
from argparse import ArgumentParser

from . import BaseTransformersCLICommand


def     __lowerCAmelCase			(			a__  )       ->  Union[str, Any]:
				return DownloadCommand(args.model						,    args.cache_dir						,    args.force						,    args.trust_remote_code  )







class 			__A( a			):




				@staticmethod
				def 				SCREAMING_SNAKE_CASE_		(     _snake_case				)    ->      int:


								'''simple docstring'''




								__a										=    parser.add_parser('''download'''				)
								download_parser.add_argument(
								    '''--cache-dir'''		,     type=_snake_case		,     default=_snake_case		,     help='''Path to location to store the models'''				)
								download_parser.add_argument(
								    '''--force'''		,     action='''store_true'''		,     help='''Force the model to be download even if already in cache-dir'''				)
								download_parser.add_argument(
								    '''--trust-remote-code'''		,     action='''store_true'''		,     help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''		,     )
								download_parser.add_argument('''model'''		,     type=_snake_case		,     help='''Name of the model to download'''				)
								download_parser.set_defaults(func=_snake_case				)




				def __init__(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      Dict:


								'''simple docstring'''




								__a										=    model
								__a										=    cache
								__a										=    force
								__a										=    trust_remote_code




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




								from ..models.auto import AutoModel, AutoTokenizer

								AutoModel.from_pretrained(
								    self._model		,     cache_dir=self._cache		,     force_download=self._force		,     trust_remote_code=self._trust_remote_code				)
								AutoTokenizer.from_pretrained(
								    self._model		,     cache_dir=self._cache		,     force_download=self._force		,     trust_remote_code=self._trust_remote_code				)

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save


def     __lowerCAmelCase			(			a__						,    a__						,    a__=1024						,    a__=1024						,    a__=False						,    **a__  )       ->  Optional[Any]:
				__a										=    AutoTokenizer.from_pretrained(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''train'''						,    **a__  )
				__a										=    tok.pad_token_id

				def get_lens(a__  ):
								__a										=    tqdm(
								    DataLoader(a__						,    batch_size=512						,    num_workers=8						,    shuffle=a__						,    collate_fn=ds.collate_fn  )						,    desc=str(ds.len_file  )						,    )
								__a										=    []
								for batch in dl:
												__a										=    batch['''input_ids'''].ne(a__  ).sum(1  ).tolist()
												__a										=    batch['''labels'''].ne(a__  ).sum(1  ).tolist()
												if consider_target:
																for src, tgt in zip(a__						,    a__  ):
																				max_lens.append(max(a__						,    a__  )  )
												else:
																max_lens.extend(a__  )
								return max_lens

				__a										=    get_lens(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''val'''						,    **a__  )
				__a										=    get_lens(a__  )
				pickle_save(a__						,    train_ds.len_file  )
				pickle_save(a__						,    val_ds.len_file  )


if __name__ == "__main__":
					fire.Fire(save_len_file)