"""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


__lowercase		:		str														=							logging.get_logger(__name__)

__lowercase		:		Any														=							"▁"

__lowercase		:		str														=							{
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
}

__lowercase		:		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"
        )
    },
}

__lowercase		:		int														=							{
    "facebook/s2t-small-librispeech-asr": 1024,
}

__lowercase		:		Union[str, Any]														=							["pt", "fr", "ru", "nl", "ro", "it", "es", "de"]

__lowercase		:		List[str]														=							{"mustc": MUSTC_LANGS}



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					VOCAB_FILES_NAMES
    UpperCamelCase_	:					List[Any]							=					PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase_	:					List[Any]							=					MAX_MODEL_INPUT_SIZES
    UpperCamelCase_	:					Optional[Any]							=					['''input_ids''', '''attention_mask''']

    UpperCamelCase_	:					List[int]							=					[]
    def __init__(		self							:     List[str]	,      A_							:     str	,      A_							:     Tuple	,      A_							:     Union[str, Any]="<s>"	,      A_							:     Union[str, Any]="</s>"	,      A_							:     Optional[int]="<pad>"	,      A_							:     Union[str, Any]="<unk>"	,      A_							:     List[str]=False	,      A_							:     Union[str, Any]=False	,      A_							:     Optional[int]=None	,      A_							:     Tuple=None	,      A_							:     Optional[Dict[str, Any]] = None	,      **A_							:     Optional[int]	,      )    ->       None:
         __snake_case				=							{} 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_	,      )
         __snake_case				=							do_upper_case
         __snake_case				=							do_lower_case

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

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

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

              self.set_tgt_lang_special_tokens(self._tgt_lang    )
         else:
              __snake_case				=							{}
    @property
    def  lowercase					(		self							:     Optional[int]    )    ->       int:
         return len(self.encoder    )
    @property
    def  lowercase					(		self							:     str    )    ->       str:
         return self._tgt_lang
    @tgt_lang.setter
    def  lowercase					(		self							:     Tuple	,      A_							:     Union[str, Any]    )    ->       None:
         __snake_case				=							new_tgt_lang
         self.set_tgt_lang_special_tokens(A_    )
    def  lowercase					(		self							:     List[str]	,      A_							:     str    )    ->       None:
         __snake_case				=							self.lang_code_to_id[tgt_lang]
         __snake_case				=							[lang_code_id]
    def  lowercase					(		self							:     Dict	,      A_							:     str    )    ->       List[str]:
         return self.sp_model.encode(A_	,      out_type=A_    )
    def  lowercase					(		self							:     List[Any]	,      A_							:     int    )    ->       List[Any]:
         return self.encoder.get(A_	,      self.encoder[self.unk_token]    )
    def  lowercase					(		self							:     Optional[int]	,      A_							:     int    )    ->       str:
         return self.decoder.get(A_	,      self.unk_token    )
    def  lowercase					(		self							:     str	,      A_							:     List[str]    )    ->       str:
         __snake_case				=							[]
         __snake_case				=							''''''
         for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                   __snake_case				=							self.sp_model.decode(A_    )
                   out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                   __snake_case				=							[]
              else:
                   current_sub_tokens.append(A_    )
         __snake_case				=							self.sp_model.decode(A_    )
         out_string += decoded.upper() if self.do_upper_case else decoded
         return out_string.strip()
    def  lowercase					(		self							:     Any	,      A_							:     List[str]	,      A_							:     Tuple=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  lowercase					(		self							:     Dict	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None	,      A_							:     bool = False    )    ->       List[int]:

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

         __snake_case				=							[1] * len(self.prefix_tokens    )
         __snake_case				=							[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  lowercase					(		self							:     Any    )    ->       Dict:
         __snake_case				=							self.encoder.copy()
         vocab.update(self.added_tokens_encoder    )
         return vocab
    def __getstate__(		self							:     Union[str, Any]    )    ->       Dict:
         __snake_case				=							self.__dict__.copy()
         __snake_case				=							None
         return state
    def __setstate__(		self							:     Dict	,      A_							:     Dict    )    ->       None:
         __snake_case				=							d

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

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







    def  lowercase					(		self							:     int	,      A_							:     str	,      A_							:     Optional[str] = None    )    ->       Tuple[str]:
         __snake_case				=							Path(A_    )
         assert save_dir.is_dir(), f"{save_directory} should be a directory"
         __snake_case				=							save_dir / (
             (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
         )
         __snake_case				=							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:
                   __snake_case				=							self.sp_model.serialized_model_proto()
                   fi.write(A_    )

         return (str(A_    ), str(A_    ))






def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     __snake_case				=							sentencepiece.SentencePieceProcessor(**snake_case)
     spm.Load(str(snake_case))
     return spm





def    SCREAMING_SNAKE_CASE       (     snake_case):
     with open(snake_case,      '''r''') as f:
          return json.load(snake_case)





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     with open(snake_case,      '''w''') as f:
          json.dump(snake_case,      snake_case,      indent=2)
"""simple docstring"""



from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=_UpperCAmelCase					)
class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					field(default='''summarization'''							, metadata={'''include_in_asdict_even_if_is_default''': True}					)
    UpperCamelCase_	:					ClassVar[Features]							=					Features({'''text''': Value('''string'''					)}					)
    UpperCamelCase_	:					ClassVar[Features]							=					Features({'''summary''': Value('''string'''					)}					)
    UpperCamelCase_	:					str							=					"text"
    UpperCamelCase_	:					str							=					"summary"
    @property
    def  lowercase					(		self							:     List[Any]    )    ->       Dict[str, str]:
         return {self.text_column: "text", self.summary_column: "summary"}