code: 
  
  
  
  
  
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
  
  
  enable_full_determinism()
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					unittest.TestCase):
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          torch.manual_seed(0							)
          a__			:      Optional[int]            =UNetaDModel(
              block_out_channels=(3_2, 6_4)	,   layers_per_block=2	,   sample_size=3_2	,   in_channels=3	,   out_channels=3	,   down_block_types=("DownBlock2D", "AttnDownBlock2D")	,   up_block_types=("AttnUpBlock2D", "UpBlock2D")	,   )
          return model
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Optional[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Tuple            ="cpu"
          a__			:      int            =self.dummy_uncond_unet
          a__			:      List[Any]            =DDPMScheduler()
  
          a__			:      Tuple            =DDPMPipeline(unet=lowerCAmelCase__	,   scheduler=lowerCAmelCase__							)
          ddpm.to(lowerCAmelCase__							)
          ddpm.set_progress_bar_config(disable=lowerCAmelCase__							)
  
          a__			:      List[str]            =torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
          a__			:      Optional[int]            =ddpm(generator=lowerCAmelCase__	,   num_inference_steps=2	,   output_type="numpy"							).images
  
          a__			:      Optional[Any]            =torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
          a__			:      Dict            =ddpm(generator=lowerCAmelCase__	,   num_inference_steps=2	,   output_type="numpy"	,   return_dict=lowerCAmelCase__							)[0]
  
          a__			:      List[str]            =image[0, -3:, -3:, -1]
          a__			:      List[Any]            =image_from_tuple[0, -3:, -3:, -1]
  
          assert image.shape == (1, 3_2, 3_2, 3)
          a__			:      Optional[Any]            =np.array(
              [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.0_00, 1.199E-03, 2.648E-04, 5.101E-04]							)
  
          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      int            =self.dummy_uncond_unet
          a__			:      List[str]            =DDPMScheduler(prediction_type="sample"							)
  
          a__			:      Optional[int]            =DDPMPipeline(unet=lowerCAmelCase__	,   scheduler=lowerCAmelCase__							)
          ddpm.to(lowerCAmelCase__							)
          ddpm.set_progress_bar_config(disable=lowerCAmelCase__							)
  
          a__			:      str            =torch.manual_seed(0							)
          a__			:      List[str]            =ddpm(generator=lowerCAmelCase__	,   num_inference_steps=2	,   output_type="numpy"							).images
  
          a__			:      Optional[Any]            =torch.manual_seed(0							)
          a__			:      Dict            =ddpm(generator=lowerCAmelCase__	,   num_inference_steps=2	,   output_type="numpy"							)[0]
  
          a__			:      Any            =image[0, -3:, -3:, -1]
          a__			:      Optional[Any]            =image_eps[0, -3:, -3:, -1]
  
          assert image.shape == (1, 3_2, 3_2, 3)
          a__			:      List[str]            =1E-2 if torch_device != "mps" else 3E-2
          assert np.abs(image_slice.flatten() - image_eps_slice.flatten()							).max() < tolerance
  
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class        __lowerCAmelCase (					unittest.TestCase):
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      int            ="google/ddpm-cifar10-32"
  
          a__			:      int            =UNetaDModel.from_pretrained(lowerCAmelCase__							)
          a__			:      Any            =DDPMScheduler.from_pretrained(lowerCAmelCase__							)
  
          a__			:      List[Any]            =DDPMPipeline(unet=lowerCAmelCase__	,   scheduler=lowerCAmelCase__							)
          ddpm.to(lowerCAmelCase__							)
          ddpm.set_progress_bar_config(disable=lowerCAmelCase__							)
  
          a__			:      List[Any]            =torch.manual_seed(0							)
          a__			:      Dict            =ddpm(generator=lowerCAmelCase__	,   output_type="numpy"							).images
  
          a__			:      str            =image[0, -3:, -3:, -1]
  
          assert image.shape == (1, 3_2, 3_2, 3)
          a__			:      List[Any]            =np.array([0.42_00, 0.35_88, 0.19_39, 0.38_47, 0.33_82, 0.26_47, 0.41_55, 0.35_82, 0.33_85]							)
          assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import importlib
  import sys
  from argparse import REMAINDER, ArgumentParser
  from pathlib import Path
  
  import torch_xla.distributed.xla_multiprocessing as xmp
  def   _A (	):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Optional[int]            =ArgumentParser(
        description=(
            "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
        )		)
  
    # Optional arguments for the launch helper
    parser.add_argument("--num_cores" , type=SCREAMING_SNAKE_CASE , default=1 , help="Number of TPU cores to use (1 or 8)."		)
  
    # positional
    parser.add_argument(
        "training_script" , type=SCREAMING_SNAKE_CASE , help=(
            "The full path to the single TPU training "
            "program/script to be launched in parallel, "
            "followed by all the arguments for the "
            "training script"
        ) , )
  
    # rest from the training program
    parser.add_argument("training_script_args" , nargs=SCREAMING_SNAKE_CASE		)
  
    return parser.parse_args()
  def   _A (	):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Optional[Any]            =parse_args()
  
    # Import training_script as a module.
    a__			:      Tuple            =Path(args.training_script		)
    sys.path.append(str(script_fpath.parent.resolve()		)		)
    a__			:      Tuple            =script_fpath.stem
    a__			:      Any            =importlib.import_module(SCREAMING_SNAKE_CASE		)
  
    # Patch sys.argv
    a__			:      List[str]            =[args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores		)]
  
    xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores		)
  
  
  if __name__ == "__main__":
       main()
  
  
  
  
  
  
  