


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


A			:						Tuple        =  logging.get_logger(__name__)



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					['''pixel_values''']

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




							"""simple docstring"""


							super().__init__(**_UpperCAmelCase						)

							lowercase__	     =						do_rescale
							lowercase__	     =						rescale_factor
							lowercase__	     =						do_pad
							lowercase__	     =						pad_size

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		np.ndarray ,   _UpperCAmelCase       :		float ,   _UpperCAmelCase       :		Optional[Union[str, ChannelDimension]] = None ,   **_UpperCAmelCase       :		Tuple						)		->  np.ndarray:




							"""simple docstring"""


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

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		np.ndarray ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		Optional[Union[str, ChannelDimension]] = None						)		->  int:




							"""simple docstring"""


							lowercase__     ,      lowercase__	     =						get_image_size(_UpperCAmelCase						)
							lowercase__	     =						(old_height // size + 1) * size - old_height
							lowercase__	     =						(old_width // size + 1) * size - old_width

							return pad(_UpperCAmelCase ,   ((0, pad_height), (0, pad_width)) ,   mode="""symmetric""" ,   data_format=_UpperCAmelCase						)


					def     lowerCamelCase__	(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       :		List[str] ,   )		->  int:




							"""simple docstring"""


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

							lowercase__	     =						make_list_of_images(_UpperCAmelCase						)

							if not valid_images(_UpperCAmelCase						):
									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.
							lowercase__	     =						[to_numpy_array(_UpperCAmelCase						) for image in images]

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

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

							lowercase__	     =						[to_channel_dimension_format(_UpperCAmelCase ,   _UpperCAmelCase						) for image in images]

							lowercase__	     =						{"""pixel_values""": images}
							return BatchFeature(data=_UpperCAmelCase ,   tensor_type=_UpperCAmelCase						)




import baseaa


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				bytes:
 '''simple docstring'''


 return baseaa.aaaencode(string.encode('utf-8'						)						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	bytes						)		->				str:
 '''simple docstring'''


 return baseaa.aaadecode(_UpperCamelCase						).decode('utf-8'						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

