






"""simple docstring"""




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  UpperCamelCase    (			unittest.TestCase   ):


 @property
 def _lowercase			(  self	:		Optional[Any]							)      -> Any:
   torch.manual_seed(0							)
   _a						: Optional[Any]    =			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	:		str							)      -> Dict:
   _a						: Optional[Any]    =			"""cpu"""
   _a						: Optional[Any]    =			self.dummy_uncond_unet
   _a						: str    =			DDPMScheduler()

   _a						: Optional[int]    =			DDPMPipeline(unet=__snake_case							,		scheduler=__snake_case							)
   ddpm.to(__snake_case							)
   ddpm.set_progress_bar_config(disable=__snake_case							)

   _a						: Optional[Any]    =			torch.Generator(device=__snake_case							).manual_seed(0							)
   _a						: Dict    =			ddpm(generator=__snake_case							,		num_inference_steps=2							,		output_type="""numpy"""							).images

   _a						: str    =			torch.Generator(device=__snake_case							).manual_seed(0							)
   _a						: List[Any]    =			ddpm(generator=__snake_case							,		num_inference_steps=2							,		output_type="""numpy"""							,		return_dict=__snake_case							)[0]

   _a						: int    =			image[0, -3:, -3:, -1]
   _a						: Optional[Any]    =			image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   _a						: int    =			np.array(
       [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.0_0_0, 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	:		List[str]							)      -> Union[str, Any]:
   _a						: Any    =			self.dummy_uncond_unet
   _a						: Optional[Any]    =			DDPMScheduler(prediction_type="""sample"""							)

   _a						: Dict    =			DDPMPipeline(unet=__snake_case							,		scheduler=__snake_case							)
   ddpm.to(__snake_case							)
   ddpm.set_progress_bar_config(disable=__snake_case							)

   _a						: Any    =			torch.manual_seed(0							)
   _a						: Optional[int]    =			ddpm(generator=__snake_case							,		num_inference_steps=2							,		output_type="""numpy"""							).images

   _a						: Union[str, Any]    =			torch.manual_seed(0							)
   _a						: Optional[Any]    =			ddpm(generator=__snake_case							,		num_inference_steps=2							,		output_type="""numpy"""							)[0]

   _a						: int    =			image[0, -3:, -3:, -1]
   _a						: str    =			image_eps[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 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  UpperCamelCase    (			unittest.TestCase   ):


 def _lowercase			(  self	:		Union[str, Any]							)      -> List[Any]:
   _a						: Optional[int]    =			"""google/ddpm-cifar10-32"""

   _a						: Dict    =			UNetaDModel.from_pretrained(__snake_case							)
   _a						: Tuple    =			DDPMScheduler.from_pretrained(__snake_case							)

   _a						: Any    =			DDPMPipeline(unet=__snake_case							,		scheduler=__snake_case							)
   ddpm.to(__snake_case							)
   ddpm.set_progress_bar_config(disable=__snake_case							)

   _a						: int    =			torch.manual_seed(0							)
   _a						: Any    =			ddpm(generator=__snake_case							,		output_type="""numpy"""							).images

   _a						: Any    =			image[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   _a						: List[Any]    =			np.array([0.4_2_0_0, 0.3_5_8_8, 0.1_9_3_9, 0.3_8_4_7, 0.3_3_8_2, 0.2_6_4_7, 0.4_1_5_5, 0.3_5_8_2, 0.3_3_8_5]							)
   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2









"""simple docstring"""




import json
import logging
import os
import subprocess
from argparse import ArgumentParser


_snake_case					   =     logging.getLogger(__name__)

def     lowerCAmelCase__  (  ):



  '''simple docstring'''
  _a						: Any    =			ArgumentParser()
  _a   ,						_a						: str    =			parser.parse_known_args()
  for arg in unknown:
    if arg.startswith(("""-""", """--""")       ):
      parser.add_argument(arg.split("""="""       )[0]       )

  return parser.parse_args()

def     lowerCAmelCase__  (  ):



  '''simple docstring'''
  _a						: Optional[Any]    =			parse_args()
  _a						: Any    =			8_8_8_8
  _a						: List[str]    =			int(os.environ["""SM_NUM_GPUS"""]       )
  _a						: Union[str, Any]    =			json.loads(os.environ["""SM_HOSTS"""]       )
  _a						: Tuple    =			len(UpperCamelCase__       )
  _a						: Optional[int]    =			os.environ["""SM_CURRENT_HOST"""]
  _a						: Optional[Any]    =			hosts.index(UpperCamelCase__       )
  _a						: List[str]    =			"""INFO"""

  if num_nodes > 1:
    _a						: int    =			F"""python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {''.join(['nested fstring' for parameter,value in args.__dict__.items()]       )}"""
  else:
    _a						: Any    =			F"""python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {''.join(['nested fstring' for parameter,value in args.__dict__.items()]       )}"""
  try:
    subprocess.run(UpperCamelCase__  ,     shell=UpperCamelCase__       )
  except Exception as e:
    logger.info(UpperCamelCase__       )


if __name__ == "__main__":
 main()

