code: 
  
  
  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   lowerCAmelCase__ (       a):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				["image_processor", "tokenizer"]
       __SCREAMING_SNAKE_CASE						    =				"LayoutLMv2ImageProcessor"
       __SCREAMING_SNAKE_CASE						    =				("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")
  
  
       def __init__(		self				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		**__lowerCamelCase)    ->       Tuple:
              _A   : Any       					=				None
              if "feature_extractor" in kwargs:
                     warnings.warn(
                         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                         " instead."				,		__lowerCamelCase				,		)
                     _A   : List[Any]       					=				kwargs.pop("feature_extractor")
  
              _A   : 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				,		__lowerCamelCase				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = True				,		__lowerCamelCase = False				,		__lowerCamelCase = False				,		__lowerCamelCase = None				,		__lowerCamelCase = 0				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = False				,		__lowerCamelCase = False				,		__lowerCamelCase = False				,		__lowerCamelCase = False				,		__lowerCamelCase = True				,		__lowerCamelCase = None				,		**__lowerCamelCase				,		)    ->       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
              _A   : 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):
                            _A   : Any       					=				[text]  # add batch dimension (as the image processor always adds a batch dimension)
                     _A   : str       					=				features["words"]
  
              _A   : Dict       					=				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
              _A   : Dict       					=				features.pop("pixel_values")
              if return_overflowing_tokens is True:
                     _A   : List[Any]       					=				self.get_overflowing_images(__lowerCamelCase				,		encoded_inputs["overflow_to_sample_mapping"])
              _A   : Optional[Any]       					=				images
  
              return encoded_inputs
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       Optional[Any]:
              # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
              _A   : Dict       					=				[]
              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				,		*__lowerCamelCase				,		**__lowerCamelCase)    ->       Optional[Any]:
              return self.tokenizer.batch_decode(*__lowerCamelCase				,		**__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		*__lowerCamelCase				,		**__lowerCamelCase)    ->       int:
              return self.tokenizer.decode(*__lowerCamelCase				,		**__lowerCamelCase)
  
  
       @property
       def 				_lowerCamelCase				(		self)    ->       List[str]:
              return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  
  
       @property
       def 				_lowerCamelCase				(		self)    ->       str:
              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)    ->       List[str]:
              warnings.warn(
                  "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,		__lowerCamelCase				,		)
              return self.image_processor
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowerCAmelCase__   	=				{
      'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
  }
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['SegformerFeatureExtractor']
      lowerCAmelCase__   	=				['SegformerImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'SegformerDecodeHead',
          'SegformerForImageClassification',
          'SegformerForSemanticSegmentation',
          'SegformerLayer',
          'SegformerModel',
          'SegformerPreTrainedModel',
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSegformerDecodeHead',
          'TFSegformerForImageClassification',
          'TFSegformerForSemanticSegmentation',
          'TFSegformerModel',
          'TFSegformerPreTrainedModel',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig
  
      try:
          if not is_vision_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .feature_extraction_segformer import SegformerFeatureExtractor
          from .image_processing_segformer import SegformerImageProcessor
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_segformer import (
              SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              SegformerDecodeHead,
              SegformerForImageClassification,
              SegformerForSemanticSegmentation,
              SegformerLayer,
              SegformerModel,
              SegformerPreTrainedModel,
          )
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_segformer import (
              TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFSegformerDecodeHead,
              TFSegformerForImageClassification,
              TFSegformerForSemanticSegmentation,
              TFSegformerModel,
              TFSegformerPreTrainedModel,
          )
  
  else:
      import sys
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  