


"""simple docstring"""


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   snake_case						(				_a:     Dict		,   _a:     int							)->		List[str]:
      '''simple docstring'''


      lowerCamelCase__           =		[]
      for part_id in partition_order:
            lowerCamelCase__           =		df.where(F'SPARK_PARTITION_ID() = {part_id}'							).collect()
            for row_idx, row in enumerate(_a							):
                  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   snake_case						(				)->		List[str]:
      '''simple docstring'''


      lowerCamelCase__           =		pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
      lowerCamelCase__           =		spark.range(100							).repartition(1							)
      lowerCamelCase__           =		Spark(_a							)
      # 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   snake_case						(				)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
      lowerCamelCase__           =		spark.range(10							).repartition(2							)
      lowerCamelCase__           =		[1, 0]
      lowerCamelCase__           =		_generate_iterable_examples(_a		,   _a							)  # Reverse the partitions.
      lowerCamelCase__           =		_get_expected_row_ids_and_row_dicts_for_partition_order(_a		,   _a							)

      for i, (row_id, row_dict) in enumerate(generate_fn()							):
            lowerCamelCase__    ,  lowerCamelCase__           =		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   snake_case						(				)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
      lowerCamelCase__           =		spark.range(10							).repartition(1							)
      lowerCamelCase__           =		SparkExamplesIterable(_a							)
      assert it.n_shards == 1
      for i, (row_id, row_dict) in enumerate(_a							):
            assert row_id == F'0_{i}'
            assert row_dict == {"id": i}



@require_not_windows
@require_dill_gt_0_3_2
def   snake_case						(				)->		Tuple:
      '''simple docstring'''


      lowerCamelCase__           =		pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
      lowerCamelCase__           =		spark.range(30							).repartition(3							)
      # Mock the generator so that shuffle reverses the partition indices.
      with patch('numpy.random.Generator'							) as generator_mock:
            lowerCamelCase__           =		lambda _a							: x.reverse()
            lowerCamelCase__           =		_get_expected_row_ids_and_row_dicts_for_partition_order(_a		,   [2, 1, 0]							)

            lowerCamelCase__           =		SparkExamplesIterable(_a							).shuffle_data_sources(_a							)
            assert shuffled_it.n_shards == 3
            for i, (row_id, row_dict) in enumerate(_a							):
                  lowerCamelCase__    ,  lowerCamelCase__           =		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   snake_case						(				)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
      lowerCamelCase__           =		spark.range(20							).repartition(4							)

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

      # Partitions 1 and 3
      lowerCamelCase__           =		SparkExamplesIterable(_a							).shard_data_sources(worker_id=1		,   num_workers=2							)
      assert shard_it_a.n_shards == 2
      lowerCamelCase__           =		_get_expected_row_ids_and_row_dicts_for_partition_order(_a		,   [1, 3]							)
      for i, (row_id, row_dict) in enumerate(_a							):
            lowerCamelCase__    ,  lowerCamelCase__           =		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   snake_case						(				)->		Optional[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		pyspark.sql.SparkSession.builder.master('local[*]'							).appName('pyspark'							).getOrCreate()
      lowerCamelCase__           =		spark.range(100							).repartition(1							)
      lowerCamelCase__           =		Spark(_a							)
      # 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() == 100







"""simple docstring"""


def   snake_case						(				_a:     int = 4000000							)->		int:
      '''simple docstring'''


      lowerCamelCase__           =		[0, 1]
      lowerCamelCase__           =		0
      while fib[i] <= n:
            fib.append(fib[i] + fib[i + 1]							)
            if fib[i + 2] > n:
                  break
            i += 1
      lowerCamelCase__           =		0
      for j in range(len(_a							) - 1							):
            if fib[j] % 2 == 0:
                  total += fib[j]

      return total


if __name__ == "__main__":
   print(f"""{solution() = }""")



