






from typing import Dict, Optional, Tuple

import torch





def 						A_					(						lowercase_			)				->torch.Tensor:







 """simple docstring"""

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





def 						A_					(						lowercase_	,  lowercase_	,  )				->Tuple[torch.Tensor, torch.Tensor]:







 """simple docstring"""

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





def 						A_					(						lowercase_	,  lowercase_ = 3_1	,  lowercase_ = 6_4	,  **lowercase_	,  )				->Dict[str, torch.Tensor]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		torch.linspace(0	,  lowercase_	,  steps=(no_bins - 1)	,  device=logits.device			)

 SCREAMING_SNAKE_CASE				    =		torch.nn.functional.softmax(lowercase_	,  dim=-1			)
 SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		_calculate_expected_aligned_error(
     alignment_confidence_breaks=lowercase_	,  aligned_distance_error_probs=lowercase_	,  )

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





def 						A_					(						lowercase_	,  lowercase_ = None	,  lowercase_ = 3_1	,  lowercase_ = 6_4	,  lowercase_ = 1e-8	,  **lowercase_	,  )				->torch.Tensor:







 """simple docstring"""

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

 SCREAMING_SNAKE_CASE				    =		torch.linspace(0	,  lowercase_	,  steps=(no_bins - 1)	,  device=logits.device			)

 SCREAMING_SNAKE_CASE				    =		_calculate_bin_centers(lowercase_			)
 torch.sum(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		logits.shape[-2]
 SCREAMING_SNAKE_CASE				    =		max(lowercase_	,  1_9			)

 SCREAMING_SNAKE_CASE				    =		1.2_4 * (clipped_n - 1_5) ** (1.0 / 3) - 1.8

 SCREAMING_SNAKE_CASE				    =		torch.nn.functional.softmax(lowercase_	,  dim=-1			)

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

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

 SCREAMING_SNAKE_CASE				    =		per_alignment * residue_weights

 SCREAMING_SNAKE_CASE				    =		(weighted == torch.max(lowercase_			)).nonzero()[0]
 return per_alignment[tuple(lowercase_			)]












from collections import namedtuple

__UpperCAmelCase      =				namedtuple("from_to", "from_ to")

__UpperCAmelCase      =				{
    "millimeter": "mm",
    "centimeter": "cm",
    "meter": "m",
    "kilometer": "km",
    "inch": "in",
    "inche": "in",  # Trailing 's' has been stripped off
    "feet": "ft",
    "foot": "ft",
    "yard": "yd",
    "mile": "mi",
}

__UpperCAmelCase      =				{
    "mm": from_to(0.001, 1_0_0_0),
    "cm": from_to(0.01, 1_0_0),
    "m": from_to(1, 1),
    "km": from_to(1_0_0_0, 0.001),
    "in": from_to(0.0_254, 39.3_701),
    "ft": from_to(0.3_048, 3.28_084),
    "yd": from_to(0.9_144, 1.09_361),
    "mi": from_to(1_609.34, 0.000_621_371),
}





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->float:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		from_type.lower().rstrip('s'			)
 SCREAMING_SNAKE_CASE				    =		TYPE_CONVERSION.get(lowercase_	,  lowercase_			)
 SCREAMING_SNAKE_CASE				    =		to_type.lower().rstrip('s'			)
 SCREAMING_SNAKE_CASE				    =		TYPE_CONVERSION.get(lowercase_	,  lowercase_			)
 if new_from not in METRIC_CONVERSION:
  SCREAMING_SNAKE_CASE				    =		(
      f'''Invalid \'from_type\' value: {from_type!r}.\n'''
      f'''Conversion abbreviations are: {', '.join(lowercase_			)}'''
  )
  raise ValueError(lowercase_			)
 if new_to not in METRIC_CONVERSION:
  SCREAMING_SNAKE_CASE				    =		(
      f'''Invalid \'to_type\' value: {to_type!r}.\n'''
      f'''Conversion abbreviations are: {', '.join(lowercase_			)}'''
  )
  raise ValueError(lowercase_			)
 return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
      import doctest

      doctest.testmod()




