
"""simple docstring"""




import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


snake_case					  =						get_logger(__name__)


if TYPE_CHECKING:
	from .builder import DatasetBuilder

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = None     ):
					SCREAMING_SNAKE_CASE           =      importlib.import_module(SCREAMING_SNAKE_CASE_     )

					# TODO(QL): always update the module to add subsequent new authentication
					if hasattr(SCREAMING_SNAKE_CASE_,	'_patched_for_streaming'     ) and module._patched_for_streaming:
										return

					def wrap_auth(SCREAMING_SNAKE_CASE_     ):
										@wraps(SCREAMING_SNAKE_CASE_     )
										def wrapper(*SCREAMING_SNAKE_CASE_,	**SCREAMING_SNAKE_CASE_     ):
															return function(*SCREAMING_SNAKE_CASE_,	use_auth_token=SCREAMING_SNAKE_CASE_,	**SCREAMING_SNAKE_CASE_     )

										SCREAMING_SNAKE_CASE           =      'wrap_auth'
										return wrapper

					# open files in a streaming fashion
					patch_submodule(SCREAMING_SNAKE_CASE_,	'open',	wrap_auth(SCREAMING_SNAKE_CASE_     )     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'os.listdir',	wrap_auth(SCREAMING_SNAKE_CASE_     )     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'os.walk',	wrap_auth(SCREAMING_SNAKE_CASE_     )     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'glob.glob',	wrap_auth(SCREAMING_SNAKE_CASE_     )     ).start()
					# allow to navigate in remote zip files
					patch_submodule(SCREAMING_SNAKE_CASE_,	'os.path.join',	SCREAMING_SNAKE_CASE_     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'os.path.dirname',	SCREAMING_SNAKE_CASE_     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'os.path.basename',	SCREAMING_SNAKE_CASE_     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'os.path.relpath',	SCREAMING_SNAKE_CASE_     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'os.path.split',	SCREAMING_SNAKE_CASE_     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'os.path.splitext',	SCREAMING_SNAKE_CASE_     ).start()
					# allow checks on paths
					patch_submodule(SCREAMING_SNAKE_CASE_,	'os.path.exists',	wrap_auth(SCREAMING_SNAKE_CASE_     )     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'os.path.isdir',	wrap_auth(SCREAMING_SNAKE_CASE_     )     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'os.path.isfile',	wrap_auth(SCREAMING_SNAKE_CASE_     )     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'os.path.getsize',	wrap_auth(SCREAMING_SNAKE_CASE_     )     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'pathlib.Path',	SCREAMING_SNAKE_CASE_     ).start()
					# file readers
					patch_submodule(SCREAMING_SNAKE_CASE_,	'gzip.open',	wrap_auth(SCREAMING_SNAKE_CASE_     )     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'numpy.load',	wrap_auth(SCREAMING_SNAKE_CASE_     )     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'pandas.read_csv',	wrap_auth(SCREAMING_SNAKE_CASE_     ),	attrs=['__version__']     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'pandas.read_excel',	wrap_auth(SCREAMING_SNAKE_CASE_     ),	attrs=['__version__']     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'scipy.io.loadmat',	wrap_auth(SCREAMING_SNAKE_CASE_     ),	attrs=['__version__']     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'xml.etree.ElementTree.parse',	wrap_auth(SCREAMING_SNAKE_CASE_     )     ).start()
					patch_submodule(SCREAMING_SNAKE_CASE_,	'xml.dom.minidom.parse',	wrap_auth(SCREAMING_SNAKE_CASE_     )     ).start()
					SCREAMING_SNAKE_CASE           =      True



def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					extend_module_for_streaming(builder.__module__,	use_auth_token=builder.use_auth_token     )
					# if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
					if not builder.__module__.startswith('datasets.'     ):  # check that it's not a packaged builder like csv
										for imports in get_imports(inspect.getfile(builder.__class__     )     ):
															if imports[0] == "internal":
																				SCREAMING_SNAKE_CASE           =      imports[1]
																				SCREAMING_SNAKE_CASE           =      '.'.join(builder.__module__.split('.'     )[:-1] + [internal_import_name]     )
																				extend_module_for_streaming(SCREAMING_SNAKE_CASE_,	use_auth_token=builder.use_auth_token     )

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
					from .builder import DatasetBuilder

					SCREAMING_SNAKE_CASE           =      [
					    cls.__module__
					    for cls in type(SCREAMING_SNAKE_CASE_     ).__mro__[1:]  # make sure it's not the same module we've already patched
					    if issubclass(cls,	SCREAMING_SNAKE_CASE_     ) and cls.__module__ != DatasetBuilder.__module__
					]  # check it's not a standard builder from datasets.builder
					for module in parent_builder_modules:
										extend_module_for_streaming(SCREAMING_SNAKE_CASE_,	use_auth_token=builder.use_auth_token     )


"""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     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					# we don't care about the past rewards in this model

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

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

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

					SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      model(
					    states=SCREAMING_SNAKE_CASE_,	actions=SCREAMING_SNAKE_CASE_,	rewards=SCREAMING_SNAKE_CASE_,	returns_to_go=SCREAMING_SNAKE_CASE_,	timesteps=SCREAMING_SNAKE_CASE_,	attention_mask=SCREAMING_SNAKE_CASE_,	return_dict=SCREAMING_SNAKE_CASE_,	)

					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					  =						1_0_0_0
snake_case					  =						'cuda'
snake_case					  =						1_0_0_0.0  # normalization for rewards/returns
snake_case					  =						3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
snake_case					  =						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,
    ]
)
snake_case					  =						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,
    ]
)
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(1_0):
	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
