




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


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


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      ["pixel_values"]


    def __init__(			self      :	Optional[int]					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	Union[int, float] = 1 / 255					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	int = 8					, **UpperCAmelCase__      :	Optional[Any]					, )						->None:





           '''simple docstring'''

           super().__init__(**lowerCamelCase__)

           A__       =    do_rescale
           A__       =    rescale_factor
           A__       =    do_pad
           A__       =    pad_size


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	np.ndarray					, UpperCAmelCase__      :	float					, UpperCAmelCase__      :	Optional[Union[str, ChannelDimension]] = None					, **UpperCAmelCase__      :	Union[str, Any])						->np.ndarray:





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	Any					, UpperCAmelCase__      :	np.ndarray					, UpperCAmelCase__      :	int					, UpperCAmelCase__      :	Optional[Union[str, ChannelDimension]] = None)						->int:





           '''simple docstring'''

           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        SCREAMING_SNAKE_CASE							(			self      :	int					, UpperCAmelCase__      :	ImageInput					, UpperCAmelCase__      :	Optional[bool] = None					, UpperCAmelCase__      :	Optional[float] = None					, UpperCAmelCase__      :	Optional[bool] = None					, UpperCAmelCase__      :	Optional[int] = None					, UpperCAmelCase__      :	Optional[Union[str, TensorType]] = None					, UpperCAmelCase__      :	Union[str, ChannelDimension] = ChannelDimension.FIRST					, **UpperCAmelCase__      :	Any					, )						->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__)






'''simple docstring'''


import math






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		return math.sqrt(__lowerCAmelCase      ) * math.sqrt(__lowerCAmelCase      ) == num






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				int	    =					0
		_UpperCAmelCase						:				Tuple	    =					n
		while left <= right:
				_UpperCAmelCase						:				int	    =					(left + right) // 2
				if mid**2 == n:
						return True
				elif mid**2 > n:
						_UpperCAmelCase						:				str	    =					mid - 1
				else:
						_UpperCAmelCase						:				List[str]	    =					mid + 1
		return False


if __name__ == "__main__":
						import doctest

						doctest.testmod()



