

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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


A_       : Any         =			logging.get_logger(__name__)

A_       : Optional[int]         =			'▁'

A_       : Union[str, Any]         =			{'vocab_file': 'sentencepiece.bpe.model'}

A_       : str         =			{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

A_       : List[Any]         =			{
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
A_       : int         =			['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Dict  							=       VOCAB_FILES_NAMES
					UpperCAmelCase__:   Optional[int]  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:   Union[str, Any]  							=       PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:   Union[str, Any]  							=       ['''input_ids''', '''attention_mask''']

					UpperCAmelCase__:   List[int]  							=       []
					UpperCAmelCase__:   List[int]  							=       []






					def __init__( self    ,       A__    ,       A__=None    ,       A__=None    ,       A__="</s>"    ,       A__="</s>"    ,       A__="<s>"    ,       A__="<unk>"    ,       A__="<pad>"    ,       A__="<mask>"    ,       A__ = None    ,       **A__    ,       ):
						# Mask token behave like a normal word, i.e. include the space before it
						A__							:    Union[str, Any]        =		AddedToken(A__    ,       lstrip=A__    ,       rstrip=A__			) if isinstance(A__    ,       A__			) else mask_token

						A__							:    str        =		{} if sp_model_kwargs is None else sp_model_kwargs

						A__							:    str        =		kwargs.get("""additional_special_tokens"""    ,       []			)
						kwargs["additional_special_tokens"] += [
						    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
						]

						super().__init__(
						    src_lang=A__    ,       tgt_lang=A__    ,       eos_token=A__    ,       unk_token=A__    ,       sep_token=A__    ,       cls_token=A__    ,       pad_token=A__    ,       mask_token=A__    ,       sp_model_kwargs=self.sp_model_kwargs    ,       **A__    ,       )

						A__							:    Any        =		spm.SentencePieceProcessor(**self.sp_model_kwargs			)
						self.sp_model.Load(str(A__			)			)
						A__							:    Union[str, Any]        =		vocab_file

						# Original fairseq vocab and spm vocab must be "aligned":
						# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
						# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
						# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
						# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

						# Mimic fairseq token-to-id alignment for the first 4 token
						A__							:    Tuple        =		{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

						# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
						A__							:    Union[str, Any]        =		1

						A__							:    Optional[int]        =		len(self.sp_model			)
						A__							:    Union[str, Any]        =		{
						    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(A__			)
						}
						A__							:    Union[str, Any]        =		{v: k for k, v in self.lang_code_to_id.items()}
						A__							:    Union[str, Any]        =		len(self.sp_model			) + len(self.lang_code_to_id			) + self.fairseq_offset

						self.fairseq_tokens_to_ids.update(self.lang_code_to_id			)
						A__							:    Any        =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}

						A__							:    Any        =		src_lang if src_lang is not None else """en_XX"""
						A__							:    Union[str, Any]        =		self.lang_code_to_id[self._src_lang]
						A__							:    Dict        =		tgt_lang
						self.set_src_lang_special_tokens(self._src_lang			)






					@property
					def   __A			( self			):
						return len(self.sp_model			) + len(self.lang_code_to_id			) + self.fairseq_offset + 1  # Plus 1 for the mask token






					@property
					def   __A			( self			):
						return self._src_lang






					@src_lang.setter
					def   __A			( self    ,       A__			):
						A__							:    Union[str, Any]        =		new_src_lang
						self.set_src_lang_special_tokens(self._src_lang			)






					def __getstate__( self			):
						A__							:    str        =		self.__dict__.copy()
						A__							:    Tuple        =		None
						return state






					def __setstate__( self    ,       A__			):
						A__							:    List[str]        =		d

						# for backward compatibility
						if not hasattr(self    ,       """sp_model_kwargs"""			):
							A__							:    Any        =		{}

						A__							:    Any        =		spm.SentencePieceProcessor(**self.sp_model_kwargs			)
						self.sp_model.Load(self.vocab_file			)






					def   __A			( self			):
						A__							:    Union[str, Any]        =		{self.convert_ids_to_tokens(A__			): i for i in range(self.vocab_size			)}
						vocab.update(self.added_tokens_encoder			)
						return vocab






					def   __A			( self    ,       A__			):
						return self.sp_model.encode(A__    ,       out_type=A__			)






					def   __A			( self    ,       A__			):
						if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
						A__							:    Union[str, Any]        =		self.sp_model.PieceToId(A__			)

						# Need to return unknown token if the SP model returned 0
						return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






					def   __A			( self    ,       A__			):
						if index in self.fairseq_ids_to_tokens:
							return self.fairseq_ids_to_tokens[index]
						return self.sp_model.IdToPiece(index - self.fairseq_offset			)






					def   __A			( self    ,       A__			):
						A__							:    List[Any]        =		[]
						A__							:    List[str]        =		""""""
						A__							:    Any        =		False
						for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
								if not prev_is_special:
									out_string += " "
								out_string += self.sp_model.decode(A__			) + token
								A__							:    Union[str, Any]        =		True
								A__							:    Union[str, Any]        =		[]
							else:
								current_sub_tokens.append(A__			)
								A__							:    Optional[int]        =		False
						out_string += self.sp_model.decode(A__			)
						return out_string.strip()






					def   __A			( self    ,       A__    ,       A__ = None			):
						if not os.path.isdir(A__			):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
							return
						A__							:    int        =		os.path.join(
						    A__    ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

						if os.path.abspath(self.vocab_file			) != os.path.abspath(A__			) and os.path.isfile(self.vocab_file			):
							copyfile(self.vocab_file    ,       A__			)
						elif not os.path.isfile(self.vocab_file			):
							with open(A__    ,       """wb"""			) as fi:
								A__							:    Optional[Any]        =		self.sp_model.serialized_model_proto()
								fi.write(A__			)

						return (out_vocab_file,)






					def   __A			( self    ,       A__    ,       A__ = None    ,       A__ = False			):

						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=A__    ,       token_ids_a=A__    ,       already_has_special_tokens=A__			)

						A__							:    Optional[int]        =		[1] * len(self.prefix_tokens			)
						A__							:    List[Any]        =		[1] * len(self.suffix_tokens			)
						if token_ids_a is None:
							return prefix_ones + ([0] * len(A__			)) + suffix_ones
						return prefix_ones + ([0] * len(A__			)) + ([0] * len(A__			)) + suffix_ones






					def   __A			( self    ,       A__    ,       A__ = None			):
						if token_ids_a is None:
							return self.prefix_tokens + token_ids_a + self.suffix_tokens
						# We don't expect to process pairs, but leave the pair logic for API consistency
						return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       A__    ,       **A__			):
						if src_lang is None or tgt_lang is None:
							raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""			)
						A__							:    List[str]        =		src_lang
						A__							:    Union[str, Any]        =		self(A__    ,       add_special_tokens=A__    ,       return_tensors=A__    ,       **A__			)
						A__							:    str        =		self.convert_tokens_to_ids(A__			)
						A__							:    Optional[Any]        =		tgt_lang_id
						return inputs






					def   __A			( self    ,       A__    ,       A__ = "en_XX"    ,       A__ = None    ,       A__ = "ro_RO"    ,       **A__    ,       ):
						A__							:    Optional[int]        =		src_lang
						A__							:    Union[str, Any]        =		tgt_lang
						return super().prepare_seqaseq_batch(A__    ,       A__    ,       **A__			)






					def   __A			( self			):
						return self.set_src_lang_special_tokens(self.src_lang			)






					def   __A			( self			):
						return self.set_tgt_lang_special_tokens(self.tgt_lang			)






					def   __A			( self    ,       A__			):
						A__							:    Dict        =		self.lang_code_to_id[src_lang]
						A__							:    List[str]        =		[self.cur_lang_code_id]
						A__							:    Any        =		[self.eos_token_id]






					def   __A			( self    ,       A__			):
						A__							:    Union[str, Any]        =		self.lang_code_to_id[tgt_lang]
						A__							:    Any        =		[self.cur_lang_code_id]
						A__							:    List[str]        =		[self.eos_token_id]






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


A_       : List[str]         =			{
    '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_       : Optional[int]         =			'hopper-medium-v2'
	A_       : List[Any]         =			gym.make(env_name)

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

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

			# execute action in environment
			A_       ,			A_       ,			A_       ,			A_       : Dict         =			env.step(denorm_actions)
			A_       : List[str]         =			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_       : int         =			next_observation
	except KeyboardInterrupt:
		pass

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



