




"""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     __snake_case   (							_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase						):


    """simple docstring"""







    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

SCREAMING_SNAKE_CASE_      =					gym.make('Hopper-v3')
SCREAMING_SNAKE_CASE_      =					env.observation_space.shape[0]
SCREAMING_SNAKE_CASE_      =					env.action_space.shape[0]
SCREAMING_SNAKE_CASE_      =					1000
SCREAMING_SNAKE_CASE_      =					'cuda'
SCREAMING_SNAKE_CASE_      =					1000.0  # normalization for rewards/returns
SCREAMING_SNAKE_CASE_      =					3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
SCREAMING_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,
    ]
)
SCREAMING_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,
    ]
)
SCREAMING_SNAKE_CASE_      =					torch.from_numpy(state_mean).to(device=device)
SCREAMING_SNAKE_CASE_      =					torch.from_numpy(state_std).to(device=device)

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

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

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

        SCREAMING_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),
        )
        SCREAMING_SNAKE_CASE_      =					action
        SCREAMING_SNAKE_CASE_      =					action.detach().cpu().numpy()

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

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

        SCREAMING_SNAKE_CASE_      =					target_return[0, -1] - (reward / scale)
        SCREAMING_SNAKE_CASE_      =					torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
        SCREAMING_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 os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch





def     __snake_case   (							):


    """simple docstring"""







    UpperCamelCase										=  Dataset.from_dict({'''i''': range(17						)}						)
    UpperCamelCase										=  len(_lowercase						)
    UpperCamelCase										=  3
    UpperCamelCase										=  [
        split_dataset_by_node(_lowercase ,rank=_lowercase ,world_size=_lowercase						) for rank in range(_lowercase						)
    ]
    assert sum(len(_lowercase						) for ds in datasets_per_rank						) == full_size
    assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size





def     __snake_case   (							):


    """simple docstring"""







    def gen():
        return ({"i": i} for i in range(17						))

    UpperCamelCase										=  3
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)
    UpperCamelCase										=  [
        split_dataset_by_node(_lowercase ,rank=_lowercase ,world_size=_lowercase						) for rank in range(_lowercase						)
    ]
    assert sum(len(list(_lowercase						)						) for ds in datasets_per_rank						) == full_size
    assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size





@pytest.mark.parametrize('''shards_per_node''' ,[1, 2, 3]						)
def     __snake_case   (							_lowercase						):


    """simple docstring"""







    def gen(_lowercase						):
        for shard in shards:
            yield from ({"i": i, "shard": shard} for i in range(17						))

    UpperCamelCase										=  3
    UpperCamelCase										=  shards_per_node * world_size
    UpperCamelCase										=  {'''shards''': [f'shard_{shard_idx}.txt' for shard_idx in range(_lowercase						)]}
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase ,gen_kwargs=_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)
    assert full_ds.n_shards == world_size * shards_per_node
    UpperCamelCase										=  [
        split_dataset_by_node(_lowercase ,rank=_lowercase ,world_size=_lowercase						) for rank in range(_lowercase						)
    ]
    assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
    assert sum(len(list(_lowercase						)						) for ds in datasets_per_rank						) == full_size
    assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size





def     __snake_case   (							):


    """simple docstring"""







    def gen():
        return ({"i": i} for i in range(17						))

    UpperCamelCase										=  2
    UpperCamelCase										=  IterableDataset.from_generator(_lowercase						)
    UpperCamelCase										=  len(list(_lowercase						)						)

    UpperCamelCase										=  split_dataset_by_node(_lowercase ,rank=0 ,world_size=_lowercase						).shuffle(seed=42						)
    assert len(list(_lowercase						)						) == 1 + full_size // world_size
    with pytest.raises(_lowercase						):
        split_dataset_by_node(_lowercase ,rank=0 ,world_size=_lowercase						).shuffle()

    UpperCamelCase										=  split_dataset_by_node(full_ds.shuffle(seed=42						) ,rank=0 ,world_size=_lowercase						)
    assert len(list(_lowercase						)						) == 1 + full_size // world_size
    with pytest.raises(_lowercase						):
        split_dataset_by_node(full_ds.shuffle() ,rank=0 ,world_size=_lowercase						)





@pytest.mark.parametrize('''streaming''' ,[False, True]						)
@require_torch
@pytest.mark.skipif(os.name == '''nt''' ,reason='''execute_subprocess_async doesn\'t support windows'''						)
@pytest.mark.integration
def     __snake_case   (							_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  2
    UpperCamelCase										=  get_torch_dist_unique_port()
    UpperCamelCase										=  Path(_lowercase						).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
    UpperCamelCase										=  f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
    UpperCamelCase										=  f'\n        --streaming={streaming}\n    '.split()
    UpperCamelCase										=  [sys.executable] + distributed_args + args
    execute_subprocess_async(_lowercase ,env=os.environ.copy()						)





@pytest.mark.parametrize(
    '''nproc_per_node, num_workers''' ,[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ] ,)
@require_torch
@pytest.mark.skipif(os.name == '''nt''' ,reason='''execute_subprocess_async doesn\'t support windows'''						)
@pytest.mark.integration
def     __snake_case   (							_lowercase ,_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  True
    UpperCamelCase										=  get_torch_dist_unique_port()
    UpperCamelCase										=  Path(_lowercase						).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
    UpperCamelCase										=  f'\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '.split()
    UpperCamelCase										=  f'\n        --streaming={streaming}\n        --num_workers={num_workers}\n    '.split()
    UpperCamelCase										=  [sys.executable] + distributed_args + args
    execute_subprocess_async(_lowercase ,env=os.environ.copy()						)