



'''simple docstring'''

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


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Any   				=					'xglm'
							lowerCAmelCase       :    Optional[Any]   				=					['past_key_values']

							lowerCAmelCase       :    Optional[Any]   				=					{
							    'num_attention_heads': 'attention_heads',
							    'hidden_size': 'd_model',
							    'num_hidden_layers': 'num_layers',
							}







							def __init__(							self					:       List[Any]	,_UpperCAmelCase					:       Dict=256008	,_UpperCAmelCase					:       str=2048	,_UpperCAmelCase					:       Optional[int]=1024	,_UpperCAmelCase					:       int=4096	,_UpperCAmelCase					:       List[Any]=24	,_UpperCAmelCase					:       str=16	,_UpperCAmelCase					:       Optional[Any]="gelu"	,_UpperCAmelCase					:       Dict=0.1	,_UpperCAmelCase					:       str=0.1	,_UpperCAmelCase					:       Optional[int]=0.0	,_UpperCAmelCase					:       Any=0.0	,_UpperCAmelCase					:       List[Any]=0.02	,_UpperCAmelCase					:       int=True	,_UpperCAmelCase					:       Union[str, Any]=True	,_UpperCAmelCase					:       Dict=2	,_UpperCAmelCase					:       Any=1	,_UpperCAmelCase					:       List[str]=0	,_UpperCAmelCase					:       Optional[Any]=2	,**_UpperCAmelCase					:       Union[str, Any]	,):
									_a     :      Optional[int]        =							vocab_size
									_a     :      str        =							max_position_embeddings
									_a     :      Any        =							d_model
									_a     :      List[str]        =							ffn_dim
									_a     :      Tuple        =							num_layers
									_a     :      Optional[int]        =							attention_heads
									_a     :      Dict        =							activation_function
									_a     :      Optional[int]        =							dropout
									_a     :      Union[str, Any]        =							attention_dropout
									_a     :      List[Any]        =							activation_dropout
									_a     :      Dict        =							layerdrop
									_a     :      Any        =							init_std
									_a     :      Optional[int]        =							scale_embedding  # scale factor will be sqrt(d_model) if True
									_a     :      Optional[Any]        =							use_cache

									super().__init__(
									    pad_token_id=_UpperCAmelCase	,bos_token_id=_UpperCAmelCase	,eos_token_id=_UpperCAmelCase	,decoder_start_token_id=_UpperCAmelCase	,**_UpperCAmelCase	,)









'''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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=False						,			**lowerCAmelCase_  )			->      List[Any]:
		_a     :      str        =							AutoTokenizer.from_pretrained(lowerCAmelCase_  )
		_a     :      List[Any]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='train'						,			**lowerCAmelCase_  )
		_a     :      List[str]        =							tok.pad_token_id

		def get_lens(lowerCAmelCase_  ):
				_a     :      Dict        =							tqdm(
				    DataLoader(lowerCAmelCase_						,			batch_size=512						,			num_workers=8						,			shuffle=lowerCAmelCase_						,			collate_fn=ds.collate_fn  )						,			desc=str(ds.len_file  )						,			)
				_a     :      Union[str, Any]        =							[]
				for batch in dl:
						_a     :      Optional[Any]        =							batch['input_ids'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						_a     :      Optional[Any]        =							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

		_a     :      str        =							get_lens(lowerCAmelCase_  )
		_a     :      Optional[int]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='val'						,			**lowerCAmelCase_  )
		_a     :      Dict        =							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)




