

import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 	UpperCAmelCase__		(	unittest.TestCase	):






		"""simple docstring"""


		@property
		def 				lowercase_		(							self						:		Union[str, Any] )		->							Optional[int]:
							torch.manual_seed(0 )
							SCREAMING_SNAKE_CASE__   =   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 				lowercase_		(							self						:		Any )		->							Any:
							SCREAMING_SNAKE_CASE__   =   self.dummy_uncond_unet
							SCREAMING_SNAKE_CASE__   =   PNDMScheduler()

							SCREAMING_SNAKE_CASE__   =   PNDMPipeline(unet=_SCREAMING_SNAKE_CASE     ,       scheduler=_SCREAMING_SNAKE_CASE )
							pndm.to(_SCREAMING_SNAKE_CASE )
							pndm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

							SCREAMING_SNAKE_CASE__   =   torch.manual_seed(0 )
							SCREAMING_SNAKE_CASE__   =   pndm(generator=_SCREAMING_SNAKE_CASE     ,       num_inference_steps=20     ,       output_type='''numpy''' ).images

							SCREAMING_SNAKE_CASE__   =   torch.manual_seed(0 )
							SCREAMING_SNAKE_CASE__   =   pndm(generator=_SCREAMING_SNAKE_CASE     ,       num_inference_steps=20     ,       output_type='''numpy'''     ,       return_dict=_SCREAMING_SNAKE_CASE )[0]

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

							assert image.shape == (1, 32, 32, 3)
							SCREAMING_SNAKE_CASE__   =   np.array([1.0, 1.0, 0.0, 1.0, 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 	UpperCAmelCase__		(	unittest.TestCase	):






		"""simple docstring"""


		def 				lowercase_		(							self						:		Tuple )		->							str:
							SCREAMING_SNAKE_CASE__   =   '''google/ddpm-cifar10-32'''

							SCREAMING_SNAKE_CASE__   =   UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE )
							SCREAMING_SNAKE_CASE__   =   PNDMScheduler()

							SCREAMING_SNAKE_CASE__   =   PNDMPipeline(unet=_SCREAMING_SNAKE_CASE     ,       scheduler=_SCREAMING_SNAKE_CASE )
							pndm.to(_SCREAMING_SNAKE_CASE )
							pndm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
							SCREAMING_SNAKE_CASE__   =   torch.manual_seed(0 )
							SCREAMING_SNAKE_CASE__   =   pndm(generator=_SCREAMING_SNAKE_CASE     ,       output_type='''numpy''' ).images

							SCREAMING_SNAKE_CASE__   =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							SCREAMING_SNAKE_CASE__   =   np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125] )

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





from argparse import ArgumentParser

from .env import EnvironmentCommand
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   ArgumentParser('''Diffusers CLI tool'''				,		usage='''diffusers-cli <command> [<args>]'''    )
     SCREAMING_SNAKE_CASE__   =   parser.add_subparsers(help='''diffusers-cli command helpers'''    )

     # Register commands
     EnvironmentCommand.register_subcommand(_A    )

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

     if not hasattr(_A				,		'''func'''    ):
          parser.print_help()
          exit(1    )

     # Run
     SCREAMING_SNAKE_CASE__   =   args.func(_A    )
     service.run()


if __name__ == "__main__":
       main()


