



"""simple docstring"""

import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
							pass

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


A_						:    Any				=      logging.get_logger(__name__)

A_						:    List[str]				=      {
    'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[Any]  						= '''align_text_model'''





	def __init__(      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       Any=30_522     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=768     ,       _SCREAMING_SNAKE_CASE				:       Tuple=12     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=12     ,       _SCREAMING_SNAKE_CASE				:       List[str]=3_072     ,       _SCREAMING_SNAKE_CASE				:       Dict="gelu"     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=0.1     ,       _SCREAMING_SNAKE_CASE				:       Tuple=0.1     ,       _SCREAMING_SNAKE_CASE				:       Any=512     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=2     ,       _SCREAMING_SNAKE_CASE				:       int=0.0_2     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=1E-1_2     ,       _SCREAMING_SNAKE_CASE				:       Dict=0     ,       _SCREAMING_SNAKE_CASE				:       List[Any]="absolute"     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=True     ,       **_SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       )						->	List[str]:





					"""simple docstring"""


					super().__init__(**_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     vocab_size
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     hidden_size
					SCREAMING_SNAKE_CASE						:							Tuple							=     num_hidden_layers
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     num_attention_heads
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     hidden_act
					SCREAMING_SNAKE_CASE						:							str							=     intermediate_size
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     hidden_dropout_prob
					SCREAMING_SNAKE_CASE						:							Tuple							=     attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE						:							str							=     max_position_embeddings
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     type_vocab_size
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     initializer_range
					SCREAMING_SNAKE_CASE						:							str							=     layer_norm_eps
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     position_embedding_type
					SCREAMING_SNAKE_CASE						:							Dict							=     use_cache
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     pad_token_id







	@classmethod
	def 			_lowerCAmelCase     (      cls				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Union[str, os.PathLike]     ,       **_SCREAMING_SNAKE_CASE				:       str    )						->	"PretrainedConfig":





					"""simple docstring"""


					cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     cls.get_config_dict(_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )

					# get the text config dict if we are loading from AlignConfig
					if config_dict.get('model_type'    ) == "align":
									SCREAMING_SNAKE_CASE						:							List[Any]							=     config_dict['text_config']

					if "model_type" in config_dict and hasattr(cls     ,       'model_type'    ) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
									    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""    )

					return cls.from_dict(_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= '''align_vision_model'''





	def __init__(      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       int = 3     ,       _SCREAMING_SNAKE_CASE				:       int = 600     ,       _SCREAMING_SNAKE_CASE				:       float = 2.0     ,       _SCREAMING_SNAKE_CASE				:       float = 3.1     ,       _SCREAMING_SNAKE_CASE				:       int = 8     ,       _SCREAMING_SNAKE_CASE				:       List[int] = [3, 3, 5, 3, 5, 5, 3]     ,       _SCREAMING_SNAKE_CASE				:       List[int] = [32, 16, 24, 40, 80, 112, 192]     ,       _SCREAMING_SNAKE_CASE				:       List[int] = [16, 24, 40, 80, 112, 192, 320]     ,       _SCREAMING_SNAKE_CASE				:       List[int] = []     ,       _SCREAMING_SNAKE_CASE				:       List[int] = [1, 2, 2, 2, 1, 2, 1]     ,       _SCREAMING_SNAKE_CASE				:       List[int] = [1, 2, 2, 3, 3, 4, 1]     ,       _SCREAMING_SNAKE_CASE				:       List[int] = [1, 6, 6, 6, 6, 6, 6]     ,       _SCREAMING_SNAKE_CASE				:       float = 0.2_5     ,       _SCREAMING_SNAKE_CASE				:       str = "swish"     ,       _SCREAMING_SNAKE_CASE				:       int = 2_560     ,       _SCREAMING_SNAKE_CASE				:       str = "mean"     ,       _SCREAMING_SNAKE_CASE				:       float = 0.0_2     ,       _SCREAMING_SNAKE_CASE				:       float = 0.0_0_1     ,       _SCREAMING_SNAKE_CASE				:       float = 0.9_9     ,       _SCREAMING_SNAKE_CASE				:       float = 0.2     ,       **_SCREAMING_SNAKE_CASE				:       str     ,       )						->	Union[str, Any]:





					"""simple docstring"""


					super().__init__(**_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Any							=     num_channels
					SCREAMING_SNAKE_CASE						:							Dict							=     image_size
					SCREAMING_SNAKE_CASE						:							List[str]							=     width_coefficient
					SCREAMING_SNAKE_CASE						:							List[Any]							=     depth_coefficient
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     depth_divisor
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     kernel_sizes
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     in_channels
					SCREAMING_SNAKE_CASE						:							Dict							=     out_channels
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     depthwise_padding
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     strides
					SCREAMING_SNAKE_CASE						:							Any							=     num_block_repeats
					SCREAMING_SNAKE_CASE						:							str							=     expand_ratios
					SCREAMING_SNAKE_CASE						:							Any							=     squeeze_expansion_ratio
					SCREAMING_SNAKE_CASE						:							Any							=     hidden_act
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     hidden_dim
					SCREAMING_SNAKE_CASE						:							Tuple							=     pooling_type
					SCREAMING_SNAKE_CASE						:							int							=     initializer_range
					SCREAMING_SNAKE_CASE						:							List[Any]							=     batch_norm_eps
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     batch_norm_momentum
					SCREAMING_SNAKE_CASE						:							Tuple							=     drop_connect_rate
					SCREAMING_SNAKE_CASE						:							Dict							=     sum(_SCREAMING_SNAKE_CASE    ) * 4







	@classmethod
	def 			_lowerCAmelCase     (      cls				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Union[str, os.PathLike]     ,       **_SCREAMING_SNAKE_CASE				:       Optional[Any]    )						->	"PretrainedConfig":





					"""simple docstring"""


					cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[Any]							=     cls.get_config_dict(_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )

					# get the vision config dict if we are loading from AlignConfig
					if config_dict.get('model_type'    ) == "align":
									SCREAMING_SNAKE_CASE						:							Dict							=     config_dict['vision_config']

					if "model_type" in config_dict and hasattr(cls     ,       'model_type'    ) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
									    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""    )

					return cls.from_dict(_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[Any]  						= '''align'''
	_SCREAMING_SNAKE_CASE	:		List[Any]  						= True





	def __init__(      self				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=None     ,       _SCREAMING_SNAKE_CASE				:       List[str]=None     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=640     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=1.0     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=0.0_2     ,       **_SCREAMING_SNAKE_CASE				:       Tuple     ,       )						->	Union[str, Any]:





					"""simple docstring"""


					super().__init__(**_SCREAMING_SNAKE_CASE    )

					if text_config is None:
									SCREAMING_SNAKE_CASE						:							Any							=     {}
									logger.info('text_config is None. Initializing the AlignTextConfig with default values.'    )

					if vision_config is None:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     {}
									logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.'    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     AlignTextConfig(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     AlignVisionConfig(**_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     projection_dim
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     temperature_init_value
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     initializer_range





	@classmethod
	def 			_lowerCAmelCase     (      cls				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       AlignTextConfig     ,       _SCREAMING_SNAKE_CASE				:       AlignVisionConfig     ,       **_SCREAMING_SNAKE_CASE				:       Optional[Any]    )						->	str:





					"""simple docstring"""


					return cls(text_config=text_config.to_dict()     ,       vision_config=vision_config.to_dict()     ,       **_SCREAMING_SNAKE_CASE    )







	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     copy.deepcopy(self.__dict__    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     self.text_config.to_dict()
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.vision_config.to_dict()
					SCREAMING_SNAKE_CASE						:							Tuple							=     self.__class__.model_type
					return output










"""simple docstring"""

import argparse
import json

from tqdm import tqdm
def  __snake_case							(	)     ->       Any:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							str							=     argparse.ArgumentParser()

				# Required parameters
				parser.add_argument(
				    '--src_path'							,		type=__A							,		default='biencoder-nq-dev.json'							,		help='Path to raw DPR training data'							,		)
				parser.add_argument(
				    '--evaluation_set'							,		type=__A							,		help='where to store parsed evaluation_set file'							,		)
				parser.add_argument(
				    '--gold_data_path'							,		type=__A							,		help='where to store parsed gold_data_path file'							,		)
				SCREAMING_SNAKE_CASE						:							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:
								SCREAMING_SNAKE_CASE						:							Tuple							=     json.load(__A     )
								for dpr_record in tqdm(__A     ):
												SCREAMING_SNAKE_CASE						:							Tuple							=     dpr_record['question']
												SCREAMING_SNAKE_CASE						:							Optional[int]							=     [context['title'] for context in dpr_record['positive_ctxs']]
												eval_file.write(question + '\n'     )
												gold_file.write('\t'.join(__A     ) + '\n'     )


if __name__ == "__main__":
							main()





