






"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a			:    Optional[Any]				 =		{
    """configuration_chinese_clip""": [
        """CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ChineseCLIPConfig""",
        """ChineseCLIPOnnxConfig""",
        """ChineseCLIPTextConfig""",
        """ChineseCLIPVisionConfig""",
    ],
    """processing_chinese_clip""": ["""ChineseCLIPProcessor"""],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a			:    Tuple				 =		["""ChineseCLIPFeatureExtractor"""]
			a			:    List[Any]				 =		["""ChineseCLIPImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a			:    int				 =		[
			    """CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """ChineseCLIPModel""",
			    """ChineseCLIPPreTrainedModel""",
			    """ChineseCLIPTextModel""",
			    """ChineseCLIPVisionModel""",
			]

if TYPE_CHECKING:
			from .configuration_chinese_clip import (
			    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    ChineseCLIPConfig,
			    ChineseCLIPOnnxConfig,
			    ChineseCLIPTextConfig,
			    ChineseCLIPVisionConfig,
			)
			from .processing_chinese_clip import ChineseCLIPProcessor

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_chinese_clip import (
						    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ChineseCLIPModel,
						    ChineseCLIPPreTrainedModel,
						    ChineseCLIPTextModel,
						    ChineseCLIPVisionModel,
						)

else:
			import sys

			a			:    Tuple				 =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)














"""simple docstring"""







from pathlib import Path

import fire
from tqdm import tqdm





def 						lowercase__(A="ro" ,		A="en" ,		A="wmt16" ,		A=None						)    ->None:




						"""simple docstring"""



						try:
												import datasets
						except (ModuleNotFoundError, ImportError):
												raise ImportError("run pip install datasets"						)
						lowercase__    :				int=	f'''{src_lang}-{tgt_lang}'''
						print(f'''Converting {dataset}-{pair}'''						)
						lowercase__    :				List[Any]=	datasets.load_dataset(A ,		A						)
						if save_dir is None:
												lowercase__    :				Union[str, Any]=	f'''{dataset}-{pair}'''
						lowercase__    :				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
												lowercase__    :				Any=	"val" if split == "validation" else split
												lowercase__    :				List[Any]=	save_dir.joinpath(f'''{fn}.source'''						)
												lowercase__    :				Optional[Any]=	save_dir.joinpath(f'''{fn}.target'''						)
												lowercase__    :				Optional[int]=	src_path.open("w+"						)
												lowercase__    :				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]						):
																		lowercase__    :				int=	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)






