






"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase		:  Dict						=		logging.get_logger(__name__)

_UpperCamelCase		:  Tuple						=		{
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}

class        a     (   a_	):
    UpperCAmelCase_ :     int	     ="swinv2"

    UpperCAmelCase_ :     Dict	     ={
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }







    def __init__(			self							,		_lowerCamelCase=2_2_4							,		_lowerCamelCase=4							,		_lowerCamelCase=3							,		_lowerCamelCase=9_6							,		_lowerCamelCase=[2, 2, 6, 2]							,		_lowerCamelCase=[3, 6, 1_2, 2_4]							,		_lowerCamelCase=7							,		_lowerCamelCase=4.0							,		_lowerCamelCase=True							,		_lowerCamelCase=0.0							,		_lowerCamelCase=0.0							,		_lowerCamelCase=0.1							,		_lowerCamelCase="gelu"							,		_lowerCamelCase=False							,		_lowerCamelCase=0.0_2							,		_lowerCamelCase=1e-5							,		_lowerCamelCase=3_2							,		**_lowerCamelCase							,		):
      super().__init__(**_lowerCamelCase       )

      lowercase							    =       image_size
      lowercase							    =       patch_size
      lowercase							    =       num_channels
      lowercase							    =       embed_dim
      lowercase							    =       depths
      lowercase							    =       len(_lowerCamelCase       )
      lowercase							    =       num_heads
      lowercase							    =       window_size
      lowercase							    =       mlp_ratio
      lowercase							    =       qkv_bias
      lowercase							    =       hidden_dropout_prob
      lowercase							    =       attention_probs_dropout_prob
      lowercase							    =       drop_path_rate
      lowercase							    =       hidden_act
      lowercase							    =       use_absolute_embeddings
      lowercase							    =       layer_norm_eps
      lowercase							    =       initializer_range
      lowercase							    =       encoder_stride
      # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      lowercase							    =       int(embed_dim * 2 ** (len(_lowerCamelCase       ) - 1)       )
      lowercase							    =       (0, 0, 0, 0)










"""simple docstring"""



import argparse
import json

from tqdm import tqdm
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       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'		,					)
  lowercase							    =       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:
    lowercase							    =       json.load(__snake_case    )
    for dpr_record in tqdm(__snake_case    ):
      lowercase							    =       dpr_record['question']
      lowercase							    =       [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()


