



'''simple docstring'''



import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def 					UpperCAmelCase   (						A  :				List[Any]					,		A  :				Optional[int]				):
							assert isinstance(A					,		A				)
							assert dataset.num_rows == 4
							assert dataset.num_columns == 1
							assert dataset.column_names == ["text"]
							for feature, expected_dtype in expected_features.items():
														assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory'''					,		[False, True]				)
def 					UpperCAmelCase   (						A  :				Optional[Any]					,		A  :				Any					,		A  :				Union[str, Any]				):
							SCREAMING_SNAKE_CASE					:      int     					=			tmp_path / '''cache'''
							SCREAMING_SNAKE_CASE					:      Any     					=			{'''text''': '''string'''}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														SCREAMING_SNAKE_CASE					:      Any     					=			TextDatasetReader(A					,		cache_dir=A					,		keep_in_memory=A				).read()
							_check_text_dataset(A					,		A				)
@pytest.mark.parametrize(
    '''features'''					,		[
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]					,		)
def 					UpperCAmelCase   (						A  :				List[str]					,		A  :				str					,		A  :				int				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			tmp_path / '''cache'''
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			{'''text''': '''string'''}
							SCREAMING_SNAKE_CASE					:      str     					=			features.copy() if features else default_expected_features
							SCREAMING_SNAKE_CASE					:      int     					=			(
							    Features({feature: Value(A				) for feature, dtype in features.items()}				) if features is not None else None
							)
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			TextDatasetReader(A					,		features=A					,		cache_dir=A				).read()
							_check_text_dataset(A					,		A				)
@pytest.mark.parametrize('''split'''					,		[None, NamedSplit('''train'''				), '''train''', '''test''']				)
def 					UpperCAmelCase   (						A  :				Dict					,		A  :				str					,		A  :				Union[str, Any]				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			tmp_path / '''cache'''
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			{'''text''': '''string'''}
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			TextDatasetReader(A					,		cache_dir=A					,		split=A				).read()
							_check_text_dataset(A					,		A				)
							assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type'''					,		[str, list]				)
def 					UpperCAmelCase   (						A  :				List[str]					,		A  :				Optional[Any]					,		A  :				Tuple				):
							if issubclass(A					,		A				):
														SCREAMING_SNAKE_CASE					:      Tuple     					=			text_path
							elif issubclass(A					,		A				):
														SCREAMING_SNAKE_CASE					:      int     					=			[text_path]
							SCREAMING_SNAKE_CASE					:      str     					=			tmp_path / '''cache'''
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			{'''text''': '''string'''}
							SCREAMING_SNAKE_CASE					:      Tuple     					=			TextDatasetReader(A					,		cache_dir=A				).read()
							_check_text_dataset(A					,		A				)
def 					UpperCAmelCase   (						A  :				Optional[int]					,		A  :				Tuple					,		A  :				Union[str, Any]=("train",)				):
							assert isinstance(A					,		A				)
							for split in splits:
														SCREAMING_SNAKE_CASE					:      int     					=			dataset_dict[split]
														assert dataset.num_rows == 4
														assert dataset.num_columns == 1
														assert dataset.column_names == ["text"]
														for feature, expected_dtype in expected_features.items():
																					assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory'''					,		[False, True]				)
def 					UpperCAmelCase   (						A  :				Dict					,		A  :				Optional[Any]					,		A  :				str				):
							SCREAMING_SNAKE_CASE					:      str     					=			tmp_path / '''cache'''
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			{'''text''': '''string'''}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														SCREAMING_SNAKE_CASE					:      List[str]     					=			TextDatasetReader({'''train''': text_path}					,		cache_dir=A					,		keep_in_memory=A				).read()
							_check_text_datasetdict(A					,		A				)
@pytest.mark.parametrize(
    '''features'''					,		[
        None,
        {'''text''': '''string'''},
        {'''text''': '''int32'''},
        {'''text''': '''float32'''},
    ]					,		)
def 					UpperCAmelCase   (						A  :				str					,		A  :				str					,		A  :				str				):
							SCREAMING_SNAKE_CASE					:      int     					=			tmp_path / '''cache'''
							# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			{'''text''': '''string'''}
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			features.copy() if features else default_expected_features
							SCREAMING_SNAKE_CASE					:      int     					=			(
							    Features({feature: Value(A				) for feature, dtype in features.items()}				) if features is not None else None
							)
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			TextDatasetReader({'''train''': text_path}					,		features=A					,		cache_dir=A				).read()
							_check_text_datasetdict(A					,		A				)






@pytest.mark.parametrize('''split'''					,		[None, NamedSplit('''train'''				), '''train''', '''test''']				)
def 					UpperCAmelCase   (						A  :				Optional[int]					,		A  :				Any					,		A  :				int				):
							if split:
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			{split: text_path}
							else:
														SCREAMING_SNAKE_CASE					:      Tuple     					=			'''train'''
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			{'''train''': text_path, '''test''': text_path}
							SCREAMING_SNAKE_CASE					:      Any     					=			tmp_path / '''cache'''
							SCREAMING_SNAKE_CASE					:      Tuple     					=			{'''text''': '''string'''}
							SCREAMING_SNAKE_CASE					:      str     					=			TextDatasetReader(A					,		cache_dir=A				).read()
							_check_text_datasetdict(A					,		A					,		splits=list(path.keys()				)				)
							assert all(dataset[split].split == split for split in path.keys()				)









'''simple docstring'''



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


lowerCAmelCase_     :       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__":
							lowerCAmelCase_     :       int								=						'hopper-medium-v2'
							lowerCAmelCase_     :       str								=						gym.make(env_name)

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

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

																					# execute action in environment
																					lowerCAmelCase_			,							lowerCAmelCase_			,							lowerCAmelCase_			,							lowerCAmelCase_     :       Union[str, Any]								=						env.step(denorm_actions)
																					lowerCAmelCase_     :       Optional[int]								=						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())

																					lowerCAmelCase_     :       Union[str, Any]								=						next_observation
							except KeyboardInterrupt:
														pass

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




