






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					(       UpperCamelCase_   :			Tuple      ,				UpperCamelCase_   :			Optional[int]      ,				UpperCamelCase_   :			str      ,				UpperCamelCase_   :			int      ,				UpperCamelCase_   :			Any      ,				UpperCamelCase_   :			int				)	->				Tuple:
						"""simple docstring"""







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

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

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

						lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__	 =  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

a_ :    Dict							=      gym.make('''Hopper-v3''')
a_ :    Union[str, Any]							=      env.observation_space.shape[0]
a_ :    Union[str, Any]							=      env.action_space.shape[0]
a_ :    Tuple							=      1000
a_ :    Dict							=      '''cuda'''
a_ :    Optional[Any]							=      1000.0  # normalization for rewards/returns
a_ :    Optional[int]							=      3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a_ :    Union[str, Any]							=      np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
a_ :    Union[str, Any]							=      np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
a_ :    Tuple							=      torch.from_numpy(state_mean).to(device=device)
a_ :    Optional[Any]							=      torch.from_numpy(state_std).to(device=device)

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

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

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

		a_ :    int							=      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),
		)
		a_ :    List[str]							=      action
		a_ :    Optional[int]							=      action.detach().cpu().numpy()

		a_,  a_,  a_,  a_ :    Optional[int]							=      env.step(action)

		a_ :    Any							=      torch.from_numpy(state).to(device=device).reshape(1, state_dim)
		a_ :    List[str]							=      torch.cat([states, cur_state], dim=0)
		a_ :    str							=      reward

		a_ :    int							=      target_return[0, -1] - (reward / scale)
		a_ :    Dict							=      torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
		a_ :    Dict							=      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














import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


a_							=      logging.get_logger(__name__)

a_							=      {
    '''facebook/musicgen-small''': '''https://huggingface.co/facebook/musicgen-small/resolve/main/config.json''',
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}


class        lowercase__       (     _UpperCAmelCase		):
	a_             ="""musicgen_decoder"""
	a_             =["""past_key_values"""]

	def __init__(							self						,      __UpperCAmelCase=2048						,      __UpperCAmelCase=2048						,      __UpperCAmelCase=24						,      __UpperCAmelCase=4096						,      __UpperCAmelCase=16						,      __UpperCAmelCase=0.0						,      __UpperCAmelCase=True						,      __UpperCAmelCase="gelu"						,      __UpperCAmelCase=1024						,      __UpperCAmelCase=0.1						,      __UpperCAmelCase=0.0						,      __UpperCAmelCase=0.0						,      __UpperCAmelCase=0.02						,      __UpperCAmelCase=False						,      __UpperCAmelCase=4						,      __UpperCAmelCase=2048						,      __UpperCAmelCase=2048						,      __UpperCAmelCase=None						,      __UpperCAmelCase=False						,      **__UpperCAmelCase						,      )->   Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  vocab_size
							lowerCAmelCase__	 =  max_position_embeddings
							lowerCAmelCase__	 =  hidden_size
							lowerCAmelCase__	 =  ffn_dim
							lowerCAmelCase__	 =  num_hidden_layers
							lowerCAmelCase__	 =  num_attention_heads
							lowerCAmelCase__	 =  dropout
							lowerCAmelCase__	 =  attention_dropout
							lowerCAmelCase__	 =  activation_dropout
							lowerCAmelCase__	 =  activation_function
							lowerCAmelCase__	 =  initializer_factor
							lowerCAmelCase__	 =  layerdrop
							lowerCAmelCase__	 =  use_cache
							lowerCAmelCase__	 =  scale_embedding  # scale factor will be sqrt(d_model) if True
							lowerCAmelCase__	 =  num_codebooks
							super().__init__(
							    pad_token_id=__UpperCAmelCase						,      bos_token_id=__UpperCAmelCase						,      eos_token_id=__UpperCAmelCase						,      tie_word_embeddings=__UpperCAmelCase						,      **__UpperCAmelCase						,      )





class        lowercase__       (     _UpperCAmelCase		):
	a_             ="""musicgen"""
	a_             =True

	def __init__(							self						,      **__UpperCAmelCase    )->   Any:







							'''simple docstring'''





							super().__init__(**__UpperCAmelCase    )
							if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
													raise ValueError("Config has to be initialized with text_encoder, audio_encoder and decoder config"    )

							lowerCAmelCase__	 =  kwargs.pop("text_encoder"    )
							lowerCAmelCase__	 =  text_encoder_config.pop("model_type"    )

							lowerCAmelCase__	 =  kwargs.pop("audio_encoder"    )
							lowerCAmelCase__	 =  audio_encoder_config.pop("model_type"    )

							lowerCAmelCase__	 =  kwargs.pop("decoder"    )

							lowerCAmelCase__	 =  AutoConfig.for_model(__UpperCAmelCase						,      **__UpperCAmelCase    )
							lowerCAmelCase__	 =  AutoConfig.for_model(__UpperCAmelCase						,      **__UpperCAmelCase    )
							lowerCAmelCase__	 =  MusicgenDecoderConfig(**__UpperCAmelCase    )
							lowerCAmelCase__	 =  True

	@classmethod
	def 						UpperCAmelCase					(							cls						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      **__UpperCAmelCase						,      )->   Tuple:







							'''simple docstring'''





							return cls(
							    text_encoder=text_encoder_config.to_dict()						,      audio_encoder=audio_encoder_config.to_dict()						,      decoder=decoder_config.to_dict()						,      **__UpperCAmelCase						,      )


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







							'''simple docstring'''





							lowerCAmelCase__	 =  copy.deepcopy(self.__dict__    )
							lowerCAmelCase__	 =  self.text_encoder.to_dict()
							lowerCAmelCase__	 =  self.audio_encoder.to_dict()
							lowerCAmelCase__	 =  self.decoder.to_dict()
							lowerCAmelCase__	 =  self.__class__.model_type
							return output






