






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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_a      	=  logging.get_logger(__name__)





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      ["pixel_values"]

	def __init__(							self       ,    UpperCAmelCase = True       ,    UpperCAmelCase = None       ,    UpperCAmelCase = PILImageResampling.BILINEAR       ,    UpperCAmelCase = True       ,    UpperCAmelCase = None       ,    UpperCAmelCase = True       ,    UpperCAmelCase = 1 / 255       ,    UpperCAmelCase = True       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							super().__init__(**UpperCAmelCase    )
							_UpperCAmelCase									=			size if size is not None else {'shortest_edge': 256}
							_UpperCAmelCase									=			get_size_dict(UpperCAmelCase       ,    default_to_square=UpperCAmelCase    )
							_UpperCAmelCase									=			crop_size if crop_size is not None else {'height': 224, 'width': 224}
							_UpperCAmelCase									=			get_size_dict(UpperCAmelCase    )
							_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 IMAGENET_STANDARD_MEAN
							_UpperCAmelCase									=			image_std if image_std is not None else IMAGENET_STANDARD_STD

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase = PILImageResampling.BICUBIC       ,    UpperCAmelCase = None       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			get_size_dict(UpperCAmelCase       ,    default_to_square=UpperCAmelCase    )
							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(UpperCAmelCase       ,    size=size['shortest_edge']       ,    default_to_square=UpperCAmelCase    )
							return resize(UpperCAmelCase       ,    size=UpperCAmelCase       ,    resample=UpperCAmelCase       ,    data_format=UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			get_size_dict(UpperCAmelCase    )
							return center_crop(UpperCAmelCase       ,    size=(size['height'], size['width'])       ,    data_format=UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    **UpperCAmelCase    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = ChannelDimension.FIRST       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							_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(UpperCAmelCase       ,    default_to_square=UpperCAmelCase    )
							_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(UpperCAmelCase    )
							_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									=			make_list_of_images(UpperCAmelCase    )

							if not valid_images(UpperCAmelCase    ):
													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.'    )

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

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

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

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

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

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

							_UpperCAmelCase									=			{'pixel_values': images}
							return BatchFeature(data=UpperCAmelCase       ,    tensor_type=UpperCAmelCase    )








def __A    (      __lowerCAmelCase	)->  list:





						"""simple docstring"""



						if len(__lowerCAmelCase	) < 2:
												return collection

						def circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	) -> bool:
												_UpperCAmelCase									=			False

												if low == high:
																		return swapped

												_UpperCAmelCase									=			low
												_UpperCAmelCase									=			high

												while left < right:
																		if collection[left] > collection[right]:
																								_UpperCAmelCase					, _UpperCAmelCase									=			(
																								    collection[right],
																								    collection[left],
																								)
																								_UpperCAmelCase									=			True

																		left += 1
																		right -= 1

												if left == right and collection[left] > collection[right + 1]:
																		_UpperCAmelCase					, _UpperCAmelCase									=			(
																		    collection[right + 1],
																		    collection[left],
																		)

																		_UpperCAmelCase									=			True

												_UpperCAmelCase									=			low + int((high - low) / 2	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		mid + 1 ,		__lowerCAmelCase	)

												return swapped or left_swap or right_swap

						_UpperCAmelCase									=			True

						while is_not_sorted is True:
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		0 ,		len(__lowerCAmelCase	) - 1	)

						return collection


if __name__ == "__main__":
		_a      	=  input('''Enter numbers separated by a comma:\n''').strip()
		_a      	=  [int(item) for item in user_input.split(''',''')]
		print(circle_sort(unsorted))
