






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__    ):






  """simple docstring"""





  A_						=   ["""image_processor""", """tokenizer"""]
  A_						=   """LayoutLMv2ImageProcessor"""
  A_						=   ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")




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

     UpperCamelCase_									=     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 = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = 0		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = False		,		_UpperCAmelCase = False		,		_UpperCAmelCase = False		,		_UpperCAmelCase = False		,		_UpperCAmelCase = True		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)			-> BatchEncoding:
     # 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
     UpperCamelCase_									=     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 ):
           UpperCamelCase_									=     [text]  # add batch dimension (as the image processor always adds a batch dimension)
        UpperCamelCase_									=     features['words']

     UpperCamelCase_									=     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
     UpperCamelCase_									=     features.pop('pixel_values' )
     if return_overflowing_tokens is True:
        UpperCamelCase_									=     self.get_overflowing_images(_UpperCAmelCase		,		encoded_inputs['overflow_to_sample_mapping'] )
     UpperCamelCase_									=     images

     return encoded_inputs




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Any:
     # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
     UpperCamelCase_									=     []
     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    _UpperCAmelCase     (     self		,		*_UpperCAmelCase		,		**_UpperCAmelCase )			-> Optional[Any]:
     return self.tokenizer.batch_decode(*_UpperCAmelCase		,		**_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		*_UpperCAmelCase		,		**_UpperCAmelCase )			-> Union[str, Any]:
     return self.tokenizer.decode(*_UpperCAmelCase		,		**_UpperCAmelCase )




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




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













def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   return not any(
       neighbour == 1 and colored_vertices[i] == color
       for i, neighbour in enumerate(__lowercase))






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase):

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

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






def     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     [-1] * len(__lowercase)

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

   return []





