



"""simple docstring"""

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A_						:    Optional[Any]				=      logging.get_logger(__name__)
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= ['''pixel_values''']





	def __init__(      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       int = 32     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=PILImageResampling.BILINEAR     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       **_SCREAMING_SNAKE_CASE				:       List[str]     ,       )						->	None:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     do_resize
					SCREAMING_SNAKE_CASE						:							List[Any]							=     do_rescale
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     size_divisor
					SCREAMING_SNAKE_CASE						:							List[Any]							=     resample
					super().__init__(**_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       np.ndarray     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Optional[ChannelDimension] = None     ,       **_SCREAMING_SNAKE_CASE				:       Tuple    )						->	np.ndarray:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Optional[Any]							=     get_image_size(_SCREAMING_SNAKE_CASE    )
					# Rounds the height and width down to the closest multiple of size_divisor
					SCREAMING_SNAKE_CASE						:							List[str]							=     height // size_divisor * size_divisor
					SCREAMING_SNAKE_CASE						:							int							=     width // size_divisor * size_divisor
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     resize(_SCREAMING_SNAKE_CASE     ,       (new_h, new_w)     ,       resample=_SCREAMING_SNAKE_CASE     ,       data_format=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )
					return image





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       np.ndarray     ,       _SCREAMING_SNAKE_CASE				:       float     ,       _SCREAMING_SNAKE_CASE				:       Optional[ChannelDimension] = None     ,       **_SCREAMING_SNAKE_CASE				:       str    )						->	np.ndarray:





					"""simple docstring"""


					return rescale(image=_SCREAMING_SNAKE_CASE     ,       scale=_SCREAMING_SNAKE_CASE     ,       data_format=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )







	def 			_lowerCAmelCase     (      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]     ,       _SCREAMING_SNAKE_CASE				:       Optional[bool] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[int] = None     ,       _SCREAMING_SNAKE_CASE				:       Tuple=None     ,       _SCREAMING_SNAKE_CASE				:       Optional[bool] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[TensorType, str]] = None     ,       _SCREAMING_SNAKE_CASE				:       ChannelDimension = ChannelDimension.FIRST     ,       **_SCREAMING_SNAKE_CASE				:       str     ,       )						->	BatchFeature:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     do_resize if do_resize is not None else self.do_resize
					SCREAMING_SNAKE_CASE						:							str							=     do_rescale if do_rescale is not None else self.do_rescale
					SCREAMING_SNAKE_CASE						:							List[str]							=     size_divisor if size_divisor is not None else self.size_divisor
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     resample if resample is not None else self.resample

					if do_resize and size_divisor is None:
									raise ValueError('size_divisor is required for resizing'    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     make_list_of_images(_SCREAMING_SNAKE_CASE    )

					if not valid_images(_SCREAMING_SNAKE_CASE    ):
									raise ValueError('Invalid image(s)'    )

					# All transformations expect numpy arrays.
					SCREAMING_SNAKE_CASE						:							Any							=     [to_numpy_array(_SCREAMING_SNAKE_CASE    ) for img in images]

					if do_resize:
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     [self.resize(_SCREAMING_SNAKE_CASE     ,       size_divisor=_SCREAMING_SNAKE_CASE     ,       resample=_SCREAMING_SNAKE_CASE    ) for image in images]

					if do_rescale:
									SCREAMING_SNAKE_CASE						:							str							=     [self.rescale(_SCREAMING_SNAKE_CASE     ,       scale=1 / 255    ) for image in images]

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     [to_channel_dimension_format(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) for image in images]

					SCREAMING_SNAKE_CASE						:							Any							=     {'pixel_values': images}
					return BatchFeature(data=_SCREAMING_SNAKE_CASE     ,       tensor_type=_SCREAMING_SNAKE_CASE    )










"""simple docstring"""

from __future__ import annotations
def  __snake_case							(	__A      :						list[int]     )     ->       list[int]:






				'''simple docstring'''
				if len(__A     ) == 0:
								return array

				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Optional[int]							=     min(__A     ), max(__A     )

				# Compute the variables
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     _max - _min + 1
				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     [0] * holes_range, [0] * holes_range

				# Make the sorting.
				for i in array:
								SCREAMING_SNAKE_CASE						:							List[Any]							=     i - _min
								SCREAMING_SNAKE_CASE						:							Dict							=     i
								holes_repeat[index] += 1

				# Makes the array back by replacing the numbers.
				SCREAMING_SNAKE_CASE						:							int							=     0
				for i in range(__A     ):
								while holes_repeat[i] > 0:
												SCREAMING_SNAKE_CASE						:							int							=     holes[i]
												index += 1
												holes_repeat[i] -= 1

    # Returns the sorted array.
				return array


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							A_						:    Optional[Any]				=      input('Enter numbers separated by comma:\n')
							A_						:    Union[str, Any]				=      [int(x) for x in user_input.split(',')]
							print(pigeon_sort(unsorted))





