






'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase__						:Dict          =			logging.get_logger(__name__)

UpperCAmelCase__						:Tuple          =			{
    """SenseTime/deformable-detr""": """https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json""",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     Dict   		=			'deformable_detr'
       snake_case__    :     Any   		=			{
           'hidden_size': 'd_model',
           'num_attention_heads': 'encoder_attention_heads',
       }




       def __init__(					self      : str	,    A__      : str=True	,    A__      : List[Any]=None	,    A__      : Dict=3	,    A__      : Dict=300	,    A__      : List[str]=1024	,    A__      : Any=6	,    A__      : Dict=1024	,    A__      : Dict=8	,    A__      : int=6	,    A__      : int=1024	,    A__      : List[str]=8	,    A__      : List[str]=0.0	,    A__      : Union[str, Any]=True	,    A__      : str="relu"	,    A__      : Dict=256	,    A__      : List[Any]=0.1	,    A__      : int=0.0	,    A__      : Tuple=0.0	,    A__      : str=0.02	,    A__      : Dict=1.0	,    A__      : Union[str, Any]=True	,    A__      : str=False	,    A__      : List[Any]="sine"	,    A__      : str="resnet50"	,    A__      : Optional[Any]=True	,    A__      : Optional[Any]=False	,    A__      : Union[str, Any]=4	,    A__      : Dict=4	,    A__      : Optional[int]=4	,    A__      : Optional[Any]=False	,    A__      : Dict=300	,    A__      : Union[str, Any]=False	,    A__      : str=1	,    A__      : Optional[int]=5	,    A__      : List[str]=2	,    A__      : int=1	,    A__      : List[str]=1	,    A__      : Dict=5	,    A__      : Union[str, Any]=2	,    A__      : str=0.1	,    A__      : int=0.25	,    A__      : str=False	,    **A__      : Union[str, Any]	,    ):


            """simple docstring"""







            if backbone_config is not None and use_timm_backbone:
                 raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""					)

            if not use_timm_backbone:
                 if backbone_config is None:
                      logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""					)
                      __lowerCamelCase						:       Optional[Any]				   =				CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]					)
                 elif isinstance(A__	,    A__					):
                      __lowerCamelCase						:       Any				   =				backbone_config.get("""model_type"""					)
                      __lowerCamelCase						:       Any				   =				CONFIG_MAPPING[backbone_model_type]
                      __lowerCamelCase						:       Any				   =				config_class.from_dict(A__					)
            __lowerCamelCase						:       Any				   =				use_timm_backbone
            __lowerCamelCase						:       Tuple				   =				backbone_config
            __lowerCamelCase						:       Union[str, Any]				   =				num_channels
            __lowerCamelCase						:       Dict				   =				num_queries
            __lowerCamelCase						:       Union[str, Any]				   =				max_position_embeddings
            __lowerCamelCase						:       Dict				   =				d_model
            __lowerCamelCase						:       str				   =				encoder_ffn_dim
            __lowerCamelCase						:       Optional[Any]				   =				encoder_layers
            __lowerCamelCase						:       List[Any]				   =				encoder_attention_heads
            __lowerCamelCase						:       Any				   =				decoder_ffn_dim
            __lowerCamelCase						:       Dict				   =				decoder_layers
            __lowerCamelCase						:       Union[str, Any]				   =				decoder_attention_heads
            __lowerCamelCase						:       str				   =				dropout
            __lowerCamelCase						:       Optional[int]				   =				attention_dropout
            __lowerCamelCase						:       Dict				   =				activation_dropout
            __lowerCamelCase						:       List[str]				   =				activation_function
            __lowerCamelCase						:       Dict				   =				init_std
            __lowerCamelCase						:       Dict				   =				init_xavier_std
            __lowerCamelCase						:       Optional[Any]				   =				encoder_layerdrop
            __lowerCamelCase						:       Optional[Any]				   =				auxiliary_loss
            __lowerCamelCase						:       List[Any]				   =				position_embedding_type
            __lowerCamelCase						:       Tuple				   =				backbone
            __lowerCamelCase						:       Optional[Any]				   =				use_pretrained_backbone
            __lowerCamelCase						:       Any				   =				dilation
            # deformable attributes
            __lowerCamelCase						:       List[Any]				   =				num_feature_levels
            __lowerCamelCase						:       Any				   =				encoder_n_points
            __lowerCamelCase						:       List[str]				   =				decoder_n_points
            __lowerCamelCase						:       int				   =				two_stage
            __lowerCamelCase						:       int				   =				two_stage_num_proposals
            __lowerCamelCase						:       List[str]				   =				with_box_refine
            if two_stage is True and with_box_refine is False:
                 raise ValueError("""If two_stage is True, with_box_refine must be True."""					)
            # Hungarian matcher
            __lowerCamelCase						:       Tuple				   =				class_cost
            __lowerCamelCase						:       Any				   =				bbox_cost
            __lowerCamelCase						:       Any				   =				giou_cost
            # Loss coefficients
            __lowerCamelCase						:       Any				   =				mask_loss_coefficient
            __lowerCamelCase						:       List[Any]				   =				dice_loss_coefficient
            __lowerCamelCase						:       Union[str, Any]				   =				bbox_loss_coefficient
            __lowerCamelCase						:       Any				   =				giou_loss_coefficient
            __lowerCamelCase						:       Any				   =				eos_coefficient
            __lowerCamelCase						:       List[str]				   =				focal_alpha
            __lowerCamelCase						:       Optional[int]				   =				disable_custom_kernels
            super().__init__(is_encoder_decoder=A__	,    **A__					)




       @property
       def 					a_					(					self      : Dict					):


            """simple docstring"""







            return self.encoder_attention_heads




       @property
       def 					a_					(					self      : Dict					):


            """simple docstring"""







            return self.d_model




       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				copy.deepcopy(self.__dict__					)
            if self.backbone_config is not None:
                 __lowerCamelCase						:       Union[str, Any]				   =				self.backbone_config.to_dict()
            __lowerCamelCase						:       List[Any]				   =				self.__class__.model_type
            return output











'''simple docstring'''

import argparse
import json

from tqdm import tqdm
def     __lowercase  ()   ->     Tuple:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				argparse.ArgumentParser()

     # Required parameters
     parser.add_argument(
         """--src_path""",						type=_lowercase,						default="""biencoder-nq-dev.json""",						help="""Path to raw DPR training data""",						)
     parser.add_argument(
         """--evaluation_set""",						type=_lowercase,						help="""where to store parsed evaluation_set file""",						)
     parser.add_argument(
         """--gold_data_path""",						type=_lowercase,						help="""where to store parsed gold_data_path file""",						)
     __lowerCamelCase						:       Tuple				   =				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						:       int				   =				json.load(_lowercase     )
          for dpr_record in tqdm(_lowercase     ):
               __lowerCamelCase						:       List[Any]				   =				dpr_record["""question"""]
               __lowerCamelCase						:       List[str]				   =				[context["""title"""] for context in dpr_record["""positive_ctxs"""]]
               eval_file.write(question + """\n"""     )
               gold_file.write("""\t""".join(_lowercase     ) + """\n"""     )


if __name__ == "__main__":
   main()



