






'''simple docstring'''







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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def 		_UpperCAmelCase (				__A				: int    ,		__A				: Optional[Any]    ,		__A				: Optional[Any]=10_24    ,		__A				: str=10_24    ,		__A				: int=False    ,		**__A				: Optional[Any]   ):
				a_		:	int									=   AutoTokenizer.from_pretrained(__A   )
				a_		:	Tuple									=   SeqaSeqDataset(__A    ,		__A    ,		__A    ,		__A    ,		type_path='''train'''    ,		**__A   )
				a_		:	Any									=   tok.pad_token_id

				def get_lens(__A				: str   ):
								a_		:	Any									=   tqdm(
								    DataLoader(__A    ,		batch_size=5_12    ,		num_workers=8    ,		shuffle=__A    ,		collate_fn=ds.collate_fn   )    ,		desc=str(ds.len_file   )    ,		)
								a_		:	Dict									=   []
								for batch in dl:
												a_		:	List[str]									=   batch['''input_ids'''].ne(__A   ).sum(1   ).tolist()
												a_		:	List[Any]									=   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_		:	Dict									=   get_lens(__A   )
				a_		:	Dict									=   SeqaSeqDataset(__A    ,		__A    ,		__A    ,		__A    ,		type_path='''val'''    ,		**__A   )
				a_		:	str									=   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)












'''simple docstring'''







import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
__lowerCAmelCase									=   object()

# For specifying empty leaf dict `{}`
__lowerCAmelCase									=   object()





def 		_UpperCAmelCase (				__A				: List[str]    ,		__A				: Tuple   ):
				a_		:	List[Any]									=   tuple((re.compile(x + '''$'''   ) for x in qs)   )
				for i in range(len(__A   ) - len(__A   ) + 1   ):
								a_		:	Union[str, Any]									=   [x.match(__A   ) for x, y in zip(__A    ,		ks[i:]   )]
								if matches and all(__A   ):
												return True
				return False





def 		_UpperCAmelCase (				__A				: List[str]   ):
				def replace(__A				: int    ,		__A				: Union[str, Any]   ):
								for rule, replacement in rules:
												if _match(__A    ,		__A   ):
																return replacement
								return val

				return replace





def 		_UpperCAmelCase (				):
				return [
				    # embeddings
				    (("transformer", "wpe", "embedding"), P('''mp'''    ,		__A   )),
				    (("transformer", "wte", "embedding"), P('''mp'''    ,		__A   )),
				    # atention
				    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__A    ,		'''mp'''   )),
				    (("attention", "out_proj", "kernel"), P('''mp'''    ,		__A   )),
				    (("attention", "out_proj", "bias"), None),
				    # mlp
				    (("mlp", "c_fc", "kernel"), P(__A    ,		'''mp'''   )),
				    (("mlp", "c_fc", "bias"), P('''mp'''   )),
				    (("mlp", "c_proj", "kernel"), P('''mp'''    ,		__A   )),
				    (("mlp", "c_proj", "bias"), None),
				    # layer norms
				    ((r"ln_\d+", "bias"), None),
				    ((r"\d+", r"ln_\d+", "scale"), None),
				    (("ln_f", "bias"), None),
				    (("ln_f", "scale"), None),
				]





def 		_UpperCAmelCase (				__A				: Union[str, Any]   ):
				a_		:	Tuple									=   _get_partition_rules()
				a_		:	Tuple									=   _replacement_rules(__A   )
				a_		:	Optional[Any]									=   {k: _unmatched for k in flatten_dict(__A   )}
				a_		:	Optional[Any]									=   {k: replace(__A    ,		__A   ) for k, v in initd.items()}
				assert _unmatched not in result.values(), "Incomplete partition spec."
				return freeze(unflatten_dict(__A   )   )




