






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								=		logging.get_logger(__name__)

class 				lowerCAmelCase_	(		SCREAMING_SNAKE_CASE_			):
				_UpperCamelCase					:       Union[str, Any]							       = ["pixel_values"]


				def __init__(					self ,						_lowerCAmelCase = True ,						_lowerCAmelCase = 3_2 ,						_lowerCAmelCase=PILImageResampling.BILINEAR ,						_lowerCAmelCase = True ,						**_lowerCAmelCase ,						):
											_lowercase							:   List[str] 							=   do_resize
											_lowercase							:   List[str] 							=   do_rescale
											_lowercase							:   int 							=   size_divisor
											_lowercase							:   int 							=   resample
											super().__init__(**_a      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase = None ,						**_lowerCAmelCase      ):
											_lowercase							:   Optional[Any] 							=   get_image_size(_a      )
											# Rounds the height and width down to the closest multiple of size_divisor
											_lowercase							:   List[Any] 							=   height // size_divisor * size_divisor
											_lowercase							:   Union[str, Any] 							=   width // size_divisor * size_divisor
											_lowercase							:   str 							=   resize(_a ,						(new_h, new_w) ,						resample=_a ,						data_format=_a ,						**_a      )
											return image


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase = None ,						**_lowerCAmelCase      ):
											return rescale(image=_a ,						scale=_a ,						data_format=_a ,						**_a      )


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase = None ,						_lowerCAmelCase = None ,						_lowerCAmelCase=None ,						_lowerCAmelCase = None ,						_lowerCAmelCase = None ,						_lowerCAmelCase = ChannelDimension.FIRST ,						**_lowerCAmelCase ,						):
											_lowercase							:   Dict 							=   do_resize if do_resize is not None else self.do_resize
											_lowercase							:   Optional[int] 							=   do_rescale if do_rescale is not None else self.do_rescale
											_lowercase							:   List[str] 							=   size_divisor if size_divisor is not None else self.size_divisor
											_lowercase							:   List[str] 							=   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'      )

											_lowercase							:   List[Any] 							=   make_list_of_images(_a      )

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

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

											if do_resize:
																		_lowercase							:   Union[str, Any] 							=   [self.resize(_a ,						size_divisor=_a ,						resample=_a      ) for image in images]

											if do_rescale:
																		_lowercase							:   Optional[Any] 							=   [self.rescale(_a ,						scale=1 / 2_5_5      ) for image in images]

											_lowercase							:   Union[str, Any] 							=   [to_channel_dimension_format(_a ,						_a      ) for image in images]

											_lowercase							:   Any 							=   {'pixel_values': images}
											return BatchFeature(data=_a ,						tensor_type=_a      )









'''simple docstring'''





def 			a_	(				_lowerCAmelCase			) -> int:

  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			):
    raise TypeError('only integers accepted as input'			)
  else:
    __lowerCamelCase			:			Any       			=					str(abs(_lowerCAmelCase			)			)
    __lowerCamelCase			:			Tuple       			=					[list(_lowerCAmelCase			) for char in range(len(_lowerCAmelCase			)			)]
    for index in range(len(_lowerCAmelCase			)			):
      num_transpositions[index].pop(_lowerCAmelCase			)
    return max(
        int(''.join(list(_lowerCAmelCase			)			)			) for transposition in num_transpositions			)


if __name__ == "__main__":
   __import__('doctest').testmod()






