


'''simple docstring'''


import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin






def 			__lowerCAmelCase			(__lowerCAmelCase=2   ,			__lowerCAmelCase=True   ,			__lowerCAmelCase=False ):
		_UpperCAmelCase		:							Dict             =   Accelerator(step_scheduler_with_optimizer=__lowerCAmelCase   ,			split_batches=__lowerCAmelCase )
		_UpperCAmelCase		:							List[str]             =   torch.nn.Linear(2   ,			4 )
		_UpperCAmelCase		:							Optional[int]             =   torch.optim.AdamW(model.parameters()   ,			lr=1.0 )
		_UpperCAmelCase		:							str             =   torch.optim.lr_scheduler.OneCycleLR(__lowerCAmelCase   ,			max_lr=0.0_1   ,			steps_per_epoch=2   ,			epochs=1 )
		_UpperCAmelCase		:							Any             =   accelerator.prepare(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase )

		# Optimizer has stepped
		scheduler.step()
		if step_scheduler_with_optimizer or (num_processes == 1):
				assert (
				    scheduler.scheduler.last_epoch == num_processes
				), F"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
		else:
				assert (
				    scheduler.scheduler.last_epoch != num_processes
				), F"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""






def 			__lowerCAmelCase			(__lowerCAmelCase=2   ,			__lowerCAmelCase=True   ,			__lowerCAmelCase=False ):
		_UpperCAmelCase		:							List[Any]             =   Accelerator(step_scheduler_with_optimizer=__lowerCAmelCase   ,			split_batches=__lowerCAmelCase )
		_UpperCAmelCase		:							Optional[int]             =   torch.nn.Linear(2   ,			4 )
		_UpperCAmelCase		:							List[str]             =   torch.optim.AdamW(model.parameters()   ,			lr=1.0 )
		_UpperCAmelCase		:							Optional[int]             =   torch.optim.lr_scheduler.LambdaLR(__lowerCAmelCase   ,			lr_lambda=lambda __lowerCAmelCase : 1 - n / 10 )
		_UpperCAmelCase		:							str             =   accelerator.prepare(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase )

		# Optimizer has stepped
		_UpperCAmelCase		:							Optional[int]             =   False
		scheduler.step()
		_UpperCAmelCase		:							Optional[int]             =   1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
		assert (
		    scheduler.get_last_lr()[0] == expected_lr
		), F"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

		# Optimizer has not stepped
		_UpperCAmelCase		:							Tuple             =   True
		scheduler.step()
		if not step_scheduler_with_optimizer:
				_UpperCAmelCase		:							Optional[Any]             =   1 - 2 / 10
		assert (
		    scheduler.get_last_lr()[0] == expected_lr
		), F"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""






def 			__lowerCAmelCase			(__lowerCAmelCase = 2 ):
		from transformers import get_linear_schedule_with_warmup

		_UpperCAmelCase		:							int             =   [1, 2, 4]
		for num_steps in steps:
				_UpperCAmelCase		:							str             =   GradientAccumulationPlugin(num_steps=__lowerCAmelCase   ,			adjust_scheduler=num_steps > 1 )
				_UpperCAmelCase		:							Any             =   Accelerator(gradient_accumulation_plugin=__lowerCAmelCase )
				_UpperCAmelCase		:							int             =   torch.nn.Linear(2   ,			4 )
				_UpperCAmelCase		:							Optional[int]             =   torch.optim.AdamW(model.parameters()   ,			lr=10.0 )
				_UpperCAmelCase		:							str             =   get_linear_schedule_with_warmup(optimizer=__lowerCAmelCase   ,			num_warmup_steps=0   ,			num_training_steps=20 )

				_UpperCAmelCase		:							List[Any]             =   accelerator.prepare(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase )

				for i in range(10 * num_steps ):
						with accelerator.accumulate(__lowerCAmelCase ):
								optimizer.step()
								scheduler.step()

						if i == (10 * num_steps - 2):
								assert (
								    scheduler.get_last_lr()[0] != 0
								), F"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
				assert (
				    scheduler.get_last_lr()[0] == 0
				), F"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
				GradientState._reset_state()






@require_cpu
class 						lowerCAmelCase__      (      unittest.TestCase						):




			def 				lowerCAmelCase__     (		self	:  Optional[int]			) ->Optional[Any]:



					'''simple docstring'''







					debug_launcher(partial(lowerCamelCase__						,	num_processes=1			)						,	num_processes=1			)
					debug_launcher(partial(lowerCamelCase__						,	num_processes=1						,	split_batches=lowerCamelCase__			)						,	num_processes=1			)




			def 				lowerCAmelCase__     (		self	:  Tuple			) ->Optional[Any]:



					'''simple docstring'''







					debug_launcher(partial(lowerCamelCase__						,	num_processes=1			)						,	num_processes=1			)
					debug_launcher(partial(lowerCamelCase__						,	num_processes=1						,	split_batches=lowerCamelCase__			)						,	num_processes=1			)




			def 				lowerCAmelCase__     (		self	:  Any			) ->Any:



					'''simple docstring'''







					debug_launcher(partial(lowerCamelCase__						,	num_processes=1						,	step_scheduler_with_optimizer=lowerCamelCase__			)						,	num_processes=1			)




			def 				lowerCAmelCase__     (		self	:  int			) ->Optional[int]:



					'''simple docstring'''







					debug_launcher(partial(lowerCamelCase__						,	num_processes=1						,	step_scheduler_with_optimizer=lowerCamelCase__			)						,	num_processes=1			)




			def 				lowerCAmelCase__     (		self	:  List[Any]			) ->List[Any]:



					'''simple docstring'''







					AcceleratorState._reset_state(lowerCamelCase__			)
					debug_launcher(lowerCamelCase__			)
					debug_launcher(partial(lowerCamelCase__						,	num_processes=1						,	split_batches=lowerCamelCase__			)						,	num_processes=1			)




			def 				lowerCAmelCase__     (		self	:  List[Any]			) ->Optional[Any]:



					'''simple docstring'''







					AcceleratorState._reset_state(lowerCamelCase__			)
					debug_launcher(lowerCamelCase__			)
					debug_launcher(partial(lowerCamelCase__						,	num_processes=1						,	split_batches=lowerCamelCase__			)						,	num_processes=1			)




			def 				lowerCAmelCase__     (		self	:  int			) ->Tuple:



					'''simple docstring'''







					AcceleratorState._reset_state(lowerCamelCase__			)
					debug_launcher(partial(lowerCamelCase__						,	step_scheduler_with_optimizer=lowerCamelCase__			)			)




			def 				lowerCAmelCase__     (		self	:  List[str]			) ->List[Any]:



					'''simple docstring'''







					AcceleratorState._reset_state(lowerCamelCase__			)
					debug_launcher(partial(lowerCamelCase__						,	step_scheduler_with_optimizer=lowerCamelCase__			)			)






			@require_huggingface_suite
			def 				lowerCAmelCase__     (		self	:  Tuple			) ->Any:



					'''simple docstring'''







					AcceleratorState._reset_state(lowerCamelCase__			)
					debug_launcher(partial(lowerCamelCase__						,	num_processes=1			)			)
					debug_launcher(lowerCamelCase__			)




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       a__      (					a  ,   a  ,   a  ,   a  ,   a  ,   a							)		->	int:
       # we don't care about the past rewards in this model

       A_							:       Dict	      =				states.reshape(1  ,   -1  ,   model.config.state_dim							)
       A_							:       Dict	      =				actions.reshape(1  ,   -1  ,   model.config.act_dim							)
       A_							:       Dict	      =				returns_to_go.reshape(1  ,   -1  ,   1							)
       A_							:       Tuple	      =				timesteps.reshape(1  ,   -1							)

       if model.config.max_length is not None:
              A_							:       int	      =				states[:, -model.config.max_length :]
              A_							:       Optional[int]	      =				actions[:, -model.config.max_length :]
              A_							:       Any	      =				returns_to_go[:, -model.config.max_length :]
              A_							:       Tuple	      =				timesteps[:, -model.config.max_length :]

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

       A_      ,							A_      ,							A_							:       List[str]	      =				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        =       gym.make('Hopper-v3')
_lowerCAmelCase        =       env.observation_space.shape[0]
_lowerCAmelCase        =       env.action_space.shape[0]
_lowerCAmelCase        =       1_0_0_0
_lowerCAmelCase        =       'cuda'
_lowerCAmelCase        =       1000.0  # normalization for rewards/returns
_lowerCAmelCase        =       3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCAmelCase        =       np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
_lowerCAmelCase        =       np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
_lowerCAmelCase        =       torch.from_numpy(state_mean).to(device=device)
_lowerCAmelCase        =       torch.from_numpy(state_std).to(device=device)

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

for ep in range(1_0):
    _lowerCAmelCase  ,			_lowerCAmelCase        =       0, 0
    _lowerCAmelCase        =       env.reset()
    _lowerCAmelCase        =       torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
    _lowerCAmelCase        =       torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
    _lowerCAmelCase        =       torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
    _lowerCAmelCase        =       torch.zeros(0, device=device, dtype=torch.floataa)

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

        _lowerCAmelCase        =       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        =       action
        _lowerCAmelCase        =       action.detach().cpu().numpy()

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

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

        _lowerCAmelCase        =       target_return[0, -1] - (reward / scale)
        _lowerCAmelCase        =       torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
        _lowerCAmelCase        =       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


