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    lowerCAmelCase_ (     )     ->				Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  ArgumentParser("Transformers CLI tool",  usage="transformers-cli <command> [<args>]"							)
      UpperCAmelCase__								=  parser.add_subparsers(help="transformers-cli command helpers"							)

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

      # Let's go
      UpperCAmelCase__								=  parser.parse_args()

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

      # Run
      UpperCAmelCase__								=  args.func(__A							)
      service.run()


if __name__ == "__main__":
      main()






'''simple docstring'''
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCamelCase :      Optional[int]					 =     False







class 							A__    (       unittest.TestCase  ):
  pass







@slow
@require_torch_gpu
class 							A__    (       unittest.TestCase  ):





  def 						A  (		self      :	Tuple )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion' )
       pipe.to(_a )
       pipe.set_progress_bar_config(disable=_a )

       _SCREAMING_SNAKE_CASE							    =load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
       _SCREAMING_SNAKE_CASE							    =torch.manual_seed(0 )
       _SCREAMING_SNAKE_CASE							    =pipe(
           image=_a							,					generator=_a							,					guidance_scale=7.5							,					num_inference_steps=50							,					output_type='numpy'							,					).images

       _SCREAMING_SNAKE_CASE							    =image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       _SCREAMING_SNAKE_CASE							    =np.array([0.04_41, 0.04_69, 0.05_07, 0.05_75, 0.06_32, 0.06_50, 0.08_65, 0.09_09, 0.09_45] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
