




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


UpperCamelCase__         =      logging.get_logger(__name__)

UpperCamelCase__         =      {
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}
class       a__   ( snake_case__				):
						_a							:   str =   """fnet"""






						def __init__(       self		,      _A=3_2_0_0_0		,      _A=7_6_8		,      _A=1_2		,      _A=3_0_7_2		,      _A="gelu_new"		,      _A=0.1		,      _A=5_1_2		,      _A=4		,      _A=0.02		,      _A=1E-1_2		,      _A=False		,      _A=5_1_2		,      _A=3		,      _A=1		,      _A=2		,      **_A		,      ):




											"""simple docstring"""



											super().__init__(pad_token_id=_A		,      bos_token_id=_A		,      eos_token_id=_A		,      **_A      )

											__lowerCAmelCase           =	vocab_size
											__lowerCAmelCase           =	max_position_embeddings
											__lowerCAmelCase           =	hidden_size
											__lowerCAmelCase           =	num_hidden_layers
											__lowerCAmelCase           =	intermediate_size
											__lowerCAmelCase           =	hidden_act
											__lowerCAmelCase           =	hidden_dropout_prob
											__lowerCAmelCase           =	initializer_range
											__lowerCAmelCase           =	type_vocab_size
											__lowerCAmelCase           =	layer_norm_eps
											__lowerCAmelCase           =	use_tpu_fourier_optimizations
											__lowerCAmelCase           =	tpu_short_seq_length










from pathlib import Path

import fire
from tqdm import tqdm







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict="ro"				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]="en"				,				SCREAMING_SNAKE_CASE_							:    Optional[Any]="wmt16"				,				SCREAMING_SNAKE_CASE_							:    List[str]=None					):
					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError("run pip install datasets"					)
					__lowerCAmelCase           =	F"""{src_lang}-{tgt_lang}"""
					print(F"""Converting {dataset}-{pair}"""					)
					__lowerCAmelCase           =	datasets.load_dataset(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					if save_dir is None:
										__lowerCAmelCase           =	F"""{dataset}-{pair}"""
					__lowerCAmelCase           =	Path(SCREAMING_SNAKE_CASE_					)
					save_dir.mkdir(exist_ok=SCREAMING_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
										__lowerCAmelCase           =	"val" if split == "validation" else split
										__lowerCAmelCase           =	save_dir.joinpath(F"""{fn}.source"""					)
										__lowerCAmelCase           =	save_dir.joinpath(F"""{fn}.target"""					)
										__lowerCAmelCase           =	src_path.open("w+"					)
										__lowerCAmelCase           =	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]					):
															__lowerCAmelCase           =	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)




