


from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					NDArray[floataa]			,     SCREAMING_SNAKE_CASE__			:					NDArray[floataa]			,     SCREAMING_SNAKE_CASE__			:					list[int]			,     SCREAMING_SNAKE_CASE__			:					int			,     ):
     __UpperCamelCase							,      __UpperCamelCase    							=coefficient_matrix.shape
     __UpperCamelCase							,      __UpperCamelCase    							=constant_matrix.shape

     if rowsa != colsa:
          __UpperCamelCase    							=F'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
          raise ValueError(SCREAMING_SNAKE_CASE__				)

     if colsa != 1:
          __UpperCamelCase    							=F'Constant matrix must be nx1 but received {rowsa}x{colsa}'
          raise ValueError(SCREAMING_SNAKE_CASE__				)

     if rowsa != rowsa:
          __UpperCamelCase    							=(
              'Coefficient and constant matrices dimensions must be nxn and nx1 but '
              F'received {rowsa}x{colsa} and {rowsa}x{colsa}'
          )
          raise ValueError(SCREAMING_SNAKE_CASE__				)

     if len(SCREAMING_SNAKE_CASE__				) != rowsa:
          __UpperCamelCase    							=(
              'Number of initial values must be equal to number of rows in coefficient '
              F'matrix but received {len(SCREAMING_SNAKE_CASE__				)} and {rowsa}'
          )
          raise ValueError(SCREAMING_SNAKE_CASE__				)

     if iterations <= 0:
          raise ValueError('Iterations must be at least 1'				)

     __UpperCamelCase    							=np.concatenate(
         (coefficient_matrix, constant_matrix)			,     axis=1				)

     __UpperCamelCase							,      __UpperCamelCase    							=table.shape

     strictly_diagonally_dominant(SCREAMING_SNAKE_CASE__				)

     # Iterates the whole matrix for given number of times
     for _ in range(SCREAMING_SNAKE_CASE__				):
          __UpperCamelCase    							=[]
          for row in range(SCREAMING_SNAKE_CASE__				):
               __UpperCamelCase    							=0
               for col in range(SCREAMING_SNAKE_CASE__				):
                    if col == row:
                         __UpperCamelCase    							=table[row][col]
                    elif col == cols - 1:
                         __UpperCamelCase    							=table[row][col]
                    else:
                         temp += (-1) * table[row][col] * init_val[col]
               __UpperCamelCase    							=(temp + val) / denom
               new_val.append(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=new_val

     return [float(SCREAMING_SNAKE_CASE__				) for i in new_val]

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					NDArray[floataa]				):
     __UpperCamelCase							,      __UpperCamelCase    							=table.shape

     __UpperCamelCase    							=True

     for i in range(0			,     SCREAMING_SNAKE_CASE__				):
          __UpperCamelCase    							=0
          for j in range(0			,     cols - 1				):
               if i == j:
                    continue
               else:
                    total += table[i][j]

          if table[i][i] <= total:
               raise ValueError('Coefficient matrix is not strictly diagonally dominant'				)

     return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
   import doctest

   doctest.testmod()









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


_A		       =						logging.get_logger(__name__)

_A		       =						'▁'

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

_A		       =						{
    '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',
    },
}

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






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Any      =  VOCAB_FILES_NAMES
    UpperCAmelCase__						:		int      =  PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__						:		Tuple      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__						:		str      =  ["input_ids", "attention_mask"]






    def __init__(			self					,   A_					,   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
         __UpperCamelCase    							=AddedToken(A_					,   lstrip=A_					,   rstrip=A_    ) if isinstance(A_					,   A_    ) else mask_token

         __UpperCamelCase    							={} 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_					,   )

         __UpperCamelCase    							=vocab_file
         __UpperCamelCase    							=monolingual_vocab_file
         __UpperCamelCase    							=spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.Load(str(A_    )    )

         # Load the reduced vocab

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

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






    def __getstate__(			self    )  ->       Any:
         __UpperCamelCase    							=self.__dict__.copy()
         __UpperCamelCase    							=None
         __UpperCamelCase    							=self.sp_model.serialized_model_proto()
         return state






    def __setstate__(			self					,   A_    )  ->       List[str]:
         __UpperCamelCase    							=d

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

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






    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]
         __UpperCamelCase    							=[self.cls_token_id]
         __UpperCamelCase    							=[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]:
         __UpperCamelCase    							=[self.sep_token_id]
         __UpperCamelCase    							=[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    )  ->       Any:
         return len(self.fairseq_ids_to_tokens    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							={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_    )  ->       str:
         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         else:
              return self.unk_token_id






    def _a					(			self					,   A_    )  ->       int:
         return self.fairseq_ids_to_tokens[index]






    def _a					(			self					,   A_    )  ->       List[Any]:
         __UpperCamelCase    							=''.join(A_    ).replace(A_					,   ' '    ).strip()
         return out_string






    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
         __UpperCamelCase    							=os.path.join(
             A_					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase    							=os.path.join(
             A_					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_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:
                   __UpperCamelCase    							=self.sp_model.serialized_model_proto()
                   fi.write(A_    )

         if os.path.abspath(self.monolingual_vocab_file    ) != os.path.abspath(
             A_    ) and os.path.isfile(self.monolingual_vocab_file    ):
              copyfile(self.monolingual_vocab_file					,   A_    )
         elif not os.path.isfile(self.monolingual_vocab_file    ):
              with open(A_					,   '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(A_    )} \n'    )

         return out_vocab_file, out_monolingual_vocab_file





