





'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


UpperCAmelCase__						:Union[str, Any]          =			logging.get_logger(__name__)

UpperCAmelCase__						:Union[str, Any]          =			{
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}







class 	SCREAMING_SNAKE_CASE	(      __lowerCAmelCase  ):
       snake_case__    :     Tuple   		=			'''dpt'''




       def __init__(					self      : Tuple	,    A__      : List[str]=768	,    A__      : Optional[int]=12	,    A__      : Any=12	,    A__      : str=3072	,    A__      : Optional[int]="gelu"	,    A__      : Dict=0.0	,    A__      : Dict=0.0	,    A__      : Tuple=0.02	,    A__      : List[str]=1e-1_2	,    A__      : Dict=384	,    A__      : List[str]=16	,    A__      : List[Any]=3	,    A__      : Union[str, Any]=False	,    A__      : List[str]=True	,    A__      : Optional[int]=[2, 5, 8, 11]	,    A__      : Optional[Any]="project"	,    A__      : Tuple=[4, 2, 1, 0.5]	,    A__      : Dict=[96, 192, 384, 768]	,    A__      : Optional[Any]=256	,    A__      : int=-1	,    A__      : Any=False	,    A__      : Optional[Any]=True	,    A__      : List[Any]=0.4	,    A__      : Dict=255	,    A__      : Optional[int]=0.1	,    A__      : str=[1, 1024, 24, 24]	,    A__      : List[str]=[0, 1]	,    A__      : Tuple=None	,    **A__      : Tuple	,    ):


            """simple docstring"""







            super().__init__(**lowerCAmelCase_					)

            __lowerCamelCase						:       Union[str, Any]				   =				hidden_size
            __lowerCamelCase						:       List[Any]				   =				is_hybrid

            if self.is_hybrid:
                 if backbone_config is None:
                      logger.info("""Initializing the config with a `BiT` backbone."""					)
                      __lowerCamelCase						:       List[Any]				   =				{
                          """global_padding""": """same""",
                          """layer_type""": """bottleneck""",
                          """depths""": [3, 4, 9],
                          """out_features""": ["""stage1""", """stage2""", """stage3"""],
                          """embedding_dynamic_padding""": True,
                      }
                      __lowerCamelCase						:       Any				   =				BitConfig(**lowerCAmelCase_					)
                 elif isinstance(lowerCAmelCase_	,    lowerCAmelCase_					):
                      logger.info("""Initializing the config with a `BiT` backbone."""					)
                      __lowerCamelCase						:       Union[str, Any]				   =				BitConfig(**lowerCAmelCase_					)
                 elif isinstance(lowerCAmelCase_	,    lowerCAmelCase_					):
                      __lowerCamelCase						:       Any				   =				backbone_config
                 else:
                      raise ValueError(
                          f"backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."					)

                 __lowerCamelCase						:       Optional[int]				   =				backbone_featmap_shape
                 __lowerCamelCase						:       Optional[int]				   =				neck_ignore_stages

                 if readout_type != "project":
                      raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode."""					)
            else:
                 __lowerCamelCase						:       Union[str, Any]				   =				None
                 __lowerCamelCase						:       List[str]				   =				None
                 __lowerCamelCase						:       Optional[Any]				   =				[]

            __lowerCamelCase						:       Any				   =				num_hidden_layers
            __lowerCamelCase						:       Optional[int]				   =				num_attention_heads
            __lowerCamelCase						:       Optional[int]				   =				intermediate_size
            __lowerCamelCase						:       str				   =				hidden_act
            __lowerCamelCase						:       Tuple				   =				hidden_dropout_prob
            __lowerCamelCase						:       Optional[int]				   =				attention_probs_dropout_prob
            __lowerCamelCase						:       str				   =				initializer_range
            __lowerCamelCase						:       Optional[Any]				   =				layer_norm_eps
            __lowerCamelCase						:       Tuple				   =				image_size
            __lowerCamelCase						:       Tuple				   =				patch_size
            __lowerCamelCase						:       Any				   =				num_channels
            __lowerCamelCase						:       str				   =				qkv_bias
            __lowerCamelCase						:       List[Any]				   =				backbone_out_indices
            if readout_type not in ["ignore", "add", "project"]:
                 raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']"""					)
            __lowerCamelCase						:       List[Any]				   =				readout_type
            __lowerCamelCase						:       Optional[Any]				   =				reassemble_factors
            __lowerCamelCase						:       Any				   =				neck_hidden_sizes
            __lowerCamelCase						:       str				   =				fusion_hidden_size
            __lowerCamelCase						:       List[Any]				   =				head_in_index
            __lowerCamelCase						:       List[Any]				   =				use_batch_norm_in_fusion_residual
            # auxiliary head attributes (semantic segmentation)
            __lowerCamelCase						:       Optional[Any]				   =				use_auxiliary_head
            __lowerCamelCase						:       List[Any]				   =				auxiliary_loss_weight
            __lowerCamelCase						:       Union[str, Any]				   =				semantic_loss_ignore_index
            __lowerCamelCase						:       Dict				   =				semantic_classifier_dropout




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				copy.deepcopy(self.__dict__					)

            if output["backbone_config"] is not None:
                 __lowerCamelCase						:       Dict				   =				self.backbone_config.to_dict()

            __lowerCamelCase						:       Optional[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()



