



import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer
def 		UpperCamelCase	(   snake_case__       :    Dict       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    int       ,     snake_case__       :    Union[str, Any]=1024	)	->     List[str]:
   UpperCamelCase	,     UpperCamelCase    :  str							=	[], []

   UpperCamelCase    :  List[str]							=	list(zip(snake_case__       ,     snake_case__	)	)
   UpperCamelCase	,     UpperCamelCase    :  Dict							=	sorted_examples[0]

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

   for src, tgt in tqdm(sorted_examples[1:]	):
      UpperCamelCase    :  str							=	new_src + ' ' + src
      UpperCamelCase    :  Optional[int]							=	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    :  List[str]							=	src, tgt
      else:  # can fit, keep adding
         UpperCamelCase	,     UpperCamelCase    :  Dict							=	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 		UpperCamelCase	(   snake_case__       :    Union[str, Any]       ,     snake_case__       :    Path       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    Tuple	)	->     Optional[int]:
   UpperCamelCase    :  Optional[int]							=	Path(snake_case__	)
   save_path.mkdir(exist_ok=snake_case__	)
   for split in ["train"]:
      UpperCamelCase	,     UpperCamelCase    :  int							=	data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
      UpperCamelCase    :  List[Any]							=	[x.rstrip() for x in Path(snake_case__	).open().readlines()]
      UpperCamelCase    :  List[str]							=	[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    :  Optional[int]							=	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 		UpperCamelCase	(   )	->     List[str]:
   UpperCamelCase    :  Union[str, Any]							=	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    :  Optional[Any]							=	parser.parse_args()
   UpperCamelCase    :  List[str]							=	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_torch_available


__UpperCAmelCase          =       {
    '''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:
    __UpperCAmelCase          =       [
        '''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:
    __UpperCAmelCase          =       [
        '''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

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



