
'''simple docstring'''
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


_SCREAMING_SNAKE_CASE =	logging.get_logger(__name__)
class      __UpperCAmelCase							(    lowercase_							):



					'''simple docstring'''





					_UpperCamelCase  	=		["""pixel_values"""]



					def __init__(  self    :	Optional[int]			,					_lowercase    :	bool = True			,					_lowercase    :	Dict[str, int] = None			,					_lowercase    :	PILImageResampling = PIL.Image.NEAREST			,					_lowercase    :	bool = False			,					_lowercase    :	Dict[str, int] = None			,					_lowercase    :	Union[int, float] = 1 / 255			,					_lowercase    :	bool = False			,					_lowercase    :	bool = True			,					_lowercase    :	bool = True			,					_lowercase    :	Optional[Union[float, List[float]]] = None			,					_lowercase    :	Optional[Union[float, List[float]]] = None			,					_lowercase    :	bool = True			,					**_lowercase    :	Optional[int]			,					)						->       Optional[int]:
								super().__init__(**_lowercase)
								A_       		=   size if size is not None else {'height': 346, 'width': 346}
								A_       		=   get_size_dict(_lowercase)
								A_       		=   crop_size if crop_size is not None else {'height': 289, 'width': 289}
								A_       		=   get_size_dict(_lowercase			,					param_name='crop_size')

								A_       		=   do_resize
								A_       		=   size
								A_       		=   resample
								A_       		=   do_center_crop
								A_       		=   crop_size
								A_       		=   do_rescale
								A_       		=   rescale_factor
								A_       		=   rescale_offset
								A_       		=   do_normalize
								A_       		=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								A_       		=   image_std if image_std is not None else IMAGENET_STANDARD_STD
								A_       		=   include_top



					def 						__snake_case	(  self    :	Dict			,					_lowercase    :	np.ndarray			,					_lowercase    :	Dict[str, int]			,					_lowercase    :	PILImageResampling = PIL.Image.NEAREST			,					_lowercase    :	Optional[Union[str, ChannelDimension]] = None			,					**_lowercase    :	Dict			,					)						->       Optional[int]:
								A_       		=   get_size_dict(_lowercase)
								if "height" not in size or "width" not in size:
											raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}')
								return resize(
								    _lowercase			,					size=(size['height'], size['width'])			,					resample=_lowercase			,					data_format=_lowercase			,					**_lowercase)



					def 						__snake_case	(  self    :	str			,					_lowercase    :	np.ndarray			,					_lowercase    :	Dict[str, int]			,					_lowercase    :	Optional[Union[str, ChannelDimension]] = None			,					**_lowercase    :	Union[str, Any]			,					)						->       Tuple:
								A_       		=   get_size_dict(_lowercase)
								if "height" not in size or "width" not in size:
											raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}')
								return center_crop(_lowercase			,					size=(size['height'], size['width'])			,					data_format=_lowercase			,					**_lowercase)



					def 						__snake_case	(  self    :	Tuple			,					_lowercase    :	np.ndarray			,					_lowercase    :	Union[int, float]			,					_lowercase    :	bool = True			,					_lowercase    :	Optional[Union[str, ChannelDimension]] = None			,					**_lowercase    :	List[str]			,					)						->       Optional[int]:







								if offset:
											A_       		=   (image - 127.5) * scale
											if data_format is not None:
														A_       		=   to_channel_dimension_format(_lowercase			,					_lowercase)
											A_       		=   rescaled_image.astype(np.floataa)
								else:
											A_       		=   rescale(_lowercase			,					scale=_lowercase			,					data_format=_lowercase			,					**_lowercase)
								return rescaled_image



					def 						__snake_case	(  self    :	List[Any]			,					_lowercase    :	np.ndarray			,					_lowercase    :	Union[float, List[float]]			,					_lowercase    :	Union[float, List[float]]			,					_lowercase    :	Optional[Union[str, ChannelDimension]] = None			,					**_lowercase    :	List[Any]			,					)						->       int:
								return normalize(_lowercase			,					mean=_lowercase			,					std=_lowercase			,					data_format=_lowercase			,					**_lowercase)



					def 						__snake_case	(  self    :	Optional[int]			,					_lowercase    :	ImageInput			,					_lowercase    :	bool = None			,					_lowercase    :	Dict[str, int] = None			,					_lowercase    :	Union[str, Any]=None			,					_lowercase    :	bool = None			,					_lowercase    :	Dict[str, int] = None			,					_lowercase    :	bool = None			,					_lowercase    :	float = None			,					_lowercase    :	bool = None			,					_lowercase    :	bool = None			,					_lowercase    :	Optional[Union[float, List[float]]] = None			,					_lowercase    :	Optional[Union[float, List[float]]] = None			,					_lowercase    :	bool = None			,					_lowercase    :	Optional[Union[str, TensorType]] = None			,					_lowercase    :	ChannelDimension = ChannelDimension.FIRST			,					**_lowercase    :	List[Any]			,					)						->       Tuple:
								A_       		=   do_resize if do_resize is not None else self.do_resize
								A_       		=   resample if resample is not None else self.resample
								A_       		=   do_center_crop if do_center_crop is not None else self.do_center_crop
								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_       		=   rescale_offset if rescale_offset is not None else self.rescale_offset
								A_       		=   do_normalize if do_normalize is not None else self.do_normalize
								A_       		=   image_mean if image_mean is not None else self.image_mean
								A_       		=   image_std if image_std is not None else self.image_std
								A_       		=   include_top if include_top is not None else self.include_top

								A_       		=   size if size is not None else self.size
								A_       		=   get_size_dict(_lowercase)
								A_       		=   crop_size if crop_size is not None else self.crop_size
								A_       		=   get_size_dict(_lowercase			,					param_name='crop_size')

								A_       		=   make_list_of_images(_lowercase)

								if not valid_images(_lowercase):
											raise ValueError(
											    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
											    'torch.Tensor, tf.Tensor or jax.ndarray.')

								if do_resize and size is None or resample is None:
											raise ValueError('Size and resample must be specified if do_resize is True.')

								if do_center_crop and crop_size is None:
											raise ValueError('Crop size must be specified if do_center_crop is True.')

								if do_rescale and rescale_factor is None:
											raise ValueError('Rescale factor must be specified if do_rescale is True.')

								if do_normalize and (image_mean is None or image_std is None):
											raise ValueError('Image mean and std must be specified if do_normalize is True.')

								# All transformations expect numpy arrays.
								A_       		=   [to_numpy_array(_lowercase) for image in images]

								if do_resize:
											A_       		=   [self.resize(image=_lowercase			,					size=_lowercase			,					resample=_lowercase) for image in images]

								if do_center_crop:
											A_       		=   [self.center_crop(image=_lowercase			,					size=_lowercase) for image in images]

								if do_rescale:
											A_       		=   [self.rescale(image=_lowercase			,					scale=_lowercase			,					offset=_lowercase) for image in images]

								if do_normalize:
											A_       		=   [self.normalize(image=_lowercase			,					mean=_lowercase			,					std=_lowercase) for image in images]

								if include_top:
											A_       		=   [self.normalize(image=_lowercase			,					mean=[0, 0, 0]			,					std=_lowercase) for image in images]

								A_       		=   [to_channel_dimension_format(_lowercase			,					_lowercase) for image in images]

								A_       		=   {'pixel_values': images}
								return BatchFeature(data=_lowercase			,					tensor_type=_lowercase)










'''simple docstring'''
from __future__ import annotations

import sys





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )		->		tuple[int, int]:
			# base cases
			if abs(SCREAMING_SNAKE_CASE_    ) == 1:
						return a, 0
			elif abs(SCREAMING_SNAKE_CASE_    ) == 1:
						return 0, b

			A_		,   A_       		=   a, b
			A_		,   A_       		=   1, 0
			A_		,   A_       		=   0, 1

			while remainder != 0:
						A_       		=   old_remainder // remainder
						A_		,   A_       		=   remainder, old_remainder - quotient * remainder
						A_		,   A_       		=   coeff_a, old_coeff_a - quotient * coeff_a
						A_		,   A_       		=   coeff_b, old_coeff_b - quotient * coeff_b

			# sign correction for negative numbers
			if a < 0:
						A_       		=   -old_coeff_a
			if b < 0:
						A_       		=   -old_coeff_b

			return old_coeff_a, old_coeff_b





def  lowerCamelCase(       )		->		Tuple:
			if len(sys.argv    ) < 3:
						print('2 integer arguments required'    )
						return 1
			A_       		=   int(sys.argv[1]    )
			A_       		=   int(sys.argv[2]    )
			print(extended_euclidean_algorithm(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )    )
			return 0


if __name__ == "__main__":
							raise SystemExit(main())




