"""simple docstring"""



import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment





def   _lowerCamelCase ( *UpperCAmelCase__					)							->     str:


   '''simple docstring'''


   a__     				=		torch.cuda.device_count()
   print(f'''Device count: {num_processes}'''					)
   with patch_environment(
       world_size=UpperCAmelCase__,master_addr='127.0.01',master_port='29500',mixed_precision=args[1].mixed_precision					):
      a__     				=		PrepareForLaunch(UpperCAmelCase__,distributed_type='MULTI_GPU'					)
      torch.multiprocessing.start_processes(UpperCAmelCase__,args=UpperCAmelCase__,nprocs=UpperCAmelCase__,start_method='spawn'					)


if __name__ == "__main__":
     # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
     # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

     # on-demand GPU
     # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
     __magic_name__    				=					rh.cluster(name="rh-cluster", instance_type="V100:4", provider="cheapest", use_spot=False)  # multi GPU
     gpu.up_if_not()

     # on-prem GPU
     # gpu = rh.cluster(
     #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
     #       )

     # Set up remote function
     __magic_name__    				=					[
         "pip:./",
         "transformers",
         "datasets",
         "evaluate",
         "tqdm",
         "scipy",
         "scikit-learn",
         "tensorboard",
         "torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117",
     ]
     __magic_name__    				=					rh.function(fn=launch_train, system=gpu, reqs=reqs, name="train_bert_glue")

     # Define train args/config, run train function
     __magic_name__    				=					argparse.Namespace(cpu=False, mixed_precision="fp16")
     __magic_name__    				=					{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     launch_train_gpu(config, train_args, stream_logs=True)

     # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
     # gpu.install_packages(reqs)
     # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])



"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 def __init__(   self		:      Dict					,   _snake_case		:      List[str]					,   _snake_case		:      Any				)				->			Optional[Any]:




    '''simple docstring'''






    super().__init__()
    self.register_modules(unet=_snake_case					,   scheduler=_snake_case				)



 @torch.no_grad()
 def __call__(   self		:      int					,   _snake_case		:      int = 1					,   _snake_case		:      Optional[torch.Generator] = None					,   _snake_case		:      int = 50					,   _snake_case		:      Optional[str] = "pil"					,   _snake_case		:      bool = True					,   **_snake_case		:      str					,   )				->			Union[ImagePipelineOutput, Tuple]:




    '''simple docstring'''






    a__     				=		torch.randn(
        (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,   generator=_snake_case					,   )
    a__     				=		image.to(self.device				)

    # set step values
    self.scheduler.set_timesteps(_snake_case				)

    for t in self.progress_bar(self.scheduler.timesteps				):
       # 1. predict noise model_output
       a__     				=		self.unet(_snake_case					,   _snake_case				).sample

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       a__     				=		self.scheduler.step(_snake_case					,   _snake_case					,   _snake_case				).prev_sample

    a__     				=		(image / 2 + 0.5).clamp(0					,   1				)
    a__     				=		image.cpu().permute(0					,   2					,   3					,   1				).numpy()
    if output_type == "pil":
       a__     				=		self.numpy_to_pil(_snake_case				)

    if not return_dict:
       return (image,), "This is a local test"

    return ImagePipelineOutput(images=_snake_case				), "This is a local test"


