

import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Optional[int]:

     """simple docstring"""







     _lowerCAmelCase      ,			_lowerCAmelCase   :					Union[str, Any]        =    image.size
     _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[Any]        =    (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     _lowerCAmelCase   :					Tuple        =    image.resize((w, h)		,							resample=PIL_INTERPOLATION["lanczos"]				)
     _lowerCAmelCase   :					int        =    np.array(SCREAMING_SNAKE_CASE				).astype(np.floataa				) / 255.0
     _lowerCAmelCase   :					int        =    image[None].transpose(0		,							3		,							1		,							2				)
     _lowerCAmelCase   :					Optional[Any]        =    torch.from_numpy(SCREAMING_SNAKE_CASE				)
     return 2.0 * image - 1.0





class A__    ( A ):



 """simple docstring"""







 def __init__(		self :	int     ,						A_ :	VQModel     ,						A_ :	UNetaDModel     ,						A_ :	Union[
         DDIMScheduler,
         PNDMScheduler,
         LMSDiscreteScheduler,
         EulerDiscreteScheduler,
         EulerAncestralDiscreteScheduler,
         DPMSolverMultistepScheduler,
     ]     ,						):






      '''simple docstring'''




      super().__init__()
      self.register_modules(vqvae=A_     ,						unet=A_     ,						scheduler=A_       )


 @torch.no_grad()
 def __call__(		self :	List[str]     ,						A_ :	Union[torch.Tensor, PIL.Image.Image] = None     ,						A_ :	Optional[int] = 1     ,						A_ :	Optional[int] = 1_0_0     ,						A_ :	Optional[float] = 0.0     ,						A_ :	Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,						A_ :	Optional[str] = "pil"     ,						A_ :	bool = True     ,						):






      '''simple docstring'''




      if isinstance(A_     ,						PIL.Image.Image       ):
           _lowerCAmelCase   :					List[Any]        =    1
      elif isinstance(A_     ,						torch.Tensor       ):
           _lowerCAmelCase   :					int        =    image.shape[0]
      else:
           raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(A_       )}'''       )

      if isinstance(A_     ,						PIL.Image.Image       ):
           _lowerCAmelCase   :					Optional[int]        =    preprocess(A_       )

      _lowerCAmelCase      ,			_lowerCAmelCase   :					List[Any]        =    image.shape[-2:]

      # in_channels should be 6: 3 for latents, 3 for low resolution image
      _lowerCAmelCase   :					int        =    (batch_size, self.unet.config.in_channels // 2, height, width)
      _lowerCAmelCase   :					int        =    next(self.unet.parameters()       ).dtype

      _lowerCAmelCase   :					List[Any]        =    randn_tensor(A_     ,						generator=A_     ,						device=self.device     ,						dtype=A_       )

      _lowerCAmelCase   :					int        =    image.to(device=self.device     ,						dtype=A_       )

      # set timesteps and move to the correct device
      self.scheduler.set_timesteps(A_     ,						device=self.device       )
      _lowerCAmelCase   :					Any        =    self.scheduler.timesteps

      # scale the initial noise by the standard deviation required by the scheduler
      _lowerCAmelCase   :					Tuple        =    latents * self.scheduler.init_noise_sigma

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      _lowerCAmelCase   :					Optional[Any]        =    "eta" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
      _lowerCAmelCase   :					List[str]        =    {}
      if accepts_eta:
           _lowerCAmelCase   :					Optional[Any]        =    eta

      for t in self.progress_bar(A_       ):
           # concat latents and low resolution image in the channel dimension.
           _lowerCAmelCase   :					Dict        =    torch.cat([latents, image]     ,						dim=1       )
           _lowerCAmelCase   :					str        =    self.scheduler.scale_model_input(A_     ,						A_       )
           # predict the noise residual
           _lowerCAmelCase   :					Dict        =    self.unet(A_     ,						A_       ).sample
           # compute the previous noisy sample x_t -> x_t-1
           _lowerCAmelCase   :					Union[str, Any]        =    self.scheduler.step(A_     ,						A_     ,						A_     ,						**A_       ).prev_sample

      # decode the image latents with the VQVAE
      _lowerCAmelCase   :					List[str]        =    self.vqvae.decode(A_       ).sample
      _lowerCAmelCase   :					Dict        =    torch.clamp(A_     ,						-1.0     ,						1.0       )
      _lowerCAmelCase   :					Optional[int]        =    image / 2 + 0.5
      _lowerCAmelCase   :					int        =    image.cpu().permute(0     ,						2     ,						3     ,						1       ).numpy()

      if output_type == "pil":
           _lowerCAmelCase   :					List[str]        =    self.numpy_to_pil(A_       )

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=A_       )









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				)       ->      Dict[str, torch.Tensor]:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    []
     _lowerCAmelCase   :					Tuple        =    []
     _lowerCAmelCase   :					List[Any]        =    []

     for rt in rc.restypes:
          _lowerCAmelCase   :					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]				)
          _lowerCAmelCase   :					str        =    {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				)

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

     _lowerCAmelCase   :					Any        =    residx_atomaa_mask
     _lowerCAmelCase   :					Dict        =    residx_atomaa_to_atomaa.long()

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

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

     _lowerCAmelCase   :					Tuple        =    restype_atomaa_mask[protein_aatype]
     _lowerCAmelCase   :					List[str]        =    residx_atomaa_mask

     return protein







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Dict[str, np.ndarray]:

     """simple docstring"""







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






