

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


A_ :				Union[str, Any]						 =    logging.get_logger(__name__)



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     Union[str, Any]													= ['''pixel_values''']






					def __init__(				self					, A__ = True					, A__ = 32					, A__=PILImageResampling.BILINEAR					, A__ = True					, **A__					, ):
						A__							:					int       =	do_resize
						A__							:					Tuple       =	do_rescale
						A__							:					Tuple       =	size_divisor
						A__							:					List[Any]       =	resample
						super().__init__(**A__    )






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






					def 						__A    (				self					, A__					, A__					, A__ = None					, **A__    ):
						return rescale(image=A__					, scale=A__					, data_format=A__					, **A__    )






					def 						__A    (				self					, A__					, A__ = None					, A__ = None					, A__=None					, A__ = None					, A__ = None					, A__ = ChannelDimension.FIRST					, **A__					, ):
						A__							:					Any       =	do_resize if do_resize is not None else self.do_resize
						A__							:					Union[str, Any]       =	do_rescale if do_rescale is not None else self.do_rescale
						A__							:					int       =	size_divisor if size_divisor is not None else self.size_divisor
						A__							:					Optional[Any]       =	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__							:					str       =	make_list_of_images(A__    )

						if not valid_images(A__    ):
							raise ValueError("""Invalid image(s)"""    )

						# All transformations expect numpy arrays.
						A__							:					int       =	[to_numpy_array(A__    ) for img in images]

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

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

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

						A__							:					int       =	{"""pixel_values""": images}
						return BatchFeature(data=A__					, tensor_type=A__    )






def 		UpperCamelCase      (lowercase_:				int      )  ->   int:
	if not isinstance(lowercase_					,   lowercase_      ):
		raise TypeError("""Input value must be an 'int' type"""      )

	A__							:					int       =	0
	while number:
		position += 1
		number >>= 1

	return position


if __name__ == "__main__":
	import doctest

	doctest.testmod()



