




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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


UpperCamelCase								=	logging.get_logger(__name__)





class 					__lowerCamelCase			(       SCREAMING_SNAKE_CASE_       ):




    """simple docstring"""

    snake_case__   = ["pixel_values"]


    def __init__(			self	:	Any  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = PIL.Image.BICUBIC  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	Union[int, float] = 1 / 255  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Optional[Any]  ,      ) ->			None:
         super().__init__(**__snake_case  )
         lowerCAmelCase__            =					size if size is not None else {"""height""": 256, """width""": 256}
         lowerCAmelCase__            =					get_size_dict(__snake_case  )
         lowerCAmelCase__            =					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
         lowerCAmelCase__            =					get_size_dict(__snake_case  ,      param_name="crop_size"  )

         lowerCAmelCase__            =					do_resize
         lowerCAmelCase__            =					size
         lowerCAmelCase__            =					resample
         lowerCAmelCase__            =					do_center_crop
         lowerCAmelCase__            =					crop_size
         lowerCAmelCase__            =					do_rescale
         lowerCAmelCase__            =					rescale_factor
         lowerCAmelCase__            =					do_normalize
         lowerCAmelCase__            =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowerCAmelCase__            =					image_std if image_std is not None else IMAGENET_STANDARD_STD


    def   a (			self	:	int  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int]  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = PIL.Image.BICUBIC  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	int  ,      ) ->			np.ndarray:
         lowerCAmelCase__            =					get_size_dict(__snake_case  )
         if "height" not in size or "width" not in size:
              raise ValueError(f'The size dictionary must have 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   a (			self	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	List[str]  ,      ) ->			np.ndarray:
         lowerCAmelCase__            =					get_size_dict(__snake_case  )
         if "height" not in size or "width" not in size:
              raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'  )
         return center_crop(__snake_case  ,      size=(size["height"], size["width"])  ,      data_format=__snake_case  ,      **__snake_case  )


    def   a (			self	:	Any  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Union[int, float]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ,      ) ->			Any:
         return rescale(__snake_case  ,      scale=__snake_case  ,      data_format=__snake_case  ,      **__snake_case  )


    def   a (			self	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Union[float, List[float]]  ,      SCREAMING_SNAKE_CASE__	:	Union[float, List[float]]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Optional[Any]  ,      ) ->			np.ndarray:
         return normalize(__snake_case  ,      mean=__snake_case  ,      std=__snake_case  ,      data_format=__snake_case  ,      **__snake_case  )


    def   a (			self	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	ImageInput  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	Dict=None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	float = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, TensorType]] = None  ,      SCREAMING_SNAKE_CASE__	:	ChannelDimension = ChannelDimension.FIRST  ,      **SCREAMING_SNAKE_CASE__	:	List[Any]  ,      ) ->			PIL.Image.Image:
         lowerCAmelCase__            =					do_resize if do_resize is not None else self.do_resize
         lowerCAmelCase__            =					resample if resample is not None else self.resample
         lowerCAmelCase__            =					do_center_crop if do_center_crop is not None else self.do_center_crop
         lowerCAmelCase__            =					do_rescale if do_rescale is not None else self.do_rescale
         lowerCAmelCase__            =					rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCAmelCase__            =					do_normalize if do_normalize is not None else self.do_normalize
         lowerCAmelCase__            =					image_mean if image_mean is not None else self.image_mean
         lowerCAmelCase__            =					image_std if image_std is not None else self.image_std

         lowerCAmelCase__            =					size if size is not None else self.size
         lowerCAmelCase__            =					get_size_dict(__snake_case  )
         lowerCAmelCase__            =					crop_size if crop_size is not None else self.crop_size
         lowerCAmelCase__            =					get_size_dict(__snake_case  ,      param_name="crop_size"  )

         lowerCAmelCase__            =					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_resize and size is None or resample is None:
              raise ValueError("Size and resample 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."  )

         # All transformations expect numpy arrays.
         lowerCAmelCase__            =					[to_numpy_array(__snake_case  ) for image in images]

         if do_resize:
              lowerCAmelCase__            =					[self.resize(image=__snake_case  ,      size=__snake_case  ,      resample=__snake_case  ) for image in images]

         if do_center_crop:
              lowerCAmelCase__            =					[self.center_crop(image=__snake_case  ,      size=__snake_case  ) for image in images]

         if do_rescale:
              lowerCAmelCase__            =					[self.rescale(image=__snake_case  ,      scale=__snake_case  ) for image in images]

         if do_normalize:
              lowerCAmelCase__            =					[self.normalize(image=__snake_case  ,      mean=__snake_case  ,      std=__snake_case  ) for image in images]

         lowerCAmelCase__            =					[to_channel_dimension_format(__snake_case  ,      __snake_case  ) for image in images]

         lowerCAmelCase__            =					{"""pixel_values""": images}
         return BatchFeature(data=__snake_case  ,      tensor_type=__snake_case  )








"""simple docstring"""





def   a    (     __UpperCAmelCase      :    list[int]  )    ->							float:
						if not nums:  # Makes sure that the list is not empty
												raise ValueError("""List is empty"""  )

						__magic_name__:	Dict							=       sum(__UpperCAmelCase  ) / len(__UpperCAmelCase  )  # Calculate the average
						return sum(abs(x - average  ) for x in nums  ) / len(__UpperCAmelCase  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()



