



'''simple docstring'''







import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    ErnieMForInformationExtraction,
							    ErnieMForMultipleChoice,
							    ErnieMForQuestionAnswering,
							    ErnieMForSequenceClassification,
							    ErnieMForTokenClassification,
							    ErnieMModel,
							)
							from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST




class 				__lowerCAmelCase:






			def __init__(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Dict=13      ,       SCREAMING_SNAKE_CASE  :							List[str]=7      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=True      ,       SCREAMING_SNAKE_CASE  :							Any=True      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=True      ,       SCREAMING_SNAKE_CASE  :							str=99      ,       SCREAMING_SNAKE_CASE  :							int=32      ,       SCREAMING_SNAKE_CASE  :							Any=5      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=4      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=37      ,       SCREAMING_SNAKE_CASE  :							List[Any]="gelu"      ,       SCREAMING_SNAKE_CASE  :							List[Any]=0.1      ,       SCREAMING_SNAKE_CASE  :							Any=0.1      ,       SCREAMING_SNAKE_CASE  :							Tuple=512      ,       SCREAMING_SNAKE_CASE  :							List[str]=16      ,       SCREAMING_SNAKE_CASE  :							Any=2      ,       SCREAMING_SNAKE_CASE  :							Dict=0.02      ,       SCREAMING_SNAKE_CASE  :							str=3      ,       SCREAMING_SNAKE_CASE  :							Any=4      ,       SCREAMING_SNAKE_CASE  :							Any=None      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:int										=      parent
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      batch_size
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      seq_length
					SCREAMING_SNAKE_CASE_						:int										=      is_training
					SCREAMING_SNAKE_CASE_						:Any										=      use_input_mask
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      use_labels
					SCREAMING_SNAKE_CASE_						:List[str]										=      vocab_size
					SCREAMING_SNAKE_CASE_						:str										=      hidden_size
					SCREAMING_SNAKE_CASE_						:List[Any]										=      num_hidden_layers
					SCREAMING_SNAKE_CASE_						:Any										=      num_attention_heads
					SCREAMING_SNAKE_CASE_						:Dict										=      intermediate_size
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      hidden_act
					SCREAMING_SNAKE_CASE_						:Tuple										=      hidden_dropout_prob
					SCREAMING_SNAKE_CASE_						:Dict										=      attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE_						:List[str]										=      max_position_embeddings
					SCREAMING_SNAKE_CASE_						:Dict										=      type_vocab_size
					SCREAMING_SNAKE_CASE_						:Dict										=      type_sequence_label_size
					SCREAMING_SNAKE_CASE_						:List[Any]										=      initializer_range
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      num_labels
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      num_choices
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      scope






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size	)

					SCREAMING_SNAKE_CASE_						:str										=      None
					if self.use_input_mask:
							SCREAMING_SNAKE_CASE_						:Any										=      random_attention_mask([self.batch_size, self.seq_length]	)

					SCREAMING_SNAKE_CASE_						:Any										=      None
					SCREAMING_SNAKE_CASE_						:str										=      None
					SCREAMING_SNAKE_CASE_						:int										=      None
					if self.use_labels:
							SCREAMING_SNAKE_CASE_						:str										=      ids_tensor([self.batch_size]      ,       self.type_sequence_label_size	)
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      ids_tensor([self.batch_size, self.seq_length]      ,       self.num_labels	)
							SCREAMING_SNAKE_CASE_						:str										=      ids_tensor([self.batch_size]      ,       self.num_choices	)

					SCREAMING_SNAKE_CASE_						:Dict										=      self.get_config()

					return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size	)

					SCREAMING_SNAKE_CASE_						:str										=      None
					if self.use_input_mask:
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      random_attention_mask([self.batch_size, self.seq_length]	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.get_config()

					return config, input_ids, input_mask






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					return ErnieMConfig(
					    vocab_size=self.vocab_size      ,       hidden_size=self.hidden_size      ,       num_hidden_layers=self.num_hidden_layers      ,       num_attention_heads=self.num_attention_heads      ,       intermediate_size=self.intermediate_size      ,       hidden_act=self.hidden_act      ,       hidden_dropout_prob=self.hidden_dropout_prob      ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,       max_position_embeddings=self.max_position_embeddings      ,       type_vocab_size=self.type_vocab_size      ,       initializer_range=self.initializer_range      ,       )






			def 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Any      ,       SCREAMING_SNAKE_CASE  :							Any      ,       SCREAMING_SNAKE_CASE  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      ErnieMModel(config=SCREAMING_SNAKE_CASE	)
					model.to(SCREAMING_SNAKE_CASE	)
					model.eval()
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      model(SCREAMING_SNAKE_CASE      ,       return_dict=SCREAMING_SNAKE_CASE	)
					self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)	)






			def 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Any      ,       SCREAMING_SNAKE_CASE  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      ErnieMForQuestionAnswering(config=SCREAMING_SNAKE_CASE	)
					model.to(SCREAMING_SNAKE_CASE	)
					model.eval()
					SCREAMING_SNAKE_CASE_						:Any										=      model(
					    SCREAMING_SNAKE_CASE      ,       attention_mask=SCREAMING_SNAKE_CASE      ,       start_positions=SCREAMING_SNAKE_CASE      ,       end_positions=SCREAMING_SNAKE_CASE      ,       )
					self.parent.assertEqual(result.start_logits.shape      ,       (self.batch_size, self.seq_length)	)
					self.parent.assertEqual(result.end_logits.shape      ,       (self.batch_size, self.seq_length)	)






			def 				_lowercase	(   self  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      ErnieMForInformationExtraction(config=SCREAMING_SNAKE_CASE	)
					model.to(SCREAMING_SNAKE_CASE	)
					model.eval()
					SCREAMING_SNAKE_CASE_						:Dict										=      torch.ones_like(SCREAMING_SNAKE_CASE      ,       dtype=torch.floataa	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      model(
					    SCREAMING_SNAKE_CASE      ,       attention_mask=SCREAMING_SNAKE_CASE      ,       start_positions=SCREAMING_SNAKE_CASE      ,       end_positions=SCREAMING_SNAKE_CASE      ,       )
					self.parent.assertEqual(result.start_logits.shape      ,       (self.batch_size, self.seq_length)	)
					self.parent.assertEqual(result.end_logits.shape      ,       (self.batch_size, self.seq_length)	)






			def 				_lowercase	(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							Dict      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							Any      ,       SCREAMING_SNAKE_CASE  :							Any      ,       SCREAMING_SNAKE_CASE  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.num_labels
					SCREAMING_SNAKE_CASE_						:Any										=      ErnieMForSequenceClassification(SCREAMING_SNAKE_CASE	)
					model.to(SCREAMING_SNAKE_CASE	)
					model.eval()
					SCREAMING_SNAKE_CASE_						:Dict										=      model(SCREAMING_SNAKE_CASE      ,       attention_mask=SCREAMING_SNAKE_CASE      ,       labels=SCREAMING_SNAKE_CASE	)
					self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)	)






			def 				_lowercase	(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Any      ,       SCREAMING_SNAKE_CASE  :							Any      ,       SCREAMING_SNAKE_CASE  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:int										=      self.num_labels
					SCREAMING_SNAKE_CASE_						:Any										=      ErnieMForTokenClassification(config=SCREAMING_SNAKE_CASE	)
					model.to(SCREAMING_SNAKE_CASE	)
					model.eval()
					input_ids.to(SCREAMING_SNAKE_CASE	)
					input_mask.to(SCREAMING_SNAKE_CASE	)
					token_labels.to(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Any										=      model(SCREAMING_SNAKE_CASE      ,       attention_mask=SCREAMING_SNAKE_CASE      ,       labels=SCREAMING_SNAKE_CASE	)

					self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.num_labels)	)






			def 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							Any      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.num_choices
					SCREAMING_SNAKE_CASE_						:str										=      ErnieMForMultipleChoice(config=SCREAMING_SNAKE_CASE	)
					model.to(SCREAMING_SNAKE_CASE	)
					model.eval()
					SCREAMING_SNAKE_CASE_						:int										=      input_ids.unsqueeze(1	).expand(-1      ,       self.num_choices      ,       -1	).contiguous()
					SCREAMING_SNAKE_CASE_						:Dict										=      input_mask.unsqueeze(1	).expand(-1      ,       self.num_choices      ,       -1	).contiguous()
					SCREAMING_SNAKE_CASE_						:Any										=      model(
					    SCREAMING_SNAKE_CASE      ,       attention_mask=SCREAMING_SNAKE_CASE      ,       labels=SCREAMING_SNAKE_CASE      ,       )
					self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_choices)	)






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      self.prepare_config_and_inputs()
					(
					    (
					    SCREAMING_SNAKE_CASE_
					)						,		(
					    SCREAMING_SNAKE_CASE_
					)						,		(
					    SCREAMING_SNAKE_CASE_
					)						,		(
					    SCREAMING_SNAKE_CASE_
					)						,		(
					    SCREAMING_SNAKE_CASE_
					)						,		(
					    SCREAMING_SNAKE_CASE_
					)						,		
					)						:Tuple										=      config_and_inputs
					SCREAMING_SNAKE_CASE_						:List[str]										=      {'input_ids': input_ids, 'attention_mask': input_mask}
					return config, inputs_dict




@require_torch
class 				__lowerCAmelCase(							lowerCAmelCase__		,			lowerCAmelCase__		,			unittest.TestCase      ):
			__snake_case  :		Dict		=      (
			    (
			        ErnieMModel,
			        ErnieMForMultipleChoice,
			        ErnieMForQuestionAnswering,
			        ErnieMForSequenceClassification,
			        ErnieMForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			__snake_case  :		Union[str, Any]		=      ()
			__snake_case  :		List[str]		=      (
			    {
			        'feature-extraction': ErnieMModel,
			        'question-answering': ErnieMForQuestionAnswering,
			        'text-classification': ErnieMForSequenceClassification,
			        'token-classification': ErnieMForTokenClassification,
			        'zero-shot': ErnieMForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			__snake_case  :		Union[str, Any]		=      False






			def 				_lowercase	(   self  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							int	):




					"""simple docstring"""







					if pipeline_test_casse_name == "QAPipelineTests":
							return True

					return False






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      ErnieMModelTester(self	)
					SCREAMING_SNAKE_CASE_						:Any										=      ConfigTester(self      ,       config_class=SCREAMING_SNAKE_CASE      ,       hidden_size=37	)






			def 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







					self.config_tester.run_common_tests()






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:int										=      self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
							SCREAMING_SNAKE_CASE_						:int										=      type
							self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_information_extraction(*SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE	)






			@slow
			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      ErnieMModel.from_pretrained(SCREAMING_SNAKE_CASE	)
							self.assertIsNotNone(SCREAMING_SNAKE_CASE	)




@require_torch
class 				__lowerCAmelCase(							unittest.TestCase      ):






			@slow
			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      ErnieMModel.from_pretrained('susnato/ernie-m-base_pytorch'	)
					model.eval()
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      torch.tensor([[0, 1, 2, 3, 4, 5]]	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      model(SCREAMING_SNAKE_CASE	)[0]

					# TODO Replace vocab size
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      768

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      torch.Size((1, 6, hidden_size)	)
					self.assertEqual(output.shape      ,       SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      torch.tensor(
					    [[[-0.00_12, 0.12_45, -0.02_14], [-0.07_42, 0.02_44, -0.07_71], [-0.03_33, 0.11_64, -0.15_54]]]	)

					self.assertTrue(torch.allclose(output[:, :3, :3]      ,       SCREAMING_SNAKE_CASE      ,       atol=1E-3	)	)




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





	__snake_case      :		Dict         =							states.reshape(1	,  -1	,  model.config.state_dim	)
	__snake_case      :		List[str]         =							actions.reshape(1	,  -1	,  model.config.act_dim	)
	__snake_case      :		Union[str, Any]         =							returns_to_go.reshape(1	,  -1	,  1	)
	__snake_case      :		Any         =							timesteps.reshape(1	,  -1	)

	if model.config.max_length is not None:
		__snake_case      :		Union[str, Any]         =							states[:, -model.config.max_length :]
		__snake_case      :		Any         =							actions[:, -model.config.max_length :]
		__snake_case      :		Tuple         =							returns_to_go[:, -model.config.max_length :]
		__snake_case      :		Union[str, Any]         =							timesteps[:, -model.config.max_length :]

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

	__snake_case      ,				__snake_case      ,				__snake_case      :		Optional[int]         =							model(
	    states=_snake_case	,  actions=_snake_case	,  rewards=_snake_case	,  returns_to_go=_snake_case	,  timesteps=_snake_case	,  attention_mask=_snake_case	,  return_dict=_snake_case	,  )

	return action_preds[0, -1]


# build the environment

SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							gym.make("""Hopper-v3""")
SCREAMING_SNAKE_CASE					:       List[str]							   =							env.observation_space.shape[0]
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							env.action_space.shape[0]
SCREAMING_SNAKE_CASE					:       Optional[int]							   =							1000
SCREAMING_SNAKE_CASE					:       Tuple							   =							"""cuda"""
SCREAMING_SNAKE_CASE					:       List[Any]							   =							1_000.0  # normalization for rewards/returns
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							np.array(
    [
        1.311_279,
        -0.08_469_521,
        -0.5_382_719,
        -0.07_201_576,
        0.04_932_366,
        2.1_066_856,
        -0.15_017_354,
        0.00_878_345,
        -0.2_848_186,
        -0.18_540_096,
        -0.28_461_286,
    ]
)
SCREAMING_SNAKE_CASE					:       List[Any]							   =							np.array(
    [
        0.17_790_751,
        0.05_444_621,
        0.21_297_139,
        0.14_530_419,
        0.6_124_444,
        0.85_174_465,
        1.4_515_252,
        0.6_751_696,
        1.536_239,
        1.6_160_746,
        5.6_072_536,
    ]
)
SCREAMING_SNAKE_CASE					:       List[str]							   =							torch.from_numpy(state_mean).to(device=device)
SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							torch.from_numpy(state_std).to(device=device)

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

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

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

						SCREAMING_SNAKE_CASE					:       Dict							   =							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),
						)
						SCREAMING_SNAKE_CASE					:       Optional[int]							   =							action
						SCREAMING_SNAKE_CASE					:       List[Any]							   =							action.detach().cpu().numpy()

						SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE   ,    SCREAMING_SNAKE_CASE					:       List[str]							   =							env.step(action)

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

						SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							target_return[0, -1] - (reward / scale)
						SCREAMING_SNAKE_CASE					:       Dict							   =							torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
						SCREAMING_SNAKE_CASE					:       List[Any]							   =							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





