


import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase=1024  )		->				int:
 UpperCamelCase			,					UpperCamelCase			:      str 			=     [], []

 UpperCamelCase			:      Optional[int] 			=     list(zip(__snake_case							,     __snake_case  )  )
 UpperCamelCase			,					UpperCamelCase			:      List[str] 			=     sorted_examples[0]

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

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

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



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



def 	A_       (  )		->				Tuple:
 UpperCamelCase			:      Optional[int] 			=     argparse.ArgumentParser()
 parser.add_argument("--tok_name"							,     type=__snake_case							,     help="like facebook/bart-large-cnn,t5-base, etc."  )
 parser.add_argument("--max_seq_len"							,     type=__snake_case							,     default=128  )
 parser.add_argument("--data_dir"							,     type=__snake_case  )
 parser.add_argument("--save_path"							,     type=__snake_case  )
 UpperCamelCase			:      Dict 			=     parser.parse_args()
 UpperCamelCase			:      List[Any] 			=     AutoTokenizer.from_pretrained(args.tok_name  )
 return pack_data_dir(__snake_case							,     Path(args.data_dir  )							,     args.max_seq_len							,     args.save_path  )


if __name__ == "__main__":
    packer_cli()







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase :  int										=  {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  Dict										=  ["""ConvBertTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  int										=  [
        """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ConvBertForMaskedLM""",
        """ConvBertForMultipleChoice""",
        """ConvBertForQuestionAnswering""",
        """ConvBertForSequenceClassification""",
        """ConvBertForTokenClassification""",
        """ConvBertLayer""",
        """ConvBertModel""",
        """ConvBertPreTrainedModel""",
        """load_tf_weights_in_convbert""",
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  str										=  [
        """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFConvBertForMaskedLM""",
        """TFConvBertForMultipleChoice""",
        """TFConvBertForQuestionAnswering""",
        """TFConvBertForSequenceClassification""",
        """TFConvBertForTokenClassification""",
        """TFConvBertLayer""",
        """TFConvBertModel""",
        """TFConvBertPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
    from .tokenization_convbert import ConvBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_convbert_fast import ConvBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convbert import (
            CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvBertForMaskedLM,
            ConvBertForMultipleChoice,
            ConvBertForQuestionAnswering,
            ConvBertForSequenceClassification,
            ConvBertForTokenClassification,
            ConvBertLayer,
            ConvBertModel,
            ConvBertPreTrainedModel,
            load_tf_weights_in_convbert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_convbert import (
            TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFConvBertForMaskedLM,
            TFConvBertForMultipleChoice,
            TFConvBertForQuestionAnswering,
            TFConvBertForSequenceClassification,
            TFConvBertForTokenClassification,
            TFConvBertLayer,
            TFConvBertModel,
            TFConvBertPreTrainedModel,
        )


else:
    import sys

    __lowerCamelCase :  List[str]										=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
