
"""simple docstring"""




from typing import Callable, Optional, Union

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


__lowerCAmelCase							:  Any 				=		logging.get_logger(__name__)

__lowerCAmelCase							:  List[Any] 				=		{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	snake_case__							:		Optional[int]         =		"prophetnet"
	snake_case__							:		Tuple         =		["past_key_values"]
	snake_case__							:		Optional[int]         =		{
	    "num_attention_heads": "num_encoder_attention_heads",
	}


	def __init__(  self :Any       ,    __magic_name__ :int = 0.1       ,    __magic_name__ :Any = "gelu"       ,    __magic_name__ :Any = 30522       ,    __magic_name__ :str = 1024       ,    __magic_name__ :Optional[Any] = 4096       ,    __magic_name__ :int = 12       ,    __magic_name__ :Tuple = 16       ,    __magic_name__ :Optional[int] = 4096       ,    __magic_name__ :str = 12       ,    __magic_name__ :Dict = 16       ,    __magic_name__ :Optional[Any] = 0.1       ,    __magic_name__ :List[Any] = 0.1       ,    __magic_name__ :int = 512       ,    __magic_name__ :Any = 0.02       ,    __magic_name__ :List[Any] = True       ,    __magic_name__ :Union[str, Any] = True       ,    __magic_name__ :Dict = 0       ,    __magic_name__ :Any = 2       ,    __magic_name__ :Any = 32       ,    __magic_name__ :Union[str, Any] = 128       ,    __magic_name__ :Optional[Any] = False       ,    __magic_name__ :List[Any] = 0.0       ,    __magic_name__ :Tuple = True       ,    __magic_name__ :Optional[int] = 0       ,    __magic_name__ :Union[str, Any] = 1       ,    __magic_name__ :Union[str, Any] = 2       ,    **__magic_name__ :str       ,    )			->   Optional[int]:

			'''simple docstring'''






			a__   		=     vocab_size
			a__   		=     hidden_size
			a__   		=     encoder_ffn_dim
			a__   		=     num_encoder_layers
			a__   		=     num_encoder_attention_heads
			a__   		=     decoder_ffn_dim
			a__   		=     num_decoder_layers
			a__   		=     num_decoder_attention_heads
			a__   		=     max_position_embeddings
			a__   		=     init_std  # Normal(0, this parameter)
			a__   		=     activation_function

			# parameters for prophetnet
			a__   		=     ngram
			a__   		=     num_buckets
			a__   		=     relative_max_distance
			a__   		=     disable_ngram_loss
			a__   		=     eps

			# 3 Types of Dropout
			a__   		=     attention_dropout
			a__   		=     activation_dropout
			a__   		=     dropout

			a__   		=     use_cache

			super().__init__(
			    pad_token_id=lowercase__       ,    bos_token_id=lowercase__       ,    eos_token_id=lowercase__       ,    is_encoder_decoder=lowercase__       ,    add_cross_attention=lowercase__       ,    decoder_start_token_id=lowercase__       ,    **lowercase__       ,    )


	@property
	def    _UpperCamelCase							(  self :List[str]			)			->   int:

			'''simple docstring'''






			return self.num_encoder_layers + self.num_decoder_layers






	@num_hidden_layers.setter
	def    _UpperCamelCase							(  self :Any       ,    __magic_name__ :List[Any]			)			->   List[Any]:

			'''simple docstring'''






			raise NotImplementedError(
			    '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
			    ''' `num_decoder_layers`.'''			)






"""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
