






"""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_albert import AlbertTokenizer
else:
	A__	:    Union[str, Any]			 = None

A__	:    Any			 = logging.get_logger(__name__)
A__	:    Any			 = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A__	:    List[Any]			 = {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

A__	:    Optional[int]			 = {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

A__	:    Optional[Any]			 = '▁'







class        lowercase__      (							__a       ):
			_UpperCAmelCase		:List[str]             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:int             =			PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:Any             =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:List[str]             =			AlbertTokenizer


			def __init__(				self					:  List[str]       ,	snake_case__					:  List[Any]=None       ,	snake_case__					:  int=None       ,	snake_case__					:  Union[str, Any]=True       ,	snake_case__					:  Tuple=True       ,	snake_case__					:  Any=False       ,	snake_case__					:  Union[str, Any]="[CLS]"       ,	snake_case__					:  Any="[SEP]"       ,	snake_case__					:  Any="<unk>"       ,	snake_case__					:  str="[SEP]"       ,	snake_case__					:  int="<pad>"       ,	snake_case__					:  Optional[Any]="[CLS]"       ,	snake_case__					:  List[str]="[MASK]"       ,	**snake_case__					:  str       ,	):
										lowerCamelCase_	:							Tuple									=(
										    AddedToken(UpperCamelCase__       ,	lstrip=UpperCamelCase__       ,	rstrip=UpperCamelCase__       ,	normalized=UpperCamelCase__ )
										    if isinstance(UpperCamelCase__       ,	UpperCamelCase__ )
										    else mask_token
										)

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

										lowerCamelCase_	:							int									=do_lower_case
										lowerCamelCase_	:							str									=remove_space
										lowerCamelCase_	:							Any									=keep_accents
										lowerCamelCase_	:							Any									=vocab_file
										lowerCamelCase_	:							List[Any]									=False if not self.vocab_file else True


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  List[int]       ,	snake_case__					:  Optional[List[int]] = None ):
										lowerCamelCase_	:							List[str]									=[self.sep_token_id]
										lowerCamelCase_	:							Optional[Any]									=[self.cls_token_id]
										if token_ids_a is None:
																	return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  List[int]       ,	snake_case__					:  Optional[List[int]] = None ):
										lowerCamelCase_	:							Union[str, Any]									=[self.sep_token_id]
										lowerCamelCase_	:							Any									=[self.cls_token_id]

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


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  str       ,	snake_case__					:  Optional[str] = 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(UpperCamelCase__ ):
																	logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
																	return
										lowerCamelCase_	:							Optional[Any]									=os.path.join(
										    UpperCamelCase__       ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

										return (out_vocab_file,)











"""simple docstring"""



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


A__	:    int			 = {
    '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__	:    str			 = 'hopper-medium-v2'
						A__	:    Dict			 = gym.make(env_name)

						A__	:    List[Any]			 = ValueGuidedRLPipeline.from_pretrained(
						    'bglick13/hopper-medium-v2-value-function-hor32',
						    env=env,
						)

						env.seed(0)
						A__	:    Dict			 = env.reset()
						A__	:    Optional[int]			 = 0
						A__	:    str			 = 0
						A__	:    List[Any]			 = 1_000
						A__	:    Tuple			 = [obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		A__	:    Union[str, Any]			 = pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		A__ ,						A__ ,						A__ ,						A__	:    Any			 = env.step(denorm_actions)
																		A__	:    List[Any]			 = 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__	:    Optional[Any]			 = next_observation
						except KeyboardInterrupt:
												pass

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



