


'''simple docstring'''







from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


a__  :					str        =       transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def _UpperCamelCase       (     __A    )							->		Any:


      '''simple docstring'''



      if isinstance(SCREAMING_SNAKE_CASE__      ,			torch.Tensor    ):
            return image
      elif isinstance(SCREAMING_SNAKE_CASE__      ,			PIL.Image.Image    ):
            UpperCamelCase__										=				[image]

      UpperCamelCase__										=				[trans(img.convert("RGB"    )    ) for img in image]
      UpperCamelCase__										=				torch.stack(SCREAMING_SNAKE_CASE__    )
      return image



class 					lowercase_     (    _snake_case   ):


    def __init__(     self  ,					a  ,					a    ):
          super().__init__()

          # make sure scheduler can always be converted to DDIM
          UpperCamelCase__										=				DDIMScheduler.from_config(scheduler.config    )

          self.register_modules(unet=__snake_case  ,					scheduler=__snake_case    )


    def    __a							(     self  ,					a    ):
          if strength < 0 or strength > 1:
                raise ValueError(f'''The value of strength should in [0.0, 1.0] but is {strength}'''    )


    def    __a							(     self  ,					a  ,					a  ,					a    ):
          # get the original timestep using init_timestep
          UpperCamelCase__										=				min(int(num_inference_steps * strength    )  ,					__snake_case    )

          UpperCamelCase__										=				max(num_inference_steps - init_timestep  ,					0    )
          UpperCamelCase__										=				self.scheduler.timesteps[t_start:]

          return timesteps, num_inference_steps - t_start


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a  ,					a  ,					a=None    ):
          if not isinstance(__snake_case  ,					(torch.Tensor, PIL.Image.Image, list)    ):
                raise ValueError(
                    f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__snake_case    )}'''    )

          UpperCamelCase__										=				image.to(device=__snake_case  ,					dtype=__snake_case    )

          if isinstance(__snake_case  ,					__snake_case    ) and len(__snake_case    ) != batch_size:
                raise ValueError(
                    f'''You have passed a list of generators of length {len(__snake_case    )}, but requested an effective batch'''
                    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''    )

          UpperCamelCase__										=				init_latents.shape
          UpperCamelCase__										=				randn_tensor(__snake_case  ,					generator=__snake_case  ,					device=__snake_case  ,					dtype=__snake_case    )

          # get latents
          print("add noise to latents at timestep"  ,					__snake_case    )
          UpperCamelCase__										=				self.scheduler.add_noise(__snake_case  ,					__snake_case  ,					__snake_case    )
          UpperCamelCase__										=				init_latents

          return latents


    @torch.no_grad()
    def __call__(     self  ,					a = None  ,					a = 0.8  ,					a = 1  ,					a = None  ,					a = 0.0  ,					a = 50  ,					a = None  ,					a = "pil"  ,					a = True  ,					):
          self.check_inputs(__snake_case    )

          # 2. Preprocess image
          UpperCamelCase__										=				preprocess(__snake_case    )

          # 3. set timesteps
          self.scheduler.set_timesteps(__snake_case  ,					device=self.device    )
          UpperCamelCase__										=				self.get_timesteps(__snake_case  ,					__snake_case  ,					self.device    )
          UpperCamelCase__										=				timesteps[:1].repeat(__snake_case    )

          # 4. Prepare latent variables
          UpperCamelCase__										=				self.prepare_latents(__snake_case  ,					__snake_case  ,					__snake_case  ,					self.unet.dtype  ,					self.device  ,					__snake_case    )
          UpperCamelCase__										=				latents

          # 5. Denoising loop
          for t in self.progress_bar(__snake_case    ):
                # 1. predict noise model_output
                UpperCamelCase__										=				self.unet(__snake_case  ,					__snake_case    ).sample

                # 2. predict previous mean of image x_t-1 and add variance depending on eta
                # eta corresponds to η in paper and should be between [0, 1]
                # do x_t -> x_t-1
                UpperCamelCase__										=				self.scheduler.step(
                    __snake_case  ,					__snake_case  ,					__snake_case  ,					eta=__snake_case  ,					use_clipped_model_output=__snake_case  ,					generator=__snake_case  ,					).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(__snake_case    )

          if not return_dict:
                return (image, latent_timestep.item())

          return ImagePipelineOutput(images=__snake_case    )









'''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 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Tuple     		=      []
				_SCREAMING_SNAKE_CASE :       Dict     		=      []
				_SCREAMING_SNAKE_CASE :       str     		=      []

				for rt in rc.restypes:
								_SCREAMING_SNAKE_CASE :       Optional[int]     		=      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]     )
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      {name: i for i, name in enumerate(SCREAMING_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     )

				_SCREAMING_SNAKE_CASE :       Dict     		=      torch.tensor(
				    SCREAMING_SNAKE_CASE__							,      dtype=torch.intaa							,      device=protein["""aatype"""].device							,      )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      torch.tensor(
				    SCREAMING_SNAKE_CASE__							,      dtype=torch.intaa							,      device=protein["""aatype"""].device							,      )
				_SCREAMING_SNAKE_CASE :       str     		=      torch.tensor(
				    SCREAMING_SNAKE_CASE__							,      dtype=torch.floataa							,      device=protein["""aatype"""].device							,      )
				_SCREAMING_SNAKE_CASE :       int     		=      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
				_SCREAMING_SNAKE_CASE :       List[str]     		=      restype_atomaa_to_atomaa[protein_aatype]
				_SCREAMING_SNAKE_CASE :       str     		=      restype_atomaa_mask[protein_aatype]

				_SCREAMING_SNAKE_CASE :       List[Any]     		=      residx_atomaa_mask
				_SCREAMING_SNAKE_CASE :       List[str]     		=      residx_atomaa_to_atomaa.long()

				# create the gather indices for mapping back
				_SCREAMING_SNAKE_CASE :       int     		=      restype_atomaa_to_atomaa[protein_aatype]
				_SCREAMING_SNAKE_CASE :       Dict     		=      residx_atomaa_to_atomaa.long()

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

				_SCREAMING_SNAKE_CASE :       int     		=      restype_atomaa_mask[protein_aatype]
				_SCREAMING_SNAKE_CASE :       int     		=      residx_atomaa_mask

				return protein







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      tree_map(lambda SCREAMING_SNAKE_CASE__     : torch.tensor(SCREAMING_SNAKE_CASE__							,      device=batch["""aatype"""].device     )							,      SCREAMING_SNAKE_CASE__							,      np.ndarray     )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      tensor_tree_map(lambda SCREAMING_SNAKE_CASE__     : np.array(SCREAMING_SNAKE_CASE__     )							,      make_atomaa_masks(SCREAMING_SNAKE_CASE__     )     )
				return out




