

import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

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





    '''simple docstring'''






    A__      ,						A__          =   [], []

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

    def is_too_big(SCREAMING_SNAKE_CASE__			:	Union[str, Any]      ):
        return tok(SCREAMING_SNAKE_CASE__    ,			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(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__      )
            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(SCREAMING_SNAKE_CASE__      )
        finished_tgt.append(SCREAMING_SNAKE_CASE__      )
    return finished_src, finished_tgt
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	Path    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	str      )							->      Optional[int]:





    '''simple docstring'''






    A__          =   Path(SCREAMING_SNAKE_CASE__      )
    save_path.mkdir(exist_ok=SCREAMING_SNAKE_CASE__      )
    for split in ["train"]:
        A__      ,						A__          =   data_dir / f'{split}.source', data_dir / f'{split}.target'
        A__          =   [x.rstrip() for x in Path(SCREAMING_SNAKE_CASE__      ).open().readlines()]
        A__          =   [x.rstrip() for x in Path(SCREAMING_SNAKE_CASE__      ).open().readlines()]
        A__      ,						A__          =   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"]:
        A__      ,						A__          =   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 						_snake_case(    )							->      Optional[int]:





    '''simple docstring'''






    A__          =   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__      )
    A__          =   parser.parse_args()
    A__          =   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()




import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Union[str, Any]:





    '''simple docstring'''






    return EnvironmentCommand()


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @staticmethod
    def 						snake_case__     (      lowercase_ :       ArgumentParser		)->    Dict:





        '''simple docstring'''







        A__          =   parser.add_parser('env'		)
        download_parser.set_defaults(func=lowercase_		)


    def 						snake_case__     (      self :       List[Any]		)->    List[str]:





        '''simple docstring'''







        A__          =   huggingface_hub.__version__

        A__          =   'not installed'
        A__          =   'NA'
        if is_torch_available():
            import torch

            A__          =   torch.__version__
            A__          =   torch.cuda.is_available()

        A__          =   'not installed'
        if is_transformers_available():
            import transformers

            A__          =   transformers.__version__

        A__          =   'not installed'
        if is_accelerate_available():
            import accelerate

            A__          =   accelerate.__version__

        A__          =   'not installed'
        if is_xformers_available():
            import xformers

            A__          =   xformers.__version__

        A__          =   {
            '`diffusers` version': version,
            'Platform': platform.platform(),
            'Python version': platform.python_version(),
            'PyTorch version (GPU?)': F'{pt_version} ({pt_cuda_available})',
            'Huggingface_hub version': hub_version,
            'Transformers version': transformers_version,
            'Accelerate version': accelerate_version,
            'xFormers version': xformers_version,
            'Using GPU in script?': '<fill in>',
            'Using distributed or parallel set-up in script?': '<fill in>',
        }

        print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'		)
        print(self.format_dict(lowercase_		)		)

        return info


    @staticmethod
    def 						snake_case__     (      lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]		) + "\n"

