






"""simple docstring"""






import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def 					_A			(__a    ,				__a							) ->       Dict:
    """simple docstring"""






    assert isinstance(__a    ,				__a							)
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
        assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('''keep_in_memory'''    ,				[False, True]							)
def 					_A			(__a    ,				__a    ,				__a							) ->       Optional[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Tuple    						=		tmp_path / '''cache'''
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		ParquetDatasetReader(__a    ,				cache_dir=__a    ,				keep_in_memory=__a							).read()
    _check_parquet_dataset(__a    ,				__a							)



@pytest.mark.parametrize(
    '''features'''    ,				[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]    ,				)
def 					_A			(__a    ,				__a    ,				__a							) ->       Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Any    						=		tmp_path / '''cache'''
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		features.copy() if features else default_expected_features
    SCREAMING_SNAKE_CASE_      :  int    						=		(
        Features({feature: Value(__a							) for feature, dtype in features.items()}							) if features is not None else None
    )
    SCREAMING_SNAKE_CASE_      :  Dict    						=		ParquetDatasetReader(__a    ,				features=__a    ,				cache_dir=__a							).read()
    _check_parquet_dataset(__a    ,				__a							)



@pytest.mark.parametrize('''split'''    ,				[None, NamedSplit('''train'''							), '''train''', '''test''']							)
def 					_A			(__a    ,				__a    ,				__a							) ->       List[str]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		tmp_path / '''cache'''
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		ParquetDatasetReader(__a    ,				cache_dir=__a    ,				split=__a							).read()
    _check_parquet_dataset(__a    ,				__a							)
    assert dataset.split == split if split else "train"



@pytest.mark.parametrize('''path_type'''    ,				[str, list]							)
def 					_A			(__a    ,				__a    ,				__a							) ->       Dict:
    """simple docstring"""






    if issubclass(__a    ,				__a							):
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		parquet_path
    elif issubclass(__a    ,				__a							):
        SCREAMING_SNAKE_CASE_      :  int    						=		[parquet_path]
    SCREAMING_SNAKE_CASE_      :  Dict    						=		tmp_path / '''cache'''
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		ParquetDatasetReader(__a    ,				cache_dir=__a							).read()
    _check_parquet_dataset(__a    ,				__a							)



def 					_A			(__a    ,				__a    ,				__a=("train",)							) ->       Any:
    """simple docstring"""






    assert isinstance(__a    ,				__a							)
    for split in splits:
        SCREAMING_SNAKE_CASE_      :  Any    						=		dataset_dict[split]
        assert dataset.num_rows == 4
        assert dataset.num_columns == 3
        assert dataset.column_names == ["col_1", "col_2", "col_3"]
        for feature, expected_dtype in expected_features.items():
            assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize('''keep_in_memory'''    ,				[False, True]							)
def 					_A			(__a    ,				__a    ,				__a							) ->       Tuple:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		tmp_path / '''cache'''
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        SCREAMING_SNAKE_CASE_      :  int    						=		ParquetDatasetReader(
            {'''train''': parquet_path}    ,				cache_dir=__a    ,				keep_in_memory=__a							).read()
    _check_parquet_datasetdict(__a    ,				__a							)



@pytest.mark.parametrize(
    '''features'''    ,				[
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]    ,				)
def 					_A			(__a    ,				__a    ,				__a							) ->       str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		tmp_path / '''cache'''
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		features.copy() if features else default_expected_features
    SCREAMING_SNAKE_CASE_      :  str    						=		(
        Features({feature: Value(__a							) for feature, dtype in features.items()}							) if features is not None else None
    )
    SCREAMING_SNAKE_CASE_      :  Dict    						=		ParquetDatasetReader({'''train''': parquet_path}    ,				features=__a    ,				cache_dir=__a							).read()
    _check_parquet_datasetdict(__a    ,				__a							)



@pytest.mark.parametrize('''split'''    ,				[None, NamedSplit('''train'''							), '''train''', '''test''']							)
def 					_A			(__a    ,				__a    ,				__a							) ->       Dict:
    """simple docstring"""






    if split:
        SCREAMING_SNAKE_CASE_      :  Any    						=		{split: parquet_path}
    else:
        SCREAMING_SNAKE_CASE_      :  Dict    						=		'''train'''
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		{'''train''': parquet_path, '''test''': parquet_path}
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		tmp_path / '''cache'''
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		ParquetDatasetReader(__a    ,				cache_dir=__a							).read()
    _check_parquet_datasetdict(__a    ,				__a    ,				splits=list(path.keys()							)							)
    assert all(dataset[split].split == split for split in path.keys()							)



def 					_A			(__a    ,				__a							) ->       int:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  str    						=		ParquetDatasetWriter(__a    ,				tmp_path / '''foo.parquet'''							)
    assert writer.write() > 0
    SCREAMING_SNAKE_CASE_      :  Any    						=		pq.ParquetFile(tmp_path / '''foo.parquet'''							)
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		pf.read()
    assert dataset.data.table == output_table



def 					_A			(__a    ,				__a							) ->       str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  int    						=		str(shared_datadir / '''test_image_rgb.jpg'''							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		{'''image''': [image_path]}
    SCREAMING_SNAKE_CASE_      :  Dict    						=		Features({'''image''': Image()}							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		Dataset.from_dict(__a    ,				features=__a							)
    SCREAMING_SNAKE_CASE_      :  Any    						=		ParquetDatasetWriter(__a    ,				tmp_path / '''foo.parquet'''							)
    assert writer.write() > 0

    SCREAMING_SNAKE_CASE_      :  Any    						=		Dataset.from_parquet(str(tmp_path / '''foo.parquet'''							)							)
    assert dataset.features == reloaded_dataset.features

    SCREAMING_SNAKE_CASE_      :  str    						=		ParquetDatasetReader(str(tmp_path / '''foo.parquet'''							)    ,				streaming=__a							).read()
    assert dataset.features == reloaded_iterable_dataset.features





@pytest.mark.parametrize(
    '''feature, expected'''    ,				[
        (Features({'''foo''': Value('''int32'''							)}							), None),
        (Features({'''image''': Image(), '''foo''': Value('''int32'''							)}							), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({'''nested''': Sequence(Audio()							)}							), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]    ,				)
def 					_A			(__a    ,				__a							) ->       str:
    """simple docstring"""






    assert get_writer_batch_size(__a							) == expected










import math
def 						_a    (      a :int )							->			list:
 a		  =      [True] * n
 a		  =      False
 a		  =      False
 a		  =      True

 for i in range(3	,							int(n**0.5 + 1 )	,							2 ):
  a		  =      i * 2
  while index < n:
   a		  =      False
   a		  =      index + i

 a		  =      [2]

 for i in range(3	,							a	,							2 ):
  if is_prime[i]:
   primes.append(a )

 return primes
def 						_a    (      a :int = 999_966_663_333 )							->			int:
 a		  =      math.floor(math.sqrt(a ) ) + 100
 a		  =      prime_sieve(a )

 a		  =      0
 a		  =      0
 a		  =      primes[prime_index]

 while (last_prime**2) <= limit:
  a		  =      primes[prime_index + 1]

  a		  =      last_prime**2
  a		  =      next_prime**2

  # Get numbers divisible by lps(current)
  a		  =      lower_bound + last_prime
  while upper_bound > current <= limit:
   matches_sum += current
   current += last_prime

  # Reset the upper_bound
  while (upper_bound - next_prime) > limit:
   upper_bound -= next_prime

  # Add the numbers divisible by ups(current)
  a		  =      upper_bound - next_prime
  while current > lower_bound:
   matches_sum += current
   current -= next_prime

  # Remove the numbers divisible by both ups and lps
  a		  =      0
  while upper_bound > current <= limit:
   if current <= lower_bound:
    # Increment the current number
    current += last_prime * next_prime
    continue

   if current > limit:
    break

   # Remove twice since it was added by both ups and lps
   matches_sum -= current * 2

   # Increment the current number
   current += last_prime * next_prime

  # Setup for next pair
  a		  =      next_prime
  prime_index += 1

 return matches_sum


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


