



'''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 						a_		(     UpperCAmelCase_    ):
							UpperCAmelCase			:		Any        =				['''image_processor''', '''tokenizer''']
							UpperCAmelCase			:		str        =				'''LayoutLMv2ImageProcessor'''
							UpperCAmelCase			:		Optional[int]        =				('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')


							def __init__(				self   :						Dict      ,						a_   :						str=None      ,						a_   :						str=None      ,						**a_   :						Tuple			)			->					Optional[Any]:

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

										snake_case:    List[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__(lowerCamelCase__      ,						lowerCamelCase__			)


							def __call__(				self   :						Dict      ,						a_   :						Tuple      ,						a_   :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,						a_   :						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,						a_   :						Union[List[List[int]], List[List[List[int]]]] = None      ,						a_   :						Optional[Union[List[int], List[List[int]]]] = None      ,						a_   :						bool = True      ,						a_   :						Union[bool, str, PaddingStrategy] = False      ,						a_   :						Union[bool, str, TruncationStrategy] = None      ,						a_   :						Optional[int] = None      ,						a_   :						int = 0      ,						a_   :						Optional[int] = None      ,						a_   :						Optional[bool] = None      ,						a_   :						Optional[bool] = None      ,						a_   :						bool = False      ,						a_   :						bool = False      ,						a_   :						bool = False      ,						a_   :						bool = False      ,						a_   :						bool = True      ,						a_   :						Optional[Union[str, TensorType]] = None      ,						**a_   :						str      ,						)			->					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
										snake_case:    Dict      							=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__			):
																snake_case:    Dict      							=[text]  # add batch dimension (as the image processor always adds a batch dimension)
													snake_case:    List[Any]      							=features["""words"""]

										snake_case:    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=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
										snake_case:    Optional[int]      							=features.pop('pixel_values'			)
										if return_overflowing_tokens is True:
													snake_case:    Tuple      							=self.get_overflowing_images(lowerCamelCase__      ,						encoded_inputs['overflow_to_sample_mapping']			)
										snake_case:    Optional[Any]      							=images

										return encoded_inputs


							def    UpperCamelCase     (				self   :						List[Any]      ,						a_   :						Optional[int]      ,						a_   :						Tuple			)			->					int:
										snake_case:    Tuple      							=[]
										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   :						List[str]      ,						*a_   :						Optional[Any]      ,						**a_   :						List[Any]			)			->					Dict:
										return self.tokenizer.batch_decode(*lowerCamelCase__      ,						**lowerCamelCase__			)


							def    UpperCamelCase     (				self   :						Optional[int]      ,						*a_   :						Dict      ,						**a_   :						Tuple			)			->					str:
										return self.tokenizer.decode(*lowerCamelCase__      ,						**lowerCamelCase__			)


							@property
							def    UpperCamelCase     (				self   :						Any			)			->					Optional[int]:
										return ["input_ids", "bbox", "attention_mask", "image"]


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












from __future__ import annotations


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					Tuple					,							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Tuple							)     ->  Optional[Any]:  # noqa: E741
   while r - l > 1:
      __UpperCamelCase	:			int					=					(l + r) // 2
      if v[m] >= key:
         __UpperCamelCase	:			Dict					=					m
      else:
         __UpperCamelCase	:			Optional[Any]					=					m  # noqa: E741
   return r


def 				__lowerCamelCase     (							__lowerCAmelCase							:					list[int]							)     ->  int:
   if len(__lowerCAmelCase							) == 0:
      return 0

   __UpperCamelCase	:			Optional[Any]					=					[0] * len(__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[int]					=					1

   __UpperCamelCase	:			Union[str, Any]					=					v[0]

   for i in range(1					,							len(__lowerCAmelCase							)							):
      if v[i] < tail[0]:
         __UpperCamelCase	:			Dict					=					v[i]
      elif v[i] > tail[length - 1]:
         __UpperCamelCase	:			Optional[int]					=					v[i]
         length += 1
      else:
         __UpperCamelCase	:			List[Any]					=					v[i]

   return length


if __name__ == "__main__":
       import doctest

       doctest.testmod()



