



"""simple docstring"""




import json
import os
import re
import warnings
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


_UpperCamelCase:				str    							=  logging.get_logger(__name__)

_UpperCamelCase:				List[str]    							=  {
    'source_spm': 'source.spm',
    'target_spm': 'target.spm',
    'vocab': 'vocab.json',
    'target_vocab_file': 'target_vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
}

_UpperCamelCase:				str    							=  {
    'source_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'
    },
    'target_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'
    },
    'vocab': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'
    },
    'tokenizer_config_file': {
        'Helsinki-NLP/opus-mt-en-de': (
            'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'
        )
    },
}

_UpperCamelCase:				str    							=  {'Helsinki-NLP/opus-mt-en-de': 5_1_2}
_UpperCamelCase:				Union[str, Any]    							=  {}



class   a__		(						lowerCAmelCase__     ):
   _lowerCamelCase												=				VOCAB_FILES_NAMES
   _lowerCamelCase												=				PRETRAINED_VOCAB_FILES_MAP
   _lowerCamelCase												=				PRETRAINED_INIT_CONFIGURATION
   _lowerCamelCase												=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _lowerCamelCase												=				['input_ids', 'attention_mask']
   _lowerCamelCase												=				re.compile('>>.+<<'     )  # type: re.Pattern


   def __init__(							self      :							Any,     lowerCAmelCase      :							Optional[Any],     lowerCAmelCase      :							str,     lowerCAmelCase      :							int,     lowerCAmelCase      :							Optional[Any]=None,     lowerCAmelCase      :							Optional[Any]=None,     lowerCAmelCase      :							str=None,     lowerCAmelCase      :							Optional[Any]="<unk>",     lowerCAmelCase      :							Optional[Any]="</s>",     lowerCAmelCase      :							int="<pad>",     lowerCAmelCase      :							Union[str, Any]=512,     lowerCAmelCase      :							Tuple = None,     lowerCAmelCase      :							List[Any]=False,     **lowerCAmelCase      :							Any,     )    ->       Optional[Any]:
         lowercase  :      Union[str, Any]					=   {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
             source_lang=_lowerCamelCase,     target_lang=_lowerCamelCase,     unk_token=_lowerCamelCase,     eos_token=_lowerCamelCase,     pad_token=_lowerCamelCase,     model_max_length=_lowerCamelCase,     sp_model_kwargs=self.sp_model_kwargs,     target_vocab_file=_lowerCamelCase,     separate_vocabs=_lowerCamelCase,     **_lowerCamelCase,     )
         assert Path(_lowerCamelCase			).exists(), f'''cannot find spm source {source_spm}'''

         lowercase  :      Tuple					=   separate_vocabs
         lowercase  :      Optional[int]					=   load_json(_lowerCamelCase			)
         if self.unk_token not in self.encoder:
               raise KeyError('<unk> token must be in vocab'			)
         assert self.pad_token in self.encoder

         if separate_vocabs:
               lowercase  :      str					=   load_json(_lowerCamelCase			)
               lowercase  :      List[Any]					=   {v: k for k, v in self.target_encoder.items()}
               lowercase  :      Optional[Any]					=   []
         else:
               lowercase  :      Any					=   {v: k for k, v in self.encoder.items()}
               lowercase  :      list					=   [k for k in self.encoder if k.startswith('>>'			) and k.endswith('<<'			)]

         lowercase  :      Any					=   source_lang
         lowercase  :      int					=   target_lang
         lowercase  :      List[Any]					=   [source_spm, target_spm]

         # load SentencePiece model for pre-processing
         lowercase  :      str					=   load_spm(_lowerCamelCase,     self.sp_model_kwargs			)
         lowercase  :      Optional[int]					=   load_spm(_lowerCamelCase,     self.sp_model_kwargs			)
         lowercase  :      Optional[int]					=   self.spm_source
         lowercase  :      int					=   self.encoder

         # Multilingual target side: default to using first supported language code.

         self._setup_normalizer()


   def        lowercase				(							self      :							Any			)    ->       Union[str, Any]:

         try:
               from sacremoses import MosesPunctNormalizer

               lowercase  :      Optional[int]					=   MosesPunctNormalizer(self.source_lang			).normalize
         except (ImportError, FileNotFoundError):
               warnings.warn('Recommended: pip install sacremoses.'			)
               lowercase  :      List[str]					=   lambda lowerCAmelCase			: x


   def        lowercase				(							self      :							Union[str, Any],     lowerCAmelCase      :							Union[str, Any]			)    ->       List[str]:
         return self.punc_normalizer(_lowerCamelCase			) if x else ""


   def        lowercase				(							self      :							str,     lowerCAmelCase      :							Optional[int]			)    ->       Tuple:
         return self.current_encoder.get(_lowerCamelCase,     self.current_encoder[self.unk_token]			)


   def        lowercase				(							self      :							Tuple,     lowerCAmelCase      :							List[str]			)    ->       int:
         lowercase  :      Dict					=   self.language_code_re.match(_lowerCamelCase			)
         lowercase  :      list					=   [match.group(0			)] if match else []
         return code, self.language_code_re.sub('',     _lowerCamelCase			)


   def        lowercase				(							self      :							str,     lowerCAmelCase      :							List[Any]			)    ->       int:
         lowercase  :      List[Any]					=   self.remove_language_code(_lowerCamelCase			)
         lowercase  :      Any					=   self.current_spm.encode(_lowerCamelCase,     out_type=_lowerCamelCase			)
         return code + pieces


   def        lowercase				(							self      :							Union[str, Any],     lowerCAmelCase      :							List[Any]			)    ->       Tuple:
         return self.decoder.get(_lowerCamelCase,     self.unk_token			)


   def        lowercase				(							self      :							List[Any],     lowerCAmelCase      :							str,     **lowerCAmelCase      :							List[str]			)    ->       List[Any]:
         return super().batch_decode(_lowerCamelCase,     **_lowerCamelCase			)


   def        lowercase				(							self      :							Optional[int],     lowerCAmelCase      :							Tuple,     **lowerCAmelCase      :							List[Any]			)    ->       Optional[Any]:
         return super().decode(_lowerCamelCase,     **_lowerCamelCase			)


   def        lowercase				(							self      :							Union[str, Any],     lowerCAmelCase      :							Union[str, Any]			)    ->       Optional[int]:
         lowercase  :      str					=   self.spm_source if self._decode_use_source_tokenizer else self.spm_target
         lowercase  :      List[str]					=   []
         lowercase  :      Tuple					=   """"""
         for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                     out_string += sp_model.decode_pieces(_lowerCamelCase			) + token + " "
                     lowercase  :      Any					=   []
               else:
                     current_sub_tokens.append(_lowerCamelCase			)
         out_string += sp_model.decode_pieces(_lowerCamelCase			)
         return out_string.strip()


   def        lowercase				(							self      :							Optional[Any],     lowerCAmelCase      :							int,     lowerCAmelCase      :							List[str]=None			)    ->       int:

         if token_ids_a is None:
               return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]


   def        lowercase				(							self      :							List[str]			)    ->       str:
         lowercase  :      List[str]					=   self.spm_source
         lowercase  :      Union[str, Any]					=   self.encoder


   def        lowercase				(							self      :							Tuple			)    ->       Union[str, Any]:
         lowercase  :      List[Any]					=   self.spm_target
         if self.separate_vocabs:
               lowercase  :      Tuple					=   self.target_encoder


   @property
   def        lowercase				(							self      :							Union[str, Any]			)    ->       Optional[Any]:
         return len(self.encoder			)


   def        lowercase				(							self      :							List[str],     lowerCAmelCase      :							Optional[int],     lowerCAmelCase      :							int = None			)    ->       Optional[int]:

         if not os.path.isdir(_lowerCamelCase			):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
               return
         lowercase  :      Tuple					=   []

         if self.separate_vocabs:
               lowercase  :      Any					=   os.path.join(
                   _lowerCamelCase,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab'],     )
               lowercase  :      List[Any]					=   os.path.join(
                   _lowerCamelCase,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file'],     )
               save_json(self.encoder,     _lowerCamelCase			)
               save_json(self.target_encoder,     _lowerCamelCase			)
               saved_files.append(_lowerCamelCase			)
               saved_files.append(_lowerCamelCase			)
         else:
               lowercase  :      Optional[Any]					=   os.path.join(
                   _lowerCamelCase,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']			)
               save_json(self.encoder,     _lowerCamelCase			)
               saved_files.append(_lowerCamelCase			)

         for spm_save_filename, spm_orig_path, spm_model in zip(
             [VOCAB_FILES_NAMES['source_spm'], VOCAB_FILES_NAMES['target_spm']],     self.spm_files,     [self.spm_source, self.spm_target],     ):
               lowercase  :      Tuple					=   os.path.join(
                   _lowerCamelCase,     (filename_prefix + '-' if filename_prefix else '') + spm_save_filename			)
               if os.path.abspath(_lowerCamelCase			) != os.path.abspath(_lowerCamelCase			) and os.path.isfile(_lowerCamelCase			):
                     copyfile(_lowerCamelCase,     _lowerCamelCase			)
                     saved_files.append(_lowerCamelCase			)
               elif not os.path.isfile(_lowerCamelCase			):
                     with open(_lowerCamelCase,     'wb'			) as fi:
                           lowercase  :      List[Any]					=   spm_model.serialized_model_proto()
                           fi.write(_lowerCamelCase			)
                     saved_files.append(_lowerCamelCase			)

         return tuple(_lowerCamelCase			)


   def        lowercase				(							self      :							Dict			)    ->       List[str]:
         return self.get_src_vocab()


   def        lowercase				(							self      :							Any			)    ->       Union[str, Any]:
         return dict(self.encoder,     **self.added_tokens_encoder			)


   def        lowercase				(							self      :							Any			)    ->       Any:
         return dict(self.target_encoder,     **self.added_tokens_decoder			)


   def __getstate__(							self      :							Any			)    ->       int:
         lowercase  :      Union[str, Any]					=   self.__dict__.copy()
         state.update(
             {k: None for k in ['spm_source', 'spm_target', 'current_spm', 'punc_normalizer', 'target_vocab_file']}			)
         return state


   def __setstate__(							self      :							List[str],     lowerCAmelCase      :							Optional[int]			)    ->       List[str]:
         lowercase  :      List[Any]					=   d

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

         lowercase  :      Dict					=   (load_spm(_lowerCamelCase,     self.sp_model_kwargs			) for f in self.spm_files)
         lowercase  :      Tuple					=   self.spm_source
         self._setup_normalizer()


   def        lowercase				(							self      :							Union[str, Any],     *lowerCAmelCase      :							List[str],     **lowerCAmelCase      :							List[Any]			)    ->       str:
         return 1


   def        lowercase				(							self      :							Any,     lowerCAmelCase      :							List[Any]			)    ->       Optional[int]:
         lowercase  :      Optional[Any]					=   set(self.all_special_ids			)  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id			)  # <unk> is only sometimes special
         return [1 if x in all_special_ids else 0 for x in seq]






   def        lowercase				(							self      :							List[Any],     lowerCAmelCase      :							Optional[int],     lowerCAmelCase      :							List[Any] = None,     lowerCAmelCase      :							List[str] = False			)    ->       Any:

         if already_has_special_tokens:
               return self._special_token_mask(_lowerCamelCase			)
         elif token_ids_a is None:
               return self._special_token_mask(_lowerCamelCase			) + [1]
         else:
               return self._special_token_mask(token_ids_a + token_ids_a			) + [1]




def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase				)     ->			sentencepiece.SentencePieceProcessor:






      '''simple docstring'''


      lowercase  :      List[Any]					=   sentencepiece.SentencePieceProcessor(**_UpperCAmelCase				)
      spm.Load(_UpperCAmelCase				)
      return spm


def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase				)     ->			None:






      '''simple docstring'''


      with open(_UpperCAmelCase	,      'w'				) as f:
            json.dump(_UpperCAmelCase	,      _UpperCAmelCase	,      indent=2				)





def   lowercase__     (			_UpperCAmelCase				)     ->			Union[Dict, List]:






      '''simple docstring'''


      with open(_UpperCAmelCase	,      'r'				) as f:
            return json.load(_UpperCAmelCase				)





"""simple docstring"""




from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode


def   lowercase__     (			_UpperCAmelCase=10				)     ->			Any:






      '''simple docstring'''


      for _ in tqdm(range(0	,      args.n_examples	,      _UpperCAmelCase				)				):
            yield [next(_UpperCAmelCase				)[args.text_column] for _ in range(_UpperCAmelCase				)]


# Configuration
_UpperCamelCase:				Union[str, Any]    							=  HfArgumentParser(TokenizerTrainingArguments)
_UpperCamelCase:				Dict    							=  parser.parse_args()

# Base tokenizer
_UpperCamelCase:				List[Any]    							=  AutoTokenizer.from_pretrained(args.base_tokenizer)
_UpperCamelCase:				int    							=  list(bytes_to_unicode().values())

# Load dataset
_UpperCamelCase:				Union[str, Any]    							=  load_dataset(args.dataset_name, split='train', streaming=True)
_UpperCamelCase:				Union[str, Any]    							=  iter(dataset)


# Training and saving
_UpperCamelCase:				Union[str, Any]    							=  tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)





new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
