


"""simple docstring"""


import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
   import sqlitea

   import sqlalchemy



class   _a      (      SCREAMING_SNAKE_CASE_ ):
    def __init__( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					Union[str, "sqlalchemy.sql.Selectable"]							,					SCREAMING_SNAKE_CASE__							:					Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]							,					SCREAMING_SNAKE_CASE__							:					Optional[Features] = None							,					SCREAMING_SNAKE_CASE__							:					str = None							,					SCREAMING_SNAKE_CASE__							:					bool = False							,					**SCREAMING_SNAKE_CASE__							:					Tuple							,					):
          super().__init__(features=SCREAMING_SNAKE_CASE__							,					cache_dir=SCREAMING_SNAKE_CASE__							,					keep_in_memory=SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		Sql(
              cache_dir=SCREAMING_SNAKE_CASE__							,					features=SCREAMING_SNAKE_CASE__							,					sql=SCREAMING_SNAKE_CASE__							,					con=SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__							,					)


    def      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__           =		None
          lowerCamelCase__           =		None
          lowerCamelCase__           =		None
          lowerCamelCase__           =		None

          self.builder.download_and_prepare(
              download_config=SCREAMING_SNAKE_CASE__							,					download_mode=SCREAMING_SNAKE_CASE__							,					verification_mode=SCREAMING_SNAKE_CASE__							,					base_path=SCREAMING_SNAKE_CASE__							,					)

          # Build dataset for splits
          lowerCamelCase__           =		self.builder.as_dataset(
              split='train'							,					verification_mode=SCREAMING_SNAKE_CASE__							,					in_memory=self.keep_in_memory				)
          return dataset





class   _a      :
    def __init__( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Dataset							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]							,					SCREAMING_SNAKE_CASE__							:					Optional[int] = None							,					SCREAMING_SNAKE_CASE__							:					Optional[int] = None							,					**SCREAMING_SNAKE_CASE__							:					List[Any]							,					):
          if num_proc is not None and num_proc <= 0:
                raise ValueError(F'num_proc {num_proc} must be an integer > 0.'				)

          lowerCamelCase__           =		dataset
          lowerCamelCase__           =		name
          lowerCamelCase__           =		con
          lowerCamelCase__           =		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          lowerCamelCase__           =		num_proc
          lowerCamelCase__           =		to_sql_kwargs
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          lowerCamelCase__           =		self.to_sql_kwargs.pop('sql'							,					SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		self.to_sql_kwargs.pop('con'							,					SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		self.to_sql_kwargs.pop('index'							,					SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self._write(index=SCREAMING_SNAKE_CASE__							,					**self.to_sql_kwargs				)
          return written
    def      _UpperCamelCase       ( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Any				):
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		args
          lowerCamelCase__           =		{**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
          lowerCamelCase__           =		query_table(
              table=self.dataset.data							,					key=slice(SCREAMING_SNAKE_CASE__							,					offset + self.batch_size				)							,					indices=self.dataset._indices							,					)
          lowerCamelCase__           =		batch.to_pandas()
          lowerCamelCase__           =		df.to_sql(self.name							,					self.con							,					index=SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				)
          return num_rows or len(SCREAMING_SNAKE_CASE__				)


    def      _UpperCamelCase       ( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					**SCREAMING_SNAKE_CASE__							:					List[str]				):
          lowerCamelCase__           =		0

          if self.num_proc is None or self.num_proc == 1:
                for offset in logging.tqdm(
                    range(0							,					len(self.dataset				)							,					self.batch_size				)							,					unit='ba'							,					disable=not logging.is_progress_bar_enabled()							,					desc='Creating SQL from Arrow format'							,					):
                      written += self._batch_sql((offset, index, to_sql_kwargs)				)
          else:
                lowerCamelCase__    ,  lowerCamelCase__           =		len(self.dataset				), self.batch_size
                with multiprocessing.Pool(self.num_proc				) as pool:
                      for num_rows in logging.tqdm(
                          pool.imap(
                              self._batch_sql							,					[(offset, index, to_sql_kwargs) for offset in range(0							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)]							,					)							,					total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size							,					unit='ba'							,					disable=not logging.is_progress_bar_enabled()							,					desc='Creating SQL from Arrow format'							,					):
                            written += num_rows

          return written







"""simple docstring"""


import argparse
import json

from tqdm import tqdm



def   snake_case						(				)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		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'		,   )
      lowerCamelCase__           =		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__           =		json.load(_a							)
            for dpr_record in tqdm(_a							):
                  lowerCamelCase__           =		dpr_record['question']
                  lowerCamelCase__           =		[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()



