
"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xlnet import XLNetTokenizer
else:
							lowerCamelCase__           =	None


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase__           =	{
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase__           =	{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

lowerCamelCase__           =	"""▁"""

# Segments (not really needed)
lowerCamelCase__           =	0
lowerCamelCase__           =	1
lowerCamelCase__           =	2
lowerCamelCase__           =	3
lowerCamelCase__           =	4




class 				A__ (			_lowerCamelCase):
		A_		: Tuple            =			VOCAB_FILES_NAMES
		A_		: List[str]            =			PRETRAINED_VOCAB_FILES_MAP
		A_		: Tuple            =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_		: Any            =			'left'
		A_		: str            =			XLNetTokenizer

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE="<s>" ,	_SCREAMING_SNAKE_CASE="</s>" ,	_SCREAMING_SNAKE_CASE="<unk>" ,	_SCREAMING_SNAKE_CASE="<sep>" ,	_SCREAMING_SNAKE_CASE="<pad>" ,	_SCREAMING_SNAKE_CASE="<cls>" ,	_SCREAMING_SNAKE_CASE="<mask>" ,	_SCREAMING_SNAKE_CASE=["<eop>", "<eod>"] ,	**_SCREAMING_SNAKE_CASE ,	):
			# Mask token behave like a normal word, i.e. include the space before it
			__lowerCAmelCase    :		int		    =   AddedToken(_SCREAMING_SNAKE_CASE ,	lstrip=_SCREAMING_SNAKE_CASE ,	rstrip=_SCREAMING_SNAKE_CASE				) if isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) else mask_token

			super().__init__(
			    vocab_file=_SCREAMING_SNAKE_CASE ,	tokenizer_file=_SCREAMING_SNAKE_CASE ,	do_lower_case=_SCREAMING_SNAKE_CASE ,	remove_space=_SCREAMING_SNAKE_CASE ,	keep_accents=_SCREAMING_SNAKE_CASE ,	bos_token=_SCREAMING_SNAKE_CASE ,	eos_token=_SCREAMING_SNAKE_CASE ,	unk_token=_SCREAMING_SNAKE_CASE ,	sep_token=_SCREAMING_SNAKE_CASE ,	pad_token=_SCREAMING_SNAKE_CASE ,	cls_token=_SCREAMING_SNAKE_CASE ,	mask_token=_SCREAMING_SNAKE_CASE ,	additional_special_tokens=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)

			__lowerCAmelCase    :		Dict		    =   3
			__lowerCAmelCase    :		Dict		    =   do_lower_case
			__lowerCAmelCase    :		List[str]		    =   remove_space
			__lowerCAmelCase    :		Any		    =   keep_accents
			__lowerCAmelCase    :		int		    =   vocab_file
			__lowerCAmelCase    :		Union[str, Any]		    =   False if not self.vocab_file else True

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
			__lowerCAmelCase    :		int		    =   [self.sep_token_id]
			__lowerCAmelCase    :		str		    =   [self.cls_token_id]
			if token_ids_a is None:
				return token_ids_a + sep + cls
			return token_ids_a + sep + token_ids_a + sep + cls

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
			__lowerCAmelCase    :		Tuple		    =   [self.sep_token_id]
			__lowerCAmelCase    :		List[Any]		    =   [2]

			if token_ids_a is None:
				return len(token_ids_a + sep				) * [0] + cls_segment_id
			return len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1] + cls_segment_id







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
			if not self.can_save_slow_tokenizer:
				raise ValueError(
				    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
				    'tokenizer.'				)

			if not os.path.isdir(_SCREAMING_SNAKE_CASE				):
				logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
				return
			__lowerCAmelCase    :		str		    =   os.path.join(
			    _SCREAMING_SNAKE_CASE ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

			if os.path.abspath(self.vocab_file				) != os.path.abspath(_SCREAMING_SNAKE_CASE				):
				copyfile(self.vocab_file ,	_SCREAMING_SNAKE_CASE				)

			return (out_vocab_file,)

"""simple docstring"""







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


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

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

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

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

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