



'''simple docstring'''





from typing import Dict, Optional, Tuple

import torch

def  a_  (							_lowerCAmelCase )   ->   torch.Tensor:
  __lowerCamelCase : Any					 =				boundaries[1] - boundaries[0]
  __lowerCamelCase : Union[str, Any]					 =				boundaries + step / 2
  __lowerCamelCase : Any					 =				torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1 )] ,dim=0 )
  return bin_centers

def  a_  (							_lowerCAmelCase ,_lowerCAmelCase ,)   ->   Tuple[torch.Tensor, torch.Tensor]:
  __lowerCamelCase : str					 =				_calculate_bin_centers(_lowerCAmelCase )
  return (
      torch.sum(aligned_distance_error_probs * bin_centers ,dim=-1 ),
      bin_centers[-1],
  )

def  a_  (							_lowerCAmelCase ,_lowerCAmelCase = 31 ,_lowerCAmelCase = 64 ,**_lowerCAmelCase ,)   ->   Dict[str, torch.Tensor]:
  __lowerCamelCase : Optional[int]					 =				torch.linspace(0 ,_lowerCAmelCase ,steps=(no_bins - 1) ,device=logits.device )

  __lowerCamelCase : Optional[Any]					 =				torch.nn.functional.softmax(_lowerCAmelCase ,dim=-1 )
  __lowerCamelCase ,__lowerCamelCase : Optional[int]					 =				_calculate_expected_aligned_error(
      alignment_confidence_breaks=_lowerCAmelCase ,aligned_distance_error_probs=_lowerCAmelCase ,)

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


def  a_  (							_lowerCAmelCase ,_lowerCAmelCase = None ,_lowerCAmelCase = 31 ,_lowerCAmelCase = 64 ,_lowerCAmelCase = 1E-8 ,**_lowerCAmelCase ,)   ->   torch.Tensor:
  if residue_weights is None:
    __lowerCamelCase : Union[str, Any]					 =				logits.new_ones(logits.shape[-2] )

  __lowerCamelCase : Union[str, Any]					 =				torch.linspace(0 ,_lowerCAmelCase ,steps=(no_bins - 1) ,device=logits.device )

  __lowerCamelCase : Optional[int]					 =				_calculate_bin_centers(_lowerCAmelCase )
  torch.sum(_lowerCAmelCase )
  __lowerCamelCase : Union[str, Any]					 =				logits.shape[-2]
  __lowerCamelCase : List[Any]					 =				max(_lowerCAmelCase ,19 )

  __lowerCamelCase : List[Any]					 =				1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

  __lowerCamelCase : Tuple					 =				torch.nn.functional.softmax(_lowerCAmelCase ,dim=-1 )

  __lowerCamelCase : Tuple					 =				1.0 / (1 + (bin_centers**2) / (da**2))
  __lowerCamelCase : List[str]					 =				torch.sum(probs * tm_per_bin ,dim=-1 )

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

  __lowerCamelCase : Tuple					 =				per_alignment * residue_weights

  __lowerCamelCase : Optional[Any]					 =				(weighted == torch.max(_lowerCAmelCase )).nonzero()[0]
  return per_alignment[tuple(_lowerCAmelCase )]











'''simple docstring'''





from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase							       =		logging.get_logger(__name__)

_UpperCamelCase							       =		{
    'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/config.json',
    # See all LED models at https://huggingface.co/models?filter=led
}






class       lowerCamelCase_					(       SCREAMING_SNAKE_CASE_  ):
  """simple docstring"""




  a_							      ="""led"""
  a_							      ={
      """num_attention_heads""": """encoder_attention_heads""",
      """hidden_size""": """d_model""",
      """attention_probs_dropout_prob""": """attention_dropout""",
      """initializer_range""": """init_std""",
  }




  def __init__(				self   :			str    ,			_a   :			Union[str, Any]=5_0265    ,			_a   :			Any=1_6384    ,			_a   :			List[Any]=1024    ,			_a   :			List[Any]=12    ,			_a   :			Any=4096    ,			_a   :			int=16    ,			_a   :			int=12    ,			_a   :			Optional[int]=4096    ,			_a   :			Tuple=16    ,			_a   :			Optional[Any]=0.0    ,			_a   :			Tuple=0.0    ,			_a   :			List[Any]=True    ,			_a   :			Optional[Any]=True    ,			_a   :			Any="gelu"    ,			_a   :			List[Any]=1024    ,			_a   :			List[str]=0.1    ,			_a   :			Dict=0.0    ,			_a   :			Tuple=0.0    ,			_a   :			Any=0.02    ,			_a   :			Optional[int]=2    ,			_a   :			Optional[int]=0.0    ,			_a   :			int=1    ,			_a   :			List[Any]=0    ,			_a   :			str=2    ,			_a   :			Union[List[int], int] = 512    ,			**_a   :			Optional[int]    ,			)      ->      Tuple:
    __lowerCamelCase : Optional[int]					 =				vocab_size
    __lowerCamelCase : List[str]					 =				max_encoder_position_embeddings
    __lowerCamelCase : Any					 =				max_decoder_position_embeddings
    __lowerCamelCase : Optional[Any]					 =				d_model
    __lowerCamelCase : str					 =				encoder_ffn_dim
    __lowerCamelCase : List[Any]					 =				encoder_layers
    __lowerCamelCase : Union[str, Any]					 =				encoder_attention_heads
    __lowerCamelCase : Optional[Any]					 =				decoder_ffn_dim
    __lowerCamelCase : Union[str, Any]					 =				decoder_layers
    __lowerCamelCase : Optional[Any]					 =				decoder_attention_heads
    __lowerCamelCase : Optional[Any]					 =				dropout
    __lowerCamelCase : List[Any]					 =				attention_dropout
    __lowerCamelCase : Optional[int]					 =				activation_dropout
    __lowerCamelCase : Optional[int]					 =				activation_function
    __lowerCamelCase : Union[str, Any]					 =				init_std
    __lowerCamelCase : str					 =				encoder_layerdrop
    __lowerCamelCase : Tuple					 =				decoder_layerdrop
    __lowerCamelCase : Dict					 =				classifier_dropout
    __lowerCamelCase : Any					 =				use_cache
    __lowerCamelCase : Union[str, Any]					 =				encoder_layers
    __lowerCamelCase : Any					 =				attention_window

    super().__init__(
        pad_token_id=_a    ,			bos_token_id=_a    ,			eos_token_id=_a    ,			is_encoder_decoder=_a    ,			decoder_start_token_id=_a    ,			**_a    ,			)






