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 (
      get_resize_output_image_size,
      normalize,
      pad,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      get_image_size,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, logging
  from ...utils.import_utils import is_vision_available
  
  
  A           =   logging.get_logger(__name__)
  
  
  if is_vision_available():
       import PIL
  
  
  
  class  lowerCAmelCase__   (       SCREAMING_SNAKE_CASE__			):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    _UpperCamelCase						= ['pixel_values']
  
    def __init__(self  : Union[str, Any]	, lowerCAmelCase_  : List[str] = True	, lowerCAmelCase_  : List[str] = None	, lowerCAmelCase_  : Any = PILImageResampling.BILINEAR	, lowerCAmelCase_  : Any = True	, lowerCAmelCase_  : int = False	, lowerCAmelCase_  : List[Any] = True	, lowerCAmelCase_  : List[str] = True	, lowerCAmelCase_  : Tuple = 1 / 2_55	, lowerCAmelCase_  : Dict = True	, lowerCAmelCase_  : Union[str, Any] = None	, lowerCAmelCase_  : Any = None	, **lowerCAmelCase_  : Union[str, Any]	, )   ->       None:
          super().__init__(**lowerCAmelCase_    )
  
          snake_case_						       =							size if size is not None else {"height": 25_60, "width": 19_20}
          if isinstance(lowerCAmelCase_	, (tuple, list)    ):
                # The previous feature extractor size parameter was in (width, height) format
                snake_case_						       =							size[::-1]
          snake_case_						       =							get_size_dict(lowerCAmelCase_    )
  
          snake_case_						       =							do_resize
          snake_case_						       =							size
          snake_case_						       =							resample
          snake_case_						       =							do_thumbnail
          snake_case_						       =							do_align_long_axis
          snake_case_						       =							do_pad
          snake_case_						       =							do_rescale
          snake_case_						       =							rescale_factor
          snake_case_						       =							do_normalize
          snake_case_						       =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          snake_case_						       =							image_std if image_std is not None else IMAGENET_STANDARD_STD
  
    def 		_lowerCamelCase	(self  : List[str]	, lowerCAmelCase_  : Union[str, Any]	, lowerCAmelCase_  : Union[str, Any]	, lowerCAmelCase_  : Tuple = None    )   ->       np.ndarray:
          snake_case_       ,	snake_case_						       =							get_image_size(lowerCAmelCase_    )
          snake_case_       ,	snake_case_						       =							size["height"], size["width"]
  
          if (output_width < output_height and input_width > input_height) or (
              output_width > output_height and input_width < input_height
          ):
                snake_case_						       =							np.rotaa(lowerCAmelCase_	, 3    )
  
          if data_format is not None:
                snake_case_						       =							to_channel_dimension_format(lowerCAmelCase_	, lowerCAmelCase_    )
  
          return image
  
    def 		_lowerCamelCase	(self  : Union[str, Any]	, lowerCAmelCase_  : Union[str, Any]	, lowerCAmelCase_  : str	, lowerCAmelCase_  : Union[str, Any] = False	, lowerCAmelCase_  : Any = None	, )   ->       np.ndarray:
          snake_case_       ,	snake_case_						       =							size["height"], size["width"]
          snake_case_       ,	snake_case_						       =							get_image_size(lowerCAmelCase_    )
  
          snake_case_						       =							output_width - input_width
          snake_case_						       =							output_height - input_height
  
          if random_padding:
                snake_case_						       =							np.random.randint(low=0	, high=delta_height + 1    )
                snake_case_						       =							np.random.randint(low=0	, high=delta_width + 1    )
          else:
                snake_case_						       =							delta_height // 2
                snake_case_						       =							delta_width // 2
  
          snake_case_						       =							delta_height - pad_top
          snake_case_						       =							delta_width - pad_left
  
          snake_case_						       =							((pad_top, pad_bottom), (pad_left, pad_right))
          return pad(lowerCAmelCase_	, lowerCAmelCase_	, data_format=lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : Tuple	, *lowerCAmelCase_  : Union[str, Any]	, **lowerCAmelCase_  : Dict    )   ->       Any:
          logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."    )
          return self.pad_image(*lowerCAmelCase_	, **lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : Tuple	, lowerCAmelCase_  : Optional[Any]	, lowerCAmelCase_  : Optional[int]	, lowerCAmelCase_  : str = PILImageResampling.BICUBIC	, lowerCAmelCase_  : int = None	, **lowerCAmelCase_  : List[str]	, )   ->       np.ndarray:
          snake_case_       ,	snake_case_						       =							get_image_size(lowerCAmelCase_    )
          snake_case_       ,	snake_case_						       =							size["height"], size["width"]
  
          # We always resize to the smallest of either the input or output size.
          snake_case_						       =							min(lowerCAmelCase_	, lowerCAmelCase_    )
          snake_case_						       =							min(lowerCAmelCase_	, lowerCAmelCase_    )
  
          if height == input_height and width == input_width:
                return image
  
          if input_height > input_width:
                snake_case_						       =							int(input_width * height / input_height    )
          elif input_width > input_height:
                snake_case_						       =							int(input_height * width / input_width    )
  
          return resize(
              lowerCAmelCase_	, size=(height, width)	, resample=lowerCAmelCase_	, reducing_gap=2.0	, data_format=lowerCAmelCase_	, **lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : Optional[Any]	, lowerCAmelCase_  : Optional[Any]	, lowerCAmelCase_  : Optional[Any]	, lowerCAmelCase_  : List[Any] = PILImageResampling.BICUBIC	, lowerCAmelCase_  : Tuple = None	, **lowerCAmelCase_  : Union[str, Any]	, )   ->       np.ndarray:
          snake_case_						       =							get_size_dict(lowerCAmelCase_    )
          snake_case_						       =							min(size["height"]	, size["width"]    )
          snake_case_						       =							get_resize_output_image_size(lowerCAmelCase_	, size=lowerCAmelCase_	, default_to_square=lowerCAmelCase_    )
          snake_case_						       =							resize(lowerCAmelCase_	, size=lowerCAmelCase_	, resample=lowerCAmelCase_	, data_format=lowerCAmelCase_	, **lowerCAmelCase_    )
          return resized_image
  
    def 		_lowerCamelCase	(self  : Optional[Any]	, lowerCAmelCase_  : str	, lowerCAmelCase_  : int	, lowerCAmelCase_  : List[str] = None	, **lowerCAmelCase_  : Optional[int]	, )   ->       Any:
          return rescale(lowerCAmelCase_	, scale=lowerCAmelCase_	, data_format=lowerCAmelCase_	, **lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : Optional[Any]	, lowerCAmelCase_  : Dict	, lowerCAmelCase_  : List[str]	, lowerCAmelCase_  : Tuple	, lowerCAmelCase_  : str = None	, **lowerCAmelCase_  : int	, )   ->       np.ndarray:
          return normalize(lowerCAmelCase_	, mean=lowerCAmelCase_	, std=lowerCAmelCase_	, data_format=lowerCAmelCase_	, **lowerCAmelCase_    )
  
  
    def 		_lowerCamelCase	(self  : Tuple	, lowerCAmelCase_  : str	, lowerCAmelCase_  : List[str] = None	, lowerCAmelCase_  : Any = None	, lowerCAmelCase_  : int = None	, lowerCAmelCase_  : Dict = None	, lowerCAmelCase_  : Tuple = None	, lowerCAmelCase_  : List[Any] = None	, lowerCAmelCase_  : Optional[int] = False	, lowerCAmelCase_  : Dict = None	, lowerCAmelCase_  : Any = None	, lowerCAmelCase_  : Union[str, Any] = None	, lowerCAmelCase_  : Dict = None	, lowerCAmelCase_  : int = None	, lowerCAmelCase_  : int = None	, lowerCAmelCase_  : List[str] = ChannelDimension.FIRST	, **lowerCAmelCase_  : Tuple	, )   ->       PIL.Image.Image:
          snake_case_						       =							do_resize if do_resize is not None else self.do_resize
          snake_case_						       =							size if size is not None else self.size
          if isinstance(lowerCAmelCase_	, (tuple, list)    ):
                # Previous feature extractor had size in (width, height) format
                snake_case_						       =							size[::-1]
          snake_case_						       =							get_size_dict(lowerCAmelCase_    )
          snake_case_						       =							resample if resample is not None else self.resample
          snake_case_						       =							do_thumbnail if do_thumbnail is not None else self.do_thumbnail
          snake_case_						       =							do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
          snake_case_						       =							do_pad if do_pad is not None else self.do_pad
          snake_case_						       =							do_rescale if do_rescale is not None else self.do_rescale
          snake_case_						       =							rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case_						       =							do_normalize if do_normalize is not None else self.do_normalize
          snake_case_						       =							image_mean if image_mean is not None else self.image_mean
          snake_case_						       =							image_std if image_std is not None else self.image_std
  
          snake_case_						       =							make_list_of_images(lowerCAmelCase_    )
  
          if not valid_images(lowerCAmelCase_    ):
                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."    )
  
          if do_pad and size is None:
                raise ValueError("Size must be specified if do_pad 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.
          snake_case_						       =							[to_numpy_array(lowerCAmelCase_    ) for image in images]
  
          if do_align_long_axis:
                snake_case_						       =							[self.align_long_axis(lowerCAmelCase_	, size=lowerCAmelCase_    ) for image in images]
  
          if do_resize:
                snake_case_						       =							[self.resize(image=lowerCAmelCase_	, size=lowerCAmelCase_	, resample=lowerCAmelCase_    ) for image in images]
  
          if do_thumbnail:
                snake_case_						       =							[self.thumbnail(image=lowerCAmelCase_	, size=lowerCAmelCase_    ) for image in images]
  
          if do_pad:
                snake_case_						       =							[self.pad_image(image=lowerCAmelCase_	, size=lowerCAmelCase_	, random_padding=lowerCAmelCase_    ) for image in images]
  
          if do_rescale:
                snake_case_						       =							[self.rescale(image=lowerCAmelCase_	, scale=lowerCAmelCase_    ) for image in images]
  
          if do_normalize:
                snake_case_						       =							[self.normalize(image=lowerCAmelCase_	, mean=lowerCAmelCase_	, std=lowerCAmelCase_    ) for image in images]
  
          snake_case_						       =							[to_channel_dimension_format(lowerCAmelCase_	, lowerCAmelCase_    ) for image in images]
  
          snake_case_						       =							{"pixel_values": images}
          return BatchFeature(data=lowerCAmelCase_	, tensor_type=lowerCAmelCase_    )
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  A           =   {
      "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
  }
  
  try:
       if not is_vision_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   ["DetaImageProcessor"]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   [
           "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
  
       A           =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  