ) ) )
# This model should also work if `image` is set to None
__snake_case = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{'''score''': 0.4_2_5_1, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.0_8_1_9, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def a (self : Dict ):
"""simple docstring"""
__snake_case = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=a__ )
__snake_case = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=a__ , revision='''3dc6de3''' , max_seq_len=50 , )
__snake_case = INVOICE_URL
__snake_case = '''What is the invoice number?'''
__snake_case = dqa_pipeline(image=a__ , question=a__ , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{'''score''': 0.9_9_9_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_9_9_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
__snake_case = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
[
{'''score''': 0.9_9_9_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_9_9_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
__snake_case = list(zip(*apply_tesseract(load_image(a__ ) , a__ , '''''' ) ) )
# This model should also work if `image` is set to None
__snake_case = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{'''score''': 0.9_9_9_9, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.9_9_9_8, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
@slow
@require_torch
def a (self : Tuple ):
"""simple docstring"""
__snake_case = pipeline(
'''document-question-answering''' , model='''naver-clova-ix/donut-base-finetuned-docvqa''' , tokenizer=AutoTokenizer.from_pretrained('''naver-clova-ix/donut-base-finetuned-docvqa''' ) , feature_extractor='''naver-clova-ix/donut-base-finetuned-docvqa''' , )
__snake_case = INVOICE_URL
__snake_case = '''What is the invoice number?'''
__snake_case = dqa_pipeline(image=a__ , question=a__ , top_k=2 )
self.assertEqual(nested_simplify(a__ , decimals=4 ) , [{'''answer''': '''us-001'''}] )
@require_tf
@unittest.skip('''Document question answering not implemented in TF''' )
def a (self : List[str] ):
"""simple docstring"""
pass
| 24 | 1 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def A ( _lowercase , _lowercase = "cpu" , _lowercase = None ):
SCREAMING_SNAKE_CASE : Optional[int] = torch.load(_lowercase , map_location=_lowercase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(_lowercase , torch.Tensor ):
raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin''' )
SCREAMING_SNAKE_CASE : List[Any] = v.half()
if save_path is None: # overwrite src_path
SCREAMING_SNAKE_CASE : str = src_path
torch.save(_lowercase , _lowercase )
if __name__ == "__main__":
fire.Fire(convert)
| 369 | import copy
import random
from transformers import CLIPTokenizer
class lowercase__ ( UpperCamelCase_):
def __init__( self : str , *UpperCamelCase__ : Optional[int] , **UpperCamelCase__ : str ):
'''simple docstring'''
super().__init__(*UpperCamelCase__ , **UpperCamelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = {}
def __A ( self : List[str] , UpperCamelCase__ : Union[str, Any] , *UpperCamelCase__ : int , **UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = super().add_tokens(UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__ )
if num_added_tokens == 0:
raise ValueError(
f"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
''' `placeholder_token` that is not already in the tokenizer.''' )
def __A ( self : Any , UpperCamelCase__ : Union[str, Any] , *UpperCamelCase__ : Dict , UpperCamelCase__ : List[str]=1 , **UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = []
if num_vec_per_token == 1:
self.try_adding_tokens(UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__ )
output.append(UpperCamelCase__ )
else:
SCREAMING_SNAKE_CASE : Optional[int] = []
for i in range(UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : Optional[int] = placeholder_token + f"""_{i}"""
self.try_adding_tokens(UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__ )
output.append(UpperCamelCase__ )
# handle cases where there is a new placeholder token that contains the current placeholder token but is larger
for token in self.token_map:
if token in placeholder_token:
raise ValueError(
f"""The tokenizer already has placeholder token {token} that can get confused with"""
f""" {placeholder_token}keep placeholder tokens independent""" )
SCREAMING_SNAKE_CASE : Optional[int] = output
def __A ( self : Tuple , UpperCamelCase__ : Any , UpperCamelCase__ : Tuple=False , UpperCamelCase__ : List[str]=1.0 ):
'''simple docstring'''
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
SCREAMING_SNAKE_CASE : List[str] = []
for i in range(len(UpperCamelCase__ ) ):
output.append(self.replace_placeholder_tokens_in_text(text[i] , vector_shuffle=UpperCamelCase__ ) )
return output
for placeholder_token in self.token_map:
if placeholder_token in text:
SCREAMING_SNAKE_CASE : Dict = self.token_map[placeholder_token]
SCREAMING_SNAKE_CASE : Tuple = tokens[: 1 + int(len(UpperCamelCase__ ) * prop_tokens_to_load )]
if vector_shuffle:
SCREAMING_SNAKE_CASE : List[Any] = copy.copy(UpperCamelCase__ )
random.shuffle(UpperCamelCase__ )
SCREAMING_SNAKE_CASE : Optional[int] = text.replace(UpperCamelCase__ , ''' '''.join(UpperCamelCase__ ) )
return text
def __call__( self : List[Any] , UpperCamelCase__ : Any , *UpperCamelCase__ : int , UpperCamelCase__ : List[str]=False , UpperCamelCase__ : Optional[Any]=1.0 , **UpperCamelCase__ : Any ):
'''simple docstring'''
return super().__call__(
self.replace_placeholder_tokens_in_text(
UpperCamelCase__ , vector_shuffle=UpperCamelCase__ , prop_tokens_to_load=UpperCamelCase__ ) , *UpperCamelCase__ , **UpperCamelCase__ , )
def __A ( self : Any , UpperCamelCase__ : Optional[int] , *UpperCamelCase__ : List[Any] , UpperCamelCase__ : Any=False , UpperCamelCase__ : Optional[Any]=1.0 , **UpperCamelCase__ : str ):
'''simple docstring'''
return super().encode(
self.replace_placeholder_tokens_in_text(
UpperCamelCase__ , vector_shuffle=UpperCamelCase__ , prop_tokens_to_load=UpperCamelCase__ ) , *UpperCamelCase__ , **UpperCamelCase__ , )
| 258 | 0 |
'''simple docstring'''
import math
def a__ ( lowercase : list, lowercase : int = 0, lowercase : int = 0 ) -> list:
"""simple docstring"""
_UpperCamelCase = end or len(lowercase )
for i in range(lowercase, lowercase ):
_UpperCamelCase = i
_UpperCamelCase = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
_UpperCamelCase = array[temp_index - 1]
temp_index -= 1
_UpperCamelCase = temp_index_value
return array
def a__ ( lowercase : list, lowercase : int, lowercase : int ) -> None: # Max Heap
"""simple docstring"""
_UpperCamelCase = index
_UpperCamelCase = 2 * index + 1 # Left Node
_UpperCamelCase = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
_UpperCamelCase = left_index
if right_index < heap_size and array[largest] < array[right_index]:
_UpperCamelCase = right_index
if largest != index:
_UpperCamelCase , _UpperCamelCase = array[largest], array[index]
heapify(lowercase, lowercase, lowercase )
def a__ ( lowercase : list ) -> list:
"""simple docstring"""
_UpperCamelCase = len(lowercase )
for i in range(n // 2, -1, -1 ):
heapify(lowercase, lowercase, lowercase )
for i in range(n - 1, 0, -1 ):
_UpperCamelCase , _UpperCamelCase = array[0], array[i]
heapify(lowercase, 0, lowercase )
return array
def a__ ( lowercase : list, lowercase : int, lowercase : int, lowercase : int ) -> int:
"""simple docstring"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def a__ ( lowercase : list, lowercase : int, lowercase : int, lowercase : int ) -> int:
"""simple docstring"""
_UpperCamelCase = low
_UpperCamelCase = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
_UpperCamelCase , _UpperCamelCase = array[j], array[i]
i += 1
def a__ ( lowercase : list ) -> list:
"""simple docstring"""
if len(lowercase ) == 0:
return array
_UpperCamelCase = 2 * math.ceil(math.loga(len(lowercase ) ) )
_UpperCamelCase = 16
return intro_sort(lowercase, 0, len(lowercase ), lowercase, lowercase )
def a__ ( lowercase : list, lowercase : int, lowercase : int, lowercase : int, lowercase : int ) -> list:
"""simple docstring"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(lowercase )
max_depth -= 1
_UpperCamelCase = median_of_a(lowercase, lowercase, start + ((end - start) // 2) + 1, end - 1 )
_UpperCamelCase = partition(lowercase, lowercase, lowercase, lowercase )
intro_sort(lowercase, lowercase, lowercase, lowercase, lowercase )
_UpperCamelCase = p
return insertion_sort(lowercase, lowercase, lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase__ : Any = input('Enter numbers separated by a comma : ').strip()
lowercase__ : Any = [float(item) for item in user_input.split(',')]
print(sort(unsorted))
| 324 |
'''simple docstring'''
import itertools
import string
from collections.abc import Generator, Iterable
def a__ ( lowercase : Iterable[str], lowercase : int ) -> Generator[tuple[str, ...], None, None]:
"""simple docstring"""
_UpperCamelCase = iter(lowercase )
while True:
_UpperCamelCase = tuple(itertools.islice(lowercase, lowercase ) )
if not chunk:
return
yield chunk
def a__ ( lowercase : str ) -> str:
"""simple docstring"""
_UpperCamelCase = ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
_UpperCamelCase = ''''''
if len(lowercase ) < 2:
return dirty
for i in range(len(lowercase ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(lowercase ) & 1:
clean += "X"
return clean
def a__ ( lowercase : str ) -> list[str]:
"""simple docstring"""
_UpperCamelCase = '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
_UpperCamelCase = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(lowercase )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(lowercase )
return table
def a__ ( lowercase : str, lowercase : str ) -> str:
"""simple docstring"""
_UpperCamelCase = generate_table(lowercase )
_UpperCamelCase = prepare_input(lowercase )
_UpperCamelCase = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(lowercase, 2 ):
_UpperCamelCase , _UpperCamelCase = divmod(table.index(lowercase ), 5 )
_UpperCamelCase , _UpperCamelCase = divmod(table.index(lowercase ), 5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def a__ ( lowercase : str, lowercase : str ) -> str:
"""simple docstring"""
_UpperCamelCase = generate_table(lowercase )
_UpperCamelCase = ''''''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(lowercase, 2 ):
_UpperCamelCase , _UpperCamelCase = divmod(table.index(lowercase ), 5 )
_UpperCamelCase , _UpperCamelCase = divmod(table.index(lowercase ), 5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 324 | 1 |
import argparse
import json
import subprocess
def __lowerCamelCase ( snake_case__ ,snake_case__ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = (
F'curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\"'
""" https://api.github.com/repos/huggingface/transformers/actions/runners"""
)
_SCREAMING_SNAKE_CASE = subprocess.run(A__ ,shell=A__ ,stdout=subprocess.PIPE )
_SCREAMING_SNAKE_CASE = output.stdout.decode("""utf-8""" )
_SCREAMING_SNAKE_CASE = json.loads(A__ )
_SCREAMING_SNAKE_CASE = status["""runners"""]
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(A__ )
# save the result so we can report them on Slack
with open("""offline_runners.txt""" ,"""w""" ) as fp:
fp.write(json.dumps(A__ ) )
if len(A__ ) > 0:
_SCREAMING_SNAKE_CASE = """\n""".join([x["""name"""] for x in offline_runners] )
raise ValueError(F'The following runners are offline:\n{failed}' )
if __name__ == "__main__":
def __lowerCamelCase ( snake_case__ ) -> int:
"""simple docstring"""
return values.split(""",""" )
UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--target_runners''',
default=None,
type=list_str,
required=True,
help='''Comma-separated list of runners to check status.''',
)
parser.add_argument(
'''--token''', default=None, type=str, required=True, help='''A token that has actions:read permission.'''
)
UpperCamelCase = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 358 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def __lowerCamelCase ( snake_case__ ,snake_case__="shi-labs/oneformer_demo" ) -> Union[str, Any]:
"""simple docstring"""
with open(hf_hub_download(snake_case__ ,snake_case__ ,repo_type="""dataset""" ) ,"""r""" ) as f:
_SCREAMING_SNAKE_CASE = json.load(snake_case__ )
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
for key, info in class_info.items():
_SCREAMING_SNAKE_CASE = info["""name"""]
class_names.append(info["""name"""] )
if info["isthing"]:
thing_ids.append(int(snake_case__ ) )
_SCREAMING_SNAKE_CASE = thing_ids
_SCREAMING_SNAKE_CASE = class_names
return metadata
class __UpperCAmelCase (unittest.TestCase ):
def __init__( self: List[Any] , UpperCAmelCase_: List[Any] , UpperCAmelCase_: Optional[Any]=7 , UpperCAmelCase_: Union[str, Any]=3 , UpperCAmelCase_: Optional[int]=30 , UpperCAmelCase_: List[str]=400 , UpperCAmelCase_: List[str]=None , UpperCAmelCase_: List[Any]=True , UpperCAmelCase_: Tuple=True , UpperCAmelCase_: Union[str, Any]=[0.5, 0.5, 0.5] , UpperCAmelCase_: int=[0.5, 0.5, 0.5] , UpperCAmelCase_: List[str]=10 , UpperCAmelCase_: Optional[int]=False , UpperCAmelCase_: Optional[int]=255 , UpperCAmelCase_: Tuple="shi-labs/oneformer_demo" , UpperCAmelCase_: Union[str, Any]="ade20k_panoptic.json" , UpperCAmelCase_: Union[str, Any]=10 , ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = batch_size
_SCREAMING_SNAKE_CASE = num_channels
_SCREAMING_SNAKE_CASE = min_resolution
_SCREAMING_SNAKE_CASE = max_resolution
_SCREAMING_SNAKE_CASE = do_resize
_SCREAMING_SNAKE_CASE = {"""shortest_edge""": 32, """longest_edge""": 1_333} if size is None else size
_SCREAMING_SNAKE_CASE = do_normalize
_SCREAMING_SNAKE_CASE = image_mean
_SCREAMING_SNAKE_CASE = image_std
_SCREAMING_SNAKE_CASE = class_info_file
_SCREAMING_SNAKE_CASE = prepare_metadata(UpperCAmelCase_ , UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = num_text
_SCREAMING_SNAKE_CASE = repo_path
# for the post_process_functions
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 10
_SCREAMING_SNAKE_CASE = 10
_SCREAMING_SNAKE_CASE = 3
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = num_labels
_SCREAMING_SNAKE_CASE = do_reduce_labels
_SCREAMING_SNAKE_CASE = ignore_index
def UpperCamelCase ( self: Optional[int] ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def UpperCamelCase ( self: int , UpperCAmelCase_: Union[str, Any] , UpperCAmelCase_: List[str]=False ):
'''simple docstring'''
if not batched:
_SCREAMING_SNAKE_CASE = image_inputs[0]
if isinstance(UpperCAmelCase_ , Image.Image ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = image.size
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = image.shape[1], image.shape[2]
if w < h:
_SCREAMING_SNAKE_CASE = int(self.size["""shortest_edge"""] * h / w )
_SCREAMING_SNAKE_CASE = self.size["""shortest_edge"""]
elif w > h:
_SCREAMING_SNAKE_CASE = self.size["""shortest_edge"""]
_SCREAMING_SNAKE_CASE = int(self.size["""shortest_edge"""] * w / h )
else:
_SCREAMING_SNAKE_CASE = self.size["""shortest_edge"""]
_SCREAMING_SNAKE_CASE = self.size["""shortest_edge"""]
else:
_SCREAMING_SNAKE_CASE = []
for image in image_inputs:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_SCREAMING_SNAKE_CASE = max(UpperCAmelCase_ , key=lambda UpperCAmelCase_ : item[0] )[0]
_SCREAMING_SNAKE_CASE = max(UpperCAmelCase_ , key=lambda UpperCAmelCase_ : item[1] )[1]
return expected_height, expected_width
def UpperCamelCase ( self: Any ):
'''simple docstring'''
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , )
@require_torch
@require_vision
class __UpperCAmelCase (_UpperCAmelCase ,unittest.TestCase ):
__snake_case : Union[str, Any] = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
__snake_case : int = image_processing_class
def UpperCamelCase ( self: Optional[int] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = OneFormerImageProcessorTester(self )
@property
def UpperCamelCase ( self: int ):
'''simple docstring'''
return self.image_processing_tester.prepare_image_processor_dict()
def UpperCamelCase ( self: int ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCAmelCase_ , """image_mean""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """image_std""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """do_normalize""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """do_resize""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """size""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """ignore_index""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """class_info_file""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """num_text""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """repo_path""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """metadata""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """do_reduce_labels""" ) )
def UpperCamelCase ( self: Optional[int] ):
'''simple docstring'''
pass
def UpperCamelCase ( self: Optional[Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processing_tester , equal_resolution=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ , Image.Image )
# Test not batched input
_SCREAMING_SNAKE_CASE = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(UpperCAmelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(UpperCAmelCase_ , batched=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = image_processor(
UpperCAmelCase_ , ["""semantic"""] * len(UpperCAmelCase_ ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: int ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processing_tester , equal_resolution=UpperCAmelCase_ , numpify=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ , np.ndarray )
# Test not batched input
_SCREAMING_SNAKE_CASE = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(UpperCAmelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(UpperCAmelCase_ , batched=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = image_processor(
UpperCAmelCase_ , ["""semantic"""] * len(UpperCAmelCase_ ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: Tuple ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processing_tester , equal_resolution=UpperCAmelCase_ , torchify=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ , torch.Tensor )
# Test not batched input
_SCREAMING_SNAKE_CASE = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(UpperCAmelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(UpperCAmelCase_ , batched=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = image_processor(
UpperCAmelCase_ , ["""semantic"""] * len(UpperCAmelCase_ ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: Optional[Any] , UpperCAmelCase_: Tuple=False , UpperCAmelCase_: Any=False , UpperCAmelCase_: str="np" ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
_SCREAMING_SNAKE_CASE = self.image_processing_tester.num_labels
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processing_tester , equal_resolution=UpperCAmelCase_ )
if with_segmentation_maps:
_SCREAMING_SNAKE_CASE = num_labels
if is_instance_map:
_SCREAMING_SNAKE_CASE = list(range(UpperCAmelCase_ ) ) * 2
_SCREAMING_SNAKE_CASE = dict(enumerate(UpperCAmelCase_ ) )
_SCREAMING_SNAKE_CASE = [
np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
_SCREAMING_SNAKE_CASE = [Image.fromarray(UpperCAmelCase_ ) for annotation in annotations]
_SCREAMING_SNAKE_CASE = image_processor(
UpperCAmelCase_ , ["""semantic"""] * len(UpperCAmelCase_ ) , UpperCAmelCase_ , return_tensors="""pt""" , instance_id_to_semantic_id=UpperCAmelCase_ , pad_and_return_pixel_mask=UpperCAmelCase_ , )
return inputs
def UpperCamelCase ( self: Union[str, Any] ):
'''simple docstring'''
pass
def UpperCamelCase ( self: Any ):
'''simple docstring'''
def common(UpperCAmelCase_: List[str]=False , UpperCAmelCase_: Optional[int]=None ):
_SCREAMING_SNAKE_CASE = self.comm_get_image_processor_inputs(
with_segmentation_maps=UpperCAmelCase_ , is_instance_map=UpperCAmelCase_ , segmentation_type=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = inputs["""mask_labels"""]
_SCREAMING_SNAKE_CASE = inputs["""class_labels"""]
_SCREAMING_SNAKE_CASE = inputs["""pixel_values"""]
_SCREAMING_SNAKE_CASE = inputs["""text_inputs"""]
# check the batch_size
for mask_label, class_label, text_input in zip(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
self.assertEqual(mask_label.shape[0] , class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] )
self.assertEqual(len(UpperCAmelCase_ ) , self.image_processing_tester.num_text )
common()
common(is_instance_map=UpperCAmelCase_ )
common(is_instance_map=UpperCAmelCase_ , segmentation_type="""pil""" )
common(is_instance_map=UpperCAmelCase_ , segmentation_type="""pil""" )
def UpperCamelCase ( self: Any ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = np.zeros((20, 50) )
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = binary_mask_to_rle(UpperCAmelCase_ )
self.assertEqual(len(UpperCAmelCase_ ) , 4 )
self.assertEqual(rle[0] , 21 )
self.assertEqual(rle[1] , 45 )
def UpperCamelCase ( self: str ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
_SCREAMING_SNAKE_CASE = self.image_processing_tester.get_fake_oneformer_outputs()
_SCREAMING_SNAKE_CASE = fature_extractor.post_process_semantic_segmentation(UpperCAmelCase_ )
self.assertEqual(len(UpperCAmelCase_ ) , self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape , (
self.image_processing_tester.height,
self.image_processing_tester.width,
) , )
_SCREAMING_SNAKE_CASE = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
_SCREAMING_SNAKE_CASE = fature_extractor.post_process_semantic_segmentation(UpperCAmelCase_ , target_sizes=UpperCAmelCase_ )
self.assertEqual(segmentation[0].shape , target_sizes[0] )
def UpperCamelCase ( self: Union[str, Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
_SCREAMING_SNAKE_CASE = self.image_processing_tester.get_fake_oneformer_outputs()
_SCREAMING_SNAKE_CASE = image_processor.post_process_instance_segmentation(UpperCAmelCase_ , threshold=0 )
self.assertTrue(len(UpperCAmelCase_ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("""segmentation""" in el )
self.assertTrue("""segments_info""" in el )
self.assertEqual(type(el["""segments_info"""] ) , UpperCAmelCase_ )
self.assertEqual(
el["""segmentation"""].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
def UpperCamelCase ( self: List[Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
_SCREAMING_SNAKE_CASE = self.image_processing_tester.get_fake_oneformer_outputs()
_SCREAMING_SNAKE_CASE = image_processor.post_process_panoptic_segmentation(UpperCAmelCase_ , threshold=0 )
self.assertTrue(len(UpperCAmelCase_ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("""segmentation""" in el )
self.assertTrue("""segments_info""" in el )
self.assertEqual(type(el["""segments_info"""] ) , UpperCAmelCase_ )
self.assertEqual(
el["""segmentation"""].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
| 125 | 0 |
'''simple docstring'''
def a__ ( lowercase : Optional[int], lowercase : Optional[Any] ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def a__ ( ) -> 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))
| 324 |
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
UpperCAmelCase__ : Tuple = logging.getLogger(__name__)
class a__ ( UpperCAmelCase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] ="""token-classification"""
def __init__( self : Tuple , UpperCAmelCase__ : Tuple ) ->Optional[Any]:
"""simple docstring"""
if type(UpperCAmelCase__ ) == dict:
SCREAMING_SNAKE_CASE : List[str] = Namespace(**UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : List[str] = import_module("""tasks""" )
try:
SCREAMING_SNAKE_CASE : str = getattr(UpperCAmelCase__ , hparams.task_type )
SCREAMING_SNAKE_CASE : 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__()}" )
SCREAMING_SNAKE_CASE : List[Any] = self.token_classification_task.get_labels(hparams.labels )
SCREAMING_SNAKE_CASE : List[Any] = CrossEntropyLoss().ignore_index
super().__init__(UpperCAmelCase__ , len(self.labels ) , self.mode )
def _lowercase ( self : List[str] , **UpperCAmelCase__ : Optional[int] ) ->Any:
"""simple docstring"""
return self.model(**UpperCAmelCase__ )
def _lowercase ( self : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str ) ->List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
SCREAMING_SNAKE_CASE : List[str] = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
SCREAMING_SNAKE_CASE : Union[str, Any] = self(**UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : int = outputs[0]
# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
return {"loss": loss}
def _lowercase ( self : Optional[Any] ) ->List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = self.hparams
for mode in ["train", "dev", "test"]:
SCREAMING_SNAKE_CASE : Any = self._feature_file(UpperCAmelCase__ )
if os.path.exists(UpperCAmelCase__ ) and not args.overwrite_cache:
logger.info("""Loading features from cached file %s""" , UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Tuple = torch.load(UpperCAmelCase__ )
else:
logger.info("""Creating features from dataset file at %s""" , args.data_dir )
SCREAMING_SNAKE_CASE : Optional[int] = self.token_classification_task.read_examples_from_file(args.data_dir , UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : List[str] = self.token_classification_task.convert_examples_to_features(
UpperCAmelCase__ , 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=UpperCAmelCase__ , 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""" , UpperCAmelCase__ )
torch.save(UpperCAmelCase__ , UpperCAmelCase__ )
def _lowercase ( self : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : bool = False ) ->DataLoader:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = self._feature_file(UpperCAmelCase__ )
logger.info("""Loading features from cached file %s""" , UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : List[Any] = torch.load(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
SCREAMING_SNAKE_CASE : int = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
if features[0].token_type_ids is not None:
SCREAMING_SNAKE_CASE : Any = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
else:
SCREAMING_SNAKE_CASE : Tuple = torch.tensor([0 for f in features] , dtype=torch.long )
# HACK(we will not use this anymore soon)
SCREAMING_SNAKE_CASE : Dict = torch.tensor([f.label_ids for f in features] , dtype=torch.long )
return DataLoader(
TensorDataset(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) , batch_size=UpperCAmelCase__ )
def _lowercase ( self : Tuple , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[Any] ) ->Tuple:
"""simple docstring"""
"""Compute validation""" ""
SCREAMING_SNAKE_CASE : Tuple = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
SCREAMING_SNAKE_CASE : str = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
SCREAMING_SNAKE_CASE : Dict = self(**UpperCAmelCase__ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = outputs[:2]
SCREAMING_SNAKE_CASE : Optional[Any] = logits.detach().cpu().numpy()
SCREAMING_SNAKE_CASE : Tuple = inputs["""labels"""].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _lowercase ( self : int , UpperCAmelCase__ : List[str] ) ->Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = torch.stack([x["""val_loss"""] for x in outputs] ).mean()
SCREAMING_SNAKE_CASE : str = np.concatenate([x["""pred"""] for x in outputs] , axis=0 )
SCREAMING_SNAKE_CASE : Tuple = np.argmax(UpperCAmelCase__ , axis=2 )
SCREAMING_SNAKE_CASE : Optional[Any] = np.concatenate([x["""target"""] for x in outputs] , axis=0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = dict(enumerate(self.labels ) )
SCREAMING_SNAKE_CASE : int = [[] for _ in range(out_label_ids.shape[0] )]
SCREAMING_SNAKE_CASE : Optional[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]] )
SCREAMING_SNAKE_CASE : Tuple = {
"""val_loss""": val_loss_mean,
"""accuracy_score""": accuracy_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"""precision""": precision_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"""recall""": recall_score(UpperCAmelCase__ , UpperCAmelCase__ ),
"""f1""": fa_score(UpperCAmelCase__ , UpperCAmelCase__ ),
}
SCREAMING_SNAKE_CASE : Optional[int] = dict(results.items() )
SCREAMING_SNAKE_CASE : Optional[Any] = results
return ret, preds_list, out_label_list
def _lowercase ( self : Dict , UpperCAmelCase__ : Union[str, Any] ) ->Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = self._eval_end(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE : Optional[int] = ret["""log"""]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _lowercase ( self : List[str] , UpperCAmelCase__ : Union[str, Any] ) ->Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = self._eval_end(UpperCAmelCase__ )
# Converting to the dict required by pl
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
# pytorch_lightning/trainer/logging.py#L139
SCREAMING_SNAKE_CASE : 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 _lowercase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : int ) ->List[Any]:
"""simple docstring"""
BaseTransformer.add_model_specific_args(UpperCAmelCase__ , UpperCAmelCase__ )
parser.add_argument(
"""--task_type""" , default="""NER""" , type=UpperCAmelCase__ , help="""Task type to fine tune in training (e.g. NER, POS, etc)""" )
parser.add_argument(
"""--max_seq_length""" , default=1_2_8 , type=UpperCAmelCase__ , 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=UpperCAmelCase__ , help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.""" , )
parser.add_argument(
"""--gpus""" , default=0 , type=UpperCAmelCase__ , 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__":
UpperCAmelCase__ : str = argparse.ArgumentParser()
add_generic_args(parser, os.getcwd())
UpperCAmelCase__ : Tuple = NERTransformer.add_model_specific_args(parser, os.getcwd())
UpperCAmelCase__ : int = parser.parse_args()
UpperCAmelCase__ : Union[str, Any] = NERTransformer(args)
UpperCAmelCase__ : str = 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
UpperCAmelCase__ : Union[str, Any] = sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
UpperCAmelCase__ : Any = model.load_from_checkpoint(checkpoints[-1])
trainer.test(model)
| 245 | 0 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class lowercase ( yaml.SafeLoader ):
'''simple docstring'''
def UpperCamelCase__ (self , __a ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = [self.constructed_objects[key_node] for key_node, _ in node.value]
UpperCAmelCase__ = [tuple(__a ) if isinstance(__a , __a ) else key for key in keys]
UpperCAmelCase__ = Counter(__a )
UpperCAmelCase__ = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(F"Got duplicate yaml keys: {duplicate_keys}" )
def UpperCamelCase__ (self , __a , __a=False ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = super().construct_mapping(__a , deep=__a )
self._check_no_duplicates_on_constructed_node(__a )
return mapping
def UpperCamelCase_( snake_case__: str ) -> Tuple[Optional[str], str]:
UpperCAmelCase__ = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
UpperCAmelCase__ = full_content[1:].index('---' ) + 1
UpperCAmelCase__ = '\n'.join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(snake_case__ )
class lowercase ( _UpperCamelCase ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = {"""train_eval_index"""} # train-eval-index in the YAML metadata
@classmethod
def UpperCamelCase__ (cls , __a ) -> "DatasetMetadata":
"""simple docstring"""
with open(__a , encoding='utf-8' ) as readme_file:
UpperCAmelCase__ , UpperCAmelCase__ = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(__a )
else:
return cls()
def UpperCamelCase__ (self , __a ) -> List[str]:
"""simple docstring"""
if path.exists():
with open(__a , encoding='utf-8' ) as readme_file:
UpperCAmelCase__ = readme_file.read()
else:
UpperCAmelCase__ = None
UpperCAmelCase__ = self._to_readme(__a )
with open(__a , 'w' , encoding='utf-8' ) as readme_file:
readme_file.write(__a )
def UpperCamelCase__ (self , __a = None ) -> str:
"""simple docstring"""
if readme_content is not None:
UpperCAmelCase__ , UpperCAmelCase__ = _split_yaml_from_readme(__a )
UpperCAmelCase__ = '---\n' + self.to_yaml_string() + '---\n' + content
else:
UpperCAmelCase__ = '---\n' + self.to_yaml_string() + '---\n'
return full_content
@classmethod
def UpperCamelCase__ (cls , __a ) -> "DatasetMetadata":
"""simple docstring"""
UpperCAmelCase__ = yaml.load(__a , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
UpperCAmelCase__ = {
(key.replace('-' , '_' ) if key.replace('-' , '_' ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**__a )
def UpperCamelCase__ (self ) -> str:
"""simple docstring"""
return yaml.safe_dump(
{
(key.replace('_' , '-' ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=__a , allow_unicode=__a , encoding='utf-8' , ).decode('utf-8' )
_UpperCamelCase = {
'''image-classification''': [],
'''translation''': [],
'''image-segmentation''': [],
'''fill-mask''': [],
'''automatic-speech-recognition''': [],
'''token-classification''': [],
'''sentence-similarity''': [],
'''audio-classification''': [],
'''question-answering''': [],
'''summarization''': [],
'''zero-shot-classification''': [],
'''table-to-text''': [],
'''feature-extraction''': [],
'''other''': [],
'''multiple-choice''': [],
'''text-classification''': [],
'''text-to-image''': [],
'''text2text-generation''': [],
'''zero-shot-image-classification''': [],
'''tabular-classification''': [],
'''tabular-regression''': [],
'''image-to-image''': [],
'''tabular-to-text''': [],
'''unconditional-image-generation''': [],
'''text-retrieval''': [],
'''text-to-speech''': [],
'''object-detection''': [],
'''audio-to-audio''': [],
'''text-generation''': [],
'''conversational''': [],
'''table-question-answering''': [],
'''visual-question-answering''': [],
'''image-to-text''': [],
'''reinforcement-learning''': [],
'''voice-activity-detection''': [],
'''time-series-forecasting''': [],
'''document-question-answering''': [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
_UpperCamelCase = ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
ap.add_argument('''readme_filepath''')
_UpperCamelCase = ap.parse_args()
_UpperCamelCase = Path(args.readme_filepath)
_UpperCamelCase = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 335 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING
_UpperCamelCase = logging.get_logger(__name__)
@add_end_docstrings(_UpperCamelCase )
class lowercase ( _UpperCamelCase ):
'''simple docstring'''
def __init__(self , **__a ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**__a )
requires_backends(self , 'vision' )
requires_backends(self , 'torch' )
if self.framework != "pt":
raise ValueError(F"The {self.__class__} is only available in PyTorch." )
self.check_model_type(__a )
def UpperCamelCase__ (self , **__a ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = {}
UpperCAmelCase__ = {}
UpperCAmelCase__ = {}
# preprocess args
if "points_per_batch" in kwargs:
UpperCAmelCase__ = kwargs['points_per_batch']
if "points_per_crop" in kwargs:
UpperCAmelCase__ = kwargs['points_per_crop']
if "crops_n_layers" in kwargs:
UpperCAmelCase__ = kwargs['crops_n_layers']
if "crop_overlap_ratio" in kwargs:
UpperCAmelCase__ = kwargs['crop_overlap_ratio']
if "crop_n_points_downscale_factor" in kwargs:
UpperCAmelCase__ = kwargs['crop_n_points_downscale_factor']
# postprocess args
if "pred_iou_thresh" in kwargs:
UpperCAmelCase__ = kwargs['pred_iou_thresh']
if "stability_score_offset" in kwargs:
UpperCAmelCase__ = kwargs['stability_score_offset']
if "mask_threshold" in kwargs:
UpperCAmelCase__ = kwargs['mask_threshold']
if "stability_score_thresh" in kwargs:
UpperCAmelCase__ = kwargs['stability_score_thresh']
if "crops_nms_thresh" in kwargs:
UpperCAmelCase__ = kwargs['crops_nms_thresh']
if "output_rle_mask" in kwargs:
UpperCAmelCase__ = kwargs['output_rle_mask']
if "output_bboxes_mask" in kwargs:
UpperCAmelCase__ = kwargs['output_bboxes_mask']
return preprocess_kwargs, forward_params, postprocess_kwargs
def __call__(self , __a , *__a , __a=None , __a=None , **__a ) -> List[str]:
"""simple docstring"""
return super().__call__(__a , *__a , num_workers=__a , batch_size=__a , **__a )
def UpperCamelCase__ (self , __a , __a=64 , __a = 0 , __a = 512 / 1500 , __a = 32 , __a = 1 , ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = load_image(__a )
UpperCAmelCase__ = self.image_processor.size['longest_edge']
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = self.image_processor.generate_crop_boxes(
__a , __a , __a , __a , __a , __a )
UpperCAmelCase__ = self.image_processor(images=__a , return_tensors='pt' )
with self.device_placement():
if self.framework == "pt":
UpperCAmelCase__ = self.get_inference_context()
with inference_context():
UpperCAmelCase__ = self._ensure_tensor_on_device(__a , device=self.device )
UpperCAmelCase__ = self.model.get_image_embeddings(model_inputs.pop('pixel_values' ) )
UpperCAmelCase__ = image_embeddings
UpperCAmelCase__ = grid_points.shape[1]
UpperCAmelCase__ = points_per_batch if points_per_batch is not None else n_points
if points_per_batch <= 0:
raise ValueError(
'Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '
'To return all points at once, set points_per_batch to None' )
for i in range(0 , __a , __a ):
UpperCAmelCase__ = grid_points[:, i : i + points_per_batch, :, :]
UpperCAmelCase__ = input_labels[:, i : i + points_per_batch]
UpperCAmelCase__ = i == n_points - points_per_batch
yield {
"input_points": batched_points,
"input_labels": labels,
"input_boxes": crop_boxes,
"is_last": is_last,
**model_inputs,
}
def UpperCamelCase__ (self , __a , __a=0.88 , __a=0.95 , __a=0 , __a=1 , ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = model_inputs.pop('input_boxes' )
UpperCAmelCase__ = model_inputs.pop('is_last' )
UpperCAmelCase__ = model_inputs.pop('original_sizes' ).tolist()
UpperCAmelCase__ = model_inputs.pop('reshaped_input_sizes' ).tolist()
UpperCAmelCase__ = self.model(**__a )
# post processing happens here in order to avoid CPU GPU copies of ALL the masks
UpperCAmelCase__ = model_outputs['pred_masks']
UpperCAmelCase__ = self.image_processor.post_process_masks(
__a , __a , __a , __a , binarize=__a )
UpperCAmelCase__ = model_outputs['iou_scores']
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = self.image_processor.filter_masks(
masks[0] , iou_scores[0] , original_sizes[0] , input_boxes[0] , __a , __a , __a , __a , )
return {
"masks": masks,
"is_last": is_last,
"boxes": boxes,
"iou_scores": iou_scores,
}
def UpperCamelCase__ (self , __a , __a=False , __a=False , __a=0.7 , ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = []
UpperCAmelCase__ = []
UpperCAmelCase__ = []
for model_output in model_outputs:
all_scores.append(model_output.pop('iou_scores' ) )
all_masks.extend(model_output.pop('masks' ) )
all_boxes.append(model_output.pop('boxes' ) )
UpperCAmelCase__ = torch.cat(__a )
UpperCAmelCase__ = torch.cat(__a )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = self.image_processor.post_process_for_mask_generation(
__a , __a , __a , __a )
UpperCAmelCase__ = defaultdict(__a )
for output in model_outputs:
for k, v in output.items():
extra[k].append(__a )
UpperCAmelCase__ = {}
if output_rle_mask:
UpperCAmelCase__ = rle_mask
if output_bboxes_mask:
UpperCAmelCase__ = bounding_boxes
return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
| 335 | 1 |
def lowerCAmelCase_ ( __A ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase__ = 0
UpperCAmelCase__ = len(__A )
for i in range(n - 1 ):
for j in range(i + 1, __A ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def lowerCAmelCase_ ( __A ) -> str:
'''simple docstring'''
if len(__A ) <= 1:
return arr, 0
UpperCAmelCase__ = len(__A ) // 2
UpperCAmelCase__ = arr[0:mid]
UpperCAmelCase__ = arr[mid:]
UpperCAmelCase__ , UpperCAmelCase__ = count_inversions_recursive(__A )
UpperCAmelCase__ , UpperCAmelCase__ = count_inversions_recursive(__A )
UpperCAmelCase__ , UpperCAmelCase__ = _count_cross_inversions(__A, __A )
UpperCAmelCase__ = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def lowerCAmelCase_ ( __A, __A ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase__ = []
UpperCAmelCase__ = UpperCAmelCase__ = UpperCAmelCase__ = 0
while i < len(__A ) and j < len(__A ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(__A ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(__A ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def lowerCAmelCase_ ( ) -> List[str]:
'''simple docstring'''
UpperCAmelCase__ = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
UpperCAmelCase__ = count_inversions_bf(__A )
UpperCAmelCase__ , UpperCAmelCase__ = count_inversions_recursive(__A )
assert num_inversions_bf == num_inversions_recursive == 8
print("number of inversions = ", __A )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
UpperCAmelCase__ = count_inversions_bf(__A )
UpperCAmelCase__ , UpperCAmelCase__ = count_inversions_recursive(__A )
assert num_inversions_bf == num_inversions_recursive == 0
print("number of inversions = ", __A )
# an empty list should also have zero inversions
UpperCAmelCase__ = []
UpperCAmelCase__ = count_inversions_bf(__A )
UpperCAmelCase__ , UpperCAmelCase__ = count_inversions_recursive(__A )
assert num_inversions_bf == num_inversions_recursive == 0
print("number of inversions = ", __A )
if __name__ == "__main__":
main()
| 65 | import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
UpperCamelCase__ = 'base_with_context'
def lowerCAmelCase_ ( __A, __A ) -> int:
'''simple docstring'''
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"] ) )
UpperCAmelCase__ = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ), requires_grad=__A )
for lyr_num, lyr in enumerate(model.encoders ):
UpperCAmelCase__ = weights[f"""layers_{lyr_num}"""]
UpperCAmelCase__ = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"] ) )
UpperCAmelCase__ = ly_weight["attention"]
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"] ) )
return model
def lowerCAmelCase_ ( __A, __A ) -> Tuple:
'''simple docstring'''
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ), requires_grad=__A )
for lyr_num, lyr in enumerate(model.encoders ):
UpperCAmelCase__ = weights[f"""layers_{lyr_num}"""]
UpperCAmelCase__ = ly_weight["attention"]
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(
torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"] ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"] ) )
return model
def lowerCAmelCase_ ( __A, __A ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(
torch.FloatTensor(weights["Embed_0"]["embedding"] ), requires_grad=__A )
UpperCAmelCase__ = nn.Parameter(
torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T ) )
for lyr_num, lyr in enumerate(model.decoders ):
UpperCAmelCase__ = weights[f"""layers_{lyr_num}"""]
UpperCAmelCase__ = nn.Parameter(
torch.FloatTensor(ly_weight["pre_self_attention_layer_norm"]["scale"] ) )
UpperCAmelCase__ = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T ) )
UpperCAmelCase__ = ly_weight["self_attention"]
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
UpperCAmelCase__ = ly_weight["MultiHeadDotProductAttention_0"]
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(
torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"] ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"] ) )
UpperCAmelCase__ = nn.Parameter(
torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"] ) )
UpperCAmelCase__ = nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T ) )
return model
def lowerCAmelCase_ ( __A ) -> int:
'''simple docstring'''
UpperCAmelCase__ = checkpoints.load_tax_checkpoint(args.checkpoint_path )
UpperCAmelCase__ = jnp.tree_util.tree_map(onp.array, __A )
UpperCAmelCase__ = [
"from __gin__ import dynamic_registration",
"from music_spectrogram_diffusion.models.diffusion import diffusion_utils",
"diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0",
"diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()",
]
UpperCAmelCase__ = os.path.join(args.checkpoint_path, "..", "config.gin" )
UpperCAmelCase__ = inference.parse_training_gin_file(__A, __A )
UpperCAmelCase__ = inference.InferenceModel(args.checkpoint_path, __A )
UpperCAmelCase__ = DDPMScheduler(beta_schedule="squaredcos_cap_v2", variance_type="fixed_large" )
UpperCAmelCase__ = SpectrogramNotesEncoder(
max_length=synth_model.sequence_length["inputs"], vocab_size=synth_model.model.module.config.vocab_size, d_model=synth_model.model.module.config.emb_dim, dropout_rate=synth_model.model.module.config.dropout_rate, num_layers=synth_model.model.module.config.num_encoder_layers, num_heads=synth_model.model.module.config.num_heads, d_kv=synth_model.model.module.config.head_dim, d_ff=synth_model.model.module.config.mlp_dim, feed_forward_proj="gated-gelu", )
UpperCAmelCase__ = SpectrogramContEncoder(
input_dims=synth_model.audio_codec.n_dims, targets_context_length=synth_model.sequence_length["targets_context"], d_model=synth_model.model.module.config.emb_dim, dropout_rate=synth_model.model.module.config.dropout_rate, num_layers=synth_model.model.module.config.num_encoder_layers, num_heads=synth_model.model.module.config.num_heads, d_kv=synth_model.model.module.config.head_dim, d_ff=synth_model.model.module.config.mlp_dim, feed_forward_proj="gated-gelu", )
UpperCAmelCase__ = TaFilmDecoder(
input_dims=synth_model.audio_codec.n_dims, targets_length=synth_model.sequence_length["targets_context"], max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time, d_model=synth_model.model.module.config.emb_dim, num_layers=synth_model.model.module.config.num_decoder_layers, num_heads=synth_model.model.module.config.num_heads, d_kv=synth_model.model.module.config.head_dim, d_ff=synth_model.model.module.config.mlp_dim, dropout_rate=synth_model.model.module.config.dropout_rate, )
UpperCAmelCase__ = load_notes_encoder(ta_checkpoint["target"]["token_encoder"], __A )
UpperCAmelCase__ = load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"], __A )
UpperCAmelCase__ = load_decoder(ta_checkpoint["target"]["decoder"], __A )
UpperCAmelCase__ = OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder" )
UpperCAmelCase__ = SpectrogramDiffusionPipeline(
notes_encoder=__A, continuous_encoder=__A, decoder=__A, scheduler=__A, melgan=__A, )
if args.save:
pipe.save_pretrained(args.output_path )
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument('--output_path', default=None, type=str, required=True, help='Path to the converted model.')
parser.add_argument(
'--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
)
parser.add_argument(
'--checkpoint_path',
default=f'''{MODEL}/checkpoint_500000''',
type=str,
required=False,
help='Path to the original jax model checkpoint.',
)
UpperCamelCase__ = parser.parse_args()
main(args)
| 65 | 1 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
snake_case_ = logging.get_logger(__name__)
snake_case_ = OrderedDict(
[
# Base model mapping
('''albert''', '''FlaxAlbertModel'''),
('''bart''', '''FlaxBartModel'''),
('''beit''', '''FlaxBeitModel'''),
('''bert''', '''FlaxBertModel'''),
('''big_bird''', '''FlaxBigBirdModel'''),
('''blenderbot''', '''FlaxBlenderbotModel'''),
('''blenderbot-small''', '''FlaxBlenderbotSmallModel'''),
('''clip''', '''FlaxCLIPModel'''),
('''distilbert''', '''FlaxDistilBertModel'''),
('''electra''', '''FlaxElectraModel'''),
('''gpt-sw3''', '''FlaxGPT2Model'''),
('''gpt2''', '''FlaxGPT2Model'''),
('''gpt_neo''', '''FlaxGPTNeoModel'''),
('''gptj''', '''FlaxGPTJModel'''),
('''longt5''', '''FlaxLongT5Model'''),
('''marian''', '''FlaxMarianModel'''),
('''mbart''', '''FlaxMBartModel'''),
('''mt5''', '''FlaxMT5Model'''),
('''opt''', '''FlaxOPTModel'''),
('''pegasus''', '''FlaxPegasusModel'''),
('''regnet''', '''FlaxRegNetModel'''),
('''resnet''', '''FlaxResNetModel'''),
('''roberta''', '''FlaxRobertaModel'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormModel'''),
('''roformer''', '''FlaxRoFormerModel'''),
('''t5''', '''FlaxT5Model'''),
('''vision-text-dual-encoder''', '''FlaxVisionTextDualEncoderModel'''),
('''vit''', '''FlaxViTModel'''),
('''wav2vec2''', '''FlaxWav2Vec2Model'''),
('''whisper''', '''FlaxWhisperModel'''),
('''xglm''', '''FlaxXGLMModel'''),
('''xlm-roberta''', '''FlaxXLMRobertaModel'''),
]
)
snake_case_ = OrderedDict(
[
# Model for pre-training mapping
('''albert''', '''FlaxAlbertForPreTraining'''),
('''bart''', '''FlaxBartForConditionalGeneration'''),
('''bert''', '''FlaxBertForPreTraining'''),
('''big_bird''', '''FlaxBigBirdForPreTraining'''),
('''electra''', '''FlaxElectraForPreTraining'''),
('''longt5''', '''FlaxLongT5ForConditionalGeneration'''),
('''mbart''', '''FlaxMBartForConditionalGeneration'''),
('''mt5''', '''FlaxMT5ForConditionalGeneration'''),
('''roberta''', '''FlaxRobertaForMaskedLM'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMaskedLM'''),
('''roformer''', '''FlaxRoFormerForMaskedLM'''),
('''t5''', '''FlaxT5ForConditionalGeneration'''),
('''wav2vec2''', '''FlaxWav2Vec2ForPreTraining'''),
('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
('''xlm-roberta''', '''FlaxXLMRobertaForMaskedLM'''),
]
)
snake_case_ = OrderedDict(
[
# Model for Masked LM mapping
('''albert''', '''FlaxAlbertForMaskedLM'''),
('''bart''', '''FlaxBartForConditionalGeneration'''),
('''bert''', '''FlaxBertForMaskedLM'''),
('''big_bird''', '''FlaxBigBirdForMaskedLM'''),
('''distilbert''', '''FlaxDistilBertForMaskedLM'''),
('''electra''', '''FlaxElectraForMaskedLM'''),
('''mbart''', '''FlaxMBartForConditionalGeneration'''),
('''roberta''', '''FlaxRobertaForMaskedLM'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMaskedLM'''),
('''roformer''', '''FlaxRoFormerForMaskedLM'''),
('''xlm-roberta''', '''FlaxXLMRobertaForMaskedLM'''),
]
)
snake_case_ = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
('''bart''', '''FlaxBartForConditionalGeneration'''),
('''blenderbot''', '''FlaxBlenderbotForConditionalGeneration'''),
('''blenderbot-small''', '''FlaxBlenderbotSmallForConditionalGeneration'''),
('''encoder-decoder''', '''FlaxEncoderDecoderModel'''),
('''longt5''', '''FlaxLongT5ForConditionalGeneration'''),
('''marian''', '''FlaxMarianMTModel'''),
('''mbart''', '''FlaxMBartForConditionalGeneration'''),
('''mt5''', '''FlaxMT5ForConditionalGeneration'''),
('''pegasus''', '''FlaxPegasusForConditionalGeneration'''),
('''t5''', '''FlaxT5ForConditionalGeneration'''),
]
)
snake_case_ = OrderedDict(
[
# Model for Image-classsification
('''beit''', '''FlaxBeitForImageClassification'''),
('''regnet''', '''FlaxRegNetForImageClassification'''),
('''resnet''', '''FlaxResNetForImageClassification'''),
('''vit''', '''FlaxViTForImageClassification'''),
]
)
snake_case_ = OrderedDict(
[
('''vision-encoder-decoder''', '''FlaxVisionEncoderDecoderModel'''),
]
)
snake_case_ = OrderedDict(
[
# Model for Causal LM mapping
('''bart''', '''FlaxBartForCausalLM'''),
('''bert''', '''FlaxBertForCausalLM'''),
('''big_bird''', '''FlaxBigBirdForCausalLM'''),
('''electra''', '''FlaxElectraForCausalLM'''),
('''gpt-sw3''', '''FlaxGPT2LMHeadModel'''),
('''gpt2''', '''FlaxGPT2LMHeadModel'''),
('''gpt_neo''', '''FlaxGPTNeoForCausalLM'''),
('''gptj''', '''FlaxGPTJForCausalLM'''),
('''opt''', '''FlaxOPTForCausalLM'''),
('''roberta''', '''FlaxRobertaForCausalLM'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForCausalLM'''),
('''xglm''', '''FlaxXGLMForCausalLM'''),
('''xlm-roberta''', '''FlaxXLMRobertaForCausalLM'''),
]
)
snake_case_ = OrderedDict(
[
# Model for Sequence Classification mapping
('''albert''', '''FlaxAlbertForSequenceClassification'''),
('''bart''', '''FlaxBartForSequenceClassification'''),
('''bert''', '''FlaxBertForSequenceClassification'''),
('''big_bird''', '''FlaxBigBirdForSequenceClassification'''),
('''distilbert''', '''FlaxDistilBertForSequenceClassification'''),
('''electra''', '''FlaxElectraForSequenceClassification'''),
('''mbart''', '''FlaxMBartForSequenceClassification'''),
('''roberta''', '''FlaxRobertaForSequenceClassification'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForSequenceClassification'''),
('''roformer''', '''FlaxRoFormerForSequenceClassification'''),
('''xlm-roberta''', '''FlaxXLMRobertaForSequenceClassification'''),
]
)
snake_case_ = OrderedDict(
[
# Model for Question Answering mapping
('''albert''', '''FlaxAlbertForQuestionAnswering'''),
('''bart''', '''FlaxBartForQuestionAnswering'''),
('''bert''', '''FlaxBertForQuestionAnswering'''),
('''big_bird''', '''FlaxBigBirdForQuestionAnswering'''),
('''distilbert''', '''FlaxDistilBertForQuestionAnswering'''),
('''electra''', '''FlaxElectraForQuestionAnswering'''),
('''mbart''', '''FlaxMBartForQuestionAnswering'''),
('''roberta''', '''FlaxRobertaForQuestionAnswering'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForQuestionAnswering'''),
('''roformer''', '''FlaxRoFormerForQuestionAnswering'''),
('''xlm-roberta''', '''FlaxXLMRobertaForQuestionAnswering'''),
]
)
snake_case_ = OrderedDict(
[
# Model for Token Classification mapping
('''albert''', '''FlaxAlbertForTokenClassification'''),
('''bert''', '''FlaxBertForTokenClassification'''),
('''big_bird''', '''FlaxBigBirdForTokenClassification'''),
('''distilbert''', '''FlaxDistilBertForTokenClassification'''),
('''electra''', '''FlaxElectraForTokenClassification'''),
('''roberta''', '''FlaxRobertaForTokenClassification'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForTokenClassification'''),
('''roformer''', '''FlaxRoFormerForTokenClassification'''),
('''xlm-roberta''', '''FlaxXLMRobertaForTokenClassification'''),
]
)
snake_case_ = OrderedDict(
[
# Model for Multiple Choice mapping
('''albert''', '''FlaxAlbertForMultipleChoice'''),
('''bert''', '''FlaxBertForMultipleChoice'''),
('''big_bird''', '''FlaxBigBirdForMultipleChoice'''),
('''distilbert''', '''FlaxDistilBertForMultipleChoice'''),
('''electra''', '''FlaxElectraForMultipleChoice'''),
('''roberta''', '''FlaxRobertaForMultipleChoice'''),
('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMultipleChoice'''),
('''roformer''', '''FlaxRoFormerForMultipleChoice'''),
('''xlm-roberta''', '''FlaxXLMRobertaForMultipleChoice'''),
]
)
snake_case_ = OrderedDict(
[
('''bert''', '''FlaxBertForNextSentencePrediction'''),
]
)
snake_case_ = OrderedDict(
[
('''speech-encoder-decoder''', '''FlaxSpeechEncoderDecoderModel'''),
('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
]
)
snake_case_ = OrderedDict(
[
('''whisper''', '''FlaxWhisperForAudioClassification'''),
]
)
snake_case_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
snake_case_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
snake_case_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
snake_case_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
snake_case_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
snake_case_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
snake_case_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
snake_case_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
snake_case_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
snake_case_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
snake_case_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
snake_case_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
snake_case_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
snake_case_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass ):
__lowerCamelCase : str = FLAX_MODEL_MAPPING
snake_case_ = auto_class_update(FlaxAutoModel)
class SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass ):
__lowerCamelCase : Dict = FLAX_MODEL_FOR_PRETRAINING_MAPPING
snake_case_ = auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')
class SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass ):
__lowerCamelCase : List[Any] = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
snake_case_ = auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')
class SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass ):
__lowerCamelCase : Optional[int] = FLAX_MODEL_FOR_MASKED_LM_MAPPING
snake_case_ = auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')
class SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass ):
__lowerCamelCase : Optional[int] = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
snake_case_ = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc='''sequence-to-sequence language modeling''', checkpoint_for_example='''t5-base'''
)
class SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass ):
__lowerCamelCase : Dict = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
snake_case_ = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)
class SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass ):
__lowerCamelCase : Tuple = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
snake_case_ = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')
class SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass ):
__lowerCamelCase : Tuple = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
snake_case_ = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)
class SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass ):
__lowerCamelCase : str = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
snake_case_ = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')
class SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass ):
__lowerCamelCase : Union[str, Any] = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
snake_case_ = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)
class SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass ):
__lowerCamelCase : Union[str, Any] = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
snake_case_ = auto_class_update(
FlaxAutoModelForImageClassification, head_doc='''image classification'''
)
class SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass ):
__lowerCamelCase : Tuple = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
snake_case_ = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')
class SCREAMING_SNAKE_CASE__ (_BaseAutoModelClass ):
__lowerCamelCase : List[str] = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
snake_case_ = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)
| 216 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case_ = {'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = ['''EncoderDecoderModel''']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = ['''TFEncoderDecoderModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = ['''FlaxEncoderDecoderModel''']
if TYPE_CHECKING:
from .configuration_encoder_decoder import EncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encoder_decoder import EncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_encoder_decoder import TFEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel
else:
import sys
snake_case_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 216 | 1 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
lowerCamelCase :int = 1_6
lowerCamelCase :Optional[int] = 3_2
def a ( lowerCamelCase__ , lowerCamelCase__ = 16 , lowerCamelCase__ = "bert-base-cased" ):
'''simple docstring'''
A_ : Optional[int] = AutoTokenizer.from_pretrained(lowerCamelCase__ )
A_ : List[str] = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowerCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
A_ : Dict = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase__ , max_length=lowerCamelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
A_ : Optional[Any] = datasets.map(
lowerCamelCase__ , batched=lowerCamelCase__ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=lowerCamelCase__ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
A_ : Optional[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowerCamelCase__ , padding="""max_length""" , max_length=1_28 , return_tensors="""pt""" )
return tokenizer.pad(lowerCamelCase__ , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
A_ : Tuple = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=lowerCamelCase__ )
A_ : Optional[Any] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCamelCase__ , collate_fn=lowerCamelCase__ , batch_size=lowerCamelCase__ )
return train_dataloader, eval_dataloader
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
model.eval()
A_ : Optional[int] = 0
for step, batch in enumerate(lowerCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
A_ : Union[str, Any] = model(**lowerCamelCase__ )
A_ : str = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
A_, A_ : Dict = accelerator.gather(
(predictions, batch["""labels"""]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(lowerCamelCase__ ) - 1:
A_ : Tuple = predictions[: len(eval_dataloader.dataset ) - samples_seen]
A_ : List[Any] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=lowerCamelCase__ , references=lowerCamelCase__ , )
A_ : str = metric.compute()
return eval_metric["accuracy"]
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : Dict = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
A_ : str = config["""lr"""]
A_ : Optional[int] = int(config["""num_epochs"""] )
A_ : Dict = int(config["""seed"""] )
A_ : Union[str, Any] = int(config["""batch_size"""] )
A_ : Dict = args.model_name_or_path
set_seed(lowerCamelCase__ )
A_, A_ : Optional[Any] = get_dataloaders(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
A_ : Optional[int] = AutoModelForSequenceClassification.from_pretrained(lowerCamelCase__ , return_dict=lowerCamelCase__ )
# Instantiate optimizer
A_ : List[str] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
A_ : List[Any] = optimizer_cls(params=model.parameters() , lr=lowerCamelCase__ )
if accelerator.state.deepspeed_plugin is not None:
A_ : List[str] = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
A_ : List[str] = 1
A_ : Tuple = (len(lowerCamelCase__ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
A_ : Optional[int] = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase__ , num_warmup_steps=0 , num_training_steps=lowerCamelCase__ , )
else:
A_ : List[Any] = DummyScheduler(lowerCamelCase__ , total_num_steps=lowerCamelCase__ , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
A_, A_, A_, A_, A_ : Union[str, Any] = accelerator.prepare(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# We need to keep track of how many total steps we have iterated over
A_ : Dict = 0
# We also need to keep track of the stating epoch so files are named properly
A_ : Union[str, Any] = 0
A_ : List[Any] = evaluate.load("""glue""" , """mrpc""" )
A_ : int = num_epochs
if args.partial_train_epoch is not None:
A_ : Optional[int] = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
A_ : int = args.resume_from_checkpoint.split("""epoch_""" )[1]
A_ : int = """"""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
A_ : List[Any] = int(lowerCamelCase__ ) + 1
A_ : int = evaluation_loop(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
accelerator.print("""resumed checkpoint performance:""" , lowerCamelCase__ )
accelerator.print("""resumed checkpoint's scheduler's lr:""" , lr_scheduler.get_lr()[0] )
accelerator.print("""resumed optimizers's lr:""" , optimizer.param_groups[0]["""lr"""] )
with open(os.path.join(args.output_dir , f'state_{starting_epoch-1}.json' ) , """r""" ) as f:
A_ : str = json.load(lowerCamelCase__ )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
A_ : Optional[Any] = {}
for epoch in range(lowerCamelCase__ , lowerCamelCase__ ):
model.train()
for step, batch in enumerate(lowerCamelCase__ ):
A_ : str = model(**lowerCamelCase__ )
A_ : str = outputs.loss
A_ : int = loss / gradient_accumulation_steps
accelerator.backward(lowerCamelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
A_ : str = f'epoch_{epoch}'
A_ : Any = os.path.join(args.output_dir , lowerCamelCase__ )
accelerator.save_state(lowerCamelCase__ )
A_ : List[str] = evaluation_loop(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
A_ : Union[str, Any] = accuracy
A_ : Optional[Any] = lr_scheduler.get_lr()[0]
A_ : Tuple = optimizer.param_groups[0]["""lr"""]
A_ : Optional[int] = epoch
A_ : Union[str, Any] = overall_step
accelerator.print(f'epoch {epoch}:' , lowerCamelCase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , f'state_{epoch}.json' ) , """w""" ) as f:
json.dump(lowerCamelCase__ , lowerCamelCase__ )
def a ( ):
'''simple docstring'''
A_ : Union[str, Any] = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=lowerCamelCase__ , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=lowerCamelCase__ , )
parser.add_argument(
"""--output_dir""" , type=lowerCamelCase__ , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--resume_from_checkpoint""" , type=lowerCamelCase__ , default=lowerCamelCase__ , help="""If the training should continue from a checkpoint folder.""" , )
parser.add_argument(
"""--partial_train_epoch""" , type=lowerCamelCase__ , default=lowerCamelCase__ , help="""If passed, the training will stop after this number of epochs.""" , )
parser.add_argument(
"""--num_epochs""" , type=lowerCamelCase__ , default=2 , help="""Number of train epochs.""" , )
A_ : Any = parser.parse_args()
A_ : Any = {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(lowerCamelCase__ , lowerCamelCase__ )
if __name__ == "__main__":
main() | 206 |
'''simple docstring'''
import torch
from diffusers import StableDiffusionPipeline
lowerCamelCase :Tuple = '''path-to-your-trained-model'''
lowerCamelCase :Optional[int] = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
lowerCamelCase :Optional[int] = '''A photo of sks dog in a bucket'''
lowerCamelCase :List[Any] = pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]
image.save('''dog-bucket.png''') | 206 | 1 |
"""simple docstring"""
_a = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
_a = [{'type': 'code', 'content': INSTALL_CONTENT}]
_a = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 144 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_a = logging.get_logger(__name__)
_a = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
_a = [
'small',
'small-base',
'medium',
'medium-base',
'intermediate',
'intermediate-base',
'large',
'large-base',
'xlarge',
'xlarge-base',
]
_a = {
'vocab_file': {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt',
'funnel-transformer/small-base': 'https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt',
'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt',
'funnel-transformer/medium-base': (
'https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'
),
'funnel-transformer/intermediate': (
'https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'
),
'funnel-transformer/intermediate-base': (
'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'
),
'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt',
'funnel-transformer/large-base': 'https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt',
'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt',
'funnel-transformer/xlarge-base': (
'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json',
'funnel-transformer/small-base': (
'https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'
),
'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json',
'funnel-transformer/medium-base': (
'https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'
),
'funnel-transformer/intermediate': (
'https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'
),
'funnel-transformer/intermediate-base': (
'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'
),
'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json',
'funnel-transformer/large-base': (
'https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'
),
'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json',
'funnel-transformer/xlarge-base': (
'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'
),
},
}
_a = {F"funnel-transformer/{name}": 5_12 for name in _model_names}
_a = {F"funnel-transformer/{name}": {'do_lower_case': True} for name in _model_names}
class _lowerCAmelCase ( lowercase ):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = VOCAB_FILES_NAMES
__UpperCAmelCase : Tuple = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : Union[str, Any] = PRETRAINED_INIT_CONFIGURATION
__UpperCAmelCase : Optional[Any] = FunnelTokenizer
__UpperCAmelCase : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase : int = 2
def __init__( self : Dict, UpperCAmelCase__ : Dict=None, UpperCAmelCase__ : Optional[int]=None, UpperCAmelCase__ : Dict=True, UpperCAmelCase__ : List[str]="<unk>", UpperCAmelCase__ : Optional[Any]="<sep>", UpperCAmelCase__ : Optional[Any]="<pad>", UpperCAmelCase__ : Union[str, Any]="<cls>", UpperCAmelCase__ : str="<mask>", UpperCAmelCase__ : Optional[Any]="<s>", UpperCAmelCase__ : Tuple="</s>", UpperCAmelCase__ : Union[str, Any]=True, UpperCAmelCase__ : Optional[int]=True, UpperCAmelCase__ : Optional[Any]=None, UpperCAmelCase__ : Dict="##", **UpperCAmelCase__ : List[str], ):
super().__init__(
UpperCAmelCase__, tokenizer_file=UpperCAmelCase__, do_lower_case=UpperCAmelCase__, unk_token=UpperCAmelCase__, sep_token=UpperCAmelCase__, pad_token=UpperCAmelCase__, cls_token=UpperCAmelCase__, mask_token=UpperCAmelCase__, bos_token=UpperCAmelCase__, eos_token=UpperCAmelCase__, clean_text=UpperCAmelCase__, tokenize_chinese_chars=UpperCAmelCase__, strip_accents=UpperCAmelCase__, wordpieces_prefix=UpperCAmelCase__, **UpperCAmelCase__, )
__lowercase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase", UpperCAmelCase__ ) != do_lower_case
or normalizer_state.get("strip_accents", UpperCAmelCase__ ) != strip_accents
or normalizer_state.get("handle_chinese_chars", UpperCAmelCase__ ) != tokenize_chinese_chars
):
__lowercase = getattr(UpperCAmelCase__, normalizer_state.pop("type" ) )
__lowercase = do_lower_case
__lowercase = strip_accents
__lowercase = tokenize_chinese_chars
__lowercase = normalizer_class(**UpperCAmelCase__ )
__lowercase = do_lower_case
def _lowercase ( self : Tuple, UpperCAmelCase__ : Any, UpperCAmelCase__ : Any=None ):
__lowercase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _lowercase ( self : str, UpperCAmelCase__ : List[int], UpperCAmelCase__ : Optional[List[int]] = None ):
__lowercase = [self.sep_token_id]
__lowercase = [self.cls_token_id]
if token_ids_a is None:
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _lowercase ( self : Optional[Any], UpperCAmelCase__ : str, UpperCAmelCase__ : Optional[str] = None ):
__lowercase = self._tokenizer.model.save(UpperCAmelCase__, name=UpperCAmelCase__ )
return tuple(UpperCAmelCase__ )
| 144 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DetaImageProcessor
class lowercase ( unittest.TestCase ):
def __init__( self , A_ , A_=7 , A_=3 , A_=30 , A_=400 , A_=True , A_=None , A_=True , A_=[0.5, 0.5, 0.5] , A_=[0.5, 0.5, 0.5] , A_=True , A_=1 / 255 , A_=True , ) -> int:
"""simple docstring"""
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
UpperCamelCase = size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = num_channels
UpperCamelCase = min_resolution
UpperCamelCase = max_resolution
UpperCamelCase = do_resize
UpperCamelCase = size
UpperCamelCase = do_normalize
UpperCamelCase = image_mean
UpperCamelCase = image_std
UpperCamelCase = do_rescale
UpperCamelCase = rescale_factor
UpperCamelCase = do_pad
def __UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def __UpperCamelCase ( self , A_ , A_=False ) -> List[str]:
"""simple docstring"""
if not batched:
UpperCamelCase = image_inputs[0]
if isinstance(A_ , Image.Image ):
UpperCamelCase , UpperCamelCase = image.size
else:
UpperCamelCase , UpperCamelCase = image.shape[1], image.shape[2]
if w < h:
UpperCamelCase = int(self.size['shortest_edge'] * h / w )
UpperCamelCase = self.size['shortest_edge']
elif w > h:
UpperCamelCase = self.size['shortest_edge']
UpperCamelCase = int(self.size['shortest_edge'] * w / h )
else:
UpperCamelCase = self.size['shortest_edge']
UpperCamelCase = self.size['shortest_edge']
else:
UpperCamelCase = []
for image in image_inputs:
UpperCamelCase , UpperCamelCase = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCamelCase = max(A_ , key=lambda A_ : item[0] )[0]
UpperCamelCase = max(A_ , key=lambda A_ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowercase ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
__lowercase : Union[str, Any] = DetaImageProcessor if is_vision_available() else None
def __UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = DetaImageProcessingTester(self )
@property
def __UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A_ , 'image_mean' ) )
self.assertTrue(hasattr(A_ , 'image_std' ) )
self.assertTrue(hasattr(A_ , 'do_normalize' ) )
self.assertTrue(hasattr(A_ , 'do_resize' ) )
self.assertTrue(hasattr(A_ , 'do_rescale' ) )
self.assertTrue(hasattr(A_ , 'do_pad' ) )
self.assertTrue(hasattr(A_ , 'size' ) )
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 18, 'longest_edge': 1_333} )
self.assertEqual(image_processor.do_pad , A_ )
def __UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
pass
def __UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
# Initialize image_processing
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=A_ )
for image in image_inputs:
self.assertIsInstance(A_ , Image.Image )
# Test not batched input
UpperCamelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
UpperCamelCase , UpperCamelCase = self.image_processor_tester.get_expected_values(A_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase , UpperCamelCase = self.image_processor_tester.get_expected_values(A_ , batched=A_ )
UpperCamelCase = image_processing(A_ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __UpperCamelCase ( self ) -> int:
"""simple docstring"""
# Initialize image_processing
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=A_ , numpify=A_ )
for image in image_inputs:
self.assertIsInstance(A_ , np.ndarray )
# Test not batched input
UpperCamelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
UpperCamelCase , UpperCamelCase = self.image_processor_tester.get_expected_values(A_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase = image_processing(A_ , return_tensors='pt' ).pixel_values
UpperCamelCase , UpperCamelCase = self.image_processor_tester.get_expected_values(A_ , batched=A_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
# Initialize image_processing
UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=A_ , torchify=A_ )
for image in image_inputs:
self.assertIsInstance(A_ , torch.Tensor )
# Test not batched input
UpperCamelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
UpperCamelCase , UpperCamelCase = self.image_processor_tester.get_expected_values(A_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase = image_processing(A_ , return_tensors='pt' ).pixel_values
UpperCamelCase , UpperCamelCase = self.image_processor_tester.get_expected_values(A_ , batched=A_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def __UpperCamelCase ( self ) -> int:
"""simple docstring"""
# prepare image and target
UpperCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r' ) as f:
UpperCamelCase = json.loads(f.read() )
UpperCamelCase = {'image_id': 39_769, 'annotations': target}
# encode them
UpperCamelCase = DetaImageProcessor()
UpperCamelCase = image_processing(images=A_ , annotations=A_ , return_tensors='pt' )
# verify pixel values
UpperCamelCase = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding['pixel_values'].shape , A_ )
UpperCamelCase = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , A_ , atol=1e-4 ) )
# verify area
UpperCamelCase = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , A_ ) )
# verify boxes
UpperCamelCase = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , A_ )
UpperCamelCase = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , A_ , atol=1e-3 ) )
# verify image_id
UpperCamelCase = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , A_ ) )
# verify is_crowd
UpperCamelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , A_ ) )
# verify class_labels
UpperCamelCase = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , A_ ) )
# verify orig_size
UpperCamelCase = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , A_ ) )
# verify size
UpperCamelCase = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , A_ ) )
@slow
def __UpperCamelCase ( self ) -> Optional[Any]:
"""simple docstring"""
# prepare image, target and masks_path
UpperCamelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r' ) as f:
UpperCamelCase = json.loads(f.read() )
UpperCamelCase = {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}
UpperCamelCase = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
UpperCamelCase = DetaImageProcessor(format='coco_panoptic' )
UpperCamelCase = image_processing(images=A_ , annotations=A_ , masks_path=A_ , return_tensors='pt' )
# verify pixel values
UpperCamelCase = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding['pixel_values'].shape , A_ )
UpperCamelCase = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , A_ , atol=1e-4 ) )
# verify area
UpperCamelCase = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , A_ ) )
# verify boxes
UpperCamelCase = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , A_ )
UpperCamelCase = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , A_ , atol=1e-3 ) )
# verify image_id
UpperCamelCase = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , A_ ) )
# verify is_crowd
UpperCamelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , A_ ) )
# verify class_labels
UpperCamelCase = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , A_ ) )
# verify masks
UpperCamelCase = 822_873
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , A_ )
# verify orig_size
UpperCamelCase = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , A_ ) )
# verify size
UpperCamelCase = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , A_ ) )
| 222 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def A ( lowercase , lowercase , lowercase ) -> str:
'''simple docstring'''
UpperCamelCase = 0
if start < end:
UpperCamelCase = randint(lowercase , lowercase )
UpperCamelCase = a[end]
UpperCamelCase = a[pivot]
UpperCamelCase = temp
UpperCamelCase , UpperCamelCase = _in_place_partition(lowercase , lowercase , lowercase )
count += _in_place_quick_sort(lowercase , lowercase , p - 1 )
count += _in_place_quick_sort(lowercase , p + 1 , lowercase )
return count
def A ( lowercase , lowercase , lowercase ) -> int:
'''simple docstring'''
UpperCamelCase = 0
UpperCamelCase = randint(lowercase , lowercase )
UpperCamelCase = a[end]
UpperCamelCase = a[pivot]
UpperCamelCase = temp
UpperCamelCase = start - 1
for index in range(lowercase , lowercase ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
UpperCamelCase = new_pivot_index + 1
UpperCamelCase = a[new_pivot_index]
UpperCamelCase = a[index]
UpperCamelCase = temp
UpperCamelCase = a[new_pivot_index + 1]
UpperCamelCase = a[end]
UpperCamelCase = temp
return new_pivot_index + 1, count
_UpperCAmelCase : Union[str, Any] = TemporaryFile()
_UpperCAmelCase : List[Any] = 100 # 1000 elements are to be sorted
_UpperCAmelCase ,_UpperCAmelCase : Any = 0, 1 # mean and standard deviation
_UpperCAmelCase : Any = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)
outfile.seek(0) # using the same array
_UpperCAmelCase : Any = np.load(outfile)
_UpperCAmelCase : str = len(M) - 1
_UpperCAmelCase : List[str] = _in_place_quick_sort(M, 0, r)
print(
"No of Comparisons for 100 elements selected from a standard normal distribution"
"is :"
)
print(z)
| 222 | 1 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
UpperCamelCase__ =logging.getLogger(__name__)
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=None ) -> Optional[Any]:
super().__init__(
__lowerCamelCase , question_encoder_tokenizer=__lowerCamelCase , generator_tokenizer=__lowerCamelCase , index=__lowerCamelCase , init_retrieval=__lowerCamelCase , )
_SCREAMING_SNAKE_CASE : List[Any] = None
def UpperCamelCase_ ( self , __lowerCamelCase ) -> Any:
logger.info("initializing retrieval" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("dist initialized" )
# needs to be set manually
_SCREAMING_SNAKE_CASE : List[str] = self._infer_socket_ifname()
# avoid clash with the NCCL port
_SCREAMING_SNAKE_CASE : List[Any] = str(distributed_port + 1 )
_SCREAMING_SNAKE_CASE : int = dist.new_group(ranks=__lowerCamelCase , backend="gloo" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("dist not initialized / main" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def UpperCamelCase_ ( self ) -> Optional[Any]:
return dist.get_rank(group=self.process_group ) == 0
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=torch.floataa ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE : Optional[int] = torch.empty(__lowerCamelCase , dtype=__lowerCamelCase )
dist.scatter(__lowerCamelCase , src=0 , scatter_list=__lowerCamelCase , group=self.process_group )
return target_tensor
def UpperCamelCase_ ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE : int = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
_SCREAMING_SNAKE_CASE : Any = next((addr for addr in addrs if addr.startswith("e" )) , __lowerCamelCase )
return ifname
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase ) -> Tuple[np.ndarray, List[dict]]:
# single GPU training
if not dist.is_initialized():
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Dict = self._main_retrieve(__lowerCamelCase , __lowerCamelCase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCamelCase )
# distributed training
_SCREAMING_SNAKE_CASE : Union[str, Any] = dist.get_world_size(group=self.process_group )
# gather logic
_SCREAMING_SNAKE_CASE : Any = None
if self._is_main():
_SCREAMING_SNAKE_CASE : Optional[Any] = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(__lowerCamelCase )]
dist.gather(torch.tensor(__lowerCamelCase ) , dst=0 , gather_list=__lowerCamelCase , group=self.process_group )
# scatter logic
_SCREAMING_SNAKE_CASE : Optional[int] = question_hidden_states.shape[0]
_SCREAMING_SNAKE_CASE : Optional[Any] = []
_SCREAMING_SNAKE_CASE : Optional[int] = []
if self._is_main():
assert len(__lowerCamelCase ) == world_size
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : int = self._main_retrieve(torch.cat(__lowerCamelCase ).numpy() , __lowerCamelCase )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[str] = torch.tensor(__lowerCamelCase ), torch.tensor(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[str] = self._chunk_tensor(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE : Tuple = self._chunk_tensor(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE : Dict = self._scattered(__lowerCamelCase , [n_queries, n_docs] , target_type=torch.intaa )
_SCREAMING_SNAKE_CASE : Optional[Any] = self._scattered(__lowerCamelCase , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(__lowerCamelCase ) | 325 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ =logging.get_logger(__name__)
UpperCamelCase__ ={
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
__snake_case = 'timesformer'
def __init__( self , __lowerCamelCase=2_2_4 , __lowerCamelCase=1_6 , __lowerCamelCase=3 , __lowerCamelCase=8 , __lowerCamelCase=7_6_8 , __lowerCamelCase=1_2 , __lowerCamelCase=1_2 , __lowerCamelCase=3_0_7_2 , __lowerCamelCase="gelu" , __lowerCamelCase=0.0 , __lowerCamelCase=0.0 , __lowerCamelCase=0.02 , __lowerCamelCase=1E-6 , __lowerCamelCase=True , __lowerCamelCase="divided_space_time" , __lowerCamelCase=0 , **__lowerCamelCase , ) -> List[str]:
super().__init__(**__lowerCamelCase )
_SCREAMING_SNAKE_CASE : int = image_size
_SCREAMING_SNAKE_CASE : str = patch_size
_SCREAMING_SNAKE_CASE : str = num_channels
_SCREAMING_SNAKE_CASE : str = num_frames
_SCREAMING_SNAKE_CASE : Dict = hidden_size
_SCREAMING_SNAKE_CASE : Any = num_hidden_layers
_SCREAMING_SNAKE_CASE : List[Any] = num_attention_heads
_SCREAMING_SNAKE_CASE : Optional[Any] = intermediate_size
_SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
_SCREAMING_SNAKE_CASE : int = hidden_dropout_prob
_SCREAMING_SNAKE_CASE : Optional[Any] = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE : int = initializer_range
_SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
_SCREAMING_SNAKE_CASE : List[str] = qkv_bias
_SCREAMING_SNAKE_CASE : Tuple = attention_type
_SCREAMING_SNAKE_CASE : Union[str, Any] = drop_path_rate | 325 | 1 |
"""simple docstring"""
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp_seed,
temporary_assignment,
zip_dict,
)
from .utils import require_tf, require_torch
def lowerCamelCase__ ( _lowerCamelCase : Union[str, Any] ) -> Tuple: # picklable for multiprocessing
return x.sum()
def lowerCamelCase__ ( _lowerCamelCase : Union[str, Any] ) -> int: # picklable for multiprocessing
return i + 1
@dataclass
class a :
SCREAMING_SNAKE_CASE : int
SCREAMING_SNAKE_CASE : str
class a ( __snake_case ):
def UpperCamelCase ( self : Dict ) -> Optional[int]:
lowerCamelCase_ = {}
lowerCamelCase_ = []
lowerCamelCase_ = 1
lowerCamelCase_ = [1, 2]
lowerCamelCase_ = {'a': 1, 'b': 2}
lowerCamelCase_ = {'a': [1, 2], 'b': [3, 4]}
lowerCamelCase_ = {'a': {'1': 1}, 'b': 2}
lowerCamelCase_ = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
lowerCamelCase_ = {}
lowerCamelCase_ = []
lowerCamelCase_ = 2
lowerCamelCase_ = [2, 3]
lowerCamelCase_ = {'a': 2, 'b': 3}
lowerCamelCase_ = {'a': [2, 3], 'b': [4, 5]}
lowerCamelCase_ = {'a': {'1': 2}, 'b': 3}
lowerCamelCase_ = {'a': 2, 'b': 3, 'c': 4, 'd': 5}
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
lowerCamelCase_ = 2
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , num_proc=__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , num_proc=__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , num_proc=__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , num_proc=__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , num_proc=__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , num_proc=__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , num_proc=__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , num_proc=__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
lowerCamelCase_ = {'a': np.eye(2 ), 'b': np.zeros(3 ), 'c': np.ones(2 )}
lowerCamelCase_ = {'a': 2, 'b': 0, 'c': 2}
lowerCamelCase_ = {
'a': np.eye(2 ).astype(__SCREAMING_SNAKE_CASE ),
'b': np.zeros(3 ).astype(__SCREAMING_SNAKE_CASE ),
'c': np.ones(2 ).astype(__SCREAMING_SNAKE_CASE ),
}
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , map_numpy=__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , map_numpy=__SCREAMING_SNAKE_CASE ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
self.assertEqual(map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , map_numpy=__SCREAMING_SNAKE_CASE , num_proc=__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , map_numpy=__SCREAMING_SNAKE_CASE , num_proc=__SCREAMING_SNAKE_CASE ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
with self.assertRaises(__SCREAMING_SNAKE_CASE ): # can't pickle a local lambda
map_nested(lambda __SCREAMING_SNAKE_CASE : x + 1 , __SCREAMING_SNAKE_CASE , num_proc=__SCREAMING_SNAKE_CASE )
def UpperCamelCase ( self : Dict ) -> List[Any]:
lowerCamelCase_ = {'a': 1, 'b': 2}
lowerCamelCase_ = {'a': 3, 'b': 4}
lowerCamelCase_ = {'a': 5, 'b': 6}
lowerCamelCase_ = sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))] )
self.assertEqual(sorted(zip_dict(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ) , __SCREAMING_SNAKE_CASE )
def UpperCamelCase ( self : str ) -> List[Any]:
class a :
SCREAMING_SNAKE_CASE : Dict = """bar"""
lowerCamelCase_ = Foo()
self.assertEqual(foo.my_attr , 'bar' )
with temporary_assignment(__SCREAMING_SNAKE_CASE , 'my_attr' , 'BAR' ):
self.assertEqual(foo.my_attr , 'BAR' )
self.assertEqual(foo.my_attr , 'bar' )
@pytest.mark.parametrize(
'iterable_length, num_proc, expected_num_proc' , [
(1, None, 1),
(1, 1, 1),
(2, None, 1),
(2, 1, 1),
(2, 2, 1),
(2, 3, 1),
(3, 2, 1),
(16, 16, 16),
(16, 17, 16),
(17, 16, 16),
] , )
def lowerCamelCase__ ( _lowerCamelCase : List[str] , _lowerCamelCase : Any , _lowerCamelCase : Optional[int] ) -> Tuple:
with patch('datasets.utils.py_utils._single_map_nested' ) as mock_single_map_nested, patch(
'datasets.parallel.parallel.Pool' ) as mock_multiprocessing_pool:
lowerCamelCase_ = {F'''{i}''': i for i in range(_lowerCamelCase )}
lowerCamelCase_ = map_nested(lambda _lowerCamelCase : x + 10 , _lowerCamelCase , num_proc=_lowerCamelCase , parallel_min_length=16 )
if expected_num_proc == 1:
assert mock_single_map_nested.called
assert not mock_multiprocessing_pool.called
else:
assert not mock_single_map_nested.called
assert mock_multiprocessing_pool.called
assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class a ( __snake_case ):
@require_tf
def UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
import tensorflow as tf
from tensorflow.keras import layers
lowerCamelCase_ = layers.Dense(2 )
def gen_random_output():
lowerCamelCase_ = tf.random.uniform((1, 3) )
return model(__SCREAMING_SNAKE_CASE ).numpy()
with temp_seed(42 , set_tensorflow=__SCREAMING_SNAKE_CASE ):
lowerCamelCase_ = gen_random_output()
with temp_seed(42 , set_tensorflow=__SCREAMING_SNAKE_CASE ):
lowerCamelCase_ = gen_random_output()
lowerCamelCase_ = gen_random_output()
np.testing.assert_equal(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@require_torch
def UpperCamelCase ( self : str ) -> Tuple:
import torch
def gen_random_output():
lowerCamelCase_ = torch.nn.Linear(3 , 2 )
lowerCamelCase_ = torch.rand(1 , 3 )
return model(__SCREAMING_SNAKE_CASE ).detach().numpy()
with temp_seed(42 , set_pytorch=__SCREAMING_SNAKE_CASE ):
lowerCamelCase_ = gen_random_output()
with temp_seed(42 , set_pytorch=__SCREAMING_SNAKE_CASE ):
lowerCamelCase_ = gen_random_output()
lowerCamelCase_ = gen_random_output()
np.testing.assert_equal(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
def UpperCamelCase ( self : Optional[int] ) -> Optional[Any]:
def gen_random_output():
return np.random.rand(1 , 3 )
with temp_seed(42 ):
lowerCamelCase_ = gen_random_output()
with temp_seed(42 ):
lowerCamelCase_ = gen_random_output()
lowerCamelCase_ = gen_random_output()
np.testing.assert_equal(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@pytest.mark.parametrize('input_data' , [{}] )
def lowerCamelCase__ ( _lowerCamelCase : Dict ) -> List[str]:
lowerCamelCase_ = NestedDataStructure(_lowerCamelCase ).data
assert output_data == input_data
@pytest.mark.parametrize(
'data, expected_output' , [
({}, []),
([], []),
('foo', ['foo']),
(['foo', 'bar'], ['foo', 'bar']),
([['foo', 'bar']], ['foo', 'bar']),
([[['foo'], ['bar']]], ['foo', 'bar']),
([[['foo'], 'bar']], ['foo', 'bar']),
({'a': 1, 'b': 2}, [1, 2]),
({'a': [1, 2], 'b': [3, 4]}, [1, 2, 3, 4]),
({'a': [[1, 2]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
({'a': [[1, 2]], 'b': [3, 4]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [[[3], [4]]]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [3, 4]}, [1, 2, 3, 4]),
({'a': [[[1], [2]]], 'b': [3, [4]]}, [1, 2, 3, 4]),
({'a': {'1': 1}, 'b': 2}, [1, 2]),
({'a': {'1': [1]}, 'b': 2}, [1, 2]),
({'a': {'1': [1]}, 'b': [2]}, [1, 2]),
] , )
def lowerCamelCase__ ( _lowerCamelCase : Optional[int] , _lowerCamelCase : str ) -> Tuple:
lowerCamelCase_ = NestedDataStructure(_lowerCamelCase ).flatten()
assert output == expected_output
def lowerCamelCase__ ( ) -> Dict:
lowerCamelCase_ = A(x=1 , y='foobar' )
lowerCamelCase_ = {'x': 1, 'y': 'foobar'}
assert asdict(_lowerCamelCase ) == expected_output
lowerCamelCase_ = {'a': {'b': A(x=10 , y='foo' )}, 'c': [A(x=20 , y='bar' )]}
lowerCamelCase_ = {'a': {'b': {'x': 10, 'y': 'foo'}}, 'c': [{'x': 20, 'y': 'bar'}]}
assert asdict(_lowerCamelCase ) == expected_output
with pytest.raises(_lowerCamelCase ):
asdict([1, A(x=10 , y='foo' )] )
def lowerCamelCase__ ( _lowerCamelCase : str ) -> Dict:
return text.split()
def lowerCamelCase__ ( _lowerCamelCase : Tuple ) -> Optional[int]:
yield (time.time(), content)
time.sleep(2 )
yield (time.time(), content)
def lowerCamelCase__ ( ) -> str:
with Pool(2 ) as pool:
lowerCamelCase_ = list(iflatmap_unordered(_lowerCamelCase , _split_text , kwargs_iterable=[{'text': 'hello there'}] * 10 ) )
assert out.count('hello' ) == 10
assert out.count('there' ) == 10
assert len(_lowerCamelCase ) == 20
# check multiprocess from pathos (uses dill for pickling)
with multiprocess.Pool(2 ) as pool:
lowerCamelCase_ = list(iflatmap_unordered(_lowerCamelCase , _split_text , kwargs_iterable=[{'text': 'hello there'}] * 10 ) )
assert out.count('hello' ) == 10
assert out.count('there' ) == 10
assert len(_lowerCamelCase ) == 20
# check that we get items as fast as possible
with Pool(2 ) as pool:
lowerCamelCase_ = []
for yield_time, content in iflatmap_unordered(
_lowerCamelCase , _aseconds_generator_of_aitems_with_timing , kwargs_iterable=[{'content': 'a'}, {'content': 'b'}] ):
assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
out.append(_lowerCamelCase )
assert out.count('a' ) == 2
assert out.count('b' ) == 2
assert len(_lowerCamelCase ) == 4
| 183 |
"""simple docstring"""
import argparse
import json
import subprocess
def lowerCamelCase__ ( _lowerCamelCase : Tuple , _lowerCamelCase : str ) -> List[Any]:
lowerCamelCase_ = []
lowerCamelCase_ = (
F'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'''
' https://api.github.com/repos/huggingface/transformers/actions/runners'
)
lowerCamelCase_ = subprocess.run(_lowerCamelCase , shell=_lowerCamelCase , stdout=subprocess.PIPE )
lowerCamelCase_ = output.stdout.decode('utf-8' )
lowerCamelCase_ = json.loads(_lowerCamelCase )
lowerCamelCase_ = status['runners']
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(_lowerCamelCase )
# save the result so we can report them on Slack
with open('offline_runners.txt' , 'w' ) as fp:
fp.write(json.dumps(_lowerCamelCase ) )
if len(_lowerCamelCase ) > 0:
lowerCamelCase_ = '\n'.join([x['name'] for x in offline_runners] )
raise ValueError(F'''The following runners are offline:\n{failed}''' )
if __name__ == "__main__":
def lowerCamelCase__ ( _lowerCamelCase : Dict ) -> Tuple:
return values.split(',' )
_SCREAMING_SNAKE_CASE : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--target_runners''',
default=None,
type=list_str,
required=True,
help='''Comma-separated list of runners to check status.''',
)
parser.add_argument(
'''--token''', default=None, type=str, required=True, help='''A token that has actions:read permission.'''
)
_SCREAMING_SNAKE_CASE : Any = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 183 | 1 |
'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
lowercase__ : int = subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
lowercase__ : Tuple = (
subprocess.check_output(f"""git diff --diff-filter=d --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()
)
lowercase__ : Dict = '''|'''.join(sys.argv[1:])
lowercase__ : Optional[int] = re.compile(rf"""^({joined_dirs}).*?\.py$""")
lowercase__ : Any = [x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''') | 190 |
'''simple docstring'''
import math
def _lowerCAmelCase ( __snake_case : int ) -> int:
if not isinstance(__snake_case , __snake_case ):
__A : List[Any] = f'Input value of [number={number}] must be an integer'
raise TypeError(__snake_case )
if number < 1:
__A : Union[str, Any] = f'Input value of [number={number}] must be > 0'
raise ValueError(__snake_case )
elif number == 1:
return 3
elif number == 2:
return 5
else:
__A : Optional[Any] = int(math.log(number // 3 , 2 ) ) + 2
__A : Union[str, Any] = [3, 5]
__A : List[Any] = 2
__A : Optional[Any] = 3
for block in range(1 , __snake_case ):
for _ in range(__snake_case ):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
lowercase__ : str = 0
try:
lowercase__ : List[str] = proth(number)
except ValueError:
print(f"""ValueError: there is no {number}th Proth number""")
continue
print(f"""The {number}th Proth number: {value}""") | 190 | 1 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = []
for part_id in partition_order:
_lowerCAmelCase = df.where(f"SPARK_PARTITION_ID() = {part_id}" ).collect()
for row_idx, row in enumerate(_a ):
expected_row_ids_and_row_dicts.append((f"{part_id}_{row_idx}", row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_lowerCAmelCase = spark.range(1_00 ).repartition(1 )
_lowerCAmelCase = Spark(_a )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=16 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_lowerCAmelCase = spark.range(10 ).repartition(2 )
_lowerCAmelCase = [1, 0]
_lowerCAmelCase = _generate_iterable_examples(_a , _a ) # Reverse the partitions.
_lowerCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , _a )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
_lowerCAmelCase = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_lowerCAmelCase = spark.range(10 ).repartition(1 )
_lowerCAmelCase = SparkExamplesIterable(_a )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(_a ):
assert row_id == f"0_{i}"
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_lowerCAmelCase = spark.range(30 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch("""numpy.random.Generator""" ) as generator_mock:
_lowerCAmelCase = lambda lowerCAmelCase : x.reverse()
_lowerCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , [2, 1, 0] )
_lowerCAmelCase = SparkExamplesIterable(_a ).shuffle_data_sources(_a )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(_a ):
_lowerCAmelCase = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_lowerCAmelCase = spark.range(20 ).repartition(4 )
# Partitions 0 and 2
_lowerCAmelCase = SparkExamplesIterable(_a ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
_lowerCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , [0, 2] )
for i, (row_id, row_dict) in enumerate(_a ):
_lowerCAmelCase = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
_lowerCAmelCase = SparkExamplesIterable(_a ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
_lowerCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(_a , [1, 3] )
for i, (row_id, row_dict) in enumerate(_a ):
_lowerCAmelCase = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase__ ( ):
"""simple docstring"""
_lowerCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_lowerCAmelCase = spark.range(1_00 ).repartition(1 )
_lowerCAmelCase = Spark(_a )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 1_00
| 70 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class _UpperCAmelCase ( lowerCAmelCase__):
def __init__( self : Optional[int] ):
snake_case_ : str = []
def _snake_case ( self : List[Any] , lowercase_ : Any , lowercase_ : Union[str, Any] , lowercase_ : List[str] , **lowercase_ : Tuple ):
self.events.append('''on_init_end''' )
def _snake_case ( self : List[Any] , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : List[str] , **lowercase_ : List[str] ):
self.events.append('''on_train_begin''' )
def _snake_case ( self : Any , lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : List[Any] , **lowercase_ : Optional[int] ):
self.events.append('''on_train_end''' )
def _snake_case ( self : str , lowercase_ : Optional[int] , lowercase_ : int , lowercase_ : Optional[Any] , **lowercase_ : List[Any] ):
self.events.append('''on_epoch_begin''' )
def _snake_case ( self : Tuple , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Union[str, Any] , **lowercase_ : Optional[Any] ):
self.events.append('''on_epoch_end''' )
def _snake_case ( self : List[str] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : int , **lowercase_ : Optional[Any] ):
self.events.append('''on_step_begin''' )
def _snake_case ( self : int , lowercase_ : int , lowercase_ : Union[str, Any] , lowercase_ : List[Any] , **lowercase_ : List[str] ):
self.events.append('''on_step_end''' )
def _snake_case ( self : str , lowercase_ : int , lowercase_ : Dict , lowercase_ : List[str] , **lowercase_ : List[str] ):
self.events.append('''on_evaluate''' )
def _snake_case ( self : Dict , lowercase_ : Union[str, Any] , lowercase_ : Any , lowercase_ : List[Any] , **lowercase_ : str ):
self.events.append('''on_predict''' )
def _snake_case ( self : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : int , **lowercase_ : Union[str, Any] ):
self.events.append('''on_save''' )
def _snake_case ( self : str , lowercase_ : Tuple , lowercase_ : Optional[int] , lowercase_ : List[str] , **lowercase_ : Any ):
self.events.append('''on_log''' )
def _snake_case ( self : Dict , lowercase_ : Optional[int] , lowercase_ : List[str] , lowercase_ : Union[str, Any] , **lowercase_ : Optional[int] ):
self.events.append('''on_prediction_step''' )
@require_torch
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : List[str] ):
snake_case_ : Tuple = tempfile.mkdtemp()
def _snake_case ( self : Tuple ):
shutil.rmtree(self.output_dir )
def _snake_case ( self : int , lowercase_ : Union[str, Any]=0 , lowercase_ : Dict=0 , lowercase_ : List[str]=64 , lowercase_ : Union[str, Any]=64 , lowercase_ : Union[str, Any]=None , lowercase_ : Any=False , **lowercase_ : List[Any] ):
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
snake_case_ : int = RegressionDataset(length=lowercase_ )
snake_case_ : Any = RegressionDataset(length=lowercase_ )
snake_case_ : int = RegressionModelConfig(a=lowercase_ , b=lowercase_ )
snake_case_ : Tuple = RegressionPreTrainedModel(lowercase_ )
snake_case_ : Any = TrainingArguments(self.output_dir , disable_tqdm=lowercase_ , report_to=[] , **lowercase_ )
return Trainer(
lowercase_ , lowercase_ , train_dataset=lowercase_ , eval_dataset=lowercase_ , callbacks=lowercase_ , )
def _snake_case ( self : Optional[int] , lowercase_ : Any , lowercase_ : List[Any] ):
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) )
# Order doesn't matter
snake_case_ : Any = sorted(lowercase_ , key=lambda lowercase_ : cb.__name__ if isinstance(lowercase_ , lowercase_ ) else cb.__class__.__name__ )
snake_case_ : List[str] = sorted(lowercase_ , key=lambda lowercase_ : cb.__name__ if isinstance(lowercase_ , lowercase_ ) else cb.__class__.__name__ )
for cba, cba in zip(lowercase_ , lowercase_ ):
if isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ ):
self.assertEqual(lowercase_ , lowercase_ )
elif isinstance(lowercase_ , lowercase_ ) and not isinstance(lowercase_ , lowercase_ ):
self.assertEqual(lowercase_ , cba.__class__ )
elif not isinstance(lowercase_ , lowercase_ ) and isinstance(lowercase_ , lowercase_ ):
self.assertEqual(cba.__class__ , lowercase_ )
else:
self.assertEqual(lowercase_ , lowercase_ )
def _snake_case ( self : Optional[Any] , lowercase_ : Tuple ):
snake_case_ : Tuple = ['''on_init_end''', '''on_train_begin''']
snake_case_ : List[Any] = 0
snake_case_ : Union[str, Any] = len(trainer.get_eval_dataloader() )
snake_case_ : List[Any] = ['''on_prediction_step'''] * len(trainer.get_eval_dataloader() ) + ['''on_log''', '''on_evaluate''']
for _ in range(trainer.state.num_train_epochs ):
expected_events.append('''on_epoch_begin''' )
for _ in range(lowercase_ ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append('''on_log''' )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append('''on_save''' )
expected_events.append('''on_epoch_end''' )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def _snake_case ( self : List[str] ):
snake_case_ : Union[str, Any] = self.get_trainer()
snake_case_ : Dict = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# Callbacks passed at init are added to the default callbacks
snake_case_ : Optional[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
snake_case_ : Optional[int] = self.get_trainer(disable_tqdm=lowercase_ )
snake_case_ : List[Any] = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
def _snake_case ( self : int ):
snake_case_ : int = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
snake_case_ : List[Any] = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(lowercase_ )
expected_callbacks.remove(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
snake_case_ : Dict = self.get_trainer()
snake_case_ : Optional[int] = trainer.pop_callback(lowercase_ )
self.assertEqual(cb.__class__ , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
trainer.add_callback(lowercase_ )
expected_callbacks.insert(0 , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
# We can also add, pop, or remove by instance
snake_case_ : Optional[int] = self.get_trainer()
snake_case_ : List[Any] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(lowercase_ )
expected_callbacks.remove(lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
snake_case_ : List[Any] = self.get_trainer()
snake_case_ : Optional[int] = trainer.callback_handler.callbacks[0]
snake_case_ : Optional[Any] = trainer.pop_callback(lowercase_ )
self.assertEqual(lowercase_ , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
trainer.add_callback(lowercase_ )
expected_callbacks.insert(0 , lowercase_ )
self.check_callbacks_equality(trainer.callback_handler.callbacks , lowercase_ )
def _snake_case ( self : List[Any] ):
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action='''ignore''' , category=lowercase_ )
snake_case_ : int = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
snake_case_ : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# Independent log/save/eval
snake_case_ : int = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
snake_case_ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
snake_case_ : List[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
snake_case_ : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
snake_case_ : List[Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy='''steps''' )
trainer.train()
snake_case_ : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
snake_case_ : Union[str, Any] = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy='''epoch''' )
trainer.train()
snake_case_ : Dict = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# A bit of everything
snake_case_ : str = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=10 , eval_steps=5 , evaluation_strategy='''steps''' , )
trainer.train()
snake_case_ : str = trainer.callback_handler.callbacks[-2].events
self.assertEqual(lowercase_ , self.get_expected_events(lowercase_ ) )
# warning should be emitted for duplicated callbacks
with patch('''transformers.trainer_callback.logger.warning''' ) as warn_mock:
snake_case_ : Dict = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(lowercase_ ) in warn_mock.call_args[0][0]
| 264 | 0 |
'''simple docstring'''
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
lowercase__ = "."
if __name__ == "__main__":
lowercase__ = os.path.join(REPO_PATH, "utils/documentation_tests.txt")
lowercase__ = []
lowercase__ = []
with open(doctest_file_path) as fp:
for line in fp:
lowercase__ = line.strip()
lowercase__ = os.path.join(REPO_PATH, line)
if not (os.path.isfile(path) or os.path.isdir(path)):
non_existent_paths.append(line)
all_paths.append(path)
if len(non_existent_paths) > 0:
lowercase__ = "\n".join(non_existent_paths)
raise ValueError(f'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
if all_paths != sorted(all_paths):
raise ValueError("Files in `utils/documentation_tests.txt` are not in alphabetical order.")
| 280 |
'''simple docstring'''
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 UpperCamelCase( UpperCAmelCase_ ):
# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
# the original model
UpperCAmelCase : List[Any] = list(s_dict.keys() )
for key in keys:
UpperCAmelCase : Union[str, Any] = R'.*/layers_(\d+)'
UpperCAmelCase : List[str] = key
if re.match(UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Union[str, Any] = re.sub(R'layers_(\d+)' , R'block/\1/layer' , UpperCAmelCase_ )
UpperCAmelCase : str = R'(encoder|decoder)\/'
if re.match(UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCAmelCase : Tuple = re.match(UpperCAmelCase_ , UpperCAmelCase_ ).groups()
if groups[0] == "encoder":
UpperCAmelCase : Union[str, Any] = re.sub(R'/mlp/' , R'/1/mlp/' , UpperCAmelCase_ )
UpperCAmelCase : List[Any] = re.sub(R'/pre_mlp_layer_norm/' , R'/1/layer_norm/' , UpperCAmelCase_ )
elif groups[0] == "decoder":
UpperCAmelCase : Tuple = re.sub(R'/mlp/' , R'/2/mlp/' , UpperCAmelCase_ )
UpperCAmelCase : Union[str, Any] = 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:
UpperCAmelCase : List[str] = new_key.replace(UpperCAmelCase_ , UpperCAmelCase_ )
print(F"""{key} -> {new_key}""" )
UpperCAmelCase : List[Any] = s_dict.pop(UpperCAmelCase_ )
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
UpperCAmelCase : Optional[int] = 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:
UpperCAmelCase : Optional[int] = 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:
UpperCAmelCase : List[str] = s_dict[key].shape[0]
UpperCAmelCase : List[Any] = s_dict[key]
for idx in range(UpperCAmelCase_ ):
UpperCAmelCase : Union[str, Any] = 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 UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
# Convert a google style config to the hugging face fromat
import regex as re
with open(UpperCAmelCase_ , 'r' ) as f:
UpperCAmelCase : Union[str, Any] = f.read()
UpperCAmelCase : Union[str, Any] = re.findall(R'(.*) = ([0-9.]*)' , UpperCAmelCase_ )
UpperCAmelCase : str = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
UpperCAmelCase : Dict = float(UpperCAmelCase_ ) if '.' in value else int(UpperCAmelCase_ )
UpperCAmelCase : str = re.findall(R'(.*activations) = \(\'(.*)\',\)' , UpperCAmelCase_ )[0]
UpperCAmelCase : Union[str, Any] = str(activation[1] )
UpperCAmelCase : Optional[int] = num_experts
UpperCAmelCase : List[str] = SwitchTransformersConfig(**UpperCAmelCase_ )
return config
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_=None , UpperCAmelCase_="./" , UpperCAmelCase_=8 ):
# Initialise PyTorch model
print(F"""Loading flax weights from : {flax_checkpoint_path}""" )
UpperCAmelCase : List[Any] = checkpoints.load_tax_checkpoint(UpperCAmelCase_ )
if gin_file is not None:
UpperCAmelCase : List[Any] = convert_gin_to_config(UpperCAmelCase_ , UpperCAmelCase_ )
else:
UpperCAmelCase : str = SwitchTransformersConfig.from_pretrained(UpperCAmelCase_ )
UpperCAmelCase : str = SwitchTransformersForConditionalGeneration(UpperCAmelCase_ )
UpperCAmelCase : str = flax_params['target']
UpperCAmelCase : Union[str, Any] = flatten_dict(UpperCAmelCase_ , sep='/' )
UpperCAmelCase : Tuple = rename_keys(UpperCAmelCase_ )
UpperCAmelCase : Optional[int] = 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,
)
| 280 | 1 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
lowerCamelCase : Tuple =False
class __a ( unittest.TestCase ):
pass
@slow
@require_torch_gpu
class __a ( unittest.TestCase ):
def __lowercase ( self : int ):
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion" )
pipe.to(SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
UpperCamelCase__ : List[Any] = torch.manual_seed(0 )
UpperCamelCase__ : List[Any] = pipe(
image=SCREAMING_SNAKE_CASE , generator=SCREAMING_SNAKE_CASE , guidance_scale=7.5 , num_inference_steps=50 , output_type="numpy" , ).images
UpperCamelCase__ : Union[str, Any] = image[0, 2_53:2_56, 2_53:2_56, -1]
assert image.shape == (1, 5_12, 5_12, 3)
UpperCamelCase__ : Optional[Any] = np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 189 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class __a ( A__ ):
def __lowercase ( self : Any ):
'''simple docstring'''
UpperCamelCase__ : List[str] = tempfile.mkdtemp()
UpperCamelCase__ : Any = 8
# DPR tok
UpperCamelCase__ : List[Any] = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
UpperCamelCase__ : Any = os.path.join(self.tmpdirname , "dpr_tokenizer" )
os.makedirs(SCREAMING_SNAKE_CASE , exist_ok=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[int] = os.path.join(SCREAMING_SNAKE_CASE , DPR_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] ) )
# BART tok
UpperCamelCase__ : str = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
UpperCamelCase__ : int = dict(zip(SCREAMING_SNAKE_CASE , range(len(SCREAMING_SNAKE_CASE ) ) ) )
UpperCamelCase__ : Optional[Any] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
UpperCamelCase__ : Union[str, Any] = {"unk_token": "<unk>"}
UpperCamelCase__ : Dict = os.path.join(self.tmpdirname , "bart_tokenizer" )
os.makedirs(SCREAMING_SNAKE_CASE , exist_ok=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : List[str] = os.path.join(SCREAMING_SNAKE_CASE , BART_VOCAB_FILES_NAMES["vocab_file"] )
UpperCamelCase__ : str = os.path.join(SCREAMING_SNAKE_CASE , BART_VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(SCREAMING_SNAKE_CASE ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(SCREAMING_SNAKE_CASE ) )
def __lowercase ( self : int ):
'''simple docstring'''
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , "dpr_tokenizer" ) )
def __lowercase ( self : Tuple ):
'''simple docstring'''
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , "dpr_tokenizer" ) )
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , "bart_tokenizer" ) )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCamelCase__ : Tuple = Dataset.from_dict(
{
"id": ["0", "1"],
"text": ["foo", "bar"],
"title": ["Foo", "Bar"],
"embeddings": [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index("embeddings" , string_factory="Flat" , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCamelCase__ : int = self.get_dummy_dataset()
UpperCamelCase__ : List[Any] = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch("transformers.models.rag.retrieval_rag.load_dataset" ) as mock_load_dataset:
UpperCamelCase__ : str = dataset
UpperCamelCase__ : Optional[int] = RagRetriever(
SCREAMING_SNAKE_CASE , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def __lowercase ( self : int , SCREAMING_SNAKE_CASE : bool ):
'''simple docstring'''
UpperCamelCase__ : Dict = self.get_dummy_dataset()
UpperCamelCase__ : Tuple = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name="custom" , )
if from_disk:
UpperCamelCase__ : Optional[int] = os.path.join(self.tmpdirname , "dataset" )
UpperCamelCase__ : List[str] = os.path.join(self.tmpdirname , "index.faiss" )
dataset.get_index("embeddings" ).save(os.path.join(self.tmpdirname , "index.faiss" ) )
dataset.drop_index("embeddings" )
dataset.save_to_disk(os.path.join(self.tmpdirname , "dataset" ) )
del dataset
UpperCamelCase__ : str = RagRetriever(
SCREAMING_SNAKE_CASE , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
UpperCamelCase__ : List[Any] = RagRetriever(
SCREAMING_SNAKE_CASE , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , SCREAMING_SNAKE_CASE ) , )
return retriever
def __lowercase ( self : int ):
'''simple docstring'''
UpperCamelCase__ : int = Dataset.from_dict(
{
"id": ["0", "1"],
"text": ["foo", "bar"],
"title": ["Foo", "Bar"],
"embeddings": [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index("embeddings" , string_factory="Flat" , metric_type=faiss.METRIC_INNER_PRODUCT )
UpperCamelCase__ : List[str] = os.path.join(self.tmpdirname , "hf_bert_base.hnswSQ8_correct_phi_128.c_index" )
dataset.save_faiss_index("embeddings" , index_file_name + ".index.dpr" )
pickle.dump(dataset["id"] , open(index_file_name + ".index_meta.dpr" , "wb" ) )
UpperCamelCase__ : Optional[int] = os.path.join(self.tmpdirname , "psgs_w100.tsv.pkl" )
UpperCamelCase__ : Tuple = {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
pickle.dump(SCREAMING_SNAKE_CASE , open(SCREAMING_SNAKE_CASE , "wb" ) )
UpperCamelCase__ : List[str] = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name="legacy" , index_path=self.tmpdirname , )
UpperCamelCase__ : List[str] = RagRetriever(
SCREAMING_SNAKE_CASE , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = 1
UpperCamelCase__ : Tuple = self.get_dummy_canonical_hf_index_retriever()
UpperCamelCase__ : str = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : List[str] = retriever.retrieve(SCREAMING_SNAKE_CASE , n_docs=SCREAMING_SNAKE_CASE )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["embeddings", "id", "text", "title"] )
self.assertEqual(len(doc_dicts[0]["id"] ) , SCREAMING_SNAKE_CASE )
self.assertEqual(doc_dicts[0]["id"][0] , "1" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0] , "0" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch("transformers.models.rag.retrieval_rag.load_dataset" ) as mock_load_dataset:
UpperCamelCase__ : Optional[int] = self.get_dummy_dataset()
retriever.save_pretrained(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Any = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : str = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase__ : Any = retriever.retrieve(SCREAMING_SNAKE_CASE , n_docs=1 )
self.assertTrue(out is not None )
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCamelCase__ : int = 1
UpperCamelCase__ : Dict = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Tuple = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : List[str] = retriever.retrieve(SCREAMING_SNAKE_CASE , n_docs=SCREAMING_SNAKE_CASE )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["embeddings", "id", "text", "title"] )
self.assertEqual(len(doc_dicts[0]["id"] ) , SCREAMING_SNAKE_CASE )
self.assertEqual(doc_dicts[0]["id"][0] , "1" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0] , "0" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def __lowercase ( self : str ):
'''simple docstring'''
UpperCamelCase__ : Any = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Tuple = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[int] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase__ : Any = retriever.retrieve(SCREAMING_SNAKE_CASE , n_docs=1 )
self.assertTrue(out is not None )
def __lowercase ( self : List[str] ):
'''simple docstring'''
UpperCamelCase__ : int = 1
UpperCamelCase__ : List[str] = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Dict = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : List[str] = retriever.retrieve(SCREAMING_SNAKE_CASE , n_docs=SCREAMING_SNAKE_CASE )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["embeddings", "id", "text", "title"] )
self.assertEqual(len(doc_dicts[0]["id"] ) , SCREAMING_SNAKE_CASE )
self.assertEqual(doc_dicts[0]["id"][0] , "1" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0] , "0" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__ : List[str] = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[int] = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Dict = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase__ : Union[str, Any] = retriever.retrieve(SCREAMING_SNAKE_CASE , n_docs=1 )
self.assertTrue(out is not None )
def __lowercase ( self : Tuple ):
'''simple docstring'''
UpperCamelCase__ : List[str] = 1
UpperCamelCase__ : Any = self.get_dummy_legacy_index_retriever()
UpperCamelCase__ : Optional[int] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Tuple = retriever.retrieve(SCREAMING_SNAKE_CASE , n_docs=SCREAMING_SNAKE_CASE )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["text", "title"] )
self.assertEqual(len(doc_dicts[0]["text"] ) , SCREAMING_SNAKE_CASE )
self.assertEqual(doc_dicts[0]["text"][0] , "bar" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["text"][0] , "foo" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : str = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : int = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase__ : int = retriever.retrieve(SCREAMING_SNAKE_CASE , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def __lowercase ( self : int ):
'''simple docstring'''
import torch
UpperCamelCase__ : Optional[Any] = 1
UpperCamelCase__ : Optional[int] = self.get_dummy_canonical_hf_index_retriever()
UpperCamelCase__ : Optional[Any] = [[5, 7], [10, 11]]
UpperCamelCase__ : List[str] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase__ : int = retriever(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , prefix=retriever.config.generator.prefix , n_docs=SCREAMING_SNAKE_CASE )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : List[str] = (
out["context_input_ids"],
out["context_attention_mask"],
out["retrieved_doc_embeds"],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
self.assertIsInstance(SCREAMING_SNAKE_CASE , np.ndarray )
UpperCamelCase__ : List[Any] = retriever(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , prefix=retriever.config.generator.prefix , n_docs=SCREAMING_SNAKE_CASE , return_tensors="pt" , )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : List[Any] = ( # noqa: F841
out["context_input_ids"],
out["context_attention_mask"],
out["retrieved_doc_embeds"],
out["doc_ids"],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(SCREAMING_SNAKE_CASE , torch.Tensor )
self.assertIsInstance(SCREAMING_SNAKE_CASE , torch.Tensor )
self.assertIsInstance(SCREAMING_SNAKE_CASE , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = self.get_dpr_ctx_encoder_tokenizer()
UpperCamelCase__ : Union[str, Any] = 1
UpperCamelCase__ : Tuple = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE )
retriever.set_ctx_encoder_tokenizer(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Tuple = [[5, 7], [10, 11]]
UpperCamelCase__ : List[str] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
UpperCamelCase__ : Optional[int] = retriever(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , prefix=retriever.config.generator.prefix , n_docs=SCREAMING_SNAKE_CASE )
self.assertEqual(
len(SCREAMING_SNAKE_CASE ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask") ) , SCREAMING_SNAKE_CASE ) # check for doc token related keys in dictionary. | 189 | 1 |
from collections.abc import Generator
from math import sin
def __UpperCamelCase ( _A : bytes ) ->bytes:
"""simple docstring"""
if len(_A ) != 32:
raise ValueError("""Input must be of length 32""" )
lowerCamelCase_ =B""""""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def __UpperCamelCase ( _A : int ) ->bytes:
"""simple docstring"""
if i < 0:
raise ValueError("""Input must be non-negative""" )
lowerCamelCase_ =format(_A , """08x""" )[-8:]
lowerCamelCase_ =B""""""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8""" )
return little_endian_hex
def __UpperCamelCase ( _A : bytes ) ->bytes:
"""simple docstring"""
lowerCamelCase_ =B""""""
for char in message:
bit_string += format(_A , """08b""" ).encode("""utf-8""" )
lowerCamelCase_ =format(len(_A ) , """064b""" ).encode("""utf-8""" )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(_A ) % 512 != 448:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )
return bit_string
def __UpperCamelCase ( _A : bytes ) ->Generator[list[int], None, None]:
"""simple docstring"""
if len(_A ) % 512 != 0:
raise ValueError("""Input must have length that's a multiple of 512""" )
for pos in range(0 , len(_A ) , 512 ):
lowerCamelCase_ =bit_string[pos : pos + 512]
lowerCamelCase_ =[]
for i in range(0 , 512 , 32 ):
block_words.append(int(to_little_endian(block[i : i + 32] ) , 2 ) )
yield block_words
def __UpperCamelCase ( _A : int ) ->int:
"""simple docstring"""
if i < 0:
raise ValueError("""Input must be non-negative""" )
lowerCamelCase_ =format(_A , """032b""" )
lowerCamelCase_ =""""""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(_A , 2 )
def __UpperCamelCase ( _A : int , _A : int ) ->int:
"""simple docstring"""
return (a + b) % 2**32
def __UpperCamelCase ( _A : int , _A : int ) ->int:
"""simple docstring"""
if i < 0:
raise ValueError("""Input must be non-negative""" )
if shift < 0:
raise ValueError("""Shift must be non-negative""" )
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def __UpperCamelCase ( _A : bytes ) ->bytes:
"""simple docstring"""
lowerCamelCase_ =preprocess(_A )
lowerCamelCase_ =[int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]
# Starting states
lowerCamelCase_ =0X67452301
lowerCamelCase_ =0XEFCDAB89
lowerCamelCase_ =0X98BADCFE
lowerCamelCase_ =0X10325476
lowerCamelCase_ =[
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(_A ):
lowerCamelCase_ =aa
lowerCamelCase_ =ba
lowerCamelCase_ =ca
lowerCamelCase_ =da
# Hash current chunk
for i in range(64 ):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
lowerCamelCase_ =d ^ (b & (c ^ d))
lowerCamelCase_ =i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
lowerCamelCase_ =c ^ (d & (b ^ c))
lowerCamelCase_ =(5 * i + 1) % 16
elif i <= 47:
lowerCamelCase_ =b ^ c ^ d
lowerCamelCase_ =(3 * i + 5) % 16
else:
lowerCamelCase_ =c ^ (b | not_aa(_A ))
lowerCamelCase_ =(7 * i) % 16
lowerCamelCase_ =(f + a + added_consts[i] + block_words[g]) % 2**32
lowerCamelCase_ =d
lowerCamelCase_ =c
lowerCamelCase_ =b
lowerCamelCase_ =sum_aa(_A , left_rotate_aa(_A , shift_amounts[i] ) )
# Add hashed chunk to running total
lowerCamelCase_ =sum_aa(_A , _A )
lowerCamelCase_ =sum_aa(_A , _A )
lowerCamelCase_ =sum_aa(_A , _A )
lowerCamelCase_ =sum_aa(_A , _A )
lowerCamelCase_ =reformat_hex(_A ) + reformat_hex(_A ) + reformat_hex(_A ) + reformat_hex(_A )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 368 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__A : List[Any] = logging.get_logger(__name__)
__A : List[Any] = [
['attention', 'attn'],
['encoder_attention', 'encoder_attn'],
['q_lin', 'q_proj'],
['k_lin', 'k_proj'],
['v_lin', 'v_proj'],
['out_lin', 'out_proj'],
['norm_embeddings', 'layernorm_embedding'],
['position_embeddings', 'embed_positions'],
['embeddings', 'embed_tokens'],
['ffn.lin', 'fc'],
]
def __UpperCamelCase ( _A : Optional[int] ) ->List[str]:
"""simple docstring"""
if k == "embeddings.weight":
return "shared.weight"
for parlai_name, hf_name in PATTERNS:
lowerCamelCase_ =k.replace(_A , _A )
if k.startswith("""encoder""" ):
lowerCamelCase_ =k.replace(""".attn""" , """.self_attn""" )
lowerCamelCase_ =k.replace("""norm1""" , """self_attn_layer_norm""" )
lowerCamelCase_ =k.replace("""norm2""" , """final_layer_norm""" )
elif k.startswith("""decoder""" ):
lowerCamelCase_ =k.replace("""norm1""" , """self_attn_layer_norm""" )
lowerCamelCase_ =k.replace("""norm2""" , """encoder_attn_layer_norm""" )
lowerCamelCase_ =k.replace("""norm3""" , """final_layer_norm""" )
return k
def __UpperCamelCase ( _A : Union[str, Any] ) ->Optional[int]:
"""simple docstring"""
lowerCamelCase_ =[
"""model.encoder.layernorm_embedding.weight""",
"""model.encoder.layernorm_embedding.bias""",
"""model.decoder.layernorm_embedding.weight""",
"""model.decoder.layernorm_embedding.bias""",
]
for k in keys:
lowerCamelCase_ =sd.pop(_A )
lowerCamelCase_ =k.replace("""layernorm_embedding""" , """layer_norm""" )
assert new_k not in sd
lowerCamelCase_ =v
__A : Any = ['START']
@torch.no_grad()
def __UpperCamelCase ( _A : List[Any] , _A : Union[str, Any] , _A : List[str] ) ->List[str]:
"""simple docstring"""
lowerCamelCase_ =torch.load(_A , map_location="""cpu""" )
lowerCamelCase_ =model["""model"""]
lowerCamelCase_ =BlenderbotConfig.from_json_file(_A )
lowerCamelCase_ =BlenderbotForConditionalGeneration(_A )
lowerCamelCase_ =m.model.state_dict().keys()
lowerCamelCase_ =[]
lowerCamelCase_ ={}
for k, v in sd.items():
if k in IGNORE_KEYS:
continue
lowerCamelCase_ =rename_state_dict_key(_A )
if new_k not in valid_keys:
failures.append([k, new_k] )
else:
lowerCamelCase_ =v
if cfg.normalize_before: # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
rename_layernorm_keys(_A )
m.model.load_state_dict(_A , strict=_A )
m.half()
m.save_pretrained(_A )
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--src_path', type=str, help='like blenderbot-model.bin')
parser.add_argument('--save_dir', default='hf_blenderbot', type=str, help='Where to save converted model.')
parser.add_argument(
'--hf_config_json', default='blenderbot-3b-config.json', type=str, help='Path to config to use'
)
__A : str = parser.parse_args()
convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)
| 49 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
a : int
a : TreeNode | None =None
a : TreeNode | None =None
lowerCAmelCase__ = namedtuple('''CoinsDistribResult''', '''moves excess''')
def a__ ( SCREAMING_SNAKE_CASE : TreeNode | None ):
'''simple docstring'''
if root is None:
return 0
# Validation
def count_nodes(SCREAMING_SNAKE_CASE : TreeNode | None ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(SCREAMING_SNAKE_CASE : TreeNode | None ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(SCREAMING_SNAKE_CASE ) != count_coins(SCREAMING_SNAKE_CASE ):
raise ValueError("The nodes number should be same as the number of coins" )
# Main calculation
def get_distrib(SCREAMING_SNAKE_CASE : TreeNode | None ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
lowerCAmelCase , lowerCAmelCase : List[str] = get_distrib(node.left )
lowerCAmelCase , lowerCAmelCase : List[str] = get_distrib(node.right )
lowerCAmelCase : Union[str, Any] = 1 - left_distrib_excess
lowerCAmelCase : List[Any] = 1 - right_distrib_excess
lowerCAmelCase : int = (
left_distrib_moves
+ right_distrib_moves
+ abs(SCREAMING_SNAKE_CASE )
+ abs(SCREAMING_SNAKE_CASE )
)
lowerCAmelCase : int = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return get_distrib(SCREAMING_SNAKE_CASE )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 108 |
"""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
lowerCAmelCase__ = get_logger(__name__)
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
a : Optional[Any] ="dummy_data"
a : int ="datasets"
a : Tuple =False
def __init__( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ = None , snake_case__ = False , snake_case__ = True , snake_case__ = None , ):
"""simple docstring"""
lowerCAmelCase : Tuple = 0
lowerCAmelCase : int = dataset_name
lowerCAmelCase : List[Any] = cache_dir
lowerCAmelCase : List[str] = use_local_dummy_data
lowerCAmelCase : List[str] = config
# download_callbacks take a single url as input
lowerCAmelCase : List[Callable] = 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
lowerCAmelCase : Tuple = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
lowerCAmelCase : Union[str, Any] = str(snake_case__ )
# to be downloaded
lowerCAmelCase : List[Any] = None
lowerCAmelCase : List[Any] = None
@property
def lowercase__ ( self ):
"""simple docstring"""
if self._dummy_file is None:
lowerCAmelCase : Any = self.download_dummy_data()
return self._dummy_file
@property
def lowercase__ ( self ):
"""simple docstring"""
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 lowercase__ ( self ):
"""simple docstring"""
return os.path.join(self.dummy_data_folder , "dummy_data.zip" )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Optional[Any] = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
lowerCAmelCase : str = cached_path(
snake_case__ , cache_dir=self.cache_dir , extract_compressed_file=snake_case__ , force_extract=snake_case__ )
return os.path.join(snake_case__ , self.dummy_file_name )
@property
def lowercase__ ( self ):
"""simple docstring"""
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def lowercase__ ( self ):
"""simple docstring"""
if self._bucket_url is None:
lowerCAmelCase : Union[str, Any] = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , "/" ) )
return self._bucket_url
@property
def lowercase__ ( self ):
"""simple docstring"""
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 lowercase__ ( self , snake_case__ , *snake_case__ ):
"""simple docstring"""
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
lowerCAmelCase : int = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
lowerCAmelCase : List[Any] = self.dummy_file_name
# special case when data_url is a dict
if isinstance(snake_case__ , snake_case__ ):
return self.create_dummy_data_dict(snake_case__ , snake_case__ )
elif isinstance(snake_case__ , (list, tuple) ):
return self.create_dummy_data_list(snake_case__ , snake_case__ )
else:
return self.create_dummy_data_single(snake_case__ , snake_case__ )
def lowercase__ ( self , snake_case__ , *snake_case__ ):
"""simple docstring"""
return self.download_and_extract(snake_case__ )
def lowercase__ ( self , snake_case__ , snake_case__ ):
"""simple docstring"""
return self.download_and_extract(snake_case__ )
def lowercase__ ( self , snake_case__ , *snake_case__ , **snake_case__ ):
"""simple docstring"""
return path
def lowercase__ ( self ):
"""simple docstring"""
return {}
def lowercase__ ( self , snake_case__ , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : List[Any] = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(snake_case__ , snake_case__ ):
for single_url in single_urls:
download_callback(snake_case__ )
else:
lowerCAmelCase : List[str] = single_urls
download_callback(snake_case__ )
# 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(snake_case__ , snake_case__ ):
lowerCAmelCase : Tuple = [os.path.join(snake_case__ , urllib.parse.quote_plus(Path(snake_case__ ).name ) ) for x in single_urls]
else:
lowerCAmelCase : int = single_urls
lowerCAmelCase : Any = os.path.join(snake_case__ , urllib.parse.quote_plus(Path(snake_case__ ).name ) )
lowerCAmelCase : Union[str, Any] = value
# make sure that values are unique
if all(isinstance(snake_case__ , snake_case__ ) 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
lowerCAmelCase : Union[str, Any] = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def lowercase__ ( self , snake_case__ , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Dict = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
lowerCAmelCase : Optional[Any] = all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}" , snake_case__ ) ) for url in data_url )
lowerCAmelCase : Any = 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):
lowerCAmelCase : int = [data_url[0]] * len(snake_case__ )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(snake_case__ )
# 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
lowerCAmelCase : Dict = os.path.join(snake_case__ , urllib.parse.quote_plus(single_url.split("/" )[-1] ) )
dummy_data_list.append(snake_case__ )
return dummy_data_list
def lowercase__ ( self , snake_case__ , snake_case__ ):
"""simple docstring"""
for download_callback in self.download_callbacks:
download_callback(snake_case__ )
# 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
lowerCAmelCase : Tuple = os.path.join(snake_case__ , urllib.parse.quote_plus(data_url.split("/" )[-1] ) )
if os.path.exists(snake_case__ ) 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 lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
def _iter_archive_members(snake_case__ ):
# this preserves the order of the members inside the ZIP archive
lowerCAmelCase : str = Path(self.dummy_file ).parent
lowerCAmelCase : Optional[Any] = path.relative_to(snake_case__ )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
lowerCAmelCase : List[Any] = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(snake_case__ )
lowerCAmelCase : List[Any] = Path(snake_case__ )
lowerCAmelCase : str = _iter_archive_members(snake_case__ ) 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(snake_case__ ).as_posix(), file_path.open("rb" )
def lowercase__ ( self , snake_case__ ):
"""simple docstring"""
if not isinstance(snake_case__ , snake_case__ ):
lowerCAmelCase : List[Any] = [paths]
for path in paths:
if os.path.isfile(snake_case__ ):
if os.path.basename(snake_case__ ).startswith((".", "__") ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(snake_case__ ):
if os.path.basename(snake_case__ ).startswith((".", "__") ):
continue
dirnames.sort()
for filename in sorted(snake_case__ ):
if filename.startswith((".", "__") ):
continue
yield os.path.join(snake_case__ , snake_case__ )
| 108 | 1 |
"""simple docstring"""
from collections.abc import Sequence
from queue import Queue
class a :
def __init__( self , _snake_case , _snake_case , _snake_case , _snake_case=None , _snake_case=None ):
"""simple docstring"""
lowerCAmelCase = start
lowerCAmelCase = end
lowerCAmelCase = val
lowerCAmelCase = (start + end) // 2
lowerCAmelCase = left
lowerCAmelCase = right
def __repr__( self ):
"""simple docstring"""
return F'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'
class a :
def __init__( self , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = collection
lowerCAmelCase = function
if self.collection:
lowerCAmelCase = self._build_tree(0 , len(_snake_case ) - 1 )
def UpperCamelCase__ ( self , _snake_case , _snake_case ):
"""simple docstring"""
self._update_tree(self.root , _snake_case , _snake_case )
def UpperCamelCase__ ( self , _snake_case , _snake_case ):
"""simple docstring"""
return self._query_range(self.root , _snake_case , _snake_case )
def UpperCamelCase__ ( self , _snake_case , _snake_case ):
"""simple docstring"""
if start == end:
return SegmentTreeNode(_snake_case , _snake_case , self.collection[start] )
lowerCAmelCase = (start + end) // 2
lowerCAmelCase = self._build_tree(_snake_case , _snake_case )
lowerCAmelCase = self._build_tree(mid + 1 , _snake_case )
return SegmentTreeNode(_snake_case , _snake_case , self.fn(left.val , right.val ) , _snake_case , _snake_case )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
if node.start == i and node.end == i:
lowerCAmelCase = val
return
if i <= node.mid:
self._update_tree(node.left , _snake_case , _snake_case )
else:
self._update_tree(node.right , _snake_case , _snake_case )
lowerCAmelCase = self.fn(node.left.val , node.right.val )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
if node.start == i and node.end == j:
return node.val
if i <= node.mid:
if j <= node.mid:
# range in left child tree
return self._query_range(node.left , _snake_case , _snake_case )
else:
# range in left child tree and right child tree
return self.fn(
self._query_range(node.left , _snake_case , node.mid ) , self._query_range(node.right , node.mid + 1 , _snake_case ) , )
else:
# range in right child tree
return self._query_range(node.right , _snake_case , _snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
if self.root is not None:
lowerCAmelCase = Queue()
queue.put(self.root )
while not queue.empty():
lowerCAmelCase = queue.get()
yield node
if node.left is not None:
queue.put(node.left )
if node.right is not None:
queue.put(node.right )
if __name__ == "__main__":
import operator
for fn in [operator.add, max, min]:
print('''*''' * 50)
__UpperCamelCase : Tuple = SegmentTree([2, 1, 5, 3, 4], fn)
for node in arr.traverse():
print(node)
print()
arr.update(1, 5)
for node in arr.traverse():
print(node)
print()
print(arr.query_range(3, 4)) # 7
print(arr.query_range(2, 2)) # 5
print(arr.query_range(1, 3)) # 13
print()
| 309 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : list[str] | None = None ):
lowerCAmelCase = word_bank or []
# create a table
lowerCAmelCase = len(_UpperCAmelCase ) + 1
lowerCAmelCase = []
for _ in range(_UpperCAmelCase ):
table.append([] )
# seed value
lowerCAmelCase = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(_UpperCAmelCase ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(_UpperCAmelCase )] == word:
lowerCAmelCase = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(_UpperCAmelCase )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(_UpperCAmelCase )]:
combination.reverse()
return table[len(_UpperCAmelCase )]
if __name__ == "__main__":
print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
print(
all_construct(
'''hexagonosaurus''',
['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
)
)
| 309 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowerCamelCase ( UpperCamelCase__ ):
_lowerCamelCase :Tuple = ["image_processor", "tokenizer"]
_lowerCamelCase :Union[str, Any] = "BlipImageProcessor"
_lowerCamelCase :str = ("BertTokenizer", "BertTokenizerFast")
def __init__( self : str , UpperCamelCase : Union[str, Any] , UpperCamelCase : List[str] ) -> int:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = False
super().__init__(_a , _a )
lowerCAmelCase__ : int = self.image_processor
def __call__( self : Union[str, Any] , UpperCamelCase : str = None , UpperCamelCase : str = None , UpperCamelCase : Any = True , UpperCamelCase : Any = False , UpperCamelCase : str = None , UpperCamelCase : str = None , UpperCamelCase : Any = 0 , UpperCamelCase : List[Any] = None , UpperCamelCase : List[Any] = None , UpperCamelCase : str = False , UpperCamelCase : List[Any] = False , UpperCamelCase : Union[str, Any] = False , UpperCamelCase : Dict = False , UpperCamelCase : Dict = False , UpperCamelCase : Dict = True , UpperCamelCase : Any = None , **UpperCamelCase : Tuple , ) -> BatchEncoding:
"""simple docstring"""
if images is None and text is None:
raise ValueError("""You have to specify either images or text.""" )
# Get only text
if images is None:
lowerCAmelCase__ : Dict = self.tokenizer
lowerCAmelCase__ : Optional[int] = self.tokenizer(
text=_a , add_special_tokens=_a , padding=_a , truncation=_a , max_length=_a , stride=_a , pad_to_multiple_of=_a , return_attention_mask=_a , return_overflowing_tokens=_a , return_special_tokens_mask=_a , return_offsets_mapping=_a , return_token_type_ids=_a , return_length=_a , verbose=_a , return_tensors=_a , **_a , )
return text_encoding
# add pixel_values
lowerCAmelCase__ : Any = self.image_processor(_a , return_tensors=_a )
if text is not None:
lowerCAmelCase__ : List[str] = self.tokenizer(
text=_a , add_special_tokens=_a , padding=_a , truncation=_a , max_length=_a , stride=_a , pad_to_multiple_of=_a , return_attention_mask=_a , return_overflowing_tokens=_a , return_special_tokens_mask=_a , return_offsets_mapping=_a , return_token_type_ids=_a , return_length=_a , verbose=_a , return_tensors=_a , **_a , )
else:
lowerCAmelCase__ : Dict = None
if text_encoding is not None:
encoding_image_processor.update(_a )
return encoding_image_processor
def _lowerCAmelCase ( self : List[Any] , *UpperCamelCase : List[Any] , **UpperCamelCase : List[str] ) -> int:
"""simple docstring"""
return self.tokenizer.batch_decode(*_a , **_a )
def _lowerCAmelCase ( self : Any , *UpperCamelCase : List[Any] , **UpperCamelCase : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return self.tokenizer.decode(*_a , **_a )
@property
def _lowerCAmelCase ( self : int ) -> int:
"""simple docstring"""
lowerCAmelCase__ : Dict = self.tokenizer.model_input_names
lowerCAmelCase__ : Tuple = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 242 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowercase ( UpperCamelCase__ ):
_a = (DPMSolverSDEScheduler,)
_a = 1_0
def a__ ( self , **_a ) -> Optional[Any]:
_A : str = {
"""num_train_timesteps""": 1100,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""noise_sampler_seed""": 0,
}
config.update(**_a )
return config
def a__ ( self ) -> Tuple:
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=_a )
def a__ ( self ) -> Optional[int]:
for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=_a , beta_end=_a )
def a__ ( self ) -> Any:
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=_a )
def a__ ( self ) -> Optional[int]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_a )
def a__ ( self ) -> Optional[int]:
_A : Any = self.scheduler_classes[0]
_A : List[str] = self.get_scheduler_config()
_A : Optional[Any] = scheduler_class(**_a )
scheduler.set_timesteps(self.num_inference_steps )
_A : Dict = self.dummy_model()
_A : Any = self.dummy_sample_deter * scheduler.init_noise_sigma
_A : Dict = sample.to(_a )
for i, t in enumerate(scheduler.timesteps ):
_A : Optional[int] = scheduler.scale_model_input(_a , _a )
_A : str = model(_a , _a )
_A : List[Any] = scheduler.step(_a , _a , _a )
_A : Optional[int] = output.prev_sample
_A : Dict = torch.sum(torch.abs(_a ) )
_A : Dict = torch.mean(torch.abs(_a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47821044921875 ) < 1e-2
assert abs(result_mean.item() - 0.2178705964565277 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59352111816406 ) < 1e-2
assert abs(result_mean.item() - 0.22342906892299652 ) < 1e-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1e-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1e-3
def a__ ( self ) -> Optional[Any]:
_A : Dict = self.scheduler_classes[0]
_A : Optional[int] = self.get_scheduler_config(prediction_type="""v_prediction""" )
_A : Optional[Any] = scheduler_class(**_a )
scheduler.set_timesteps(self.num_inference_steps )
_A : Tuple = self.dummy_model()
_A : int = self.dummy_sample_deter * scheduler.init_noise_sigma
_A : Tuple = sample.to(_a )
for i, t in enumerate(scheduler.timesteps ):
_A : int = scheduler.scale_model_input(_a , _a )
_A : Tuple = model(_a , _a )
_A : Dict = scheduler.step(_a , _a , _a )
_A : Optional[int] = output.prev_sample
_A : Optional[Any] = torch.sum(torch.abs(_a ) )
_A : List[Any] = torch.mean(torch.abs(_a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77149200439453 ) < 1e-2
assert abs(result_mean.item() - 0.16226289014816284 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1663360595703 ) < 1e-2
assert abs(result_mean.item() - 0.16688326001167297 ) < 1e-3
else:
assert abs(result_sum.item() - 119.8487548828125 ) < 1e-2
assert abs(result_mean.item() - 0.1560530662536621 ) < 1e-3
def a__ ( self ) -> List[str]:
_A : Union[str, Any] = self.scheduler_classes[0]
_A : List[Any] = self.get_scheduler_config()
_A : List[str] = scheduler_class(**_a )
scheduler.set_timesteps(self.num_inference_steps , device=_a )
_A : Union[str, Any] = self.dummy_model()
_A : Optional[Any] = self.dummy_sample_deter.to(_a ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
_A : int = scheduler.scale_model_input(_a , _a )
_A : List[Any] = model(_a , _a )
_A : Dict = scheduler.step(_a , _a , _a )
_A : Dict = output.prev_sample
_A : str = torch.sum(torch.abs(_a ) )
_A : str = torch.mean(torch.abs(_a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46957397460938 ) < 1e-2
assert abs(result_mean.item() - 0.21805934607982635 ) < 1e-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59353637695312 ) < 1e-2
assert abs(result_mean.item() - 0.22342908382415771 ) < 1e-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1e-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1e-3
def a__ ( self ) -> Union[str, Any]:
_A : List[Any] = self.scheduler_classes[0]
_A : Optional[Any] = self.get_scheduler_config()
_A : int = scheduler_class(**_a , use_karras_sigmas=_a )
scheduler.set_timesteps(self.num_inference_steps , device=_a )
_A : Optional[Any] = self.dummy_model()
_A : Dict = self.dummy_sample_deter.to(_a ) * scheduler.init_noise_sigma
_A : str = sample.to(_a )
for t in scheduler.timesteps:
_A : Optional[int] = scheduler.scale_model_input(_a , _a )
_A : List[Any] = model(_a , _a )
_A : Dict = scheduler.step(_a , _a , _a )
_A : List[str] = output.prev_sample
_A : str = torch.sum(torch.abs(_a ) )
_A : List[str] = torch.mean(torch.abs(_a ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66974135742188 ) < 1e-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1e-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63653564453125 ) < 1e-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1e-2
else:
assert abs(result_sum.item() - 170.3135223388672 ) < 1e-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1e-2
| 26 | 0 |
import os
def UpperCamelCase__( UpperCamelCase__ : str = "input.txt" )->int:
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() = }")
| 39 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__: List[Any] = logging.get_logger(__name__)
a__: Optional[Any] = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'
),
# See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE = '''unispeech'''
def __init__( self,__lowerCamelCase=32,__lowerCamelCase=768,__lowerCamelCase=12,__lowerCamelCase=12,__lowerCamelCase=3072,__lowerCamelCase="gelu",__lowerCamelCase=0.1,__lowerCamelCase=0.1,__lowerCamelCase=0.1,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=0.1,__lowerCamelCase=0.1,__lowerCamelCase=0.02,__lowerCamelCase=1E-5,__lowerCamelCase="group",__lowerCamelCase="gelu",__lowerCamelCase=(512, 512, 512, 512, 512, 512, 512),__lowerCamelCase=(5, 2, 2, 2, 2, 2, 2),__lowerCamelCase=(10, 3, 3, 3, 3, 2, 2),__lowerCamelCase=False,__lowerCamelCase=128,__lowerCamelCase=16,__lowerCamelCase=False,__lowerCamelCase=True,__lowerCamelCase=0.05,__lowerCamelCase=10,__lowerCamelCase=2,__lowerCamelCase=0.0,__lowerCamelCase=10,__lowerCamelCase=0,__lowerCamelCase=320,__lowerCamelCase=2,__lowerCamelCase=0.1,__lowerCamelCase=100,__lowerCamelCase=256,__lowerCamelCase=256,__lowerCamelCase=0.1,__lowerCamelCase="mean",__lowerCamelCase=False,__lowerCamelCase=False,__lowerCamelCase=256,__lowerCamelCase=80,__lowerCamelCase=0,__lowerCamelCase=1,__lowerCamelCase=2,__lowerCamelCase=0.5,**__lowerCamelCase,):
super().__init__(**__lowerCamelCase,pad_token_id=__lowerCamelCase,bos_token_id=__lowerCamelCase,eos_token_id=__lowerCamelCase )
A__ = hidden_size
A__ = feat_extract_norm
A__ = feat_extract_activation
A__ = list(__lowerCamelCase )
A__ = list(__lowerCamelCase )
A__ = list(__lowerCamelCase )
A__ = conv_bias
A__ = num_conv_pos_embeddings
A__ = num_conv_pos_embedding_groups
A__ = len(self.conv_dim )
A__ = num_hidden_layers
A__ = intermediate_size
A__ = hidden_act
A__ = num_attention_heads
A__ = hidden_dropout
A__ = attention_dropout
A__ = activation_dropout
A__ = feat_proj_dropout
A__ = final_dropout
A__ = layerdrop
A__ = layer_norm_eps
A__ = initializer_range
A__ = num_ctc_classes
A__ = vocab_size
A__ = do_stable_layer_norm
A__ = use_weighted_layer_sum
A__ = classifier_proj_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
f" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"
f" `len(config.conv_kernel) = {len(self.conv_kernel )}`." )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
A__ = apply_spec_augment
A__ = mask_time_prob
A__ = mask_time_length
A__ = mask_time_min_masks
A__ = mask_feature_prob
A__ = mask_feature_length
A__ = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
A__ = num_codevectors_per_group
A__ = num_codevector_groups
A__ = contrastive_logits_temperature
A__ = feat_quantizer_dropout
A__ = num_negatives
A__ = codevector_dim
A__ = proj_codevector_dim
A__ = diversity_loss_weight
# ctc loss
A__ = ctc_loss_reduction
A__ = ctc_zero_infinity
# pretraining loss
A__ = replace_prob
@property
def UpperCamelCase ( self ):
return functools.reduce(operator.mul,self.conv_stride,1 )
| 39 | 1 |
def UpperCAmelCase_ ( __snake_case ) -> int:
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_lowercase =grid[0]
for row_n in range(1 , len(__snake_case ) ):
_lowercase =grid[row_n]
_lowercase =fill_row(__snake_case , __snake_case )
_lowercase =grid[row_n]
return grid[-1][-1]
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> list:
"""simple docstring"""
current_row[0] += row_above[0]
for cell_n in range(1 , len(__snake_case ) ):
current_row[cell_n] += min(current_row[cell_n - 1] , row_above[cell_n] )
return current_row
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 |
"""simple docstring"""
def lowercase (_lowerCAmelCase = 100_0000 ):
__lowerCAmelCase = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , _lowerCAmelCase ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 301 | 0 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError('''multiplicative_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negative values''' )
snake_case__ : Any = 0
snake_case__ : Dict = str(__lowerCAmelCase )
while len(__lowerCAmelCase ) != 1:
snake_case__ : Union[str, Any] = [int(__lowerCAmelCase ) for i in num_string]
snake_case__ : Tuple = 1
for i in range(0 , len(__lowerCAmelCase ) ):
total *= numbers[i]
snake_case__ : Any = str(__lowerCAmelCase )
steps += 1
return steps
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError('''additive_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''additive_persistence() does not accept negative values''' )
snake_case__ : Dict = 0
snake_case__ : Union[str, Any] = str(__lowerCAmelCase )
while len(__lowerCAmelCase ) != 1:
snake_case__ : Optional[int] = [int(__lowerCAmelCase ) for i in num_string]
snake_case__ : Any = 0
for i in range(0 , len(__lowerCAmelCase ) ):
total += numbers[i]
snake_case__ : Tuple = str(__lowerCAmelCase )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 352 |
A__ = 256
# Modulus to hash a string
A__ = 100_0003
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : str = len(__lowerCAmelCase )
snake_case__ : Optional[int] = len(__lowerCAmelCase )
if p_len > t_len:
return False
snake_case__ : str = 0
snake_case__ : Union[str, Any] = 0
snake_case__ : Dict = 1
# Calculating the hash of pattern and substring of text
for i in range(__lowerCAmelCase ):
snake_case__ : int = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
snake_case__ : str = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
snake_case__ : str = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
snake_case__ : Any = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def _lowerCAmelCase ( ) -> None:
"""simple docstring"""
snake_case__ : Optional[int] = '''abc1abc12'''
snake_case__ : Dict = '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
snake_case__ : int = '''alskfjaldsk23adsfabcabc'''
assert rabin_karp(__lowerCAmelCase , __lowerCAmelCase ) and not rabin_karp(__lowerCAmelCase , __lowerCAmelCase )
# Test 2)
snake_case__ : int = '''ABABX'''
snake_case__ : Any = '''ABABZABABYABABX'''
assert rabin_karp(__lowerCAmelCase , __lowerCAmelCase )
# Test 3)
snake_case__ : Dict = '''AAAB'''
snake_case__ : Union[str, Any] = '''ABAAAAAB'''
assert rabin_karp(__lowerCAmelCase , __lowerCAmelCase )
# Test 4)
snake_case__ : Union[str, Any] = '''abcdabcy'''
snake_case__ : Optional[Any] = '''abcxabcdabxabcdabcdabcy'''
assert rabin_karp(__lowerCAmelCase , __lowerCAmelCase )
# Test 5)
snake_case__ : Dict = '''Lü'''
snake_case__ : Optional[Any] = '''Lüsai'''
assert rabin_karp(__lowerCAmelCase , __lowerCAmelCase )
snake_case__ : str = '''Lue'''
assert not rabin_karp(__lowerCAmelCase , __lowerCAmelCase )
print('''Success.''' )
if __name__ == "__main__":
test_rabin_karp()
| 44 | 0 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
if is_torch_available():
import torch
from transformers import TimmBackbone, TimmBackboneConfig
from ...test_pipeline_mixin import PipelineTesterMixin
class lowerCamelCase__ :
def __init__(self , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase="resnet50" , UpperCAmelCase=3 , UpperCAmelCase=3_2 , UpperCAmelCase=3 , UpperCAmelCase=True , UpperCAmelCase=True , ) -> str:
_lowercase =parent
_lowercase =out_indices if out_indices is not None else [4]
_lowercase =stage_names
_lowercase =out_features
_lowercase =backbone
_lowercase =batch_size
_lowercase =image_size
_lowercase =num_channels
_lowercase =use_pretrained_backbone
_lowercase =is_training
def __A (self ) -> Tuple:
_lowercase =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowercase =self.get_config()
return config, pixel_values
def __A (self ) -> Optional[Any]:
return TimmBackboneConfig(
image_size=self.image_size , num_channels=self.num_channels , out_features=self.out_features , out_indices=self.out_indices , stage_names=self.stage_names , use_pretrained_backbone=self.use_pretrained_backbone , backbone=self.backbone , )
def __A (self , UpperCAmelCase , UpperCAmelCase ) -> int:
_lowercase =TimmBackbone(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
with torch.no_grad():
_lowercase =model(UpperCAmelCase )
self.parent.assertEqual(
result.feature_map[-1].shape , (self.batch_size, model.channels[-1], 1_4, 1_4) , )
def __A (self ) -> List[str]:
_lowercase =self.prepare_config_and_inputs()
_lowercase , _lowercase =config_and_inputs
_lowercase ={'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
@require_timm
class lowerCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , unittest.TestCase):
SCREAMING_SNAKE_CASE__ = (TimmBackbone,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE__ = {'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
def __A (self ) -> Optional[int]:
_lowercase =TimmBackboneModelTester(self )
_lowercase =ConfigTester(self , config_class=UpperCAmelCase , has_text_modality=UpperCAmelCase )
def __A (self ) -> Tuple:
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 __A (self ) -> Optional[Any]:
_lowercase ='''resnet18'''
_lowercase ='''microsoft/resnet-18'''
_lowercase =AutoBackbone.from_pretrained(UpperCAmelCase , use_timm_backbone=UpperCAmelCase )
_lowercase =AutoBackbone.from_pretrained(UpperCAmelCase )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(len(timm_model.stage_names ) , len(transformers_model.stage_names ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
# Out indices are set to the last layer by default. For timm models, we don't know
# the number of layers in advance, so we set it to (-1,), whereas for transformers
# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
self.assertEqual(timm_model.out_indices , (-1,) )
self.assertEqual(transformers_model.out_indices , [len(timm_model.stage_names ) - 1] )
_lowercase =AutoBackbone.from_pretrained(UpperCAmelCase , use_timm_backbone=UpperCAmelCase , out_indices=[1, 2, 3] )
_lowercase =AutoBackbone.from_pretrained(UpperCAmelCase , out_indices=[1, 2, 3] )
self.assertEqual(timm_model.out_indices , transformers_model.out_indices )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
@unittest.skip('''TimmBackbone doesn\'t support feed forward chunking''' )
def __A (self ) -> Optional[Any]:
pass
@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute''' )
def __A (self ) -> Tuple:
pass
@unittest.skip('''TimmBackbone initialization is managed on the timm side''' )
def __A (self ) -> List[Any]:
pass
@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''' )
def __A (self ) -> Tuple:
pass
@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''' )
def __A (self ) -> Any:
pass
@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint''' )
def __A (self ) -> Any:
pass
@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''' )
def __A (self ) -> Optional[Any]:
pass
@unittest.skip('''model weights aren\'t tied in TimmBackbone.''' )
def __A (self ) -> Tuple:
pass
@unittest.skip('''model weights aren\'t tied in TimmBackbone.''' )
def __A (self ) -> int:
pass
@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''' )
def __A (self ) -> Tuple:
pass
@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''' )
def __A (self ) -> Tuple:
pass
@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.''' )
def __A (self ) -> int:
pass
@unittest.skip('''TimmBackbone doesn\'t support output_attentions.''' )
def __A (self ) -> List[str]:
pass
@unittest.skip('''Safetensors is not supported by timm.''' )
def __A (self ) -> Dict:
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __A (self ) -> List[Any]:
pass
def __A (self ) -> str:
_lowercase , _lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase =model_class(UpperCAmelCase )
_lowercase =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowercase =[*signature.parameters.keys()]
_lowercase =['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCAmelCase )
def __A (self ) -> Union[str, Any]:
_lowercase , _lowercase =self.model_tester.prepare_config_and_inputs_for_common()
_lowercase =True
_lowercase =self.has_attentions
# no need to test all models as different heads yield the same functionality
_lowercase =self.all_model_classes[0]
_lowercase =model_class(UpperCAmelCase )
model.to(UpperCAmelCase )
_lowercase =self._prepare_for_class(UpperCAmelCase , UpperCAmelCase )
_lowercase =model(**UpperCAmelCase )
_lowercase =outputs[0][-1]
# Encoder-/Decoder-only models
_lowercase =outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
_lowercase =outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=UpperCAmelCase )
self.assertIsNotNone(hidden_states.grad )
if self.has_attentions:
self.assertIsNotNone(attentions.grad )
def __A (self ) -> str:
_lowercase , _lowercase =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase =model_class(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_lowercase =model(**UpperCAmelCase )
self.assertEqual(len(result.feature_maps ) , len(config.out_indices ) )
self.assertEqual(len(model.channels ) , len(config.out_indices ) )
# Check output of last stage is taken if out_features=None, out_indices=None
_lowercase =copy.deepcopy(UpperCAmelCase )
_lowercase =None
_lowercase =model_class(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_lowercase =model(**UpperCAmelCase )
self.assertEqual(len(result.feature_maps ) , 1 )
self.assertEqual(len(model.channels ) , 1 )
# Check backbone can be initialized with fresh weights
_lowercase =copy.deepcopy(UpperCAmelCase )
_lowercase =False
_lowercase =model_class(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_lowercase =model(**UpperCAmelCase )
| 5 | """simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ ( lowerCAmelCase__ :list[int] ) -> int:
'''simple docstring'''
if not nums:
return 0
lowercase = nums[0]
lowercase = 0
for num in nums[1:]:
lowercase , lowercase = (
max_excluding + num,
max(lowerCAmelCase__ , lowerCAmelCase__ ),
)
return max(lowerCAmelCase__ , lowerCAmelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 197 | 0 |
'''simple docstring'''
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 A ( __snake_case ):
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
A : List[str] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''hidden_sizes''' ) )
self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''neck_hidden_sizes''' ) )
self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE , '''num_attention_heads''' ) )
class A :
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=13 , SCREAMING_SNAKE_CASE=32 , SCREAMING_SNAKE_CASE=2 , SCREAMING_SNAKE_CASE=3 , SCREAMING_SNAKE_CASE=640 , SCREAMING_SNAKE_CASE=4 , SCREAMING_SNAKE_CASE="silu" , SCREAMING_SNAKE_CASE=3 , SCREAMING_SNAKE_CASE=32 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.1 , SCREAMING_SNAKE_CASE=0.02 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=10 , SCREAMING_SNAKE_CASE=None , ) -> Dict:
"""simple docstring"""
A : Any = parent
A : Any = batch_size
A : Any = image_size
A : List[Any] = patch_size
A : List[Any] = num_channels
A : List[str] = last_hidden_size
A : int = num_attention_heads
A : List[str] = hidden_act
A : List[str] = conv_kernel_size
A : Optional[int] = output_stride
A : List[str] = hidden_dropout_prob
A : int = attention_probs_dropout_prob
A : Union[str, Any] = classifier_dropout_prob
A : int = use_labels
A : int = is_training
A : str = num_labels
A : List[str] = initializer_range
A : int = scope
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
A : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A : Union[str, Any] = None
A : Union[str, Any] = None
if self.use_labels:
A : Dict = ids_tensor([self.batch_size] , self.num_labels )
A : Dict = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
A : Optional[Any] = self.get_config()
return config, pixel_values, labels, pixel_labels
def __lowerCAmelCase ( self ) -> str:
"""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 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
A : Dict = MobileViTModel(config=SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
A : Tuple = model(SCREAMING_SNAKE_CASE )
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 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
A : Optional[int] = self.num_labels
A : Optional[Any] = MobileViTForImageClassification(SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
A : Optional[int] = model(SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
A : Dict = self.num_labels
A : Union[str, Any] = MobileViTForSemanticSegmentation(SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
A : str = model(SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
A : int = model(SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE )
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 ) -> Tuple:
"""simple docstring"""
A : Optional[int] = self.prepare_config_and_inputs()
A : List[Any] = config_and_inputs
A : Optional[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class A ( __snake_case , __snake_case , unittest.TestCase ):
__magic_name__ = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
__magic_name__ = (
{
'''feature-extraction''': MobileViTModel,
'''image-classification''': MobileViTForImageClassification,
'''image-segmentation''': MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__magic_name__ = False
__magic_name__ = False
__magic_name__ = False
__magic_name__ = False
def __lowerCAmelCase ( self ) -> str:
"""simple docstring"""
A : str = MobileViTModelTester(self )
A : Optional[int] = MobileViTConfigTester(self , config_class=SCREAMING_SNAKE_CASE , has_text_modality=SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileViT does not use inputs_embeds''' )
def __lowerCAmelCase ( self ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason='''MobileViT does not support input and output embeddings''' )
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
pass
@unittest.skip(reason='''MobileViT does not output attentions''' )
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
pass
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
A : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A : Union[str, Any] = model_class(SCREAMING_SNAKE_CASE )
A : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A : Union[str, Any] = [*signature.parameters.keys()]
A : str = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
pass
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
A : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
def check_hidden_states_output(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A : Union[str, Any] = model_class(SCREAMING_SNAKE_CASE )
model.to(SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
A : str = model(**self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
A : str = outputs.hidden_states
A : Tuple = 5
self.assertEqual(len(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
A : List[str] = 2
for i in range(len(SCREAMING_SNAKE_CASE ) ):
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 )
A : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A : Dict = 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 : Any = True
check_hidden_states_output(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> Dict:
"""simple docstring"""
A : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
A : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE )
@slow
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A : Optional[int] = MobileViTModel.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
def lowerCAmelCase_ ( ):
'''simple docstring'''
A : Optional[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class A ( unittest.TestCase ):
@cached_property
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small''' ) if is_vision_available() else None
@slow
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
A : List[Any] = MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small''' ).to(SCREAMING_SNAKE_CASE )
A : int = self.default_image_processor
A : Dict = prepare_img()
A : List[str] = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).to(SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
A : List[Any] = model(**SCREAMING_SNAKE_CASE )
# verify the logits
A : str = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE )
A : List[Any] = torch.tensor([-1.9_364, -1.2_327, -0.4_653] ).to(SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE , atol=1e-4 ) )
@slow
def __lowerCAmelCase ( self ) -> Optional[Any]:
"""simple docstring"""
A : int = MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
A : Optional[int] = model.to(SCREAMING_SNAKE_CASE )
A : Tuple = MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
A : Union[str, Any] = prepare_img()
A : str = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).to(SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
A : List[Any] = model(**SCREAMING_SNAKE_CASE )
A : List[Any] = outputs.logits
# verify the logits
A : Optional[Any] = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , SCREAMING_SNAKE_CASE )
A : List[Any] = torch.tensor(
[
[[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
[[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
[[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
] , device=SCREAMING_SNAKE_CASE , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , SCREAMING_SNAKE_CASE , atol=1e-4 ) )
@slow
def __lowerCAmelCase ( self ) -> List[str]:
"""simple docstring"""
A : int = MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
A : Dict = model.to(SCREAMING_SNAKE_CASE )
A : Any = MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
A : List[str] = prepare_img()
A : Union[str, Any] = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).to(SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
A : Dict = model(**SCREAMING_SNAKE_CASE )
A : Union[str, Any] = outputs.logits.detach().cpu()
A : str = image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE , target_sizes=[(50, 60)] )
A : List[str] = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , SCREAMING_SNAKE_CASE )
A : Union[str, Any] = image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE )
A : Optional[int] = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , SCREAMING_SNAKE_CASE ) | 366 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
A : Tuple = []
def generate(snake_case__ , snake_case__ ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , snake_case__ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
A, A : Optional[Any] = arr[k - 1], arr[i]
else: # k is odd
A, A : Optional[Any] = arr[k - 1], arr[0]
generate(k - 1 , snake_case__ )
generate(len(snake_case__ ) , snake_case__ )
return res
if __name__ == "__main__":
lowercase : List[str] = input('Enter numbers separated by a comma:\n').strip()
lowercase : int = [int(item) for item in user_input.split(',')]
print(heaps(arr))
| 311 | 0 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case_ ( __A ):
__A : List[Any] = (UnCLIPScheduler,)
def __UpperCamelCase ( self : Union[str, Any] , **lowercase_ : List[str] ) -> List[str]:
lowercase__ : int = {
"num_train_timesteps": 10_00,
"variance_type": "fixed_small_log",
"clip_sample": True,
"clip_sample_range": 1.0,
"prediction_type": "epsilon",
}
config.update(**lowercase_ )
return config
def __UpperCamelCase ( self : Tuple ) -> Tuple:
for timesteps in [1, 5, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=lowercase_ )
def __UpperCamelCase ( self : Optional[int] ) -> Union[str, Any]:
for variance in ["fixed_small_log", "learned_range"]:
self.check_over_configs(variance_type=lowercase_ )
def __UpperCamelCase ( self : int ) -> List[Any]:
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=lowercase_ )
def __UpperCamelCase ( self : str ) -> int:
for clip_sample_range in [1, 5, 10, 20]:
self.check_over_configs(clip_sample_range=lowercase_ )
def __UpperCamelCase ( self : int ) -> Optional[int]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=lowercase_ )
def __UpperCamelCase ( self : str ) -> Union[str, Any]:
for time_step in [0, 5_00, 9_99]:
for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=lowercase_ , prev_timestep=lowercase_ )
def __UpperCamelCase ( self : Union[str, Any] ) -> int:
lowercase__ : Dict = self.scheduler_classes[0]
lowercase__ : Optional[Any] = self.get_scheduler_config(variance_type="fixed_small_log" )
lowercase__ : Optional[int] = scheduler_class(**lowercase_ )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.00_00E-10 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(4_87 ) - 0.0_54_96_25 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(9_99 ) - 0.9_99_49_87 ) ) < 1E-5
def __UpperCamelCase ( self : Union[str, Any] ) -> Optional[Any]:
lowercase__ : Dict = self.scheduler_classes[0]
lowercase__ : Optional[Any] = self.get_scheduler_config(variance_type="learned_range" )
lowercase__ : Any = scheduler_class(**lowercase_ )
lowercase__ : Tuple = 0.5
assert scheduler._get_variance(1 , predicted_variance=lowercase_ ) - -10.1_71_27_90 < 1E-5
assert scheduler._get_variance(4_87 , predicted_variance=lowercase_ ) - -5.7_99_80_52 < 1E-5
assert scheduler._get_variance(9_99 , predicted_variance=lowercase_ ) - -0.0_01_00_11 < 1E-5
def __UpperCamelCase ( self : str ) -> List[Any]:
lowercase__ : Union[str, Any] = self.scheduler_classes[0]
lowercase__ : List[Any] = self.get_scheduler_config()
lowercase__ : Optional[Any] = scheduler_class(**lowercase_ )
lowercase__ : Union[str, Any] = scheduler.timesteps
lowercase__ : List[Any] = self.dummy_model()
lowercase__ : str = self.dummy_sample_deter
lowercase__ : int = torch.manual_seed(0 )
for i, t in enumerate(lowercase_ ):
# 1. predict noise residual
lowercase__ : Optional[int] = model(lowercase_ , lowercase_ )
# 2. predict previous mean of sample x_t-1
lowercase__ : int = scheduler.step(lowercase_ , lowercase_ , lowercase_ , generator=lowercase_ ).prev_sample
lowercase__ : List[str] = pred_prev_sample
lowercase__ : List[str] = torch.sum(torch.abs(lowercase_ ) )
lowercase__ : List[Any] = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 2_52.2_68_24_95 ) < 1E-2
assert abs(result_mean.item() - 0.3_28_47_43 ) < 1E-3
def __UpperCamelCase ( self : Optional[Any] ) -> List[str]:
lowercase__ : Optional[Any] = self.scheduler_classes[0]
lowercase__ : Any = self.get_scheduler_config()
lowercase__ : List[Any] = scheduler_class(**lowercase_ )
scheduler.set_timesteps(25 )
lowercase__ : Union[str, Any] = scheduler.timesteps
lowercase__ : Tuple = self.dummy_model()
lowercase__ : int = self.dummy_sample_deter
lowercase__ : Optional[Any] = torch.manual_seed(0 )
for i, t in enumerate(lowercase_ ):
# 1. predict noise residual
lowercase__ : Tuple = model(lowercase_ , lowercase_ )
if i + 1 == timesteps.shape[0]:
lowercase__ : int = None
else:
lowercase__ : Union[str, Any] = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
lowercase__ : str = scheduler.step(
lowercase_ , lowercase_ , lowercase_ , prev_timestep=lowercase_ , generator=lowercase_ ).prev_sample
lowercase__ : str = pred_prev_sample
lowercase__ : Dict = torch.sum(torch.abs(lowercase_ ) )
lowercase__ : List[Any] = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 2_58.2_04_49_83 ) < 1E-2
assert abs(result_mean.item() - 0.3_36_20_38 ) < 1E-3
def __UpperCamelCase ( self : Any ) -> Tuple:
pass
def __UpperCamelCase ( self : Optional[int] ) -> Optional[Any]:
pass
| 87 | 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
UpperCamelCase = logging.get_logger(__name__)
if is_vision_available():
import PIL
class snake_case_ ( __A ):
__A : str = ["pixel_values"]
def __init__( self : int , lowercase_ : bool = True , lowercase_ : Dict[str, int] = None , lowercase_ : PILImageResampling = PILImageResampling.BICUBIC , lowercase_ : bool = True , lowercase_ : Dict[str, int] = None , lowercase_ : bool = True , lowercase_ : Union[int, float] = 1 / 2_55 , lowercase_ : bool = True , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : bool = True , **lowercase_ : Union[str, Any] , ) -> None:
super().__init__(**lowercase_ )
lowercase__ : Tuple = size if size is not None else {"shortest_edge": 2_24}
lowercase__ : Tuple = get_size_dict(lowercase_ , default_to_square=lowercase_ )
lowercase__ : List[str] = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
lowercase__ : Tuple = get_size_dict(lowercase_ , default_to_square=lowercase_ , param_name="crop_size" )
lowercase__ : Dict = do_resize
lowercase__ : List[Any] = size
lowercase__ : int = resample
lowercase__ : Union[str, Any] = do_center_crop
lowercase__ : Optional[int] = crop_size
lowercase__ : List[str] = do_rescale
lowercase__ : int = rescale_factor
lowercase__ : List[Any] = do_normalize
lowercase__ : Union[str, Any] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
lowercase__ : str = image_std if image_std is not None else OPENAI_CLIP_STD
lowercase__ : Dict = do_convert_rgb
def __UpperCamelCase ( self : Optional[Any] , lowercase_ : np.ndarray , lowercase_ : Dict[str, int] , lowercase_ : PILImageResampling = PILImageResampling.BICUBIC , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : Union[str, Any] , ) -> np.ndarray:
lowercase__ : str = get_size_dict(lowercase_ , default_to_square=lowercase_ )
if "shortest_edge" not in size:
raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
lowercase__ : Dict = get_resize_output_image_size(lowercase_ , size=size["shortest_edge"] , default_to_square=lowercase_ )
return resize(lowercase_ , size=lowercase_ , resample=lowercase_ , data_format=lowercase_ , **lowercase_ )
def __UpperCamelCase ( self : int , lowercase_ : np.ndarray , lowercase_ : Dict[str, int] , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : int , ) -> np.ndarray:
lowercase__ : Optional[Any] = get_size_dict(lowercase_ )
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(lowercase_ , size=(size["height"], size["width"]) , data_format=lowercase_ , **lowercase_ )
def __UpperCamelCase ( self : Optional[Any] , lowercase_ : np.ndarray , lowercase_ : Union[int, float] , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : Optional[Any] , ) -> Any:
return rescale(lowercase_ , scale=lowercase_ , data_format=lowercase_ , **lowercase_ )
def __UpperCamelCase ( self : str , lowercase_ : np.ndarray , lowercase_ : Union[float, List[float]] , lowercase_ : Union[float, List[float]] , lowercase_ : Optional[Union[str, ChannelDimension]] = None , **lowercase_ : str , ) -> np.ndarray:
return normalize(lowercase_ , mean=lowercase_ , std=lowercase_ , data_format=lowercase_ , **lowercase_ )
def __UpperCamelCase ( self : Optional[Any] , lowercase_ : ImageInput , lowercase_ : bool = None , lowercase_ : Dict[str, int] = None , lowercase_ : PILImageResampling = None , lowercase_ : bool = None , lowercase_ : int = None , lowercase_ : bool = None , lowercase_ : float = None , lowercase_ : bool = None , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : Optional[Union[float, List[float]]] = None , lowercase_ : bool = None , lowercase_ : Optional[Union[str, TensorType]] = None , lowercase_ : Optional[ChannelDimension] = ChannelDimension.FIRST , **lowercase_ : Union[str, Any] , ) -> PIL.Image.Image:
lowercase__ : int = do_resize if do_resize is not None else self.do_resize
lowercase__ : Dict = size if size is not None else self.size
lowercase__ : List[Any] = get_size_dict(lowercase_ , param_name="size" , default_to_square=lowercase_ )
lowercase__ : Dict = resample if resample is not None else self.resample
lowercase__ : int = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase__ : Dict = crop_size if crop_size is not None else self.crop_size
lowercase__ : List[str] = get_size_dict(lowercase_ , param_name="crop_size" , default_to_square=lowercase_ )
lowercase__ : int = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ : Dict = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ : Union[str, Any] = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ : int = image_mean if image_mean is not None else self.image_mean
lowercase__ : List[str] = image_std if image_std is not None else self.image_std
lowercase__ : Union[str, Any] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
lowercase__ : Union[str, Any] = make_list_of_images(lowercase_ )
if not valid_images(lowercase_ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
lowercase__ : Dict = [convert_to_rgb(lowercase_ ) for image in images]
# All transformations expect numpy arrays.
lowercase__ : Optional[Any] = [to_numpy_array(lowercase_ ) for image in images]
if do_resize:
lowercase__ : List[Any] = [self.resize(image=lowercase_ , size=lowercase_ , resample=lowercase_ ) for image in images]
if do_center_crop:
lowercase__ : int = [self.center_crop(image=lowercase_ , size=lowercase_ ) for image in images]
if do_rescale:
lowercase__ : str = [self.rescale(image=lowercase_ , scale=lowercase_ ) for image in images]
if do_normalize:
lowercase__ : Optional[int] = [self.normalize(image=lowercase_ , mean=lowercase_ , std=lowercase_ ) for image in images]
lowercase__ : Optional[Any] = [to_channel_dimension_format(lowercase_ , lowercase_ ) for image in images]
lowercase__ : List[str] = {"pixel_values": images}
return BatchFeature(data=lowercase_ , tensor_type=lowercase_ )
| 87 | 1 |
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Union[str, Any] = abs(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: Any = 0
while n > 0:
res += n % 10
n //= 10
return res
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Optional[Any] = abs(_UpperCAmelCase )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def A_ ( _UpperCAmelCase ):
return sum(int(_UpperCAmelCase ) for c in str(abs(_UpperCAmelCase ) ) )
def A_ ( ):
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(_UpperCAmelCase , _UpperCAmelCase ) -> None:
SCREAMING_SNAKE_CASE_: str = f"{func.__name__}({value})"
SCREAMING_SNAKE_CASE_: Any = timeit(f"__main__.{call}" , setup="import __main__" )
print(f"{call:56} = {func(_UpperCAmelCase )} -- {timing:.4f} seconds" )
for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(_UpperCAmelCase , _UpperCAmelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 354 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DeformableDetrImageProcessor
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Any , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Optional[Any]=7 , lowerCAmelCase__ : Tuple=3 , lowerCAmelCase__ : Optional[int]=30 , lowerCAmelCase__ : Dict=400 , lowerCAmelCase__ : int=True , lowerCAmelCase__ : Dict=None , lowerCAmelCase__ : Union[str, Any]=True , lowerCAmelCase__ : Any=[0.5, 0.5, 0.5] , lowerCAmelCase__ : Optional[Any]=[0.5, 0.5, 0.5] , lowerCAmelCase__ : List[Any]=True , lowerCAmelCase__ : Tuple=1 / 255 , lowerCAmelCase__ : int=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
SCREAMING_SNAKE_CASE_: Optional[Any] = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
SCREAMING_SNAKE_CASE_: str = parent
SCREAMING_SNAKE_CASE_: Tuple = batch_size
SCREAMING_SNAKE_CASE_: Tuple = num_channels
SCREAMING_SNAKE_CASE_: Union[str, Any] = min_resolution
SCREAMING_SNAKE_CASE_: Tuple = max_resolution
SCREAMING_SNAKE_CASE_: List[Any] = do_resize
SCREAMING_SNAKE_CASE_: Optional[int] = size
SCREAMING_SNAKE_CASE_: Optional[int] = do_normalize
SCREAMING_SNAKE_CASE_: Any = image_mean
SCREAMING_SNAKE_CASE_: Dict = image_std
SCREAMING_SNAKE_CASE_: Tuple = do_rescale
SCREAMING_SNAKE_CASE_: int = rescale_factor
SCREAMING_SNAKE_CASE_: int = do_pad
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def _SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase__ : Dict , lowerCAmelCase__ : int=False):
if not batched:
SCREAMING_SNAKE_CASE_: List[str] = image_inputs[0]
if isinstance(lowerCAmelCase__ , Image.Image):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Union[str, Any] = image.size
else:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Union[str, Any] = image.shape[1], image.shape[2]
if w < h:
SCREAMING_SNAKE_CASE_: List[Any] = int(self.size["shortest_edge"] * h / w)
SCREAMING_SNAKE_CASE_: Union[str, Any] = self.size["shortest_edge"]
elif w > h:
SCREAMING_SNAKE_CASE_: Any = self.size["shortest_edge"]
SCREAMING_SNAKE_CASE_: Union[str, Any] = int(self.size["shortest_edge"] * w / h)
else:
SCREAMING_SNAKE_CASE_: int = self.size["shortest_edge"]
SCREAMING_SNAKE_CASE_: Dict = self.size["shortest_edge"]
else:
SCREAMING_SNAKE_CASE_: int = []
for image in image_inputs:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[int] = self.get_expected_values([image])
expected_values.append((expected_height, expected_width))
SCREAMING_SNAKE_CASE_: Tuple = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__: item[0])[0]
SCREAMING_SNAKE_CASE_: Optional[Any] = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__: item[1])[1]
return expected_height, expected_width
@require_torch
@require_vision
class __lowercase ( UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
_UpperCAmelCase : Any = DeformableDetrImageProcessor if is_vision_available() else None
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
SCREAMING_SNAKE_CASE_: int = DeformableDetrImageProcessingTester(self)
@property
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
return self.image_processor_tester.prepare_image_processor_dict()
def _SCREAMING_SNAKE_CASE ( self : List[str]):
SCREAMING_SNAKE_CASE_: Dict = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(lowerCAmelCase__ , "image_mean"))
self.assertTrue(hasattr(lowerCAmelCase__ , "image_std"))
self.assertTrue(hasattr(lowerCAmelCase__ , "do_normalize"))
self.assertTrue(hasattr(lowerCAmelCase__ , "do_resize"))
self.assertTrue(hasattr(lowerCAmelCase__ , "do_rescale"))
self.assertTrue(hasattr(lowerCAmelCase__ , "do_pad"))
self.assertTrue(hasattr(lowerCAmelCase__ , "size"))
def _SCREAMING_SNAKE_CASE ( self : Tuple):
SCREAMING_SNAKE_CASE_: Dict = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 1333})
self.assertEqual(image_processor.do_pad , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Dict = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=lowerCAmelCase__)
self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84})
self.assertEqual(image_processor.do_pad , lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]):
pass
def _SCREAMING_SNAKE_CASE ( self : List[Any]):
# Initialize image_processing
SCREAMING_SNAKE_CASE_: List[Any] = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
SCREAMING_SNAKE_CASE_: Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__)
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image)
# Test not batched input
SCREAMING_SNAKE_CASE_: Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt").pixel_values
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[str] = self.image_processor_tester.get_expected_values(lowerCAmelCase__)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[Any] = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[str] = image_processing(lowerCAmelCase__ , return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _SCREAMING_SNAKE_CASE ( self : str):
# Initialize image_processing
SCREAMING_SNAKE_CASE_: Optional[Any] = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
SCREAMING_SNAKE_CASE_: List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__)
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray)
# Test not batched input
SCREAMING_SNAKE_CASE_: str = image_processing(image_inputs[0] , return_tensors="pt").pixel_values
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[str] = self.image_processor_tester.get_expected_values(lowerCAmelCase__)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE_: Any = image_processing(lowerCAmelCase__ , return_tensors="pt").pixel_values
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: int = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__)
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _SCREAMING_SNAKE_CASE ( self : List[Any]):
# Initialize image_processing
SCREAMING_SNAKE_CASE_: List[Any] = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
SCREAMING_SNAKE_CASE_: int = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__)
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor)
# Test not batched input
SCREAMING_SNAKE_CASE_: Dict = image_processing(image_inputs[0] , return_tensors="pt").pixel_values
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__)
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE_: Optional[Any] = image_processing(lowerCAmelCase__ , return_tensors="pt").pixel_values
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Optional[Any] = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__)
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def _SCREAMING_SNAKE_CASE ( self : Tuple):
# prepare image and target
SCREAMING_SNAKE_CASE_: Optional[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r") as f:
SCREAMING_SNAKE_CASE_: str = json.loads(f.read())
SCREAMING_SNAKE_CASE_: Optional[int] = {"image_id": 3_9769, "annotations": target}
# encode them
SCREAMING_SNAKE_CASE_: str = DeformableDetrImageProcessor()
SCREAMING_SNAKE_CASE_: Dict = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , return_tensors="pt")
# verify pixel values
SCREAMING_SNAKE_CASE_: Optional[Any] = torch.Size([1, 3, 800, 1066])
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: str = torch.tensor([0.2796, 0.3138, 0.3481])
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , lowerCAmelCase__ , atol=1E-4))
# verify area
SCREAMING_SNAKE_CASE_: int = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438])
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , lowerCAmelCase__))
# verify boxes
SCREAMING_SNAKE_CASE_: str = torch.Size([6, 4])
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Dict = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , lowerCAmelCase__ , atol=1E-3))
# verify image_id
SCREAMING_SNAKE_CASE_: str = torch.tensor([3_9769])
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__))
# verify is_crowd
SCREAMING_SNAKE_CASE_: int = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__))
# verify class_labels
SCREAMING_SNAKE_CASE_: Tuple = torch.tensor([75, 75, 63, 65, 17, 17])
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__))
# verify orig_size
SCREAMING_SNAKE_CASE_: Union[str, Any] = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__))
# verify size
SCREAMING_SNAKE_CASE_: str = torch.tensor([800, 1066])
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__))
@slow
def _SCREAMING_SNAKE_CASE ( self : Tuple):
# prepare image, target and masks_path
SCREAMING_SNAKE_CASE_: Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r") as f:
SCREAMING_SNAKE_CASE_: List[Any] = json.loads(f.read())
SCREAMING_SNAKE_CASE_: Optional[Any] = {"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}
SCREAMING_SNAKE_CASE_: int = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")
# encode them
SCREAMING_SNAKE_CASE_: Any = DeformableDetrImageProcessor(format="coco_panoptic")
SCREAMING_SNAKE_CASE_: Optional[Any] = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , masks_path=lowerCAmelCase__ , return_tensors="pt")
# verify pixel values
SCREAMING_SNAKE_CASE_: Dict = torch.Size([1, 3, 800, 1066])
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: List[Any] = torch.tensor([0.2796, 0.3138, 0.3481])
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , lowerCAmelCase__ , atol=1E-4))
# verify area
SCREAMING_SNAKE_CASE_: Optional[Any] = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147])
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , lowerCAmelCase__))
# verify boxes
SCREAMING_SNAKE_CASE_: List[str] = torch.Size([6, 4])
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Optional[Any] = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , lowerCAmelCase__ , atol=1E-3))
# verify image_id
SCREAMING_SNAKE_CASE_: Any = torch.tensor([3_9769])
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__))
# verify is_crowd
SCREAMING_SNAKE_CASE_: Union[str, Any] = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__))
# verify class_labels
SCREAMING_SNAKE_CASE_: List[Any] = torch.tensor([17, 17, 63, 75, 75, 93])
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__))
# verify masks
SCREAMING_SNAKE_CASE_: Tuple = 82_2873
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , lowerCAmelCase__)
# verify orig_size
SCREAMING_SNAKE_CASE_: str = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__))
# verify size
SCREAMING_SNAKE_CASE_: Optional[int] = torch.tensor([800, 1066])
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__))
| 127 | 0 |
from __future__ import annotations
def lowerCamelCase__ (_UpperCAmelCase = 4):
SCREAMING_SNAKE_CASE = abs(_UpperCAmelCase) or 4
return [[1 + x + y * row_size for x in range(_UpperCAmelCase)] for y in range(_UpperCAmelCase)]
def lowerCamelCase__ (_UpperCAmelCase):
return reverse_row(transpose(_UpperCAmelCase))
# OR.. transpose(reverse_column(matrix))
def lowerCamelCase__ (_UpperCAmelCase):
return reverse_row(reverse_column(_UpperCAmelCase))
# OR.. reverse_column(reverse_row(matrix))
def lowerCamelCase__ (_UpperCAmelCase):
return reverse_column(transpose(_UpperCAmelCase))
# OR.. transpose(reverse_row(matrix))
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [list(_UpperCAmelCase) for x in zip(*_UpperCAmelCase)]
return matrix
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = matrix[::-1]
return matrix
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [x[::-1] for x in matrix]
return matrix
def lowerCamelCase__ (_UpperCAmelCase):
for i in matrix:
print(*_UpperCAmelCase)
if __name__ == "__main__":
a_ : Tuple = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 90 counterclockwise:\n')
print_matrix(rotate_aa(matrix))
a_ : List[Any] = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 180:\n')
print_matrix(rotate_aaa(matrix))
a_ : Tuple = make_matrix()
print('\norigin:\n')
print_matrix(matrix)
print('\nrotate 270 counterclockwise:\n')
print_matrix(rotate_aaa(matrix))
| 137 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ : str = {'configuration_mbart': ['MBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MBartConfig', 'MBartOnnxConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Optional[Any] = ['MBartTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : int = ['MBartTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : List[str] = [
'MBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'MBartForCausalLM',
'MBartForConditionalGeneration',
'MBartForQuestionAnswering',
'MBartForSequenceClassification',
'MBartModel',
'MBartPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Optional[int] = [
'TFMBartForConditionalGeneration',
'TFMBartModel',
'TFMBartPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : List[Any] = [
'FlaxMBartForConditionalGeneration',
'FlaxMBartForQuestionAnswering',
'FlaxMBartForSequenceClassification',
'FlaxMBartModel',
'FlaxMBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
a_ : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 137 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE = {
"""configuration_poolformer""": [
"""POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""PoolFormerConfig""",
"""PoolFormerOnnxConfig""",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = ["""PoolFormerFeatureExtractor"""]
_SCREAMING_SNAKE_CASE = ["""PoolFormerImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
"""POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""PoolFormerForImageClassification""",
"""PoolFormerModel""",
"""PoolFormerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_poolformer import (
POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
PoolFormerConfig,
PoolFormerOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_poolformer import PoolFormerFeatureExtractor
from .image_processing_poolformer import PoolFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_poolformer import (
POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
PoolFormerForImageClassification,
PoolFormerModel,
PoolFormerPreTrainedModel,
)
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 165 | from __future__ import annotations
def lowercase( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> tuple[float, list[float]]:
'''simple docstring'''
UpperCamelCase = list(range(len(UpperCamelCase_ ) ) )
UpperCamelCase = [v / w for v, w in zip(UpperCamelCase_ , UpperCamelCase_ )]
index.sort(key=lambda UpperCamelCase_ : ratio[i] , reverse=UpperCamelCase_ )
UpperCamelCase = 0
UpperCamelCase = [0] * len(UpperCamelCase_ )
for i in index:
if weight[i] <= capacity:
UpperCamelCase = 1
max_value += value[i]
capacity -= weight[i]
else:
UpperCamelCase = capacity / weight[i]
max_value += value[i] * capacity / weight[i]
break
return max_value, fractions
if __name__ == "__main__":
import doctest
doctest.testmod()
| 165 | 1 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __lowerCAmelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , unittest.TestCase ):
_a = IFInpaintingSuperResolutionPipeline
_a = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
_a = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""} )
_a = PipelineTesterMixin.required_optional_params - {"""latents"""}
def A__ ( self ) -> int:
'''simple docstring'''
return self._get_superresolution_dummy_components()
def A__ ( self , lowerCAmelCase , lowerCAmelCase=0 ) -> Optional[Any]:
'''simple docstring'''
if str(lowerCAmelCase ).startswith('mps' ):
_lowercase =torch.manual_seed(lowerCAmelCase )
else:
_lowercase =torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
_lowercase =floats_tensor((1, 3, 16, 16) , rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
_lowercase =floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
_lowercase =floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
_lowercase ={
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'original_image': original_image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def A__ ( self ) -> Dict:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def A__ ( self ) -> Any:
'''simple docstring'''
super().test_save_load_floataa(expected_max_diff=1e-1 )
def A__ ( self ) -> Any:
'''simple docstring'''
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def A__ ( self ) -> Tuple:
'''simple docstring'''
self._test_save_load_local()
def A__ ( self ) -> str:
'''simple docstring'''
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 205 |
# 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
lowercase_ = 'Create a default config file for Accelerate with only a few flags set.'
def a ( A__ : Optional[Any]="no" , A__ : str = default_json_config_file , A__ : bool = False ) -> Optional[int]:
"""simple docstring"""
_lowercase =Path(A__ )
path.parent.mkdir(parents=A__ , exist_ok=A__ )
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
_lowercase =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}''' )
_lowercase ={
'compute_environment': 'LOCAL_MACHINE',
'mixed_precision': mixed_precision,
}
if torch.cuda.is_available():
_lowercase =torch.cuda.device_count()
_lowercase =num_gpus
_lowercase =False
if num_gpus > 1:
_lowercase ='MULTI_GPU'
else:
_lowercase ='NO'
elif is_xpu_available() and use_xpu:
_lowercase =torch.xpu.device_count()
_lowercase =num_xpus
_lowercase =False
if num_xpus > 1:
_lowercase ='MULTI_XPU'
else:
_lowercase ='NO'
elif is_npu_available():
_lowercase =torch.npu.device_count()
_lowercase =num_npus
_lowercase =False
if num_npus > 1:
_lowercase ='MULTI_NPU'
else:
_lowercase ='NO'
else:
_lowercase =0
_lowercase =True
_lowercase =1
_lowercase ='NO'
_lowercase =ClusterConfig(**A__ )
config.to_json_file(A__ )
return path
def a ( A__ : Dict , A__ : Optional[Any] ) -> List[Any]:
"""simple docstring"""
_lowercase =parser.add_parser('default' , parents=A__ , help=A__ , formatter_class=A__ )
parser.add_argument(
'--config_file' , default=A__ , 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=A__ , 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=A__ )
return parser
def a ( A__ : List[str] ) -> Any:
"""simple docstring"""
_lowercase =write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(F'''accelerate configuration saved at {config_file}''' )
| 205 | 1 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
lowercase__ = logging.getLogger(__name__)
@dataclass
@add_start_docstrings(TrainingArguments.__doc__ )
class __snake_case ( __lowerCAmelCase ):
a__ = field(default=__lowerCAmelCase , metadata={"""help""": """Whether to use SortishSampler or not."""} )
a__ = field(
default=__lowerCAmelCase , metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""} )
a__ = field(
default=__lowerCAmelCase , metadata={
"""help""": (
"""The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default """
"""to the `max_length` value of the model configuration."""
)
} , )
a__ = field(
default=__lowerCAmelCase , metadata={
"""help""": (
"""The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default """
"""to the `num_beams` value of the model configuration."""
)
} , )
a__ = field(
default=__lowerCAmelCase , metadata={
"""help""": """Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."""
} , )
def lowerCamelCase_ ( self) -> List[Any]:
'''simple docstring'''
a__: Optional[int] = super().to_dict()
for k, v in d.items():
if isinstance(lowercase , lowercase):
a__: Any = v.to_dict()
return d
| 350 | """simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
lowercase__ = logging.get_logger(__name__)
lowercase__ = Dict[str, Any]
lowercase__ = List[Prediction]
@add_end_docstrings(__lowerCAmelCase )
class __snake_case ( __lowerCAmelCase ):
def __init__( self , *lowercase , **lowercase) -> Dict:
'''simple docstring'''
super().__init__(*lowercase , **lowercase)
if self.framework == "tf":
raise ValueError(f'The {self.__class__} is only available in PyTorch.')
requires_backends(self , 'vision')
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))
def lowerCamelCase_ ( self , **lowercase) -> int:
'''simple docstring'''
a__: Optional[Any] = {}
if "threshold" in kwargs:
a__: Dict = kwargs['threshold']
return {}, {}, postprocess_kwargs
def __call__( self , *lowercase , **lowercase) -> Union[Predictions, List[Prediction]]:
'''simple docstring'''
return super().__call__(*lowercase , **lowercase)
def lowerCamelCase_ ( self , lowercase) -> List[Any]:
'''simple docstring'''
a__: Optional[Any] = load_image(lowercase)
a__: List[Any] = torch.IntTensor([[image.height, image.width]])
a__: Any = self.image_processor(images=[image] , return_tensors='pt')
if self.tokenizer is not None:
a__: Any = self.tokenizer(text=inputs['words'] , boxes=inputs['boxes'] , return_tensors='pt')
a__: List[str] = target_size
return inputs
def lowerCamelCase_ ( self , lowercase) -> int:
'''simple docstring'''
a__: Any = model_inputs.pop('target_size')
a__: Union[str, Any] = self.model(**lowercase)
a__: List[str] = outputs.__class__({'target_size': target_size, **outputs})
if self.tokenizer is not None:
a__: Union[str, Any] = model_inputs['bbox']
return model_outputs
def lowerCamelCase_ ( self , lowercase , lowercase=0.9) -> Optional[Any]:
'''simple docstring'''
a__: int = model_outputs['target_size']
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
a__ , a__: str = target_size[0].tolist()
def unnormalize(lowercase):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 10_00),
(height * bbox[1] / 10_00),
(width * bbox[2] / 10_00),
(height * bbox[3] / 10_00),
]))
a__ , a__: Optional[Any] = model_outputs['logits'].squeeze(0).softmax(dim=-1).max(dim=-1)
a__: str = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
a__: Union[str, Any] = [unnormalize(lowercase) for bbox in model_outputs['bbox'].squeeze(0)]
a__: Dict = ['score', 'label', 'box']
a__: Any = [dict(zip(lowercase , lowercase)) for vals in zip(scores.tolist() , lowercase , lowercase) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
a__: List[str] = self.image_processor.post_process_object_detection(lowercase , lowercase , lowercase)
a__: Tuple = raw_annotations[0]
a__: List[str] = raw_annotation['scores']
a__: int = raw_annotation['labels']
a__: int = raw_annotation['boxes']
a__: List[Any] = scores.tolist()
a__: Any = [self.model.config.idalabel[label.item()] for label in labels]
a__: Dict = [self._get_bounding_box(lowercase) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
a__: Optional[Any] = ['score', 'label', 'box']
a__: List[Any] = [
dict(zip(lowercase , lowercase))
for vals in zip(raw_annotation['scores'] , raw_annotation['labels'] , raw_annotation['boxes'])
]
return annotation
def lowerCamelCase_ ( self , lowercase) -> Dict[str, int]:
'''simple docstring'''
if self.framework != "pt":
raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.')
a__ , a__ , a__ , a__: List[Any] = box.int().tolist()
a__: Optional[int] = {
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax,
}
return bbox
| 203 | 0 |
from __future__ import annotations
from fractions import Fraction
def UpperCAmelCase ( a_ , a_ ) -> bool:
"""simple docstring"""
return (
num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
)
def UpperCAmelCase ( a_ ) -> list[str]:
"""simple docstring"""
__A = []
__A = 1_1
__A = int("1" + "0" * digit_len )
for num in range(a_ , a_ ):
while den <= 9_9:
if (num != den) and (num % 1_0 == den // 1_0) and (den % 1_0 != 0):
if is_digit_cancelling(a_ , a_ ):
solutions.append(F'''{num}/{den}''' )
den += 1
num += 1
__A = 1_0
return solutions
def UpperCAmelCase ( a_ = 2 ) -> int:
"""simple docstring"""
__A = 1.0
for fraction in fraction_list(a_ ):
__A = Fraction(a_ )
result *= frac.denominator / frac.numerator
return int(a_ )
if __name__ == "__main__":
print(solution())
| 15 |
from __future__ import annotations
from collections.abc import Iterator
class __lowerCAmelCase :
def __init__( self :Optional[Any] , __magic_name__ :int ):
'''simple docstring'''
a = value
a = None
a = None
class __lowerCAmelCase :
def __init__( self :str , __magic_name__ :Node ):
'''simple docstring'''
a = tree
def lowerCamelCase__ ( self :str , __magic_name__ :Node | None ):
'''simple docstring'''
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self :Tuple ):
'''simple docstring'''
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 228 | 0 |
"""simple docstring"""
import math
def lowercase ( A_ )-> int:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
a : Dict = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_lowerCamelCase )
if number < 1:
a : Union[str, Any] = F'''Input value of [number={number}] must be > 0'''
raise ValueError(_lowerCamelCase )
elif number == 1:
return 3
elif number == 2:
return 5
else:
a : Union[str, Any] = int(math.log(number // 3 , 2 ) ) + 2
a : List[str] = [3, 5]
a : Optional[Any] = 2
a : Optional[Any] = 3
for block in range(1 , _lowerCamelCase ):
for _ in range(_lowerCamelCase ):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
__lowercase = 0
try:
__lowercase = proth(number)
except ValueError:
print(f'''ValueError: there is no {number}th Proth number''')
continue
print(f'''The {number}th Proth number: {value}''')
| 363 |
"""simple docstring"""
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment
def lowercase ( )-> Union[str, Any]:
'''simple docstring'''
a : Union[str, Any] = torch.nn.Linear(2 , 4 )
a : Tuple = torch.optim.AdamW(model.parameters() , lr=1.0 )
a : Union[str, Any] = torch.optim.lr_scheduler.OneCycleLR(A_ , max_lr=0.0_1 , steps_per_epoch=2 , epochs=1 )
a : List[str] = DataLoader(TensorDataset(torch.tensor([1, 2, 3] ) ) )
a : int = DataLoader(TensorDataset(torch.tensor([4, 5, 6] ) ) )
return model, optimizer, scheduler, train_dl, valid_dl
def lowercase ( A_ )-> List[Any]:
'''simple docstring'''
return (model.weight.abs().sum() + model.bias.abs().sum()).item()
def lowercase ( A_ )-> Tuple:
'''simple docstring'''
a : Optional[int] = torch.nn.Linear(*tuple(model.weight.T.shape ) ).state_dict()
model.load_state_dict(A_ )
class _A ( _a ):
"""simple docstring"""
@require_cuda
def __snake_case ( self : Any):
a : List[str] = Accelerator()
assert PartialState._shared_state["_cpu"] is False
assert PartialState._shared_state["device"].type == "cuda"
with self.assertRaises(__UpperCAmelCase):
a : Any = Accelerator(cpu=__UpperCAmelCase)
def __snake_case ( self : List[Any]):
a : str = Accelerator()
a : Optional[Any] = GradientState()
assert state.num_steps == 1
a : Dict = 4
assert state.num_steps == 4
assert state.sync_gradients is True
a : Optional[int] = False
assert state.sync_gradients is False
GradientState._reset_state()
def __snake_case ( self : str):
a : int = Accelerator()
a , a , a , a , a : Tuple = create_components()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) : Tuple = accelerator.prepare(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)
self.assertTrue(prepared_model in accelerator._models)
self.assertTrue(prepared_optimizer in accelerator._optimizers)
self.assertTrue(prepared_scheduler in accelerator._schedulers)
self.assertTrue(prepared_train_dl in accelerator._dataloaders)
self.assertTrue(prepared_valid_dl in accelerator._dataloaders)
def __snake_case ( self : Dict):
a : Dict = Accelerator()
a , a , a , a , a : Any = create_components()
accelerator.prepare(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)
accelerator.free_memory()
self.assertTrue(len(accelerator._models) == 0)
self.assertTrue(len(accelerator._optimizers) == 0)
self.assertTrue(len(accelerator._schedulers) == 0)
self.assertTrue(len(accelerator._dataloaders) == 0)
def __snake_case ( self : int):
PartialState._reset_state()
# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
def noop(*__UpperCAmelCase : Union[str, Any] , **__UpperCAmelCase : Any):
pass
with patch("torch.cuda.set_device" , __UpperCAmelCase), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"):
a : int = Accelerator()
self.assertEqual(str(accelerator.state.device) , "cuda:64")
def __snake_case ( self : List[str]):
a : Tuple = Accelerator()
a , a , a , a , a : Optional[Any] = create_components()
accelerator.prepare(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)
a : Dict = get_signature(__UpperCAmelCase)
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(__UpperCAmelCase)
# make sure random weights don't match
load_random_weights(__UpperCAmelCase)
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) > 1e-3)
# make sure loaded weights match
accelerator.load_state(__UpperCAmelCase)
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) < 1e-3)
def __snake_case ( self : Optional[int]):
a : str = Accelerator()
a , a , a , a , a : Dict = create_components()
accelerator.prepare(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)
a : Union[str, Any] = get_signature(__UpperCAmelCase)
# saving hook
def save_config(__UpperCAmelCase : Tuple , __UpperCAmelCase : int , __UpperCAmelCase : Union[str, Any]):
a : Tuple = {"class_name": models[0].__class__.__name__}
with open(os.path.join(__UpperCAmelCase , "data.json") , "w") as f:
json.dump(__UpperCAmelCase , __UpperCAmelCase)
# loading hook
def load_config(__UpperCAmelCase : Dict , __UpperCAmelCase : Union[str, Any]):
with open(os.path.join(__UpperCAmelCase , "data.json") , "r") as f:
a : Optional[Any] = json.load(__UpperCAmelCase)
a : Tuple = config["class_name"]
a : Optional[int] = accelerator.register_save_state_pre_hook(__UpperCAmelCase)
a : Tuple = accelerator.register_load_state_pre_hook(__UpperCAmelCase)
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(__UpperCAmelCase)
# make sure random weights don't match with hooks
load_random_weights(__UpperCAmelCase)
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) > 1e-3)
# random class name to verify correct one is loaded
a : int = "random"
# make sure loaded weights match with hooks
accelerator.load_state(__UpperCAmelCase)
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) < 1e-3)
# mode.class_name is loaded from config
self.assertTrue(model.class_name == model.__class__.__name__)
# remove hooks
save_hook.remove()
load_hook.remove()
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(__UpperCAmelCase)
# make sure random weights don't match with hooks removed
load_random_weights(__UpperCAmelCase)
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) > 1e-3)
# random class name to verify correct one is loaded
a : Dict = "random"
# make sure loaded weights match with hooks removed
accelerator.load_state(__UpperCAmelCase)
self.assertTrue(abs(model_signature - get_signature(__UpperCAmelCase)) < 1e-3)
# mode.class_name is NOT loaded from config
self.assertTrue(model.class_name != model.__class__.__name__)
def __snake_case ( self : Optional[Any]):
a : List[str] = Accelerator()
a , a , a , a , a : int = create_components()
a : Tuple = None
# This should work
a , a , a , a , a , a : Any = accelerator.prepare(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)
self.assertTrue(dummy_obj is None)
def __snake_case ( self : List[str]):
a : str = Accelerator()
a , a , a , a , a : List[Any] = create_components()
a : Union[str, Any] = [1, 2, 3]
# This should work
a , a , a , a , a , a : str = accelerator.prepare(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)
self.assertEqual(
getattr(__UpperCAmelCase , "_is_accelerate_prepared" , __UpperCAmelCase) , __UpperCAmelCase , "Dummy object should have `_is_accelerate_prepared` set to `True`" , )
self.assertEqual(
getattr(__UpperCAmelCase , "_is_accelerate_prepared" , __UpperCAmelCase) , __UpperCAmelCase , "Model is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(__UpperCAmelCase , "_is_accelerate_prepared" , __UpperCAmelCase) , __UpperCAmelCase , "Optimizer is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(__UpperCAmelCase , "_is_accelerate_prepared" , __UpperCAmelCase) , __UpperCAmelCase , "Scheduler is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(__UpperCAmelCase , "_is_accelerate_prepared" , __UpperCAmelCase) , __UpperCAmelCase , "Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(__UpperCAmelCase , "_is_accelerate_prepared" , __UpperCAmelCase) , __UpperCAmelCase , "Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`" , )
@slow
@require_bnb
def __snake_case ( self : Optional[int]):
from transformers import AutoModelForCausalLM
a : Optional[int] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , load_in_abit=__UpperCAmelCase , device_map={"": 0} , )
a : Tuple = Accelerator()
# This should work
a : List[Any] = accelerator.prepare(__UpperCAmelCase)
@slow
@require_bnb
def __snake_case ( self : Optional[int]):
from transformers import AutoModelForCausalLM
a : Dict = Accelerator()
with init_empty_weights():
a : Any = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , )
model.tie_weights()
a : Union[str, Any] = infer_auto_device_map(__UpperCAmelCase)
a : str = "cpu"
a : int = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , device_map=__UpperCAmelCase , load_in_abit=__UpperCAmelCase , llm_inta_enable_fpaa_cpu_offload=__UpperCAmelCase)
# This should not work and get value error
with self.assertRaises(__UpperCAmelCase):
a : Optional[int] = accelerator.prepare(__UpperCAmelCase)
@slow
@require_bnb
@require_multi_gpu
def __snake_case ( self : Optional[int]):
from transformers import AutoModelForCausalLM
a : Union[str, Any] = {"distributed_type": DistributedType.MULTI_GPU}
with init_empty_weights():
a : List[str] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , )
model.tie_weights()
a : Any = infer_auto_device_map(__UpperCAmelCase)
a : Dict = 1
a : Any = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , load_in_abit=__UpperCAmelCase , device_map=__UpperCAmelCase , )
a : int = Accelerator()
# This should not work and get value error
with self.assertRaises(__UpperCAmelCase):
a : Optional[int] = accelerator.prepare(__UpperCAmelCase)
PartialState._reset_state()
@slow
@require_bnb
@require_multi_gpu
def __snake_case ( self : Tuple):
from transformers import AutoModelForCausalLM
with init_empty_weights():
a : List[str] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , )
a : Tuple = infer_auto_device_map(__UpperCAmelCase)
a : str = 1
a : Optional[int] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , load_in_abit=__UpperCAmelCase , device_map=__UpperCAmelCase , )
a : str = Accelerator()
# This should work
a : Any = accelerator.prepare(__UpperCAmelCase)
@require_cuda
def __snake_case ( self : List[Any]):
a : Tuple = torch.nn.Linear(10 , 10)
a : int = torch.optim.SGD(model.parameters() , lr=0.01)
a : Optional[Any] = Accelerator(cpu=__UpperCAmelCase)
a : List[str] = accelerator.prepare(__UpperCAmelCase)
| 226 | 0 |
"""simple docstring"""
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A: Tuple = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
A: int = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
__lowerCAmelCase : Dict = 'mask2former'
__lowerCAmelCase : int = ['swin']
__lowerCAmelCase : Tuple = {'hidden_size': 'hidden_dim'}
def __init__( self , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = 256 , _SCREAMING_SNAKE_CASE = 256 , _SCREAMING_SNAKE_CASE = 256 , _SCREAMING_SNAKE_CASE = 1024 , _SCREAMING_SNAKE_CASE = "relu" , _SCREAMING_SNAKE_CASE = 6 , _SCREAMING_SNAKE_CASE = 10 , _SCREAMING_SNAKE_CASE = 8 , _SCREAMING_SNAKE_CASE = 0.0 , _SCREAMING_SNAKE_CASE = 2048 , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = 4 , _SCREAMING_SNAKE_CASE = 255 , _SCREAMING_SNAKE_CASE = 100 , _SCREAMING_SNAKE_CASE = 0.1 , _SCREAMING_SNAKE_CASE = 2.0 , _SCREAMING_SNAKE_CASE = 5.0 , _SCREAMING_SNAKE_CASE = 5.0 , _SCREAMING_SNAKE_CASE = 12544 , _SCREAMING_SNAKE_CASE = 3.0 , _SCREAMING_SNAKE_CASE = 0.75 , _SCREAMING_SNAKE_CASE = 0.02 , _SCREAMING_SNAKE_CASE = 1.0 , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE = [4, 8, 16, 32] , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> List[str]:
'''simple docstring'''
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.""" )
UpperCAmelCase : Optional[Any] = CONFIG_MAPPING["""swin"""](
image_size=224 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=_SCREAMING_SNAKE_CASE , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] , )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCAmelCase : Optional[int] = backbone_config.pop("""model_type""" )
UpperCAmelCase : int = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase : Dict = config_class.from_dict(_SCREAMING_SNAKE_CASE )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. "
F"Supported model types: {','.join(self.backbones_supported )}" )
UpperCAmelCase : Any = backbone_config
UpperCAmelCase : List[Any] = feature_size
UpperCAmelCase : Optional[int] = mask_feature_size
UpperCAmelCase : Any = hidden_dim
UpperCAmelCase : Optional[int] = encoder_feedforward_dim
UpperCAmelCase : Optional[Any] = activation_function
UpperCAmelCase : Optional[int] = encoder_layers
UpperCAmelCase : Dict = decoder_layers
UpperCAmelCase : Optional[Any] = num_attention_heads
UpperCAmelCase : List[Any] = dropout
UpperCAmelCase : Optional[Any] = dim_feedforward
UpperCAmelCase : Optional[Any] = pre_norm
UpperCAmelCase : List[str] = enforce_input_projection
UpperCAmelCase : List[Any] = common_stride
UpperCAmelCase : Tuple = ignore_value
UpperCAmelCase : List[Any] = num_queries
UpperCAmelCase : Dict = no_object_weight
UpperCAmelCase : int = class_weight
UpperCAmelCase : List[str] = mask_weight
UpperCAmelCase : str = dice_weight
UpperCAmelCase : Optional[int] = train_num_points
UpperCAmelCase : Union[str, Any] = oversample_ratio
UpperCAmelCase : str = importance_sample_ratio
UpperCAmelCase : Tuple = init_std
UpperCAmelCase : Any = init_xavier_std
UpperCAmelCase : Optional[int] = use_auxiliary_loss
UpperCAmelCase : Any = feature_strides
UpperCAmelCase : Any = output_auxiliary_logits
UpperCAmelCase : str = decoder_layers
super().__init__(**_SCREAMING_SNAKE_CASE )
@classmethod
def SCREAMING_SNAKE_CASE ( cls , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
return cls(
backbone_config=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
def SCREAMING_SNAKE_CASE ( self ) -> Dict[str, any]:
'''simple docstring'''
UpperCAmelCase : int = copy.deepcopy(self.__dict__ )
UpperCAmelCase : int = self.backbone_config.to_dict()
UpperCAmelCase : Optional[Any] = self.__class__.model_type
return output
| 109 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A: Optional[Any] = {
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_tapas": ["TapasTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A: Optional[int] = [
"TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
"TapasForMaskedLM",
"TapasForQuestionAnswering",
"TapasForSequenceClassification",
"TapasModel",
"TapasPreTrainedModel",
"load_tf_weights_in_tapas",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A: List[str] = [
"TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFTapasForMaskedLM",
"TFTapasForQuestionAnswering",
"TFTapasForSequenceClassification",
"TFTapasModel",
"TFTapasPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
else:
import sys
A: Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 109 | 1 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_a : Optional[Any]= logging.get_logger(__name__)
_a : int= {'vocab_file': 'vocab.txt'}
_a : Any= {
'vocab_file': {
'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
},
}
_a : Optional[Any]= {
'openbmb/cpm-ant-10b': 1_024,
}
def __UpperCAmelCase ( UpperCAmelCase_ ) -> Optional[int]:
'''simple docstring'''
__snake_case : Optional[int] = collections.OrderedDict()
with open(snake_case_ , 'r' , encoding='utf-8' ) as reader:
__snake_case : int = reader.readlines()
for index, token in enumerate(snake_case_ ):
__snake_case : Any = token.rstrip('\n' )
__snake_case : List[Any] = index
return vocab
class UpperCamelCase ( _lowerCAmelCase ):
def __init__(self : Optional[int] , _A : List[str] , _A : str="<unk>" , _A : Optional[Any]=2_00) -> int:
__snake_case : str = vocab
__snake_case : Tuple = unk_token
__snake_case : Optional[Any] = max_input_chars_per_word
def _lowercase (self : int , _A : List[Any]) -> str:
__snake_case : Dict = list(_lowercase)
if len(_lowercase) > self.max_input_chars_per_word:
return [self.unk_token]
__snake_case : List[str] = 0
__snake_case : List[Any] = []
while start < len(_lowercase):
__snake_case : Union[str, Any] = len(_lowercase)
__snake_case : Optional[Any] = None
while start < end:
__snake_case : Any = ''.join(chars[start:end])
if substr in self.vocab:
__snake_case : Tuple = substr
break
end -= 1
if cur_substr is None:
sub_tokens.append(self.unk_token)
start += 1
else:
sub_tokens.append(_lowercase)
__snake_case : List[str] = end
return sub_tokens
class UpperCamelCase ( _lowerCAmelCase ):
UpperCAmelCase : List[str] = VOCAB_FILES_NAMES
UpperCAmelCase : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase : Optional[Any] = ["input_ids", "attention_mask"]
UpperCAmelCase : List[Any] = False
def __init__(self : List[Any] , _A : Union[str, Any] , _A : Optional[Any]="<d>" , _A : int="</d>" , _A : Tuple="<s>" , _A : List[str]="</s>" , _A : Optional[int]="<pad>" , _A : str="<unk>" , _A : int="</n>" , _A : Optional[int]="</_>" , _A : List[str]="left" , **_A : Optional[Any] , ) -> str:
requires_backends(self , ['jieba'])
super().__init__(
bod_token=_lowercase , eod_token=_lowercase , bos_token=_lowercase , eos_token=_lowercase , pad_token=_lowercase , unk_token=_lowercase , line_token=_lowercase , space_token=_lowercase , padding_side=_lowercase , **_lowercase , )
__snake_case : int = bod_token
__snake_case : Optional[Any] = eod_token
__snake_case : Dict = load_vocab(_lowercase)
__snake_case : List[str] = self.encoder[space_token]
__snake_case : Optional[Any] = self.encoder[line_token]
del self.encoder[space_token]
del self.encoder[line_token]
__snake_case : Union[str, Any] = collections.OrderedDict(sorted(self.encoder.items() , key=lambda _A: x[1]))
__snake_case : Any = {v: k for k, v in self.encoder.items()}
__snake_case : Any = WordpieceTokenizer(vocab=self.encoder , unk_token=self.unk_token)
@property
def _lowercase (self : int) -> List[Any]:
return self.encoder[self.bod_token]
@property
def _lowercase (self : Optional[int]) -> Tuple:
return self.encoder[self.eod_token]
@property
def _lowercase (self : Optional[Any]) -> Optional[Any]:
return self.encoder["\n"]
@property
def _lowercase (self : Dict) -> List[Any]:
return len(self.encoder)
def _lowercase (self : Any) -> str:
return dict(self.encoder , **self.added_tokens_encoder)
def _lowercase (self : str , _A : Dict) -> str:
__snake_case : Optional[int] = []
for x in jieba.cut(_lowercase , cut_all=_lowercase):
output_tokens.extend(self.wordpiece_tokenizer.tokenize(_lowercase))
return output_tokens
def _lowercase (self : Optional[Any] , _A : Optional[int] , **_A : Dict) -> str:
__snake_case : List[str] = [i for i in token_ids if i >= 0]
__snake_case : List[str] = [
x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
]
return super()._decode(_lowercase , **_lowercase)
def _lowercase (self : Any , _A : int) -> int:
return token in self.encoder
def _lowercase (self : int , _A : List[str]) -> Dict:
return "".join(_lowercase)
def _lowercase (self : int , _A : int) -> List[Any]:
return self.encoder.get(_lowercase , self.encoder.get(self.unk_token))
def _lowercase (self : List[Any] , _A : Any) -> List[Any]:
return self.decoder.get(_lowercase , self.unk_token)
def _lowercase (self : Any , _A : str , _A : Optional[str] = None) -> Optional[Any]:
if os.path.isdir(_lowercase):
__snake_case : int = os.path.join(
_lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
else:
__snake_case : Dict = (filename_prefix + '-' if filename_prefix else '') + save_directory
__snake_case : Tuple = 0
if " " in self.encoder:
__snake_case : Optional[Any] = self.encoder[' ']
del self.encoder[" "]
if "\n" in self.encoder:
__snake_case : Any = self.encoder['\n']
del self.encoder["\n"]
__snake_case : List[Any] = collections.OrderedDict(sorted(self.encoder.items() , key=lambda _A: x[1]))
with open(_lowercase , 'w' , encoding='utf-8') as writer:
for token, token_index in self.encoder.items():
if index != token_index:
logger.warning(
f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
' Please check that the vocabulary is not corrupted!')
__snake_case : str = token_index
writer.write(token + '\n')
index += 1
return (vocab_file,)
def _lowercase (self : Any , _A : List[int] , _A : List[int] = None) -> int:
if token_ids_a is None:
return [self.bos_token_id] + token_ids_a
return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a
def _lowercase (self : Union[str, Any] , _A : List[int] , _A : Optional[List[int]] = None , _A : bool = False) -> int:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowercase , token_ids_a=_lowercase , already_has_special_tokens=_lowercase)
if token_ids_a is not None:
return [1] + ([0] * len(_lowercase)) + [1] + ([0] * len(_lowercase))
return [1] + ([0] * len(_lowercase))
| 370 | """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 | 0 |
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