





import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):


   def __init__(					self    :       Optional[Any]    ,  a    :       UNetaDModel    ,  a    :       UNetaDModel    ,  a    :       DDPMScheduler    ,  a    :       Any    ,  )->     Dict:





     """simple docstring"""







     super().__init__()
     lowercase__			    =						value_function
     lowercase__			    =						unet
     lowercase__			    =						scheduler
     lowercase__			    =						env
     lowercase__			    =						env.get_dataset()
     lowercase__			    =						{}
     for key in self.data.keys():
       try:
         lowercase__			    =						self.data[key].mean()
       except:  # noqa: E722
         pass
     lowercase__			    =						{}
     for key in self.data.keys():
       try:
         lowercase__			    =						self.data[key].std()
       except:  # noqa: E722
         pass
     lowercase__			    =						env.observation_space.shape[0]
     lowercase__			    =						env.action_space.shape[0]


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       Any    ,  a    :       int )->     Dict:





     """simple docstring"""







     return (x_in - self.means[key]) / self.stds[key]


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int]    ,  a    :       str    ,  a    :       List[str] )->     str:





     """simple docstring"""







     return x_in * self.stds[key] + self.means[key]


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       Tuple )->     Tuple:





     """simple docstring"""







     if type(a ) is dict:
       return {k: self.to_torch(a ) for k, v in x_in.items()}
     elif torch.is_tensor(a ):
       return x_in.to(self.unet.device )
     return torch.tensor(a    ,  device=self.unet.device )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       Optional[int]    ,  a    :       Dict    ,  a    :       Optional[Any] )->     List[Any]:





     """simple docstring"""







     for key, val in cond.items():
       lowercase__			    =						val.clone()
     return x_in


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple    ,  a    :       Optional[Any]    ,  a    :       Any    ,  a    :       Optional[Any]    ,  a    :       Optional[int] )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						x.shape[0]
     lowercase__			    =						None
     for i in tqdm.tqdm(self.scheduler.timesteps ):
       # create batch of timesteps to pass into model
       lowercase__			    =						torch.full((batch_size,)    ,  a    ,  device=self.unet.device    ,  dtype=torch.long )
       for _ in range(a ):
         with torch.enable_grad():
           x.requires_grad_()

           # permute to match dimension for pre-trained models
           lowercase__			    =						self.value_function(x.permute(0    ,  2    ,  1 )    ,  a ).sample
           lowercase__			    =						torch.autograd.grad([y.sum()]    ,  [x] )[0]

           lowercase__			    =						self.scheduler._get_variance(a )
           lowercase__			    =						torch.exp(0.5 * posterior_variance )
           lowercase__			    =						model_std * grad

         lowercase__			    =						0
         lowercase__			    =						x.detach()
         lowercase__			    =						x + scale * grad
         lowercase__			    =						self.reset_xa(a    ,  a    ,  self.action_dim )

       lowercase__			    =						self.unet(x.permute(0    ,  2    ,  1 )    ,  a ).sample.permute(0    ,  2    ,  1 )

       # TODO: verify deprecation of this kwarg
       lowercase__			    =						self.scheduler.step(a    ,  a    ,  a    ,  predict_epsilon=a )['prev_sample']

       # apply conditions to the trajectory (set the initial state)
       lowercase__			    =						self.reset_xa(a    ,  a    ,  self.action_dim )
       lowercase__			    =						self.to_torch(a )
     return x, y


   def __call__(					self    :       Any    ,  a    :       Tuple    ,  a    :       int=64    ,  a    :       Tuple=32    ,  a    :       List[Any]=2    ,  a    :       List[str]=0.1 )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						self.normalize(a    ,  'observations' )
     lowercase__			    =						obs[None].repeat(a    ,  axis=0 )

     lowercase__			    =						{0: self.to_torch(a )}
     lowercase__			    =						(batch_size, planning_horizon, self.state_dim + self.action_dim)

     # generate initial noise and apply our conditions (to make the trajectories start at current state)
     lowercase__			    =						randn_tensor(a    ,  device=self.unet.device )
     lowercase__			    =						self.reset_xa(a    ,  a    ,  self.action_dim )
     lowercase__			    =						self.to_torch(a )

     # run the diffusion process
     lowercase__    ,					lowercase__			    =						self.run_diffusion(a    ,  a    ,  a    ,  a )

     # sort output trajectories by value
     lowercase__			    =						y.argsort(0    ,  descending=a ).squeeze()
     lowercase__			    =						x[sorted_idx]
     lowercase__			    =						sorted_values[:, :, : self.action_dim]
     lowercase__			    =						actions.detach().cpu().numpy()
     lowercase__			    =						self.de_normalize(a    ,  key='actions' )

     # select the action with the highest value
     if y is not None:
       lowercase__			    =						0
     else:
       # if we didn't run value guiding, select a random action
       lowercase__			    =						np.random.randint(0    ,  a )

     lowercase__			    =						denorm_actions[selected_index, 0]
     return denorm_actions









import argparse
import json
import subprocess




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Optional[int]:
  lowercase__			    =						[]

  lowercase__			    =						(
      F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""""
      ' https://api.github.com/repos/huggingface/transformers/actions/runners'
  )
  lowercase__			    =						subprocess.run(_SCREAMING_SNAKE_CASE	,		shell=_SCREAMING_SNAKE_CASE	,		stdout=subprocess.PIPE	)
  lowercase__			    =						output.stdout.decode('utf-8'	)
  lowercase__			    =						json.loads(_SCREAMING_SNAKE_CASE	)

  lowercase__			    =						status['runners']
  for runner in runners:
    if runner["name"] in target_runners:
      if runner["status"] == "offline":
        offline_runners.append(_SCREAMING_SNAKE_CASE	)

    # save the result so we can report them on Slack
  with open('offline_runners.txt'	,		'w'	) as fp:
    fp.write(json.dumps(_SCREAMING_SNAKE_CASE	)	)

  if len(_SCREAMING_SNAKE_CASE	) > 0:
    lowercase__			    =						'\n'.join([x['name'] for x in offline_runners]	)
    raise ValueError(F"""The following runners are offline:\n{failed}"""	)


if __name__ == "__main__":




      def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     str:
        return values.split(','	)

      lowercase_					     =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--target_runners""",
          default=None,
          type=list_str,
          required=True,
          help="""Comma-separated list of runners to check status.""",
      )

      parser.add_argument(
          """--token""", default=None, type=str, required=True, help="""A token that has actions:read permission."""
      )
      lowercase_					     =	parser.parse_args()


      get_runner_status(args.target_runners, args.token)


