'''simple docstring'''


import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=10_24						)			->List[str]:
  lowercase_     ,	lowercase_     =       [], []

  lowercase_     =       list(zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)						)
  lowercase_     ,	lowercase_     =       sorted_examples[0]

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

  for src, tgt in tqdm(sorted_examples[1:]						):
    lowercase_     =       new_src + """ """ + src
    lowercase_     =       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_						)
      lowercase_     ,	lowercase_     =       src, tgt
    else:  # can fit, keep adding
      lowercase_     ,	lowercase_     =       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_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->List[str]:
  lowercase_     =       Path(SCREAMING_SNAKE_CASE_						)
  save_path.mkdir(exist_ok=SCREAMING_SNAKE_CASE_						)
  for split in ["train"]:
    lowercase_     ,	lowercase_     =       data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
    lowercase_     =       [x.rstrip() for x in Path(SCREAMING_SNAKE_CASE_						).open().readlines()]
    lowercase_     =       [x.rstrip() for x in Path(SCREAMING_SNAKE_CASE_						).open().readlines()]
    lowercase_     ,	lowercase_     =       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"]:
    lowercase_     ,	lowercase_     =       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_      (       )			->Optional[Any]:
  lowercase_     =       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=1_28						)
  parser.add_argument("""--data_dir""" ,	type=SCREAMING_SNAKE_CASE_						)
  parser.add_argument("""--save_path""" ,	type=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       parser.parse_args()
  lowercase_     =       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__)

