



import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					float:


				"""simple docstring"""

				return np.dot(__lowerCAmelCase  ,  __lowerCAmelCase      )


class       a							:



		def __init__(     self     :Tuple   ,*,
		    __lowercase     :float = np.inf   ,__lowercase     :str = "linear"   ,__lowercase     :float = 0.0   ,):
						snake_case__	:      List[str]     		=	regularization
						snake_case__	:      int     		=	gamma
						if kernel == "linear":
										snake_case__	:      int     		=	self.__linear
						elif kernel == "rbf":
										if self.gamma == 0:
														raise ValueError('''rbf kernel requires gamma'''  )
										if not isinstance(self.gamma   ,(float, int)  ):
														raise ValueError('''gamma must be float or int'''  )
										if not self.gamma > 0:
														raise ValueError('''gamma must be > 0'''  )
										snake_case__	:      List[str]     		=	self.__rbf
										# in the future, there could be a default value like in sklearn
										# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
										# previously it was 1/(n_features)
						else:
										snake_case__	:      Optional[Any]     		=	F"""Unknown kernel: {kernel}"""
										raise ValueError(__lowercase  )



		def 	__lowerCamelCase			(     self     :Dict   ,__lowercase     :ndarray   ,__lowercase     :ndarray  ):
						return np.dot(__lowercase   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :int   ,__lowercase     :ndarray   ,__lowercase     :ndarray  ):
						return np.exp(-(self.gamma * norm_squared(vectora - vectora  ))  )



		def 	__lowerCamelCase			(     self     :Union[str, Any]   ,__lowercase     :list[ndarray]   ,__lowercase     :ndarray  ):
						snake_case__	:      Tuple     		=	observations
						snake_case__	:      Dict     		=	classes

						# using Wolfe's Dual to calculate w.
						# Primal problem: minimize 1/2*norm_squared(w)
						#   constraint: yn(w . xn + b) >= 1
						#
						# With l a vector
						# Dual problem: maximize sum_n(ln) -
						#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
						#   constraint: self.C >= ln >= 0
						#           and sum_n(ln*yn) = 0
						# Then we get w using w = sum_n(ln*yn*xn)
						# At the end we can get b ~= mean(yn - w . xn)
						#
						# Since we use kernels, we only need l_star to calculate b
						# and to classify observations

						((snake_case__)		, )	:      List[Any]     		=	np.shape(__lowercase  )

						def to_minimize(__lowercase     :ndarray  ) -> float:
										snake_case__	:      List[str]     		=	0
										((snake_case__)		, )	:      List[str]     		=	np.shape(__lowercase  )
										for i in range(__lowercase  ):
														for j in range(__lowercase  ):
																		s += (
																		    candidate[i]
																		    * candidate[j]
																		    * classes[i]
																		    * classes[j]
																		    * self.kernel(observations[i]   ,observations[j]  )
																		)
										return 1 / 2 * s - sum(__lowercase  )

						snake_case__	:      Tuple     		=	LinearConstraint(__lowercase   ,0   ,0  )
						snake_case__	:      str     		=	Bounds(0   ,self.regularization  )

						snake_case__	:      List[str]     		=	minimize(
						    __lowercase   ,np.ones(__lowercase  )   ,bounds=__lowercase   ,constraints=[ly_contraint]  ).x
						snake_case__	:      Tuple     		=	l_star

						# calculating mean offset of separation plane to points
						snake_case__	:      Dict     		=	0
						for i in range(__lowercase  ):
										for j in range(__lowercase  ):
														s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
														    observations[i]   ,observations[j]  )
						snake_case__	:      Dict     		=	s / n






		def 	__lowerCamelCase			(     self     :Optional[int]   ,__lowercase     :ndarray  ):
						snake_case__	:      Optional[int]     		=	sum(
						    self.optimum[n]
						    * self.classes[n]
						    * self.kernel(self.observations[n]   ,__lowercase  )
						    for n in range(len(self.classes  )  )  )
						return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
				import doctest

				doctest.testmod()






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 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Tuple:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	[]
				for part_id in partition_order:
								snake_case__	:      Any     		=	df.where(f"""SPARK_PARTITION_ID() = {part_id}"""      ).collect()
								for row_idx, row in enumerate(__lowerCAmelCase      ):
												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 			_lowerCAmelCase       (					)   ->					Tuple:


				"""simple docstring"""

				snake_case__	:      Any     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      Optional[Any]     		=	spark.range(100      ).repartition(1      )
				snake_case__	:      Optional[int]     		=	Spark(__lowerCAmelCase      )
				# 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 			_lowerCAmelCase       (					)   ->					Optional[int]:


				"""simple docstring"""

				snake_case__	:      Any     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      Dict     		=	spark.range(10      ).repartition(2      )
				snake_case__	:      Any     		=	[1, 0]
				snake_case__	:      Tuple     		=	_generate_iterable_examples(__lowerCAmelCase  ,  __lowerCAmelCase      )  # Reverse the partitions.
				snake_case__	:      Any     		=	_get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCAmelCase  ,  __lowerCAmelCase      )

				for i, (row_id, row_dict) in enumerate(generate_fn()      ):
								snake_case__		, snake_case__	:      Union[str, 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 			_lowerCAmelCase       (					)   ->					Any:


				"""simple docstring"""

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



@require_not_windows
@require_dill_gt_0_3_2
def 			_lowerCAmelCase       (					)   ->					Dict:


				"""simple docstring"""

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

								snake_case__	:      List[str]     		=	SparkExamplesIterable(__lowerCAmelCase      ).shuffle_data_sources(__lowerCAmelCase      )
								assert shuffled_it.n_shards == 3
								for i, (row_id, row_dict) in enumerate(__lowerCAmelCase      ):
												snake_case__		, snake_case__	:      Optional[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 			_lowerCAmelCase       (					)   ->					List[Any]:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      Dict     		=	spark.range(20      ).repartition(4      )

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

				# Partitions 1 and 3
				snake_case__	:      List[str]     		=	SparkExamplesIterable(__lowerCAmelCase      ).shard_data_sources(worker_id=1  ,  num_workers=2      )
				assert shard_it_a.n_shards == 2
				snake_case__	:      List[Any]     		=	_get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCAmelCase  ,  [1, 3]      )
				for i, (row_id, row_dict) in enumerate(__lowerCAmelCase      ):
								snake_case__		, snake_case__	:      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 			_lowerCAmelCase       (					)   ->					Dict:


				"""simple docstring"""

				snake_case__	:      int     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      Dict     		=	spark.range(100      ).repartition(1      )
				snake_case__	:      Tuple     		=	Spark(__lowerCAmelCase      )
				# 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

