






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				(	__A    ):





		'''simple docstring'''



		UpperCAmelCase__   :					Optional[Any]					     =    ["""image_processor""", """tokenizer"""]
		UpperCAmelCase__   :					str					     =    """LayoutLMv2ImageProcessor"""
		UpperCAmelCase__   :					Union[str, Any]					     =    ("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")


		def __init__(    self			,     UpperCamelCase__=None			,     UpperCamelCase__=None			,     **UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= None
						if "feature_extractor" in kwargs:
										warnings.warn(
										    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
										    ' instead.'			,     UpperCamelCase__			,     )
										SCREAMING_SNAKE_CASE_ :						List[str]    				= kwargs.pop('feature_extractor'							)

						SCREAMING_SNAKE_CASE_ :						Any    				= 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__(UpperCamelCase__			,     UpperCamelCase__							)


		def __call__(    self			,     UpperCamelCase__			,     UpperCamelCase__ = None			,     UpperCamelCase__ = None			,     UpperCamelCase__ = None			,     UpperCamelCase__ = None			,     UpperCamelCase__ = True			,     UpperCamelCase__ = False			,     UpperCamelCase__ = False			,     UpperCamelCase__ = None			,     UpperCamelCase__ = 0			,     UpperCamelCase__ = None			,     UpperCamelCase__ = None			,     UpperCamelCase__ = None			,     UpperCamelCase__ = False			,     UpperCamelCase__ = False			,     UpperCamelCase__ = False			,     UpperCamelCase__ = False			,     UpperCamelCase__ = True			,     UpperCamelCase__ = None			,     **UpperCamelCase__			,     ):
						# verify input
						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
						SCREAMING_SNAKE_CASE_ :						List[str]    				= self.image_processor(images=UpperCamelCase__			,     return_tensors=UpperCamelCase__							)

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

						SCREAMING_SNAKE_CASE_ :						List[Any]    				= 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=UpperCamelCase__			,     add_special_tokens=UpperCamelCase__			,     padding=UpperCamelCase__			,     truncation=UpperCamelCase__			,     max_length=UpperCamelCase__			,     stride=UpperCamelCase__			,     pad_to_multiple_of=UpperCamelCase__			,     return_token_type_ids=UpperCamelCase__			,     return_attention_mask=UpperCamelCase__			,     return_overflowing_tokens=UpperCamelCase__			,     return_special_tokens_mask=UpperCamelCase__			,     return_offsets_mapping=UpperCamelCase__			,     return_length=UpperCamelCase__			,     verbose=UpperCamelCase__			,     return_tensors=UpperCamelCase__			,     **UpperCamelCase__			,     )

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

						return encoded_inputs


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__							):
						# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
						SCREAMING_SNAKE_CASE_ :						Tuple    				= []
						for sample_idx in overflow_to_sample_mapping:
										images_with_overflow.append(images[sample_idx]							)

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

						return images_with_overflow


		def 	__snake_case      (    self			,     *UpperCamelCase__			,     **UpperCamelCase__							):
						return self.tokenizer.batch_decode(*UpperCamelCase__			,     **UpperCamelCase__							)


		def 	__snake_case      (    self			,     *UpperCamelCase__			,     **UpperCamelCase__							):
						return self.tokenizer.decode(*UpperCamelCase__			,     **UpperCamelCase__							)


		@property
		def 	__snake_case      (    self							):
						return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


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






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







def        _lowerCamelCase(      lowerCAmelCase__					:		int = 99_8001   ):





				'''simple docstring'''







				for number in range(n - 1     ,     9999     ,     -1   ):
								SCREAMING_SNAKE_CASE_ :						int    				= str(lowerCAmelCase__   )

								# checks whether 'str_number' is a palindrome.
								if str_number == str_number[::-1]:
												SCREAMING_SNAKE_CASE_ :						str    				= 999

												# if 'number' is a product of two 3-digit numbers
												# then number is the answer otherwise fetch next number.
												while divisor != 99:
																if (number % divisor == 0) and (len(str(number // divisor   )   ) == 3.0):
																				return number
																divisor -= 1
				raise ValueError('That number is larger than our acceptable range.'   )


if __name__ == "__main__":
				print(f"""{solution() = }""")