






"""simple docstring"""





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       __A		(   SCREAMING_SNAKE_CASE_			):

					def __init__(			self  :      int     ,		__snake_case  :      UNetaDModel     ,		__snake_case  :      UNetaDModel     ,		__snake_case  :      DDPMScheduler     ,		__snake_case  :      str     ,		)			->							Tuple:
											super().__init__()
											__magic_name__:	Dict							=       value_function
											__magic_name__:	Optional[Any]							=       unet
											__magic_name__:	Dict							=       scheduler
											__magic_name__:	Optional[int]							=       env
											__magic_name__:	List[Any]							=       env.get_dataset()
											__magic_name__:	str							=       {}
											for key in self.data.keys():
																	try:
																							__magic_name__:	Any							=       self.data[key].mean()
																	except:  # noqa: E722
																							pass
											__magic_name__:	Optional[int]							=       {}
											for key in self.data.keys():
																	try:
																							__magic_name__:	Dict							=       self.data[key].std()
																	except:  # noqa: E722
																							pass
											__magic_name__:	List[str]							=       env.observation_space.shape[0]
											__magic_name__:	Any							=       env.action_space.shape[0]

					def   lowerCamelCase__   (			self  :      Any     ,		__snake_case  :      Dict     ,		__snake_case  :      int					)			->							Optional[int]:
											return (x_in - self.means[key]) / self.stds[key]

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      List[Any]					)			->							Optional[int]:
											return x_in * self.stds[key] + self.means[key]

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

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      str     ,		__snake_case  :      Dict     ,		__snake_case  :      Tuple					)			->							Optional[Any]:
											for key, val in cond.items():
																	__magic_name__:	int							=       val.clone()
											return x_in

					def   lowerCamelCase__   (			self  :      Any     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Any     ,		__snake_case  :      Any     ,		__snake_case  :      List[Any]					)			->							List[Any]:
											__magic_name__:	str							=       x.shape[0]
											__magic_name__:	Optional[Any]							=       None
											for i in tqdm.tqdm(self.scheduler.timesteps					):
																	# create batch of timesteps to pass into model
																	__magic_name__:	Dict							=       torch.full((batch_size,)     ,		__snake_case     ,		device=self.unet.device     ,		dtype=torch.long					)
																	for _ in range(__snake_case					):
																							with torch.enable_grad():
																													x.requires_grad_()

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

																													__magic_name__:	Optional[int]							=       self.scheduler._get_variance(__snake_case					)
																													__magic_name__:	Optional[Any]							=       torch.exp(0.5 * posterior_variance					)
																													__magic_name__:	List[Any]							=       model_std * grad

																							__magic_name__:	List[str]							=       0
																							__magic_name__:	Any							=       x.detach()
																							__magic_name__:	Dict							=       x + scale * grad
																							__magic_name__:	Optional[Any]							=       self.reset_xa(__snake_case     ,		__snake_case     ,		self.action_dim					)

																	__magic_name__:	int							=       self.unet(x.permute(0     ,		2     ,		1					)     ,		__snake_case					).sample.permute(0     ,		2     ,		1					)

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

																	# apply conditions to the trajectory (set the initial state)
																	__magic_name__:	Dict							=       self.reset_xa(__snake_case     ,		__snake_case     ,		self.action_dim					)
																	__magic_name__:	Union[str, Any]							=       self.to_torch(__snake_case					)
											return x, y






					def __call__(			self  :      Optional[Any]     ,		__snake_case  :      int     ,		__snake_case  :      Tuple=6_4     ,		__snake_case  :      Union[str, Any]=3_2     ,		__snake_case  :      str=2     ,		__snake_case  :      Any=0.1					)			->							int:
											# normalize the observations and create  batch dimension
											__magic_name__:	List[str]							=       self.normalize(__snake_case     ,		"""observations"""					)
											__magic_name__:	Optional[int]							=       obs[None].repeat(__snake_case     ,		axis=0					)

											__magic_name__:	str							=       {0: self.to_torch(__snake_case					)}
											__magic_name__:	Any							=       (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)
											__magic_name__:	int							=       randn_tensor(__snake_case     ,		device=self.unet.device					)
											__magic_name__:	Union[str, Any]							=       self.reset_xa(__snake_case     ,		__snake_case     ,		self.action_dim					)
											__magic_name__:	int							=       self.to_torch(__snake_case					)

											# run the diffusion process
											__magic_name__,			__magic_name__:	int							=       self.run_diffusion(__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case					)

											# sort output trajectories by value
											__magic_name__:	List[str]							=       y.argsort(0     ,		descending=__snake_case					).squeeze()
											__magic_name__:	List[str]							=       x[sorted_idx]
											__magic_name__:	Dict							=       sorted_values[:, :, : self.action_dim]
											__magic_name__:	str							=       actions.detach().cpu().numpy()
											__magic_name__:	Optional[Any]							=       self.de_normalize(__snake_case     ,		key="""actions"""					)

											# select the action with the highest value
											if y is not None:
																	__magic_name__:	Tuple							=       0
											else:
																	# if we didn't run value guiding, select a random action
																	__magic_name__:	List[str]							=       np.random.randint(0     ,		__snake_case					)

											__magic_name__:	int							=       denorm_actions[selected_index, 0]
											return denorm_actions






"""simple docstring"""
import requests




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> None:
      UpperCAmelCase__			:							List[str]							=    {"""Content-Type""": """application/json"""}
      UpperCAmelCase__			:							List[str]							=    requests.post(lowerCAmelCase			,	json={"""text""": message_body}			,	headers=lowerCAmelCase    )
      if response.status_code != 2_00:
            UpperCAmelCase__			:							str							=    (
                """Request to slack returned an error """
                F"""{response.status_code}, the response is:\n{response.text}"""
            )
            raise ValueError(lowerCAmelCase    )


if __name__ == "__main__":
    # Set the slack url to the one provided by Slack when you create the webhook at
    # https://my.slack.com/services/new/incoming-webhook/
    send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""")

