


"""simple docstring"""





import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def      lowercase__						(	lowerCamelCase  :	Optional[int]					,						lowerCamelCase  :	Tuple					,						lowerCamelCase  :	str					,						lowerCamelCase  :	Any=1_0_2_4					)       ->  List[Any]:
  lowerCAmelCase__					,		lowerCAmelCase__     :     int										=       [], []

  lowerCAmelCase__     :     Optional[int]										=       list(zip(UpperCAmelCase_					,						UpperCAmelCase_					)					)
  lowerCAmelCase__					,		lowerCAmelCase__     :     Any										=       sorted_examples[0]

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

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

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





def      lowercase__						(	lowerCamelCase  :	Union[str, Any]					,						lowerCamelCase  :	Path					,						lowerCamelCase  :	int					,						lowerCamelCase  :	Dict					)       ->  str:
  lowerCAmelCase__     :     Tuple										=       Path(UpperCAmelCase_					)
  save_path.mkdir(exist_ok=UpperCAmelCase_					)
  for split in ["train"]:
    lowerCAmelCase__					,		lowerCAmelCase__     :     str										=       data_dir / F"{split}.source", data_dir / F"{split}.target"
    lowerCAmelCase__     :     Any										=       [x.rstrip() for x in Path(UpperCAmelCase_					).open().readlines()]
    lowerCAmelCase__     :     int										=       [x.rstrip() for x in Path(UpperCAmelCase_					).open().readlines()]
    lowerCAmelCase__					,		lowerCAmelCase__     :     Dict										=       pack_examples(UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_					)
    print(F"packed {split} split from {len(UpperCAmelCase_					)} examples -> {len(UpperCAmelCase_					)}."					)
    Path(save_path / F"{split}.source"					).open("w"					).write("\n".join(UpperCAmelCase_					)					)
    Path(save_path / F"{split}.target"					).open("w"					).write("\n".join(UpperCAmelCase_					)					)
  for split in ["val", "test"]:
    lowerCAmelCase__					,		lowerCAmelCase__     :     Dict										=       data_dir / F"{split}.source", data_dir / F"{split}.target"
    shutil.copyfile(UpperCAmelCase_					,						save_path / F"{split}.source"					)
    shutil.copyfile(UpperCAmelCase_					,						save_path / F"{split}.target"					)





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


if __name__ == "__main__":
   packer_cli()





"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase     =							{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							[
			    """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """WavLMForAudioFrameClassification""",
			    """WavLMForCTC""",
			    """WavLMForSequenceClassification""",
			    """WavLMForXVector""",
			    """WavLMModel""",
			    """WavLMPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavlm import (
						    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavLMForAudioFrameClassification,
						    WavLMForCTC,
						    WavLMForSequenceClassification,
						    WavLMForXVector,
						    WavLMModel,
						    WavLMPreTrainedModel,
						)

else:
			import sys

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




