
import argparse
import json

from tqdm import tqdm




def        __UpperCamelCase(    ):
 __a			:  List[str]		    =  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'''  ,       )
 __a			:  Any		    =  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:
  __a			:  Union[str, Any]		    =  json.load(_snake_case  )
  for dpr_record in tqdm(_snake_case  ):
   __a			:  List[Any]		    =  dpr_record['''question''']
   __a			:  Dict		    =  [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()


from typing import TYPE_CHECKING

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


lowercase__          ={
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['LayoutLMv2TokenizerFast']

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['LayoutLMv2FeatureExtractor']
      lowercase__          =['LayoutLMv2ImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
          'LayoutLMv2ForQuestionAnswering',
          'LayoutLMv2ForSequenceClassification',
          'LayoutLMv2ForTokenClassification',
          'LayoutLMv2Layer',
          'LayoutLMv2Model',
          'LayoutLMv2PreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaLayer,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )
else:
      import sys

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