






"""simple docstring"""


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


lowercase_				     =     logging.get_logger(__name__)

lowercase_				     =     '▁'

lowercase_				     =     {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

lowercase_				     =     {
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

lowercase_				     =     {'vinai/bartpho-syllable': 1024}


class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:	str						 = VOCAB_FILES_NAMES
   A_						:	Optional[int]						 = PRETRAINED_VOCAB_FILES_MAP
   A_						:	Optional[int]						 = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_						:	Tuple						 = ["input_ids", "attention_mask"]


   def __init__(	self :     List[str],       _lowerCamelCase :     Union[str, Any],       _lowerCamelCase :     List[Any],       _lowerCamelCase :     List[str]="<s>",       _lowerCamelCase :     Union[str, Any]="</s>",       _lowerCamelCase :     Any="</s>",       _lowerCamelCase :     Dict="<s>",       _lowerCamelCase :     Dict="<unk>",       _lowerCamelCase :     List[Any]="<pad>",       _lowerCamelCase :     Tuple="<mask>",       _lowerCamelCase :     Optional[Dict[str, Any]] = None,       **_lowerCamelCase :     List[Any],       ):

     '''simple docstring'''
     # Mask token behave like a normal word, i.e. include the space before it
     __A									= AddedToken(_lowerCamelCase,       lstrip=_lowerCamelCase,       rstrip=_lowerCamelCase  ) if isinstance(_lowerCamelCase,       _lowerCamelCase  ) else mask_token

     __A									= {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=_lowerCamelCase,       eos_token=_lowerCamelCase,       unk_token=_lowerCamelCase,       sep_token=_lowerCamelCase,       cls_token=_lowerCamelCase,       pad_token=_lowerCamelCase,       mask_token=_lowerCamelCase,       sp_model_kwargs=self.sp_model_kwargs,       **_lowerCamelCase,       )

     __A									= vocab_file
     __A									= monolingual_vocab_file
     __A									= spm.SentencePieceProcessor(**self.sp_model_kwargs  )
     self.sp_model.Load(str(_lowerCamelCase  )  )

     # Load the reduced vocab

     # Keep order of special tokens for backward compatibility
     __A									= {}
     __A									= 0
     for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
       if str(_lowerCamelCase  ) not in self.fairseq_tokens_to_ids:
         __A									= cnt
         cnt += 1
     with open(_lowerCamelCase,       '''r''',       encoding='''utf-8'''  ) as f:
       for line in f.readlines():
         __A									= line.strip().split()[0]
         __A									= len(self.fairseq_tokens_to_ids  )
     if str(_lowerCamelCase  ) not in self.fairseq_tokens_to_ids:
       __A									= len(self.fairseq_tokens_to_ids  )

     __A									= {v: k for k, v in self.fairseq_tokens_to_ids.items()}


   def __getstate__(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= self.__dict__.copy()
     __A									= None
     __A									= self.sp_model.serialized_model_proto()
     return state


   def __setstate__(	self :     Union[str, Any],       _lowerCamelCase :     Dict  ):

     '''simple docstring'''
     __A									= d

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

     __A									= spm.SentencePieceProcessor(**self.sp_model_kwargs  )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any,       _lowerCamelCase :     List[int],       _lowerCamelCase :     Optional[List[int]] = None  ):

     '''simple docstring'''

     if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     __A									= [self.cls_token_id]
     __A									= [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     List[int],       _lowerCamelCase :     Optional[List[int]] = None,       _lowerCamelCase :     bool = False  ):

     '''simple docstring'''

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

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


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict,       _lowerCamelCase :     List[int],       _lowerCamelCase :     Optional[List[int]] = None  ):

     '''simple docstring'''
     __A									= [self.sep_token_id]
     __A									= [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 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     return len(self.fairseq_ids_to_tokens  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     __A									= {self.convert_ids_to_tokens(_lowerCamelCase  ): i for i in range(self.vocab_size  )}
     vocab.update(self.added_tokens_encoder  )
     return vocab


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any,       _lowerCamelCase :     str  ):

     '''simple docstring'''
     return self.sp_model.encode(_lowerCamelCase,       out_type=_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any],       _lowerCamelCase :     List[Any]  ):

     '''simple docstring'''
     if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
     else:
       return self.unk_token_id


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict,       _lowerCamelCase :     Optional[int]  ):

     '''simple docstring'''
     return self.fairseq_ids_to_tokens[index]


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= ''''''.join(_lowerCamelCase  ).replace(_lowerCamelCase,       ''' '''  ).strip()
     return out_string






   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any],       _lowerCamelCase :     str,       _lowerCamelCase :     Optional[str] = None  ):

     '''simple docstring'''
     if not os.path.isdir(_lowerCamelCase  ):
       logger.error(f'Vocabulary path ({save_directory}) should be a directory'  )
       return
     __A									= os.path.join(
         _lowerCamelCase,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )
     __A									= os.path.join(
         _lowerCamelCase,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file'''],       )

     if os.path.abspath(self.vocab_file  ) != os.path.abspath(_lowerCamelCase  ) and os.path.isfile(self.vocab_file  ):
       copyfile(self.vocab_file,       _lowerCamelCase  )
     elif not os.path.isfile(self.vocab_file  ):
       with open(_lowerCamelCase,       '''wb'''  ) as fi:
         __A									= self.sp_model.serialized_model_proto()
         fi.write(_lowerCamelCase  )

     if os.path.abspath(self.monolingual_vocab_file  ) != os.path.abspath(
         _lowerCamelCase  ) and os.path.isfile(self.monolingual_vocab_file  ):
       copyfile(self.monolingual_vocab_file,       _lowerCamelCase  )
     elif not os.path.isfile(self.monolingual_vocab_file  ):
       with open(_lowerCamelCase,       '''w''',       encoding='''utf-8'''  ) as fp:
         for token in self.fairseq_tokens_to_ids:
           if token not in self.all_special_tokens:
             fp.write(f'{str(_lowerCamelCase  )} \n'  )

     return out_vocab_file, out_monolingual_vocab_file












"""simple docstring"""


def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= current_set.copy()
  for row_index, row in enumerate(__UpperCamelCase ):
    __A									= row[0]
    for column_index, column in enumerate(__UpperCamelCase ):
      if magnitude == 0:
        __A									= column
        continue
      __A									= column / magnitude
    # Subtract to cancel term
  __A									= current_set[0]
  __A									= [first_row]
  __A									= current_set[1::]
  for row in current_set:
    __A									= []
    # If first term is 0, it is already in form we want, so we preserve it
    if row[0] == 0:
      final_set.append(__UpperCamelCase )
      continue
    for column_index in range(len(__UpperCamelCase ) ):
      temp_row.append(first_row[column_index] - row[column_index] )
    final_set.append(__UpperCamelCase )
  # Create next recursion iteration set
  if len(final_set[0] ) != 3:
    __A									= final_set[0]
    __A									= []
    __A									= []
    for row in final_set[1::]:
      current_first_column.append(row[0] )
      next_iteration.append(row[1::] )
    __A									= simplify(__UpperCamelCase )
    for i in range(len(__UpperCamelCase ) ):
      resultant[i].insert(0		,       current_first_column[i] )
    resultant.insert(0		,       __UpperCamelCase )
    __A									= resultant
  return final_set



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  if len(__UpperCamelCase ) == 0:
    raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
  __A									= len(__UpperCamelCase ) + 1
  if any(len(__UpperCamelCase ) != _length for item in equations ):
    raise IndexError('''solve_simultaneous() requires n lists of length n+1''' )
  for row in equations:
    if any(not isinstance(__UpperCamelCase		,       (int, float) ) for column in row ):
      raise ValueError('''solve_simultaneous() requires lists of integers''' )
  if len(__UpperCamelCase ) == 1:
    return [equations[0][-1] / equations[0][0]]
  __A									= equations.copy()
  if any(0 in row for row in data_set ):
    __A									= data_set.copy()
    __A									= []
    for row_index, row in enumerate(__UpperCamelCase ):
      if 0 not in row:
        __A									= data_set.pop(__UpperCamelCase )
        break
    if not full_row:
      raise ValueError('''solve_simultaneous() requires at least 1 full equation''' )
    data_set.insert(0		,       __UpperCamelCase )
  __A									= data_set.copy()
  __A									= simplify(__UpperCamelCase )
  __A									= simplified[::-1]
  __A									= []
  for row in simplified:
    __A									= row[-1]
    if not solutions:
      if row[-2] == 0:
        solutions.append(0 )
        continue
      solutions.append(current_solution / row[-2] )
      continue
    __A									= row.copy()[: len(__UpperCamelCase ) - 1 :]
    while temp_row[0] == 0:
      temp_row.pop(0 )
    if len(__UpperCamelCase ) == 0:
      solutions.append(0 )
      continue
    __A									= temp_row[1::]
    __A									= temp_row[::-1]
    for column_index, column in enumerate(__UpperCamelCase ):
      current_solution -= column * solutions[column_index]
    solutions.append(__UpperCamelCase )
  __A									= []
  for item in solutions:
    final.append(float(round(__UpperCamelCase		,       5 ) ) )
  return final[::-1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       lowercase_				     =     [
           [2, 1, 1, 1, 1, 4],
           [1, 2, 1, 1, 1, 5],
           [1, 1, 2, 1, 1, 6],
           [1, 1, 1, 2, 1, 7],
           [1, 1, 1, 1, 2, 8],
       ]
       print(solve_simultaneous(eq))
       print(solve_simultaneous([[4, 2]]))




