


'''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 	lowerCAmelCase__			(			UpperCAmelCase__	):




			def __init__(		self  :     List[Any]	,  lowerCamelCase__  :     UNetaDModel	,  lowerCamelCase__  :     UNetaDModel	,  lowerCamelCase__  :     DDPMScheduler	,  lowerCamelCase__  :     Optional[Any]	,  )   ->str:



					'''simple docstring'''







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




			def 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int )   ->Any:



					'''simple docstring'''







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




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     Optional[Any]	,  lowerCamelCase__  :     Union[str, Any] )   ->str:



					'''simple docstring'''







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




			def 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     Union[str, Any] )   ->int:



					'''simple docstring'''







					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 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     Dict )   ->Optional[int]:



					'''simple docstring'''







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




			def 							lowerCAmelCase__    (		self  :     List[Any]	,  lowerCamelCase__  :     Any	,  lowerCamelCase__  :     Optional[Any]	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     int )   ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      x.shape[0]
					_UpperCAmelCase		:       Tuple								=      None
					for i in tqdm.tqdm(self.scheduler.timesteps ):
							# create batch of timesteps to pass into model
							_UpperCAmelCase		:       str								=      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
											_UpperCAmelCase		:       Optional[int]								=      self.value_function(x.permute(0	,  2	,  1 )	,  lowerCamelCase__ ).sample
											_UpperCAmelCase		:       Optional[int]								=      torch.autograd.grad([y.sum()]	,  [x] )[0]

											_UpperCAmelCase		:       List[str]								=      self.scheduler._get_variance(lowerCamelCase__ )
											_UpperCAmelCase		:       str								=      torch.exp(0.5 * posterior_variance )
											_UpperCAmelCase		:       str								=      model_std * grad

									_UpperCAmelCase		:       str								=      0
									_UpperCAmelCase		:       Union[str, Any]								=      x.detach()
									_UpperCAmelCase		:       Optional[Any]								=      x + scale * grad
									_UpperCAmelCase		:       Union[str, Any]								=      self.reset_xa(lowerCamelCase__	,  lowerCamelCase__	,  self.action_dim )

							_UpperCAmelCase		:       int								=      self.unet(x.permute(0	,  2	,  1 )	,  lowerCamelCase__ ).sample.permute(0	,  2	,  1 )

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

							# apply conditions to the trajectory (set the initial state)
							_UpperCAmelCase		:       Union[str, Any]								=      self.reset_xa(lowerCamelCase__	,  lowerCamelCase__	,  self.action_dim )
							_UpperCAmelCase		:       Optional[int]								=      self.to_torch(lowerCamelCase__ )
					return x, y






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



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      self.normalize(lowerCamelCase__	,  "observations" )
					_UpperCAmelCase		:       str								=      obs[None].repeat(lowerCamelCase__	,  axis=0 )

					_UpperCAmelCase		:       List[str]								=      {0: self.to_torch(lowerCamelCase__ )}
					_UpperCAmelCase		:       Union[str, 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)
					_UpperCAmelCase		:       Tuple								=      randn_tensor(lowerCamelCase__	,  device=self.unet.device )
					_UpperCAmelCase		:       Any								=      self.reset_xa(lowerCamelCase__	,  lowerCamelCase__	,  self.action_dim )
					_UpperCAmelCase		:       Optional[int]								=      self.to_torch(lowerCamelCase__ )

					# run the diffusion process
					_UpperCAmelCase					,    _UpperCAmelCase		:       Any								=      self.run_diffusion(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )

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

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

					_UpperCAmelCase		:       List[str]								=      denorm_actions[selected_index, 0]
					return denorm_actions







'''simple docstring'''


def  __lowerCAmelCase (__lowerCAmelCase   ):  # noqa: E741
		_UpperCAmelCase		:       List[str]								=      len(__lowerCAmelCase   )
		_UpperCAmelCase		:       str								=      0
		_UpperCAmelCase		:       List[str]								=      [0] * n
		_UpperCAmelCase		:       int								=      [False] * n
		_UpperCAmelCase		:       Dict								=      [False] * n

		def dfs(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
				if parent == root:
						out_edge_count += 1
				_UpperCAmelCase		:       List[Any]								=      True
				_UpperCAmelCase		:       str								=      at

				for to in l[at]:
						if to == parent:
								pass
						elif not visited[to]:
								_UpperCAmelCase		:       List[str]								=      dfs(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )
								_UpperCAmelCase		:       Tuple								=      min(low[at]      , low[to]   )

								# AP found via bridge
								if at < low[to]:
										_UpperCAmelCase		:       Dict								=      True
								# AP found via cycle
								if at == low[to]:
										_UpperCAmelCase		:       Dict								=      True
						else:
								_UpperCAmelCase		:       Optional[int]								=      min(low[at]      , __lowerCAmelCase   )
				return out_edge_count

		for i in range(__lowerCAmelCase   ):
				if not visited[i]:
						_UpperCAmelCase		:       str								=      0
						_UpperCAmelCase		:       Tuple								=      dfs(__lowerCAmelCase      , __lowerCAmelCase      , -1      , __lowerCAmelCase   )
						_UpperCAmelCase		:       Optional[int]								=      out_edge_count > 1

		for x in range(len(__lowerCAmelCase   )   ):
				if is_art[x] is True:
						print(__lowerCAmelCase   )


# Adjacency list of graph
lowerCamelCase__    	=     {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}





compute_ap(data)



