





'''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						(a__					):
  _lowercase  :	UNetaDModel
  _lowercase  :	ScoreSdeVeScheduler







  def __init__(		self     ,     lowercase__     ,     lowercase__		)		->						Optional[int]:






        """simple docstring"""





        super().__init__()
        self.register_modules(unet=lowercase__     ,     scheduler=lowercase__		)







  @torch.no_grad()
  def __call__(		self     ,     lowercase__ = 1     ,     lowercase__ = 2_000     ,     lowercase__ = None     ,     lowercase__ = "pil"     ,     lowercase__ = True     ,     **lowercase__     ,     )		->						Union[ImagePipelineOutput, Tuple]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     self.unet.config.sample_size
        _snake_case		:	int					     =     (batch_size, 3, img_size, img_size)

        _snake_case		:	Dict					     =     self.unet

        _snake_case		:	List[str]					     =     randn_tensor(lowercase__     ,     generator=lowercase__		) * self.scheduler.init_noise_sigma
        _snake_case		:	Tuple					     =     sample.to(self.device		)

        self.scheduler.set_timesteps(lowercase__		)
        self.scheduler.set_sigmas(lowercase__		)

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

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

              # prediction step
              _snake_case		:	int					     =     model(lowercase__     ,     lowercase__		).sample
              _snake_case		:	List[Any]					     =     self.scheduler.step_pred(lowercase__     ,     lowercase__     ,     lowercase__     ,     generator=lowercase__		)

              _snake_case     ,							_snake_case		:	List[Any]					     =     output.prev_sample, output.prev_sample_mean

        _snake_case		:	List[str]					     =     sample_mean.clamp(0     ,     1		)
        _snake_case		:	List[Any]					     =     sample.cpu().permute(0     ,     2     ,     3     ,     1		).numpy()
        if output_type == "pil":
              _snake_case		:	Any					     =     self.numpy_to_pil(lowercase__		)

        if not return_dict:
              return (sample,)

        return ImagePipelineOutput(images=lowercase__		)









'''simple docstring'''

from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands




def    _a    (							):





      """simple docstring"""



      _snake_case		:	List[Any]					     =     ArgumentParser('''Transformers CLI tool'''			,      usage='''transformers-cli <command> [<args>]'''	)
      _snake_case		:	List[str]					     =     parser.add_subparsers(help='''transformers-cli command helpers'''	)

      # Register commands
      ConvertCommand.register_subcommand(lowerCAmelCase_	)
      DownloadCommand.register_subcommand(lowerCAmelCase_	)
      EnvironmentCommand.register_subcommand(lowerCAmelCase_	)
      RunCommand.register_subcommand(lowerCAmelCase_	)
      ServeCommand.register_subcommand(lowerCAmelCase_	)
      UserCommands.register_subcommand(lowerCAmelCase_	)
      AddNewModelCommand.register_subcommand(lowerCAmelCase_	)
      AddNewModelLikeCommand.register_subcommand(lowerCAmelCase_	)
      LfsCommands.register_subcommand(lowerCAmelCase_	)
      PTtoTFCommand.register_subcommand(lowerCAmelCase_	)

      # Let's go
      _snake_case		:	str					     =     parser.parse_args()

      if not hasattr(lowerCAmelCase_			,      '''func'''	):
            parser.print_help()
            exit(1	)

      # Run
      _snake_case		:	Union[str, Any]					     =     args.func(lowerCAmelCase_	)
      service.run()


if __name__ == "__main__":
   main()


