



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


_snake_case		=  {
    "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__":
						_snake_case		=  "hopper-medium-v2"
						_snake_case		=  gym.make(env_name)

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

						env.seed(0)
						_snake_case		=  env.reset()
						_snake_case		=  0
						_snake_case		=  0
						_snake_case		=  1000
						_snake_case		=  [obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		_snake_case		=  pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		_snake_case,						_snake_case,						_snake_case,						_snake_case		=  env.step(denorm_actions)
																		_snake_case		=  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())

																		_snake_case		=  next_observation
						except KeyboardInterrupt:
												pass

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





import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs






@pytest.mark.parametrize(
    "kwargs, expected"				, [
        ({"num_shards": 0, "max_num_jobs": 1}, []),
        ({"num_shards": 10, "max_num_jobs": 1}, [range(10  )]),
        ({"num_shards": 10, "max_num_jobs": 10}, [range(_lowerCamelCase				, i + 1  ) for i in range(10  )]),
        ({"num_shards": 1, "max_num_jobs": 10}, [range(1  )]),
        ({"num_shards": 10, "max_num_jobs": 3}, [range(0				, 4  ), range(4				, 7  ), range(7				, 10  )]),
        ({"num_shards": 3, "max_num_jobs": 10}, [range(0				, 1  ), range(1				, 2  ), range(2				, 3  )]),
    ]				, )
def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[Any]    =    _distribute_shards(**_lowerCamelCase  )
							assert out == expected






@pytest.mark.parametrize(
    "gen_kwargs, max_num_jobs, expected"				, [
        ({"foo": 0}, 10, [{"foo": 0}]),
        ({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
        ({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
        ({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
        ({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
    ]				, )
def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[int]    =    _split_gen_kwargs(_lowerCamelCase				, _lowerCamelCase  )
							assert out == expected






@pytest.mark.parametrize(
    "gen_kwargs, expected"				, [
        ({"foo": 0}, 1),
        ({"shards": [0]}, 1),
        ({"shards": [0, 1, 2, 3]}, 4),
        ({"shards": [0, 1, 2, 3], "foo": 0}, 4),
        ({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
        ({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
    ]				, )
def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							if expected is RuntimeError:
														with pytest.raises(_lowerCamelCase  ):
																					_number_of_shards_in_gen_kwargs(_lowerCamelCase  )
							else:
														_lowerCAmelCase     :							Optional[int]    =    _number_of_shards_in_gen_kwargs(_lowerCamelCase  )
														assert out == expected
