




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 						lowercase							(   UpperCamelCase__		):

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

     def 					a__	(  self		,     _a		,     _a				)						->							Union[str, Any]:
           return (x_in - self.means[key]) / self.stds[key]

     def 					a__	(  self		,     _a		,     _a				)						->							Optional[int]:
           return x_in * self.stds[key] + self.means[key]

     def 					a__	(  self		,     _a				)						->							List[str]:
           if type(_a				) is dict:
                 return {k: self.to_torch(_a				) for k, v in x_in.items()}
           elif torch.is_tensor(_a				):
                 return x_in.to(self.unet.device				)
           return torch.tensor(_a		,     device=self.unet.device				)

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							Optional[Any]:
           for key, val in cond.items():
                 _A							:     int			=						val.clone()
           return x_in

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a				)						->							Optional[int]:
           _A							:     Union[str, Any]			=						x.shape[0]
           _A							:     Any			=						None
           for i in tqdm.tqdm(self.scheduler.timesteps				):
                 # create batch of timesteps to pass into model
                 _A							:     Dict			=						torch.full((batch_size,)		,     _a		,     device=self.unet.device		,     dtype=torch.long				)
                 for _ in range(_a				):
                       with torch.enable_grad():
                             x.requires_grad_()

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

                             _A							:     Optional[int]			=						self.scheduler._get_variance(_a				)
                             _A							:     List[Any]			=						torch.exp(0.5 * posterior_variance				)
                             _A							:     Tuple			=						model_std * grad

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

                 _A							:     str			=						self.unet(x.permute(0		,     2		,     1				)		,     _a				).sample.permute(0		,     2		,     1				)

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

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



     def __call__(  self		,     _a		,     _a=64		,     _a=32		,     _a=2		,     _a=0.1				)						->							Any:
           # normalize the observations and create  batch dimension
           _A							:     Any			=						self.normalize(_a		,     """observations"""				)
           _A							:     Optional[int]			=						obs[None].repeat(_a		,     axis=0				)

           _A							:     List[str]			=						{0: self.to_torch(_a				)}
           _A							:     int			=						(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							:     Optional[int]			=						randn_tensor(_a		,     device=self.unet.device				)
           _A							:     int			=						self.reset_xa(_a		,     _a		,     self.action_dim				)
           _A							:     int			=						self.to_torch(_a				)

           # run the diffusion process
           _A			,   _A							:     List[Any]			=						self.run_diffusion(_a		,     _a		,     _a		,     _a				)

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

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

           _A							:     Any			=						denorm_actions[selected_index, 0]
           return denorm_actions







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       {
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
    # See all FNet models at https://huggingface.co/models?filter=fnet
}







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						"fnet"

     def __init__(  self		,     _a=3_2000		,     _a=768		,     _a=12		,     _a=3072		,     _a="gelu_new"		,     _a=0.1		,     _a=512		,     _a=4		,     _a=0.02		,     _a=1e-12		,     _a=False		,     _a=512		,     _a=3		,     _a=1		,     _a=2		,     **_a		,     )						->							int:
           super().__init__(pad_token_id=_a		,     bos_token_id=_a		,     eos_token_id=_a		,     **_a				)

           _A							:     Any			=						vocab_size
           _A							:     str			=						max_position_embeddings
           _A							:     Optional[Any]			=						hidden_size
           _A							:     List[str]			=						num_hidden_layers
           _A							:     List[str]			=						intermediate_size
           _A							:     List[Any]			=						hidden_act
           _A							:     List[str]			=						hidden_dropout_prob
           _A							:     List[str]			=						initializer_range
           _A							:     List[Any]			=						type_vocab_size
           _A							:     List[Any]			=						layer_norm_eps
           _A							:     List[str]			=						use_tpu_fourier_optimizations
           _A							:     str			=						tpu_short_seq_length

