
'''simple docstring'''

from typing import TYPE_CHECKING

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


lowercase__         =      {
    '''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PerceiverConfig''', '''PerceiverOnnxConfig'''],
    '''tokenization_perceiver''': ['''PerceiverTokenizer'''],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      ['''PerceiverFeatureExtractor''']
 lowercase__         =      ['''PerceiverImageProcessor''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     '''PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''PerceiverForImageClassificationConvProcessing''',
     '''PerceiverForImageClassificationFourier''',
     '''PerceiverForImageClassificationLearned''',
     '''PerceiverForMaskedLM''',
     '''PerceiverForMultimodalAutoencoding''',
     '''PerceiverForOpticalFlow''',
     '''PerceiverForSequenceClassification''',
     '''PerceiverLayer''',
     '''PerceiverModel''',
     '''PerceiverPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
 from .tokenization_perceiver import PerceiverTokenizer

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_perceiver import PerceiverFeatureExtractor
  from .image_processing_perceiver import PerceiverImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_perceiver import (
      PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
      PerceiverForImageClassificationConvProcessing,
      PerceiverForImageClassificationFourier,
      PerceiverForImageClassificationLearned,
      PerceiverForMaskedLM,
      PerceiverForMultimodalAutoencoding,
      PerceiverForOpticalFlow,
      PerceiverForSequenceClassification,
      PerceiverLayer,
      PerceiverModel,
      PerceiverPreTrainedModel,
  )

else:
 import sys

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







'''simple docstring'''

import argparse
import json

from tqdm import tqdm




def 				UpperCamelCase(					):
 UpperCAmelCase	:     List[Any]						 =			argparse.ArgumentParser()

 # Required parameters
 parser.add_argument(
     '--src_path'  ,	type=UpperCAmelCase_  ,	default='biencoder-nq-dev.json'  ,	help='Path to raw DPR training data'  ,	)
 parser.add_argument(
     '--evaluation_set'  ,	type=UpperCAmelCase_  ,	help='where to store parsed evaluation_set file'  ,	)
 parser.add_argument(
     '--gold_data_path'  ,	type=UpperCAmelCase_  ,	help='where to store parsed gold_data_path file'  ,	)
 UpperCAmelCase	:     int						 =			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:
  UpperCAmelCase	:     int						 =			json.load(UpperCAmelCase_     )
  for dpr_record in tqdm(UpperCAmelCase_     ):
   UpperCAmelCase	:     Any						 =			dpr_record['question']
   UpperCAmelCase	:     List[str]						 =			[context['title'] for context in dpr_record['positive_ctxs']]
   eval_file.write(question + '\n'     )
   gold_file.write('\t'.join(UpperCAmelCase_     ) + '\n'     )


if __name__ == "__main__":
 main()




