






"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase						:    Tuple		=						logging.get_logger(__name__)

_UpperCamelCase						:    List[str]		=						{
    'google/pegasus-x-base': 'https://huggingface.co/google/pegasus-x-base/resolve/main/config.json',
    'google/pegasus-x-large': 'https://huggingface.co/google/pegasus-x-large/resolve/main/config.json',
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}

class 			a	(  a_      ):
    UpperCAmelCase_					:	int      	="pegasus_x"
    UpperCAmelCase_					:	List[Any]      	=["past_key_values"]
    UpperCAmelCase_					:	Optional[int]      	={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







    def __init__(     self				,						_lowerCamelCase=9_6_1_0_3				,						_lowerCamelCase=1_6_3_8_4				,						_lowerCamelCase=1_6				,						_lowerCamelCase=4_0_9_6				,						_lowerCamelCase=1_6				,						_lowerCamelCase=1_6				,						_lowerCamelCase=4_0_9_6				,						_lowerCamelCase=1_6				,						_lowerCamelCase=0.0				,						_lowerCamelCase=0.0				,						_lowerCamelCase=True				,						_lowerCamelCase=True				,						_lowerCamelCase="gelu"				,						_lowerCamelCase=1_0_2_4				,						_lowerCamelCase=0.1				,						_lowerCamelCase=0.0				,						_lowerCamelCase=0.0				,						_lowerCamelCase=0.0_2				,						_lowerCamelCase=0				,						_lowerCamelCase=True				,						_lowerCamelCase=0				,						_lowerCamelCase=1				,						_lowerCamelCase=1				,						_lowerCamelCase=3_2				,						_lowerCamelCase=5_1_2				,						_lowerCamelCase=True				,						**_lowerCamelCase				,						):
      lowercase				       =			vocab_size
      lowercase				       =			max_position_embeddings
      lowercase				       =			d_model
      lowercase				       =			encoder_ffn_dim
      lowercase				       =			encoder_layers
      lowercase				       =			encoder_attention_heads
      lowercase				       =			decoder_ffn_dim
      lowercase				       =			decoder_layers
      lowercase				       =			decoder_attention_heads
      lowercase				       =			dropout
      lowercase				       =			attention_dropout
      lowercase				       =			activation_dropout
      lowercase				       =			activation_function
      lowercase				       =			init_std
      lowercase				       =			encoder_layerdrop
      lowercase				       =			decoder_layerdrop
      lowercase				       =			use_cache
      lowercase				       =			encoder_layers
      lowercase				       =			scale_embedding  # scale factor will be sqrt(d_model) if True

      lowercase				       =			num_global_tokens
      lowercase				       =			block_size
      lowercase				       =			stagger_local_blocks

      super().__init__(
          pad_token_id=_lowerCamelCase				,						eos_token_id=_lowerCamelCase				,						is_encoder_decoder=_lowerCamelCase				,						decoder_start_token_id=_lowerCamelCase				,						forced_eos_token_id=_lowerCamelCase				,						**_lowerCamelCase				,						)







    @property
    def      UpperCamelCase_ (     self			):
      return self.encoder_attention_heads







    @property
    def      UpperCamelCase_ (     self			):
      return self.d_model




"""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
