


import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer




def       a_ (       SCREAMING_SNAKE_CASE__   : int						,     SCREAMING_SNAKE_CASE__   : Optional[int]						,     SCREAMING_SNAKE_CASE__   : Optional[int]						,     SCREAMING_SNAKE_CASE__   : List[Any]=1_024	):
       '''simple docstring'''





       _lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =[], []

       _lowerCamelCase			:						List[str]            =list(zip(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)	)
       _lowerCamelCase      ,	_lowerCamelCase			:						Optional[Any]            =sorted_examples[0]

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

       for src, tgt in tqdm(sorted_examples[1:]	):
              _lowerCamelCase			:						Optional[int]            =new_src + ' ' + src
              _lowerCamelCase			:						str            =new_tgt + ' ' + tgt
              if is_too_big(SCREAMING_SNAKE_CASE__	) or is_too_big(SCREAMING_SNAKE_CASE__	):  # cant fit, finalize example
                     finished_src.append(SCREAMING_SNAKE_CASE__	)
                     finished_tgt.append(SCREAMING_SNAKE_CASE__	)
                     _lowerCamelCase      ,	_lowerCamelCase			:						Union[str, Any]            =src, tgt
              else:  # can fit, keep adding
                     _lowerCamelCase      ,	_lowerCamelCase			:						Tuple            =cand_src, cand_tgt

    # cleanup
       if new_src:
              assert new_tgt
              finished_src.append(SCREAMING_SNAKE_CASE__	)
              finished_tgt.append(SCREAMING_SNAKE_CASE__	)
       return finished_src, finished_tgt




def       a_ (       SCREAMING_SNAKE_CASE__   : List[str]						,     SCREAMING_SNAKE_CASE__   : Path						,     SCREAMING_SNAKE_CASE__   : Union[str, Any]						,     SCREAMING_SNAKE_CASE__   : Any	):
       '''simple docstring'''





       _lowerCamelCase			:						str            =Path(SCREAMING_SNAKE_CASE__	)
       save_path.mkdir(exist_ok=SCREAMING_SNAKE_CASE__	)
       for split in ["train"]:
              _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
              _lowerCamelCase			:						Tuple            =[x.rstrip() for x in Path(SCREAMING_SNAKE_CASE__	).open().readlines()]
              _lowerCamelCase			:						int            =[x.rstrip() for x in Path(SCREAMING_SNAKE_CASE__	).open().readlines()]
              _lowerCamelCase      ,	_lowerCamelCase			:						int            =pack_examples(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
              print(F'''packed {split} split from {len(SCREAMING_SNAKE_CASE__	)} examples -> {len(SCREAMING_SNAKE_CASE__	)}.'''	)
              Path(save_path / F'''{split}.source'''	).open('w'	).write('\n'.join(SCREAMING_SNAKE_CASE__	)	)
              Path(save_path / F'''{split}.target'''	).open('w'	).write('\n'.join(SCREAMING_SNAKE_CASE__	)	)
       for split in ["val", "test"]:
              _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
              shutil.copyfile(SCREAMING_SNAKE_CASE__						,     save_path / F'''{split}.source'''	)
              shutil.copyfile(SCREAMING_SNAKE_CASE__						,     save_path / F'''{split}.target'''	)




def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						int            =argparse.ArgumentParser()
       parser.add_argument('--tok_name'						,     type=SCREAMING_SNAKE_CASE__						,     help='like facebook/bart-large-cnn,t5-base, etc.'	)
       parser.add_argument('--max_seq_len'						,     type=SCREAMING_SNAKE_CASE__						,     default=128	)
       parser.add_argument('--data_dir'						,     type=SCREAMING_SNAKE_CASE__	)
       parser.add_argument('--save_path'						,     type=SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						Any            =parser.parse_args()
       _lowerCamelCase			:						Tuple            =AutoTokenizer.from_pretrained(args.tok_name	)
       return pack_data_dir(SCREAMING_SNAKE_CASE__						,     Path(args.data_dir	)						,     args.max_seq_len						,     args.save_path	)


if __name__ == "__main__":
 packer_cli()






from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class A    (  UpperCamelCase_	):






      def __init__(   self		:	Union[str, Any]     ,  lowercase_		:	NestedDataStructureLike[PathLike]     ,  lowercase_		:	Optional[NamedSplit] = None     ,  lowercase_		:	Optional[Features] = None     ,  lowercase_		:	str = None     ,  lowercase_		:	bool = False     ,  lowercase_		:	bool = False     ,  lowercase_		:	Optional[int] = None     ,  **lowercase_		:	Optional[int]     ,  )   ->		Union[str, Any]:
             """simple docstring"""




             super().__init__(
                 lowercase_     ,  split=lowercase_     ,  features=lowercase_     ,  cache_dir=lowercase_     ,  keep_in_memory=lowercase_     ,  streaming=lowercase_     ,  num_proc=lowercase_     ,  **lowercase_     ,  )
             _lowerCamelCase			:						Optional[int]            =path_or_paths if isinstance(lowercase_     ,  lowercase_					) else {self.split: path_or_paths}
             _lowerCamelCase			:						int            =Text(
                 cache_dir=lowercase_     ,  data_files=lowercase_     ,  features=lowercase_     ,  **lowercase_     ,  )






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Dict:
             """simple docstring"""




             if self.streaming:
                    _lowerCamelCase			:						Optional[Any]            =self.builder.as_streaming_dataset(split=self.split					)
             # Build regular (map-style) dataset
             else:
                    _lowerCamelCase			:						List[str]            =None
                    _lowerCamelCase			:						int            =None
                    _lowerCamelCase			:						Optional[Any]            =None
                    _lowerCamelCase			:						Any            =None

                    self.builder.download_and_prepare(
                        download_config=lowercase_     ,  download_mode=lowercase_     ,  verification_mode=lowercase_     ,  base_path=lowercase_     ,  num_proc=self.num_proc     ,  )
                    _lowerCamelCase			:						Tuple            =self.builder.as_dataset(
                        split=self.split     ,  verification_mode=lowercase_     ,  in_memory=self.keep_in_memory					)
             return dataset


