




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


a													=			logging.get_logger(__name__)


if is_vision_available():
				import PIL




class       _A				(		__lowercase     ):
		__a					    =       ["""pixel_values"""]






		def __init__(    self						,		_SCREAMING_SNAKE_CASE = True						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC						,		_SCREAMING_SNAKE_CASE = True						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = True						,		_SCREAMING_SNAKE_CASE = 1 / 255						,		_SCREAMING_SNAKE_CASE = True						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = True						,		**_SCREAMING_SNAKE_CASE						,		):
								super().__init__(**_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			size if size is not None else {"""shortest_edge""": 224}
								_UpperCAmelCase      =			get_size_dict(_SCREAMING_SNAKE_CASE						,		default_to_square=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								_UpperCAmelCase      =			get_size_dict(_SCREAMING_SNAKE_CASE						,		default_to_square=_SCREAMING_SNAKE_CASE						,		param_name="""crop_size"""			)

								_UpperCAmelCase      =			do_resize
								_UpperCAmelCase      =			size
								_UpperCAmelCase      =			resample
								_UpperCAmelCase      =			do_center_crop
								_UpperCAmelCase      =			crop_size
								_UpperCAmelCase      =			do_rescale
								_UpperCAmelCase      =			rescale_factor
								_UpperCAmelCase      =			do_normalize
								_UpperCAmelCase      =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								_UpperCAmelCase      =			image_std if image_std is not None else OPENAI_CLIP_STD
								_UpperCAmelCase      =			do_convert_rgb






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC						,		_SCREAMING_SNAKE_CASE = None						,		**_SCREAMING_SNAKE_CASE						,		):
								_UpperCAmelCase      =			get_size_dict(_SCREAMING_SNAKE_CASE						,		default_to_square=_SCREAMING_SNAKE_CASE			)
								if "shortest_edge" not in size:
														raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"			)
								_UpperCAmelCase      =			get_resize_output_image_size(_SCREAMING_SNAKE_CASE						,		size=size["""shortest_edge"""]						,		default_to_square=_SCREAMING_SNAKE_CASE			)
								return resize(_SCREAMING_SNAKE_CASE						,		size=_SCREAMING_SNAKE_CASE						,		resample=_SCREAMING_SNAKE_CASE						,		data_format=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None						,		**_SCREAMING_SNAKE_CASE						,		):
								_UpperCAmelCase      =			get_size_dict(_SCREAMING_SNAKE_CASE			)
								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(_SCREAMING_SNAKE_CASE						,		size=(size["""height"""], size["""width"""])						,		data_format=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None						,		**_SCREAMING_SNAKE_CASE						,		):
								return rescale(_SCREAMING_SNAKE_CASE						,		scale=_SCREAMING_SNAKE_CASE						,		data_format=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None						,		**_SCREAMING_SNAKE_CASE						,		):
								return normalize(_SCREAMING_SNAKE_CASE						,		mean=_SCREAMING_SNAKE_CASE						,		std=_SCREAMING_SNAKE_CASE						,		data_format=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST						,		**_SCREAMING_SNAKE_CASE						,		):
								_UpperCAmelCase      =			do_resize if do_resize is not None else self.do_resize
								_UpperCAmelCase      =			size if size is not None else self.size
								_UpperCAmelCase      =			get_size_dict(_SCREAMING_SNAKE_CASE						,		param_name="""size"""						,		default_to_square=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			resample if resample is not None else self.resample
								_UpperCAmelCase      =			do_center_crop if do_center_crop is not None else self.do_center_crop
								_UpperCAmelCase      =			crop_size if crop_size is not None else self.crop_size
								_UpperCAmelCase      =			get_size_dict(_SCREAMING_SNAKE_CASE						,		param_name="""crop_size"""						,		default_to_square=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			do_rescale if do_rescale is not None else self.do_rescale
								_UpperCAmelCase      =			rescale_factor if rescale_factor is not None else self.rescale_factor
								_UpperCAmelCase      =			do_normalize if do_normalize is not None else self.do_normalize
								_UpperCAmelCase      =			image_mean if image_mean is not None else self.image_mean
								_UpperCAmelCase      =			image_std if image_std is not None else self.image_std
								_UpperCAmelCase      =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								_UpperCAmelCase      =			make_list_of_images(_SCREAMING_SNAKE_CASE			)

								if not valid_images(_SCREAMING_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:
														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:
														_UpperCAmelCase      =			[convert_to_rgb(_SCREAMING_SNAKE_CASE			) for image in images]

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

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

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

								if do_rescale:
														_UpperCAmelCase      =			[self.rescale(image=_SCREAMING_SNAKE_CASE						,		scale=_SCREAMING_SNAKE_CASE			) for image in images]

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

								_UpperCAmelCase      =			[to_channel_dimension_format(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			) for image in images]

								_UpperCAmelCase      =			{"""pixel_values""": images}
								return BatchFeature(data=_SCREAMING_SNAKE_CASE						,		tensor_type=_SCREAMING_SNAKE_CASE			)





from __future__ import annotations




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				list[int]:
						if len(snake_case       ) == 0:
												return array

						_UpperCAmelCase    ,					_UpperCAmelCase      =			min(snake_case       ), max(snake_case       )

						# Compute the variables
						_UpperCAmelCase      =			_max - _min + 1
						_UpperCAmelCase    ,					_UpperCAmelCase      =			[0] * holes_range, [0] * holes_range

						# Make the sorting.
						for i in array:
												_UpperCAmelCase      =			i - _min
												_UpperCAmelCase      =			i
												holes_repeat[index] += 1

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

    # Returns the sorted array.
						return array


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				a													=			input("Enter numbers separated by comma:\n")
				a													=			[int(x) for x in user_input.split(",")]
				print(pigeon_sort(unsorted))