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
  
  
  SCREAMING_SNAKE_CASE       = logging.get_logger(__name__)
  
  
  
  
  class 			UpperCAmelCase_						(		A_							):
      lowercase__		      =   ['''pixel_values''']
  
  
  
  
      def __init__(					self		: Any			,				snake_case_		: bool = True			,				snake_case_		: Dict[str, int] = None			,				snake_case_		: bool = True			,				snake_case_		: Dict[str, int] = None			,				snake_case_		: PILImageResampling = PILImageResampling.BICUBIC			,				snake_case_		: bool = True			,				snake_case_		: Union[int, float] = 1 / 255			,				snake_case_		: bool = True			,				snake_case_		: Optional[Union[float, List[float]]] = None			,				snake_case_		: Optional[Union[float, List[float]]] = None			,				**snake_case_		: List[Any]			,				)   ->							None:
  
  
  
  
       '''simple docstring'''
  
       super().__init__(**snake_case_   )
       A__ =       crop_size if crop_size is not None else {"height": 256, "width": 256}
       A__ =       get_size_dict(snake_case_			,				param_name="crop_size"   )
       A__ =       size if size is not None else {"height": 224, "width": 224}
       A__ =       get_size_dict(snake_case_   )
  
       A__ =       do_center_crop
       A__ =       crop_size
       A__ =       do_resize
       A__ =       size
       A__ =       resample
       A__ =       do_rescale
       A__ =       rescale_factor
       A__ =       do_normalize
       A__ =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       A__ =       image_std if image_std is not None else IMAGENET_DEFAULT_STD
  
  
  
  
      def 							__magic_name__							(					self		: Dict			,				snake_case_		: np.ndarray			,				snake_case_		: Dict[str, int]			,				snake_case_		: Optional[int] = None			,				snake_case_		: Optional[Union[str, ChannelDimension]] = None			,				**snake_case_		: Tuple			,				)   ->							np.ndarray:
  
  
  
  
       '''simple docstring'''
  
       A__ =       self.size if size is None else size
       A__ =       get_size_dict(snake_case_   )
       A__ =       get_size_dict(snake_case_			,				param_name="crop_size"   )
  
       A__,   A__ =       get_image_size(snake_case_   )
       A__ =       min(snake_case_			,				snake_case_   )
       A__ =       (size["height"] / crop_size["height"]) * min_dim
       A__ =       (size["width"] / crop_size["width"]) * min_dim
       return center_crop(snake_case_			,				size=(cropped_height, cropped_width)			,				data_format=snake_case_			,				**snake_case_   )
  
  
  
  
      def 							__magic_name__							(					self		: List[str]			,				snake_case_		: np.ndarray			,				snake_case_		: Dict[str, int]			,				snake_case_		: PILImageResampling = PIL.Image.BICUBIC			,				snake_case_		: Optional[Union[str, ChannelDimension]] = None			,				**snake_case_		: Tuple			,				)   ->							np.ndarray:
  
  
  
  
       '''simple docstring'''
  
       A__ =       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 							__magic_name__							(					self		: Optional[Any]			,				snake_case_		: np.ndarray			,				snake_case_		: Union[int, float]			,				snake_case_		: Optional[Union[str, ChannelDimension]] = None			,				**snake_case_		: List[Any]			,				)   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       return rescale(snake_case_			,				scale=snake_case_			,				data_format=snake_case_			,				**snake_case_   )
  
  
  
  
      def 							__magic_name__							(					self		: Any			,				snake_case_		: np.ndarray			,				snake_case_		: Union[float, List[float]]			,				snake_case_		: Union[float, List[float]]			,				snake_case_		: Optional[Union[str, ChannelDimension]] = None			,				**snake_case_		: Optional[int]			,				)   ->							np.ndarray:
  
  
  
  
       '''simple docstring'''
  
       return normalize(snake_case_			,				mean=snake_case_			,				std=snake_case_			,				data_format=snake_case_			,				**snake_case_   )
  
  
  
  
      def 							__magic_name__							(					self		: Union[str, Any]			,				snake_case_		: ImageInput			,				snake_case_		: Optional[bool] = None			,				snake_case_		: Optional[Dict[str, int]] = None			,				snake_case_		: Optional[bool] = None			,				snake_case_		: Optional[Dict[str, int]] = None			,				snake_case_		: PILImageResampling = None			,				snake_case_		: Optional[bool] = None			,				snake_case_		: Optional[float] = None			,				snake_case_		: Optional[bool] = None			,				snake_case_		: Optional[Union[float, List[float]]] = None			,				snake_case_		: Optional[Union[float, List[float]]] = None			,				snake_case_		: Optional[Union[str, TensorType]] = None			,				snake_case_		: ChannelDimension = ChannelDimension.FIRST			,				**snake_case_		: List[str]			,				)   ->							PIL.Image.Image:
  
  
  
  
       '''simple docstring'''
  
       A__ =       do_center_crop if do_center_crop is not None else self.do_center_crop
       A__ =       crop_size if crop_size is not None else self.crop_size
       A__ =       get_size_dict(snake_case_			,				param_name="crop_size"   )
       A__ =       do_resize if do_resize is not None else self.do_resize
       A__ =       size if size is not None else self.size
       A__ =       get_size_dict(snake_case_   )
       A__ =       resample if resample is not None else self.resample
       A__ =       do_rescale if do_rescale is not None else self.do_rescale
       A__ =       rescale_factor if rescale_factor is not None else self.rescale_factor
       A__ =       do_normalize if do_normalize is not None else self.do_normalize
       A__ =       image_mean if image_mean is not None else self.image_mean
       A__ =       image_std if image_std is not None else self.image_std
  
       A__ =       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.
       A__ =       [to_numpy_array(snake_case_   ) for image in images]
  
       if do_center_crop:
        A__ =       [self.center_crop(snake_case_			,				snake_case_			,				size=snake_case_   ) for image in images]
  
       if do_resize:
        A__ =       [self.resize(image=snake_case_			,				size=snake_case_			,				resample=snake_case_   ) for image in images]
  
       if do_rescale:
        A__ =       [self.rescale(image=snake_case_			,				scale=snake_case_   ) for image in images]
  
       if do_normalize:
        A__ =       [self.normalize(image=snake_case_			,				mean=snake_case_			,				std=snake_case_   ) for image in images]
  
       A__ =       [to_channel_dimension_format(snake_case_			,				snake_case_   ) for image in images]
  
       A__ =       {"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_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE       = {
      "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
  }
  
  try:
        if not is_vision_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = ["DetaImageProcessor"]
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = [
            "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
            "DetaForObjectDetection",
            "DetaModel",
            "DetaPreTrainedModel",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
        try:
              if not is_vision_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .image_processing_deta import DetaImageProcessor
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_deta import (
                  DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DetaForObjectDetection,
                  DetaModel,
                  DetaPreTrainedModel,
              )
  
  else:
        import sys
  
        SCREAMING_SNAKE_CASE       = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  