






from pathlib import Path

import fire
from tqdm import tqdm


def   _A   (				SCREAMING_SNAKE_CASE__  :	int="ro"     ,  SCREAMING_SNAKE_CASE__  :	Tuple="en"     ,  SCREAMING_SNAKE_CASE__  :	int="wmt16"     ,  SCREAMING_SNAKE_CASE__  :	Optional[Any]=None       ):
    try:
        import datasets
    except (ModuleNotFoundError, ImportError):
        raise ImportError('''run pip install datasets'''       )
    UpperCamelCase       :Any								=   F'''{src_lang}-{tgt_lang}'''
    print(F'''Converting {dataset}-{pair}'''       )
    UpperCamelCase       :Optional[Any]								=   datasets.load_dataset(A_     ,  A_       )
    if save_dir is None:
        UpperCamelCase       :List[Any]								=   F'''{dataset}-{pair}'''
    UpperCamelCase       :str								=   Path(A_       )
    save_dir.mkdir(exist_ok=A_       )

    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       :Union[str, Any]								=   '''val''' if split == '''validation''' else split
        UpperCamelCase       :List[str]								=   save_dir.joinpath(F'''{fn}.source'''       )
        UpperCamelCase       :Optional[Any]								=   save_dir.joinpath(F'''{fn}.target'''       )
        UpperCamelCase       :Optional[Any]								=   src_path.open('''w+'''       )
        UpperCamelCase       :Optional[int]								=   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       :Dict								=   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"""





import torch

from transformers import AutoModel







class 				SCREAMING_SNAKE_CASE					(		torch.nn.Module       ):






					"""simple docstring"""



					def __init__(       self			:				Tuple   ,lowercase_			:				Dict="sayef/fsner-bert-base-uncased"		):
						super(lowercase_   ,self		).__init__()

						lowerCAmelCase__	:						int  				=	AutoModel.from_pretrained(lowercase_   ,return_dict=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	torch.nn.CosineSimilarity(3   ,1E-08		)
						lowerCAmelCase__	:						List[str]  				=	torch.nn.Softmax(dim=1		)







					def        __lowerCAmelCase (       self			:				str   ,**lowercase_			:				int		):
						return self.bert(**lowercase_		).last_hidden_state







					def        __lowerCAmelCase (       self			:				List[Any]   ,lowercase_			:				Optional[int]		):
						return token_embeddings.sum(2   ,keepdim=lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				int   ,lowercase_			:				str   ,lowercase_			:				Tuple=1		):
						return self.softmax(T * self.cos(lowercase_   ,lowercase_		)		)







					def        __lowerCAmelCase (       self			:				Optional[Any]   ,lowercase_			:				str   ,lowercase_			:				Union[str, Any]		):
						lowerCAmelCase__	:						List[Any]  				=	W_supports['''sizes'''].tolist()
						lowerCAmelCase__	:						Dict  				=	W_supports['''start_token_id'''].item()
						lowerCAmelCase__	:						Union[str, Any]  				=	W_supports['''end_token_id'''].item()

						del W_supports["sizes"]
						del W_supports["start_token_id"]
						del W_supports["end_token_id"]

						lowerCAmelCase__	:						Optional[Any]  				=	self.BERT(**lowercase_		)
						lowerCAmelCase__	:						int  				=	self.BERT(**lowercase_		)

						lowerCAmelCase__	:						List[str]  				=	None
						lowerCAmelCase__	:						Union[str, Any]  				=	None

						lowerCAmelCase__	:						int  				=	W_supports['''input_ids'''] == start_token_id
						lowerCAmelCase__	:						Optional[Any]  				=	W_supports['''input_ids'''] == end_token_id

						for i, size in enumerate(lowercase_		):
							if i == 0:
								lowerCAmelCase__	:						str  				=	0
							else:
								lowerCAmelCase__	:						List[Any]  				=	support_sizes[i - 1]

							lowerCAmelCase__	:						Optional[Any]  				=	S[s : s + size][start_token_masks[s : s + size]]
							lowerCAmelCase__	:						List[Any]  				=	S[s : s + size][end_token_masks[s : s + size]]

							lowerCAmelCase__	:						Union[str, Any]  				=	torch.matmul(q[i]   ,s_start.T		).sum(1		).softmax(0		)
							lowerCAmelCase__	:						Any  				=	torch.matmul(q[i]   ,s_end.T		).sum(1		).softmax(0		)

							if p_starts is not None:
								lowerCAmelCase__	:						List[Any]  				=	torch.vstack((p_starts, p_start)		)
								lowerCAmelCase__	:						List[Any]  				=	torch.vstack((p_ends, p_end)		)
							else:
								lowerCAmelCase__	:						Union[str, Any]  				=	p_start
								lowerCAmelCase__	:						str  				=	p_end

						return p_starts, p_ends




