




from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					List[str]:
     a     =				[]
     for part_id in partition_order:
          a     =				df.where(f'''SPARK_PARTITION_ID() = {part_id}''').collect()
          for row_idx, row in enumerate(__UpperCamelCase):
               expected_row_ids_and_row_dicts.append((f'''{part_id}_{row_idx}''', row.asDict()))
     return expected_row_ids_and_row_dicts

@require_not_windows
@require_dill_gt_0_3_2
def  SCREAMING_SNAKE_CASE							(       )      ->					Tuple:
     a     =				pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
     a     =				spark.range(1_00).repartition(1)
     a     =				Spark(__UpperCamelCase)
     # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
     # that each partition can hold 2 rows.
     spark_builder._repartition_df_if_needed(max_shard_size=16)
     # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
     assert spark_builder.df.rdd.getNumPartitions() == 50

@require_not_windows
@require_dill_gt_0_3_2
def  SCREAMING_SNAKE_CASE							(       )      ->					Union[str, Any]:
     a     =				pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
     a     =				spark.range(10).repartition(2)
     a     =				[1, 0]
     a     =				_generate_iterable_examples(__UpperCamelCase		, __UpperCamelCase)  # Reverse the partitions.
     a     =				_get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCamelCase		, __UpperCamelCase)

     for i, (row_id, row_dict) in enumerate(generate_fn()):
          a  ,				a     =				expected_row_ids_and_row_dicts[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict

@require_not_windows
@require_dill_gt_0_3_2
def  SCREAMING_SNAKE_CASE							(       )      ->					Optional[int]:
     a     =				pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
     a     =				spark.range(10).repartition(1)
     a     =				SparkExamplesIterable(__UpperCamelCase)
     assert it.n_shards == 1
     for i, (row_id, row_dict) in enumerate(__UpperCamelCase):
          assert row_id == f'''0_{i}'''
          assert row_dict == {"id": i}

@require_not_windows
@require_dill_gt_0_3_2
def  SCREAMING_SNAKE_CASE							(       )      ->					List[str]:
     a     =				pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
     a     =				spark.range(30).repartition(3)
     # Mock the generator so that shuffle reverses the partition indices.
     with patch("numpy.random.Generator") as generator_mock:
          a     =				lambda __UpperCamelCase: x.reverse()
          a     =				_get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCamelCase		, [2, 1, 0])

          a     =				SparkExamplesIterable(__UpperCamelCase).shuffle_data_sources(__UpperCamelCase)
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(__UpperCamelCase):
               a  ,				a     =				expected_row_ids_and_row_dicts[i]
               assert row_id == expected_row_id
               assert row_dict == expected_row_dict

@require_not_windows
@require_dill_gt_0_3_2
def  SCREAMING_SNAKE_CASE							(       )      ->					Optional[Any]:
     a     =				pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
     a     =				spark.range(20).repartition(4)

     # Partitions 0 and 2
     a     =				SparkExamplesIterable(__UpperCamelCase).shard_data_sources(worker_id=0		, num_workers=2)
     assert shard_it_a.n_shards == 2
     a     =				_get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCamelCase		, [0, 2])
     for i, (row_id, row_dict) in enumerate(__UpperCamelCase):
          a  ,				a     =				expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict

     # Partitions 1 and 3
     a     =				SparkExamplesIterable(__UpperCamelCase).shard_data_sources(worker_id=1		, num_workers=2)
     assert shard_it_a.n_shards == 2
     a     =				_get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCamelCase		, [1, 3])
     for i, (row_id, row_dict) in enumerate(__UpperCamelCase):
          a  ,				a     =				expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict





@require_not_windows
@require_dill_gt_0_3_2
def  SCREAMING_SNAKE_CASE							(       )      ->					Union[str, Any]:
     a     =				pyspark.sql.SparkSession.builder.master("local[*]").appName("pyspark").getOrCreate()
     a     =				spark.range(1_00).repartition(1)
     a     =				Spark(__UpperCamelCase)
     # Choose a small max_shard_size for maximum partitioning.
     spark_builder._repartition_df_if_needed(max_shard_size=1)
     # The new number of partitions should not be greater than the number of rows.
     assert spark_builder.df.rdd.getNumPartitions() == 1_00







import math

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					bool:

     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(__UpperCamelCase) + 1)		, 6):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase = 0.1)      ->					int:
     a     =				3
     a     =				3

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()

