"""simple docstring"""







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  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  ["image_processor", "tokenizer"]
				_snake_case     =  "LayoutLMv2ImageProcessor"
				_snake_case     =  ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")







				def __init__( self    :       Dict			,					lowerCamelCase_    :       List[Any]=None			,					lowerCamelCase_    :       int=None			,					**lowerCamelCase_    :       Dict						):

					"""simple docstring"""






					if "feature_extractor" in kwargs:
						warnings.warn(
						    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
						    ' instead.'			,					lowerCamelCase_			,					)
						_lowercase :    int							=							kwargs.pop('feature_extractor'						)

					_lowercase :    int							=							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__(lowerCamelCase_			,					lowerCamelCase_						)







				def __call__( self    :       Optional[Any]			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,					lowerCamelCase_    :       Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,					lowerCamelCase_    :       Union[List[List[int]], List[List[List[int]]]] = None			,					lowerCamelCase_    :       Optional[Union[List[int], List[List[int]]]] = None			,					lowerCamelCase_    :       bool = True			,					lowerCamelCase_    :       Union[bool, str, PaddingStrategy] = False			,					lowerCamelCase_    :       Union[bool, str, TruncationStrategy] = None			,					lowerCamelCase_    :       Optional[int] = None			,					lowerCamelCase_    :       int = 0			,					lowerCamelCase_    :       Optional[int] = None			,					lowerCamelCase_    :       Optional[bool] = None			,					lowerCamelCase_    :       Optional[bool] = None			,					lowerCamelCase_    :       bool = False			,					lowerCamelCase_    :       bool = False			,					lowerCamelCase_    :       bool = False			,					lowerCamelCase_    :       bool = False			,					lowerCamelCase_    :       bool = True			,					lowerCamelCase_    :       Optional[Union[str, TensorType]] = None			,					**lowerCamelCase_    :       Tuple			,					):

					"""simple docstring"""






					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.'						)

					if return_overflowing_tokens is True and return_offsets_mapping is False:
						raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'						)

					# first, apply the image processor
					_lowercase :    Optional[int]							=							self.image_processor(images=lowerCamelCase_			,					return_tensors=lowerCamelCase_						)

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

					_lowercase :    List[str]							=							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=lowerCamelCase_			,					add_special_tokens=lowerCamelCase_			,					padding=lowerCamelCase_			,					truncation=lowerCamelCase_			,					max_length=lowerCamelCase_			,					stride=lowerCamelCase_			,					pad_to_multiple_of=lowerCamelCase_			,					return_token_type_ids=lowerCamelCase_			,					return_attention_mask=lowerCamelCase_			,					return_overflowing_tokens=lowerCamelCase_			,					return_special_tokens_mask=lowerCamelCase_			,					return_offsets_mapping=lowerCamelCase_			,					return_length=lowerCamelCase_			,					verbose=lowerCamelCase_			,					return_tensors=lowerCamelCase_			,					**lowerCamelCase_			,					)

					# add pixel values
					_lowercase :    Tuple							=							features.pop('pixel_values'						)
					if return_overflowing_tokens is True:
						_lowercase :    Dict							=							self.get_overflowing_images(lowerCamelCase_			,					encoded_inputs['overflow_to_sample_mapping']						)
					_lowercase :    Optional[Any]							=							images

					return encoded_inputs







				def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       List[str]						):

					"""simple docstring"""






					_lowercase :    Any							=							[]
					for sample_idx in overflow_to_sample_mapping:
						images_with_overflow.append(images[sample_idx]						)

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

					return images_with_overflow







				def 							__UpperCAmelCase					( self    :       Optional[int]			,					*lowerCamelCase_    :       List[Any]			,					**lowerCamelCase_    :       List[Any]						):

					"""simple docstring"""






					return self.tokenizer.batch_decode(*lowerCamelCase_			,					**lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Optional[Any]			,					*lowerCamelCase_    :       str			,					**lowerCamelCase_    :       Optional[Any]						):

					"""simple docstring"""






					return self.tokenizer.decode(*lowerCamelCase_			,					**lowerCamelCase_						)







				@property
				def 							__UpperCAmelCase					( self    :       str						):

					"""simple docstring"""






					return ["input_ids", "bbox", "attention_mask", "image"]







				@property
				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					warnings.warn(
					    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'			,					lowerCamelCase_			,					)
					return self.image_processor_class







				@property
				def 							__UpperCAmelCase					( self    :       str						):

					"""simple docstring"""






					warnings.warn(
					    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'			,					lowerCamelCase_			,					)
					return self.image_processor




"""simple docstring"""







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							len(__UpperCAmelCase			)
	_lowercase :    Any							=							len(__UpperCAmelCase			)
	_lowercase :    List[Any]							=							[[False for _ in range(m + 1			)] for _ in range(n + 1			)]
	_lowercase :    Union[str, Any]							=							True
	for i in range(__UpperCAmelCase			):
		for j in range(m + 1			):
			if dp[i][j]:
				if j < m and a[i].upper() == b[j]:
					_lowercase :    Optional[int]							=							True
				if a[i].islower():
					_lowercase :    Union[str, Any]							=							True
	return dp[n][m]


if __name__ == "__main__":
						import doctest

						doctest.testmod()



