
import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer


def 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case=1_024							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Dict					=		[], []

  __SCREAMING_SNAKE_CASE      :	List[Any]					=		list(zip(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_							)							)
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	List[str]					=		sorted_examples[0]

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

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

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


def 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case							):





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


def 							a__					(							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	str					=		argparse.ArgumentParser()
  parser.add_argument('''--tok_name'''       ,			type=SCREAMING_SNAKE_CASE_       ,			help='''like facebook/bart-large-cnn,t5-base, etc.'''							)
  parser.add_argument('''--max_seq_len'''       ,			type=SCREAMING_SNAKE_CASE_       ,			default=128							)
  parser.add_argument('''--data_dir'''       ,			type=SCREAMING_SNAKE_CASE_							)
  parser.add_argument('''--save_path'''       ,			type=SCREAMING_SNAKE_CASE_							)
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		parser.parse_args()
  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		AutoTokenizer.from_pretrained(args.tok_name							)
  return pack_data_dir(SCREAMING_SNAKE_CASE_       ,			Path(args.data_dir							)       ,			args.max_seq_len       ,			args.save_path							)


if __name__ == "__main__":
   packer_cli()

'''simple docstring'''


from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__snake_case						  =		{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __snake_case						  =		[
           """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTNeoXJapaneseForCausalLM""",
           """GPTNeoXJapaneseLayer""",
           """GPTNeoXJapaneseModel""",
           """GPTNeoXJapanesePreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
       from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neox_japanese import (
                  GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoXJapaneseForCausalLM,
                  GPTNeoXJapaneseLayer,
                  GPTNeoXJapaneseModel,
                  GPTNeoXJapanesePreTrainedModel,
              )


else:
       import sys

       __snake_case						  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

