
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_mbart import MBartTokenizer
else:
      lowercase_       :						str     					=	None


lowercase_       :						str     					=	logging.get_logger(__name__)


lowercase_       :						Union[str, Any]     					=	{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase_       :						int     					=	{
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''',
        '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''',
    },
}

lowercase_       :						List[Any]     					=	{
    '''facebook/mbart-large-en-ro''': 10_24,
    '''facebook/mbart-large-cc25''': 10_24,
}

# fmt: off
lowercase_       :						List[Any]     					=	['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']
class 			UpperCamelCase      (							_UpperCAmelCase  ):
     A__      =			VOCAB_FILES_NAMES
     A__      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A__      =			PRETRAINED_VOCAB_FILES_MAP
     A__      =			["""input_ids""", """attention_mask"""]
     A__      =			MBartTokenizer

     A__      =			[]
     A__      =			[]
     def __init__(     self  ,     snake_case__=None  ,     snake_case__=None  ,     snake_case__="<s>"  ,     snake_case__="</s>"  ,     snake_case__="</s>"  ,     snake_case__="<s>"  ,     snake_case__="<unk>"  ,     snake_case__="<pad>"  ,     snake_case__="<mask>"  ,     snake_case__=None  ,     snake_case__=None  ,     snake_case__=None  ,     **snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = AddedToken(lowercase__  ,     lstrip=lowercase__  ,     rstrip=lowercase__				) if isinstance(lowercase__  ,     lowercase__				) else mask_token

       super().__init__(
           vocab_file=lowercase__  ,     tokenizer_file=lowercase__  ,     bos_token=lowercase__  ,     eos_token=lowercase__  ,     sep_token=lowercase__  ,     cls_token=lowercase__  ,     unk_token=lowercase__  ,     pad_token=lowercase__  ,     mask_token=lowercase__  ,     src_lang=lowercase__  ,     tgt_lang=lowercase__  ,     additional_special_tokens=lowercase__  ,     **lowercase__  ,     )

       _SCREAMING_SNAKE_CASE   :				Dict							    = vocab_file
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = False if not self.vocab_file else True

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = FAIRSEQ_LANGUAGE_CODES.copy()

       if additional_special_tokens is not None:
         # Only add those special tokens if they are not already there.
         _additional_special_tokens.extend(
             [t for t in additional_special_tokens if t not in _additional_special_tokens]				)

       self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}				)
       _SCREAMING_SNAKE_CASE   :				Any							    = {
           lang_code: self.convert_tokens_to_ids(lowercase__				) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

       _SCREAMING_SNAKE_CASE   :				Tuple							    = src_lang if src_lang is not None else """en_XX"""
       _SCREAMING_SNAKE_CASE   :				Tuple							    = self.convert_tokens_to_ids(self._src_lang				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = tgt_lang
       self.set_src_lang_special_tokens(self._src_lang				)
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return self._src_lang
     @src_lang.setter
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = new_src_lang
       self.set_src_lang_special_tokens(self._src_lang				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = None				):




       """simple docstring"""


       if token_ids_a is None:
         return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = None				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = [self.sep_token_id]
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = [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]
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     **snake_case__				):




       """simple docstring"""


       if src_lang is None or tgt_lang is None:
         raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"				)
       _SCREAMING_SNAKE_CASE   :				int							    = src_lang
       _SCREAMING_SNAKE_CASE   :				List[str]							    = self(lowercase__  ,     add_special_tokens=lowercase__  ,     return_tensors=lowercase__  ,     **lowercase__				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = self.convert_tokens_to_ids(lowercase__				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tgt_lang_id
       return inputs
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = "en_XX"  ,     snake_case__ = None  ,     snake_case__ = "ro_RO"  ,     **snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = src_lang
       _SCREAMING_SNAKE_CASE   :				int							    = tgt_lang
       return super().prepare_seqaseq_batch(lowercase__  ,     lowercase__  ,     **lowercase__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return self.set_src_lang_special_tokens(self.src_lang				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return self.set_tgt_lang_special_tokens(self.tgt_lang				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = self.convert_tokens_to_ids(lowercase__				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = []
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = [self.eos_token_id, self.cur_lang_code]

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.convert_ids_to_tokens(self.prefix_tokens				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.convert_ids_to_tokens(self.suffix_tokens				)

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = processors.TemplateProcessing(
           single=prefix_tokens_str + ["$A"] + suffix_tokens_str  ,     pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str  ,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str  ,     self.prefix_tokens + self.suffix_tokens				)				)  ,     )
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = self.convert_tokens_to_ids(lowercase__				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = [self.eos_token_id, self.cur_lang_code]

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.convert_ids_to_tokens(self.prefix_tokens				)
       _SCREAMING_SNAKE_CASE   :				int							    = self.convert_ids_to_tokens(self.suffix_tokens				)

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = processors.TemplateProcessing(
           single=prefix_tokens_str + ["$A"] + suffix_tokens_str  ,     pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str  ,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str  ,     self.prefix_tokens + self.suffix_tokens				)				)  ,     )




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = None				):




       """simple docstring"""


       if not self.can_save_slow_tokenizer:
         raise ValueError(
             "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
             "tokenizer."				)

       if not os.path.isdir(lowercase__				):
         logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''				)
         return
       _SCREAMING_SNAKE_CASE   :				List[str]							    = os.path.join(
           lowercase__  ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

       if os.path.abspath(self.vocab_file				) != os.path.abspath(lowercase__				):
         copyfile(self.vocab_file  ,     lowercase__				)

       return (out_vocab_file,)





"""simple docstring"""
import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							Optional[Any],						lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Any=1_0_2_4				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = [], []

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = list(zip(lowerCamelCase__,						lowerCamelCase__				)				)
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[Any]							    = sorted_examples[0]

  def is_too_big(lowerCamelCase__							:							List[Any]				):
    return tok(lowerCamelCase__,						return_tensors="pt"				).input_ids.shape[1] > max_tokens

  for src, tgt in tqdm(sorted_examples[1:]				):
    _SCREAMING_SNAKE_CASE   :				int							    = new_src + " " + src
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_tgt + " " + tgt
    if is_too_big(lowerCamelCase__				) or is_too_big(lowerCamelCase__				):  # cant fit, finalize example
      finished_src.append(lowerCamelCase__				)
      finished_tgt.append(lowerCamelCase__				)
      _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Any							    = src, tgt
    else:  # can fit, keep adding
      _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = cand_src, cand_tgt

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





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[str],						lowerCamelCase__							:							Path,						lowerCamelCase__							:							Dict,						lowerCamelCase__							:							List[str]				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				int							    = Path(lowerCamelCase__				)
  save_path.mkdir(exist_ok=lowerCamelCase__				)
  for split in ["train"]:
    _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Dict							    = data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
    _SCREAMING_SNAKE_CASE   :				Any							    = [x.rstrip() for x in Path(lowerCamelCase__				).open().readlines()]
    _SCREAMING_SNAKE_CASE   :				int							    = [x.rstrip() for x in Path(lowerCamelCase__				).open().readlines()]
    _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Any							    = pack_examples(lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)
    print(f'''packed {split} split from {len(lowerCamelCase__				)} examples -> {len(lowerCamelCase__				)}.'''				)
    Path(save_path / f'''{split}.source'''				).open("w"				).write("\n".join(lowerCamelCase__				)				)
    Path(save_path / f'''{split}.target'''				).open("w"				).write("\n".join(lowerCamelCase__				)				)
  for split in ["val", "test"]:
    _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
    shutil.copyfile(lowerCamelCase__,						save_path / f'''{split}.source'''				)
    shutil.copyfile(lowerCamelCase__,						save_path / f'''{split}.target'''				)





def    _lowerCAmelCase    (     )  ->    int:
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = argparse.ArgumentParser()
  parser.add_argument("--tok_name",						type=lowerCamelCase__,						help="like facebook/bart-large-cnn,t5-base, etc."				)
  parser.add_argument("--max_seq_len",						type=lowerCamelCase__,						default=1_2_8				)
  parser.add_argument("--data_dir",						type=lowerCamelCase__				)
  parser.add_argument("--save_path",						type=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = parser.parse_args()
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = AutoTokenizer.from_pretrained(args.tok_name				)
  return pack_data_dir(lowerCamelCase__,						Path(args.data_dir				),						args.max_seq_len,						args.save_path				)


if __name__ == "__main__":
      packer_cli()



