

"""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     (     UpperCAmelCase_    ):
  _UpperCAmelCase        =		["image_processor", "tokenizer"]
  _UpperCAmelCase        =		"LayoutLMv2ImageProcessor"
  _UpperCAmelCase        =		("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")

  def __init__(		self     ,_A=None     ,_A=None     ,**_A   ):
      '''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     ,)
          _lowerCAmelCase							: List[str]	=     kwargs.pop('feature_extractor'   )

      _lowerCAmelCase							: Optional[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     ,_A     ,_A = None     ,_A = None     ,_A = None     ,_A = None     ,_A = True     ,_A = False     ,_A = None     ,_A = None     ,_A = 0     ,_A = None     ,_A = None     ,_A = None     ,_A = False     ,_A = False     ,_A = False     ,_A = False     ,_A = True     ,_A = None     ,**_A     ,):
      '''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
      _lowerCAmelCase							: Optional[Any]	=     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   ):
              _lowerCAmelCase							: Union[str, Any]	=     [text]  # add batch dimension (as the image processor always adds a batch dimension)
          _lowerCAmelCase							: Any	=     features['words']

      _lowerCAmelCase							: Union[str, 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=__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
      _lowerCAmelCase							: Dict	=     features.pop('pixel_values'   )
      if return_overflowing_tokens is True:
          _lowerCAmelCase							: str	=     self.get_overflowing_images(__lowerCamelCase     ,encoded_inputs['overflow_to_sample_mapping']   )
      _lowerCAmelCase							: str	=     images

      return encoded_inputs

  def __lowerCamelCase		(		self     ,_A     ,_A   ):
      '''simple docstring'''

      _lowerCAmelCase							: Optional[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 __lowerCamelCase		(		self     ,*_A     ,**_A   ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self     ,*_A     ,**_A   ):
      '''simple docstring'''

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

  @property
  def __lowerCamelCase		(		self   ):
      '''simple docstring'''

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

  @property
  def __lowerCamelCase		(		self   ):
      '''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 __lowerCamelCase		(		self   ):
      '''simple docstring'''

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












def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    bool:
		return not any(
		    neighbour == 1 and colored_vertices[i] == color
		    for i, neighbour in enumerate(snake_case							)							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    bool:

		# Base Case
		if index == len(snake_case							):
				return True

		# Recursive Step
		for i in range(snake_case							):
				if valid_coloring(graph[index]						,   snake_case						,   snake_case							):
						# Color current vertex
						__lowercase											=     i
						# Validate coloring
						if util_color(snake_case						,   snake_case						,   snake_case						,   index + 1							):
								return True
						# Backtrack
						__lowercase											=     -1
		return False



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    list[int]:
		__lowercase											=     [-1] * len(snake_case							)

		if util_color(snake_case						,   snake_case						,   snake_case						,   0							):
				return colored_vertices

		return []




