



from typing import Dict, Optional, Tuple

import torch






def 					a(   A :					torch.Tensor )	-> torch.Tensor:
					"""simple docstring"""





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






def 					a(   A :					torch.Tensor				,  A :					torch.Tensor				,  )	-> Tuple[torch.Tensor, torch.Tensor]:
					"""simple docstring"""





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






def 					a(   A :					torch.Tensor				,  A :					int = 31				,  A :					int = 64				,  **A :					Optional[Any]				,  )	-> Dict[str, torch.Tensor]:
					"""simple docstring"""





					a        =     torch.linspace(0				,  A				,  steps=(no_bins - 1)				,  device=logits.device )

					a        =     torch.nn.functional.softmax(A				,  dim=-1 )
					a  ,       a        =     _calculate_expected_aligned_error(
					    alignment_confidence_breaks=A				,  aligned_distance_error_probs=A				,  )

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







def 					a(   A :					torch.Tensor				,  A :					Optional[torch.Tensor] = None				,  A :					int = 31				,  A :					int = 64				,  A :					float = 1e-8				,  **A :					Any				,  )	-> torch.Tensor:
					"""simple docstring"""





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

					a        =     torch.linspace(0				,  A				,  steps=(no_bins - 1)				,  device=logits.device )

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

					a        =     1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

					a        =     torch.nn.functional.softmax(A				,  dim=-1 )

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

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

					a        =     per_alignment * residue_weights

					a        =     (weighted == torch.max(A )).nonzero()[0]
					return per_alignment[tuple(A )]





import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


_lowercase:				Optional[int]       =	logging.get_logger(__name__)



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	def __init__(self	, *lowerCamelCase_	, **lowerCamelCase_	):
						"""simple docstring"""

						warnings.warn(
						    "The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
						    " Please use DetrImageProcessor instead."	, lowerCamelCase_	, )
						super().__init__(*lowerCamelCase_	, **lowerCamelCase_	)
