



from typing import Dict, Optional, Tuple

import torch

def       A_   (						__a  :					torch.Tensor		):

				"""simple docstring"""
				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       A_   (						__a  :					torch.Tensor			,		__a  :					torch.Tensor			,		):

				"""simple docstring"""
				a__					   =		_calculate_bin_centers(__a		)
				return (
				    torch.sum(aligned_distance_error_probs * bin_centers			,		dim=-1		),
				    bin_centers[-1],
				)

def       A_   (						__a  :					torch.Tensor			,		__a  :					int = 31			,		__a  :					int = 64			,		**__a  :					Any			,		):

				"""simple docstring"""
				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       A_   (						__a  :					torch.Tensor			,		__a  :					Optional[torch.Tensor] = None			,		__a  :					int = 31			,		__a  :					int = 64			,		__a  :					float = 1e-8			,		**__a  :					str			,		):

				"""simple docstring"""
				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__					   =		1.2_4 * (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		)]








def 				_lowerCAmelCase			(	_a :       str      )						->      int:
     lowerCAmelCase_       :							List[str]			 =		53_81
     for x in s:
          lowerCAmelCase_       :							Dict			 =		((hash_value << 5) + hash_value) + ord(_a      )
     return hash_value & 0XFFFFFFFF
