



"""simple docstring"""


import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_	,			lowercase_	,			lowercase_	,			lowercase_	,			lowercase_		):
      # we don't care about the past rewards in this model

      UpperCAmelCase    =				states.reshape(1	,			-1	,			model.config.state_dim		)
      UpperCAmelCase    =				actions.reshape(1	,			-1	,			model.config.act_dim		)
      UpperCAmelCase    =				returns_to_go.reshape(1	,			-1	,			1		)
      UpperCAmelCase    =				timesteps.reshape(1	,			-1		)

      if model.config.max_length is not None:
            UpperCAmelCase    =				states[:, -model.config.max_length :]
            UpperCAmelCase    =				actions[:, -model.config.max_length :]
            UpperCAmelCase    =				returns_to_go[:, -model.config.max_length :]
            UpperCAmelCase    =				timesteps[:, -model.config.max_length :]

            # pad all tokens to sequence length
            UpperCAmelCase    =				torch.cat(
                [torch.zeros(model.config.max_length - states.shape[1]		), torch.ones(states.shape[1]		)]		)
            UpperCAmelCase    =				attention_mask.to(dtype=torch.long	,			device=states.device		).reshape(1	,			-1		)
            UpperCAmelCase    =				torch.cat(
                [
                    torch.zeros(
                        (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)	,			device=states.device	,			),
                    states,
                ]	,			dim=1	,			).to(dtype=torch.floataa		)
            UpperCAmelCase    =				torch.cat(
                [
                    torch.zeros(
                        (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)	,			device=actions.device	,			),
                    actions,
                ]	,			dim=1	,			).to(dtype=torch.floataa		)
            UpperCAmelCase    =				torch.cat(
                [
                    torch.zeros(
                        (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)	,			device=returns_to_go.device	,			),
                    returns_to_go,
                ]	,			dim=1	,			).to(dtype=torch.floataa		)
            UpperCAmelCase    =				torch.cat(
                [
                    torch.zeros(
                        (timesteps.shape[0], model.config.max_length - timesteps.shape[1])	,			device=timesteps.device		),
                    timesteps,
                ]	,			dim=1	,			).to(dtype=torch.long		)
      else:
            UpperCAmelCase    =				None

      UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase    =				model(
          states=lowercase_	,			actions=lowercase_	,			rewards=lowercase_	,			returns_to_go=lowercase_	,			timesteps=lowercase_	,			attention_mask=lowercase_	,			return_dict=lowercase_	,			)

      return action_preds[0, -1]


# build the environment

snake_case_         = gym.make("""Hopper-v3""")
snake_case_         = env.observation_space.shape[0]
snake_case_         = env.action_space.shape[0]
snake_case_         = 1000
snake_case_         = """cuda"""
snake_case_         = 1000.0  # normalization for rewards/returns
snake_case_         = 3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
snake_case_         = np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
snake_case_         = np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
snake_case_         = torch.from_numpy(state_mean).to(device=device)
snake_case_         = torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
snake_case_         = DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-medium""")
snake_case_         = model.to(device)
model.eval()

for ep in range(10):
    snake_case_  ,     snake_case_         = 0, 0
    snake_case_         = env.reset()
    snake_case_         = torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
    snake_case_         = torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
    snake_case_         = torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
    snake_case_         = torch.zeros(0, device=device, dtype=torch.floataa)

    snake_case_         = torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
    for t in range(max_ep_len):
        env.render()
        # add padding
        snake_case_         = torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
        snake_case_         = torch.cat([rewards, torch.zeros(1, device=device)])

        snake_case_         = get_action(
            model,
            (states.to(dtype=torch.floataa) - state_mean) / state_std,
            actions.to(dtype=torch.floataa),
            rewards.to(dtype=torch.floataa),
            target_return.to(dtype=torch.floataa),
            timesteps.to(dtype=torch.long),
        )
        snake_case_         = action
        snake_case_         = action.detach().cpu().numpy()

        snake_case_  ,     snake_case_  ,     snake_case_  ,     snake_case_         = env.step(action)

        snake_case_         = torch.from_numpy(state).to(device=device).reshape(1, state_dim)
        snake_case_         = torch.cat([states, cur_state], dim=0)
        snake_case_         = reward

        snake_case_         = target_return[0, -1] - (reward / scale)
        snake_case_         = torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
        snake_case_         = torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

        episode_return += reward
        episode_length += 1

        if done:
            break










"""simple docstring"""


import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class    A_    (  nn.Linear       ):



						"""simple docstring"""






						def __init__(      self				:List[str]     ,						lowercase_				:int     ,						lowercase_				:int     ,						lowercase_				:bool = True     ,						lowercase_				:str = "constant"     ,						lowercase_				:float = 0.0     ,						lowercase_				:str = "topK"     ,						)       ->    Union[str, Any]:
												super(lowercase_     ,						self      ).__init__(in_features=lowercase_     ,						out_features=lowercase_     ,						bias=lowercase_      )
												assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
												UpperCAmelCase    =				pruning_method

												if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
																		UpperCAmelCase    =				mask_scale
																		UpperCAmelCase    =				mask_init
																		UpperCAmelCase    =				nn.Parameter(torch.empty(self.weight.size()      )      )
																		self.init_mask()






						def 			UpperCAmelCase__  (      self				:Optional[Any]      )       ->    Dict:
												if self.mask_init == "constant":
																		init.constant_(self.mask_scores     ,						val=self.mask_scale      )
												elif self.mask_init == "uniform":
																		init.uniform_(self.mask_scores     ,						a=-self.mask_scale     ,						b=self.mask_scale      )
												elif self.mask_init == "kaiming":
																		init.kaiming_uniform_(self.mask_scores     ,						a=math.sqrt(5      )      )






						def 			UpperCAmelCase__  (      self				:Dict     ,						lowercase_				:torch.tensor     ,						lowercase_				:float      )       ->    List[str]:
												# Get the mask
												if self.pruning_method == "topK":
																		UpperCAmelCase    =				TopKBinarizer.apply(self.mask_scores     ,						lowercase_      )
												elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
																		UpperCAmelCase    =				'sigmoied' in self.pruning_method
																		UpperCAmelCase    =				ThresholdBinarizer.apply(self.mask_scores     ,						lowercase_     ,						lowercase_      )
												elif self.pruning_method == "magnitude":
																		UpperCAmelCase    =				MagnitudeBinarizer.apply(self.weight     ,						lowercase_      )
												elif self.pruning_method == "l0":
																		UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase    =				-0.1, 1.1, 2 / 3
																		if self.training:
																								UpperCAmelCase    =				torch.zeros_like(self.mask_scores      ).uniform_().clamp(0.0001     ,						0.9999      )
																								UpperCAmelCase    =				torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b      )
																		else:
																								UpperCAmelCase    =				torch.sigmoid(self.mask_scores      )
																		UpperCAmelCase    =				s * (r - l) + l
																		UpperCAmelCase    =				s_bar.clamp(min=0.0     ,						max=1.0      )
												# Mask weights with computed mask
												UpperCAmelCase    =				mask * self.weight
												# Compute output (linear layer) with masked weights
												return nn.functional.linear(lowercase_     ,						lowercase_     ,						self.bias      )





