





'''simple docstring'''






from typing import Dict, Optional, Tuple

import torch
def      UpperCamelCase__   (	lowerCAmelCase						):





      """simple docstring"""


      _lowerCAmelCase							  =							boundaries[1] - boundaries[0]
      _lowerCAmelCase							  =							boundaries + step / 2
      _lowerCAmelCase							  =							torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1						)]							,			dim=0						)
      return bin_centers
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			):





      """simple docstring"""


      _lowerCAmelCase							  =							_calculate_bin_centers(lowerCAmelCase						)
      return (
          torch.sum(aligned_distance_error_probs * bin_centers							,			dim=-1						),
          bin_centers[-1],
      )
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase = 31							,			lowerCAmelCase = 64							,			**lowerCAmelCase							,			):





      """simple docstring"""


      _lowerCAmelCase							  =							torch.linspace(0							,			lowerCAmelCase							,			steps=(no_bins - 1)							,			device=logits.device						)

      _lowerCAmelCase							  =							torch.nn.functional.softmax(lowerCAmelCase							,			dim=-1						)
      _lowerCAmelCase  ,							_lowerCAmelCase							  =							_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      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase = None							,			lowerCAmelCase = 31							,			lowerCAmelCase = 64							,			lowerCAmelCase = 1e-8							,			**lowerCAmelCase							,			):





      """simple docstring"""


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

      _lowerCAmelCase							  =							torch.linspace(0							,			lowerCAmelCase							,			steps=(no_bins - 1)							,			device=logits.device						)

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

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

      _lowerCAmelCase							  =							torch.nn.functional.softmax(lowerCAmelCase							,			dim=-1						)

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

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

      _lowerCAmelCase							  =							per_alignment * residue_weights

      _lowerCAmelCase							  =							(weighted == torch.max(lowerCAmelCase						)).nonzero()[0]
      return per_alignment[tuple(lowerCAmelCase						)]













'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__       :     Tuple          ={
    '''configuration_oneformer''': ['''ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OneFormerConfig'''],
    '''processing_oneformer''': ['''OneFormerProcessor'''],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     List[str]          =['''OneFormerImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     Dict          =[
          '''ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''OneFormerForUniversalSegmentation''',
          '''OneFormerModel''',
          '''OneFormerPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
      from .processing_oneformer import OneFormerProcessor

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_oneformer import OneFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_oneformer import (
                ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                OneFormerForUniversalSegmentation,
                OneFormerModel,
                OneFormerPreTrainedModel,
            )


else:
      import sys

      A__       :     Optional[Any]          =_LazyModule(__name__, globals()['''__file__'''], _import_structure)






