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 center_crop, 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, is_vision_available, logging
  
  
  if is_vision_available():
   import PIL
  
  
  snake_case   :   Optional[Any]    		=		logging.get_logger(__name__)
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   ["""pixel_values"""]
  
  
  
  
  
  
  
      def __init__(		self   :  Optional[int]							,						UpperCamelCase_   :  bool = True							,						UpperCamelCase_   :  Dict[str, int] = None							,						UpperCamelCase_   :  PILImageResampling = PIL.Image.NEAREST							,						UpperCamelCase_   :  bool = False							,						UpperCamelCase_   :  Dict[str, int] = None							,						UpperCamelCase_   :  Union[int, float] = 1 / 2_5_5							,						UpperCamelCase_   :  bool = False							,						UpperCamelCase_   :  bool = True							,						UpperCamelCase_   :  bool = True							,						UpperCamelCase_   :  Optional[Union[float, List[float]]] = None							,						UpperCamelCase_   :  Optional[Union[float, List[float]]] = None							,						UpperCamelCase_   :  bool = True							,						**UpperCamelCase_   :  Union[str, Any]							,						):
  
  
  
  
  
             '''simple docstring'''
  
             super().__init__(**UpperCamelCase_   )
             __magic_name__										=						size if size is not None else {'height': 3_4_6, 'width': 3_4_6}
             __magic_name__										=						get_size_dict(UpperCamelCase_   )
             __magic_name__										=						crop_size if crop_size is not None else {'height': 2_8_9, 'width': 2_8_9}
             __magic_name__										=						get_size_dict(UpperCamelCase_							,						param_name='crop_size'   )
  
             __magic_name__										=						do_resize
             __magic_name__										=						size
             __magic_name__										=						resample
             __magic_name__										=						do_center_crop
             __magic_name__										=						crop_size
             __magic_name__										=						do_rescale
             __magic_name__										=						rescale_factor
             __magic_name__										=						rescale_offset
             __magic_name__										=						do_normalize
             __magic_name__										=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             __magic_name__										=						image_std if image_std is not None else IMAGENET_STANDARD_STD
             __magic_name__										=						include_top
  
  
  
  
  
  
  
      def 							a__ (		self   :  Any							,						UpperCamelCase_   :  np.ndarray							,						UpperCamelCase_   :  Dict[str, int]							,						UpperCamelCase_   :  PILImageResampling = PIL.Image.NEAREST							,						UpperCamelCase_   :  Optional[Union[str, ChannelDimension]] = None							,						**UpperCamelCase_   :  List[str]							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						get_size_dict(UpperCamelCase_   )
             if "height" not in size or "width" not in size:
                    raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""   )
             return resize(
                 UpperCamelCase_							,						size=(size['height'], size['width'])							,						resample=UpperCamelCase_							,						data_format=UpperCamelCase_							,						**UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Any							,						UpperCamelCase_   :  np.ndarray							,						UpperCamelCase_   :  Dict[str, int]							,						UpperCamelCase_   :  Optional[Union[str, ChannelDimension]] = None							,						**UpperCamelCase_   :  int							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						get_size_dict(UpperCamelCase_   )
             if "height" not in size or "width" not in size:
                    raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""   )
             return center_crop(UpperCamelCase_							,						size=(size['height'], size['width'])							,						data_format=UpperCamelCase_							,						**UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[Any]							,						UpperCamelCase_   :  np.ndarray							,						UpperCamelCase_   :  Union[int, float]							,						UpperCamelCase_   :  bool = True							,						UpperCamelCase_   :  Optional[Union[str, ChannelDimension]] = None							,						**UpperCamelCase_   :  int							,						):
  
  
  
  
  
             '''simple docstring'''
  
             if offset:
                    __magic_name__										=						(image - 127.5) * scale
                    if data_format is not None:
                           __magic_name__										=						to_channel_dimension_format(UpperCamelCase_							,						UpperCamelCase_   )
                    __magic_name__										=						rescaled_image.astype(np.floataa   )
             else:
                    __magic_name__										=						rescale(UpperCamelCase_							,						scale=UpperCamelCase_							,						data_format=UpperCamelCase_							,						**UpperCamelCase_   )
             return rescaled_image
  
  
  
  
  
  
  
      def 							a__ (		self   :  Tuple							,						UpperCamelCase_   :  np.ndarray							,						UpperCamelCase_   :  Union[float, List[float]]							,						UpperCamelCase_   :  Union[float, List[float]]							,						UpperCamelCase_   :  Optional[Union[str, ChannelDimension]] = None							,						**UpperCamelCase_   :  int							,						):
  
  
  
  
  
             '''simple docstring'''
  
             return normalize(UpperCamelCase_							,						mean=UpperCamelCase_							,						std=UpperCamelCase_							,						data_format=UpperCamelCase_							,						**UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]							,						UpperCamelCase_   :  ImageInput							,						UpperCamelCase_   :  bool = None							,						UpperCamelCase_   :  Dict[str, int] = None							,						UpperCamelCase_   :  int=None							,						UpperCamelCase_   :  bool = None							,						UpperCamelCase_   :  Dict[str, int] = None							,						UpperCamelCase_   :  bool = None							,						UpperCamelCase_   :  float = None							,						UpperCamelCase_   :  bool = None							,						UpperCamelCase_   :  bool = None							,						UpperCamelCase_   :  Optional[Union[float, List[float]]] = None							,						UpperCamelCase_   :  Optional[Union[float, List[float]]] = None							,						UpperCamelCase_   :  bool = None							,						UpperCamelCase_   :  Optional[Union[str, TensorType]] = None							,						UpperCamelCase_   :  ChannelDimension = ChannelDimension.FIRST							,						**UpperCamelCase_   :  List[str]							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						do_resize if do_resize is not None else self.do_resize
             __magic_name__										=						resample if resample is not None else self.resample
             __magic_name__										=						do_center_crop if do_center_crop is not None else self.do_center_crop
             __magic_name__										=						do_rescale if do_rescale is not None else self.do_rescale
             __magic_name__										=						rescale_factor if rescale_factor is not None else self.rescale_factor
             __magic_name__										=						rescale_offset if rescale_offset is not None else self.rescale_offset
             __magic_name__										=						do_normalize if do_normalize is not None else self.do_normalize
             __magic_name__										=						image_mean if image_mean is not None else self.image_mean
             __magic_name__										=						image_std if image_std is not None else self.image_std
             __magic_name__										=						include_top if include_top is not None else self.include_top
  
             __magic_name__										=						size if size is not None else self.size
             __magic_name__										=						get_size_dict(UpperCamelCase_   )
             __magic_name__										=						crop_size if crop_size is not None else self.crop_size
             __magic_name__										=						get_size_dict(UpperCamelCase_							,						param_name='crop_size'   )
  
             __magic_name__										=						make_list_of_images(UpperCamelCase_   )
  
             if not valid_images(UpperCamelCase_   ):
                    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 or resample is None:
                    raise ValueError('Size and resample must be specified if do_resize is True.'   )
  
             if do_center_crop and crop_size is None:
                    raise ValueError('Crop size must be specified if do_center_crop is True.'   )
  
             if do_rescale and rescale_factor is None:
                    raise ValueError('Rescale factor must be specified if do_rescale is True.'   )
  
             if do_normalize and (image_mean is None or image_std is None):
                    raise ValueError('Image mean and std must be specified if do_normalize is True.'   )
  
             # All transformations expect numpy arrays.
             __magic_name__										=						[to_numpy_array(UpperCamelCase_   ) for image in images]
  
             if do_resize:
                    __magic_name__										=						[self.resize(image=UpperCamelCase_							,						size=UpperCamelCase_							,						resample=UpperCamelCase_   ) for image in images]
  
             if do_center_crop:
                    __magic_name__										=						[self.center_crop(image=UpperCamelCase_							,						size=UpperCamelCase_   ) for image in images]
  
             if do_rescale:
                    __magic_name__										=						[self.rescale(image=UpperCamelCase_							,						scale=UpperCamelCase_							,						offset=UpperCamelCase_   ) for image in images]
  
             if do_normalize:
                    __magic_name__										=						[self.normalize(image=UpperCamelCase_							,						mean=UpperCamelCase_							,						std=UpperCamelCase_   ) for image in images]
  
             if include_top:
                    __magic_name__										=						[self.normalize(image=UpperCamelCase_							,						mean=[0, 0, 0]							,						std=UpperCamelCase_   ) for image in images]
  
             __magic_name__										=						[to_channel_dimension_format(UpperCamelCase_							,						UpperCamelCase_   ) for image in images]
  
             __magic_name__										=						{'pixel_values': images}
             return BatchFeature(data=UpperCamelCase_							,						tensor_type=UpperCamelCase_   )
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  snake_case   :   str    		=		{"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   Union[str, Any]    		=		[
       """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SEWDForCTC""",
       """SEWDForSequenceClassification""",
       """SEWDModel""",
       """SEWDPreTrainedModel""",
   ]
  
  if TYPE_CHECKING:
   from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_sew_d import (
        SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWDForCTC,
        SEWDForSequenceClassification,
        SEWDModel,
        SEWDPreTrainedModel,
    )
  
  
  else:
   import sys
  
   snake_case   :   Union[str, Any]    		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)