






'''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 (
    center_crop,
    get_resize_output_image_size,
    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


__A    	=   logging.get_logger(__name__)




class a_    (    UpperCamelCase_							):
   _snake_case        =							["""pixel_values"""]







   def __init__(self					,   __a = True					,   __a = None					,   __a = PILImageResampling.BILINEAR					,   __a = True					,   __a = None					,   __a = True					,   __a = 1 / 2_5_5					,   __a = True					,   __a = None					,   __a = None					,   **__a					,   )			->      None:
      """simple docstring"""






      super().__init__(**__a)
      __snake_case				:      Optional[int]     							= size if size is not None else {'shortest_edge': 2_5_6}
      __snake_case				:      int     							= get_size_dict(__a					,   default_to_square=__a)
      __snake_case				:      Any     							= crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
      __snake_case				:      Tuple     							= get_size_dict(__a)
      __snake_case				:      Dict     							= do_resize
      __snake_case				:      Any     							= size
      __snake_case				:      Optional[int]     							= resample
      __snake_case				:      int     							= do_center_crop
      __snake_case				:      int     							= crop_size
      __snake_case				:      int     							= do_rescale
      __snake_case				:      Optional[int]     							= rescale_factor
      __snake_case				:      Optional[int]     							= do_normalize
      __snake_case				:      Any     							= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      __snake_case				:      str     							= image_std if image_std is not None else IMAGENET_STANDARD_STD







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a = PILImageResampling.BICUBIC					,   __a = None					,   **__a					,   )			->      np.ndarray:
      """simple docstring"""






      __snake_case				:      str     							= get_size_dict(__a					,   default_to_square=__a)
      if "shortest_edge" not in size:
         raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""")
      __snake_case				:      List[str]     							= get_resize_output_image_size(__a					,   size=size['shortest_edge']					,   default_to_square=__a)
      return resize(__a					,   size=__a					,   resample=__a					,   data_format=__a					,   **__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a = None					,   **__a					,   )			->      np.ndarray:
      """simple docstring"""






      __snake_case				:      Dict     							= get_size_dict(__a)
      return center_crop(__a					,   size=(size['height'], size['width'])					,   data_format=__a					,   **__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a = None					,   **__a)			->      np.ndarray:
      """simple docstring"""






      return rescale(__a					,   scale=__a					,   data_format=__a					,   **__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a					,   __a = None					,   **__a					,   )			->      np.ndarray:
      """simple docstring"""






      return normalize(__a					,   mean=__a					,   std=__a					,   data_format=__a					,   **__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = ChannelDimension.FIRST					,   **__a					,   )			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= do_resize if do_resize is not None else self.do_resize
      __snake_case				:      Union[str, Any]     							= size if size is not None else self.size
      __snake_case				:      List[Any]     							= get_size_dict(__a					,   default_to_square=__a)
      __snake_case				:      Tuple     							= resample if resample is not None else self.resample
      __snake_case				:      Union[str, Any]     							= do_center_crop if do_center_crop is not None else self.do_center_crop
      __snake_case				:      Dict     							= crop_size if crop_size is not None else self.crop_size
      __snake_case				:      str     							= get_size_dict(__a)
      __snake_case				:      List[str]     							= do_rescale if do_rescale is not None else self.do_rescale
      __snake_case				:      Dict     							= rescale_factor if rescale_factor is not None else self.rescale_factor
      __snake_case				:      List[str]     							= do_normalize if do_normalize is not None else self.do_normalize
      __snake_case				:      Union[str, Any]     							= image_mean if image_mean is not None else self.image_mean
      __snake_case				:      Any     							= image_std if image_std is not None else self.image_std

      __snake_case				:      Optional[int]     							= make_list_of_images(__a)

      if not valid_images(__a):
         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_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.
      __snake_case				:      Union[str, Any]     							= [to_numpy_array(__a) for image in images]

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

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

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

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

      __snake_case				:      Union[str, Any]     							= [to_channel_dimension_format(__a					,   __a) for image in images]

      __snake_case				:      Any     							= {'pixel_values': images}
      return BatchFeature(data=__a					,   tensor_type=__a)







'''simple docstring'''






from functools import lru_cache




@lru_cache
def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> int:







   """simple docstring"""
   if num < 0:
      raise ValueError('Number should not be negative.'  )

   return 1 if num in (0, 1) else num * factorial(num - 1  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()