"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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


_UpperCamelCase								=		logging.get_logger(__name__)
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						List[str]					     =				['pixel_values']





			def __init__(			self   ,      snake_case_ = True   ,      snake_case_ = None   ,      snake_case_ = 0.9   ,      snake_case_ = PILImageResampling.BICUBIC   ,      snake_case_ = True   ,      snake_case_ = None   ,      snake_case_ = 1 / 255   ,      snake_case_ = True   ,      snake_case_ = True   ,      snake_case_ = None   ,      snake_case_ = None   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case_							)
										A__       :     str								=			size if size is not None else {"""shortest_edge""": 224}
										A__       :     List[str]								=			get_size_dict(snake_case_   ,      default_to_square=snake_case_							)
										A__       :     Dict								=			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										A__       :     List[str]								=			get_size_dict(snake_case_   ,      param_name="""crop_size"""							)

										A__       :     Tuple								=			do_resize
										A__       :     Optional[int]								=			size
										A__       :     str								=			crop_pct
										A__       :     int								=			resample
										A__       :     List[Any]								=			do_center_crop
										A__       :     Any								=			crop_size
										A__       :     Any								=			do_rescale
										A__       :     List[Any]								=			rescale_factor
										A__       :     Optional[int]								=			do_normalize
										A__       :     Optional[int]								=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
										A__       :     Dict								=			image_std if image_std is not None else IMAGENET_DEFAULT_STD





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_ = None   ,      snake_case_ = PILImageResampling.BICUBIC   ,      snake_case_ = None   ,      **snake_case_   ,      ):







										'''simple docstring'''


										A__       :     List[str]								=			get_size_dict(snake_case_   ,      default_to_square=snake_case_							)
										if "shortest_edge" not in size and ("height" not in size or "width" not in size):
																	raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''							)
										if crop_pct is not None:
																	if "shortest_edge" in size:
																								A__       :     Tuple								=			int(size["""shortest_edge"""] / crop_pct							)
																	elif "height" in size and "width" in size:
																								if size["height"] == size["width"]:
																															A__       :     Optional[int]								=			int(size["""height"""] / crop_pct							)
																								else:
																															A__       :     Tuple								=			(int(size["""height"""] / crop_pct							), int(size["""width"""] / crop_pct							))
																	else:
																								raise ValueError("""Invalid size for resize: {}""".format(snake_case_							)							)

																	A__       :     Union[str, Any]								=			get_resize_output_image_size(snake_case_   ,      size=snake_case_   ,      default_to_square=snake_case_							)
										else:
																	if "shortest_edge" in size:
																								A__       :     List[Any]								=			get_resize_output_image_size(snake_case_   ,      size=size["""shortest_edge"""]   ,      default_to_square=snake_case_							)
																	elif "height" in size and "width" in size:
																								A__       :     int								=			(size["""height"""], size["""width"""])
																	else:
																								raise ValueError("""Invalid size for resize: {}""".format(snake_case_							)							)

										return resize(snake_case_   ,      size=snake_case_   ,      resample=snake_case_   ,      data_format=snake_case_   ,      **snake_case_							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_ = None   ,      **snake_case_   ,      ):







										'''simple docstring'''


										A__       :     str								=			get_size_dict(snake_case_							)
										if "height" not in size or "width" not in size:
																	raise ValueError(F'''size must contain \'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   ,      snake_case_   ,      snake_case_   ,      snake_case_ = None   ,      **snake_case_   ,      ):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_   ,      snake_case_ = None   ,      **snake_case_   ,      ):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = ChannelDimension.FIRST   ,      **snake_case_   ,      ):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			do_resize if do_resize is not None else self.do_resize
										A__       :     Any								=			crop_pct if crop_pct is not None else self.crop_pct
										A__       :     Optional[Any]								=			resample if resample is not None else self.resample
										A__       :     Dict								=			do_center_crop if do_center_crop is not None else self.do_center_crop
										A__       :     int								=			do_rescale if do_rescale is not None else self.do_rescale
										A__       :     Optional[Any]								=			rescale_factor if rescale_factor is not None else self.rescale_factor
										A__       :     List[str]								=			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__       :     List[Any]								=			image_std if image_std is not None else self.image_std

										A__       :     List[str]								=			size if size is not None else self.size
										A__       :     List[Any]								=			get_size_dict(snake_case_   ,      default_to_square=snake_case_							)
										A__       :     str								=			crop_size if crop_size is not None else self.crop_size
										A__       :     int								=			get_size_dict(snake_case_   ,      param_name="""crop_size"""							)

										A__       :     Optional[int]								=			make_list_of_images(snake_case_							)

										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."""							)

										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_pct is None:
																	raise ValueError("""Crop_pct 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__       :     List[str]								=			[to_numpy_array(snake_case_							) for image in images]

										if do_resize:
																	A__       :     Any								=			[self.resize(image=snake_case_   ,      size=snake_case_   ,      crop_pct=snake_case_   ,      resample=snake_case_							) for image in images]

										if do_center_crop:
																	A__       :     Tuple								=			[self.center_crop(image=snake_case_   ,      size=snake_case_							) for image in images]

										if do_rescale:
																	A__       :     int								=			[self.rescale(image=snake_case_   ,      scale=snake_case_							) for image in images]

										if do_normalize:
																	A__       :     Optional[Any]								=			[self.normalize(image=snake_case_   ,      mean=snake_case_   ,      std=snake_case_							) for image in images]

										A__       :     int								=			[to_channel_dimension_format(snake_case_   ,      snake_case_							) for image in images]

										A__       :     List[str]								=			{"""pixel_values""": images}
										return BatchFeature(data=snake_case_   ,      tensor_type=snake_case_							)






"""simple docstring"""



from __future__ import annotations

def  _A(		lowerCAmelCase							):
							if len(lowerCAmelCase							) == 0:
														return []
							A__				,							A__       :     Dict								=			min(lowerCAmelCase							), max(lowerCAmelCase							)
							A__       :     List[Any]								=			int(max_value - min_value							) + 1
							A__       :     list[list]								=			[[] for _ in range(lowerCAmelCase							)]

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

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


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]





