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 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, logging
  
  
  lowerCAmelCase__   	=				logging.get_logger(__name__)
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       UpperCamelCase_):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				["pixel_values"]
  
  
       def __init__(		self				,		__lowerCamelCase = True				,		__lowerCamelCase = None				,		__lowerCamelCase = PILImageResampling.BILINEAR				,		__lowerCamelCase = True				,		__lowerCamelCase = 1 / 2_5_5				,		__lowerCamelCase = True				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		**__lowerCamelCase				,		)    ->       None:
              super().__init__(**__UpperCamelCase)
              _A   : Tuple       					=				size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
              _A   : Tuple       					=				get_size_dict(__UpperCamelCase)
              _A   : Dict       					=				do_resize
              _A   : str       					=				do_rescale
              _A   : Optional[int]       					=				do_normalize
              _A   : Dict       					=				size
              _A   : str       					=				resample
              _A   : int       					=				rescale_factor
              _A   : Dict       					=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
              _A   : int       					=				image_std if image_std is not None else IMAGENET_STANDARD_STD
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase = PILImageResampling.BILINEAR				,		__lowerCamelCase = None				,		**__lowerCamelCase				,		)    ->       np.ndarray:
              _A   : List[Any]       					=				get_size_dict(__UpperCamelCase)
              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(
                  __UpperCamelCase				,		size=(size["height"], size["width"])				,		resample=__UpperCamelCase				,		data_format=__UpperCamelCase				,		**__UpperCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase = None				,		**__lowerCamelCase)    ->       np.ndarray:
              return rescale(__UpperCamelCase				,		scale=__UpperCamelCase				,		data_format=__UpperCamelCase				,		**__UpperCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase = None				,		**__lowerCamelCase				,		)    ->       np.ndarray:
              return normalize(__UpperCamelCase				,		mean=__UpperCamelCase				,		std=__UpperCamelCase				,		data_format=__UpperCamelCase				,		**__UpperCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = ChannelDimension.FIRST				,		**__lowerCamelCase				,		)    ->       Any:
              _A   : Optional[Any]       					=				do_resize if do_resize is not None else self.do_resize
              _A   : str       					=				do_rescale if do_rescale is not None else self.do_rescale
              _A   : str       					=				do_normalize if do_normalize is not None else self.do_normalize
              _A   : Optional[int]       					=				resample if resample is not None else self.resample
              _A   : Union[str, Any]       					=				rescale_factor if rescale_factor is not None else self.rescale_factor
              _A   : int       					=				image_mean if image_mean is not None else self.image_mean
              _A   : Dict       					=				image_std if image_std is not None else self.image_std
  
              _A   : Optional[Any]       					=				size if size is not None else self.size
              _A   : Tuple       					=				get_size_dict(__UpperCamelCase)
  
              _A   : Any       					=				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:
                     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.")
  
              # All transformations expect numpy arrays.
              _A   : Optional[Any]       					=				[to_numpy_array(__UpperCamelCase) for image in images]
  
              if do_resize:
                     _A   : Dict       					=				[self.resize(image=__UpperCamelCase				,		size=__UpperCamelCase				,		resample=__UpperCamelCase) for image in images]
  
              if do_rescale:
                     _A   : List[str]       					=				[self.rescale(image=__UpperCamelCase				,		scale=__UpperCamelCase) for image in images]
  
              if do_normalize:
                     _A   : int       					=				[self.normalize(image=__UpperCamelCase				,		mean=__UpperCamelCase				,		std=__UpperCamelCase) for image in images]
  
              _A   : int       					=				[to_channel_dimension_format(__UpperCamelCase				,		__UpperCamelCase) for image in images]
  
              _A   : List[Any]       					=				{'''pixel_values''': images}
              return BatchFeature(data=__UpperCamelCase				,		tensor_type=__UpperCamelCase)
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowerCAmelCase__   	=				{
      'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
  }
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['DetaImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          '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
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  