



from pathlib import Path

import fire
from tqdm import tqdm



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_="ro"    ,	SCREAMING_SNAKE_CASE_="en"    ,	SCREAMING_SNAKE_CASE_="wmt16"    ,	SCREAMING_SNAKE_CASE_=None	):





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

			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
						lowercase__       	=					'''val''' if split == '''validation''' else split
						lowercase__       	=					save_dir.joinpath(f'''{fn}.source'''	)
						lowercase__       	=					save_dir.joinpath(f'''{fn}.target'''	)
						lowercase__       	=					src_path.open("w+"	)
						lowercase__       	=					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]	):
									lowercase__       	=					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)











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase							:       List[str]					 =	{
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase							:       str					 =	[
							    """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GPTBigCodeForSequenceClassification""",
							    """GPTBigCodeForTokenClassification""",
							    """GPTBigCodeForCausalLM""",
							    """GPTBigCodeModel""",
							    """GPTBigCodePreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_bigcode import (
														    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTBigCodeForCausalLM,
														    GPTBigCodeForSequenceClassification,
														    GPTBigCodeForTokenClassification,
														    GPTBigCodeModel,
														    GPTBigCodePreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase							:       List[str]					 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



