





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


__A     =    {
    "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     =    "hopper-medium-v2"
     __A     =    gym.make(env_name)

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

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

               # execute action in environment
               __A ,     __A ,     __A ,     __A     =    env.step(denorm_actions)
               __A     =    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     =    next_observation
     except KeyboardInterrupt:
          pass

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








import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


__A     =    get_logger(__name__)



class        _SCREAMING_SNAKE_CASE						(     enum.Enum   ):







 '''simple docstring'''
 lowercase_		=      "all_checks"
 lowercase_		=      "basic_checks"
 lowercase_		=      "no_checks"



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a=None		)					->		Optional[int]:







  """simple docstring"""





  if expected_checksums is None:
    logger.info("Unable to verify checksums."		)
    return
  if len(set(__a		) - set(__a		)		) > 0:
    raise ExpectedMoreDownloadedFiles(str(set(__a		) - set(__a		)		)		)
  if len(set(__a		) - set(__a		)		) > 0:
    raise UnexpectedDownloadedFile(str(set(__a		) - set(__a		)		)		)
  lowerCamelCase__:				List[Any]        =[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
  lowerCamelCase__:				Union[str, Any]        =" for " + verification_name if verification_name is not None else ""
  if len(__a		) > 0:
    raise NonMatchingChecksumError(
        F"""Checksums didn't match{for_verification_name}:\n"""
        F"""{bad_urls}\n"""
        "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"		)
  logger.info("All the checksums matched successfully" + for_verification_name		)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Any:







  """simple docstring"""





  if expected_splits is None:
    logger.info("Unable to verify splits sizes."		)
    return
  if len(set(__a		) - set(__a		)		) > 0:
    raise ExpectedMoreSplits(str(set(__a		) - set(__a		)		)		)
  if len(set(__a		) - set(__a		)		) > 0:
    raise UnexpectedSplits(str(set(__a		) - set(__a		)		)		)
  lowerCamelCase__:				Optional[int]        =[
      {"expected": expected_splits[name], "recorded": recorded_splits[name]}
      for name in expected_splits
      if expected_splits[name].num_examples != recorded_splits[name].num_examples
  ]
  if len(__a		) > 0:
    raise NonMatchingSplitsSizesError(str(__a		)		)
  logger.info("All the splits matched successfully."		)





def 					lowerCAmelCase_			(	__a     ,   __a = True		)					->		dict:







  """simple docstring"""





  if record_checksum:
    lowerCamelCase__:				str        =shaaaa()
    with open(__a     ,   "rb"		) as f:
      for chunk in iter(lambda: f.read(1 << 20		)     ,   b""		):
        m.update(__a		)
      lowerCamelCase__:				Dict        =m.hexdigest()
  else:
    lowerCamelCase__:				List[str]        =None
  return {"num_bytes": os.path.getsize(__a		), "checksum": checksum}






def 					lowerCAmelCase_			(	__a		)					->		int:







  """simple docstring"""





  if dataset_size and config.IN_MEMORY_MAX_SIZE:
    return dataset_size < config.IN_MEMORY_MAX_SIZE
  else:
    return False

