
"""simple docstring"""



import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def 						__snake_case	(     SCREAMING_SNAKE_CASE:       Tuple  ,	SCREAMING_SNAKE_CASE:       int  ,	SCREAMING_SNAKE_CASE:       Optional[Any]  ,	SCREAMING_SNAKE_CASE:       Optional[int]=1024    ):






							"""simple docstring"""



							_lowerCAmelCase						,		_lowerCAmelCase							=      [], []

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

							def is_too_big(SCREAMING_SNAKE_CASE:       List[Any]    ):
														return tok(UpperCamelCase__  ,	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(UpperCamelCase__    ) or is_too_big(UpperCamelCase__    ):  # cant fit, finalize example
																					finished_src.append(UpperCamelCase__    )
																					finished_tgt.append(UpperCamelCase__    )
																					_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(UpperCamelCase__    )
														finished_tgt.append(UpperCamelCase__    )
							return finished_src, finished_tgt






def 						__snake_case	(     SCREAMING_SNAKE_CASE:       int  ,	SCREAMING_SNAKE_CASE:       Path  ,	SCREAMING_SNAKE_CASE:       List[str]  ,	SCREAMING_SNAKE_CASE:       Optional[int]    ):






							"""simple docstring"""



							_lowerCAmelCase							=      Path(UpperCamelCase__    )
							save_path.mkdir(exist_ok=UpperCamelCase__    )
							for split in ["train"]:
														_lowerCAmelCase						,		_lowerCAmelCase							=      data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
														_lowerCAmelCase							=      [x.rstrip() for x in Path(UpperCamelCase__    ).open().readlines()]
														_lowerCAmelCase							=      [x.rstrip() for x in Path(UpperCamelCase__    ).open().readlines()]
														_lowerCAmelCase						,		_lowerCAmelCase							=      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"]:
														_lowerCAmelCase						,		_lowerCAmelCase							=      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 						__snake_case	(     ):






							"""simple docstring"""



							_lowerCAmelCase							=      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=128    )
							parser.add_argument('--data_dir'  ,	type=UpperCamelCase__    )
							parser.add_argument('--save_path'  ,	type=UpperCamelCase__    )
							_lowerCAmelCase							=      parser.parse_args()
							_lowerCAmelCase							=      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)





