code: 
  
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, logging
  
  
  _SCREAMING_SNAKE_CASE    :				Union[str, Any]       =					logging.get_logger(__name__)
  
  class 						A__      (					snake_case__			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	['pixel_values']
      def __init__( self    ,      __snake_case = True    ,      __snake_case = None    ,      __snake_case = PILImageResampling.BILINEAR    ,      __snake_case = True    ,      __snake_case = 1 / 2_5_5    ,      __snake_case = True    ,      __snake_case = None    ,      __snake_case = None    ,      **__snake_case    ,      ):
           super().__init__(**__snake_case					)
           snake_case					=       size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
           snake_case					=       get_size_dict(__snake_case					)
           snake_case					=       do_resize
           snake_case					=       do_rescale
           snake_case					=       do_normalize
           snake_case					=       size
           snake_case					=       resample
           snake_case					=       rescale_factor
           snake_case					=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           snake_case					=       image_std if image_std is not None else IMAGENET_STANDARD_STD
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case = PILImageResampling.BILINEAR    ,      __snake_case = None    ,      **__snake_case    ,      ):
           snake_case					=       get_size_dict(__snake_case					)
           if "height" not in size or "width" not in size:
                raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''					)
           return resize(
               __snake_case    ,      size=(size['''height'''], size['''width'''])    ,      resample=__snake_case    ,      data_format=__snake_case    ,      **__snake_case					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case = None    ,      **__snake_case					):
           return rescale(__snake_case    ,      scale=__snake_case    ,      data_format=__snake_case    ,      **__snake_case					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case = None    ,      **__snake_case    ,      ):
           return normalize(__snake_case    ,      mean=__snake_case    ,      std=__snake_case    ,      data_format=__snake_case    ,      **__snake_case					)
  
  
  
  
      def 	a_    ( self    ,      __snake_case    ,      __snake_case = None    ,      __snake_case = None    ,      __snake_case = None    ,      __snake_case = None    ,      __snake_case = None    ,      __snake_case = None    ,      __snake_case = None    ,      __snake_case = None    ,      __snake_case = None    ,      __snake_case = ChannelDimension.FIRST    ,      **__snake_case    ,      ):
           snake_case					=       do_resize if do_resize is not None else self.do_resize
           snake_case					=       do_rescale if do_rescale is not None else self.do_rescale
           snake_case					=       do_normalize if do_normalize is not None else self.do_normalize
           snake_case					=       resample if resample is not None else self.resample
           snake_case					=       rescale_factor if rescale_factor is not None else self.rescale_factor
           snake_case					=       image_mean if image_mean is not None else self.image_mean
           snake_case					=       image_std if image_std is not None else self.image_std
  
           snake_case					=       size if size is not None else self.size
           snake_case					=       get_size_dict(__snake_case					)
  
           snake_case					=       make_list_of_images(__snake_case					)
  
           if not valid_images(__snake_case					):
                raise ValueError(
                    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                    '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)
  
           if do_resize and size is None:
                raise ValueError('''Size must be specified if do_resize is True.'''					)
  
           if do_rescale and rescale_factor is None:
                raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)
  
           # All transformations expect numpy arrays.
           snake_case					=       [to_numpy_array(__snake_case					) for image in images]
  
           if do_resize:
                snake_case					=       [self.resize(image=__snake_case    ,      size=__snake_case    ,      resample=__snake_case					) for image in images]
  
           if do_rescale:
                snake_case					=       [self.rescale(image=__snake_case    ,      scale=__snake_case					) for image in images]
  
           if do_normalize:
                snake_case					=       [self.normalize(image=__snake_case    ,      mean=__snake_case    ,      std=__snake_case					) for image in images]
  
           snake_case					=       [to_channel_dimension_format(__snake_case    ,      __snake_case					) for image in images]
  
           snake_case					=       {'''pixel_values''': images}
           return BatchFeature(data=__snake_case    ,      tensor_type=__snake_case					)
  
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				List[str]       =					[
       "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMForMaskedLM",
       "LayoutLMForSequenceClassification",
       "LayoutLMForTokenClassification",
       "LayoutLMForQuestionAnswering",
       "LayoutLMModel",
       "LayoutLMPreTrainedModel",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
       "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFLayoutLMForMaskedLM",
       "TFLayoutLMForSequenceClassification",
       "TFLayoutLMForTokenClassification",
       "TFLayoutLMForQuestionAnswering",
       "TFLayoutLMMainLayer",
       "TFLayoutLMModel",
       "TFLayoutLMPreTrainedModel",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_layoutlm import (
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
        LayoutLMPreTrainedModel,
    )
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_layoutlm import (
        TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMForMaskedLM,
        TFLayoutLMForQuestionAnswering,
        TFLayoutLMForSequenceClassification,
        TFLayoutLMForTokenClassification,
        TFLayoutLMMainLayer,
        TFLayoutLMModel,
        TFLayoutLMPreTrainedModel,
    )
  
  else:
   import sys
  
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  