



from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):





				if not inplace:
								_UpperCAmelCase						:  str     					=	ma + ma
				else:
								ma += ma

				return ma


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase			):
				_UpperCAmelCase						:  Dict     					=	-1 * len(__lowerCAmelCase			)
				_UpperCAmelCase						:  Dict     					=	list(range(len(tensor.shape[:zero_index]			)			)			)
				return tensor.permute(first_inds + [zero_index + i for i in inds]			)


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase			):
				return t.reshape(t.shape[:-no_dims] + (-1,)			)


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase = 1e-4			):
				_UpperCAmelCase						:  List[str]     					=	mask.expand(*value.shape			)
				return torch.sum(mask * value      ,					dim=__lowerCAmelCase			) / (eps + torch.sum(__lowerCAmelCase      ,					dim=__lowerCAmelCase			))


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase = 2.3_125      ,					_UpperCamelCase = 21.6_875      ,					_UpperCamelCase = 64			):
				_UpperCAmelCase						:  Union[str, Any]     					=	torch.linspace(__lowerCAmelCase      ,					__lowerCAmelCase      ,					no_bins - 1      ,					device=pts.device			)
				_UpperCAmelCase						:  int     					=	torch.sqrt(torch.sum((pts.unsqueeze(-2			) - pts.unsqueeze(-3			)) ** 2      ,					dim=-1			)			)
				return torch.bucketize(__lowerCAmelCase      ,					__lowerCAmelCase			)


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase			):
				_UpperCAmelCase						:  str     					=	dicts[0]
				_UpperCAmelCase						:  Any     					=	{}
				for k, v in first.items():
								_UpperCAmelCase						:  List[Any]     					=	[d[k] for d in dicts]
								if isinstance(__lowerCAmelCase      ,					__lowerCAmelCase			):
												_UpperCAmelCase						:  str     					=	dict_multimap(__lowerCAmelCase      ,					__lowerCAmelCase			)
								else:
												_UpperCAmelCase						:  List[Any]     					=	fn(__lowerCAmelCase			)

				return new_dict


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase			):
				_UpperCAmelCase						:  Any     					=	v_bins.view(((1,) * len(x.shape			)) + (len(__lowerCAmelCase			),)			)
				_UpperCAmelCase						:  Union[str, Any]     					=	x[..., None] - reshaped_bins
				_UpperCAmelCase						:  int     					=	torch.argmin(torch.abs(__lowerCAmelCase			)      ,					dim=-1			)
				return nn.functional.one_hot(__lowerCAmelCase      ,					num_classes=len(__lowerCAmelCase			)			).float()


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase = 0      ,					_UpperCamelCase = 0			):
				_UpperCAmelCase						:  List[Union[slice, torch.Tensor]]     					=	[]
				for i, s in enumerate(data.shape[:no_batch_dims]			):
								_UpperCAmelCase						:  List[str]     					=	torch.arange(__lowerCAmelCase			)
								_UpperCAmelCase						:  Optional[Any]     					=	r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape			) - i - 1)))			)
								ranges.append(__lowerCAmelCase			)

				_UpperCAmelCase						:  List[Union[slice, torch.Tensor]]     					=	[slice(__lowerCAmelCase			) for _ in range(len(data.shape			) - no_batch_dims			)]
				_UpperCAmelCase						:  Optional[int]     					=	inds
				ranges.extend(__lowerCAmelCase			)
				# Matt note: Editing this to get around the behaviour of using a list as an array index changing
				# in recent Numpy versions
				return data[tuple(__lowerCAmelCase			)]


UpperCAmelCase__			:   int      			=    TypeVar('T')


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):
				_UpperCAmelCase						:  Dict[Any, Union[dict, list, tuple, Any]]     					=	{}
				for k, v in dic.items():
								if isinstance(__lowerCAmelCase      ,					__lowerCAmelCase			):
												_UpperCAmelCase						:  Optional[Any]     					=	dict_map(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase			)
								else:
												_UpperCAmelCase						:  List[str]     					=	tree_map(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase			)

				return new_dict


@overload
def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):
				...


@overload
def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):
				...


@overload
def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):
				...


@overload
def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):
				...


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):





				if isinstance(__lowerCAmelCase      ,					__lowerCAmelCase			):
								return dict_map(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase			)
				elif isinstance(__lowerCAmelCase      ,					__lowerCAmelCase			):
								return [tree_map(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase			) for x in tree]
				elif isinstance(__lowerCAmelCase      ,					__lowerCAmelCase			):
								return tuple(tree_map(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase			) for x in tree			)
				elif isinstance(__lowerCAmelCase      ,					__lowerCAmelCase			):
								return fn(__lowerCAmelCase			)
				else:
								print(type(__lowerCAmelCase			)			)
								raise ValueError('''Not supported'''			)






UpperCAmelCase__			:   Optional[Any]      			=    partial(tree_map, leaf_type=torch.Tensor)

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_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	Optional[Any]:






      '''simple docstring'''





      UpperCAmelCase     :			Any     	=states.reshape(1		,       -1		,       model.config.state_dim    )
      UpperCAmelCase     :			Optional[int]     	=actions.reshape(1		,       -1		,       model.config.act_dim    )
      UpperCAmelCase     :			Optional[Any]     	=returns_to_go.reshape(1		,       -1		,       1    )
      UpperCAmelCase     :			Optional[int]     	=timesteps.reshape(1		,       -1    )

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

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

      UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase     :			int     	=model(
          states=__lowerCAmelCase		,       actions=__lowerCAmelCase		,       rewards=__lowerCAmelCase		,       returns_to_go=__lowerCAmelCase		,       timesteps=__lowerCAmelCase		,       attention_mask=__lowerCAmelCase		,       return_dict=__lowerCAmelCase		,       )

      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         =     10_00
__snake_case         =     '''cuda'''
__snake_case         =     1000.0  # normalization for rewards/returns
__snake_case         =     36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case         =     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,
    ]
)
__snake_case         =     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,
    ]
)
__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
