





"""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    _UpperCAmelCase  (      lowerCamelCase__					):
					UpperCamelCase						=				['image_processor', 'tokenizer']
					UpperCamelCase						=				'LayoutLMv2ImageProcessor'
					UpperCamelCase						=				('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')


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

												A						=    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     :int	,			__UpperCamelCase     :Dict	,			__UpperCamelCase     :str = None	,			__UpperCamelCase     :Tuple = None	,			__UpperCamelCase     :List[Any] = None	,			__UpperCamelCase     :Union[str, Any] = None	,			__UpperCamelCase     :Any = True	,			__UpperCamelCase     :List[Any] = False	,			__UpperCamelCase     :int = False	,			__UpperCamelCase     :Any = None	,			__UpperCamelCase     :Optional[Any] = 0	,			__UpperCamelCase     :Optional[int] = None	,			__UpperCamelCase     :Optional[int] = None	,			__UpperCamelCase     :Optional[int] = None	,			__UpperCamelCase     :int = False	,			__UpperCamelCase     :Union[str, Any] = False	,			__UpperCamelCase     :Dict = False	,			__UpperCamelCase     :Optional[int] = False	,			__UpperCamelCase     :Tuple = True	,			__UpperCamelCase     :Union[str, Any] = None	,			**__UpperCamelCase     :List[Any]	,			):






												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
												A						=    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_						):
																										A						=    [text]  # add batch dimension (as the image processor always adds a batch dimension)
																			A						=    features["words"]

												A						=    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
												A						=    features.pop("pixel_values"						)
												if return_overflowing_tokens is True:
																			A						=    self.get_overflowing_images(lowercase_	,			encoded_inputs["overflow_to_sample_mapping"]						)
												A						=    images

												return encoded_inputs


					def       lowerCamelCase			(   self     :int	,			__UpperCamelCase     :Optional[Any]	,			__UpperCamelCase     :List[Any]						):
												A						=    []
												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     :str	,			*__UpperCamelCase     :str	,			**__UpperCamelCase     :Tuple						):
												return self.tokenizer.batch_decode(*lowercase_	,			**lowercase_						)


					def       lowerCamelCase			(   self     :int	,			*__UpperCamelCase     :str	,			**__UpperCamelCase     :List[str]						):
												return self.tokenizer.decode(*lowercase_	,			**lowercase_						)


					@property
					def       lowerCamelCase			(   self     :List[str]						):
												return ["input_ids", "bbox", "token_type_ids", "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     :Any						):
												warnings.warn(
												    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."	,			lowercase_	,			)
												return self.image_processor





def      UpperCAmelCase__							(     _A :					Optional[int]      , _A :					Optional[int]				):





  '''simple docstring'''







  sequence.append(_A				)

  a__    =0
  while sequence[index] != target:
    index += 1

  sequence.pop()

  if index == len(_A				):
    return None

  return index


if __name__ == "__main__":
       lowerCamelCase 	=   input('''Enter numbers separated by comma:\n''').strip()
       lowerCamelCase 	=   [int(item) for item in user_input.split(''',''')]

       lowerCamelCase 	=   input('''Enter a single number to be found in the list:\n''')
       lowerCamelCase 	=   int(target_input)
       lowerCamelCase 	=   sentinel_linear_search(sequence, target)
       if result is not None:
              print(f"""{target} found at positions: {result}""")
       else:
              print('''Not found''')
