





"""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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Tuple				   =			tmp_path / """cache"""
						UpperCamelCase				:		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():
												UpperCamelCase				:		Dict				   =			ParquetDatasetReader(SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE							,     keep_in_memory=SCREAMING_SNAKE_CASE     ).read()
						_check_parquet_dataset(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

@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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Any				   =			tmp_path / """cache"""
						UpperCamelCase				:		Optional[Any]				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase				:		Union[str, Any]				   =			features.copy() if features else default_expected_features
						UpperCamelCase				:		Optional[int]				   =			(
						    Features({feature: Value(SCREAMING_SNAKE_CASE     ) for feature, dtype in features.items()}     ) if features is not None else None
						)
						UpperCamelCase				:		Tuple				   =			ParquetDatasetReader(SCREAMING_SNAKE_CASE							,     features=SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						_check_parquet_dataset(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

@pytest.mark.parametrize("""split"""							,     [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[Any]				   =			tmp_path / """cache"""
						UpperCamelCase				:		Optional[int]				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase				:		Union[str, Any]				   =			ParquetDatasetReader(SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE							,     split=SCREAMING_SNAKE_CASE     ).read()
						_check_parquet_dataset(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						assert dataset.split == split if split else "train"

@pytest.mark.parametrize("""path_type"""							,     [str, list]     )
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						if issubclass(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		int				   =			parquet_path
						elif issubclass(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		List[Any]				   =			[parquet_path]
						UpperCamelCase				:		List[str]				   =			tmp_path / """cache"""
						UpperCamelCase				:		Dict				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase				:		List[str]				   =			ParquetDatasetReader(SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						_check_parquet_dataset(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=("train",)     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						for split in splits:
												UpperCamelCase				:		Union[str, 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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Tuple				   =			tmp_path / """cache"""
						UpperCamelCase				:		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():
												UpperCamelCase				:		str				   =			ParquetDatasetReader(
												    {"""train""": parquet_path}							,     cache_dir=SCREAMING_SNAKE_CASE							,     keep_in_memory=SCREAMING_SNAKE_CASE     ).read()
						_check_parquet_datasetdict(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

@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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Tuple				   =			tmp_path / """cache"""
						UpperCamelCase				:		str				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase				:		Union[str, Any]				   =			features.copy() if features else default_expected_features
						UpperCamelCase				:		int				   =			(
						    Features({feature: Value(SCREAMING_SNAKE_CASE     ) for feature, dtype in features.items()}     ) if features is not None else None
						)
						UpperCamelCase				:		Union[str, Any]				   =			ParquetDatasetReader({"""train""": parquet_path}							,     features=SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						_check_parquet_datasetdict(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

@pytest.mark.parametrize("""split"""							,     [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						if split:
												UpperCamelCase				:		Optional[int]				   =			{split: parquet_path}
						else:
												UpperCamelCase				:		Any				   =			"""train"""
												UpperCamelCase				:		str				   =			{"""train""": parquet_path, """test""": parquet_path}
						UpperCamelCase				:		Union[str, Any]				   =			tmp_path / """cache"""
						UpperCamelCase				:		Union[str, Any]				   =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						UpperCamelCase				:		Optional[Any]				   =			ParquetDatasetReader(SCREAMING_SNAKE_CASE							,     cache_dir=SCREAMING_SNAKE_CASE     ).read()
						_check_parquet_datasetdict(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     splits=list(path.keys()     )     )
						assert all(dataset[split].split == split for split in path.keys()     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Any				   =			ParquetDatasetWriter(SCREAMING_SNAKE_CASE							,     tmp_path / """foo.parquet"""     )
						assert writer.write() > 0
						UpperCamelCase				:		str				   =			pq.ParquetFile(tmp_path / """foo.parquet"""     )
						UpperCamelCase				:		Optional[Any]				   =			pf.read()
						assert dataset.data.table == output_table

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Any				   =			str(shared_datadir / """test_image_rgb.jpg"""     )
						UpperCamelCase				:		Any				   =			{"""image""": [image_path]}
						UpperCamelCase				:		List[Any]				   =			Features({"""image""": Image()}     )
						UpperCamelCase				:		Optional[Any]				   =			Dataset.from_dict(SCREAMING_SNAKE_CASE							,     features=SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Tuple				   =			ParquetDatasetWriter(SCREAMING_SNAKE_CASE							,     tmp_path / """foo.parquet"""     )
						assert writer.write() > 0

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

						UpperCamelCase				:		Dict				   =			ParquetDatasetReader(str(tmp_path / """foo.parquet"""     )							,     streaming=SCREAMING_SNAKE_CASE     ).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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						assert get_writer_batch_size(SCREAMING_SNAKE_CASE     ) == expected







'''simple docstring'''







from __future__ import annotations

from math import gcd






def       lowercase					(_A ,    _A = 2 ,    _A = 1 ,    _A = 3 ,    ):







       """simple docstring"""







       if num < 2:
              raise ValueError('The input value cannot be less than 2'       )

       # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
       # algorithm struggles to find factors that are divisible by two.
       # As a workaround, we specifically check for two and even inputs.
       #   See: https://math.stackexchange.com/a/2856214/165820
       if num > 2 and num % 2 == 0:
              return 2

       # Pollard's Rho algorithm requires a function that returns pseudorandom
       # values between 0 <= X < ``num``.  It doesn't need to be random in the
       # sense that the output value is cryptographically secure or difficult
       # to calculate, it only needs to be random in the sense that all output
       # values should be equally likely to appear.
       # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
       # However, the success of Pollard's algorithm isn't guaranteed and is
       # determined in part by the initial seed and the chosen random function.
       # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
       # where ``C`` is a value that we can modify between each attempt.
       def rand_fn(_A ,    _A ,    _A       ) -> int:
              return (pow(_A ,    2       ) + step) % modulus

       for _ in range(_A       ):
              # These track the position within the cycle detection logic.
              _lowerCAmelCase     :				Dict       =							seed
              _lowerCAmelCase     :				int       =							seed

              while True:
                     # At each iteration, the tortoise moves one step and the hare moves two.
                     _lowerCAmelCase     :				str       =							rand_fn(_A ,    _A ,    _A       )
                     _lowerCAmelCase     :				Optional[int]       =							rand_fn(_A ,    _A ,    _A       )
                     _lowerCAmelCase     :				Union[str, Any]       =							rand_fn(_A ,    _A ,    _A       )

                     # At some point both the tortoise and the hare will enter a cycle whose
                     # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
                     # the tortoise and hare will end up on the same value modulo ``p``.
                     # We can detect when this happens because the position difference between
                     # the tortoise and the hare will share a common divisor with ``num``.
                     _lowerCAmelCase     :				Optional[int]       =							gcd(hare - tortoise ,    _A       )

                     if divisor == 1:
                            # No common divisor yet, just keep searching.
                            continue
                     else:
                            # We found a common divisor!
                            if divisor == num:
                                   # Unfortunately, the divisor is ``num`` itself and is useless.
                                   break
                            else:
                                   # The divisor is a nontrivial factor of ``num``!
                                   return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
              _lowerCAmelCase     :				Tuple       =							hare

              # The new step value for the random function can just be incremented.
              # At first the results will be similar to what the old function would
              # have produced, but the value will quickly diverge after a bit.
              step += 1

       # We haven't found a divisor within the requested number of attempts.
       # We were unlucky or ``num`` itself is actually prime.
       return None


if __name__ == "__main__":
       import argparse

       lowerCAmelCase     :  Tuple											=    argparse.ArgumentParser()
       parser.add_argument(
           """num""",
           type=int,
           help="""The value to find a divisor of""",
       )
       parser.add_argument(
           """--attempts""",
           type=int,
           default=3,
           help="""The number of attempts before giving up""",
       )
       lowerCAmelCase     :  List[str]											=    parser.parse_args()

       lowerCAmelCase     :  List[str]											=    pollard_rho(args.num, attempts=args.attempts)
       if divisor is None:
              print(F'''{args.num} is probably prime''')
       else:
              lowerCAmelCase     :  Union[str, Any]											=    args.num // divisor
              print(F'''{args.num} = {divisor} * {quotient}''')

