

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_ :				Optional[int]						 =    logging.get_logger(__name__)

A_ :				Any						 =    '▁'

A_ :				Dict						 =    {
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
}

A_ :				Optional[int]						 =    {
    '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_ :				str						 =    {
    'facebook/s2t-small-librispeech-asr': 1024,
}

A_ :				Any						 =    ['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']

A_ :				str						 =    {'mustc': MUSTC_LANGS}



class 					_a		(UpperCAmelCase__       ):


     '''simple docstring'''






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

     UpperCAmelCase__:     List[int]													= []






     def __init__(				self					, A__					, A__					, A__="<s>"					, A__="</s>"					, A__="<pad>"					, A__="<unk>"					, A__=False					, A__=False					, A__=None					, A__=None					, A__ = None					, **A__					, ):
      A__							:					Optional[int]       =	{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=A__					, eos_token=A__					, unk_token=A__					, pad_token=A__					, do_upper_case=A__					, do_lower_case=A__					, tgt_lang=A__					, lang_codes=A__					, sp_model_kwargs=self.sp_model_kwargs					, **A__					, )
      A__							:					Optional[Any]       =	do_upper_case
      A__							:					str       =	do_lower_case

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

      if lang_codes is not None:
       A__							:					Optional[Any]       =	lang_codes
       A__							:					str       =	LANGUAGES[lang_codes]
       A__							:					List[Any]       =	[F"""<lang:{lang}>""" for lang in self.langs]
       A__							:					Any       =	{lang: self.sp_model.PieceToId(F"""<lang:{lang}>"""    ) for lang in self.langs}

       A__							:					Tuple       =	self.lang_tokens
       A__							:					Any       =	tgt_lang if tgt_lang is not None else self.langs[0]

       self.set_tgt_lang_special_tokens(self._tgt_lang    )
      else:
       A__							:					List[Any]       =	{}






     @property
     def 						__A    (				self    ):
      return len(self.encoder    )






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






     @tgt_lang.setter
     def 						__A    (				self					, A__    ):
      A__							:					Dict       =	new_tgt_lang
      self.set_tgt_lang_special_tokens(A__    )






     def 						__A    (				self					, A__    ):
      A__							:					int       =	self.lang_code_to_id[tgt_lang]
      A__							:					Any       =	[lang_code_id]






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






     def 						__A    (				self					, A__    ):
      return self.encoder.get(A__					, self.encoder[self.unk_token]    )






     def 						__A    (				self					, A__    ):
      return self.decoder.get(A__					, self.unk_token    )






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






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




      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					, 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__							:					List[Any]       =	[1] * len(self.prefix_tokens    )
      A__							:					Union[str, Any]       =	[1]
      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__							:					List[Any]       =	self.encoder.copy()
      vocab.update(self.added_tokens_encoder    )
      return vocab






     def __getstate__(				self    ):
      A__							:					List[str]       =	self.__dict__.copy()
      A__							:					Optional[Any]       =	None
      return state






     def __setstate__(				self					, A__    ):
      A__							:					Any       =	d

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

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






     def 						__A    (				self					, A__					, A__ = None    ):
      A__							:					List[str]       =	Path(A__    )
      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					, A__    )

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

      return (str(A__    ), str(A__    ))



def 		UpperCamelCase      (lowercase_:				List[str]					,   lowercase_:				List[str]      )  ->   str:
 A__							:					Optional[Any]       =	sentencepiece.SentencePieceProcessor(**lowercase_      )
 spm.Load(str(lowercase_      )      )
 return spm

def 		UpperCamelCase      (lowercase_:				Union[str, Any]      )  ->   Union[str, Any]:
 with open(lowercase_					,   """r"""      ) as f:
  return json.load(lowercase_      )


def 		UpperCamelCase      (lowercase_:				Optional[Any]					,   lowercase_:				Dict      )  ->   List[str]:
 with open(lowercase_					,   """w"""      ) as f:
  json.dump(lowercase_					,   lowercase_					,   indent=2      )






from functools import lru_cache

@lru_cache
def 		UpperCamelCase      (lowercase_:				int      )  ->   int:
	if num < 0:
		raise ValueError("""Number should not be negative."""      )

	return 1 if num in (0, 1) else num * factorial(num - 1      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



