


a					  =     'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					# Make sure the supplied data is a bytes-like object
					if not isinstance(UpperCAmelCase__						,							UpperCAmelCase__  ):
										lowercase_  							=			F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
										raise TypeError(UpperCAmelCase__  )

					lowercase_  							=			"""""".join(bin(UpperCAmelCase__  )[2:].zfill(8  ) for byte in data  )

					lowercase_  							=			len(UpperCAmelCase__  ) % 6 != 0

					if padding_needed:
										# The padding that will be added later
										lowercase_  							=			B"""=""" * ((6 - len(UpperCAmelCase__  ) % 6) // 2)

										# Append binary_stream with arbitrary binary digits (0's by default) to make its
										# length a multiple of 6.
										binary_stream += "0" * (6 - len(UpperCAmelCase__  ) % 6)
					else:
										lowercase_  							=			B""""""

					# Encode every 6 binary digits to their corresponding Base64 character
					return (
					    "".join(
					        B64_CHARSET[int(binary_stream[index : index + 6]						,							2  )]
					        for index in range(0						,							len(UpperCAmelCase__  )						,							6  )  ).encode()
					    + padding
					)


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					# Make sure encoded_data is either a string or a bytes-like object
					if not isinstance(UpperCAmelCase__						,							UpperCAmelCase__  ) and not isinstance(UpperCAmelCase__						,							UpperCAmelCase__  ):
										lowercase_  							=			(
										    """argument should be a bytes-like object or ASCII string, """
										    F'''not \'{encoded_data.__class__.__name__}\''''
										)
										raise TypeError(UpperCAmelCase__  )

					# In case encoded_data is a bytes-like object, make sure it contains only
					# ASCII characters so we convert it to a string object
					if isinstance(UpperCAmelCase__						,							UpperCAmelCase__  ):
										try:
															lowercase_  							=			encoded_data.decode("""utf-8"""  )
										except UnicodeDecodeError:
															raise ValueError("""base64 encoded data should only contain ASCII characters"""  )

					lowercase_  							=			encoded_data.count("""="""  )

					# Check if the encoded string contains non base64 characters
					if padding:
										assert all(
										    char in B64_CHARSET for char in encoded_data[:-padding]  ), "Invalid base64 character(s) found."
					else:
										assert all(
										    char in B64_CHARSET for char in encoded_data  ), "Invalid base64 character(s) found."

					# Check the padding
					assert len(UpperCAmelCase__  ) % 4 == 0 and padding < 3, "Incorrect padding"

					if padding:
										# Remove padding if there is one
										lowercase_  							=			encoded_data[:-padding]

										lowercase_  							=			"""""".join(
										    bin(B64_CHARSET.index(UpperCAmelCase__  )  )[2:].zfill(6  ) for char in encoded_data  )[: -padding * 2]
					else:
										lowercase_  							=			"""""".join(
										    bin(B64_CHARSET.index(UpperCAmelCase__  )  )[2:].zfill(6  ) for char in encoded_data  )

					lowercase_  							=			[
					    int(binary_stream[index : index + 8]						,							2  )
					    for index in range(0						,							len(UpperCAmelCase__  )						,							8  )
					]

					return bytes(UpperCAmelCase__  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()






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					  =     logging.get_logger(__name__)







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  str						=   ['pixel_values']

					def __init__(     self       :   List[Any]							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   Union[int, float] = 1 / 255							,					UpperCamelCase__       :   bool = True							,					UpperCamelCase__       :   int = 8							,					**UpperCamelCase__       :   Optional[Any]							,					):






										'''simple docstring'''






										super().__init__(**UpperCamelCase__       )

										lowercase_  							=			do_rescale
										lowercase_  							=			rescale_factor
										lowercase_  							=			do_pad
										lowercase_  							=			pad_size

					def   UpperCAmelCase__       (     self       :   int							,					UpperCamelCase__       :   np.ndarray							,					UpperCamelCase__       :   float							,					UpperCamelCase__       :   Optional[Union[str, ChannelDimension]] = None							,					**UpperCamelCase__       :   Optional[Any]       ):






										'''simple docstring'''






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

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






										'''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   UpperCAmelCase__       (     self       :   Optional[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__       :   Dict							,					):






										'''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__       )


