






'''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 							__magic_name__     (	__A				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Union[str, Any]        =  ['image_processor', 'tokenizer']
	SCREAMING_SNAKE_CASE_					:      str        =  'LayoutLMv2ImageProcessor'
	SCREAMING_SNAKE_CASE_					:      Dict        =  ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')
	def __init__(							self  ,      _lowercase=None  ,      _lowercase=None  ,      **_lowercase       )						->  Optional[int]:

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

							lowercase_      :   Dict        =			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       )
	def __call__(							self  ,      _lowercase  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = True  ,      _lowercase = False  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = 0  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = False  ,      _lowercase = False  ,      _lowercase = False  ,      _lowercase = False  ,      _lowercase = True  ,      _lowercase = None  ,      **_lowercase  ,      )						->  BatchEncoding:

							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_      :   Tuple        =			self.image_processor(images=_lowercase  ,      return_tensors=_lowercase       )

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

							lowercase_      :   Optional[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=_lowercase  ,      add_special_tokens=_lowercase  ,      padding=_lowercase  ,      truncation=_lowercase  ,      max_length=_lowercase  ,      stride=_lowercase  ,      pad_to_multiple_of=_lowercase  ,      return_token_type_ids=_lowercase  ,      return_attention_mask=_lowercase  ,      return_overflowing_tokens=_lowercase  ,      return_special_tokens_mask=_lowercase  ,      return_offsets_mapping=_lowercase  ,      return_length=_lowercase  ,      verbose=_lowercase  ,      return_tensors=_lowercase  ,      **_lowercase  ,      )

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

							return encoded_inputs
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase       )						->  str:
							lowercase_      :   str        =			[]
							for sample_idx in overflow_to_sample_mapping:
													images_with_overflow.append(images[sample_idx]       )

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

							return images_with_overflow
	def        lowerCamelCase__		(							self  ,      *_lowercase  ,      **_lowercase       )						->  int:
							return self.tokenizer.batch_decode(*_lowercase  ,      **_lowercase       )
	def        lowerCamelCase__		(							self  ,      *_lowercase  ,      **_lowercase       )						->  str:
							return self.tokenizer.decode(*_lowercase  ,      **_lowercase       )
	@property
	def        lowerCamelCase__		(							self       )						->  List[str]:
							return ["input_ids", "bbox", "attention_mask", "image"]
	@property
	def        lowerCamelCase__		(							self       )						->  List[str]:
							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,      _lowercase  ,      )
							return self.image_processor_class







	@property
	def        lowerCamelCase__		(							self       )						->  List[str]:
							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,      _lowercase  ,      )
							return self.image_processor








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	str							)			->			str:





						"""simple docstring"""
						lowercase_      :   Dict        =			0

						# if input_string is "aba" than new_input_string become "a|b|a"
						lowercase_      :   Dict        =			''
						lowercase_      :   Any        =			''

						# append each character + "|" in new_string for range(0, length-1)
						for i in input_string[: len(a							) - 1]:
												new_input_string += i + "|"
						# append last character
						new_input_string += input_string[-1]

						# we will store the starting and ending of previous furthest ending palindromic
						# substring
						lowercase_  ,      lowercase_      :   Dict        =			0, 0

						# length[i] shows the length of palindromic substring with center i
						lowercase_      :   List[Any]        =			[1 for i in range(len(a							)							)]

						# for each character in new_string find corresponding palindromic string
						lowercase_      :   Dict        =			0
						for j in range(len(a							)							):
												lowercase_      :   Tuple        =			1 if j > r else min(length[l + r - j] // 2						,			r - j + 1							)
												while (
												    j - k >= 0
												    and j + k < len(a							)
												    and new_input_string[k + j] == new_input_string[j - k]
												):
																		k += 1

												lowercase_      :   int        =			2 * k - 1

												# does this string is ending after the previously explored end (that is r) ?
												# if yes the update the new r to the last index of this
												if j + k - 1 > r:
																		lowercase_      :   Tuple        =			j - k + 1  # noqa: E741
																		lowercase_      :   Tuple        =			j + k - 1

												# update max_length and start position
												if max_length < length[j]:
																		lowercase_      :   Tuple        =			length[j]
																		lowercase_      :   List[Any]        =			j

    # create that string
						lowercase_      :   str        =			new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
						for i in s:
												if i != "|":
																		output_string += i

						return output_string


if __name__ == "__main__":
						import doctest

						doctest.testmod()
