


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

import sentencepiece as spm

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


_snake_case            = logging.get_logger(__name__)

_snake_case            = {"vocab_file": "sentencepiece.bpe.model"}

_snake_case            = {
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

_snake_case            = {
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

_snake_case            = "▁"







class 		lowercase							(     UpperCamelCase__	):
     _a											=		VOCAB_FILES_NAMES
     _a											=		PRETRAINED_VOCAB_FILES_MAP
     _a											=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _a											=		["input_ids", "attention_mask"]

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

           _A							:  List[str]    =		{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               bos_token=_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							:  int    =		vocab_file
           _A							:  Dict    =		spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(str(_a       )       )

           _A							:  Union[str, Any]    =		{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

           _A							:  Any    =		len(self.sp_model       ) - 1
           _A							:  Optional[int]    =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}

     def       a__			(						self	,  _a	,  _a = None       )		->							List[int]:

           if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           _A							:  Optional[int]    =		[self.cls_token_id]
           _A							:  Optional[Any]    =		[self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep

     def       a__			(						self	,  _a	,  _a = None	,  _a = 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       )

           if token_ids_a is None:
                 return [1] + ([0] * len(_a       )) + [1]
           return [1] + ([0] * len(_a       )) + [1, 1] + ([0] * len(_a       )) + [1]

     def       a__			(						self	,  _a	,  _a = None       )		->							List[int]:
           _A							:  int    =		[self.sep_token_id]
           _A							:  Any    =		[self.cls_token_id]

           if token_ids_a is None:
                 return len(cls + token_ids_a + sep       ) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]

     @property
     def       a__			(						self       )		->							Tuple:
           return len(self.sp_model       )

     def       a__			(						self       )		->							Any:
           _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       )		->							List[str]:
           return self.sp_model.encode(_a	,  out_type=_a       )

     def       a__			(						self	,  _a       )		->							List[Any]:
           if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
           _A							:  Optional[int]    =		self.sp_model.PieceToId(_a       )

           return spm_id if spm_id else self.unk_token_id

     def       a__			(						self	,  _a       )		->							Any:
           if index in self.fairseq_ids_to_tokens:
                 return self.fairseq_ids_to_tokens[index]
           return self.sp_model.IdToPiece(_a       )

     def       a__			(						self	,  _a       )		->							Optional[int]:
           _A							:  Union[str, Any]    =		[]
           _A							:  str    =		""""""
           _A							:  List[str]    =		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							:  Tuple    =		True
                       _A							:  Dict    =		[]
                 else:
                       current_sub_tokens.append(_a       )
                       _A							:  Optional[Any]    =		False
           out_string += self.sp_model.decode(_a       )
           return out_string.strip()

     def __getstate__(						self       )		->							Dict:
           _A							:  List[Any]    =		self.__dict__.copy()
           _A							:  Union[str, Any]    =		None
           return state

     def __setstate__(						self	,  _a       )		->							int:
           _A							:  Optional[int]    =		d

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

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



     def       a__			(						self	,  _a	,  _a = None       )		->							Tuple[str]:
           if not os.path.isdir(_a       ):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
                 return
           _A							:  Optional[Any]    =		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							:  Union[str, Any]    =		self.sp_model.serialized_model_proto()
                       fi.write(_a       )

           return (out_vocab_file,)







from __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  List[str]    =		list(snake_case_  )
      _A							:  List[Any]    =		list(snake_case_  )
      _A							:  Tuple    =		0
      for i in range(len(snake_case_  )  ):
            if lista[i] != lista[i]:
                  count += 1
                  _A							:  Optional[Any]    =		"""_"""
      if count > 1:
            return False
      else:
            return "".join(snake_case_  )







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  Optional[Any]    =		[]
      while True:
            _A							:  int    =		["""$"""] * len(snake_case_  )
            _A							:  Any    =		[]
            for i in range(len(snake_case_  )  ):
                  for j in range(i + 1,len(snake_case_  )  ):
                        _A							:  Tuple    =		compare_string(binary[i],binary[j]  )
                        if k is False:
                              _A							:  str    =		"""*"""
                              _A							:  str    =		"""*"""
                              temp.append("""X"""  )
            for i in range(len(snake_case_  )  ):
                  if checka[i] == "$":
                        pi.append(binary[i]  )
            if len(snake_case_  ) == 0:
                  return pi
            _A							:  Dict    =		list(set(snake_case_  )  )







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  List[str]    =		[]
      for minterm in minterms:
            _A							:  Tuple    =		""""""
            for _ in range(snake_case_  ):
                  _A							:  Optional[Any]    =		str(minterm % 2  ) + string
                  minterm //= 2
            temp.append(snake_case_  )
      return temp







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):
      _A							:  Dict    =		list(snake_case_  )
      _A							:  Tuple    =		list(snake_case_  )
      _A							:  Dict    =		0
      for i in range(len(snake_case_  )  ):
            if lista[i] != lista[i]:
                  count_n += 1
      return count_n == count







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Optional[int]    =		[]
      _A							:  str    =		[0] * len(snake_case_  )
      for i in range(len(chart[0]  )  ):
            _A							:  Union[str, Any]    =		0
            _A							:  Optional[Any]    =		-1
            for j in range(len(snake_case_  )  ):
                  if chart[j][i] == 1:
                        count += 1
                        _A							:  Dict    =		j
            if count == 1:
                  _A							:  int    =		1
      for i in range(len(snake_case_  )  ):
            if select[i] == 1:
                  for j in range(len(chart[0]  )  ):
                        if chart[i][j] == 1:
                              for k in range(len(snake_case_  )  ):
                                    _A							:  int    =		0
                  temp.append(prime_implicants[i]  )
      while True:
            _A							:  Optional[Any]    =		0
            _A							:  Tuple    =		-1
            _A							:  List[Any]    =		0
            for i in range(len(snake_case_  )  ):
                  _A							:  List[str]    =		chart[i].count(1  )
                  if count_n > max_n:
                        _A							:  Optional[int]    =		count_n
                        _A							:  Tuple    =		i

            if max_n == 0:
                  return temp

            temp.append(prime_implicants[rem]  )

            for i in range(len(chart[0]  )  ):
                  if chart[rem][i] == 1:
                        for j in range(len(snake_case_  )  ):
                              _A							:  Optional[int]    =		0







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Optional[int]    =		[[0 for x in range(len(snake_case_  )  )] for x in range(len(snake_case_  )  )]
      for i in range(len(snake_case_  )  ):
            _A							:  List[Any]    =		prime_implicants[i].count("""_"""  )
            for j in range(len(snake_case_  )  ):
                  if is_for_table(prime_implicants[i],binary[j],snake_case_  ):
                        _A							:  Union[str, Any]    =		1

      return chart







def        lowerCAmelCase_			(  ):
      _A							:  Dict    =		int(input("""Enter the no. of variables\n"""  )  )
      _A							:  Dict    =		[
          float(snake_case_  )
          for x in input(
              """Enter the decimal representation of Minterms 'Spaces Separated'\n"""  ).split()
      ]
      _A							:  int    =		decimal_to_binary(snake_case_,snake_case_  )

      _A							:  Optional[Any]    =		check(snake_case_  )
      print("""Prime Implicants are:"""  )
      print(snake_case_  )
      _A							:  int    =		prime_implicant_chart(snake_case_,snake_case_  )

      _A							:  int    =		selection(snake_case_,snake_case_  )
      print("""Essential Prime Implicants are:"""  )
      print(snake_case_  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

