



"""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       __magic_name__    (   ):
							__a		:		Tuple									=       ArgumentParser("""Transformers CLI tool"""  ,       usage="""transformers-cli <command> [<args>]"""					)
							__a		:		Dict									=       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
							__a		:		Union[str, Any]									=       parser.parse_args()

							if not hasattr(_lowerCamelCase  ,       """func"""					):
														parser.print_help()
														exit(1					)

							# Run
							__a		:		Optional[Any]									=       args.func(_lowerCamelCase					)
							service.run()


if __name__ == "__main__":
	main()






"""simple docstring"""

import torch

from diffusers import DiffusionPipeline




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def __init__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													super().__init__()

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





						def __call__(self							):





													'''simple docstring'''

													__a		:		Dict									=       torch.randn(
													    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,     )
													__a		:		Optional[Any]									=       1

													__a		:		List[str]									=       self.unet(_lowercase						,     _lowercase							).sample
													__a		:		Union[str, Any]									=       self.scheduler.step(_lowercase						,     _lowercase						,     _lowercase							).prev_sample

													__a		:		Optional[int]									=       scheduler_output - scheduler_output + torch.ones_like(_lowercase							)

													return result

