






"""simple docstring"""



import os

import pytest

from transformers.dynamic_module_utils import get_imports


A__	:    Any			 = '\nimport os\n'

A__	:    Optional[int]			 = '\ndef foo():\n    import os\n    return False\n'

A__	:    str			 = '\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

A__	:    int			 = '\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

A__	:    Dict			 = '\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

A__	:    Dict			 = '\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

A__	:    List[str]			 = '\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

A__	:    Optional[Any]			 = '\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

A__	:    Optional[Any]			 = '\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

A__	:    Optional[int]			 = '\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

A__	:    int			 = [
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]

@pytest.mark.parametrize("case"    ,						lowerCamelCase__  )
def 			_snake_case    (					lowerCamelCase__     :   Dict    ,						lowerCamelCase__     :   int  )    ->     Tuple:
							lowerCamelCase_	:							Union[str, Any]									=os.path.join(lowerCamelCase__    ,						"test_file.py"  )
							with open(lowerCamelCase__    ,						"w"  ) as _tmp_file:
														_tmp_file.write(lowerCamelCase__  )

							lowerCamelCase_	:							Tuple									=get_imports(lowerCamelCase__  )
							assert parsed_imports == ["os"]











"""simple docstring"""



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


A__	:    int			 = {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    '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__":
						A__	:    str			 = 'hopper-medium-v2'
						A__	:    Dict			 = gym.make(env_name)

						A__	:    List[Any]			 = ValueGuidedRLPipeline.from_pretrained(
						    'bglick13/hopper-medium-v2-value-function-hor32',
						    env=env,
						)

						env.seed(0)
						A__	:    Dict			 = env.reset()
						A__	:    Optional[int]			 = 0
						A__	:    str			 = 0
						A__	:    List[Any]			 = 1_000
						A__	:    Tuple			 = [obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		A__	:    Union[str, Any]			 = pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		A__ ,						A__ ,						A__ ,						A__	:    Any			 = env.step(denorm_actions)
																		A__	:    List[Any]			 = 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())

																		A__	:    Optional[Any]			 = next_observation
						except KeyboardInterrupt:
												pass

						print(f'Total reward: {total_reward}')



