




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_       (					UpperCAmelCase__	):

    '''simple docstring'''






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





           '''simple docstring'''

           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 			SCREAMING_SNAKE_CASE				(     self :      Any   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      int)		->List[str]:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      int   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      List[Any])		->Any:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      Union[str, Any])		->Dict:





           '''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 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      List[Any]   ,					UpperCAmelCase__ :      Optional[Any]   ,					UpperCAmelCase__ :      List[Any])		->Any:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      List[Any]   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      Union[str, Any]   ,					UpperCAmelCase__ :      str)		->List[Any]:





           '''simple docstring'''

           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 :      Tuple   ,					UpperCAmelCase__ :      List[Any]   ,					UpperCAmelCase__ :      List[Any]=64   ,					UpperCAmelCase__ :      Dict=32   ,					UpperCAmelCase__ :      int=2   ,					UpperCAmelCase__ :      Dict=0.1)		->List[str]:





           '''simple docstring'''

           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__					,				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"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__magic_name__       :					Any						=			logging.get_logger(__name__)

__magic_name__       :					int						=			{
    """facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}






class 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				str										=  """convnextv2"""







			def __init__(      self						,   _A=3						,   _A=4						,   _A=4						,   _A=None						,   _A=None						,   _A="gelu"						,   _A=0.02						,   _A=1e-1_2						,   _A=0.0						,   _A=2_2_4						,   _A=None						,   _A=None						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(**_A    )

									UpperCamelCase				:		Optional[Any]				   =			num_channels
									UpperCamelCase				:		int				   =			patch_size
									UpperCamelCase				:		Dict				   =			num_stages
									UpperCamelCase				:		Tuple				   =			[9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
									UpperCamelCase				:		str				   =			[3, 3, 9, 3] if depths is None else depths
									UpperCamelCase				:		List[str]				   =			hidden_act
									UpperCamelCase				:		List[str]				   =			initializer_range
									UpperCamelCase				:		List[Any]				   =			layer_norm_eps
									UpperCamelCase				:		Any				   =			drop_path_rate
									UpperCamelCase				:		Any				   =			image_size
									UpperCamelCase				:		Union[str, Any]				   =			["""stem"""] + [f"""stage{idx}""" for idx in range(1						,   len(self.depths    ) + 1    )]
									UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			get_aligned_output_features_output_indices(
									    out_features=_A						,   out_indices=_A						,   stage_names=self.stage_names    )



