


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        _lowercase      (			A__       ):






				'''simple docstring'''



				def __init__(       self			:int			,			lowerCAmelCase__			:Union[str, "sqlalchemy.sql.Selectable"]			,			lowerCAmelCase__			:Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]			,			lowerCAmelCase__			:Optional[Features] = None			,			lowerCAmelCase__			:str = None			,			lowerCAmelCase__			:bool = False			,			**lowerCAmelCase__			:Union[str, Any]			,			)		->			Optional[int]:
										super().__init__(features=lowerCAmelCase__			,			cache_dir=lowerCAmelCase__			,			keep_in_memory=lowerCAmelCase__			,			**lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Any					=		Sql(
										    cache_dir=lowerCAmelCase__			,			features=lowerCAmelCase__			,			sql=lowerCAmelCase__			,			con=lowerCAmelCase__			,			**lowerCAmelCase__			,			)



				def 		__magic_name__(       self			:List[Any]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		None
										__SCREAMING_SNAKE_CASE						:     int					=		None
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		None
										__SCREAMING_SNAKE_CASE						:     Dict					=		None

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

										# Build dataset for splits
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		self.builder.as_dataset(
										    split='''train'''			,			verification_mode=lowerCAmelCase__			,			in_memory=self.keep_in_memory	)
										return dataset




class        _lowercase      :






				'''simple docstring'''



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

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		dataset
										__SCREAMING_SNAKE_CASE						:     Tuple					=		name
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		con
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		num_proc
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		to_sql_kwargs


				def 		__magic_name__(       self			:Tuple	)		->			int:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		self.to_sql_kwargs.pop('''sql'''			,			lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		self.to_sql_kwargs.pop('''con'''			,			lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     int					=		self.to_sql_kwargs.pop('''index'''			,			lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     int					=		self._write(index=lowerCAmelCase__			,			**self.to_sql_kwargs	)
										return written


				def 		__magic_name__(       self			:Dict			,			lowerCAmelCase__			:List[Any]	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		args
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		{**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
										__SCREAMING_SNAKE_CASE						:     List[str]					=		query_table(
										    table=self.dataset.data			,			key=slice(lowerCAmelCase__			,			offset + self.batch_size	)			,			indices=self.dataset._indices			,			)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		batch.to_pandas()
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		df.to_sql(self.name			,			self.con			,			index=lowerCAmelCase__			,			**lowerCAmelCase__	)
										return num_rows or len(lowerCAmelCase__	)



				def 		__magic_name__(       self			:str			,			lowerCAmelCase__			:str			,			**lowerCAmelCase__			:Tuple	)		->			int:
										__SCREAMING_SNAKE_CASE						:     str					=		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:
																__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[int]					=		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			,			lowerCAmelCase__			,			lowerCAmelCase__	)]			,			)			,			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







from collections import defaultdict
from math import gcd



def    __lowercase   (      _SCREAMING_SNAKE_CASE = 1_50_00_00	) ->   int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						defaultdict(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						2
      while 2 * euclid_m * (euclid_m + 1) <= limit:
            for euclid_n in range((euclid_m % 2) + 1 ,						_SCREAMING_SNAKE_CASE ,						2	):
                  if gcd(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) > 1:
                        continue
                  SCREAMING_SNAKE_CASE		=						2 * euclid_m * (euclid_m + euclid_n)
                  for perimeter in range(_SCREAMING_SNAKE_CASE ,						limit + 1 ,						_SCREAMING_SNAKE_CASE	):
                        frequencies[perimeter] += 1
            euclid_m += 1
      return sum(1 for frequency in frequencies.values() if frequency == 1	)


if __name__ == "__main__":
      print(F'''{solution() = }''')


