

'''simple docstring'''




import os
import unicodedata
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


_lowerCAmelCase         =						logging.get_logger(__name__)
_lowerCAmelCase         =						{"vocab_file": "spiece.model"}

_lowerCAmelCase         =						{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    }
}

_lowerCAmelCase         =						{
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

_lowerCAmelCase         =						"▁"






class   _SCREAMING_SNAKE_CASE   (   __lowercase     ):
 __SCREAMING_SNAKE_CASE					:Dict      	=					VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE					:str      	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



 def __init__(   self		:   Dict			,				a__		:   Optional[int]			,				a__		:   Tuple=True			,				a__		:   Any=True			,				a__		:   List[Any]=False			,				a__		:   List[str]="[CLS]"			,				a__		:   List[Any]="[SEP]"			,				a__		:   List[str]="<unk>"			,				a__		:   Dict="[SEP]"			,				a__		:   Tuple="<pad>"			,				a__		:   str="[CLS]"			,				a__		:   List[str]="[MASK]"			,				a__		:   Optional[Dict[str, Any]] = None			,				**a__		:   Tuple			,				):
      # Mask token behave like a normal word, i.e. include the space before it and
      # is included in the raw text, there should be a match in a non-normalized sentence.
      __magic_name__						       =    (
          AddedToken(snake_case_			,				lstrip=snake_case_			,				rstrip=snake_case_			,				normalized=snake_case_      )
          if isinstance(snake_case_			,				snake_case_      )
          else mask_token
      )

      __magic_name__						       =    {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          do_lower_case=snake_case_			,				remove_space=snake_case_			,				keep_accents=snake_case_			,				bos_token=snake_case_			,				eos_token=snake_case_			,				unk_token=snake_case_			,				sep_token=snake_case_			,				pad_token=snake_case_			,				cls_token=snake_case_			,				mask_token=snake_case_			,				sp_model_kwargs=self.sp_model_kwargs			,				**snake_case_			,				)

      __magic_name__						       =    do_lower_case
      __magic_name__						       =    remove_space
      __magic_name__						       =    keep_accents
      __magic_name__						       =    vocab_file

      __magic_name__						       =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.Load(snake_case_      )



 @property
 def 			snake_case__  (   self		:   Optional[Any]      ):
      return len(self.sp_model      )



 def 			snake_case__  (   self		:   List[Any]      ):
      __magic_name__						       =    {self.convert_ids_to_tokens(snake_case_      ): i for i in range(self.vocab_size      )}
      vocab.update(self.added_tokens_encoder      )
      return vocab



 def __getstate__(   self		:   Union[str, Any]      ):
      __magic_name__						       =    self.__dict__.copy()
      __magic_name__						       =    None
      return state



 def __setstate__(   self		:   Any			,				a__		:   Optional[int]      ):
      __magic_name__						       =    d

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

      __magic_name__						       =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
      self.sp_model.Load(self.vocab_file      )



 def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   Union[str, Any]      ):
      if self.remove_space:
           __magic_name__						       =    ''' '''.join(inputs.strip().split()      )
      else:
           __magic_name__						       =    inputs
      __magic_name__						       =    outputs.replace('''``'''			,				'''"'''      ).replace('''\'\''''			,				'''"'''      )

      if not self.keep_accents:
           __magic_name__						       =    unicodedata.normalize('''NFKD'''			,				snake_case_      )
           __magic_name__						       =    ''''''.join([c for c in outputs if not unicodedata.combining(snake_case_      )]      )
      if self.do_lower_case:
           __magic_name__						       =    outputs.lower()

      return outputs



 def 			snake_case__  (   self		:   Optional[int]			,				a__		:   str      ):
      __magic_name__						       =    self.preprocess_text(snake_case_      )
      __magic_name__						       =    self.sp_model.encode(snake_case_			,				out_type=snake_case_      )
      __magic_name__						       =    []
      for piece in pieces:
           if len(snake_case_      ) > 1 and piece[-1] == str(''','''      ) and piece[-2].isdigit():
                __magic_name__						       =    self.sp_model.EncodeAsPieces(piece[:-1].replace(snake_case_			,				''''''      )      )
                if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                     if len(cur_pieces[0]      ) == 1:
                          __magic_name__						       =    cur_pieces[1:]
                     else:
                          __magic_name__						       =    cur_pieces[0][1:]
                cur_pieces.append(piece[-1]      )
                new_pieces.extend(snake_case_      )
           else:
                new_pieces.append(snake_case_      )

      return new_pieces



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   Dict      ):
      return self.sp_model.PieceToId(snake_case_      )



 def 			snake_case__  (   self		:   int			,				a__		:   Tuple      ):
      return self.sp_model.IdToPiece(snake_case_      )



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   Union[str, Any]      ):
      __magic_name__						       =    []
      __magic_name__						       =    ''''''
      __magic_name__						       =    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(snake_case_      ) + token
                __magic_name__						       =    True
                __magic_name__						       =    []
           else:
                current_sub_tokens.append(snake_case_      )
                __magic_name__						       =    False
      out_string += self.sp_model.decode(snake_case_      )
      return out_string.strip()



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   List[int]			,				a__		:   Optional[List[int]] = None      ):
      __magic_name__						       =    [self.sep_token_id]
      __magic_name__						       =    [self.cls_token_id]
      if token_ids_a is None:
           return cls + token_ids_a + sep
      return cls + token_ids_a + sep + token_ids_a + sep



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

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

      if token_ids_a is not None:
           return [1] + ([0] * len(snake_case_      )) + [1] + ([0] * len(snake_case_      )) + [1]
      return [1] + ([0] * len(snake_case_      )) + [1]



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   List[int]			,				a__		:   Optional[List[int]] = None      ):
      __magic_name__						       =    [self.sep_token_id]
      __magic_name__						       =    [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      ) * [0] + len(token_ids_a + sep      ) * [1]



 def 			snake_case__  (   self		:   str			,				a__		:   str			,				a__		:   Optional[str] = None      ):
      if not os.path.isdir(snake_case_      ):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
           return
      __magic_name__						       =    os.path.join(
          snake_case_			,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

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

      return (out_vocab_file,)









'''simple docstring'''




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer




def 						UpperCamelCase	(			a		,						a		,						a		,						a=1024			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    [], []

     __magic_name__						       =    list(zip(a		,						a			)			)
     __magic_name__       ,  __magic_name__						       =    sorted_examples[0]

     def is_too_big(a			):
          return tok(a		,						return_tensors='''pt'''			).input_ids.shape[1] > max_tokens

     for src, tgt in tqdm(sorted_examples[1:]			):
          __magic_name__						       =    new_src + ''' ''' + src
          __magic_name__						       =    new_tgt + ''' ''' + tgt
          if is_too_big(a			) or is_too_big(a			):  # cant fit, finalize example
               finished_src.append(a			)
               finished_tgt.append(a			)
               __magic_name__       ,  __magic_name__						       =    src, tgt
          else:  # can fit, keep adding
               __magic_name__       ,  __magic_name__						       =    cand_src, cand_tgt

    # cleanup
     if new_src:
          assert new_tgt
          finished_src.append(a			)
          finished_tgt.append(a			)
     return finished_src, finished_tgt




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     Any:






     '''simple docstring'''
     __magic_name__						       =    Path(a			)
     save_path.mkdir(exist_ok=a			)
     for split in ["train"]:
          __magic_name__       ,  __magic_name__						       =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__       ,  __magic_name__						       =    pack_examples(a		,						a		,						a		,						a			)
          print(F'''packed {split} split from {len(a			)} examples -> {len(a			)}.'''			)
          Path(save_path / F'''{split}.source'''			).open('''w'''			).write('''\n'''.join(a			)			)
          Path(save_path / F'''{split}.target'''			).open('''w'''			).write('''\n'''.join(a			)			)
     for split in ["val", "test"]:
          __magic_name__       ,  __magic_name__						       =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
          shutil.copyfile(a		,						save_path / F'''{split}.source'''			)
          shutil.copyfile(a		,						save_path / F'''{split}.target'''			)




def 						UpperCamelCase	(			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    argparse.ArgumentParser()
     parser.add_argument('''--tok_name'''		,						type=a		,						help='''like facebook/bart-large-cnn,t5-base, etc.'''			)
     parser.add_argument('''--max_seq_len'''		,						type=a		,						default=128			)
     parser.add_argument('''--data_dir'''		,						type=a			)
     parser.add_argument('''--save_path'''		,						type=a			)
     __magic_name__						       =    parser.parse_args()
     __magic_name__						       =    AutoTokenizer.from_pretrained(args.tok_name			)
     return pack_data_dir(a		,						Path(args.data_dir			)		,						args.max_seq_len		,						args.save_path			)


if __name__ == "__main__":
      packer_cli()






