"""simple docstring"""




from typing import Dict, Iterable, 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, logging


__lowerCamelCase											=  logging.get_logger(__name__)
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Optional[int]													=		['pixel_values']





						def __init__(       self		,__UpperCAmelCase = True		,__UpperCAmelCase = None		,__UpperCAmelCase = PILImageResampling.BICUBIC		,__UpperCAmelCase = True		,__UpperCAmelCase = None		,__UpperCAmelCase = True		,__UpperCAmelCase = 1 / 2_55		,__UpperCAmelCase = True		,__UpperCAmelCase = IMAGENET_DEFAULT_MEAN		,__UpperCAmelCase = IMAGENET_DEFAULT_STD		,**__UpperCAmelCase		,)					->				None:
													super().__init__(**__UpperCAmelCase     )
													A__	     =							size if size is not None else {'shortest_edge': 2_24}
													A__	     =							get_size_dict(__UpperCAmelCase		,default_to_square=__UpperCAmelCase     )
													A__	     =							crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
													A__	     =							get_size_dict(__UpperCAmelCase		,param_name='crop_size'     )

													A__	     =							do_resize
													A__	     =							size
													A__	     =							resample
													A__	     =							do_center_crop
													A__	     =							crop_size
													A__	     =							do_rescale
													A__	     =							rescale_factor
													A__	     =							do_normalize
													A__	     =							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
													A__	     =							image_std if image_std is not None else IMAGENET_DEFAULT_STD





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = PILImageResampling.BICUBIC		,__UpperCAmelCase = None		,**__UpperCAmelCase		,)					->				np.ndarray:
													A__	     =							get_size_dict(__UpperCAmelCase		,default_to_square=__UpperCAmelCase     )
													# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
													if "shortest_edge" in size:
																				A__	     =							int((2_56 / 2_24) * size['shortest_edge']     )
																				A__	     =							get_resize_output_image_size(__UpperCAmelCase		,size=__UpperCAmelCase		,default_to_square=__UpperCAmelCase     )
																				A__	     =							{'height': output_size[0], 'width': output_size[1]}
													if "height" not in size_dict or "width" not in size_dict:
																				raise ValueError(
																				    f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''     )
													return resize(
													    __UpperCAmelCase		,size=(size_dict['height'], size_dict['width'])		,resample=__UpperCAmelCase		,data_format=__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = None		,**__UpperCAmelCase		,)					->				np.ndarray:
													A__	     =							get_size_dict(__UpperCAmelCase     )
													if "height" not in size or "width" not in size:
																				raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''     )
													return center_crop(__UpperCAmelCase		,size=(size['height'], size['width'])		,data_format=__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = None		,**__UpperCAmelCase		,)					->				np.ndarray:
													return rescale(__UpperCAmelCase		,scale=__UpperCAmelCase		,data_format=__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = None		,**__UpperCAmelCase		,)					->				np.ndarray:
													return normalize(__UpperCAmelCase		,mean=__UpperCAmelCase		,std=__UpperCAmelCase		,data_format=__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       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		,)					->				BatchFeature:
													A__	     =							do_resize if do_resize is not None else self.do_resize
													A__	     =							resample if resample is not None else self.resample
													A__	     =							do_center_crop if do_center_crop is not None else self.do_center_crop
													A__	     =							do_rescale if do_rescale is not None else self.do_rescale
													A__	     =							rescale_factor if rescale_factor is not None else self.rescale_factor
													A__	     =							do_normalize if do_normalize is not None else self.do_normalize
													A__	     =							image_mean if image_mean is not None else self.image_mean
													A__	     =							image_std if image_std is not None else self.image_std

													A__	     =							size if size is not None else self.size
													A__	     =							get_size_dict(__UpperCAmelCase		,default_to_square=__UpperCAmelCase     )
													A__	     =							crop_size if crop_size is not None else self.crop_size
													A__	     =							get_size_dict(__UpperCAmelCase		,param_name='crop_size'     )

													A__	     =							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.
													A__	     =							[to_numpy_array(__UpperCAmelCase     ) for image in images]

													if do_resize:
																				A__	     =							[self.resize(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     ) for image in images]

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

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

													if do_normalize:
																				A__	     =							[self.normalize(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     ) for image in images]

													A__	     =							[to_channel_dimension_format(__UpperCAmelCase		,__UpperCAmelCase     ) for image in images]

													A__	     =							{'pixel_values': images}
													return BatchFeature(data=__UpperCAmelCase		,tensor_type=__UpperCAmelCase     )






"""simple docstring"""




def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__     ,  A__	     =							[], []
							while len(UpperCamelCase__				) > 1:
														A__     ,  A__	     =							min(UpperCamelCase__				), max(UpperCamelCase__				)
														start.append(UpperCamelCase__				)
														end.append(UpperCamelCase__				)
														collection.remove(UpperCamelCase__				)
														collection.remove(UpperCamelCase__				)
							end.reverse()
							return start + collection + end


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





