





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 		__magic_name__				( lowercase_						,		lowercase_				)		->					str:


						'''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(_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 		__magic_name__				( )		->					List[str]:


						'''simple docstring'''


						UpperCamelCase  							= pyspark.sql.SparkSession.builder.master("local[*]"				).appName("pyspark"				).getOrCreate()
						UpperCamelCase  							= spark.range(100				).repartition(1				)
						UpperCamelCase  							= 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 		__magic_name__				( )		->					Any:


						'''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(_UpperCAmelCase						,		_UpperCAmelCase				)  # Reverse the partitions.
						UpperCamelCase  							= _get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase						,		_UpperCAmelCase				)

						for i, (row_id, row_dict) in enumerate(generate_fn()				):
												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 		__magic_name__				( )		->					Tuple:


						'''simple docstring'''


						UpperCamelCase  							= pyspark.sql.SparkSession.builder.master("local[*]"				).appName("pyspark"				).getOrCreate()
						UpperCamelCase  							= spark.range(10				).repartition(1				)
						UpperCamelCase  							= 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 		__magic_name__				( )		->					List[str]:


						'''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(_UpperCAmelCase						,		[2, 1, 0]				)

												UpperCamelCase  							= SparkExamplesIterable(_UpperCAmelCase				).shuffle_data_sources(_UpperCAmelCase				)
												assert shuffled_it.n_shards == 3
												for i, (row_id, row_dict) in enumerate(_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 		__magic_name__				( )		->					List[str]:


						'''simple docstring'''


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

						# Partitions 0 and 2
						UpperCamelCase  							= SparkExamplesIterable(_UpperCAmelCase				).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(_UpperCAmelCase						,		[0, 2]				)
						for i, (row_id, row_dict) in enumerate(_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(_UpperCAmelCase				).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(_UpperCAmelCase						,		[1, 3]				)
						for i, (row_id, row_dict) in enumerate(_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 		__magic_name__				( )		->					List[Any]:


						'''simple docstring'''


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






import math




class   __lowercase						:






	def       _a(self				: Dict      ,	snake_case				: list[list[float]]      ,	snake_case				: list[int]    ) ->			int:
							_lowercase       :	List[Any]           =   0.0
							_lowercase       :	Any           =   0.0
							for i in range(len(snake_case    )    ):
													da += math.pow((sample[i] - weights[0][i])      ,	2    )
													da += math.pow((sample[i] - weights[1][i])      ,	2    )
													return 0 if da > da else 1
							return 0






	def       _a(self				: Dict      ,	snake_case				: list[list[int | float]]      ,	snake_case				: list[int]      ,	snake_case				: int      ,	snake_case				: float    ) ->			list[list[int | float]]:
							for i in range(len(snake_case    )    ):
													weights[j][i] += alpha * (sample[i] - weights[j][i])
							return weights



def 		UpperCamelCase     (       )   ->			None:






						'''simple docstring'''

						_lowercase       :	Union[str, Any]           =   [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

						# weight initialization ( n, C )
						_lowercase       :	Tuple           =   [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

						# training
						_lowercase       :	List[Any]           =   SelfOrganizingMap()
						_lowercase       :	Any           =   3
						_lowercase       :	Optional[Any]           =   0.5

						for _ in range(_UpperCAmelCase  ):
												for j in range(len(_UpperCAmelCase  )  ):
																		# training sample
																		_lowercase       :	Optional[int]           =   training_samples[j]

																		# Compute the winning vector
																		_lowercase       :	Tuple           =   self_organizing_map.get_winner(_UpperCAmelCase ,			_UpperCAmelCase  )

																		# Update the winning vector
																		_lowercase       :	str           =   self_organizing_map.update(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )

    # classify test sample
						_lowercase       :	Any           =   [0, 0, 0, 1]
						_lowercase       :	Dict           =   self_organizing_map.get_winner(_UpperCAmelCase ,			_UpperCAmelCase  )

						# results
						print(f"""Clusters that the test sample belongs to : {winner}"""  )
						print(f"""Weights that have been trained : {weights}"""  )


# running the main() function
if __name__ == "__main__":
							main()




