




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 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		42
 UpperCamelCase		     =		42

 def __init__(       self      :						int		,							A_      :						UNetaDModel		,							A_      :						ScoreSdeVeScheduler						)     ->			List[Any]:

       """simple docstring"""






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





 @torch.no_grad()
 def __call__(       self      :						List[str]		,							A_      :						int = 1		,							A_      :						int = 2000		,							A_      :						Optional[Union[torch.Generator, List[torch.Generator]]] = None		,							A_      :						Optional[str] = "pil"		,							A_      :						bool = True		,							**A_      :						str		,							)     ->			Union[ImagePipelineOutput, Tuple]:

       """simple docstring"""






       lowerCamelCase_	  =					self.unet.config.sample_size
       lowerCamelCase_	  =					(batch_size, 3, img_size, img_size)

       lowerCamelCase_	  =					self.unet

       lowerCamelCase_	  =					randn_tensor(A_		,							generator=A_						) * self.scheduler.init_noise_sigma
       lowerCamelCase_	  =					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						)						):
             lowerCamelCase_	  =					self.scheduler.sigmas[i] * torch.ones(shape[0]		,							device=self.device						)

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

             # prediction step
             lowerCamelCase_	  =					model(A_		,							A_						).sample
             lowerCamelCase_	  =					self.scheduler.step_pred(A_		,							A_		,							A_		,							generator=A_						)

             lowerCamelCase_  ,       lowerCamelCase_	  =					output.prev_sample, output.prev_sample_mean

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

       if not return_dict:
             return (sample,)

       return ImagePipelineOutput(images=A_						)









import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]  ):


      '''simple docstring'''



      return EnvironmentCommand()







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 @staticmethod
 def        a__				(       A_      :						ArgumentParser						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					parser.add_parser('env'						)
       download_parser.set_defaults(func=A_						)

 def        a__				(       self      :						Optional[Any]						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					huggingface_hub.__version__

       lowerCamelCase_	  =					'not installed'
       lowerCamelCase_	  =					'NA'
       if is_torch_available():
             import torch

             lowerCamelCase_	  =					torch.__version__
             lowerCamelCase_	  =					torch.cuda.is_available()

       lowerCamelCase_	  =					'not installed'
       if is_transformers_available():
             import transformers

             lowerCamelCase_	  =					transformers.__version__

       lowerCamelCase_	  =					'not installed'
       if is_accelerate_available():
             import accelerate

             lowerCamelCase_	  =					accelerate.__version__

       lowerCamelCase_	  =					'not installed'
       if is_xformers_available():
             import xformers

             lowerCamelCase_	  =					xformers.__version__

       lowerCamelCase_	  =					{
           '`diffusers` version': version,
           'Platform': platform.platform(),
           'Python version': platform.python_version(),
           'PyTorch version (GPU?)': f"""{pt_version} ({pt_cuda_available})""",
           'Huggingface_hub version': hub_version,
           'Transformers version': transformers_version,
           'Accelerate version': accelerate_version,
           'xFormers version': xformers_version,
           'Using GPU in script?': '<fill in>',
           'Using distributed or parallel set-up in script?': '<fill in>',
       }

       print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'						)
       print(self.format_dict(A_						)						)

       return info





 @staticmethod
 def        a__				(       A_      :						Dict						)     ->			Any:

       """simple docstring"""






       return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]						) + "\n"



