




"""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 (				_lowercase			,_lowercase    ):


    """simple docstring"""







    UpperCamelCase              =	[]
    for part_id in partition_order:
        UpperCamelCase              =	df.where(f'SPARK_PARTITION_ID() = {part_id}'    ).collect()
        for row_idx, row in enumerate(_lowercase    ):
            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 (				):


    """simple docstring"""







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


    """simple docstring"""







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

    for i, (row_id, row_dict) in enumerate(generate_fn()    ):
        UpperCamelCase       ,	UpperCamelCase              =	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 (				):


    """simple docstring"""







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





@require_not_windows
@require_dill_gt_0_3_2
def      __snake_case (				):


    """simple docstring"""







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

        UpperCamelCase              =	SparkExamplesIterable(_lowercase    ).shuffle_data_sources(_lowercase    )
        assert shuffled_it.n_shards == 3
        for i, (row_id, row_dict) in enumerate(_lowercase    ):
            UpperCamelCase       ,	UpperCamelCase              =	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 (				):


    """simple docstring"""







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

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

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


    """simple docstring"""







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


import unittest

import numpy as np


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE = None       ,					):




     '''simple docstring'''





     __A	:       List[Any]      =							np.shape(SCREAMING_SNAKE_CASE							)
     __A	:       List[str]      =							np.shape(SCREAMING_SNAKE_CASE							)
     __A	:       Dict      =							np.shape(SCREAMING_SNAKE_CASE							)

     if shape_a[0] != shape_b[0]:
          __A	:       Tuple      =							(
              "Expected the same number of rows for A and B. "
              f"Instead found A of size {shape_a} and B of size {shape_b}"
          )
          raise ValueError(SCREAMING_SNAKE_CASE							)

     if shape_b[1] != shape_c[1]:
          __A	:       Optional[Any]      =							(
              "Expected the same number of columns for B and C. "
              f"Instead found B of size {shape_b} and C of size {shape_c}"
          )
          raise ValueError(SCREAMING_SNAKE_CASE							)

     __A	:       Tuple      =							pseudo_inv
     if a_inv is None:
          try:
               __A	:       Optional[Any]      =							np.linalg.inv(SCREAMING_SNAKE_CASE							)
          except np.linalg.LinAlgError:
               raise ValueError(
                   "Input matrix A is not invertible. Cannot compute Schur complement."							)

     return mat_c - mat_b.T @ a_inv @ mat_b







class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
          __A	:       Tuple      =							np.array([[0, 3], [3, 0], [2, 3]]		)
          __A	:       Tuple      =							np.array([[2, 1], [6, 3]]		)

          __A	:       Optional[Any]      =							schur_complement(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		)

          __A	:       int      =							np.block([[a, b], [b.T, c]]		)

          __A	:       int      =							np.linalg.det(lowerCamelCase		)
          __A	:       List[str]      =							np.linalg.det(lowerCamelCase		)
          __A	:       Any      =							np.linalg.det(lowerCamelCase		)

          self.assertAlmostEqual(lowerCamelCase			,     det_a * det_s		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[int]      =							np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
          __A	:       List[Any]      =							np.array([[0, 3], [3, 0], [2, 3]]		)
          __A	:       List[str]      =							np.array([[2, 1], [6, 3]]		)

          with self.assertRaises(lowerCamelCase		):
               schur_complement(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Any      =							np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
          __A	:       Union[str, Any]      =							np.array([[0, 3], [3, 0], [2, 3]]		)
          __A	:       Optional[Any]      =							np.array([[2, 1, 3], [6, 3, 5]]		)

          with self.assertRaises(lowerCamelCase		):
               schur_complement(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      unittest.main()

