

'''simple docstring'''





from typing import Callable, Optional, Union

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


A_	= logging.get_logger(__name__)

A_	= {
    "microsoft/xprophetnet-large-wiki100-cased": (
        "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
    ),
}


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					'xlm-prophetnet'
		SCREAMING_SNAKE_CASE_           =					['past_key_values']
		SCREAMING_SNAKE_CASE_           =					{
		    'num_attention_heads': 'num_encoder_attention_heads',
		}
		def __init__(					self   ,			SCREAMING_SNAKE_CASE_ = 0.1   ,			SCREAMING_SNAKE_CASE_ = "gelu"   ,			SCREAMING_SNAKE_CASE_ = 30522   ,			SCREAMING_SNAKE_CASE_ = 1024   ,			SCREAMING_SNAKE_CASE_ = 4096   ,			SCREAMING_SNAKE_CASE_ = 12   ,			SCREAMING_SNAKE_CASE_ = 16   ,			SCREAMING_SNAKE_CASE_ = 4096   ,			SCREAMING_SNAKE_CASE_ = 12   ,			SCREAMING_SNAKE_CASE_ = 16   ,			SCREAMING_SNAKE_CASE_ = 0.1   ,			SCREAMING_SNAKE_CASE_ = 0.1   ,			SCREAMING_SNAKE_CASE_ = 512   ,			SCREAMING_SNAKE_CASE_ = 0.02   ,			SCREAMING_SNAKE_CASE_ = True   ,			SCREAMING_SNAKE_CASE_ = True   ,			SCREAMING_SNAKE_CASE_ = 0   ,			SCREAMING_SNAKE_CASE_ = 2   ,			SCREAMING_SNAKE_CASE_ = 32   ,			SCREAMING_SNAKE_CASE_ = 128   ,			SCREAMING_SNAKE_CASE_ = False   ,			SCREAMING_SNAKE_CASE_ = 0.0   ,			SCREAMING_SNAKE_CASE_ = True   ,			SCREAMING_SNAKE_CASE_ = 0   ,			SCREAMING_SNAKE_CASE_ = 1   ,			SCREAMING_SNAKE_CASE_ = 2   ,			**SCREAMING_SNAKE_CASE_   ,			)  ->     Tuple:






							'''simple docstring'''



							lowerCamelCase_  				=      vocab_size
							lowerCamelCase_  				=      hidden_size
							lowerCamelCase_  				=      encoder_ffn_dim
							lowerCamelCase_  				=      num_encoder_layers
							lowerCamelCase_  				=      num_encoder_attention_heads
							lowerCamelCase_  				=      decoder_ffn_dim
							lowerCamelCase_  				=      num_decoder_layers
							lowerCamelCase_  				=      num_decoder_attention_heads
							lowerCamelCase_  				=      max_position_embeddings
							lowerCamelCase_  				=      init_std  # Normal(0, this parameter)
							lowerCamelCase_  				=      activation_function

							# parameters for xlmprophetnet
							lowerCamelCase_  				=      ngram
							lowerCamelCase_  				=      num_buckets
							lowerCamelCase_  				=      relative_max_distance
							lowerCamelCase_  				=      disable_ngram_loss
							lowerCamelCase_  				=      eps

							# 3 Types of Dropout
							lowerCamelCase_  				=      attention_dropout
							lowerCamelCase_  				=      activation_dropout
							lowerCamelCase_  				=      dropout

							lowerCamelCase_  				=      use_cache

							super().__init__(
							    pad_token_id=SCREAMING_SNAKE_CASE_   ,			bos_token_id=SCREAMING_SNAKE_CASE_   ,			eos_token_id=SCREAMING_SNAKE_CASE_   ,			is_encoder_decoder=SCREAMING_SNAKE_CASE_   ,			add_cross_attention=SCREAMING_SNAKE_CASE_   ,			decoder_start_token_id=SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_   ,			)
		@property
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							return self.num_encoder_layers + self.num_decoder_layers






		@num_hidden_layers.setter
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     Tuple:






							'''simple docstring'''



							raise NotImplementedError(
							    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
							    ' `num_decoder_layers`.'		)









from pathlib import Path

import fire
from tqdm import tqdm




def 		lowerCAmelCase_ (						lowerCamelCase="ro"						,  lowerCamelCase="en"						,  lowerCamelCase="wmt16"						,  lowerCamelCase=None ):
				try:
								import datasets
				except (ModuleNotFoundError, ImportError):
								raise ImportError("""run pip install datasets""" )
				__magic_name__						:						Dict										=F"{src_lang}-{tgt_lang}"
				print(F"Converting {dataset}-{pair}" )
				__magic_name__						:						Dict										=datasets.load_dataset(lowerCamelCase						,  lowerCamelCase )
				if save_dir is None:
								__magic_name__						:						Optional[int]										=F"{dataset}-{pair}"
				__magic_name__						:						int										=Path(lowerCamelCase )
				save_dir.mkdir(exist_ok=lowerCamelCase )

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




