




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


UpperCAmelCase_		:					Union[str, Any]          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[str]          =  {
    "tanreinama/GPTSAN-2.8B-spout_is_uniform": (
        "https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"
    ),
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """gptsan-japanese"""
						UpperCamelCase      =       [
						    """past_key_values""",
						]
						UpperCamelCase      =       {
						    """hidden_size""": """d_model""",
						    """num_attention_heads""": """num_heads""",
						    """num_hidden_layers""": """num_layers""",
						}

						def __init__(		self :str     ,			__snake_case :str=3_60_00     ,			__snake_case :List[Any]=12_80     ,			__snake_case :Tuple=10_24     ,			__snake_case :Union[str, Any]=81_92     ,			__snake_case :Dict=40_96     ,			__snake_case :Tuple=1_28     ,			__snake_case :Union[str, Any]=10     ,			__snake_case :List[Any]=0     ,			__snake_case :int=16     ,			__snake_case :Tuple=16     ,			__snake_case :int=1_28     ,			__snake_case :Optional[Any]=0.0     ,			__snake_case :Any=1E-5     ,			__snake_case :str=False     ,			__snake_case :Dict=0.0     ,			__snake_case :str="float32"     ,			__snake_case :int=False     ,			__snake_case :int=False     ,			__snake_case :Optional[int]=False     ,			__snake_case :Tuple=0.002     ,			__snake_case :Any=False     ,			__snake_case :Optional[Any]=True     ,			__snake_case :Optional[int]=3_59_98     ,			__snake_case :Dict=3_59_95     ,			__snake_case :Optional[int]=3_59_99     ,			**__snake_case :Union[str, Any]     ,			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=vocab_size
										__magic_name__						:						Any										=max_position_embeddings
										__magic_name__						:						int										=d_model
										__magic_name__						:						Any										=d_ff
										__magic_name__						:						Dict										=d_ext
										__magic_name__						:						Union[str, Any]										=d_spout
										__magic_name__						:						List[Any]										=num_switch_layers
										__magic_name__						:						int										=num_ext_layers
										__magic_name__						:						Optional[int]										=num_switch_layers + num_ext_layers
										__magic_name__						:						Union[str, Any]										=num_heads
										__magic_name__						:						Any										=num_experts
										__magic_name__						:						Optional[int]										=expert_capacity
										__magic_name__						:						Union[str, Any]										=dropout_rate
										__magic_name__						:						Any										=layer_norm_epsilon
										__magic_name__						:						Union[str, Any]										=router_bias
										__magic_name__						:						int										=router_jitter_noise
										__magic_name__						:						str										=router_dtype
										__magic_name__						:						Optional[int]										=router_ignore_padding_tokens
										__magic_name__						:						str										=output_hidden_states
										__magic_name__						:						Dict										=output_attentions
										__magic_name__						:						Dict										=initializer_factor
										__magic_name__						:						List[str]										=output_router_logits
										__magic_name__						:						int										=use_cache

										super().__init__(
										    separator_token_id=__snake_case     ,			pad_token_id=__snake_case     ,			eos_token_id=__snake_case     ,			**__snake_case     ,			)










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)




