



"""simple docstring"""




from typing import Dict, Optional, Tuple

import torch


def 	lowercase__  (      _UpperCAmelCase					)  ->      torch.Tensor:






      '''simple docstring'''


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


def 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase						,   )  ->      Tuple[torch.Tensor, torch.Tensor]:






      '''simple docstring'''


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


def 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase = 31						,   _UpperCAmelCase = 64						,   **_UpperCAmelCase						,   )  ->      Dict[str, torch.Tensor]:






      '''simple docstring'''


      lowercase	:   Tuple											= torch.linspace(0						,   _UpperCAmelCase						,   steps=(no_bins - 1)						,   device=logits.device					)

      lowercase	:   Tuple											= torch.nn.functional.softmax(_UpperCAmelCase						,   dim=-1					)
      lowercase						,     lowercase	:   Tuple											= _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 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase = None						,   _UpperCAmelCase = 31						,   _UpperCAmelCase = 64						,   _UpperCAmelCase = 1e-8						,   **_UpperCAmelCase						,   )  ->      torch.Tensor:






      '''simple docstring'''


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

      lowercase	:   str											= torch.linspace(0						,   _UpperCAmelCase						,   steps=(no_bins - 1)						,   device=logits.device					)

      lowercase	:   Dict											= _calculate_bin_centers(_UpperCAmelCase					)
      torch.sum(_UpperCAmelCase					)
      lowercase	:   str											= logits.shape[-2]
      lowercase	:   List[str]											= max(_UpperCAmelCase						,   19					)

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

      lowercase	:   Tuple											= torch.nn.functional.softmax(_UpperCAmelCase						,   dim=-1					)

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

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

      lowercase	:   List[Any]											= per_alignment * residue_weights

      lowercase	:   str											= (weighted == torch.max(_UpperCAmelCase					)).nonzero()[0]
      return per_alignment[tuple(_UpperCAmelCase					)]





"""simple docstring"""




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


_UpperCamelCase:					Tuple     				=	logging.get_logger(__name__)

_UpperCamelCase:					List[str]     				=	{
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}


class 	a__		(    SCREAMING_SNAKE_CASE__  ):
   _lowerCamelCase       				=					'regnet'
   _lowerCamelCase       				=					['x', 'y']


   def __init__(				self   :				Union[str, Any],  lowerCAmelCase   :				Union[str, Any]=3,  lowerCAmelCase   :				Tuple=32,  lowerCAmelCase   :				Optional[int]=[128, 192, 512, 1088],  lowerCAmelCase   :				Optional[int]=[2, 6, 12, 2],  lowerCAmelCase   :				str=64,  lowerCAmelCase   :				List[str]="y",  lowerCAmelCase   :				Optional[int]="relu",  **lowerCAmelCase   :				List[Any],  )     ->							Union[str, Any]:
         super().__init__(**lowerCAmelCase      )
         if layer_type not in self.layer_types:
               raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types      )}'''      )
         lowercase	:   str											= num_channels
         lowercase	:   str											= embedding_size
         lowercase	:   int											= hidden_sizes
         lowercase	:   Tuple											= depths
         lowercase	:   str											= groups_width
         lowercase	:   List[str]											= layer_type
         lowercase	:   Optional[int]											= hidden_act
         # always downsample in the first stage
         lowercase	:   Optional[Any]											= True
