
import argparse
import json

from tqdm import tqdm






def   lowerCamelCase__							(							):




			"""simple docstring"""


			lowerCAmelCase_         =      argparse.ArgumentParser()

			# Required parameters
			parser.add_argument(
			    "--src_path"       ,				type=_snake_case       ,				default="biencoder-nq-dev.json"       ,				help="Path to raw DPR training data"       ,				)
			parser.add_argument(
			    "--evaluation_set"       ,				type=_snake_case       ,				help="where to store parsed evaluation_set file"       ,				)
			parser.add_argument(
			    "--gold_data_path"       ,				type=_snake_case       ,				help="where to store parsed gold_data_path file"       ,				)
			lowerCAmelCase_         =      parser.parse_args()

			with open(args.src_path       ,				"r"							) as src_file, open(args.evaluation_set       ,				"w"							) as eval_file, open(
			    args.gold_data_path       ,				"w"							) as gold_file:
						lowerCAmelCase_         =      json.load(_snake_case							)
						for dpr_record in tqdm(_snake_case							):
									lowerCAmelCase_         =      dpr_record["question"]
									lowerCAmelCase_         =      [context["title"] for context in dpr_record["positive_ctxs"]]
									eval_file.write(question + "\n"							)
									gold_file.write("\t".join(_snake_case							) + "\n"							)


if __name__ == "__main__":
			main()








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__          =  {
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__          =  [
     "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ClapModel",
     "ClapPreTrainedModel",
     "ClapTextModel",
     "ClapTextModelWithProjection",
     "ClapAudioModel",
     "ClapAudioModelWithProjection",
 ]
 SCREAMING_SNAKE_CASE__          =  ["ClapFeatureExtractor"]

if TYPE_CHECKING:
 from .configuration_clap import (
     CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
     ClapAudioConfig,
     ClapConfig,
     ClapTextConfig,
 )
 from .processing_clap import ClapProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_clap import ClapFeatureExtractor
  from .modeling_clap import (
      CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
      ClapAudioModel,
      ClapAudioModelWithProjection,
      ClapModel,
      ClapPreTrainedModel,
      ClapTextModel,
      ClapTextModelWithProjection,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE__          =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




