






"""simple docstring"""







from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[]
      UpperCAmelCase          =	[]
      UpperCAmelCase          =	[]

      for rt in rc.restypes:
            UpperCAmelCase          =	rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
            restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]							)
            UpperCAmelCase          =	{name: i for i, name in enumerate(_snake_case							)}
            restype_atomaa_to_atomaa_list.append(
                [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]							)

            restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]							)

      # Add dummy mapping for restype 'UNK'
      restype_atomaa_to_atomaa_list.append([0] * 14							)
      restype_atomaa_to_atomaa_list.append([0] * 37							)
      restype_atomaa_mask_list.append([0.0] * 14							)

      UpperCAmelCase          =	torch.tensor(
          _snake_case				,	dtype=torch.intaa				,	device=protein["""aatype"""].device				,	)
      UpperCAmelCase          =	torch.tensor(
          _snake_case				,	dtype=torch.intaa				,	device=protein["""aatype"""].device				,	)
      UpperCAmelCase          =	torch.tensor(
          _snake_case				,	dtype=torch.floataa				,	device=protein["""aatype"""].device				,	)
      UpperCAmelCase          =	protein["""aatype"""].to(torch.long							)

      # create the mapping for (residx, atom14) --> atom37, i.e. an array
      # with shape (num_res, 14) containing the atom37 indices for this protein
      UpperCAmelCase          =	restype_atomaa_to_atomaa[protein_aatype]
      UpperCAmelCase          =	restype_atomaa_mask[protein_aatype]

      UpperCAmelCase          =	residx_atomaa_mask
      UpperCAmelCase          =	residx_atomaa_to_atomaa.long()

      # create the gather indices for mapping back
      UpperCAmelCase          =	restype_atomaa_to_atomaa[protein_aatype]
      UpperCAmelCase          =	residx_atomaa_to_atomaa.long()

      # create the corresponding mask
      UpperCAmelCase          =	torch.zeros([21, 37]				,	dtype=torch.floataa				,	device=protein["""aatype"""].device							)
      for restype, restype_letter in enumerate(rc.restypes							):
            UpperCAmelCase          =	rc.restype_atoa[restype_letter]
            UpperCAmelCase          =	rc.residue_atoms[restype_name]
            for atom_name in atom_names:
                  UpperCAmelCase          =	rc.atom_order[atom_name]
                  UpperCAmelCase          =	1

      UpperCAmelCase          =	restype_atomaa_mask[protein_aatype]
      UpperCAmelCase          =	residx_atomaa_mask

      return protein






def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	tree_map(lambda _snake_case							: torch.tensor(_snake_case				,	device=batch["""aatype"""].device							)				,	_snake_case				,	np.ndarray							)
      UpperCAmelCase          =	tensor_tree_map(lambda _snake_case							: np.array(_snake_case							)				,	make_atomaa_masks(_snake_case							)							)
      return out











"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      42
    SCREAMING_SNAKE_CASE		=      42






    def __init__(			self     ,A     ,A ):
          super().__init__()
          self.register_modules(unet=A     ,scheduler=A )






    @torch.no_grad()
    def __call__(			self     ,A = 1     ,A = 2_000     ,A = None     ,A = "pil"     ,A = True     ,**A     ,):
          UpperCAmelCase          =	self.unet.config.sample_size
          UpperCAmelCase          =	(batch_size, 3, img_size, img_size)

          UpperCAmelCase          =	self.unet

          UpperCAmelCase          =	randn_tensor(A     ,generator=A ) * self.scheduler.init_noise_sigma
          UpperCAmelCase          =	sample.to(self.device )

          self.scheduler.set_timesteps(A )
          self.scheduler.set_sigmas(A )

          for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
                UpperCAmelCase          =	self.scheduler.sigmas[i] * torch.ones(shape[0]     ,device=self.device )

                # correction step
                for _ in range(self.scheduler.config.correct_steps ):
                      UpperCAmelCase          =	self.unet(A     ,A ).sample
                      UpperCAmelCase          =	self.scheduler.step_correct(A     ,A     ,generator=A ).prev_sample

                # prediction step
                UpperCAmelCase          =	model(A     ,A ).sample
                UpperCAmelCase          =	self.scheduler.step_pred(A     ,A     ,A     ,generator=A )

                UpperCAmelCase      , UpperCAmelCase          =	output.prev_sample, output.prev_sample_mean

          UpperCAmelCase          =	sample_mean.clamp(0     ,1 )
          UpperCAmelCase          =	sample.cpu().permute(0     ,2     ,3     ,1 ).numpy()
          if output_type == "pil":
                UpperCAmelCase          =	self.numpy_to_pil(A )

          if not return_dict:
                return (sample,)

          return ImagePipelineOutput(images=A )



