



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


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  ["pixel_values"]







						def __init__(	self      :			Union[str, Any]	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			Union[int, float] = 1 / 2_55	,	__lowerCamelCase      :			bool = True	,	__lowerCamelCase      :			int = 8	,	**__lowerCamelCase      :			int	,	)      ->				None:
									super().__init__(**__lowerCamelCase )

									A							:					List[str]              =						do_rescale
									A							:					int              =						rescale_factor
									A							:					Optional[Any]              =						do_pad
									A							:					int              =						pad_size







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	__lowerCamelCase      :			np.ndarray	,	__lowerCamelCase      :			float	,	__lowerCamelCase      :			Optional[Union[str, ChannelDimension]] = None	,	**__lowerCamelCase      :			str )      ->				np.ndarray:
									return rescale(__lowerCamelCase	,	scale=__lowerCamelCase	,	data_format=__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			np.ndarray	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Optional[Union[str, ChannelDimension]] = None )      ->				Any:
									A       , A							:					Union[str, Any]              =						get_image_size(__lowerCamelCase )
									A							:					List[Any]              =						(old_height // size + 1) * size - old_height
									A							:					List[str]              =						(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      :			Optional[int]	,	__lowerCamelCase      :			ImageInput	,	__lowerCamelCase      :			Optional[bool] = None	,	__lowerCamelCase      :			Optional[float] = None	,	__lowerCamelCase      :			Optional[bool] = None	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			Optional[Union[str, TensorType]] = None	,	__lowerCamelCase      :			Union[str, ChannelDimension] = ChannelDimension.FIRST	,	**__lowerCamelCase      :			Optional[int]	,	)      ->				Optional[Any]:
									A							:					Union[str, Any]              =						do_rescale if do_rescale is not None else self.do_rescale
									A							:					Union[str, Any]              =						rescale_factor if rescale_factor is not None else self.rescale_factor
									A							:					str              =						do_pad if do_pad is not None else self.do_pad
									A							:					Union[str, Any]              =						pad_size if pad_size is not None else self.pad_size

									A							:					Dict              =						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							:					Dict              =						[to_numpy_array(__lowerCamelCase ) for image in images]

									if do_rescale:
												A							:					List[Any]              =						[self.rescale(image=__lowerCamelCase	,	scale=__lowerCamelCase ) for image in images]

									if do_pad:
												A							:					Any              =						[self.pad(__lowerCamelCase	,	size=__lowerCamelCase ) for image in images]

									A							:					Dict              =						[to_channel_dimension_format(__lowerCamelCase	,	__lowerCamelCase ) for image in images]

									A							:					Dict              =						{"pixel_values": images}
									return BatchFeature(data=__lowerCamelCase	,	tensor_type=__lowerCamelCase )




import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__SCREAMING_SNAKE_CASE           =  """."""


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
							__SCREAMING_SNAKE_CASE           =  []
							__SCREAMING_SNAKE_CASE           =  []
							with open(doctest_file_path) as fp:
														for line in fp:
																					__SCREAMING_SNAKE_CASE           =  line.strip()
																					__SCREAMING_SNAKE_CASE           =  os.path.join(REPO_PATH, line)
																					if not (os.path.isfile(path) or os.path.isdir(path)):
																												non_existent_paths.append(line)
																					all_paths.append(path)
							if len(non_existent_paths) > 0:
														__SCREAMING_SNAKE_CASE           =  """\n""".join(non_existent_paths)
														raise ValueError(F"""`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}""")
							if all_paths != sorted(all_paths):
														raise ValueError("""Files in `utils/documentation_tests.txt` are not in alphabetical order.""")