


'''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 _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A  ,			A  ,			A  ,			A	)	->	Any:






       """simple docstring"""







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

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

              # pad all tokens to sequence length
              lowercase__				      =			torch.cat(
                  [torch.zeros(model.config.max_length - states.shape[1]	), torch.ones(states.shape[1]	)]	)
              lowercase__				      =			attention_mask.to(dtype=torch.long  ,			device=states.device	).reshape(1  ,			-1	)
              lowercase__				      =			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	)
              lowercase__				      =			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	)
              lowercase__				      =			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	)
              lowercase__				      =			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:
              lowercase__				      =			None

       lowercase__      ,lowercase__      ,lowercase__				      =			model(
           states=A  ,			actions=A  ,			rewards=A  ,			returns_to_go=A  ,			timesteps=A  ,			attention_mask=A  ,			return_dict=A  ,			)

       return action_preds[0, -1]


# build the environment

lowerCamelCase							:       Any  						=     gym.make('Hopper-v3')
lowerCamelCase							:       Optional[int]  						=     env.observation_space.shape[0]
lowerCamelCase							:       List[Any]  						=     env.action_space.shape[0]
lowerCamelCase							:       List[str]  						=     1_000
lowerCamelCase							:       List[Any]  						=     'cuda'
lowerCamelCase							:       Any  						=     1_0_0_0.0  # normalization for rewards/returns
lowerCamelCase							:       List[str]  						=     3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
lowerCamelCase							:       int  						=     np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
lowerCamelCase							:       Tuple  						=     np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
lowerCamelCase							:       str  						=     torch.from_numpy(state_mean).to(device=device)
lowerCamelCase							:       Optional[int]  						=     torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
lowerCamelCase							:       List[str]  						=     DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
lowerCamelCase							:       str  						=     model.to(device)
model.eval()

for ep in range(10):
 lowerCamelCase						,  lowerCamelCase							:       Optional[Any]  						=     0, 0
 lowerCamelCase							:       Dict  						=     env.reset()
 lowerCamelCase							:       Any  						=     torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
 lowerCamelCase							:       int  						=     torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
 lowerCamelCase							:       Any  						=     torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
 lowerCamelCase							:       Dict  						=     torch.zeros(0, device=device, dtype=torch.floataa)

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

  lowerCamelCase							:       List[Any]  						=     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),
  )
  lowerCamelCase							:       Union[str, Any]  						=     action
  lowerCamelCase							:       int  						=     action.detach().cpu().numpy()

  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase							:       Any  						=     env.step(action)

  lowerCamelCase							:       Any  						=     torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  lowerCamelCase							:       List[str]  						=     torch.cat([states, cur_state], dim=0)
  lowerCamelCase							:       Optional[Any]  						=     reward

  lowerCamelCase							:       int  						=     target_return[0, -1] - (reward / scale)
  lowerCamelCase							:       Tuple  						=     torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  lowerCamelCase							:       Tuple  						=     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 random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm

def _SCREAMING_SNAKE_CASE				(A  ,			A = False	)	->	Tuple:






       """simple docstring"""







       if device_specific:
              seed += AcceleratorState().process_index
       random.seed(A	)
       np.random.seed(A	)
       torch.manual_seed(A	)
       if is_xpu_available():
              torch.xpu.manual_seed_all(A	)
       elif is_npu_available():
              torch.npu.manual_seed_all(A	)
       else:
              torch.cuda.manual_seed_all(A	)
       # ^^ safe to call this function even if cuda is not available
       if is_tpu_available():
              xm.set_rng_state(A	)

def _SCREAMING_SNAKE_CASE				(A = None  ,			A = None	)	->	Tuple:






       """simple docstring"""







       if rng_type == RNGType.TORCH:
              lowercase__				      =			torch.get_rng_state()
       elif rng_type == RNGType.CUDA:
              lowercase__				      =			torch.cuda.get_rng_state()
       elif rng_type == RNGType.XLA:
              assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
              lowercase__				      =			torch.tensor(xm.get_rng_state()	)
       elif rng_type == RNGType.NPU:
              assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
              lowercase__				      =			torch.npu.get_rng_state()
       elif rng_type == RNGType.XPU:
              assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
              lowercase__				      =			torch.xpu.get_rng_state()
       elif rng_type == RNGType.GENERATOR:
              assert generator is not None, "Need a generator to synchronize its seed."
              lowercase__				      =			generator.get_state()

       # Broadcast the rng state from device 0 to other devices
       lowercase__				      =			AcceleratorState()
       if state.distributed_type == DistributedType.TPU:
              lowercase__				      =			rng_state.to(xm.xla_device()	)
              xm.collective_broadcast([rng_state]	)
              xm.mark_step()
              lowercase__				      =			rng_state.cpu()
       elif (
           state.distributed_type in CUDA_DISTRIBUTED_TYPES
           or state.distributed_type == DistributedType.MULTI_NPU
           or state.distributed_type == DistributedType.MULTI_XPU
       ):
              lowercase__				      =			rng_state.to(state.device	)
              torch.distributed.broadcast(A  ,			0	)
              lowercase__				      =			rng_state.cpu()
       elif state.distributed_type == DistributedType.MULTI_CPU:
              torch.distributed.broadcast(A  ,			0	)

       # Set the broadcast rng state
       if rng_type == RNGType.TORCH:
              torch.set_rng_state(A	)
       elif rng_type == RNGType.CUDA:
              torch.cuda.set_rng_state(A	)
       elif rng_type == RNGType.NPU:
              torch.npu.set_rng_state(A	)
       elif rng_type == RNGType.XPU:
              torch.xpu.set_rng_state(A	)
       elif rng_type == RNGType.XLA:
              xm.set_rng_state(rng_state.item()	)
       elif rng_type == RNGType.GENERATOR:
              generator.set_state(A	)

def _SCREAMING_SNAKE_CASE				(A  ,			A = None	)	->	int:






       """simple docstring"""







       for rng_type in rng_types:
              synchronize_rng_state(RNGType(A	)  ,			generator=A	)




