





"""simple docstring"""






import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


lowerCAmelCase_      =				set()
lowerCAmelCase_      =				get_logger(__name__)

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE = None				)		->							Union[str, Any]:






       """simple docstring"""




       def decorator(SCREAMING_SNAKE_CASE				):
              global _emitted_deprecation_warnings

              if inspect.isclass(SCREAMING_SNAKE_CASE				):
                     _UpperCAmelCase			     =  deprecated_class_or_function.__init__
                     _UpperCAmelCase			     =  deprecated_class_or_function.__name__
              else:
                     _UpperCAmelCase			     =  deprecated_class_or_function
                     _UpperCAmelCase			     =  deprecated_function.__name__
                     # Support deprecating __init__ class method: class name instead
                     _UpperCAmelCase			     =  name if name != """__init__""" else deprecated_function.__qualname__.split('.'				)[-2]

              _UpperCAmelCase			     =  (
                  F"""{name} is deprecated and will be removed in the next major version of datasets.""" + F""" {help_message}"""
                  if help_message
                  else """"""
              )

              @wraps(SCREAMING_SNAKE_CASE				)
              def wrapper(*SCREAMING_SNAKE_CASE,**SCREAMING_SNAKE_CASE				):
                     _UpperCAmelCase			     =  hash(SCREAMING_SNAKE_CASE				)
                     if func_hash not in _emitted_deprecation_warnings:
                            warnings.warn(SCREAMING_SNAKE_CASE,category=SCREAMING_SNAKE_CASE,stacklevel=2				)
                            _emitted_deprecation_warnings.add(SCREAMING_SNAKE_CASE				)
                     return deprecated_function(*SCREAMING_SNAKE_CASE,**SCREAMING_SNAKE_CASE				)

              _UpperCAmelCase			     =  """deprecated"""

              if inspect.isclass(SCREAMING_SNAKE_CASE				):
                     _UpperCAmelCase			     =  wrapper
                     return deprecated_class_or_function
              else:
                     return wrapper

       return decorator



class 						lowerCAmelCase       (   enum.EnumMeta				):






       def __getattribute__( cls     ,	a__  ):
              _UpperCAmelCase			     =  super().__getattribute__(a__  )
              if isinstance(a__     ,	enum.Enum  ) and obj._on_access:
                     obj._on_access()
              return obj






       def __getitem__( cls     ,	a__  ):
              _UpperCAmelCase			     =  super().__getitem__(a__  )
              if member._on_access:
                     member._on_access()
              return member






       def __call__( cls     ,	a__     ,	a__=None     ,	*, a__=None     ,	a__=None     ,	a__=None     ,	a__=1  ):
              _UpperCAmelCase			     =  super().__call__(a__     ,	a__     ,	module=a__     ,	qualname=a__     ,	type=a__     ,	start=a__  )
              if isinstance(a__     ,	enum.Enum  ) and obj._on_access:
                     obj._on_access()
              return obj



class 						lowerCAmelCase       (   enum.Enum      ,						metaclass=snake_case				):






       def __new__( cls     ,	a__  ):
              _UpperCAmelCase			     =  object.__new__(cls  )
              _UpperCAmelCase			     =  value
              _UpperCAmelCase			     =  member.deprecate
              return member






       @property
       def 				__A			( self  ):
              return ""






       def 				__A			( self  ):
              _UpperCAmelCase			     =  f""" {self.help_message}""" if self.help_message else """"""
              warnings.warn(
                  f"""'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."""
                  + help_message     ,	a__     ,	stacklevel=3     ,	)










'''simple docstring'''
from typing import Dict, Optional, Tuple

import torch



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Optional[int]          =					boundaries[1] - boundaries[0]
       snake_case__					:	Optional[Any]          =					boundaries + step / 2
       snake_case__					:	str          =					torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1     )]    ,       dim=0     )
       return bin_centers



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       ):






       """simple docstring"""





       snake_case__					:	str          =					_calculate_bin_centers(UpperCAmelCase     )
       return (
           torch.sum(aligned_distance_error_probs * bin_centers    ,       dim=-1     ),
           bin_centers[-1],
       )



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase = 31    ,       UpperCAmelCase = 64    ,       **UpperCAmelCase    ,       ):






       """simple docstring"""





       snake_case__					:	Dict          =					torch.linspace(0    ,       UpperCAmelCase    ,       steps=(no_bins - 1)    ,       device=logits.device     )

       snake_case__					:	str          =					torch.nn.functional.softmax(UpperCAmelCase    ,       dim=-1     )
       snake_case__       ,   snake_case__					:	List[str]          =					_calculate_expected_aligned_error(
           alignment_confidence_breaks=UpperCAmelCase    ,       aligned_distance_error_probs=UpperCAmelCase    ,       )

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






def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase = None    ,       UpperCAmelCase = 31    ,       UpperCAmelCase = 64    ,       UpperCAmelCase = 1E-8    ,       **UpperCAmelCase    ,       ):






       """simple docstring"""





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

       snake_case__					:	Optional[Any]          =					torch.linspace(0    ,       UpperCAmelCase    ,       steps=(no_bins - 1)    ,       device=logits.device     )

       snake_case__					:	int          =					_calculate_bin_centers(UpperCAmelCase     )
       torch.sum(UpperCAmelCase     )
       snake_case__					:	Dict          =					logits.shape[-2]
       snake_case__					:	Optional[Any]          =					max(UpperCAmelCase    ,       19     )

       snake_case__					:	Optional[int]          =					1.2_4 * (clipped_n - 15) ** (1.0 / 3) - 1.8

       snake_case__					:	Tuple          =					torch.nn.functional.softmax(UpperCAmelCase    ,       dim=-1     )

       snake_case__					:	int          =					1.0 / (1 + (bin_centers**2) / (da**2))
       snake_case__					:	Optional[int]          =					torch.sum(probs * tm_per_bin    ,       dim=-1     )

       snake_case__					:	Optional[Any]          =					residue_weights / (eps + residue_weights.sum())
       snake_case__					:	Any          =					torch.sum(predicted_tm_term * normed_residue_mask    ,       dim=-1     )

       snake_case__					:	Dict          =					per_alignment * residue_weights

       snake_case__					:	List[str]          =					(weighted == torch.max(UpperCAmelCase     )).nonzero()[0]
       return per_alignment[tuple(UpperCAmelCase     )]




