



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case  =logging.get_logger(__name__)

__snake_case  ={
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     ="""biogpt"""



		def __init__(self		,__lowerCamelCase=4_23_84		,__lowerCamelCase=10_24		,__lowerCamelCase=24		,__lowerCamelCase=16		,__lowerCamelCase=40_96		,__lowerCamelCase="gelu"		,__lowerCamelCase=0.1		,__lowerCamelCase=0.1		,__lowerCamelCase=10_24		,__lowerCamelCase=0.02		,__lowerCamelCase=1e-12		,__lowerCamelCase=True		,__lowerCamelCase=True		,__lowerCamelCase=0.0		,__lowerCamelCase=0.0		,__lowerCamelCase=1		,__lowerCamelCase=0		,__lowerCamelCase=2		,**__lowerCamelCase		,)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					vocab_size
						lowerCAmelCase__					:    List[Any]												=					max_position_embeddings
						lowerCAmelCase__					:    Tuple												=					hidden_size
						lowerCAmelCase__					:    Any												=					num_hidden_layers
						lowerCAmelCase__					:    Optional[Any]												=					num_attention_heads
						lowerCAmelCase__					:    Optional[int]												=					intermediate_size
						lowerCAmelCase__					:    Union[str, Any]												=					hidden_act
						lowerCAmelCase__					:    str												=					hidden_dropout_prob
						lowerCAmelCase__					:    int												=					attention_probs_dropout_prob
						lowerCAmelCase__					:    Optional[Any]												=					initializer_range
						lowerCAmelCase__					:    Optional[Any]												=					layer_norm_eps
						lowerCAmelCase__					:    int												=					scale_embedding
						lowerCAmelCase__					:    List[Any]												=					use_cache
						lowerCAmelCase__					:    Tuple												=					layerdrop
						lowerCAmelCase__					:    Optional[Any]												=					activation_dropout
						super().__init__(pad_token_id=__lowerCamelCase		,bos_token_id=__lowerCamelCase		,eos_token_id=__lowerCamelCase		,**__lowerCamelCase			)






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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def      lowerCAmelCase__		(   lowerCamelCase_		:		Dict	,lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		List[Any]=1024	,lowerCamelCase_		:		int=1024	,lowerCamelCase_		:		Dict=False	,**lowerCamelCase_		:		Tuple):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					AutoTokenizer.from_pretrained(lowerCamelCase_)
				lowerCAmelCase__					:    Optional[Any]												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''train'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    int												=					tok.pad_token_id

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

				lowerCAmelCase__					:    str												=					get_lens(lowerCamelCase_)
				lowerCAmelCase__					:    Tuple												=					SeqaSeqDataset(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,type_path='''val'''	,**lowerCamelCase_)
				lowerCAmelCase__					:    Optional[int]												=					get_lens(lowerCamelCase_)
				pickle_save(lowerCamelCase_	,train_ds.len_file)
				pickle_save(lowerCamelCase_	,val_ds.len_file)


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

