import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=1_0_2_4     ):
       lowerCAmelCase__     , lowerCAmelCase__					     = [], []

       lowerCAmelCase__					     = list(zip(lowerCAmelCase__     ,  lowerCAmelCase__     )     )
       lowerCAmelCase__     , lowerCAmelCase__					     = sorted_examples[0]

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

       for src, tgt in tqdm(sorted_examples[1:]     ):
              lowerCAmelCase__					     = new_src + ' ' + src
              lowerCAmelCase__					     = 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__     )
                     lowerCAmelCase__     , lowerCAmelCase__					     = src, tgt
              else:  # can fit, keep adding
                     lowerCAmelCase__     , lowerCAmelCase__					     = 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__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = Path(lowerCAmelCase__     )
       save_path.mkdir(exist_ok=lowerCAmelCase__     )
       for split in ["train"]:
              lowerCAmelCase__     , lowerCAmelCase__					     = data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
              lowerCAmelCase__					     = [x.rstrip() for x in Path(lowerCAmelCase__     ).open().readlines()]
              lowerCAmelCase__					     = [x.rstrip() for x in Path(lowerCAmelCase__     ).open().readlines()]
              lowerCAmelCase__     , lowerCAmelCase__					     = 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"]:
              lowerCAmelCase__     , lowerCAmelCase__					     = 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		(						):
       lowerCAmelCase__					     = 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__     )
       lowerCAmelCase__					     = parser.parse_args()
       lowerCAmelCase__					     = 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()

from typing import TYPE_CHECKING

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


lowerCAmelCase__     						=			{
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'AdaptiveEmbedding',
        'TransfoXLForSequenceClassification',
        'TransfoXLLMHeadModel',
        'TransfoXLModel',
        'TransfoXLPreTrainedModel',
        'load_tf_weights_in_transfo_xl',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFAdaptiveEmbedding',
        'TFTransfoXLForSequenceClassification',
        'TFTransfoXLLMHeadModel',
        'TFTransfoXLMainLayer',
        'TFTransfoXLModel',
        'TFTransfoXLPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
    from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_transfo_xl import (
            TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            AdaptiveEmbedding,
            TransfoXLForSequenceClassification,
            TransfoXLLMHeadModel,
            TransfoXLModel,
            TransfoXLPreTrainedModel,
            load_tf_weights_in_transfo_xl,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_transfo_xl import (
            TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFAdaptiveEmbedding,
            TFTransfoXLForSequenceClassification,
            TFTransfoXLLMHeadModel,
            TFTransfoXLMainLayer,
            TFTransfoXLModel,
            TFTransfoXLPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__     						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
