


def      _lowercase(				__a							:	List[Any]    ):
							a__     						=len(__a    )
							for i in range(length - 1    ):
														a__     						=False
														for j in range(length - 1 - i    ):
																					if collection[j] > collection[j + 1]:
																												a__     						=True
																												a__						,    a__     						=collection[j + 1], collection[j]
														if not swapped:
																					break  # Stop iteration if the collection is sorted.
							return collection


if __name__ == "__main__":
	import doctest
	import time

	doctest.testmod()

	_lowerCAmelCase:	List[str]      							=					input('Enter numbers separated by a comma:').strip()
	_lowerCAmelCase:	List[Any]      							=					[int(item) for item in user_input.split(',')]
	_lowerCAmelCase:	Optional[int]      							=					time.process_time()
	print(*bubble_sort(unsorted), sep=',')
	print(F"""Processing time: {(time.process_time() - start)%1e9 + 7}""")





from typing import Dict, Optional, Tuple

import torch






def      _lowercase(				__a							:	torch.Tensor    ):
							a__     						=boundaries[1] - boundaries[0]
							a__     						=boundaries + step / 2
							a__     						=torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1    )]						,    dim=0    )
							return bin_centers






def      _lowercase(				__a							:	torch.Tensor						,    __a							:	torch.Tensor						,    ):
							a__     						=_calculate_bin_centers(__a    )
							return (
							    torch.sum(aligned_distance_error_probs * bin_centers						,    dim=-1    ),
							    bin_centers[-1],
							)






def      _lowercase(				__a							:	torch.Tensor						,    __a							:	int = 31						,    __a							:	int = 64						,    **__a							:	List[str]						,    ):
							a__     						=torch.linspace(0						,    __a						,    steps=(no_bins - 1)						,    device=logits.device    )

							a__     						=torch.nn.functional.softmax(__a						,    dim=-1    )
							a__						,    a__     						=_calculate_expected_aligned_error(
							    alignment_confidence_breaks=__a						,    aligned_distance_error_probs=__a						,    )

							return {
							    "aligned_confidence_probs": aligned_confidence_probs,
							    "predicted_aligned_error": predicted_aligned_error,
							    "max_predicted_aligned_error": max_predicted_aligned_error,
							}







def      _lowercase(				__a							:	torch.Tensor						,    __a							:	Optional[torch.Tensor] = None						,    __a							:	int = 31						,    __a							:	int = 64						,    __a							:	float = 1e-8						,    **__a							:	str						,    ):
							if residue_weights is None:
														a__     						=logits.new_ones(logits.shape[-2]    )

							a__     						=torch.linspace(0						,    __a						,    steps=(no_bins - 1)						,    device=logits.device    )

							a__     						=_calculate_bin_centers(__a    )
							torch.sum(__a    )
							a__     						=logits.shape[-2]
							a__     						=max(__a						,    19    )

							a__     						=1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

							a__     						=torch.nn.functional.softmax(__a						,    dim=-1    )

							a__     						=1.0 / (1 + (bin_centers**2) / (da**2))
							a__     						=torch.sum(probs * tm_per_bin						,    dim=-1    )

							a__     						=residue_weights / (eps + residue_weights.sum())
							a__     						=torch.sum(predicted_tm_term * normed_residue_mask						,    dim=-1    )

							a__     						=per_alignment * residue_weights

							a__     						=(weighted == torch.max(__a    )).nonzero()[0]
							return per_alignment[tuple(__a    )]

