


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 	A_ :




      '''simple docstring'''


      def __init__(    self:	str						,				a:	Tuple						,				a:	List[Any]=13						,				a:	List[Any]=7						,				a:	int=6						,				a:	Union[str, Any]=17						,				a:	Dict=23						,				a:	Optional[Any]=11						,				a:	Any=True						,				):
       __lowerCamelCase		:							Dict		 =						parent
       __lowerCamelCase		:							Any		 =						batch_size
       __lowerCamelCase		:							str		 =						seq_length
       __lowerCamelCase		:							Optional[int]		 =						act_dim
       __lowerCamelCase		:							Union[str, Any]		 =						state_dim
       __lowerCamelCase		:							List[Any]		 =						hidden_size
       __lowerCamelCase		:							List[str]		 =						max_length
       __lowerCamelCase		:							Union[str, Any]		 =						is_training




      def 					_snake_case     (    self:	Optional[int]    ):
       __lowerCamelCase		:							Union[str, Any]		 =						floats_tensor((self.batch_size, self.seq_length, self.state_dim)    )
       __lowerCamelCase		:							Optional[Any]		 =						floats_tensor((self.batch_size, self.seq_length, self.act_dim)    )
       __lowerCamelCase		:							Tuple		 =						floats_tensor((self.batch_size, self.seq_length, 1)    )
       __lowerCamelCase		:							Optional[int]		 =						floats_tensor((self.batch_size, self.seq_length, 1)    )
       __lowerCamelCase		:							Any		 =						ids_tensor((self.batch_size, self.seq_length)						,				vocab_size=1000    )
       __lowerCamelCase		:							Any		 =						random_attention_mask((self.batch_size, self.seq_length)    )

       __lowerCamelCase		:							Union[str, Any]		 =						self.get_config()

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




      def 					_snake_case     (    self:	Union[str, 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 					_snake_case     (    self:	str						,				a:	Tuple						,				a:	Optional[int]						,				a:	Optional[Any]						,				a:	List[str]						,				a:	List[Any]						,				a:	Tuple						,				a:	str						,				):
       __lowerCamelCase		:							Optional[int]		 =						DecisionTransformerModel(config=a    )
       model.to(a    )
       model.eval()
       __lowerCamelCase		:							List[Any]		 =						model(a						,				a						,				a						,				a						,				a						,				a    )

       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 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase		:							Optional[int]		 =						self.prepare_config_and_inputs()
       (
           (
           __lowerCamelCase
       ) ,					(
           __lowerCamelCase
       ) ,					(
           __lowerCamelCase
       ) ,					(
           __lowerCamelCase
       ) ,					(
           __lowerCamelCase
       ) ,					(
           __lowerCamelCase
       ) ,					(
           __lowerCamelCase
       ) ,					
       )		:							Dict		 =						config_and_inputs
       __lowerCamelCase		:							Dict		 =						{
           '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 	A_ (	__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				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 					_snake_case     (    self:	Dict    ):
       __lowerCamelCase		:							List[str]		 =						DecisionTransformerModelTester(self    )
       __lowerCamelCase		:							List[Any]		 =						ConfigTester(self						,				config_class=a						,				hidden_size=37    )




      def 					_snake_case     (    self:	str    ):
       self.config_tester.run_common_tests()




      def 					_snake_case     (    self:	str    ):
       __lowerCamelCase		:							List[Any]		 =						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a    )




      @slow
      def 					_snake_case     (    self:	List[str]    ):
       for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowerCamelCase		:							Dict		 =						DecisionTransformerModel.from_pretrained(a    )
        self.assertIsNotNone(a    )




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase ,					__lowerCamelCase		:							Union[str, Any]		 =						self.model_tester.prepare_config_and_inputs_for_common()

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

        __lowerCamelCase		:							Union[str, Any]		 =						[
            'states',
            'actions',
            'rewards',
            'returns_to_go',
            'timesteps',
            'attention_mask',
        ]

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



@require_torch
class 	A_ (	unittest.TestCase   ):




      '''simple docstring'''


      @slow
      def 					_snake_case     (    self:	Tuple    ):
       __lowerCamelCase		:							str		 =						2  # number of steps of autoregressive prediction we will perform
       __lowerCamelCase		:							List[Any]		 =						10  # defined by the RL environment, may be normalized
       __lowerCamelCase		:							List[Any]		 =						DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-expert'    )
       __lowerCamelCase		:							Union[str, Any]		 =						model.to(a    )
       __lowerCamelCase		:							List[str]		 =						model.config
       torch.manual_seed(0    )
       __lowerCamelCase		:							int		 =						torch.randn(1						,				1						,				config.state_dim    ).to(device=a						,				dtype=torch.floataa    )  # env.reset()

       __lowerCamelCase		:							int		 =						torch.tensor(
           [[0.2_4_2_7_9_3, -0.2_8_6_9_3_0_7_4, 0.8_7_4_2_6_1_3], [0.6_7_8_1_5_2_7_4, -0.0_8_1_0_1_0_8_5, -0.1_2_9_5_2_1_4_7]]						,				device=a    )

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

       for step in range(a    ):
        __lowerCamelCase		:							str		 =						torch.cat([actions, torch.zeros(1						,				1						,				config.act_dim						,				device=a    )]						,				dim=1    )
        __lowerCamelCase		:							Dict		 =						torch.cat([rewards, torch.zeros(1						,				1						,				device=a    )]						,				dim=1    )

        __lowerCamelCase		:							Dict		 =						torch.ones(1						,				states.shape[1]    ).to(dtype=torch.long						,				device=states.device    )

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

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

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





import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


lowercase_	   =     {
    'n_samples': 6_4,
    'horizon': 3_2,
    'num_inference_steps': 2_0,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
       lowercase_	   =     'hopper-medium-v2'
       lowercase_	   =     gym.make(env_name)

       lowercase_	   =     ValueGuidedRLPipeline.from_pretrained(
           'bglick13/hopper-medium-v2-value-function-hor32',
           env=env,
       )

       env.seed(0)
       lowercase_	   =     env.reset()
       lowercase_	   =     0
       lowercase_	   =     0
       lowercase_	   =     1_0_0_0
       lowercase_	   =     [obs.copy()]
       try:
              for t in tqdm.tqdm(range(T)):
                     # call the policy
                     lowercase_	   =     pipeline(obs, planning_horizon=3_2)

                     # execute action in environment
                     lowercase_   ,lowercase_   ,lowercase_   ,lowercase_	   =     env.step(denorm_actions)
                     lowercase_	   =     env.get_normalized_score(total_reward)

                     # update return
                     total_reward += reward
                     total_score += score
                     print(
                         F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
                         F""" {total_score}"""
                     )

                     # save observations for rendering
                     rollout.append(next_observation.copy())

                     lowercase_	   =     next_observation
       except KeyboardInterrupt:
              pass

       print(F"""Total reward: {total_reward}""")

