


'''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       (       snake_case__    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			)   ->							Union[str, Any]:
      super().__init__()
      _a      =							value_function
      _a      =							unet
      _a      =							scheduler
      _a      =							env
      _a      =							env.get_dataset()
      _a      =							{}
      for key in self.data.keys():
           try:
                _a      =							self.data[key].mean()
           except:  # noqa: E722
                pass
      _a      =							{}
      for key in self.data.keys():
           try:
                _a      =							self.data[key].std()
           except:  # noqa: E722
                pass
      _a      =							env.observation_space.shape[0]
      _a      =							env.action_space.shape[0]
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							Any:
      return (x_in - self.means[key]) / self.stds[key]
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__  )   ->							Dict:
      return x_in * self.stds[key] + self.means[key]
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							int:
      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 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Union[str, Any]:
      for key, val in cond.items():
           _a      =							val.clone()
      return x_in
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Tuple:
      _a      =							x.shape[0]
      _a      =							None
      for i in tqdm.tqdm(self.scheduler.timesteps  ):
           # create batch of timesteps to pass into model
           _a      =							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
                     _a      =							self.value_function(x.permute(0 ,			2 ,			1  ) ,			UpperCAmelCase_  ).sample
                     _a      =							torch.autograd.grad([y.sum()] ,			[x]  )[0]

                     _a      =							self.scheduler._get_variance(UpperCAmelCase_  )
                     _a      =							torch.exp(0.5 * posterior_variance  )
                     _a      =							model_std * grad

                _a      =							0
                _a      =							x.detach()
                _a      =							x + scale * grad
                _a      =							self.reset_xa(UpperCAmelCase_ ,			UpperCAmelCase_ ,			self.action_dim  )

           _a      =							self.unet(x.permute(0 ,			2 ,			1  ) ,			UpperCAmelCase_  ).sample.permute(0 ,			2 ,			1  )

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

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




 def __call__(	self ,			__magic_name__ ,			__magic_name__=64 ,			__magic_name__=32 ,			__magic_name__=2 ,			__magic_name__=0.1  )   ->							int:
      _a      =							self.normalize(UpperCAmelCase_ ,			'observations'  )
      _a      =							obs[None].repeat(UpperCAmelCase_ ,			axis=0  )

      _a      =							{0: self.to_torch(UpperCAmelCase_  )}
      _a      =							(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)
      _a      =							randn_tensor(UpperCAmelCase_ ,			device=self.unet.device  )
      _a      =							self.reset_xa(UpperCAmelCase_ ,			UpperCAmelCase_ ,			self.action_dim  )
      _a      =							self.to_torch(UpperCAmelCase_  )

      # run the diffusion process
      _a      =							self.run_diffusion(UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_  )

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

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

      _a      =							denorm_actions[selected_index, 0]
      return denorm_actions





'''simple docstring'''






import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline






@pytest.mark.integration
def 		_A			()				->					Optional[Any]:




     '''simple docstring'''



     with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT						):
          with pytest.raises(lowerCAmelCase__						):
               requests.request('GET'			,  'https://huggingface.co'						)
          with pytest.raises(requests.exceptions.ConnectTimeout						):
               requests.request('GET'			,  'https://huggingface.co'			,  timeout=1.0						)






@pytest.mark.integration
def 		_A			()				->					Any:




     '''simple docstring'''



     with offline(OfflineSimulationMode.CONNECTION_FAILS						):
          with pytest.raises(requests.exceptions.ConnectionError						):
               requests.request('GET'			,  'https://huggingface.co'						)






def 		_A			()				->					Dict:




     '''simple docstring'''



     with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1						):
          with pytest.raises(lowerCAmelCase__						):
               http_head('https://huggingface.co'						)

