





"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__UpperCAmelCase						=			logging.get_logger(__name__)


if is_vision_available():
		import PIL



class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	int				     =				["pixel_values"]
				def __init__(		self    :			Optional[Any]	, a_    :			bool = True	, a_    :			Dict[str, int] = None	, a_    :			PILImageResampling = PILImageResampling.BICUBIC	, a_    :			bool = True	, a_    :			Dict[str, int] = None	, a_    :			bool = True	, a_    :			Union[int, float] = 1 / 2_55	, a_    :			bool = True	, a_    :			Optional[Union[float, List[float]]] = None	, a_    :			Optional[Union[float, List[float]]] = None	, a_    :			bool = True	, **a_    :			List[str]	, )	->	None:



							'''simple docstring'''



							super().__init__(**a_    )
							a__  :	Union[str, Any]		 =			size if size is not None else {"shortest_edge": 2_24}
							a__  :	Dict		 =			get_size_dict(a_	, default_to_square=a_    )
							a__  :	Optional[int]		 =			crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
							a__  :	Any		 =			get_size_dict(a_	, default_to_square=a_	, param_name="crop_size"    )

							a__  :	Union[str, Any]		 =			do_resize
							a__  :	Optional[Any]		 =			size
							a__  :	Optional[int]		 =			resample
							a__  :	Dict		 =			do_center_crop
							a__  :	int		 =			crop_size
							a__  :	Optional[int]		 =			do_rescale
							a__  :	Optional[int]		 =			rescale_factor
							a__  :	int		 =			do_normalize
							a__  :	int		 =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							a__  :	Any		 =			image_std if image_std is not None else OPENAI_CLIP_STD
							a__  :	str		 =			do_convert_rgb
				def       UpperCAmelCase      (		self    :			List[Any]	, a_    :			np.ndarray	, a_    :			Dict[str, int]	, a_    :			PILImageResampling = PILImageResampling.BICUBIC	, a_    :			Optional[Union[str, ChannelDimension]] = None	, **a_    :			Optional[Any]	, )	->	np.ndarray:



							'''simple docstring'''



							a__  :	int		 =			get_size_dict(a_	, default_to_square=a_    )
							if "shortest_edge" not in size:
										raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"    )
							a__  :	List[str]		 =			get_resize_output_image_size(a_	, size=size["shortest_edge"]	, default_to_square=a_    )
							return resize(a_	, size=a_	, resample=a_	, data_format=a_	, **a_    )
				def       UpperCAmelCase      (		self    :			Optional[int]	, a_    :			np.ndarray	, a_    :			Dict[str, int]	, a_    :			Optional[Union[str, ChannelDimension]] = None	, **a_    :			int	, )	->	np.ndarray:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			get_size_dict(a_    )
							if "height" not in size or "width" not in size:
										raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}"    )
							return center_crop(a_	, size=(size["height"], size["width"])	, data_format=a_	, **a_    )
				def       UpperCAmelCase      (		self    :			List[str]	, a_    :			np.ndarray	, a_    :			Union[int, float]	, a_    :			Optional[Union[str, ChannelDimension]] = None	, **a_    :			Dict	, )	->	str:



							'''simple docstring'''



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



							'''simple docstring'''



							return normalize(a_	, mean=a_	, std=a_	, data_format=a_	, **a_    )






				def       UpperCAmelCase      (		self    :			str	, a_    :			ImageInput	, a_    :			bool = None	, a_    :			Dict[str, int] = None	, a_    :			PILImageResampling = None	, a_    :			bool = None	, a_    :			int = None	, a_    :			bool = None	, a_    :			float = None	, a_    :			bool = None	, a_    :			Optional[Union[float, List[float]]] = None	, a_    :			Optional[Union[float, List[float]]] = None	, a_    :			bool = None	, a_    :			Optional[Union[str, TensorType]] = None	, a_    :			Optional[ChannelDimension] = ChannelDimension.FIRST	, **a_    :			Any	, )	->	PIL.Image.Image:



							'''simple docstring'''



							a__  :	Optional[int]		 =			do_resize if do_resize is not None else self.do_resize
							a__  :	int		 =			size if size is not None else self.size
							a__  :	List[str]		 =			get_size_dict(a_	, param_name="size"	, default_to_square=a_    )
							a__  :	List[str]		 =			resample if resample is not None else self.resample
							a__  :	List[Any]		 =			do_center_crop if do_center_crop is not None else self.do_center_crop
							a__  :	List[str]		 =			crop_size if crop_size is not None else self.crop_size
							a__  :	str		 =			get_size_dict(a_	, param_name="crop_size"	, default_to_square=a_    )
							a__  :	Optional[int]		 =			do_rescale if do_rescale is not None else self.do_rescale
							a__  :	Optional[int]		 =			rescale_factor if rescale_factor is not None else self.rescale_factor
							a__  :	int		 =			do_normalize if do_normalize is not None else self.do_normalize
							a__  :	Any		 =			image_mean if image_mean is not None else self.image_mean
							a__  :	Tuple		 =			image_std if image_std is not None else self.image_std
							a__  :	Any		 =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							a__  :	List[Any]		 =			make_list_of_images(a_    )

							if not valid_images(a_    ):
										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:
										raise ValueError("Size 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."    )

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
										a__  :	List[str]		 =			[convert_to_rgb(a_    ) for image in images]

							# All transformations expect numpy arrays.
							a__  :	Any		 =			[to_numpy_array(a_    ) for image in images]

							if do_resize:
										a__  :	int		 =			[self.resize(image=a_	, size=a_	, resample=a_    ) for image in images]

							if do_center_crop:
										a__  :	Union[str, Any]		 =			[self.center_crop(image=a_	, size=a_    ) for image in images]

							if do_rescale:
										a__  :	Tuple		 =			[self.rescale(image=a_	, scale=a_    ) for image in images]

							if do_normalize:
										a__  :	int		 =			[self.normalize(image=a_	, mean=a_	, std=a_    ) for image in images]

							a__  :	List[Any]		 =			[to_channel_dimension_format(a_	, a_    ) for image in images]

							a__  :	Union[str, Any]		 =			{"pixel_values": images}
							return BatchFeature(data=a_	, tensor_type=a_    )






"""simple docstring"""






from __future__ import annotations






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			)    ->			tuple[int, int]:



			'''simple docstring'''
			if b == 0:
						return (1, 0)
			((a__) ,	(a__))  :	List[Any]		 =			extended_euclid(lowerCAmelCase__							,			a % b			)
			a__  :	str		 =			a // b
			return (y, x - k * y)






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			)    ->			int:



			'''simple docstring'''
			((a__) ,	(a__))  :	Tuple		 =			extended_euclid(lowerCAmelCase__							,			lowerCAmelCase__			)
			a__  :	List[str]		 =			na * na
			a__  :	Union[str, Any]		 =			ra * x * na + ra * y * na
			return (n % m + m) % m






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			)    ->			int:



			'''simple docstring'''
			((a__) ,	(a__))  :	Optional[Any]		 =			extended_euclid(lowerCAmelCase__							,			lowerCAmelCase__			)
			if b < 0:
						a__  :	Optional[int]		 =			(b % n + n) % n
			return b






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			)    ->			int:



			'''simple docstring'''
			a__ ,	a__  :	List[Any]		 =			invert_modulo(lowerCAmelCase__							,			lowerCAmelCase__			), invert_modulo(lowerCAmelCase__							,			lowerCAmelCase__			)
			a__  :	Dict		 =			na * na
			a__  :	Any		 =			ra * x * na + ra * y * na
			return (n % m + m) % m


if __name__ == "__main__":
		from doctest import testmod

		testmod(name='''chinese_remainder_theorem''', verbose=True)
		testmod(name='''chinese_remainder_theorem2''', verbose=True)
		testmod(name='''invert_modulo''', verbose=True)
		testmod(name='''extended_euclid''', verbose=True)