





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__  (       _lowercase     : List[str]   ,    _lowercase     : Dict			)   ->							Union[str, Any]:




		'''simple docstring'''
		lowercase__					:      Any      =		[]
		for part_id in partition_order:
				lowercase__					:      Union[str, Any]      =		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       SCREAMING_SNAKE_CASE__  (       )   ->							Optional[Any]:




		'''simple docstring'''
		lowercase__					:      List[str]      =		pyspark.sql.SparkSession.builder.master('local[*]'			).appName('pyspark'			).getOrCreate()
		lowercase__					:      Optional[int]      =		spark.range(100			).repartition(1			)
		lowercase__					:      Any      =		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       SCREAMING_SNAKE_CASE__  (       )   ->							Dict:




		'''simple docstring'''
		lowercase__					:      List[Any]      =		pyspark.sql.SparkSession.builder.master('local[*]'			).appName('pyspark'			).getOrCreate()
		lowercase__					:      Dict      =		spark.range(10			).repartition(2			)
		lowercase__					:      str      =		[1, 0]
		lowercase__					:      Dict      =		_generate_iterable_examples(_lowercase   ,    _lowercase			)  # Reverse the partitions.
		lowercase__					:      Union[str, Any]      =		_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase   ,    _lowercase			)

		for i, (row_id, row_dict) in enumerate(generate_fn()			):
				lowercase__   ,				lowercase__					:      List[str]      =		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__  (       )   ->							Dict:




		'''simple docstring'''
		lowercase__					:      Tuple      =		pyspark.sql.SparkSession.builder.master('local[*]'			).appName('pyspark'			).getOrCreate()
		lowercase__					:      Tuple      =		spark.range(10			).repartition(1			)
		lowercase__					:      str      =		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       SCREAMING_SNAKE_CASE__  (       )   ->							Optional[Any]:




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

				lowercase__					:      Optional[Any]      =		SparkExamplesIterable(_lowercase			).shuffle_data_sources(_lowercase			)
				assert shuffled_it.n_shards == 3
				for i, (row_id, row_dict) in enumerate(_lowercase			):
						lowercase__   ,				lowercase__					:      List[Any]      =		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__  (       )   ->							Union[str, Any]:




		'''simple docstring'''
		lowercase__					:      str      =		pyspark.sql.SparkSession.builder.master('local[*]'			).appName('pyspark'			).getOrCreate()
		lowercase__					:      List[Any]      =		spark.range(20			).repartition(4			)

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

		# Partitions 1 and 3
		lowercase__					:      Optional[int]      =		SparkExamplesIterable(_lowercase			).shard_data_sources(worker_id=1   ,    num_workers=2			)
		assert shard_it_a.n_shards == 2
		lowercase__					:      Any      =		_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase   ,    [1, 3]			)
		for i, (row_id, row_dict) in enumerate(_lowercase			):
				lowercase__   ,				lowercase__					:      Optional[int]      =		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__  (       )   ->							Optional[int]:




		'''simple docstring'''
		lowercase__					:      List[str]      =		pyspark.sql.SparkSession.builder.master('local[*]'			).appName('pyspark'			).getOrCreate()
		lowercase__					:      Dict      =		spark.range(100			).repartition(1			)
		lowercase__					:      Optional[int]      =		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









import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : dict			)   ->							tuple:




		'''simple docstring'''
		return (data["data"], data["target"])


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : np.ndarray   ,    _lowercase     : np.ndarray   ,    _lowercase     : np.ndarray			)   ->							np.ndarray:




		'''simple docstring'''
		lowercase__					:      List[str]      =		XGBRegressor(verbosity=0   ,    random_state=42			)
		xgb.fit(_lowercase   ,    _lowercase			)
		# Predict target for test data
		lowercase__					:      Union[str, Any]      =		xgb.predict(_lowercase			)
		lowercase__					:      Tuple      =		predictions.reshape(len(_lowercase			)   ,    1			)
		return predictions


def       SCREAMING_SNAKE_CASE__  (       )   ->							None:




		'''simple docstring'''
		lowercase__					:      List[str]      =		fetch_california_housing()
		lowercase__   ,				lowercase__					:      Optional[int]      =		data_handling(_lowercase			)
		lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:      Optional[Any]      =		train_test_split(
		    _lowercase   ,    _lowercase   ,    test_size=0.25   ,    random_state=1			)
		lowercase__					:      Any      =		xgboost(_lowercase   ,    _lowercase   ,    _lowercase			)
		# Error printing
		print(f"""Mean Absolute Error : {mean_absolute_error(_lowercase   ,    _lowercase			)}"""			)
		print(f"""Mean Square Error  : {mean_squared_error(_lowercase   ,    _lowercase			)}"""			)


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)
			main()


