





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     ):
   '''simple docstring'''


   A_      ,				A_     :				int			      =	[], []

   A_     :				Tuple			      =	list(zip(_lowerCAmelCase  ,_lowerCAmelCase     )     )
   A_      ,				A_     :				List[Any]			      =	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:]     ):
      A_     :				Union[str, Any]			      =	new_src + """ """ + src
      A_     :				Union[str, Any]			      =	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     )
         A_      ,				A_     :				int			      =	src, tgt
      else:  # can fit, keep adding
         A_      ,				A_     :				List[str]			      =	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     ):
   '''simple docstring'''


   A_     :				List[Any]			      =	Path(_lowerCAmelCase     )
   save_path.mkdir(exist_ok=_lowerCAmelCase     )
   for split in ["train"]:
      A_      ,				A_     :				Union[str, Any]			      =	data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
      A_     :				Dict			      =	[x.rstrip() for x in Path(_lowerCAmelCase     ).open().readlines()]
      A_     :				List[str]			      =	[x.rstrip() for x in Path(_lowerCAmelCase     ).open().readlines()]
      A_      ,				A_     :				Any			      =	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"]:
      A_      ,				A_     :				List[Any]			      =	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		(  ):
   '''simple docstring'''


   A_     :				List[str]			      =	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     )
   A_     :				Optional[Any]			      =	parser.parse_args()
   A_     :				Optional[Any]			      =	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 ....configuration_utils import PretrainedConfig
from ....utils import logging


_lowerCAmelCase 				=     logging.get_logger(__name__)

# TODO: upload to AWS
_lowerCAmelCase 				=     {
    """yjernite/retribert-base-uncased""": (
        """https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"""
    ),
}

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							'''retribert'''
      def __init__(		self      ,       a__=30522      ,       a__=768      ,       a__=8      ,       a__=12      ,       a__=3072      ,       a__="gelu"      ,       a__=0.1      ,       a__=0.1      ,       a__=512      ,       a__=2      ,       a__=0.02      ,       a__=1E-12      ,       a__=True      ,       a__=128      ,       a__=0      ,       **a__      ,       ):
         super().__init__(pad_token_id=a__      ,       **a__       )

         A_     :				Union[str, Any]			      =	vocab_size
         A_     :				Optional[Any]			      =	hidden_size
         A_     :				Optional[Any]			      =	num_hidden_layers
         A_     :				List[str]			      =	num_attention_heads
         A_     :				Optional[int]			      =	hidden_act
         A_     :				Any			      =	intermediate_size
         A_     :				Tuple			      =	hidden_dropout_prob
         A_     :				Optional[Any]			      =	attention_probs_dropout_prob
         A_     :				Dict			      =	max_position_embeddings
         A_     :				Dict			      =	type_vocab_size
         A_     :				List[str]			      =	initializer_range
         A_     :				Optional[int]			      =	layer_norm_eps
         A_     :				Optional[Any]			      =	share_encoders
         A_     :				Dict			      =	projection_dim






