




'''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						(			__a				):
       def __init__(			self		:			List[str]     ,       lowercase		:			Union[str, "sqlalchemy.sql.Selectable"]     ,       lowercase		:			Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]     ,       lowercase		:			Optional[Features] = None     ,       lowercase		:			str = None     ,       lowercase		:			bool = False     ,       **lowercase		:			int     ,       ):



           '''simple docstring'''

           super().__init__(features=lowercase     ,       cache_dir=lowercase     ,       keep_in_memory=lowercase     ,       **lowercase       )
           UpperCAmelCase     =	Sql(
               cache_dir=lowercase     ,       features=lowercase     ,       sql=lowercase     ,       con=lowercase     ,       **lowercase     ,       )







       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	None
           UpperCAmelCase     =	None
           UpperCAmelCase     =	None
           UpperCAmelCase     =	None

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

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






class     _a						:
       def __init__(			self		:			Dict     ,       lowercase		:			Dataset     ,       lowercase		:			str     ,       lowercase		:			Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]     ,       lowercase		:			Optional[int] = None     ,       lowercase		:			Optional[int] = None     ,       **lowercase		:			Dict     ,       ):



           '''simple docstring'''

           if num_proc is not None and num_proc <= 0:
               raise ValueError(f"num_proc {num_proc} must be an integer > 0."       )

           UpperCAmelCase     =	dataset
           UpperCAmelCase     =	name
           UpperCAmelCase     =	con
           UpperCAmelCase     =	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           UpperCAmelCase     =	num_proc
           UpperCAmelCase     =	to_sql_kwargs
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.to_sql_kwargs.pop('''sql'''     ,       lowercase       )
           UpperCAmelCase     =	self.to_sql_kwargs.pop('''con'''     ,       lowercase       )
           UpperCAmelCase     =	self.to_sql_kwargs.pop('''index'''     ,       lowercase       )

           UpperCAmelCase     =	self._write(index=lowercase     ,       **self.to_sql_kwargs       )
           return written
       def 					A   (			self		:			Dict     ,       lowercase		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	args
           UpperCAmelCase     =	{**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
           UpperCAmelCase     =	query_table(
               table=self.dataset.data     ,       key=slice(lowercase     ,       offset + self.batch_size       )     ,       indices=self.dataset._indices     ,       )
           UpperCAmelCase     =	batch.to_pandas()
           UpperCAmelCase     =	df.to_sql(self.name     ,       self.con     ,       index=lowercase     ,       **lowercase       )
           return num_rows or len(lowercase       )







       def 					A   (			self		:			str     ,       lowercase		:			List[Any]     ,       **lowercase		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	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:
               UpperCAmelCase ,  UpperCAmelCase     =	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     ,       lowercase     ,       lowercase       )]     ,       )     ,       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'''







from __future__ import annotations

import requests





def 		snake_case_							(_a			:							str  ):
    UpperCAmelCase     =	F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
    return requests.get(_a  ).json()





def 		snake_case_							(_a			:							int = 1_0  ):
    UpperCAmelCase     =	'''https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty'''
    UpperCAmelCase     =	requests.get(_a  ).json()[:max_stories]
    return [get_hackernews_story(_a  ) for story_id in story_ids]





def 		snake_case_							(_a			:							int = 1_0  ):
    UpperCAmelCase     =	hackernews_top_stories(_a  )
    return "\n".join('''* [{title}]({url})'''.format(**_a  ) for story in stories  )


if __name__ == "__main__":
   print(hackernews_top_stories_as_markdown())




