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


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

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

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

               # execute action in environment
               _SCREAMING_SNAKE_CASE      						=	env.step(denorm_actions)
               _SCREAMING_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())

               _SCREAMING_SNAKE_CASE      						=	next_observation
     except KeyboardInterrupt:
          pass

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



from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


A_       : List[str]         =			{
    'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
    'tokenization_gpt_neox_japanese': ['GPTNeoXJapaneseTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_       : Optional[int]         =			[
	    'GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXJapaneseForCausalLM',
	    'GPTNeoXJapaneseLayer',
	    'GPTNeoXJapaneseModel',
	    'GPTNeoXJapanesePreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	A_       : Any         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



