






'''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 UpperCAmelCase_					(    __A				):




      """simple docstring"""




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


       '''simple docstring'''

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






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Any		)  ->  Dict:


       '''simple docstring'''

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






      def 				A__      ( self			:		Optional[Any]     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Tuple		)  ->  List[Any]:


       '''simple docstring'''

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






      def 				A__      ( self			:		str     ,      UpperCAmelCase			:		str		)  ->  Union[str, Any]:


       '''simple docstring'''

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






      def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		Tuple		)  ->  str:


       '''simple docstring'''

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






      def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Tuple		)  ->  Tuple:


       '''simple docstring'''

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

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

          lowercase					:   Union[str, Any]				  =self.scheduler._get_variance(UpperCAmelCase		)
          lowercase					:   Any				  =torch.exp(0.5 * posterior_variance		)
          lowercase					:   str				  =model_std * grad

         lowercase					:   Any				  =0
         lowercase					:   List[Any]				  =x.detach()
         lowercase					:   Optional[Any]				  =x + scale * grad
         lowercase					:   Any				  =self.reset_xa(UpperCAmelCase     ,      UpperCAmelCase     ,      self.action_dim		)

        lowercase					:   Dict				  =self.unet(x.permute(0     ,      2     ,      1		)     ,      UpperCAmelCase		).sample.permute(0     ,      2     ,      1		)

        # TODO: verify deprecation of this kwarg
        lowercase					:   Optional[int]				  =self.scheduler.step(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      predict_epsilon=UpperCAmelCase		)['''prev_sample''']

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






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


       '''simple docstring'''

       lowercase					:   Tuple				  =self.normalize(UpperCAmelCase     ,      '''observations'''		)
       lowercase					:   Dict				  =obs[None].repeat(UpperCAmelCase     ,      axis=0		)

       lowercase					:   str				  ={0: self.to_torch(UpperCAmelCase		)}
       lowercase					:   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)
       lowercase					:   Tuple				  =randn_tensor(UpperCAmelCase     ,      device=self.unet.device		)
       lowercase					:   Optional[Any]				  =self.reset_xa(UpperCAmelCase     ,      UpperCAmelCase     ,      self.action_dim		)
       lowercase					:   Dict				  =self.to_torch(UpperCAmelCase		)

       # run the diffusion process
       lowercase  ,		lowercase					:   Tuple				  =self.run_diffusion(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		)

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

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

       lowercase					:   List[Any]				  =denorm_actions[selected_index, 0]
       return denorm_actions











'''simple docstring'''



from __future__ import annotations

import math


def 							lowercase_				(    __A :       float					,      __A :       int			)       ->      float:



 """simple docstring"""




 lowercase					:   str				  =u
 for i in range(1					,      __A			):
  lowercase					:   Any				  =temp * (u - i)
 return temp


def 							lowercase_				(    )       ->      None:



 """simple docstring"""




 lowercase					:   List[str]				  =int(input('''enter the numbers of values: '''			)			)
 lowercase					:   list[list[float]]				  =[]
 for _ in range(__A			):
  y.append([]			)
 for i in range(__A			):
  for j in range(__A			):
   y[i].append(__A			)
   lowercase					:   List[Any]				  =0

 print('''enter the values of parameters in a list: '''			)
 lowercase					:   Optional[int]				  =list(map(__A					,      input().split()			)			)

 print('''enter the values of corresponding parameters: '''			)
 for i in range(__A			):
  lowercase					:   str				  =float(input()			)

 lowercase					:   int				  =int(input('''enter the value to interpolate: '''			)			)
 lowercase					:   Union[str, Any]				  =(value - x[0]) / (x[1] - x[0])

 # for calculating forward difference table

 for i in range(1					,      __A			):
  for j in range(n - i			):
   lowercase					:   str				  =y[j + 1][i - 1] - y[j][i - 1]

 lowercase					:   Any				  =y[0][0]
 for i in range(1					,      __A			):
  summ += (ucal(__A					,      __A			) * y[0][i]) / math.factorial(__A			)

 print(F'the value at {value} is {summ}'			)


if __name__ == "__main__":
      main()



