

"""simple docstring"""





import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	if isinstance(A_						,     A_ ):
		lowerCAmelCase__	:						List[Any]  				=	np.full((len(A_ ), sequence_length, 2)						,     A_ )
	else:
		lowerCAmelCase__	:						List[str]  				=	np.full((len(A_ ), sequence_length)						,     A_ )

	for i, tensor in enumerate(A_ ):
		if padding_side == "right":
			if isinstance(A_						,     A_ ):
				lowerCAmelCase__	:						Any  				=	tensor[:sequence_length]
			else:
				lowerCAmelCase__	:						Optional[int]  				=	tensor[:sequence_length]
		else:
			if isinstance(A_						,     A_ ):
				lowerCAmelCase__	:						str  				=	tensor[:sequence_length]
			else:
				lowerCAmelCase__	:						Optional[Any]  				=	tensor[:sequence_length]

	return out_tensor.tolist()

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						Tuple  				=	ord(A_ )
	if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
		return True
	lowerCAmelCase__	:						int  				=	unicodedata.category(A_ )
	if cat.startswith('''P''' ):
		return True
	return False







@dataclass
class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       42
					lowercase__				 =       True
					lowercase__				 =       None
					lowercase__				 =       None
					lowercase__				 =       -100
					lowercase__				 =       "pt"







					def        __lowerCAmelCase (       self			:				Optional[Any]   ,lowercase_			:				Union[str, Any]		):
						import torch

						lowerCAmelCase__	:						int  				=	'''label''' if '''label''' in features[0].keys() else '''labels'''
						lowerCAmelCase__	:						str  				=	[feature[label_name] for feature in features] if label_name in features[0].keys() else None
						lowerCAmelCase__	:						Tuple  				=	self.tokenizer.pad(
						    lowercase_   ,padding=self.padding   ,max_length=self.max_length   ,pad_to_multiple_of=self.pad_to_multiple_of   ,return_tensors='''pt''' if labels is None else None   ,)

						if labels is None:
							return batch

						lowerCAmelCase__	:						List[str]  				=	torch.tensor(batch['''entity_ids''']		).shape[1]
						lowerCAmelCase__	:						Optional[Any]  				=	self.tokenizer.padding_side
						if padding_side == "right":
							lowerCAmelCase__	:						List[str]  				=	[
							    list(lowercase_		) + [self.label_pad_token_id] * (sequence_length - len(lowercase_		)) for label in labels
							]
						else:
							lowerCAmelCase__	:						int  				=	[
							    [self.label_pad_token_id] * (sequence_length - len(lowercase_		)) + list(lowercase_		) for label in labels
							]

						lowerCAmelCase__	:						Tuple  				=	[feature['''ner_tags'''] for feature in features]
						lowerCAmelCase__	:						str  				=	padding_tensor(lowercase_   ,-1   ,lowercase_   ,lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	[feature['''original_entity_spans'''] for feature in features]
						lowerCAmelCase__	:						Optional[Any]  				=	padding_tensor(lowercase_   ,(-1, -1)   ,lowercase_   ,lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	{k: torch.tensor(lowercase_   ,dtype=torch.intaa		) for k, v in batch.items()}

						return batch







"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__UpperCamelCase  :      Optional[Any]							      =		logging.get_logger(__name__)


if is_vision_available():
							import PIL







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       ["pixel_values"]







					def __init__(       self			:				str   ,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_5_5   ,lowercase_			:				bool = True   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				bool = True   ,**lowercase_			:				Optional[Any]   ,):
						super().__init__(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	size if size is not None else {'''shortest_edge''': 2_2_4}
						lowerCAmelCase__	:						Tuple  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
						lowerCAmelCase__	:						Optional[Any]  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_   ,param_name='''crop_size'''		)

						lowerCAmelCase__	:						Dict  				=	do_resize
						lowerCAmelCase__	:						Optional[int]  				=	size
						lowerCAmelCase__	:						Dict  				=	resample
						lowerCAmelCase__	:						Optional[Any]  				=	do_center_crop
						lowerCAmelCase__	:						Dict  				=	crop_size
						lowerCAmelCase__	:						Tuple  				=	do_rescale
						lowerCAmelCase__	:						str  				=	rescale_factor
						lowerCAmelCase__	:						List[str]  				=	do_normalize
						lowerCAmelCase__	:						Tuple  				=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						lowerCAmelCase__	:						Optional[Any]  				=	image_std if image_std is not None else OPENAI_CLIP_STD
						lowerCAmelCase__	:						Optional[Any]  				=	do_convert_rgb







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				np.ndarray   ,lowercase_			:				Dict[str, int]   ,lowercase_			:				PILImageResampling = PILImageResampling.BICUBIC   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Optional[Any]   ,):
						lowerCAmelCase__	:						Dict  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_		)
						if "shortest_edge" not in size:
							raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'		)
						lowerCAmelCase__	:						Optional[Any]  				=	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        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				np.ndarray   ,lowercase_			:				Dict[str, int]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				str   ,):
						lowerCAmelCase__	:						List[str]  				=	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        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				np.ndarray   ,lowercase_			:				Union[int, float]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Union[str, Any]   ,):
						return rescale(lowercase_   ,scale=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				np.ndarray   ,lowercase_			:				Union[float, List[float]]   ,lowercase_			:				Union[float, List[float]]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				int   ,):
						return normalize(lowercase_   ,mean=lowercase_   ,std=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]   ,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_			:				List[Any]   ,):
						lowerCAmelCase__	:						Optional[int]  				=	do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__	:						Optional[int]  				=	size if size is not None else self.size
						lowerCAmelCase__	:						Union[str, Any]  				=	get_size_dict(lowercase_   ,param_name='''size'''   ,default_to_square=lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	resample if resample is not None else self.resample
						lowerCAmelCase__	:						Optional[Any]  				=	do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCAmelCase__	:						Optional[int]  				=	crop_size if crop_size is not None else self.crop_size
						lowerCAmelCase__	:						Dict  				=	get_size_dict(lowercase_   ,param_name='''crop_size'''   ,default_to_square=lowercase_		)
						lowerCAmelCase__	:						int  				=	do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase__	:						List[str]  				=	rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase__	:						Optional[int]  				=	do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__	:						str  				=	image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase__	:						str  				=	image_std if image_std is not None else self.image_std
						lowerCAmelCase__	:						int  				=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						lowerCAmelCase__	:						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:
							lowerCAmelCase__	:						Tuple  				=	[convert_to_rgb(lowercase_		) for image in images]

						# All transformations expect numpy arrays.
						lowerCAmelCase__	:						Optional[int]  				=	[to_numpy_array(lowercase_		) for image in images]

						if do_resize:
							lowerCAmelCase__	:						Optional[int]  				=	[self.resize(image=lowercase_   ,size=lowercase_   ,resample=lowercase_		) for image in images]

						if do_center_crop:
							lowerCAmelCase__	:						Tuple  				=	[self.center_crop(image=lowercase_   ,size=lowercase_		) for image in images]

						if do_rescale:
							lowerCAmelCase__	:						Tuple  				=	[self.rescale(image=lowercase_   ,scale=lowercase_		) for image in images]

						if do_normalize:
							lowerCAmelCase__	:						Union[str, Any]  				=	[self.normalize(image=lowercase_   ,mean=lowercase_   ,std=lowercase_		) for image in images]

						lowerCAmelCase__	:						Optional[Any]  				=	[to_channel_dimension_format(lowercase_   ,lowercase_		) for image in images]

						lowerCAmelCase__	:						List[Any]  				=	{'''pixel_values''': images}
						return BatchFeature(data=lowercase_   ,tensor_type=lowercase_		)




