



'''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": 1_024}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[Any]          =       VOCAB_FILES_NAMES
 __lowerCamelCase    :       int          =       PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :       Union[str, Any]          =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :       int          =       ['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:


      """simple docstring"""



      _a      =  AddedToken(A ,   lstrip=A ,   rstrip=A       ) if isinstance(A ,   A       ) else mask_token

      _a      =  {} 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      =  vocab_file
      _a      =  monolingual_vocab_file
      _a      =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
      self.sp_model.Load(str(A       )       )

      # 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(A       ) not in self.fairseq_tokens_to_ids:
                _a      =  cnt
                cnt += 1
      with open(A ,   '''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(A       ) 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       )    ->     int:


      """simple docstring"""



      _a      =  self.__dict__.copy()
      _a      =  None
      _a      =  self.sp_model.serialized_model_proto()
      return state



 def __setstate__(self ,   A       )    ->     int:


      """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 					a__  (self ,   A ,   A = None       )    ->     List[int]:


      """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 					a__  (self ,   A ,   A = None ,   A = False       )    ->     List[int]:


      """simple docstring"""



      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]:


      """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 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      return len(self.fairseq_ids_to_tokens       )



 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      _a      =  {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]:


      """simple docstring"""



      return self.sp_model.encode(A ,   out_type=A       )



 def 					a__  (self ,   A       )    ->     str:


      """simple docstring"""



      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       )    ->     List[str]:


      """simple docstring"""



      return self.fairseq_ids_to_tokens[index]



 def 					a__  (self ,   A       )    ->     List[Any]:


      """simple docstring"""



      _a      =  ''''''.join(A       ).replace(A ,   ''' '''       ).strip()
      return out_string



 def 					a__  (self ,   A ,   A = None       )    ->     Tuple[str]:


      """simple docstring"""



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








'''simple docstring'''





import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



     if isinstance(__A  ,	__A):
          _a      =  np.full((len(__A), sequence_length, 2)  ,	__A)
     else:
          _a      =  np.full((len(__A), sequence_length)  ,	__A)

     for i, tensor in enumerate(__A):
          if padding_side == "right":
               if isinstance(__A  ,	__A):
                    _a      =  tensor[:sequence_length]
               else:
                    _a      =  tensor[:sequence_length]
          else:
               if isinstance(__A  ,	__A):
                    _a      =  tensor[:sequence_length]
               else:
                    _a      =  tensor[:sequence_length]

     return out_tensor.tolist()




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  ord(__A)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
          return True
     _a      =  unicodedata.category(__A)
     if cat.startswith('''P'''):
          return True
     return False






@dataclass
class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       PreTrainedTokenizerBase
 __lowerCamelCase    :       Union[bool, str, PaddingStrategy]          =       True
 __lowerCamelCase    :       Optional[int]          =       None
 __lowerCamelCase    :       Optional[int]          =       None
 __lowerCamelCase    :       int          =       -100
 __lowerCamelCase    :       str          =       "pt"



 def 					a__  (self ,   A       )    ->     List[str]:


      """simple docstring"""



      import torch

      _a      =  '''label''' if '''label''' in features[0].keys() else '''labels'''
      _a      =  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
      _a      =  self.tokenizer.pad(
          A ,   padding=self.padding ,   max_length=self.max_length ,   pad_to_multiple_of=self.pad_to_multiple_of ,   return_tensors='''pt''' if labels is None else None ,   )

      if labels is None:
           return batch

      _a      =  torch.tensor(batch['''entity_ids''']       ).shape[1]
      _a      =  self.tokenizer.padding_side
      if padding_side == "right":
           _a      =  [
               list(A       ) + [self.label_pad_token_id] * (sequence_length - len(A       )) for label in labels
           ]
      else:
           _a      =  [
               [self.label_pad_token_id] * (sequence_length - len(A       )) + list(A       ) for label in labels
           ]

      _a      =  [feature['''ner_tags'''] for feature in features]
      _a      =  padding_tensor(A ,   -1 ,   A ,   A       )
      _a      =  [feature['''original_entity_spans'''] for feature in features]
      _a      =  padding_tensor(A ,   (-1, -1) ,   A ,   A       )
      _a      =  {k: torch.tensor(A ,   dtype=torch.intaa       ) for k, v in batch.items()}

      return batch



