





import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer


def       UpperCamelCase__(			UpperCamelCase__     :			Tuple					,	UpperCamelCase__     :			int					,	UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			Optional[int]=10_24    )->Optional[Any]:
       A__   ,      A__		   =		[], []

       A__		   =		list(zip(UpperCamelCase__					,	UpperCamelCase__    )    )
       A__   ,      A__		   =		sorted_examples[0]

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

       for src, tgt in tqdm(sorted_examples[1:]    ):
              A__		   =		new_src + ''' ''' + src
              A__		   =		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__    )
                     A__   ,      A__		   =		src, tgt
              else:  # can fit, keep adding
                     A__   ,      A__		   =		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       UpperCamelCase__(			UpperCamelCase__     :			int					,	UpperCamelCase__     :			Path					,	UpperCamelCase__     :			List[str]					,	UpperCamelCase__     :			Optional[int]    )->List[Any]:
       A__		   =		Path(UpperCamelCase__    )
       save_path.mkdir(exist_ok=UpperCamelCase__    )
       for split in ["train"]:
              A__   ,      A__		   =		data_dir / f"{split}.source", data_dir / f"{split}.target"
              A__		   =		[x.rstrip() for x in Path(UpperCamelCase__    ).open().readlines()]
              A__		   =		[x.rstrip() for x in Path(UpperCamelCase__    ).open().readlines()]
              A__   ,      A__		   =		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"]:
              A__   ,      A__		   =		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       UpperCamelCase__(			)->Optional[int]:
       A__		   =		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_28    )
       parser.add_argument('''--data_dir'''					,	type=UpperCamelCase__    )
       parser.add_argument('''--save_path'''					,	type=UpperCamelCase__    )
       A__		   =		parser.parse_args()
       A__		   =		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()












import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			int					,	UpperCamelCase__     :			List[Any]    )->Optional[int]:
       # Initialise PyTorch model
       A__		   =		RemBertConfig.from_json_file(UpperCamelCase__    )
       print('''Building PyTorch model from configuration: {}'''.format(str(UpperCamelCase__    )    )    )
       A__		   =		RemBertModel(UpperCamelCase__    )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__    )

       # Save pytorch-model
       print('''Save PyTorch model to {}'''.format(UpperCamelCase__    )    )
       torch.save(model.state_dict()					,	UpperCamelCase__    )


if __name__ == "__main__":
       a__: List[Any]							   =						argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--rembert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained RemBERT model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       a__: Union[str, Any]							   =						parser.parse_args()
       convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)





