

from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class     __magic_name__     (							_snake_case     ):


 def __init__(    self		:			List[str]      ,    lowerCAmelCase__		:			Optional[int]      ,    lowerCAmelCase__		:			Dict     )     ->   List[str]:
   super().__init__()
   self.register_modules(unet=lowerCAmelCase__      ,    scheduler=lowerCAmelCase__     )


 @torch.no_grad()
 def __call__(    self		:			Tuple      ,    lowerCAmelCase__		:			int = 1      ,    lowerCAmelCase__		:			Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,    lowerCAmelCase__		:			int = 1_0_0_0      ,    lowerCAmelCase__		:			Optional[str] = "pil"      ,    lowerCAmelCase__		:			bool = True      ,    )     ->   Union[ImagePipelineOutput, Tuple]:
   # Sample gaussian noise to begin loop
   if isinstance(self.unet.config.sample_size      ,    lowerCAmelCase__     ):
     UpperCAmelCase          =	(
         batch_size,
         self.unet.config.in_channels,
         self.unet.config.sample_size,
         self.unet.config.sample_size,
     )
   else:
     UpperCAmelCase          =	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

   if self.device.type == "mps":
     # randn does not work reproducibly on mps
     UpperCAmelCase          =	randn_tensor(lowerCAmelCase__      ,    generator=lowerCAmelCase__     )
     UpperCAmelCase          =	image.to(self.device     )
   else:
     UpperCAmelCase          =	randn_tensor(lowerCAmelCase__      ,    generator=lowerCAmelCase__      ,    device=self.device     )

   # set step values
   self.scheduler.set_timesteps(lowerCAmelCase__     )

   for t in self.progress_bar(self.scheduler.timesteps     ):
     # 1. predict noise model_output
     UpperCAmelCase          =	self.unet(lowerCAmelCase__      ,    lowerCAmelCase__     ).sample

     # 2. compute previous image: x_t -> x_t-1
     UpperCAmelCase          =	self.scheduler.step(lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ,    generator=lowerCAmelCase__     ).prev_sample

   UpperCAmelCase          =	(image / 2 + 0.5).clamp(0      ,    1     )
   UpperCAmelCase          =	image.cpu().permute(0      ,    2      ,    3      ,    1     ).numpy()
   if output_type == "pil":
     UpperCAmelCase          =	self.numpy_to_pil(lowerCAmelCase__     )

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=lowerCAmelCase__     )






from typing import Dict, Optional, Tuple

import torch
def   _lowerCAmelCase(						__A      ):
  UpperCAmelCase          =	boundaries[1] - boundaries[0]
  UpperCAmelCase          =	boundaries + step / 2
  UpperCAmelCase          =	torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1      )] ,							dim=0      )
  return bin_centers
def   _lowerCAmelCase(						__A ,							__A ,							):
  UpperCAmelCase          =	_calculate_bin_centers(__A      )
  return (
      torch.sum(aligned_distance_error_probs * bin_centers ,							dim=-1      ),
      bin_centers[-1],
  )
def   _lowerCAmelCase(						__A ,							__A = 31 ,							__A = 64 ,							**__A ,							):
  UpperCAmelCase          =	torch.linspace(0 ,							__A ,							steps=(no_bins - 1) ,							device=logits.device      )

  UpperCAmelCase          =	torch.nn.functional.softmax(__A ,							dim=-1      )
  UpperCAmelCase   ,						UpperCAmelCase          =	_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   _lowerCAmelCase(						__A ,							__A = None ,							__A = 31 ,							__A = 64 ,							__A = 1E-8 ,							**__A ,							):
  if residue_weights is None:
    UpperCAmelCase          =	logits.new_ones(logits.shape[-2]      )

  UpperCAmelCase          =	torch.linspace(0 ,							__A ,							steps=(no_bins - 1) ,							device=logits.device      )

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

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

  UpperCAmelCase          =	torch.nn.functional.softmax(__A ,							dim=-1      )

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

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

  UpperCAmelCase          =	per_alignment * residue_weights

  UpperCAmelCase          =	(weighted == torch.max(__A      )).nonzero()[0]
  return per_alignment[tuple(__A      )]



