


"""simple docstring"""




from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_				:			Dict   =	logging.get_logger(__name__)
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					Any					       =						['''pixel_values''']
      def __init__(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	bool = True					,							SCREAMING_SNAKE_CASE__    :	int = 3_2					,							SCREAMING_SNAKE_CASE__    :	Dict=PILImageResampling.BILINEAR					,							SCREAMING_SNAKE_CASE__    :	bool = True					,							**SCREAMING_SNAKE_CASE__    :	Any					,							)		->      None:
             a_ :		int							=  do_resize
             a_ :		Optional[int]							=  do_rescale
             a_ :		List[str]							=  size_divisor
             a_ :		Optional[int]							=  resample
             super().__init__(**SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	np.ndarray					,							SCREAMING_SNAKE_CASE__    :	int					,							SCREAMING_SNAKE_CASE__    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Optional[ChannelDimension] = None					,							**SCREAMING_SNAKE_CASE__    :	List[Any]					)		->      np.ndarray:
             a_ :		Any							=  get_image_size(SCREAMING_SNAKE_CASE__					)
             # Rounds the height and width down to the closest multiple of size_divisor
             a_ :		Optional[Any]							=  height // size_divisor * size_divisor
             a_ :		Dict							=  width // size_divisor * size_divisor
             a_ :		Tuple							=  resize(SCREAMING_SNAKE_CASE__					,							(new_h, new_w)					,							resample=SCREAMING_SNAKE_CASE__					,							data_format=SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
             return image
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	np.ndarray					,							SCREAMING_SNAKE_CASE__    :	float					,							SCREAMING_SNAKE_CASE__    :	Optional[ChannelDimension] = None					,							**SCREAMING_SNAKE_CASE__    :	List[Any]					)		->      np.ndarray:
             return rescale(image=SCREAMING_SNAKE_CASE__					,							scale=SCREAMING_SNAKE_CASE__					,							data_format=SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]					,							SCREAMING_SNAKE_CASE__    :	Optional[bool] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[int] = None					,							SCREAMING_SNAKE_CASE__    :	int=None					,							SCREAMING_SNAKE_CASE__    :	Optional[bool] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[Union[TensorType, str]] = None					,							SCREAMING_SNAKE_CASE__    :	ChannelDimension = ChannelDimension.FIRST					,							**SCREAMING_SNAKE_CASE__    :	Tuple					,							)		->      BatchFeature:
             a_ :		Tuple							=  do_resize if do_resize is not None else self.do_resize
             a_ :		Any							=  do_rescale if do_rescale is not None else self.do_rescale
             a_ :		List[Any]							=  size_divisor if size_divisor is not None else self.size_divisor
             a_ :		int							=  resample if resample is not None else self.resample

             if do_resize and size_divisor is None:
                    raise ValueError('size_divisor is required for resizing'					)

             a_ :		Any							=  make_list_of_images(SCREAMING_SNAKE_CASE__					)

             if not valid_images(SCREAMING_SNAKE_CASE__					):
                    raise ValueError('Invalid image(s)'					)

             # All transformations expect numpy arrays.
             a_ :		Optional[Any]							=  [to_numpy_array(SCREAMING_SNAKE_CASE__					) for img in images]

             if do_resize:
                    a_ :		Optional[Any]							=  [self.resize(SCREAMING_SNAKE_CASE__					,							size_divisor=SCREAMING_SNAKE_CASE__					,							resample=SCREAMING_SNAKE_CASE__					) for image in images]

             if do_rescale:
                    a_ :		Union[str, Any]							=  [self.rescale(SCREAMING_SNAKE_CASE__					,							scale=1 / 2_5_5					) for image in images]

             a_ :		Dict							=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					) for image in images]

             a_ :		List[Any]							=  {'pixel_values': images}
             return BatchFeature(data=SCREAMING_SNAKE_CASE__					,							tensor_type=SCREAMING_SNAKE_CASE__					)






def 	SCREAMING_SNAKE_CASE_						(      __A			:      list    )     ->		bool:

       """simple docstring"""

       if not isinstance(__A  ,   __A    ):
              raise ValueError('Input series is not valid, valid series - [2, 4, 6]'    )
       if len(__A    ) == 0:
              raise ValueError('Input list must be a non empty list'    )
       if len(__A    ) == 1:
              return True
       a_ :		Tuple							=  series[1] - series[0]
       for index in range(len(__A    ) - 1    ):
              if series[index + 1] - series[index] != common_diff:
                     return False
       return True







def 	SCREAMING_SNAKE_CASE_						(      __A			:      list    )     ->		float:

       """simple docstring"""

       if not isinstance(__A  ,   __A    ):
              raise ValueError('Input series is not valid, valid series - [2, 4, 6]'    )
       if len(__A    ) == 0:
              raise ValueError('Input list must be a non empty list'    )
       a_ :		str							=  0
       for val in series:
              answer += val
       return answer / len(__A    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


