
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





class 			lowerCAmelCase__( UpperCAmelCase_    ):






       '''simple docstring'''


       __snake_case 			=    (DPMSolverSinglestepScheduler,)
       __snake_case 			=    (('num_inference_steps', 2_5),)




       def     UpperCamelCase_			(      self    ,						**__lowerCamelCase							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							{
                'num_train_timesteps': 1_0_0_0,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
                'solver_order': 2,
                'prediction_type': 'epsilon',
                'thresholding': False,
                'sample_max_value': 1.0,
                'algorithm_type': 'dpmsolver++',
                'solver_type': 'midpoint',
                'lambda_min_clipped': -float("inf"							),
                'variance_type': None,
            }

            config.update(**_lowercase							)
            return config




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase=0    ,						**__lowerCamelCase							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							dict(self.forward_default_kwargs							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							kwargs.pop("num_inference_steps"    ,						_lowercase							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.dummy_sample
            _SCREAMING_SNAKE_CASE       :	Dict						    =							0.1 * sample
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.get_scheduler_config(**_lowercase							)
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							scheduler_class(**_lowercase							)
                 scheduler.set_timesteps(_lowercase							)
                 # copy over dummy past residuals
                 _SCREAMING_SNAKE_CASE       :	Any						    =							dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(_lowercase							)
                      _SCREAMING_SNAKE_CASE       :	int						    =							scheduler_class.from_pretrained(_lowercase							)
                      new_scheduler.set_timesteps(_lowercase							)
                      # copy over dummy past residuals
                      _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							dummy_past_residuals[: new_scheduler.config.solver_order]

                 _SCREAMING_SNAKE_CASE       :	Any						    =							sample, sample
                 for t in range(_lowercase    ,						time_step + scheduler.config.solver_order + 1							):
                      _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							scheduler.step(_lowercase    ,						_lowercase    ,						_lowercase    ,						**_lowercase							).prev_sample
                      _SCREAMING_SNAKE_CASE       :	List[Any]						    =							new_scheduler.step(_lowercase    ,						_lowercase    ,						_lowercase    ,						**_lowercase							).prev_sample

                      assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"




       def     UpperCamelCase_			(      self							)			->     List[str]:
            pass




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase=0    ,						**__lowerCamelCase							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	int						    =							dict(self.forward_default_kwargs							)
            _SCREAMING_SNAKE_CASE       :	int						    =							kwargs.pop("num_inference_steps"    ,						_lowercase							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.dummy_sample
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							0.1 * sample
            _SCREAMING_SNAKE_CASE       :	Dict						    =							[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.get_scheduler_config()
                 _SCREAMING_SNAKE_CASE       :	Any						    =							scheduler_class(**_lowercase							)
                 scheduler.set_timesteps(_lowercase							)

                 # copy over dummy past residuals (must be after setting timesteps)
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(_lowercase							)
                      _SCREAMING_SNAKE_CASE       :	Any						    =							scheduler_class.from_pretrained(_lowercase							)
                      # copy over dummy past residuals
                      new_scheduler.set_timesteps(_lowercase							)

                      # copy over dummy past residual (must be after setting timesteps)
                      _SCREAMING_SNAKE_CASE       :	Tuple						    =							dummy_past_residuals[: new_scheduler.config.solver_order]

                 _SCREAMING_SNAKE_CASE       :	Dict						    =							scheduler.step(_lowercase    ,						_lowercase    ,						_lowercase    ,						**_lowercase							).prev_sample
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							new_scheduler.step(_lowercase    ,						_lowercase    ,						_lowercase    ,						**_lowercase							).prev_sample

                 assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase=None    ,						**__lowerCamelCase							)			->     Optional[Any]:
            if scheduler is None:
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.scheduler_classes[0]
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.get_scheduler_config(**_lowercase							)
                 _SCREAMING_SNAKE_CASE       :	str						    =							scheduler_class(**_lowercase							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.get_scheduler_config(**_lowercase							)
            _SCREAMING_SNAKE_CASE       :	int						    =							scheduler_class(**_lowercase							)

            _SCREAMING_SNAKE_CASE       :	Tuple						    =							1_0
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.dummy_model()
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.dummy_sample_deter
            scheduler.set_timesteps(_lowercase							)

            for i, t in enumerate(scheduler.timesteps							):
                 _SCREAMING_SNAKE_CASE       :	str						    =							model(_lowercase    ,						_lowercase							)
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							scheduler.step(_lowercase    ,						_lowercase    ,						_lowercase							).prev_sample

            return sample




       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							DPMSolverSinglestepScheduler(**self.get_scheduler_config()							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							5_0
            _SCREAMING_SNAKE_CASE       :	int						    =							self.dummy_model()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.dummy_sample_deter
            scheduler.set_timesteps(_lowercase							)

            # make sure that the first t is uneven
            for i, t in enumerate(scheduler.timesteps[3:]							):
                 _SCREAMING_SNAKE_CASE       :	int						    =							model(_lowercase    ,						_lowercase							)
                 _SCREAMING_SNAKE_CASE       :	Any						    =							scheduler.step(_lowercase    ,						_lowercase    ,						_lowercase							).prev_sample

            _SCREAMING_SNAKE_CASE       :	Any						    =							torch.mean(torch.abs(_lowercase							)							)

            assert abs(result_mean.item() - 0.2574							) < 1E-3




       def     UpperCamelCase_			(      self							)			->     Dict:
            for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=_lowercase							)




       def     UpperCamelCase_			(      self							)			->     List[str]:
            # make sure that iterating over schedulers with same config names gives same results
            # for defaults
            _SCREAMING_SNAKE_CASE       :	str						    =							DPMSolverSinglestepScheduler(**self.get_scheduler_config()							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.full_loop(scheduler=_lowercase							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							torch.mean(torch.abs(_lowercase							)							)

            assert abs(result_mean.item() - 0.2791							) < 1E-3

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							DEISMultistepScheduler.from_config(scheduler.config							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							DPMSolverMultistepScheduler.from_config(scheduler.config							)
            _SCREAMING_SNAKE_CASE       :	int						    =							UniPCMultistepScheduler.from_config(scheduler.config							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							DPMSolverSinglestepScheduler.from_config(scheduler.config							)

            _SCREAMING_SNAKE_CASE       :	Any						    =							self.full_loop(scheduler=_lowercase							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							torch.mean(torch.abs(_lowercase							)							)

            assert abs(result_mean.item() - 0.2791							) < 1E-3




       def     UpperCamelCase_			(      self							)			->     Dict:
            self.check_over_configs(thresholding=_lowercase							)
            for order in [1, 2, 3]:
                 for solver_type in ["midpoint", "heun"]:
                      for threshold in [0.5, 1.0, 2.0]:
                           for prediction_type in ["epsilon", "sample"]:
                                self.check_over_configs(
                                    thresholding=_lowercase    ,						prediction_type=_lowercase    ,						sample_max_value=_lowercase    ,						algorithm_type="dpmsolver++"    ,						solver_order=_lowercase    ,						solver_type=_lowercase    ,						)




       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=_lowercase							)




       def     UpperCamelCase_			(      self							)			->     Dict:
            for algorithm_type in ["dpmsolver", "dpmsolver++"]:
                 for solver_type in ["midpoint", "heun"]:
                      for order in [1, 2, 3]:
                           for prediction_type in ["epsilon", "sample"]:
                                self.check_over_configs(
                                    solver_order=_lowercase    ,						solver_type=_lowercase    ,						prediction_type=_lowercase    ,						algorithm_type=_lowercase    ,						)
                                _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.full_loop(
                                    solver_order=_lowercase    ,						solver_type=_lowercase    ,						prediction_type=_lowercase    ,						algorithm_type=_lowercase    ,						)
                                assert not torch.isnan(_lowercase							).any(), "Samples have nan numbers"




       def     UpperCamelCase_			(      self							)			->     int:
            self.check_over_configs(lower_order_final=_lowercase							)
            self.check_over_configs(lower_order_final=_lowercase							)




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            self.check_over_configs(lambda_min_clipped=-float("inf"							)							)
            self.check_over_configs(lambda_min_clipped=-5.1							)




       def     UpperCamelCase_			(      self							)			->     Tuple:
            self.check_over_configs(variance_type=_lowercase							)
            self.check_over_configs(variance_type="learned_range"							)




       def     UpperCamelCase_			(      self							)			->     Dict:
            for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_forward(num_inference_steps=_lowercase    ,						time_step=0							)




       def     UpperCamelCase_			(      self							)			->     int:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.full_loop()
            _SCREAMING_SNAKE_CASE       :	int						    =							torch.mean(torch.abs(_lowercase							)							)

            assert abs(result_mean.item() - 0.2791							) < 1E-3




       def     UpperCamelCase_			(      self							)			->     Any:
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.full_loop(use_karras_sigmas=_lowercase							)
            _SCREAMING_SNAKE_CASE       :	int						    =							torch.mean(torch.abs(_lowercase							)							)

            assert abs(result_mean.item() - 0.2248							) < 1E-3




       def     UpperCamelCase_			(      self							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.full_loop(prediction_type="v_prediction"							)
            _SCREAMING_SNAKE_CASE       :	str						    =							torch.mean(torch.abs(_lowercase							)							)

            assert abs(result_mean.item() - 0.1453							) < 1E-3




       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.full_loop(prediction_type="v_prediction"    ,						use_karras_sigmas=_lowercase							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							torch.mean(torch.abs(_lowercase							)							)

            assert abs(result_mean.item() - 0.0649							) < 1E-3







       def     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.get_scheduler_config(thresholding=_lowercase    ,						dynamic_thresholding_ratio=0							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							scheduler_class(**_lowercase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							1_0
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.dummy_model()
            _SCREAMING_SNAKE_CASE       :	int						    =							self.dummy_sample_deter.half()
            scheduler.set_timesteps(_lowercase							)

            for i, t in enumerate(scheduler.timesteps							):
                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							model(_lowercase    ,						_lowercase							)
                 _SCREAMING_SNAKE_CASE       :	int						    =							scheduler.step(_lowercase    ,						_lowercase    ,						_lowercase							).prev_sample

            assert sample.dtype == torch.floataa






import inspect
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import DecisionTransformerModel
       from transformers.models.decision_transformer.modeling_decision_transformer import (
           DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
       )





class 			lowerCAmelCase__:






       '''simple docstring'''


       def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase=1_3    ,						__lowerCamelCase=7    ,						__lowerCamelCase=6    ,						__lowerCamelCase=1_7    ,						__lowerCamelCase=2_3    ,						__lowerCamelCase=1_1    ,						__lowerCamelCase=True    ,						)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							parent
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							batch_size
            _SCREAMING_SNAKE_CASE       :	int						    =							seq_length
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							act_dim
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							state_dim
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							hidden_size
            _SCREAMING_SNAKE_CASE       :	Any						    =							max_length
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							is_training




       def     UpperCamelCase_			(      self							)			->     List[Any]:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							floats_tensor((self.batch_size, self.seq_length, self.state_dim)							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							floats_tensor((self.batch_size, self.seq_length, self.act_dim)							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							floats_tensor((self.batch_size, self.seq_length, 1)							)
            _SCREAMING_SNAKE_CASE       :	str						    =							floats_tensor((self.batch_size, self.seq_length, 1)							)
            _SCREAMING_SNAKE_CASE       :	str						    =							ids_tensor((self.batch_size, self.seq_length)    ,						vocab_size=1_0_0_0							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							random_attention_mask((self.batch_size, self.seq_length)							)

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.get_config()

            return (
                config,
                states,
                actions,
                rewards,
                returns_to_go,
                timesteps,
                attention_mask,
            )




       def     UpperCamelCase_			(      self							)			->     List[Any]:
            return DecisionTransformerConfig(
                batch_size=self.batch_size    ,						seq_length=self.seq_length    ,						act_dim=self.act_dim    ,						state_dim=self.state_dim    ,						hidden_size=self.hidden_size    ,						max_length=self.max_length    ,						)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						)			->     Any:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							DecisionTransformerModel(config=__lowerCamelCase							)
            model.to(__lowerCamelCase							)
            model.eval()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							model(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							)

            self.parent.assertEqual(result.state_preds.shape    ,						states.shape							)
            self.parent.assertEqual(result.action_preds.shape    ,						actions.shape							)
            self.parent.assertEqual(result.return_preds.shape    ,						returns_to_go.shape							)
            self.parent.assertEqual(
                result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length * 3, self.hidden_size)							)  # seq length *3 as there are 3 modelities: states, returns and actions







       def     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	int						    =							self.prepare_config_and_inputs()
            (
                (
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			
            )       :	Optional[Any]						    =							config_and_inputs
            _SCREAMING_SNAKE_CASE       :	Any						    =							{
                "states": states,
                "actions": actions,
                "rewards": rewards,
                "returns_to_go": returns_to_go,
                "timesteps": timesteps,
                "attention_mask": attention_mask,
            }
            return config, inputs_dict





@require_torch
class 			lowerCAmelCase__( __lowercase  ,     __lowercase  ,     __lowercase  ,     unittest.TestCase    ):






       '''simple docstring'''


       __snake_case 			=    (DecisionTransformerModel,) if is_torch_available() else ()
       __snake_case 			=    ()
       __snake_case 			=    {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

       # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
       __snake_case 			=    False

       # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	int						    =							DecisionTransformerModelTester(self							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							ConfigTester(self    ,						config_class=__lowerCamelCase    ,						hidden_size=3_7							)




       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            self.config_tester.run_common_tests()




       def     UpperCamelCase_			(      self							)			->     List[Any]:
            _SCREAMING_SNAKE_CASE       :	str						    =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCamelCase							)




       @slow
       def     UpperCamelCase_			(      self							)			->     Dict:
            for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _SCREAMING_SNAKE_CASE       :	str						    =							DecisionTransformerModel.from_pretrained(__lowerCamelCase							)
                 self.assertIsNotNone(__lowerCamelCase							)







       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Dict						    =							self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							model_class(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							inspect.signature(model.forward							)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 _SCREAMING_SNAKE_CASE       :	int						    =							[*signature.parameters.keys()]

                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							[
                     "states",
                     "actions",
                     "rewards",
                     "returns_to_go",
                     "timesteps",
                     "attention_mask",
                 ]

                 self.assertListEqual(arg_names[: len(__lowerCamelCase							)]    ,						__lowerCamelCase							)





@require_torch
class 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


       @slow
       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	str						    =							2  # number of steps of autoregressive prediction we will perform
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							1_0  # defined by the RL environment, may be normalized
            _SCREAMING_SNAKE_CASE       :	int						    =							DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert"							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							model.to(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							model.config
            torch.manual_seed(0							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							torch.randn(1    ,						1    ,						config.state_dim							).to(device=__lowerCamelCase    ,						dtype=torch.floataa							)  # env.reset()

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							torch.tensor(
                [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]    ,						device=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							torch.tensor(__lowerCamelCase    ,						device=__lowerCamelCase    ,						dtype=torch.floataa							).reshape(1    ,						1    ,						1							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							state
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							torch.zeros(1    ,						0    ,						config.act_dim    ,						device=__lowerCamelCase    ,						dtype=torch.floataa							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							torch.zeros(1    ,						0    ,						device=__lowerCamelCase    ,						dtype=torch.floataa							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							torch.tensor(0    ,						device=__lowerCamelCase    ,						dtype=torch.long							).reshape(1    ,						1							)

            for step in range(__lowerCamelCase							):
                 _SCREAMING_SNAKE_CASE       :	int						    =							torch.cat([actions, torch.zeros(1    ,						1    ,						config.act_dim    ,						device=__lowerCamelCase							)]    ,						dim=1							)
                 _SCREAMING_SNAKE_CASE       :	Any						    =							torch.cat([rewards, torch.zeros(1    ,						1    ,						device=__lowerCamelCase							)]    ,						dim=1							)

                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							torch.ones(1    ,						states.shape[1]							).to(dtype=torch.long    ,						device=states.device							)

                 with torch.no_grad():
                      _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							model(
                          states=__lowerCamelCase    ,						actions=__lowerCamelCase    ,						rewards=__lowerCamelCase    ,						returns_to_go=__lowerCamelCase    ,						timesteps=__lowerCamelCase    ,						attention_mask=__lowerCamelCase    ,						return_dict=__lowerCamelCase    ,						)

                 self.assertEqual(action_pred.shape    ,						actions.shape							)
                 self.assertTrue(torch.allclose(action_pred[0, -1]    ,						expected_outputs[step]    ,						atol=1E-4							)							)
                 _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Tuple						    =							(  # env.step(action)
                     torch.randn(1    ,						1    ,						config.state_dim							).to(device=__lowerCamelCase    ,						dtype=torch.floataa							),
                     1.0,
                     False,
                     {},
                 )

                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							action_pred[0, -1]
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							torch.cat([states, state]    ,						dim=1							)
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							returns_to_go[0, -1] - reward
                 _SCREAMING_SNAKE_CASE       :	str						    =							torch.cat([returns_to_go, pred_return.reshape(1    ,						1    ,						1							)]    ,						dim=1							)
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							torch.cat(
                     [timesteps, torch.ones((1, 1)    ,						device=__lowerCamelCase    ,						dtype=torch.long							) * (step + 1)]    ,						dim=1							)