




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
			import PIL

if is_torch_available():
			import torch


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					["pixel_values"]


				def __init__(							self:   Optional[Any]		,					_lowerCamelCase:   bool = True		,					_lowerCamelCase:   Dict[str, int] = None		,					_lowerCamelCase:   PILImageResampling = PILImageResampling.BILINEAR		,					_lowerCamelCase:   bool = True		,					_lowerCamelCase:   Union[int, float] = 1 / 2_55		,					_lowerCamelCase:   bool = True		,					_lowerCamelCase:   Dict[str, int] = None		,					_lowerCamelCase:   bool = True		,					**_lowerCamelCase:   Any		,					):
											super().__init__(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    size if size is not None else {'''shortest_edge''': 2_24}
											SCREAMING_SNAKE_CASE_   	=    get_size_dict(_lowerCamelCase		,					default_to_square=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    crop_size if crop_size is not None else {'''height''': 2_56, '''width''': 2_56}
											SCREAMING_SNAKE_CASE_   	=    get_size_dict(_lowerCamelCase		,					param_name='''crop_size'''		)

											SCREAMING_SNAKE_CASE_   	=    do_resize
											SCREAMING_SNAKE_CASE_   	=    size
											SCREAMING_SNAKE_CASE_   	=    resample
											SCREAMING_SNAKE_CASE_   	=    do_rescale
											SCREAMING_SNAKE_CASE_   	=    rescale_factor
											SCREAMING_SNAKE_CASE_   	=    do_center_crop
											SCREAMING_SNAKE_CASE_   	=    crop_size
											SCREAMING_SNAKE_CASE_   	=    do_flip_channel_order


				def     _A							(							self:   List[str]		,					_lowerCamelCase:   np.ndarray		,					_lowerCamelCase:   Dict[str, int]		,					_lowerCamelCase:   PILImageResampling = PIL.Image.BILINEAR		,					_lowerCamelCase:   Optional[Union[str, ChannelDimension]] = None		,					**_lowerCamelCase:   List[Any]		,					):
											SCREAMING_SNAKE_CASE_   	=    get_size_dict(_lowerCamelCase		,					default_to_square=_lowerCamelCase		)
											if "shortest_edge" not in size:
																		raise ValueError(f"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"		)
											SCREAMING_SNAKE_CASE_   	=    get_resize_output_image_size(_lowerCamelCase		,					size=size['''shortest_edge''']		,					default_to_square=_lowerCamelCase		)
											return resize(_lowerCamelCase		,					size=_lowerCamelCase		,					resample=_lowerCamelCase		,					data_format=_lowerCamelCase		,					**_lowerCamelCase		)


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   np.ndarray		,					_lowerCamelCase:   Dict[str, int]		,					_lowerCamelCase:   Optional[Union[str, ChannelDimension]] = None		,					**_lowerCamelCase:   List[str]		,					):
											SCREAMING_SNAKE_CASE_   	=    get_size_dict(_lowerCamelCase		)
											if "height" not in size or "width" not in size:
																		raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"		)
											return center_crop(_lowerCamelCase		,					size=(size['''height'''], size['''width'''])		,					data_format=_lowerCamelCase		,					**_lowerCamelCase		)


				def     _A							(							self:   str		,					_lowerCamelCase:   np.ndarray		,					_lowerCamelCase:   Union[int, float]		,					_lowerCamelCase:   Optional[Union[str, ChannelDimension]] = None		,					**_lowerCamelCase:   Union[str, Any]		,					):
											return rescale(_lowerCamelCase		,					scale=_lowerCamelCase		,					data_format=_lowerCamelCase		,					**_lowerCamelCase		)


				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   np.ndarray		,					_lowerCamelCase:   Optional[Union[str, ChannelDimension]] = None		):
											return flip_channel_order(_lowerCamelCase		,					data_format=_lowerCamelCase		)


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   ImageInput		,					_lowerCamelCase:   bool = None		,					_lowerCamelCase:   Dict[str, int] = None		,					_lowerCamelCase:   PILImageResampling = None		,					_lowerCamelCase:   bool = None		,					_lowerCamelCase:   float = None		,					_lowerCamelCase:   bool = None		,					_lowerCamelCase:   Dict[str, int] = None		,					_lowerCamelCase:   bool = None		,					_lowerCamelCase:   Optional[Union[str, TensorType]] = None		,					_lowerCamelCase:   ChannelDimension = ChannelDimension.FIRST		,					**_lowerCamelCase:   Any		,					):
											SCREAMING_SNAKE_CASE_   	=    do_resize if do_resize is not None else self.do_resize
											SCREAMING_SNAKE_CASE_   	=    resample if resample is not None else self.resample
											SCREAMING_SNAKE_CASE_   	=    do_rescale if do_rescale is not None else self.do_rescale
											SCREAMING_SNAKE_CASE_   	=    rescale_factor if rescale_factor is not None else self.rescale_factor
											SCREAMING_SNAKE_CASE_   	=    do_center_crop if do_center_crop is not None else self.do_center_crop
											SCREAMING_SNAKE_CASE_   	=    (
											    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
											)

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

											SCREAMING_SNAKE_CASE_   	=    make_list_of_images(_lowerCamelCase		)

											if not valid_images(_lowerCamelCase		):
																		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_rescale and rescale_factor is None:
																		raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

											if do_center_crop and crop_size is None:
																		raise ValueError('''Crop size must be specified if do_center_crop is True.'''		)

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

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

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

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

											# the pretrained checkpoints assume images are BGR, not RGB
											if do_flip_channel_order:
																		SCREAMING_SNAKE_CASE_   	=    [self.flip_channel_order(image=_lowerCamelCase		) for image in images]

											SCREAMING_SNAKE_CASE_   	=    [to_channel_dimension_format(_lowerCamelCase		,					_lowerCamelCase		) for image in images]

											SCREAMING_SNAKE_CASE_   	=    {'''pixel_values''': images}
											return BatchFeature(data=_lowerCamelCase		,					tensor_type=_lowerCamelCase		)





				def     _A							(							self:   Dict		,					_lowerCamelCase:   int		,					_lowerCamelCase:   List[Tuple] = None		):
											SCREAMING_SNAKE_CASE_   	=    outputs.logits

											# Resize logits and compute semantic segmentation maps
											if target_sizes is not None:
																		if len(_lowerCamelCase		) != len(_lowerCamelCase		):
																									raise ValueError(
																									    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''		)

																		if is_torch_tensor(_lowerCamelCase		):
																									SCREAMING_SNAKE_CASE_   	=    target_sizes.numpy()

																		SCREAMING_SNAKE_CASE_   	=    []

																		for idx in range(len(_lowerCamelCase		)		):
																									SCREAMING_SNAKE_CASE_   	=    torch.nn.functional.interpolate(
																									    logits[idx].unsqueeze(dim=0		)		,					size=target_sizes[idx]		,					mode='''bilinear'''		,					align_corners=_lowerCamelCase		)
																									SCREAMING_SNAKE_CASE_   	=    resized_logits[0].argmax(dim=0		)
																									semantic_segmentation.append(_lowerCamelCase		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    logits.argmax(dim=1		)
																		SCREAMING_SNAKE_CASE_   	=    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

											return semantic_segmentation








def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    [], []
							while len(_lowerCAmelCase     ) > 1:
														SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    min(_lowerCAmelCase     ), max(_lowerCAmelCase     )
														start.append(_lowerCAmelCase     )
														end.append(_lowerCAmelCase     )
														collection.remove(_lowerCAmelCase     )
														collection.remove(_lowerCAmelCase     )
							end.reverse()
							return start + collection + end


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


