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,
      convert_to_rgb,
      get_resize_output_image_size,
      normalize,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      OPENAI_CLIP_MEAN,
      OPENAI_CLIP_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  __a   				=      logging.get_logger(__name__)
  
  
  if is_vision_available():
    import PIL
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   (			A__   ):
        A				:							List[str]    							=     ['pixel_values']
  
  
        def __init__(							self							,						SCREAMING_SNAKE_CASE__ = True							,						SCREAMING_SNAKE_CASE__ = None							,						SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC							,						SCREAMING_SNAKE_CASE__ = True							,						SCREAMING_SNAKE_CASE__ = None							,						SCREAMING_SNAKE_CASE__ = True							,						SCREAMING_SNAKE_CASE__ = 1 / 255							,						SCREAMING_SNAKE_CASE__ = True							,						SCREAMING_SNAKE_CASE__ = None							,						SCREAMING_SNAKE_CASE__ = None							,						SCREAMING_SNAKE_CASE__ = True							,						**SCREAMING_SNAKE_CASE__							,						):
              super().__init__(**SCREAMING_SNAKE_CASE__		)
              lowercase			:   int					   =		size if size is not None else {'''shortest_edge''': 224}
              lowercase			:   Optional[Any]					   =		get_size_dict(SCREAMING_SNAKE_CASE__							,						default_to_square=SCREAMING_SNAKE_CASE__		)
              lowercase			:   Union[str, Any]					   =		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
              lowercase			:   Any					   =		get_size_dict(SCREAMING_SNAKE_CASE__		)
  
              lowercase			:   Any					   =		do_resize
              lowercase			:   Union[str, Any]					   =		size
              lowercase			:   Optional[int]					   =		resample
              lowercase			:   Optional[int]					   =		do_center_crop
              lowercase			:   Dict					   =		crop_size
              lowercase			:   Tuple					   =		do_rescale
              lowercase			:   str					   =		rescale_factor
              lowercase			:   Optional[Any]					   =		do_normalize
              lowercase			:   Optional[int]					   =		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
              lowercase			:   Dict					   =		image_std if image_std is not None else OPENAI_CLIP_STD
              lowercase			:   Any					   =		do_convert_rgb
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC							,						SCREAMING_SNAKE_CASE__ = None							,						**SCREAMING_SNAKE_CASE__							,						):
              lowercase			:   Optional[int]					   =		get_size_dict(SCREAMING_SNAKE_CASE__							,						default_to_square=SCREAMING_SNAKE_CASE__		)
              lowercase			:   Optional[int]					   =		get_resize_output_image_size(
                  SCREAMING_SNAKE_CASE__							,						size=(size['''height'''], size['''width'''])							,						default_to_square=SCREAMING_SNAKE_CASE__		)
              return resize(SCREAMING_SNAKE_CASE__							,						size=SCREAMING_SNAKE_CASE__							,						resample=SCREAMING_SNAKE_CASE__							,						data_format=SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__ = None							,						**SCREAMING_SNAKE_CASE__							,						):
              lowercase			:   Dict					   =		get_size_dict(SCREAMING_SNAKE_CASE__		)
              return center_crop(SCREAMING_SNAKE_CASE__							,						size=(size['''height'''], size['''width'''])							,						data_format=SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__ = None							,						**SCREAMING_SNAKE_CASE__							,						):
              return rescale(SCREAMING_SNAKE_CASE__							,						scale=SCREAMING_SNAKE_CASE__							,						data_format=SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__ = None							,						**SCREAMING_SNAKE_CASE__							,						):
              return normalize(SCREAMING_SNAKE_CASE__							,						mean=SCREAMING_SNAKE_CASE__							,						std=SCREAMING_SNAKE_CASE__							,						data_format=SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		)
  
  
  
  
  
  
        def        __lowerCamelCase			(							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__ = None							,						SCREAMING_SNAKE_CASE__ = None							,						SCREAMING_SNAKE_CASE__ = None							,						SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST							,						**SCREAMING_SNAKE_CASE__							,						):
              lowercase			:   List[Any]					   =		do_resize if do_resize is not None else self.do_resize
              lowercase			:   Optional[int]					   =		size if size is not None else self.size
              lowercase			:   List[Any]					   =		get_size_dict(SCREAMING_SNAKE_CASE__							,						default_to_square=SCREAMING_SNAKE_CASE__		)
              lowercase			:   Optional[int]					   =		resample if resample is not None else self.resample
              lowercase			:   Tuple					   =		do_center_crop if do_center_crop is not None else self.do_center_crop
              lowercase			:   int					   =		crop_size if crop_size is not None else self.crop_size
              lowercase			:   Any					   =		get_size_dict(SCREAMING_SNAKE_CASE__		)
              lowercase			:   Optional[int]					   =		do_rescale if do_rescale is not None else self.do_rescale
              lowercase			:   Optional[int]					   =		rescale_factor if rescale_factor is not None else self.rescale_factor
              lowercase			:   Optional[Any]					   =		do_normalize if do_normalize is not None else self.do_normalize
              lowercase			:   Tuple					   =		image_mean if image_mean is not None else self.image_mean
              lowercase			:   int					   =		image_std if image_std is not None else self.image_std
              lowercase			:   Union[str, Any]					   =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
  
              lowercase			:   int					   =		make_list_of_images(SCREAMING_SNAKE_CASE__		)
  
              if not valid_images(SCREAMING_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_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.'''		)
  
              # PIL RGBA images are converted to RGB
              if do_convert_rgb:
                    lowercase			:   Union[str, Any]					   =		[convert_to_rgb(SCREAMING_SNAKE_CASE__		) for image in images]
  
              # All transformations expect numpy arrays.
              lowercase			:   List[Any]					   =		[to_numpy_array(SCREAMING_SNAKE_CASE__		) for image in images]
  
              if do_resize:
                    lowercase			:   Optional[Any]					   =		[self.resize(image=SCREAMING_SNAKE_CASE__							,						size=SCREAMING_SNAKE_CASE__							,						resample=SCREAMING_SNAKE_CASE__		) for image in images]
  
              if do_center_crop:
                    lowercase			:   List[Any]					   =		[self.center_crop(image=SCREAMING_SNAKE_CASE__							,						size=SCREAMING_SNAKE_CASE__		) for image in images]
  
              if do_rescale:
                    lowercase			:   Union[str, Any]					   =		[self.rescale(image=SCREAMING_SNAKE_CASE__							,						scale=SCREAMING_SNAKE_CASE__		) for image in images]
  
              if do_normalize:
                    lowercase			:   Optional[Any]					   =		[self.normalize(image=SCREAMING_SNAKE_CASE__							,						mean=SCREAMING_SNAKE_CASE__							,						std=SCREAMING_SNAKE_CASE__		) for image in images]
  
              lowercase			:   List[Any]					   =		[to_channel_dimension_format(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		) for image in images]
  
              lowercase			:   Union[str, Any]					   =		{'''pixel_values''': images}
              return BatchFeature(data=SCREAMING_SNAKE_CASE__							,						tensor_type=SCREAMING_SNAKE_CASE__		)
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  __a   				=      {
      '''configuration_decision_transformer''': [
          '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
          '''DecisionTransformerConfig''',
      ],
  }
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __a   				=      [
        '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''DecisionTransformerGPT2Model''',
        '''DecisionTransformerGPT2PreTrainedModel''',
        '''DecisionTransformerModel''',
        '''DecisionTransformerPreTrainedModel''',
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_decision_transformer import (
        DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        DecisionTransformerConfig,
    )
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )
  
  
  else:
    import sys
  
    __a   				=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  