





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


UpperCAmelCase						:   Dict			  =     logging.get_logger(__name__)


if is_vision_available():
				import PIL





class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  Dict					    =	['''pixel_values''']



				def __init__(			self     ,					_A = True     ,					_A = None     ,					_A = PILImageResampling.BICUBIC     ,					_A = True     ,					_A = None     ,					_A = True     ,					_A = 1 / 255     ,					_A = True     ,					_A = None     ,					_A = None     ,					_A = True     ,					**_A     ,					):
									super().__init__(**_A     )
									__A	:						Tuple       =				size if size is not None else {'shortest_edge': 224}
									__A	:						Optional[Any]       =				get_size_dict(_A     ,					default_to_square=_A     )
									__A	:						Optional[Any]       =				crop_size if crop_size is not None else {'height': 224, 'width': 224}
									__A	:						Any       =				get_size_dict(_A     ,					default_to_square=_A     ,					param_name='crop_size'     )

									__A	:						Any       =				do_resize
									__A	:						Dict       =				size
									__A	:						Optional[int]       =				resample
									__A	:						Union[str, Any]       =				do_center_crop
									__A	:						Optional[Any]       =				crop_size
									__A	:						int       =				do_rescale
									__A	:						Tuple       =				rescale_factor
									__A	:						Union[str, Any]       =				do_normalize
									__A	:						int       =				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
									__A	:						Any       =				image_std if image_std is not None else OPENAI_CLIP_STD
									__A	:						Union[str, Any]       =				do_convert_rgb



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A = PILImageResampling.BICUBIC     ,					_A = None     ,					**_A     ,					):
									__A	:						List[str]       =				get_size_dict(_A     ,					default_to_square=_A     )
									if "shortest_edge" not in size:
														raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""     )
									__A	:						Union[str, Any]       =				get_resize_output_image_size(_A     ,					size=size['shortest_edge']     ,					default_to_square=_A     )
									return resize(_A     ,					size=_A     ,					resample=_A     ,					data_format=_A     ,					**_A     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A = None     ,					**_A     ,					):
									__A	:						Optional[int]       =				get_size_dict(_A     )
									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(_A     ,					size=(size['height'], size['width'])     ,					data_format=_A     ,					**_A     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A = None     ,					**_A     ,					):
									return rescale(_A     ,					scale=_A     ,					data_format=_A     ,					**_A     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A = None     ,					**_A     ,					):
									return normalize(_A     ,					mean=_A     ,					std=_A     ,					data_format=_A     ,					**_A     )





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = None     ,					_A = ChannelDimension.FIRST     ,					**_A     ,					):
									__A	:						Dict       =				do_resize if do_resize is not None else self.do_resize
									__A	:						Union[str, Any]       =				size if size is not None else self.size
									__A	:						int       =				get_size_dict(_A     ,					param_name='size'     ,					default_to_square=_A     )
									__A	:						Dict       =				resample if resample is not None else self.resample
									__A	:						Any       =				do_center_crop if do_center_crop is not None else self.do_center_crop
									__A	:						Any       =				crop_size if crop_size is not None else self.crop_size
									__A	:						Optional[int]       =				get_size_dict(_A     ,					param_name='crop_size'     ,					default_to_square=_A     )
									__A	:						Dict       =				do_rescale if do_rescale is not None else self.do_rescale
									__A	:						List[Any]       =				rescale_factor if rescale_factor is not None else self.rescale_factor
									__A	:						List[Any]       =				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	:						Optional[Any]       =				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									__A	:						Any       =				make_list_of_images(_A     )

									if not valid_images(_A     ):
														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:
														__A	:						Union[str, Any]       =				[convert_to_rgb(_A     ) for image in images]

									# All transformations expect numpy arrays.
									__A	:						Any       =				[to_numpy_array(_A     ) for image in images]

									if do_resize:
														__A	:						int       =				[self.resize(image=_A     ,					size=_A     ,					resample=_A     ) for image in images]

									if do_center_crop:
														__A	:						str       =				[self.center_crop(image=_A     ,					size=_A     ) for image in images]

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

									if do_normalize:
														__A	:						List[str]       =				[self.normalize(image=_A     ,					mean=_A     ,					std=_A     ) for image in images]

									__A	:						Union[str, Any]       =				[to_channel_dimension_format(_A     ,					_A     ) for image in images]

									__A	:						List[Any]       =				{'pixel_values': images}
									return BatchFeature(data=_A     ,					tensor_type=_A     )











def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Tuple:
					__A    ,				__A	:						Optional[Any]       =				[], []
					while len(a  ) > 1:
										__A    ,				__A	:						Any       =				min(a  ), max(a  )
										start.append(a  )
										end.append(a  )
										collection.remove(a  )
										collection.remove(a  )
					end.reverse()
					return start + collection + end


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




