code: 
  
  
  '''simple docstring'''
  
  
  
  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
  
  
  A__	:      Union[str, Any]				=						logging.get_logger(__name__)
  
  
  
  
  
  class      UpperCAmelCase_						(snake_case__						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      str							  =    ['pixel_values']
  
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 1 / 2_55    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  Optional[int]:
           super().__init__(**__snake_case			)
           __lowerCamelCase     :			Optional[Any]              =	size if size is not None else {'height': 2_24, 'width': 2_24}
           __lowerCamelCase     :			Optional[int]              =	get_size_dict(__snake_case			)
           __lowerCamelCase     :			List[str]              =	do_resize
           __lowerCamelCase     :			Optional[int]              =	do_rescale
           __lowerCamelCase     :			List[str]              =	do_normalize
           __lowerCamelCase     :			Union[str, Any]              =	size
           __lowerCamelCase     :			Tuple              =	resample
           __lowerCamelCase     :			str              =	rescale_factor
           __lowerCamelCase     :			Dict              =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           __lowerCamelCase     :			Tuple              =	image_std if image_std is not None else IMAGENET_STANDARD_STD
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR    ,  SCREAMING_SNAKE_CASE_ = None    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  Tuple:
           __lowerCamelCase     :			Any              =	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    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = None    ,  **SCREAMING_SNAKE_CASE_			)       ->  Any:
           return rescale(__snake_case    ,  scale=__snake_case    ,  data_format=__snake_case    ,  **__snake_case			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = None    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  List[Any]:
           return normalize(__snake_case    ,  mean=__snake_case    ,  std=__snake_case    ,  data_format=__snake_case    ,  **__snake_case			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = None    ,  SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  List[str]:
           __lowerCamelCase     :			int              =	do_resize if do_resize is not None else self.do_resize
           __lowerCamelCase     :			Optional[int]              =	do_rescale if do_rescale is not None else self.do_rescale
           __lowerCamelCase     :			str              =	do_normalize if do_normalize is not None else self.do_normalize
           __lowerCamelCase     :			Any              =	resample if resample is not None else self.resample
           __lowerCamelCase     :			str              =	rescale_factor if rescale_factor is not None else self.rescale_factor
           __lowerCamelCase     :			List[Any]              =	image_mean if image_mean is not None else self.image_mean
           __lowerCamelCase     :			Optional[int]              =	image_std if image_std is not None else self.image_std
  
           __lowerCamelCase     :			Dict              =	size if size is not None else self.size
           __lowerCamelCase     :			int              =	get_size_dict(__snake_case			)
  
           __lowerCamelCase     :			Union[str, Any]              =	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.
           __lowerCamelCase     :			Optional[Any]              =	[to_numpy_array(__snake_case			) for image in images]
  
           if do_resize:
                __lowerCamelCase     :			Tuple              =	[self.resize(image=__snake_case    ,  size=__snake_case    ,  resample=__snake_case			) for image in images]
  
           if do_rescale:
                __lowerCamelCase     :			Any              =	[self.rescale(image=__snake_case    ,  scale=__snake_case			) for image in images]
  
           if do_normalize:
                __lowerCamelCase     :			Optional[int]              =	[self.normalize(image=__snake_case    ,  mean=__snake_case    ,  std=__snake_case			) for image in images]
  
           __lowerCamelCase     :			Tuple              =	[to_channel_dimension_format(__snake_case    ,  __snake_case			) for image in images]
  
           __lowerCamelCase     :			Tuple              =	{'pixel_values': images}
           return BatchFeature(data=__snake_case    ,  tensor_type=__snake_case			)
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  A__	:      List[str]				=						{
      """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
      """tokenization_flaubert""": ["""FlaubertTokenizer"""],
  }
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      int				=						[
            """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """FlaubertForMultipleChoice""",
            """FlaubertForQuestionAnswering""",
            """FlaubertForQuestionAnsweringSimple""",
            """FlaubertForSequenceClassification""",
            """FlaubertForTokenClassification""",
            """FlaubertModel""",
            """FlaubertWithLMHeadModel""",
            """FlaubertPreTrainedModel""",
        ]
  
  try:
        if not is_tf_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        A__	:      int				=						[
            """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """TFFlaubertForMultipleChoice""",
            """TFFlaubertForQuestionAnsweringSimple""",
            """TFFlaubertForSequenceClassification""",
            """TFFlaubertForTokenClassification""",
            """TFFlaubertModel""",
            """TFFlaubertPreTrainedModel""",
            """TFFlaubertWithLMHeadModel""",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
        from .tokenization_flaubert import FlaubertTokenizer
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_flaubert import (
                  FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaubertForMultipleChoice,
                  FlaubertForQuestionAnswering,
                  FlaubertForQuestionAnsweringSimple,
                  FlaubertForSequenceClassification,
                  FlaubertForTokenClassification,
                  FlaubertModel,
                  FlaubertPreTrainedModel,
                  FlaubertWithLMHeadModel,
              )
  
        try:
              if not is_tf_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_tf_flaubert import (
                  TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFFlaubertForMultipleChoice,
                  TFFlaubertForQuestionAnsweringSimple,
                  TFFlaubertForSequenceClassification,
                  TFFlaubertForTokenClassification,
                  TFFlaubertModel,
                  TFFlaubertPreTrainedModel,
                  TFFlaubertWithLMHeadModel,
              )
  
  else:
        import sys
  
        A__	:      Tuple				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  