





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













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()






