


"""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		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      )      ->						Dict:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Any:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Any:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Dict:

       '''simple docstring'''




       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		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						List[str]:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						List[Any]:

       '''simple docstring'''




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

                   snake_case       : str    =       self.scheduler._get_variance(snake_case__		)
                   snake_case       : Dict    =       torch.exp(0.5 * posterior_variance		)
                   snake_case       : Dict    =       model_std * grad

               snake_case       : Tuple    =       0
               snake_case       : List[str]    =       x.detach()
               snake_case       : str    =       x + scale * grad
               snake_case       : List[str]    =       self.reset_xa(snake_case__		,      snake_case__		,      self.action_dim		)

           snake_case       : Optional[Any]    =       self.unet(x.permute(0		,      2		,      1		)		,      snake_case__		).sample.permute(0		,      2		,      1		)

           # TODO: verify deprecation of this kwarg
           snake_case       : List[str]    =       self.scheduler.step(snake_case__		,      snake_case__		,      snake_case__		,      predict_epsilon=snake_case__		)["prev_sample"]

           # apply conditions to the trajectory (set the initial state)
           snake_case       : str    =       self.reset_xa(snake_case__		,      snake_case__		,      self.action_dim		)
           snake_case       : List[str]    =       self.to_torch(snake_case__		)
       return x, y







   def __call__(      self		,      UpperCamelCase__		,      UpperCamelCase__=64		,      UpperCamelCase__=32		,      UpperCamelCase__=2		,      UpperCamelCase__=0.1		)      ->						List[str]:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       self.normalize(snake_case__		,      "observations"		)
       snake_case       : str    =       obs[None].repeat(snake_case__		,      axis=0		)

       snake_case       : int    =       {0: self.to_torch(snake_case__		)}
       snake_case       : List[str]    =       (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)
       snake_case       : Any    =       randn_tensor(snake_case__		,      device=self.unet.device		)
       snake_case       : int    =       self.reset_xa(snake_case__		,      snake_case__		,      self.action_dim		)
       snake_case       : Tuple    =       self.to_torch(snake_case__		)

       # run the diffusion process
       snake_case   ,snake_case       : Any    =       self.run_diffusion(snake_case__		,      snake_case__		,      snake_case__		,      snake_case__		)

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

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

       snake_case       : Tuple    =       denorm_actions[selected_index, 0]
       return denorm_actions






"""simple docstring"""




from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		return getitem, k







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		return setitem, k, v







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		return delitem, k







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	*lowerCamelCase__  ):





		"""simple docstring"""




		try:
				return fun(lowerCamelCase__      ,	*lowerCamelCase__  ), None
		except Exception as e:
				return None, e


__lowerCAmelCase     :				Optional[Any]    				=    (
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
)

__lowerCAmelCase     :				Optional[int]    				=    [
    _set("key_a", "val_a"),
    _set("key_a", "val_b"),
]

__lowerCAmelCase     :				List[Any]    				=    [
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
    _del("key_a"),
    _del("key_b"),
    _set("key_a", "val_a"),
    _del("key_a"),
]

__lowerCAmelCase     :				int    				=    [
    _get("key_a"),
    _del("key_a"),
    _set("key_a", "val_a"),
    _del("key_a"),
    _del("key_a"),
    _get("key_a"),
]

__lowerCAmelCase     :				Optional[Any]    				=    [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

__lowerCAmelCase     :				Any    				=    [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set("key_a", "val_b"),
]







@pytest.mark.parametrize(
    """operations"""      ,	(
        pytest.param(_add_items      ,	id="""add items"""  ),
        pytest.param(_overwrite_items      ,	id="""overwrite items"""  ),
        pytest.param(_delete_items      ,	id="""delete items"""  ),
        pytest.param(_access_absent_items      ,	id="""access absent items"""  ),
        pytest.param(_add_with_resize_up      ,	id="""add with resize up"""  ),
        pytest.param(_add_with_resize_down      ,	id="""add with resize down"""  ),
    )      ,	)
def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = HashMap(initial_block_size=4  )
		lowerCAmelCase__   = {}
		for _, (fun, *args) in enumerate(lowerCamelCase__  ):
				lowerCAmelCase__ ,       lowerCAmelCase__   = _run_operation(lowerCamelCase__      ,	lowerCamelCase__      ,	*lowerCamelCase__  )
				lowerCAmelCase__ ,       lowerCAmelCase__   = _run_operation(lowerCamelCase__      ,	lowerCamelCase__      ,	*lowerCamelCase__  )
				assert my_res == py_res
				assert str(lowerCamelCase__  ) == str(lowerCamelCase__  )
				assert set(lowerCamelCase__  ) == set(lowerCamelCase__  )
				assert len(lowerCamelCase__  ) == len(lowerCamelCase__  )
				assert set(my.items()  ) == set(py.items()  )







def 				_UpperCAmelCase		(							):





		"""simple docstring"""




		def is_public(lowerCamelCase__  ) -> bool:
				return not name.startswith("""_"""  )

		lowerCAmelCase__   = {name for name in dir({}  ) if is_public(lowerCamelCase__  )}
		lowerCAmelCase__   = {name for name in dir(HashMap()  ) if is_public(lowerCamelCase__  )}

		assert dict_public_names > hash_public_names




