

from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__lowercase				      =						logging.get_logger(__name__)

class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       str  					=    ['pixel_values']

   def __init__(				self	:    List[str]			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Union[int, float] = 1 / 2_5_5			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    int = 8			,						**lowerCamelCase__	:    Any			,						) ->			None:
         """simple docstring"""





         super().__init__(**lowerCamelCase__       )

         A_							       =		do_rescale
         A_							       =		rescale_factor
         A_							       =		do_pad
         A_							       =		pad_size

   def     UpperCamelCase					(				self	:    Any			,						lowerCamelCase__	:    np.ndarray			,						lowerCamelCase__	:    float			,						lowerCamelCase__	:    Optional[Union[str, ChannelDimension]] = None			,						**lowerCamelCase__	:    Optional[int]       ) ->			np.ndarray:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    List[Any]			,						lowerCamelCase__	:    np.ndarray			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Optional[Union[str, ChannelDimension]] = None       ) ->			Dict:
         """simple docstring"""





         A_       ,A_							       =		get_image_size(lowerCamelCase__       )
         A_							       =		(old_height // size + 1) * size - old_height
         A_							       =		(old_width // size + 1) * size - old_width

         return pad(lowerCamelCase__			,						((0, pad_height), (0, pad_width))			,						mode='''symmetric'''			,						data_format=lowerCamelCase__       )





   def     UpperCamelCase					(				self	:    Any			,						lowerCamelCase__	:    ImageInput			,						lowerCamelCase__	:    Optional[bool] = None			,						lowerCamelCase__	:    Optional[float] = None			,						lowerCamelCase__	:    Optional[bool] = None			,						lowerCamelCase__	:    Optional[int] = None			,						lowerCamelCase__	:    Optional[Union[str, TensorType]] = None			,						lowerCamelCase__	:    Union[str, ChannelDimension] = ChannelDimension.FIRST			,						**lowerCamelCase__	:    str			,						) ->			str:
         """simple docstring"""





         A_							       =		do_rescale if do_rescale is not None else self.do_rescale
         A_							       =		rescale_factor if rescale_factor is not None else self.rescale_factor
         A_							       =		do_pad if do_pad is not None else self.do_pad
         A_							       =		pad_size if pad_size is not None else self.pad_size

         A_							       =		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_rescale and rescale_factor is None:
               raise ValueError('''Rescale factor must be specified if do_rescale is True.'''       )

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

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

         if do_pad:
               A_							       =		[self.pad(lowerCamelCase__			,						size=lowerCamelCase__       ) for image in images]

         A_							       =		[to_channel_dimension_format(lowerCamelCase__			,						lowerCamelCase__       ) for image in images]

         A_							       =		{'''pixel_values''': images}
         return BatchFeature(data=lowerCamelCase__			,						tensor_type=lowerCamelCase__       )








from __future__ import annotations







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None   ):
      '''simple docstring'''







      if start is None:
            A_							       =		0

      if end is None:
            A_							       =		len(SCREAMING_SNAKE_CASE   ) - 1

      if start >= end:
            return

      A_							       =		(start + end) // 2

      slowsort(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
      slowsort(SCREAMING_SNAKE_CASE		,				mid + 1		,				SCREAMING_SNAKE_CASE   )

      if sequence[end] < sequence[mid]:
            A_       ,A_							       =		sequence[mid], sequence[end]

      slowsort(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				end - 1   )


if __name__ == "__main__":
      from doctest import testmod

      testmod()





