



import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase=10_24   ):
						SCREAMING_SNAKE_CASE_:			str  		=	[], []

						SCREAMING_SNAKE_CASE_:			List[str]  		=	list(zip(_UpperCAmelCase    ,     _UpperCAmelCase   )   )
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	sorted_examples[0]

						def is_too_big(_UpperCAmelCase   ):
												return tok(_UpperCAmelCase    ,     return_tensors="pt"   ).input_ids.shape[1] > max_tokens

						for src, tgt in tqdm(sorted_examples[1:]   ):
												SCREAMING_SNAKE_CASE_:			str  		=	new_src + " " + src
												SCREAMING_SNAKE_CASE_:			Tuple  		=	new_tgt + " " + tgt
												if is_too_big(_UpperCAmelCase   ) or is_too_big(_UpperCAmelCase   ):  # cant fit, finalize example
																		finished_src.append(_UpperCAmelCase   )
																		finished_tgt.append(_UpperCAmelCase   )
																		SCREAMING_SNAKE_CASE_:			int  		=	src, tgt
												else:  # can fit, keep adding
																		SCREAMING_SNAKE_CASE_:			List[str]  		=	cand_src, cand_tgt

    # cleanup
						if new_src:
												assert new_tgt
												finished_src.append(_UpperCAmelCase   )
												finished_tgt.append(_UpperCAmelCase   )
						return finished_src, finished_tgt







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	Path(_UpperCAmelCase   )
						save_path.mkdir(exist_ok=_UpperCAmelCase   )
						for split in ["train"]:
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	data_dir / f"{split}.source", data_dir / f"{split}.target"
												SCREAMING_SNAKE_CASE_:			Tuple  		=	[x.rstrip() for x in Path(_UpperCAmelCase   ).open().readlines()]
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	[x.rstrip() for x in Path(_UpperCAmelCase   ).open().readlines()]
												SCREAMING_SNAKE_CASE_:			List[str]  		=	pack_examples(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )
												print(f"packed {split} split from {len(_UpperCAmelCase   )} examples -> {len(_UpperCAmelCase   )}."   )
												Path(save_path / f"{split}.source"   ).open("w"   ).write("\n".join(_UpperCAmelCase   )   )
												Path(save_path / f"{split}.target"   ).open("w"   ).write("\n".join(_UpperCAmelCase   )   )
						for split in ["val", "test"]:
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	data_dir / f"{split}.source", data_dir / f"{split}.target"
												shutil.copyfile(_UpperCAmelCase    ,     save_path / f"{split}.source"   )
												shutil.copyfile(_UpperCAmelCase    ,     save_path / f"{split}.target"   )







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	argparse.ArgumentParser()
						parser.add_argument("--tok_name"    ,     type=_UpperCAmelCase    ,     help="like facebook/bart-large-cnn,t5-base, etc."   )
						parser.add_argument("--max_seq_len"    ,     type=_UpperCAmelCase    ,     default=1_28   )
						parser.add_argument("--data_dir"    ,     type=_UpperCAmelCase   )
						parser.add_argument("--save_path"    ,     type=_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	parser.parse_args()
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	AutoTokenizer.from_pretrained(args.tok_name   )
						return pack_data_dir(_UpperCAmelCase    ,     Path(args.data_dir   )    ,     args.max_seq_len    ,     args.save_path   )


if __name__ == "__main__":
						packer_cli()









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


lowerCAmelCase							:  Tuple            =						{
    """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							:  Optional[int]            =						"""hopper-medium-v2"""
						lowerCAmelCase							:  Optional[int]            =						gym.make(env_name)

						lowerCAmelCase							:  Optional[int]            =						ValueGuidedRLPipeline.from_pretrained(
						    """bglick13/hopper-medium-v2-value-function-hor32""",
						    env=env,
						)

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

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

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






