



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


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

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

    env.seed(0)
    _UpperCAmelCase	       =	env.reset()
    _UpperCAmelCase	       =	0
    _UpperCAmelCase	       =	0
    _UpperCAmelCase	       =	1_000
    _UpperCAmelCase	       =	[obs.copy()]
    try:
        for t in tqdm.tqdm(range(T)):
            # call the policy
            _UpperCAmelCase	       =	pipeline(obs, planning_horizon=32)

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

            _UpperCAmelCase	       =	next_observation
    except KeyboardInterrupt:
        pass

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








from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader



class     lowerCAmelCase_		(  a__							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = "arrow",      **SCREAMING_SNAKE_CASE_,      )					->		int:
          super().__init__(
              split=SCREAMING_SNAKE_CASE_,      features=SCREAMING_SNAKE_CASE_,      cache_dir=SCREAMING_SNAKE_CASE_,      keep_in_memory=SCREAMING_SNAKE_CASE_,      streaming=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )
          UpperCamelCase   :  Any						     =     load_from_cache_file
          UpperCamelCase   :  Any						     =     file_format
          UpperCamelCase   :  str						     =     Spark(
              df=SCREAMING_SNAKE_CASE_,      features=SCREAMING_SNAKE_CASE_,      cache_dir=SCREAMING_SNAKE_CASE_,      working_dir=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )






       def  snake_case_	(      self	)					->		Tuple:
          if self.streaming:
             return self.builder.as_streaming_dataset(split=self.split	)
          UpperCamelCase   :  Dict						     =     None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
          self.builder.download_and_prepare(
              download_mode=SCREAMING_SNAKE_CASE_,      file_format=self._file_format,      )
          return self.builder.as_dataset(split=self.split	)



