
"""simple docstring"""





from pathlib import Path

import fire
from tqdm import tqdm



def 				lowerCamelCase__		(      __snake_case="ro",					__snake_case="en",					__snake_case="wmt16",					__snake_case=None     )							->		None:


					"""simple docstring"""





					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError('''run pip install datasets'''     )
					_UpperCamelCase										=  F'''{src_lang}-{tgt_lang}'''
					print(F'''Converting {dataset}-{pair}'''     )
					_UpperCamelCase										=  datasets.load_dataset(__snake_case,					__snake_case     )
					if save_dir is None:
										_UpperCamelCase										=  F'''{dataset}-{pair}'''
					_UpperCamelCase										=  Path(__snake_case     )
					save_dir.mkdir(exist_ok=__snake_case     )

					for split in ds.keys():
										print(F'''Splitting {split} with {ds[split].num_rows} records'''     )

										# to save to val.source, val.target like summary datasets
										_UpperCamelCase										=  '''val''' if split == '''validation''' else split
										_UpperCamelCase										=  save_dir.joinpath(F'''{fn}.source'''     )
										_UpperCamelCase										=  save_dir.joinpath(F'''{fn}.target'''     )
										_UpperCamelCase										=  src_path.open('''w+'''     )
										_UpperCamelCase										=  tgt_path.open('''w+'''     )

										# reader is the bottleneck so writing one record at a time doesn't slow things down
										for x in tqdm(ds[split]     ):
															_UpperCamelCase										=  x['''translation''']
															src_fp.write(ex[src_lang] + '''\n'''     )
															tgt_fp.write(ex[tgt_lang] + '''\n'''     )

					print(F'''Saved {dataset} dataset to {save_dir}'''     )


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








"""simple docstring"""





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


_a														=		logging.get_logger(__name__)

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






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'biogpt'



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






							'''simple docstring'''
							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  scale_embedding
							_UpperCamelCase										=  use_cache
							_UpperCamelCase										=  layerdrop
							_UpperCamelCase										=  activation_dropout
							super().__init__(pad_token_id=__a    ,		bos_token_id=__a    ,		eos_token_id=__a    ,		**__a)






