




'''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 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_		,				lowercase_		,				lowercase_		,				lowercase_    ):



		'''simple docstring'''







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

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

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

		_snake_case			,       _snake_case			,       _snake_case      			=			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

_lowerCamelCase				:		Optional[int]				      =    gym.make('Hopper-v3')
_lowerCamelCase				:		Dict				      =    env.observation_space.shape[0]
_lowerCamelCase				:		Any				      =    env.action_space.shape[0]
_lowerCamelCase				:		Dict				      =    1_000
_lowerCamelCase				:		Optional[int]				      =    'cuda'
_lowerCamelCase				:		int				      =    1000.0  # normalization for rewards/returns
_lowerCamelCase				:		Optional[int]				      =    3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCamelCase				:		List[str]				      =    np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
_lowerCamelCase				:		Dict				      =    np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
_lowerCamelCase				:		Dict				      =    torch.from_numpy(state_mean).to(device=device)
_lowerCamelCase				:		List[Any]				      =    torch.from_numpy(state_std).to(device=device)

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

for ep in range(10):
						_lowerCamelCase      ,_lowerCamelCase				:		List[Any]				      =    0, 0
						_lowerCamelCase				:		Union[str, Any]				      =    env.reset()
						_lowerCamelCase				:		Any				      =    torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
						_lowerCamelCase				:		Union[str, Any]				      =    torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
						_lowerCamelCase				:		List[str]				      =    torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
						_lowerCamelCase				:		str				      =    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				:		Union[str, Any]				      =    torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
												_lowerCamelCase				:		Any				      =    torch.cat([rewards, torch.zeros(1, device=device)])

												_lowerCamelCase				:		Tuple				      =    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				:		List[Any]				      =    action
												_lowerCamelCase				:		Any				      =    action.detach().cpu().numpy()

												_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase				:		List[Any]				      =    env.step(action)

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

												_lowerCamelCase				:		str				      =    target_return[0, -1] - (reward / scale)
												_lowerCamelCase				:		int				      =    torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
												_lowerCamelCase				:		Optional[int]				      =    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 subprocess
from argparse import ArgumentParser
from typing import List, Union

from huggingface_hub.hf_api import HfFolder, create_repo, whoami
from requests.exceptions import HTTPError

from . import BaseTransformersCLICommand





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					@staticmethod
					def 							__SCREAMING_SNAKE_CASE      (       __UpperCamelCase					):
							_snake_case      			=			parser.add_parser("login",    help="Log in using the same credentials as on huggingface.co"					)
							login_parser.set_defaults(func=lambda __UpperCamelCase					: LoginCommand(__UpperCamelCase					)					)
							_snake_case      			=			parser.add_parser("whoami",    help="Find out which huggingface.co account you are logged in as."					)
							whoami_parser.set_defaults(func=lambda __UpperCamelCase					: WhoamiCommand(__UpperCamelCase					)					)
							_snake_case      			=			parser.add_parser("logout",    help="Log out"					)
							logout_parser.set_defaults(func=lambda __UpperCamelCase					: LogoutCommand(__UpperCamelCase					)					)

							# new system: git-based repo system
							_snake_case      			=			parser.add_parser(
							    "repo",    help="Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos.",    )
							_snake_case      			=			repo_parser.add_subparsers(
							    help="Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands"					)
							_snake_case      			=			repo_subparsers.add_parser(
							    "create",    help="Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co"					)
							repo_create_parser.add_argument(
							    "name",    type=__UpperCamelCase,    help="Name for your model's repo. Will be namespaced under your username to build the model id.",    )
							repo_create_parser.add_argument("--organization",    type=__UpperCamelCase,    help="Optional: organization namespace."					)
							repo_create_parser.add_argument("-y",    "--yes",    action="store_true",    help="Optional: answer Yes to the prompt"					)
							repo_create_parser.set_defaults(func=lambda __UpperCamelCase					: RepoCreateCommand(__UpperCamelCase					)					)





class 	lowerCamelCase_				:




					'''simple docstring'''






					lowercase__       =					"""\u001b[1m"""
					lowercase__       =					"""\u001b[31m"""
					lowercase__       =					"""\u001b[90m"""
					lowercase__       =					"""\u001b[0m"""






					@classmethod
					def 							__SCREAMING_SNAKE_CASE      (       cls,    __UpperCamelCase					):
							return f'''{cls._bold}{s}{cls._reset}'''






					@classmethod
					def 							__SCREAMING_SNAKE_CASE      (       cls,    __UpperCamelCase					):
							return f'''{cls._bold}{cls._red}{s}{cls._reset}'''






					@classmethod
					def 							__SCREAMING_SNAKE_CASE      (       cls,    __UpperCamelCase					):
							return f'''{cls._gray}{s}{cls._reset}'''






def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			[max(len(str(lowercase_    )    ) for x in col    ) for col in zip(*lowercase_		,				lowercase_    )]
		_snake_case      			=			("{{:{}}} " * len(lowercase_    )).format(*lowercase_    )
		_snake_case      			=			[]
		lines.append(row_format.format(*lowercase_    )    )
		lines.append(row_format.format(*["-" * w for w in col_widths]    )    )
		for row in rows:
				lines.append(row_format.format(*lowercase_    )    )
		return "\n".join(lowercase_    )





class 	lowerCamelCase_				:




					'''simple docstring'''






					def __init__(       self,    __UpperCamelCase					):
							_snake_case      			=			args





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							print(
							    ANSI.red(
							        "ERROR! `huggingface-cli login` uses an outdated login mechanism "
							        "that is not compatible with the Hugging Face Hub backend anymore. "
							        "Please use `huggingface-cli login instead."					)					)





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							print(
							    ANSI.red(
							        "WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use "
							        "`huggingface-cli whoami` instead."					)					)
							_snake_case      			=			HfFolder.get_token()
							if token is None:
									print("Not logged in"					)
									exit()
							try:
									_snake_case			,       _snake_case      			=			whoami(__UpperCamelCase					)
									print(__UpperCamelCase					)
									if orgs:
											print(ANSI.bold("orgs: "					),    ",".join(__UpperCamelCase					)					)
							except HTTPError as e:
									print(__UpperCamelCase					)
									print(ANSI.red(e.response.text					)					)
									exit(1					)





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							print(
							    ANSI.red(
							        "ERROR! `transformers-cli logout` uses an outdated logout mechanism "
							        "that is not compatible with the Hugging Face Hub backend anymore. "
							        "Please use `huggingface-cli logout instead."					)					)





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							print(
							    ANSI.red(
							        "WARNING! Managing repositories through transformers-cli is deprecated. "
							        "Please use `huggingface-cli` instead."					)					)
							_snake_case      			=			HfFolder.get_token()
							if token is None:
									print("Not logged in"					)
									exit(1					)
							try:
									_snake_case      			=			subprocess.check_output(["git", "--version"]					).decode("utf-8"					)
									print(ANSI.gray(stdout.strip()					)					)
							except FileNotFoundError:
									print("Looks like you do not have git installed, please install."					)

							try:
									_snake_case      			=			subprocess.check_output(["git-lfs", "--version"]					).decode("utf-8"					)
									print(ANSI.gray(stdout.strip()					)					)
							except FileNotFoundError:
									print(
									    ANSI.red(
									        "Looks like you do not have git-lfs installed, please install."
									        " You can install from https://git-lfs.github.com/."
									        " Then run `git lfs install` (you only have to do this once)."					)					)
							print(""					)

							_snake_case			,       _snake_case      			=			whoami(__UpperCamelCase					)
							_snake_case      			=			self.args.organization if self.args.organization is not None else user
							_snake_case      			=			f'''{namespace}/{self.args.name}'''
							print(f'''You are about to create {ANSI.bold(__UpperCamelCase					)}'''					)

							if not self.args.yes:
									_snake_case      			=			input("Proceed? [Y/n] "					).lower()
									if not (choice == "" or choice == "y" or choice == "yes"):
											print("Abort"					)
											exit()
							try:
									_snake_case      			=			create_repo(__UpperCamelCase,    name=self.args.name,    organization=self.args.organization					)
							except HTTPError as e:
									print(__UpperCamelCase					)
									print(ANSI.red(e.response.text					)					)
									exit(1					)
							print("\nYour repo now lives at:"					)
							print(f'''  {ANSI.bold(__UpperCamelCase					)}'''					)
							print("\nYou can clone it locally with the command below, and commit/push as usual."					)
							print(f'''\n  git clone {url}'''					)
							print(""					)





