



import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class  __magic_name__   (							unittest.TestCase						):






  @property
  def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Union[str, Any]:
        '''simple docstring'''







        torch.manual_seed(0      )
        UpperCAmelCase         =     UNetaDModel(
            block_out_channels=(32, 64)				,		layers_per_block=2				,		sample_size=32				,		in_channels=3				,		out_channels=3				,		down_block_types=("DownBlock2D", "AttnDownBlock2D")				,		up_block_types=("AttnUpBlock2D", "UpBlock2D")				,		)
        return model







  def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					Any:
        '''simple docstring'''







        UpperCAmelCase         =     self.dummy_uncond_unet
        UpperCAmelCase         =     KarrasVeScheduler()

        UpperCAmelCase         =     KarrasVePipeline(unet=_lowercase				,		scheduler=_lowercase      )
        pipe.to(_lowercase      )
        pipe.set_progress_bar_config(disable=_lowercase      )

        UpperCAmelCase         =     torch.manual_seed(0      )
        UpperCAmelCase         =     pipe(num_inference_steps=2				,		generator=_lowercase				,		output_type="numpy"      ).images

        UpperCAmelCase         =     torch.manual_seed(0      )
        UpperCAmelCase         =     pipe(num_inference_steps=2				,		generator=_lowercase				,		output_type="numpy"				,		return_dict=_lowercase      )[0]

        UpperCAmelCase         =     image[0, -3:, -3:, -1]
        UpperCAmelCase         =     image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        UpperCAmelCase         =     np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

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





@slow
@require_torch
class  __magic_name__   (							unittest.TestCase						):






  def SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					Dict:
        '''simple docstring'''







        UpperCAmelCase         =     """google/ncsnpp-celebahq-256"""
        UpperCAmelCase         =     UNetaDModel.from_pretrained(_lowercase      )
        UpperCAmelCase         =     KarrasVeScheduler()

        UpperCAmelCase         =     KarrasVePipeline(unet=_lowercase				,		scheduler=_lowercase      )
        pipe.to(_lowercase      )
        pipe.set_progress_bar_config(disable=_lowercase      )

        UpperCAmelCase         =     torch.manual_seed(0      )
        UpperCAmelCase         =     pipe(num_inference_steps=20				,		generator=_lowercase				,		output_type="numpy"      ).images

        UpperCAmelCase         =     image[0, -3:, -3:, -1]
        assert image.shape == (1, 2_56, 2_56, 3)
        UpperCAmelCase         =     np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86]      )

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








from argparse import ArgumentParser

from .env import EnvironmentCommand

def    lowerCamelCase_() ->       Any:
						UpperCAmelCase         =     ArgumentParser("Diffusers CLI tool"    ,   usage="diffusers-cli <command> [<args>]"    )
						UpperCAmelCase         =     parser.add_subparsers(help="diffusers-cli command helpers"    )

						# Register commands
						EnvironmentCommand.register_subcommand(lowerCamelCase_    )

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

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

						# Run
						UpperCAmelCase         =     args.func(lowerCamelCase_    )
						service.run()


if __name__ == "__main__":
		main()



