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 center_crop, normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_DEFAULT_MEAN,
      IMAGENET_DEFAULT_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      get_image_size,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  if is_vision_available():
         import PIL
  
  
  A_		:			Any										=   logging.get_logger(__name__)
  class 					A_   (      snake_case__						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       ["pixel_values"]
  
  
      def __init__(self     ,      lowercase__ = True     ,      lowercase__ = None     ,      lowercase__ = True     ,      lowercase__ = None     ,      lowercase__ = PILImageResampling.BICUBIC     ,      lowercase__ = True     ,      lowercase__ = 1 / 255     ,      lowercase__ = True     ,      lowercase__ = None     ,      lowercase__ = None     ,      **lowercase__     ,      )     ->      Optional[int]:
          super().__init__(**__snake_case  )
          __UpperCAmelCase									=			crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
          __UpperCAmelCase									=			get_size_dict(__snake_case     ,      param_name='''crop_size'''  )
          __UpperCAmelCase									=			size if size is not None else {'''height''': 224, '''width''': 224}
          __UpperCAmelCase									=			get_size_dict(__snake_case  )
  
          __UpperCAmelCase									=			do_center_crop
          __UpperCAmelCase									=			crop_size
          __UpperCAmelCase									=			do_resize
          __UpperCAmelCase									=			size
          __UpperCAmelCase									=			resample
          __UpperCAmelCase									=			do_rescale
          __UpperCAmelCase									=			rescale_factor
          __UpperCAmelCase									=			do_normalize
          __UpperCAmelCase									=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          __UpperCAmelCase									=			image_std if image_std is not None else IMAGENET_DEFAULT_STD
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__ = None     ,      lowercase__ = None     ,      **lowercase__     ,      )     ->      List[str]:
          __UpperCAmelCase									=			self.size if size is None else size
          __UpperCAmelCase									=			get_size_dict(__snake_case  )
          __UpperCAmelCase									=			get_size_dict(__snake_case     ,      param_name='''crop_size'''  )
  
          __UpperCAmelCase					,     __UpperCAmelCase									=			get_image_size(__snake_case  )
          __UpperCAmelCase									=			min(__snake_case     ,      __snake_case  )
          __UpperCAmelCase									=			(size['''height'''] / crop_size['''height''']) * min_dim
          __UpperCAmelCase									=			(size['''width'''] / crop_size['''width''']) * min_dim
          return center_crop(__snake_case     ,      size=(cropped_height, cropped_width)     ,      data_format=__snake_case     ,      **__snake_case  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__ = PIL.Image.BICUBIC     ,      lowercase__ = None     ,      **lowercase__     ,      )     ->      List[Any]:
          __UpperCAmelCase									=			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      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__ = None     ,      **lowercase__     ,      )     ->      List[Any]:
          return rescale(__snake_case     ,      scale=__snake_case     ,      data_format=__snake_case     ,      **__snake_case  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__ = None     ,      **lowercase__     ,      )     ->      int:
          return normalize(__snake_case     ,      mean=__snake_case     ,      std=__snake_case     ,      data_format=__snake_case     ,      **__snake_case  )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = None     ,      lowercase__ = ChannelDimension.FIRST     ,      **lowercase__     ,      )     ->      List[Any]:
          __UpperCAmelCase									=			do_center_crop if do_center_crop is not None else self.do_center_crop
          __UpperCAmelCase									=			crop_size if crop_size is not None else self.crop_size
          __UpperCAmelCase									=			get_size_dict(__snake_case     ,      param_name='''crop_size'''  )
          __UpperCAmelCase									=			do_resize if do_resize is not None else self.do_resize
          __UpperCAmelCase									=			size if size is not None else self.size
          __UpperCAmelCase									=			get_size_dict(__snake_case  )
          __UpperCAmelCase									=			resample if resample is not None else self.resample
          __UpperCAmelCase									=			do_rescale if do_rescale is not None else self.do_rescale
          __UpperCAmelCase									=			rescale_factor if rescale_factor is not None else self.rescale_factor
          __UpperCAmelCase									=			do_normalize if do_normalize is not None else self.do_normalize
          __UpperCAmelCase									=			image_mean if image_mean is not None else self.image_mean
          __UpperCAmelCase									=			image_std if image_std is not None else self.image_std
  
          __UpperCAmelCase									=			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_center_crop and crop_size is None:
              raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''  )
  
          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.'''  )
  
          if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('''Image mean and image standard deviation must be specified if do_normalize is True.'''  )
  
          # All transformations expect numpy arrays.
          __UpperCAmelCase									=			[to_numpy_array(__snake_case  ) for image in images]
  
          if do_center_crop:
              __UpperCAmelCase									=			[self.center_crop(__snake_case     ,      __snake_case     ,      size=__snake_case  ) for image in images]
  
          if do_resize:
              __UpperCAmelCase									=			[self.resize(image=__snake_case     ,      size=__snake_case     ,      resample=__snake_case  ) for image in images]
  
          if do_rescale:
              __UpperCAmelCase									=			[self.rescale(image=__snake_case     ,      scale=__snake_case  ) for image in images]
  
          if do_normalize:
              __UpperCAmelCase									=			[self.normalize(image=__snake_case     ,      mean=__snake_case     ,      std=__snake_case  ) for image in images]
  
          __UpperCAmelCase									=			[to_channel_dimension_format(__snake_case     ,      __snake_case  ) for image in images]
  
          __UpperCAmelCase									=			{'''pixel_values''': images}
          return BatchFeature(data=__snake_case     ,      tensor_type=__snake_case  )
  
  
  
  
  
  
style_context: 
  
  import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto.configuration_auto import AutoConfig
  
  
  A_		:			int										=   logging.get_logger(__name__)
  class 					A_   (      _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       "speech-encoder-decoder"
      a__     							=       True
  
  
      def __init__(self     ,      **lowercase__  )     ->      Optional[int]:
          super().__init__(**lowercase__  )
          if "encoder" not in kwargs or "decoder" not in kwargs:
              raise ValueError(
                  F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
                  F''' `decoder` sub-configurations are passed, but only {kwargs}'''  )
  
          __UpperCAmelCase									=			kwargs.pop('''encoder'''  )
          __UpperCAmelCase									=			encoder_config.pop('''model_type'''  )
          __UpperCAmelCase									=			kwargs.pop('''decoder'''  )
          __UpperCAmelCase									=			decoder_config.pop('''model_type'''  )
  
          __UpperCAmelCase									=			AutoConfig.for_model(lowercase__     ,      **lowercase__  )
          __UpperCAmelCase									=			AutoConfig.for_model(lowercase__     ,      **lowercase__  )
          __UpperCAmelCase									=			True
  
  
      @classmethod
      def      lowerCAmelCase_						(cls     ,      lowercase__     ,      lowercase__     ,      **lowercase__  )     ->      PretrainedConfig:
          logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''  )
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			True
  
          return cls(encoder=encoder_config.to_dict()     ,      decoder=decoder_config.to_dict()     ,      **lowercase__  )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          __UpperCAmelCase									=			copy.deepcopy(self.__dict__  )
          __UpperCAmelCase									=			self.encoder.to_dict()
          __UpperCAmelCase									=			self.decoder.to_dict()
          __UpperCAmelCase									=			self.__class__.model_type
          return output
  
  
  
  
  