




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


lowerCamelCase__			:       Optional[Any]         =							logging.get_logger(__name__)





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 lowercase_       =   ["pixel_values"]



 def __init__(							self  :		Optional[int]    ,    _lowerCAmelCase  :		bool = True    ,    _lowerCAmelCase  :		int = 32    ,    _lowerCAmelCase  :		Union[str, Any]=PILImageResampling.BILINEAR    ,    _lowerCAmelCase  :		bool = True    ,    **_lowerCAmelCase  :		Any    ,    ):
       SCREAMING_SNAKE_CASE_     						=      do_resize
       SCREAMING_SNAKE_CASE_     						=      do_rescale
       SCREAMING_SNAKE_CASE_     						=      size_divisor
       SCREAMING_SNAKE_CASE_     						=      resample
       super().__init__(**_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Dict    ,    _lowerCAmelCase  :		np.ndarray    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		Optional[ChannelDimension] = None    ,    **_lowerCAmelCase  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      get_image_size(_lowerCAmelCase						)
       # Rounds the height and width down to the closest multiple of size_divisor
       SCREAMING_SNAKE_CASE_     						=      height // size_divisor * size_divisor
       SCREAMING_SNAKE_CASE_     						=      width // size_divisor * size_divisor
       SCREAMING_SNAKE_CASE_     						=      resize(_lowerCAmelCase    ,    (new_h, new_w)    ,    resample=_lowerCAmelCase    ,    data_format=_lowerCAmelCase    ,    **_lowerCAmelCase						)
       return image



 def 				lowerCAmelCase_  (							self  :		int    ,    _lowerCAmelCase  :		np.ndarray    ,    _lowerCAmelCase  :		float    ,    _lowerCAmelCase  :		Optional[ChannelDimension] = None    ,    **_lowerCAmelCase  :		str						):
       return rescale(image=_lowerCAmelCase    ,    scale=_lowerCAmelCase    ,    data_format=_lowerCAmelCase    ,    **_lowerCAmelCase						)




 def 				lowerCAmelCase_  (							self  :		Dict    ,    _lowerCAmelCase  :		Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]    ,    _lowerCAmelCase  :		Optional[bool] = None    ,    _lowerCAmelCase  :		Optional[int] = None    ,    _lowerCAmelCase  :		Any=None    ,    _lowerCAmelCase  :		Optional[bool] = None    ,    _lowerCAmelCase  :		Optional[Union[TensorType, str]] = None    ,    _lowerCAmelCase  :		ChannelDimension = ChannelDimension.FIRST    ,    **_lowerCAmelCase  :		List[str]    ,    ):
       SCREAMING_SNAKE_CASE_     						=      do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE_     						=      do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE_     						=      size_divisor if size_divisor is not None else self.size_divisor
       SCREAMING_SNAKE_CASE_     						=      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'						)

       SCREAMING_SNAKE_CASE_     						=      make_list_of_images(_lowerCAmelCase						)

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

       # All transformations expect numpy arrays.
       SCREAMING_SNAKE_CASE_     						=      [to_numpy_array(_lowerCAmelCase						) for img in images]

       if do_resize:
             SCREAMING_SNAKE_CASE_     						=      [self.resize(_lowerCAmelCase    ,    size_divisor=_lowerCAmelCase    ,    resample=_lowerCAmelCase						) for image in images]

       if do_rescale:
             SCREAMING_SNAKE_CASE_     						=      [self.rescale(_lowerCAmelCase    ,    scale=1 / 255						) for image in images]

       SCREAMING_SNAKE_CASE_     						=      [to_channel_dimension_format(_lowerCAmelCase    ,    _lowerCAmelCase						) for image in images]

       SCREAMING_SNAKE_CASE_     						=      {'pixel_values': images}
       return BatchFeature(data=_lowerCAmelCase    ,    tensor_type=_lowerCAmelCase						)





import random
from typing import Any

def  UpperCAmelCase_							(				__UpperCAmelCase						:     list							) ->			list[Any]:
      for _ in range(len(__UpperCAmelCase							)							):
            SCREAMING_SNAKE_CASE_     						=      random.randint(0      ,      len(__UpperCAmelCase							) - 1							)
            SCREAMING_SNAKE_CASE_     						=      random.randint(0      ,      len(__UpperCAmelCase							) - 1							)
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      data[b], data[a]
      return data


if __name__ == "__main__":
   lowerCamelCase__			:       Union[str, Any]         =							[0, 1, 2, 3, 4, 5, 6, 7]
   lowerCamelCase__			:       Optional[int]         =							['python', 'says', 'hello', '!']
   print('Fisher-Yates Shuffle:')
   print('List', integers, strings)
   print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))