




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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


__UpperCamelCase   :			Optional[int]          = logging.get_logger(__name__)





def 						__A						(      __lowerCamelCase       ) ->			List[List[ImageInput]]:
	if isinstance(__UpperCAmelCase				,     (list, tuple)       ) and isinstance(videos[0]				,     (list, tuple)       ) and is_valid_image(videos[0][0]       ):
		return videos

	elif isinstance(__UpperCAmelCase				,     (list, tuple)       ) and is_valid_image(videos[0]       ):
		return [videos]

	elif is_valid_image(__UpperCAmelCase       ):
		return [[videos]]

	raise ValueError(f'Could not make batched video from {videos}'       )



class 						__lowerCAmelCase      (      SCREAMING_SNAKE_CASE_					):
							UpperCamelCase__   	=    ['''pixel_values''']
							def __init__(		self		:str						, __magic_name__		:bool = True						, __magic_name__		:Dict[str, int] = None						, __magic_name__		:PILImageResampling = PILImageResampling.BILINEAR						, __magic_name__		:bool = True						, __magic_name__		:Dict[str, int] = None						, __magic_name__		:bool = True						, __magic_name__		:Union[int, float] = 1 / 255						, __magic_name__		:bool = True						, __magic_name__		:Optional[Union[float, List[float]]] = None						, __magic_name__		:Optional[Union[float, List[float]]] = None						, **__magic_name__		:List[str]						, ):







								'''simple docstring'''







								super().__init__(**__snake_case							)
								a									=				size if size is not None else {"""shortest_edge""": 224}
								a									=				get_size_dict(__snake_case						, default_to_square=__snake_case							)
								a									=				crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								a									=				get_size_dict(__snake_case						, param_name="""crop_size"""							)

								a									=				do_resize
								a									=				size
								a									=				do_center_crop
								a									=				crop_size
								a									=				resample
								a									=				do_rescale
								a									=				rescale_factor
								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
							def  lowerCamelCase__				(		self		:List[Any]						, __magic_name__		:np.ndarray						, __magic_name__		:Dict[str, int]						, __magic_name__		:PILImageResampling = PILImageResampling.BILINEAR						, __magic_name__		:Optional[Union[str, ChannelDimension]] = None						, **__magic_name__		:Optional[Any]						, ):







								'''simple docstring'''







								a									=				get_size_dict(__snake_case						, default_to_square=__snake_case							)
								if "shortest_edge" in size:
									a									=				get_resize_output_image_size(__snake_case						, size["""shortest_edge"""]						, default_to_square=__snake_case							)
								elif "height" in size and "width" in size:
									a									=				(size["""height"""], size["""width"""])
								else:
									raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'							)
								return resize(__snake_case						, size=__snake_case						, resample=__snake_case						, data_format=__snake_case						, **__snake_case							)
							def  lowerCamelCase__				(		self		:List[Any]						, __magic_name__		:np.ndarray						, __magic_name__		:Dict[str, int]						, __magic_name__		:Optional[Union[str, ChannelDimension]] = None						, **__magic_name__		:Optional[int]						, ):







								'''simple docstring'''







								a									=				get_size_dict(__snake_case							)
								if "height" not in size or "width" not in size:
									raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'							)
								return center_crop(__snake_case						, size=(size["""height"""], size["""width"""])						, data_format=__snake_case						, **__snake_case							)
							def  lowerCamelCase__				(		self		:List[Any]						, __magic_name__		:np.ndarray						, __magic_name__		:Union[int, float]						, __magic_name__		:Optional[Union[str, ChannelDimension]] = None						, **__magic_name__		:Optional[Any]						, ):







								'''simple docstring'''







								return rescale(__snake_case						, scale=__snake_case						, data_format=__snake_case						, **__snake_case							)
							def  lowerCamelCase__				(		self		:str						, __magic_name__		:np.ndarray						, __magic_name__		:Union[float, List[float]]						, __magic_name__		:Union[float, List[float]]						, __magic_name__		:Optional[Union[str, ChannelDimension]] = None						, **__magic_name__		:Any						, ):







								'''simple docstring'''







								return normalize(__snake_case						, mean=__snake_case						, std=__snake_case						, data_format=__snake_case						, **__snake_case							)
							def  lowerCamelCase__				(		self		:Union[str, Any]						, __magic_name__		:ImageInput						, __magic_name__		:bool = None						, __magic_name__		:Dict[str, int] = None						, __magic_name__		:PILImageResampling = None						, __magic_name__		:bool = None						, __magic_name__		:Dict[str, int] = None						, __magic_name__		:bool = None						, __magic_name__		:float = None						, __magic_name__		:bool = None						, __magic_name__		:Optional[Union[float, List[float]]] = None						, __magic_name__		:Optional[Union[float, List[float]]] = None						, __magic_name__		:Optional[ChannelDimension] = ChannelDimension.FIRST						, ):







								'''simple docstring'''







								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(__snake_case							)

								if do_resize:
									a									=				self.resize(image=__snake_case						, size=__snake_case						, resample=__snake_case							)

								if do_center_crop:
									a									=				self.center_crop(__snake_case						, size=__snake_case							)

								if do_rescale:
									a									=				self.rescale(image=__snake_case						, scale=__snake_case							)

								if do_normalize:
									a									=				self.normalize(image=__snake_case						, mean=__snake_case						, std=__snake_case							)

								a									=				to_channel_dimension_format(__snake_case						, __snake_case							)
								return image





							def  lowerCamelCase__				(		self		:str						, __magic_name__		:ImageInput						, __magic_name__		:bool = None						, __magic_name__		:Dict[str, int] = None						, __magic_name__		:PILImageResampling = None						, __magic_name__		:bool = None						, __magic_name__		:Dict[str, int] = None						, __magic_name__		:bool = None						, __magic_name__		:float = None						, __magic_name__		:bool = None						, __magic_name__		:Optional[Union[float, List[float]]] = None						, __magic_name__		:Optional[Union[float, List[float]]] = None						, __magic_name__		:Optional[Union[str, TensorType]] = None						, __magic_name__		:ChannelDimension = ChannelDimension.FIRST						, **__magic_name__		:int						, ):







								'''simple docstring'''







								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									=				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									=				size if size is not None else self.size
								a									=				get_size_dict(__snake_case						, default_to_square=__snake_case							)
								a									=				crop_size if crop_size is not None else self.crop_size
								a									=				get_size_dict(__snake_case						, param_name="""crop_size"""							)

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

								a									=				make_batched(__snake_case							)

								a									=				[
								    [
								        self._preprocess_image(
								            image=__snake_case						, do_resize=__snake_case						, size=__snake_case						, resample=__snake_case						, do_center_crop=__snake_case						, crop_size=__snake_case						, do_rescale=__snake_case						, rescale_factor=__snake_case						, do_normalize=__snake_case						, image_mean=__snake_case						, image_std=__snake_case						, data_format=__snake_case						, )
								        for img in video
								    ]
								    for video in videos
								]

								a									=				{"""pixel_values""": videos}
								return BatchFeature(data=__snake_case						, tensor_type=__snake_case							)









"""simple docstring"""





from __future__ import annotations



def   a    (     __UpperCAmelCase      :    list  )    ->							list:
						if len(__UpperCAmelCase  ) == 0:
												return []
						__magic_name__,			__magic_name__:	List[str]							=       min(__UpperCAmelCase  ), max(__UpperCAmelCase  )
						__magic_name__:	Optional[int]							=       int(max_value - min_value  ) + 1
						__magic_name__:	list[list]							=       [[] for _ in range(__UpperCAmelCase  )]

						for i in my_list:
												buckets[int(i - min_value  )].append(__UpperCAmelCase  )

						return [v for bucket in buckets for v in sorted(__UpperCAmelCase  )]


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

							testmod()
							assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
							assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]



