


import math







def        SCREAMING_SNAKE_CASE_						(			__A :			int				)	->		bool:

       """simple docstring"""

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5  ,    int(math.sqrt(__A				) + 1				)  ,    6				):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True







def        SCREAMING_SNAKE_CASE_						(			__A :			float = 0.1				)	->		int:

       """simple docstring"""

       a_					:		Dict							=  3
       a_					:		str							=  3

       while primes / (2 * j - 1) >= ratio:
              for i in range(j * j + j + 1  ,    (j + 2) * (j + 2)  ,    j + 1				):
                     primes += is_prime(__A				)
              j += 2
       return j


if __name__ == "__main__":
   import doctest

   doctest.testmod()








import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast




@dataclass
class       __lowerCAmelCase				(							datasets.BuilderConfig):
       _a           =  None







class       __lowerCAmelCase				(							datasets.ArrowBasedBuilder):
       _a           =  PandasConfig






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         return datasets.DatasetInfo(features=self.config.features )






       def     SCREAMING_SNAKE_CASE			(      self:    str				,    _lowerCAmelCase:    List[str] ):
         if not self.config.data_files:
           raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}" )
         lowercase			:int							=  dl_manager.download_and_extract(self.config.data_files )
         if isinstance(_lowerCAmelCase				,    (str, list, tuple) ):
           lowercase			:Union[str, Any]							=  data_files
           if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ):
             lowercase			:str							=  [files]
           # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
           lowercase			:Optional[int]							=  [dl_manager.iter_files(_lowerCAmelCase ) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,    gen_kwargs={"files": files} )]
         lowercase			:List[Any]							=  []
         for split_name, files in data_files.items():
           if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ):
             lowercase			:Optional[Any]							=  [files]
           # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
           lowercase			:Optional[Any]							=  [dl_manager.iter_files(_lowerCAmelCase ) for file in files]
           splits.append(datasets.SplitGenerator(name=_lowerCAmelCase				,    gen_kwargs={"files": files} ) )
         return splits






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    pa.Table ):
         if self.config.features is not None:
           # more expensive cast to support nested features with keys in a different order
           # allows str <-> int/float or str to Audio for example
           lowercase			:Any							=  table_cast(_lowerCAmelCase				,    self.config.features.arrow_schema )
         return pa_table






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    _lowerCAmelCase:    Union[str, Any] ):
         for i, file in enumerate(itertools.chain.from_iterable(_lowerCAmelCase ) ):
           with open(_lowerCAmelCase				,    "rb" ) as f:
             lowercase			:int							=  pa.Table.from_pandas(pd.read_pickle(_lowerCAmelCase ) )
             yield i, self._cast_table(_lowerCAmelCase )



