
'''simple docstring'''

import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Optional[int]  		=  """conditional_detr"""
     UpperCAmelCase_						:     str  		=  ["""past_key_values"""]
     UpperCAmelCase_						:     Tuple  		=  {
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }


     def __init__(		self			:					str					,      lowercase_			:					Optional[int]=True					,      lowercase_			:					Tuple=None					,      lowercase_			:					int=3					,      lowercase_			:					List[Any]=300					,      lowercase_			:					List[Any]=6					,      lowercase_			:					Optional[Any]=2_048					,      lowercase_			:					int=8					,      lowercase_			:					Optional[int]=6					,      lowercase_			:					Any=2_048					,      lowercase_			:					Any=8					,      lowercase_			:					Tuple=0.0					,      lowercase_			:					Any=0.0					,      lowercase_			:					Tuple=True					,      lowercase_			:					Any="relu"					,      lowercase_			:					Tuple=256					,      lowercase_			:					Optional[int]=0.1					,      lowercase_			:					Any=0.0					,      lowercase_			:					List[Any]=0.0					,      lowercase_			:					Any=0.02					,      lowercase_			:					Tuple=1.0					,      lowercase_			:					Any=False					,      lowercase_			:					Dict="sine"					,      lowercase_			:					Tuple="resnet50"					,      lowercase_			:					Tuple=True					,      lowercase_			:					Union[str, Any]=False					,      lowercase_			:					Dict=2					,      lowercase_			:					List[Any]=5					,      lowercase_			:					List[Any]=2					,      lowercase_			:					Optional[Any]=1					,      lowercase_			:					List[str]=1					,      lowercase_			:					Dict=2					,      lowercase_			:					Optional[int]=5					,      lowercase_			:					str=2					,      lowercase_			:					List[str]=0.25					,      **lowercase_			:					Optional[Any]					,      )						->     Optional[int]:
      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.'	)
        UpperCAmelCase	:     int						 =			CONFIG_MAPPING['resnet'](out_features=['stage4']	)
       elif isinstance(lowercase_					,      lowercase_	):
        UpperCAmelCase	:     Any						 =			backbone_config.get('model_type'	)
        UpperCAmelCase	:     Union[str, Any]						 =			CONFIG_MAPPING[backbone_model_type]
        UpperCAmelCase	:     Union[str, Any]						 =			config_class.from_dict(lowercase_	)

      UpperCAmelCase	:     str						 =			use_timm_backbone
      UpperCAmelCase	:     Tuple						 =			backbone_config
      UpperCAmelCase	:     Dict						 =			num_channels
      UpperCAmelCase	:     Optional[Any]						 =			num_queries
      UpperCAmelCase	:     int						 =			d_model
      UpperCAmelCase	:     Optional[Any]						 =			encoder_ffn_dim
      UpperCAmelCase	:     Optional[Any]						 =			encoder_layers
      UpperCAmelCase	:     Optional[int]						 =			encoder_attention_heads
      UpperCAmelCase	:     List[str]						 =			decoder_ffn_dim
      UpperCAmelCase	:     List[str]						 =			decoder_layers
      UpperCAmelCase	:     List[str]						 =			decoder_attention_heads
      UpperCAmelCase	:     List[str]						 =			dropout
      UpperCAmelCase	:     Tuple						 =			attention_dropout
      UpperCAmelCase	:     Dict						 =			activation_dropout
      UpperCAmelCase	:     Tuple						 =			activation_function
      UpperCAmelCase	:     Union[str, Any]						 =			init_std
      UpperCAmelCase	:     Union[str, Any]						 =			init_xavier_std
      UpperCAmelCase	:     Union[str, Any]						 =			encoder_layerdrop
      UpperCAmelCase	:     List[Any]						 =			decoder_layerdrop
      UpperCAmelCase	:     List[Any]						 =			encoder_layers
      UpperCAmelCase	:     Union[str, Any]						 =			auxiliary_loss
      UpperCAmelCase	:     Any						 =			position_embedding_type
      UpperCAmelCase	:     Optional[int]						 =			backbone
      UpperCAmelCase	:     List[Any]						 =			use_pretrained_backbone
      UpperCAmelCase	:     Any						 =			dilation
      # Hungarian matcher
      UpperCAmelCase	:     Dict						 =			class_cost
      UpperCAmelCase	:     Any						 =			bbox_cost
      UpperCAmelCase	:     List[Any]						 =			giou_cost
      # Loss coefficients
      UpperCAmelCase	:     List[str]						 =			mask_loss_coefficient
      UpperCAmelCase	:     Optional[Any]						 =			dice_loss_coefficient
      UpperCAmelCase	:     List[str]						 =			cls_loss_coefficient
      UpperCAmelCase	:     Optional[int]						 =			bbox_loss_coefficient
      UpperCAmelCase	:     Optional[Any]						 =			giou_loss_coefficient
      UpperCAmelCase	:     str						 =			focal_alpha
      super().__init__(is_encoder_decoder=lowercase_					,      **lowercase_	)


     @property
     def        UpperCAmelCase_	(		self			:					Tuple	)						->     int:
      return self.encoder_attention_heads


     @property
     def        UpperCAmelCase_	(		self			:					Dict	)						->     int:
      return self.d_model




     def        UpperCAmelCase_	(		self			:					Any	)						->     Dict:
      UpperCAmelCase	:     List[str]						 =			copy.deepcopy(self.__dict__	)
      if self.backbone_config is not None:
       UpperCAmelCase	:     List[str]						 =			self.backbone_config.to_dict()
      UpperCAmelCase	:     str						 =			self.__class__.model_type
      return output




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     str  		=  version.parse("""1.11"""							)


     @property
     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Mapping[str, Mapping[int, str]]:
      return OrderedDict(
          [
              ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
              ('pixel_mask', {0: 'batch'}),
          ]	)


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     float:
      return 1E-5




     @property
     def        UpperCAmelCase_	(		self			:					Dict	)						->     int:
      return 12







'''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()




