"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

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

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 a_       :	UNetaDModel
 a_       :	PNDMScheduler


 def __init__(   self		:      str					,   _snake_case		:      UNetaDModel					,   _snake_case		:      PNDMScheduler				)				->			List[Any]:




    '''simple docstring'''






    super().__init__()

    a__     				=		PNDMScheduler.from_config(scheduler.config				)

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



 @torch.no_grad()
 def __call__(   self		:      List[str]					,   _snake_case		:      int = 1					,   _snake_case		:      int = 50					,   _snake_case		:      Optional[Union[torch.Generator, List[torch.Generator]]] = None					,   _snake_case		:      Optional[str] = "pil"					,   _snake_case		:      bool = True					,   **_snake_case		:      Optional[int]					,   )				->			Union[ImagePipelineOutput, Tuple]:




    '''simple docstring'''






    a__     				=		randn_tensor(
        (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,   generator=_snake_case					,   device=self.device					,   )

    self.scheduler.set_timesteps(_snake_case				)
    for t in self.progress_bar(self.scheduler.timesteps				):
       a__     				=		self.unet(_snake_case					,   _snake_case				).sample

       a__     				=		self.scheduler.step(_snake_case					,   _snake_case					,   _snake_case				).prev_sample

    a__     				=		(image / 2 + 0.5).clamp(0					,   1				)
    a__     				=		image.cpu().permute(0					,   2					,   3					,   1				).numpy()
    if output_type == "pil":
       a__     				=		self.numpy_to_pil(_snake_case				)

    if not return_dict:
       return (image,)

    return ImagePipelineOutput(images=_snake_case				)



"""simple docstring"""



import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand





def   _lowerCamelCase ( UpperCAmelCase__					)							->     Any:


   '''simple docstring'''


   return EnvironmentCommand()




class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 @staticmethod
 def        _lowerCAmelCase				(   _snake_case		:      ArgumentParser				)				->			int:




    '''simple docstring'''






    a__     				=		parser.add_parser('env'					,   help='Print relevant system environment info.'				)
    download_parser.set_defaults(func=_snake_case				)


 def        _lowerCAmelCase				(   self		:      List[str]				)				->			Union[str, Any]:




    '''simple docstring'''






    a__     				=		{
        '`datasets` version': version,
        'Platform': platform.platform(),
        'Python version': platform.python_version(),
        'Huggingface_hub version': huggingface_hub.__version__,
        'PyArrow version': pyarrow.__version__,
        'Pandas version': pandas.__version__,
    }

    print('\nCopy-and-paste the text below in your GitHub issue.\n'				)
    print(self.format_dict(_snake_case				)				)

    return info



 @staticmethod
 def        _lowerCAmelCase				(   _snake_case		:      Tuple				)				->			Optional[int]:




    '''simple docstring'''






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


