
import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
						# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
						# setup instructions, if using on-demand hardware

						# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
						# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
						# Throw an error if user passes both BYO and on-demand cluster args
						# Otherwise, use default values
						_snake_case      :							Tuple     	=    argparse.ArgumentParser()
						parser.add_argument("--user", type=str, default="ubuntu")
						parser.add_argument("--host", type=str, default="localhost")
						parser.add_argument("--key_path", type=str, default=None)
						parser.add_argument("--instance", type=str, default="V100:1")
						parser.add_argument("--provider", type=str, default="cheapest")
						parser.add_argument("--use_spot", type=bool, default=False)
						parser.add_argument("--example", type=str, default="pytorch/text-generation/run_generation.py")
						_snake_case	,       _snake_case      :							List[Any]     	=    parser.parse_known_args()
						if args.host != "localhost":
												if args.instance != "V100:1" or args.provider != "cheapest":
																		raise ValueError("Cannot specify both BYO and on-demand cluster args")
												_snake_case      :							str     	=    rh.cluster(
												    name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
												)
						else:
												_snake_case      :							Tuple     	=    rh.cluster(
												    name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
												)
						_snake_case      :							Union[str, Any]     	=    args.example.rsplit("/", 1)[0]

						# Set up remote environment
						cluster.install_packages(["pip:./"])  # Installs transformers from local source
						# Note transformers is copied into the home directory on the remote machine, so we can install from there
						cluster.run([F"pip install -r transformers/examples/{example_dir}/requirements.txt"])
						cluster.run(["pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"])

						# Run example. You can bypass the CLI wrapper and paste your own code here.
						cluster.run([F"python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"])

						# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
						# from my_script... import train
						# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
						# launch_train_gpu = rh.function(fn=train,
						#                                system=gpu,
						#                                reqs=reqs,
						#                                name='train_bert_glue')
						#
						# We can pass in arguments just like we would to a function:
						# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
						#                  stream_logs=True)


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   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def __init__(			self							:str		,    lowerCAmelCase__							:UNetaDModel		,    lowerCAmelCase__							:UNetaDModel		,    lowerCAmelCase__							:DDPMScheduler		,    lowerCAmelCase__							:Optional[Any]		,    )  ->Optional[int]:
										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							:List[str]		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:str     )  ->Any:
										return (x_in - self.means[key]) / self.stds[key]





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Tuple		,    lowerCAmelCase__							:List[Any]     )  ->Optional[Any]:
										return x_in * self.stds[key] + self.means[key]





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:Optional[Any]     )  ->Tuple:
										if type(lowerCAmelCase__     ) is dict:
														return {k: self.to_torch(lowerCAmelCase__     ) for k, v in x_in.items()}
										elif torch.is_tensor(lowerCAmelCase__     ):
														return x_in.to(self.unet.device     )
										return torch.tensor(lowerCAmelCase__		,    device=self.unet.device     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:List[str]     )  ->Any:
										for key, val in cond.items():
														lowercase         =      val.clone()
										return x_in





						def        SCREAMING_SNAKE_CASE(			self							:str		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:Dict     )  ->Tuple:
										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,)		,    lowerCAmelCase__		,    device=self.unet.device		,    dtype=torch.long     )
														for _ in range(lowerCAmelCase__     ):
																		with torch.enable_grad():
																						x.requires_grad_()

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

																						lowercase         =      self.scheduler._get_variance(lowerCAmelCase__     )
																						lowercase         =      torch.exp(0.5 * posterior_variance     )
																						lowercase         =      model_std * grad

																		lowercase         =      0
																		lowercase         =      x.detach()
																		lowercase         =      x + scale * grad
																		lowercase         =      self.reset_xa(lowerCAmelCase__		,    lowerCAmelCase__		,    self.action_dim     )

														lowercase         =      self.unet(x.permute(0		,    2		,    1     )		,    lowerCAmelCase__     ).sample.permute(0		,    2		,    1     )

														# TODO: verify deprecation of this kwarg
														lowercase         =      self.scheduler.step(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    predict_epsilon=lowerCAmelCase__     )["prev_sample"]

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





						def __call__(			self							:Optional[Any]		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:Optional[Any]=64		,    lowerCAmelCase__							:int=32		,    lowerCAmelCase__							:int=2		,    lowerCAmelCase__							:List[str]=0.1     )  ->Tuple:
										# normalize the observations and create  batch dimension
										lowercase         =      self.normalize(lowerCAmelCase__		,    "observations"     )
										lowercase         =      obs[None].repeat(lowerCAmelCase__		,    axis=0     )

										lowercase         =      {0: self.to_torch(lowerCAmelCase__     )}
										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(lowerCAmelCase__		,    device=self.unet.device     )
										lowercase         =      self.reset_xa(lowerCAmelCase__		,    lowerCAmelCase__		,    self.action_dim     )
										lowercase         =      self.to_torch(lowerCAmelCase__     )

										# run the diffusion process
										lowercase     ,	lowercase         =      self.run_diffusion(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

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

										lowercase         =      denorm_actions[selected_index, 0]
										return denorm_actions
