code: 
  
  
  """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   lowercase__							(				A_							):
     __UpperCAmelCase       					=				['''image_processor''', '''tokenizer''']
     __UpperCAmelCase       					=				'''LayoutLMv2ImageProcessor'''
     __UpperCAmelCase       					=				('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      **SCREAMING_SNAKE_CASE)     ->     Optional[int]:
        _lowerCamelCase  :				Union[str, 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."""			,      SCREAMING_SNAKE_CASE			,      )
           _lowerCamelCase  :				Optional[Any]      =		kwargs.pop("""feature_extractor""")
  
        _lowerCamelCase  :				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__(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def __call__(       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = True			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = 0			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = None			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = False			,      SCREAMING_SNAKE_CASE = True			,      SCREAMING_SNAKE_CASE = None			,      **SCREAMING_SNAKE_CASE			,      )     ->     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
        _lowerCamelCase  :				Tuple      =		self.image_processor(images=SCREAMING_SNAKE_CASE			,      return_tensors=SCREAMING_SNAKE_CASE)
  
        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
              _lowerCamelCase  :				Any      =		[text]  # add batch dimension (as the image processor always adds a batch dimension)
           _lowerCamelCase  :				List[str]      =		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=SCREAMING_SNAKE_CASE			,      add_special_tokens=SCREAMING_SNAKE_CASE			,      padding=SCREAMING_SNAKE_CASE			,      truncation=SCREAMING_SNAKE_CASE			,      max_length=SCREAMING_SNAKE_CASE			,      stride=SCREAMING_SNAKE_CASE			,      pad_to_multiple_of=SCREAMING_SNAKE_CASE			,      return_token_type_ids=SCREAMING_SNAKE_CASE			,      return_attention_mask=SCREAMING_SNAKE_CASE			,      return_overflowing_tokens=SCREAMING_SNAKE_CASE			,      return_special_tokens_mask=SCREAMING_SNAKE_CASE			,      return_offsets_mapping=SCREAMING_SNAKE_CASE			,      return_length=SCREAMING_SNAKE_CASE			,      verbose=SCREAMING_SNAKE_CASE			,      return_tensors=SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE			,      )
  
        # add pixel values
        _lowerCamelCase  :				List[Any]      =		features.pop("""pixel_values""")
        if return_overflowing_tokens is True:
           _lowerCamelCase  :				List[Any]      =		self.get_overflowing_images(SCREAMING_SNAKE_CASE			,      encoded_inputs["""overflow_to_sample_mapping"""])
        _lowerCamelCase  :				Dict      =		images
  
        return encoded_inputs
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     str:
        # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
        _lowerCamelCase  :				Dict      =		[]
        for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx])
  
        if len(SCREAMING_SNAKE_CASE) != len(SCREAMING_SNAKE_CASE):
           raise ValueError(
               """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
               F' {len(SCREAMING_SNAKE_CASE)} and {len(SCREAMING_SNAKE_CASE)}')
  
        return images_with_overflow
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      *SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)     ->     Dict:
        return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      *SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)     ->     int:
        return self.tokenizer.decode(*SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)
  
  
  
  
  
     @property
     def 		UpperCamelCase_     (       self)     ->     Dict:
        return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  
  
  
  
  
     @property
     def 		UpperCamelCase_     (       self)     ->     Optional[int]:
        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,      SCREAMING_SNAKE_CASE			,      )
        return self.image_processor_class
  
  
  
  
  
     @property
     def 		UpperCamelCase_     (       self)     ->     List[str]:
        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,      SCREAMING_SNAKE_CASE			,      )
        return self.image_processor
  
  
style_context: 
  
  
  """simple docstring"""
  import os
  
  from apache_beam.io.filesystems import FileSystems
  from apache_beam.pipeline import Pipeline
  
  from .logging import get_logger
  
  
  UpperCAmelCase   =	2 << 20  # 2mb
  UpperCAmelCase   =	get_logger(__name__)
  
  
  
  
  class   lowercase__							(				A_							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Dict:
        _lowerCamelCase  :				Tuple      =		self._options.get_all_options().get("""runner""")
        return runner in [None, "DirectRunner", "PortableRunner"]
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			List[Any]	,   __snake_case       :			Any=False      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Any      =		FileSystems
     if fs.exists(__snake_case      ):
        if force_upload:
           logger.info(F'Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'      )
        else:
           logger.info(F'Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'      )
           return
     with fs.create(__snake_case      ) as remote_file:
        with open(__snake_case	,   """rb"""      ) as local_file:
           _lowerCamelCase  :				Optional[Any]      =		local_file.read(__snake_case      )
           while chunk:
              remote_file.write(__snake_case      )
              _lowerCamelCase  :				Tuple      =		local_file.read(__snake_case      )
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			List[Any]	,   __snake_case       :			Union[str, Any]=False      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				int      =		FileSystems
     if os.path.exists(__snake_case      ):
        if force_download:
           logger.info(F'Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'      )
        else:
           logger.info(F'Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'      )
           return
     with fs.open(__snake_case      ) as remote_file:
        with open(__snake_case	,   """wb"""      ) as local_file:
           _lowerCamelCase  :				str      =		remote_file.read(__snake_case      )
           while chunk:
              local_file.write(__snake_case      )
              _lowerCamelCase  :				Any      =		remote_file.read(__snake_case      )
  
  