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[Any]       =				logging.get_logger(__name__)

A   :     List[str]       =				"▁"

A   :     List[str]       =				{
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
}

A   :     List[str]       =				{
    "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   :     Union[str, Any]       =				{
    "facebook/s2t-small-librispeech-asr": 10_24,
}

A   :     int       =				["pt", "fr", "ru", "nl", "ro", "it", "es", "de"]

A   :     str       =				{"mustc": MUSTC_LANGS}


class      lowerCamelCase    (__lowerCamelCase    ):
  """simple docstring"""







  lowerCamelCase__   =  VOCAB_FILES_NAMES
  lowerCamelCase__   =  PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase__   =  MAX_MODEL_INPUT_SIZES
  lowerCamelCase__   =  ["input_ids", "attention_mask"]

  lowerCamelCase__   =  []







  def __init__(							self       :				Tuple     ,   __magic_name__       :				Dict     ,   __magic_name__       :				Union[str, Any]     ,   __magic_name__       :				Any="<s>"     ,   __magic_name__       :				List[Any]="</s>"     ,   __magic_name__       :				str="<pad>"     ,   __magic_name__       :				List[Any]="<unk>"     ,   __magic_name__       :				int=False     ,   __magic_name__       :				List[Any]=False     ,   __magic_name__       :				Tuple=None     ,   __magic_name__       :				List[Any]=None     ,   __magic_name__       :				Optional[int] = None     ,   **__magic_name__       :				Dict     ,   )     ->		None:
        SCREAMING_SNAKE_CASE_      =     {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__magic_name__     ,   eos_token=__magic_name__     ,   unk_token=__magic_name__     ,   pad_token=__magic_name__     ,   do_upper_case=__magic_name__     ,   do_lower_case=__magic_name__     ,   tgt_lang=__magic_name__     ,   lang_codes=__magic_name__     ,   sp_model_kwargs=self.sp_model_kwargs     ,   **__magic_name__     ,   )
        SCREAMING_SNAKE_CASE_      =     do_upper_case
        SCREAMING_SNAKE_CASE_      =     do_lower_case

        SCREAMING_SNAKE_CASE_      =     load_json(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     {v: k for k, v in self.encoder.items()}
        SCREAMING_SNAKE_CASE_      =     spm_file
        SCREAMING_SNAKE_CASE_      =     load_spm(__magic_name__     ,   self.sp_model_kwargs      )

        if lang_codes is not None:
              SCREAMING_SNAKE_CASE_      =     lang_codes
              SCREAMING_SNAKE_CASE_      =     LANGUAGES[lang_codes]
              SCREAMING_SNAKE_CASE_      =     [F'''<lang:{lang}>''' for lang in self.langs]
              SCREAMING_SNAKE_CASE_      =     {lang: self.sp_model.PieceToId(F'''<lang:{lang}>'''      ) for lang in self.langs}

              SCREAMING_SNAKE_CASE_      =     self.lang_tokens
              SCREAMING_SNAKE_CASE_      =     tgt_lang if tgt_lang is not None else self.langs[0]

              self.set_tgt_lang_special_tokens(self._tgt_lang      )
        else:
              SCREAMING_SNAKE_CASE_      =     {}







  @property
  def       __A   (							self       :				List[Any]      )     ->		int:
        return len(self.encoder      )







  @property
  def       __A   (							self       :				Union[str, Any]      )     ->		str:
        return self._tgt_lang







  @tgt_lang.setter
  def       __A   (							self       :				List[Any]     ,   __magic_name__       :				List[str]      )     ->		None:
        SCREAMING_SNAKE_CASE_      =     new_tgt_lang
        self.set_tgt_lang_special_tokens(__magic_name__      )







  def       __A   (							self       :				Tuple     ,   __magic_name__       :				Optional[Any]      )     ->		None:
        SCREAMING_SNAKE_CASE_      =     self.lang_code_to_id[tgt_lang]
        SCREAMING_SNAKE_CASE_      =     [lang_code_id]







  def       __A   (							self       :				Optional[Any]     ,   __magic_name__       :				Union[str, Any]      )     ->		List[str]:
        return self.sp_model.encode(__magic_name__     ,   out_type=__magic_name__      )







  def       __A   (							self       :				Optional[Any]     ,   __magic_name__       :				Union[str, Any]      )     ->		int:
        return self.encoder.get(__magic_name__     ,   self.encoder[self.unk_token]      )







  def       __A   (							self       :				Union[str, Any]     ,   __magic_name__       :				List[str]      )     ->		str:
        return self.decoder.get(__magic_name__     ,   self.unk_token      )







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







  def       __A   (							self       :				Union[str, Any]     ,   __magic_name__       :				List[Any]     ,   __magic_name__       :				Any=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       :				Union[str, Any]     ,   __magic_name__       :				int     ,   __magic_name__       :				Tuple = None     ,   __magic_name__       :				Dict = False      )     ->		List[int]:

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

        SCREAMING_SNAKE_CASE_      =     [1] * len(self.prefix_tokens      )
        SCREAMING_SNAKE_CASE_      =     [1]
        if token_ids_a is None:
              return prefix_ones + ([0] * len(__magic_name__      )) + suffix_ones
        return prefix_ones + ([0] * len(__magic_name__      )) + ([0] * len(__magic_name__      )) + suffix_ones







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







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







  def __setstate__(							self       :				Optional[int]     ,   __magic_name__       :				Optional[int]      )     ->		None:
        SCREAMING_SNAKE_CASE_      =     d

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

        SCREAMING_SNAKE_CASE_      =     load_spm(self.spm_file     ,   self.sp_model_kwargs      )







  def       __A   (							self       :				int     ,   __magic_name__       :				List[Any]     ,   __magic_name__       :				Dict = None      )     ->		Tuple[str]:
        SCREAMING_SNAKE_CASE_      =     Path(__magic_name__      )
        assert save_dir.is_dir(), F'''{save_directory} should be a directory'''
        SCREAMING_SNAKE_CASE_      =     save_dir / (
            (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
        )
        SCREAMING_SNAKE_CASE_      =     save_dir / (
            (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
        )

        save_json(self.encoder     ,   __magic_name__      )

        if os.path.abspath(self.spm_file      ) != os.path.abspath(__magic_name__      ) and os.path.isfile(self.spm_file      ):
              copyfile(self.spm_file     ,   __magic_name__      )
        elif not os.path.isfile(self.spm_file      ):
              with open(__magic_name__     ,   "wb"      ) as fi:
                    SCREAMING_SNAKE_CASE_      =     self.sp_model.serialized_model_proto()
                    fi.write(__magic_name__      )

        return (str(__magic_name__      ), str(__magic_name__      ))



def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     sentencepiece.SentencePieceProcessor(**__UpperCAmelCase )
      spm.Load(str(__UpperCAmelCase ) )
      return spm


def    a__       (						__UpperCamelCase ):
      with open(__UpperCAmelCase   ,					"r" ) as f:
            return json.load(__UpperCAmelCase )





def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      with open(__UpperCAmelCase   ,					"w" ) as f:
            json.dump(__UpperCAmelCase   ,					__UpperCAmelCase   ,					indent=2 )









import re
from pathlib import Path
from unittest import TestCase

import pytest






@pytest.mark.integration
class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Union[str, Any]:






       """simple docstring"""

       with open(__magic_name__,		encoding='''utf-8'''							) as input_file:
           UpperCamelCase__							:				Tuple          =      re.compile(R'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'''							)
           UpperCamelCase__							:				str          =      input_file.read()
           UpperCamelCase__							:				List[Any]          =      regexp.search(__magic_name__							)

       return match




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Any:






       """simple docstring"""

       with open(__magic_name__,		encoding='''utf-8'''							) as input_file:
           UpperCamelCase__							:				Dict          =      re.compile(R'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()''',		re.DOTALL							)
           UpperCamelCase__							:				Any          =      input_file.read()
           # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
           UpperCamelCase__							:				Tuple          =      regexp.finditer(__magic_name__							)

       UpperCamelCase__							:				Dict          =      [match for match in matches if match is not None and match.group(1							) is not None]
       return matches[0] if matches else None




   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				int          =      Path('''./datasets'''							)
       UpperCamelCase__							:				Any          =      list(dataset_paths.absolute().glob('''**/*.py'''							)							)

       for dataset in dataset_files:
           if self._no_encoding_on_file_open(str(__magic_name__							)							):
               raise AssertionError(f"open(...) must use utf-8 encoding in {dataset}"							)




   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      Path('''./datasets'''							)
       UpperCamelCase__							:				Optional[Any]          =      list(dataset_paths.absolute().glob('''**/*.py'''							)							)

       for dataset in dataset_files:
           if self._no_print_statements(str(__magic_name__							)							):
               raise AssertionError(f"print statement found in {dataset}. Use datasets.logger/logging instead."							)

