





"""simple docstring"""




import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a		:				Tuple  			=    logging.get_logger(__name__)

a		:				List[str]  			=    '''▁'''

a		:				Optional[int]  			=    {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

a		:				Optional[Any]  			=    {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

a		:				Tuple  			=    {
    '''facebook/s2t-small-librispeech-asr''': 1024,
}

a		:				Optional[int]  			=    ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

a		:				str  			=    {'''mustc''': MUSTC_LANGS}


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      Optional[Any]         =VOCAB_FILES_NAMES
							lowerCamelCase     :      List[Any]         =PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase     :      List[Any]         =MAX_MODEL_INPUT_SIZES
							lowerCamelCase     :      List[str]         =["""input_ids""", """attention_mask"""]

							lowerCamelCase     :      List[int]         =[]






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__="<s>"   ,      lowerCAmelCase__="</s>"   ,      lowerCAmelCase__="<pad>"   ,      lowerCAmelCase__="<unk>"   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				None:
								a   :	str			=       {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=lowerCAmelCase__   ,      eos_token=lowerCAmelCase__   ,      unk_token=lowerCAmelCase__   ,      pad_token=lowerCAmelCase__   ,      do_upper_case=lowerCAmelCase__   ,      do_lower_case=lowerCAmelCase__   ,      tgt_lang=lowerCAmelCase__   ,      lang_codes=lowerCAmelCase__   ,      sp_model_kwargs=self.sp_model_kwargs   ,      **lowerCAmelCase__   ,      )
								a   :	Tuple			=       do_upper_case
								a   :	List[Any]			=       do_lower_case

								a   :	List[str]			=       load_json(lowerCAmelCase__   )
								a   :	str			=       {v: k for k, v in self.encoder.items()}
								a   :	Tuple			=       spm_file
								a   :	int			=       load_spm(lowerCAmelCase__   ,      self.sp_model_kwargs   )

								if lang_codes is not None:
									a   :	List[Any]			=       lang_codes
									a   :	Any			=       LANGUAGES[lang_codes]
									a   :	Tuple			=       [f"""<lang:{lang}>""" for lang in self.langs]
									a   :	Tuple			=       {lang: self.sp_model.PieceToId(f"""<lang:{lang}>"""   ) for lang in self.langs}

									a   :	Dict			=       self.lang_tokens
									a   :	Union[str, Any]			=       tgt_lang if tgt_lang is not None else self.langs[0]

									self.set_tgt_lang_special_tokens(self._tgt_lang   )
								else:
									a   :	Tuple			=       {}






							@property
							def 					__a     (     self   )			->				int:
								return len(self.encoder   )






							@property
							def 					__a     (     self   )			->				str:
								return self._tgt_lang






							@tgt_lang.setter
							def 					__a     (     self   ,      lowerCAmelCase__   )			->				None:
								a   :	List[str]			=       new_tgt_lang
								self.set_tgt_lang_special_tokens(lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				None:
								a   :	int			=       self.lang_code_to_id[tgt_lang]
								a   :	Union[str, Any]			=       [lang_code_id]






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				List[str]:
								return self.sp_model.encode(lowerCAmelCase__   ,      out_type=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Any:
								return self.encoder.get(lowerCAmelCase__   ,      self.encoder[self.unk_token]   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				str:
								return self.decoder.get(lowerCAmelCase__   ,      self.unk_token   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				str:
								a   :	int			=       []
								a   :	Optional[int]			=       ""
								for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
										a   :	Optional[Any]			=       self.sp_model.decode(lowerCAmelCase__   )
										out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
										a   :	Any			=       []
									else:
										current_sub_tokens.append(lowerCAmelCase__   )
								a   :	Optional[int]			=       self.sp_model.decode(lowerCAmelCase__   )
								out_string += decoded.upper() if self.do_upper_case else decoded
								return out_string.strip()






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=None   )			->				List[int]:
								if token_ids_a is None:
									return self.prefix_tokens + token_ids_a + [self.eos_token_id]
								# 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.eos_token_id]






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = False   )			->				List[int]:

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

								a   :	int			=       [1] * len(self.prefix_tokens   )
								a   :	Tuple			=       [1]
								if token_ids_a is None:
									return prefix_ones + ([0] * len(lowerCAmelCase__   )) + suffix_ones
								return prefix_ones + ([0] * len(lowerCAmelCase__   )) + ([0] * len(lowerCAmelCase__   )) + suffix_ones






							def 					__a     (     self   )			->				Dict:
								a   :	Optional[Any]			=       self.encoder.copy()
								vocab.update(self.added_tokens_encoder   )
								return vocab






							def __getstate__(     self   )			->				Dict:
								a   :	Optional[Any]			=       self.__dict__.copy()
								a   :	Dict			=       None
								return state






							def __setstate__(     self   ,      lowerCAmelCase__   )			->				None:
								a   :	int			=       d

								# for backward compatibility
								if not hasattr(self   ,      "sp_model_kwargs"   ):
									a   :	Optional[int]			=       {}

								a   :	Union[str, Any]			=       load_spm(self.spm_file   ,      self.sp_model_kwargs   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Tuple[str]:
								a   :	str			=       Path(lowerCAmelCase__   )
								assert save_dir.is_dir(), f"""{save_directory} should be a directory"""
								a   :	Any			=       save_dir / (
								    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
								)
								a   :	Tuple			=       save_dir / (
								    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
								)

								save_json(self.encoder   ,      lowerCAmelCase__   )

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

								return (str(lowerCAmelCase__   ), str(lowerCAmelCase__   ))






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str					, _lowercase		:					Dict[str, Any]      ):






	'''simple docstring'''






	a   :	Optional[int]			=       sentencepiece.SentencePieceProcessor(**_lowercase      )
	spm.Load(str(_lowercase      )      )
	return spm






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      ):






	'''simple docstring'''






	with open(_lowercase					, "r"      ) as f:
		return json.load(_lowercase      )







def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Dict					, _lowercase		:					str      ):






	'''simple docstring'''






	with open(_lowercase					, "w"      ) as f:
		json.dump(_lowercase					, _lowercase					, indent=2      )









"""simple docstring"""




def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int = 200      )				->int:






	'''simple docstring'''






	a   :	Dict			=       [1, 2, 5, 10, 20, 50, 100, 200]
	a   :	Optional[Any]			=       [0] * (pence + 1)
	a   :	List[Any]			=       1  # base case: 1 way to make 0 pence

	for coin in coins:
		for i in range(_lowercase					, pence + 1					, 1      ):
			number_of_ways[i] += number_of_ways[i - coin]
	return number_of_ways[pence]


if __name__ == "__main__":
		assert solution(200) == 73682


