
from typing import Dict, Optional, Tuple

import torch

def    __snake_case       (  __magic_name__	):







    '''simple docstring'''


    lowercase						    =			boundaries[1] - boundaries[0]
    lowercase						    =			boundaries + step / 2
    lowercase						    =			torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1	)]		,						dim=0	)
    return bin_centers

def    __snake_case       (  __magic_name__		,						__magic_name__		,						):







    '''simple docstring'''


    lowercase						    =			_calculate_bin_centers(__magic_name__	)
    return (
        torch.sum(aligned_distance_error_probs * bin_centers		,						dim=-1	),
        bin_centers[-1],
    )

def    __snake_case       (  __magic_name__		,						__magic_name__ = 31		,						__magic_name__ = 64		,						**__magic_name__		,						):







    '''simple docstring'''


    lowercase						    =			torch.linspace(0		,						__magic_name__		,						steps=(no_bins - 1)		,						device=logits.device	)

    lowercase						    =			torch.nn.functional.softmax(__magic_name__		,						dim=-1	)
    lowercase    ,  lowercase						    =			_calculate_expected_aligned_error(
        alignment_confidence_breaks=__magic_name__		,						aligned_distance_error_probs=__magic_name__		,						)

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




def    __snake_case       (  __magic_name__		,						__magic_name__ = None		,						__magic_name__ = 31		,						__magic_name__ = 64		,						__magic_name__ = 1e-8		,						**__magic_name__		,						):







    '''simple docstring'''


    if residue_weights is None:
        lowercase						    =			logits.new_ones(logits.shape[-2]	)

    lowercase						    =			torch.linspace(0		,						__magic_name__		,						steps=(no_bins - 1)		,						device=logits.device	)

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

    lowercase						    =			1.2_4 * (clipped_n - 15) ** (1.0 / 3) - 1.8

    lowercase						    =			torch.nn.functional.softmax(__magic_name__		,						dim=-1	)

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

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

    lowercase						    =			per_alignment * residue_weights

    lowercase						    =			(weighted == torch.max(__magic_name__	)).nonzero()[0]
    return per_alignment[tuple(__magic_name__	)]


from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				if not inplace:
								lowercase						    =			ma + ma
				else:
								ma += ma

				return ma

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			-1 * len(__magic_name__	)
				lowercase						    =			list(range(len(tensor.shape[:zero_index]	)	)	)
				return tensor.permute(first_inds + [zero_index + i for i in inds]	)

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				return t.reshape(t.shape[:-no_dims] + (-1,)	)

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__		,						__magic_name__ = 1e-4	):







				'''simple docstring'''


				lowercase						    =			mask.expand(*value.shape	)
				return torch.sum(mask * value		,						dim=__magic_name__	) / (eps + torch.sum(__magic_name__		,						dim=__magic_name__	))

def    __snake_case       (  __magic_name__		,						__magic_name__ = 2.3_1_2_5		,						__magic_name__ = 2_1.6_8_7_5		,						__magic_name__ = 64	):







				'''simple docstring'''


				lowercase						    =			torch.linspace(__magic_name__		,						__magic_name__		,						no_bins - 1		,						device=pts.device	)
				lowercase						    =			torch.sqrt(torch.sum((pts.unsqueeze(-2	) - pts.unsqueeze(-3	)) ** 2		,						dim=-1	)	)
				return torch.bucketize(__magic_name__		,						__magic_name__	)

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			dicts[0]
				lowercase						    =			{}
				for k, v in first.items():
								lowercase						    =			[d[k] for d in dicts]
								if isinstance(__magic_name__		,						__magic_name__	):
												lowercase						    =			dict_multimap(__magic_name__		,						__magic_name__	)
								else:
												lowercase						    =			fn(__magic_name__	)

				return new_dict

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			v_bins.view(((1,) * len(x.shape	)) + (len(__magic_name__	),)	)
				lowercase						    =			x[..., None] - reshaped_bins
				lowercase						    =			torch.argmin(torch.abs(__magic_name__	)		,						dim=-1	)
				return nn.functional.one_hot(__magic_name__		,						num_classes=len(__magic_name__	)	).float()

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__ = 0		,						__magic_name__ = 0	):







				'''simple docstring'''


				lowercase						    =			[]
				for i, s in enumerate(data.shape[:no_batch_dims]	):
								lowercase						    =			torch.arange(__magic_name__	)
								lowercase						    =			r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape	) - i - 1)))	)
								ranges.append(__magic_name__	)

				lowercase						    =			[slice(__magic_name__	) for _ in range(len(data.shape	) - no_batch_dims	)]
				lowercase						    =			inds
				ranges.extend(__magic_name__	)
				# Matt note: Editing this to get around the behaviour of using a list as an array index changing
				# in recent Numpy versions
				return data[tuple(__magic_name__	)]


_snake_case			:       List[Any]      =						TypeVar("T")

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			{}
				for k, v in dic.items():
								if isinstance(__magic_name__		,						__magic_name__	):
												lowercase						    =			dict_map(__magic_name__		,						__magic_name__		,						__magic_name__	)
								else:
												lowercase						    =			tree_map(__magic_name__		,						__magic_name__		,						__magic_name__	)

				return new_dict

@overload
def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				...

@overload
def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				...

@overload
def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				...

@overload
def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				...

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				if isinstance(__magic_name__		,						__magic_name__	):
								return dict_map(__magic_name__		,						__magic_name__		,						__magic_name__	)
				elif isinstance(__magic_name__		,						__magic_name__	):
								return [tree_map(__magic_name__		,						__magic_name__		,						__magic_name__	) for x in tree]
				elif isinstance(__magic_name__		,						__magic_name__	):
								return tuple(tree_map(__magic_name__		,						__magic_name__		,						__magic_name__	) for x in tree	)
				elif isinstance(__magic_name__		,						__magic_name__	):
								return fn(__magic_name__	)
				else:
								print(type(__magic_name__	)	)
								raise ValueError("Not supported"	)




_snake_case			:       Union[str, Any]      =						partial(tree_map, leaf_type=torch.Tensor)
