


"""simple docstring"""




import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
       import sqlitea

       import sqlalchemy


class 	A_ (	__A   ):




      '''simple docstring'''


      def __init__(    self:	Tuple						,				a:	Optional[int]						,				a:	int						,				a:	Optional[int] = None						,				a:	Any = None						,				a:	int = False						,				**a:	List[str]						,				):
       super().__init__(features=__lowercase						,				cache_dir=__lowercase						,				keep_in_memory=__lowercase						,				**__lowercase    )
       __lowerCamelCase		:							Optional[int]		 =						Sql(
           cache_dir=__lowercase						,				features=__lowercase						,				sql=__lowercase						,				con=__lowercase						,				**__lowercase						,				)




      def 					_snake_case     (    self:	Optional[int]    ):
       __lowerCamelCase		:							int		 =						None
       __lowerCamelCase		:							int		 =						None
       __lowerCamelCase		:							str		 =						None
       __lowerCamelCase		:							str		 =						None

       self.builder.download_and_prepare(
           download_config=__lowercase						,				download_mode=__lowercase						,				verification_mode=__lowercase						,				base_path=__lowercase						,				)

       # Build dataset for splits
       __lowerCamelCase		:							Tuple		 =						self.builder.as_dataset(
           split='train'						,				verification_mode=__lowercase						,				in_memory=self.keep_in_memory    )
       return dataset



class 	A_ :




      '''simple docstring'''


      def __init__(    self:	Optional[int]						,				a:	int						,				a:	List[str]						,				a:	Dict						,				a:	Optional[Any] = None						,				a:	List[str] = None						,				**a:	List[str]						,				):
       if num_proc is not None and num_proc <= 0:
        raise ValueError(F'num_proc {num_proc} must be an integer > 0.'    )

       __lowerCamelCase		:							List[str]		 =						dataset
       __lowerCamelCase		:							List[str]		 =						name
       __lowerCamelCase		:							Dict		 =						con
       __lowerCamelCase		:							int		 =						batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
       __lowerCamelCase		:							Any		 =						num_proc
       __lowerCamelCase		:							List[str]		 =						to_sql_kwargs




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							Optional[int]		 =						self.to_sql_kwargs.pop('sql'						,				__lowercase    )
       __lowerCamelCase		:							Tuple		 =						self.to_sql_kwargs.pop('con'						,				__lowercase    )
       __lowerCamelCase		:							Tuple		 =						self.to_sql_kwargs.pop('index'						,				__lowercase    )

       __lowerCamelCase		:							int		 =						self._write(index=__lowercase						,				**self.to_sql_kwargs    )
       return written




      def 					_snake_case     (    self:	Optional[int]						,				a:	Tuple    ):
       __lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						args
       __lowerCamelCase		:							str		 =						{**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
       __lowerCamelCase		:							Dict		 =						query_table(
           table=self.dataset.data						,				key=slice(__lowercase						,				offset + self.batch_size    )						,				indices=self.dataset._indices						,				)
       __lowerCamelCase		:							Union[str, Any]		 =						batch.to_pandas()
       __lowerCamelCase		:							Optional[int]		 =						df.to_sql(self.name						,				self.con						,				index=__lowercase						,				**__lowercase    )
       return num_rows or len(__lowercase    )




      def 					_snake_case     (    self:	str						,				a:	str						,				**a:	Any    ):
       __lowerCamelCase		:							Any		 =						0

       if self.num_proc is None or self.num_proc == 1:
        for offset in logging.tqdm(
            range(0						,				len(self.dataset    )						,				self.batch_size    )						,				unit='ba'						,				disable=not logging.is_progress_bar_enabled()						,				desc='Creating SQL from Arrow format'						,				):
         written += self._batch_sql((offset, index, to_sql_kwargs)    )
       else:
        __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						len(self.dataset    ), self.batch_size
        with multiprocessing.Pool(self.num_proc    ) as pool:
         for num_rows in logging.tqdm(
             pool.imap(
                 self._batch_sql						,				[(offset, index, to_sql_kwargs) for offset in range(0						,				__lowercase						,				__lowercase    )]						,				)						,				total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,				unit='ba'						,				disable=not logging.is_progress_bar_enabled()						,				desc='Creating SQL from Arrow format'						,				):
          written += num_rows

       return written





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


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

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

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

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

                     lowercase_	   =     next_observation
       except KeyboardInterrupt:
              pass

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

