
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 				A__ (			_lowerCamelCase):
		A_		: str            =			["image_processor", "tokenizer"]
		A_		: Union[str, Any]            =			"LayoutLMv3ImageProcessor"
		A_		: List[str]            =			("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	**_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Dict		    =   None
			if "feature_extractor" in kwargs:
				warnings.warn(
				    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
				    ' instead.' ,	_SCREAMING_SNAKE_CASE ,	)
				__lowerCAmelCase    :		Any		    =   kwargs.pop('feature_extractor'				)

			__lowerCAmelCase    :		str		    =   image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
				raise ValueError('You need to specify an `image_processor`.'				)
			if tokenizer is None:
				raise ValueError('You need to specify a `tokenizer`.'				)

			super().__init__(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

		def __call__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = 0 ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):




			if self.image_processor.apply_ocr and (boxes is not None):
				raise ValueError(
				    'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'				)

			if self.image_processor.apply_ocr and (word_labels is not None):
				raise ValueError(
				    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'				)

			# first, apply the image processor
			__lowerCAmelCase    :		Dict		    =   self.image_processor(images=_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE				)

			# second, apply the tokenizer
			if text is not None and self.image_processor.apply_ocr and text_pair is None:
				if isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
					__lowerCAmelCase    :		Any		    =   [text]  # add batch dimension (as the image processor always adds a batch dimension)
				__lowerCAmelCase    :		Union[str, Any]		    =   features['words']

			__lowerCAmelCase    :		Optional[int]		    =   self.tokenizer(
			    text=text if text is not None else features['words'] ,	text_pair=text_pair if text_pair is not None else None ,	boxes=boxes if boxes is not None else features['boxes'] ,	word_labels=_SCREAMING_SNAKE_CASE ,	add_special_tokens=_SCREAMING_SNAKE_CASE ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=_SCREAMING_SNAKE_CASE ,	stride=_SCREAMING_SNAKE_CASE ,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,	return_token_type_ids=_SCREAMING_SNAKE_CASE ,	return_attention_mask=_SCREAMING_SNAKE_CASE ,	return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,	return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE ,	return_length=_SCREAMING_SNAKE_CASE ,	verbose=_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)

			# add pixel values
			__lowerCAmelCase    :		List[Any]		    =   features.pop('pixel_values'				)
			if return_overflowing_tokens is True:
				__lowerCAmelCase    :		Dict		    =   self.get_overflowing_images(_SCREAMING_SNAKE_CASE ,	encoded_inputs['overflow_to_sample_mapping']				)
			__lowerCAmelCase    :		str		    =   images

			return encoded_inputs

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[str]		    =   []
			for sample_idx in overflow_to_sample_mapping:
				images_with_overflow.append(images[sample_idx]				)

			if len(_SCREAMING_SNAKE_CASE				) != len(_SCREAMING_SNAKE_CASE				):
				raise ValueError(
				    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
				    f" {len(_SCREAMING_SNAKE_CASE				)} and {len(_SCREAMING_SNAKE_CASE				)}"				)

			return images_with_overflow

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		@property
		def 							__lowerCamelCase							(   self				):
			return ["input_ids", "bbox", "attention_mask", "pixel_values"]

		@property
		def 							__lowerCamelCase							(   self				):
			warnings.warn(
			    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' ,	_SCREAMING_SNAKE_CASE ,	)
			return self.image_processor_class







		@property
		def 							__lowerCamelCase							(   self				):
			warnings.warn(
			    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' ,	_SCREAMING_SNAKE_CASE ,	)
			return self.image_processor

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase = False	):

	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		str		    =   F"Expected string as input, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)
	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		Union[str, Any]		    =   F"Expected boolean as use_pascal parameter, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)

	__lowerCAmelCase    :		Tuple		    =   input_str.split('_'	)

	__lowerCAmelCase    :		int		    =   0 if use_pascal else 1

	__lowerCAmelCase    :		Any		    =   words[start_index:]

	__lowerCAmelCase    :		Any		    =   [word[0].upper() + word[1:] for word in words_to_capitalize]

	__lowerCAmelCase    :		Any		    =   '' if use_pascal else words[0]

	return "".join([initial_word, *capitalized_words]	)


if __name__ == "__main__":
							from doctest import testmod

							testmod()