




import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
	import sqlitea

	import sqlalchemy


__lowerCamelCase :    str      						=      datasets.utils.logging.get_logger(__name__)


@dataclass
class  lowerCamelCase     (		datasets.BuilderConfig    ):






			'''simple docstring'''





			UpperCamelCase__										=None
			UpperCamelCase__										=None
			UpperCamelCase__										=None
			UpperCamelCase__										=True
			UpperCamelCase__										=None
			UpperCamelCase__										=None
			UpperCamelCase__										=None
			UpperCamelCase__										=1_0_0_0_0
			UpperCamelCase__										=None

			def 				UpperCAmelCase__							(			self      : List[str]	)    -> Union[str, Any]:
								if self.sql is None:
													raise ValueError('''sql must be specified'''	)
								if self.con is None:
													raise ValueError('''con must be specified'''	)

			def 				UpperCAmelCase__							(			self      : Dict							,    lowerCamelCase_      : dict							,    lowerCamelCase_      : Optional[datasets.Features] = None							,    )    -> str:
								__magic_name__				:						Optional[int]     				=				config_kwargs.copy()
								# We need to stringify the Selectable object to make its hash deterministic

								# The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
								__magic_name__				:						Optional[int]     				=				config_kwargs['''sql''']
								if not isinstance(lowerCamelCase_							,    lowerCamelCase_	):
													if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
																		import sqlalchemy

																		if isinstance(lowerCamelCase_							,    sqlalchemy.sql.Selectable	):
																							__magic_name__				:						Tuple     				=				sqlalchemy.create_engine(config_kwargs['''con'''].split('''://'''	)[0] + '''://'''	)
																							__magic_name__				:						Optional[Any]     				=				str(sql.compile(dialect=engine.dialect	)	)
																							__magic_name__				:						Any     				=				sql_str
																		else:
																							raise TypeError(
																							    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowerCamelCase_	)}: {sql}'''	)
													else:
																		raise TypeError(
																		    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowerCamelCase_	)}: {sql}'''	)
								__magic_name__				:						Tuple     				=				config_kwargs['''con''']
								if not isinstance(lowerCamelCase_							,    lowerCamelCase_	):
													__magic_name__				:						Dict     				=				id(lowerCamelCase_	)
													logger.info(
													    F'''SQL connection \'con\' of type {type(lowerCamelCase_	)} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'''	)

								return super().create_config_id(lowerCamelCase_							,    custom_features=lowerCamelCase_	)


			@property
			def 				UpperCAmelCase__							(			self      : Optional[Any]	)    -> int:
								__magic_name__				:						Union[str, Any]     				=				{
								    '''index_col''': self.index_col,
								    '''columns''': self.columns,
								    '''params''': self.params,
								    '''coerce_float''': self.coerce_float,
								    '''parse_dates''': self.parse_dates,
								}
								return pd_read_sql_kwargs






class  lowerCamelCase     (		datasets.ArrowBasedBuilder    ):






			'''simple docstring'''





			UpperCamelCase__										=SqlConfig

			def 				UpperCAmelCase__							(			self      : List[str]	)    -> Optional[int]:
								return datasets.DatasetInfo(features=self.config.features	)

			def 				UpperCAmelCase__							(			self      : Optional[int]							,    lowerCamelCase_      : Optional[Any]	)    -> Tuple:
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,    gen_kwargs={}	)]

			def 				UpperCAmelCase__							(			self      : Any							,    lowerCamelCase_      : pa.Table	)    -> pa.Table:
								if self.config.features is not None:
													__magic_name__				:						Any     				=				self.config.features.arrow_schema
													if all(not require_storage_cast(lowerCamelCase_	) for feature in self.config.features.values()	):
																		# cheaper cast
																		__magic_name__				:						Optional[int]     				=				pa.Table.from_arrays([pa_table[field.name] for field in schema]							,    schema=lowerCamelCase_	)
													else:
																		# more expensive cast; allows str <-> int/float or str to Audio for example
																		__magic_name__				:						Optional[Any]     				=				table_cast(lowerCamelCase_							,    lowerCamelCase_	)
								return pa_table


			def 				UpperCAmelCase__							(			self      : int	)    -> Tuple:
								__magic_name__				:						Optional[int]     				=				self.config.chunksize
								__magic_name__				:						Optional[Any]     				=				pd.read_sql(
								    self.config.sql							,    self.config.con							,    chunksize=lowerCamelCase_							,    **self.config.pd_read_sql_kwargs	)
								__magic_name__				:						List[str]     				=				[sql_reader] if chunksize is None else sql_reader
								for chunk_idx, df in enumerate(lowerCamelCase_	):
													__magic_name__				:						str     				=				pa.Table.from_pandas(lowerCamelCase_	)
													yield chunk_idx, self._cast_table(lowerCamelCase_	)









import math
from collections.abc import Generator







def 		lowercase__ (		__A:						int					):





					'''simple docstring'''






					__magic_name__				:						Generator     				=				(i for i in range(1				,(max_n + 1)					))
					for i in (n for n in numbers if n > 1):
										for j in range(2				,__A					):
															if (i % j) == 0:
																				break
										else:
															yield i







def 		lowercase__ (		__A:						int					):





					'''simple docstring'''






					__magic_name__				:						Generator     				=				(i for i in range(1				,(max_n + 1)					))
					for i in (n for n in numbers if n > 1):
										# only need to check for factors up to sqrt(i)
										__magic_name__				:						Tuple     				=				int(math.sqrt(__A					)					) + 1
										for j in range(2				,__A					):
															if (i % j) == 0:
																				break
										else:
															yield i







def 		lowercase__ (		__A:						int					):





					'''simple docstring'''






					__magic_name__				:						Generator     				=				(i for i in range(1				,(max_n + 1)				,2					))
					# It's useless to test even numbers as they will not be prime
					if max_n > 2:
										yield 2  # Because 2 will not be tested, it's necessary to yield it now
					for i in (n for n in numbers if n > 1):
										__magic_name__				:						str     				=				int(math.sqrt(__A					)					) + 1
										for j in range(3				,__A				,2					):
															# As we removed the even numbers, we don't need them now
															if (i % j) == 0:
																				break
										else:
															yield i







def 		lowercase__ (		):





					'''simple docstring'''






					from timeit import timeit

					__magic_name__				:						Dict     				=				'''from __main__ import slow_primes, primes, fast_primes'''
					print(timeit('''slow_primes(1_000_000_000_000)'''				,setup=__A				,number=1_0_0_0_0_0_0					)					)
					print(timeit('''primes(1_000_000_000_000)'''				,setup=__A				,number=1_0_0_0_0_0_0					)					)
					print(timeit('''fast_primes(1_000_000_000_000)'''				,setup=__A				,number=1_0_0_0_0_0_0					)					)


if __name__ == "__main__":
	__lowerCamelCase :    Union[str, Any]      						=      int(input('''Calculate primes up to:\n>> ''').strip())
	for ret in primes(number):
		print(ret)
	benchmark()



