


'''simple docstring'''



from random import shuffle

import tensorflow as tf
from numpy import array






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  str		, _SCREAMING_SNAKE_CASE :  Union[str, Any]  ):
					__a     :			int          =    int(A__  )
					assert noofclusters < len(A__  )

					# Find out the dimensionality
					__a     :			Union[str, Any]          =    len(vectors[0]  )

					# Will help select random centroids from among the available vectors
					__a     :			Any          =    list(range(len(A__  )  )  )
					shuffle(A__  )

					# GRAPH OF COMPUTATION
					# We initialize a new graph and set it as the default during each run
					# of this algorithm. This ensures that as this function is called
					# multiple times, the default graph doesn't keep getting crowded with
					# unused ops and Variables from previous function calls.

					__a     :			Optional[int]          =    tf.Graph()

					with graph.as_default():
										# SESSION OF COMPUTATION

										__a     :			int          =    tf.Session()

										##CONSTRUCTING THE ELEMENTS OF COMPUTATION

										##First lets ensure we have a Variable vector for each centroid,
										##initialized to one of the vectors from the available data points
										__a     :			Any          =    [
										    tf.Variable(vectors[vector_indices[i]]  ) for i in range(A__  )
										]
										##These nodes will assign the centroid Variables the appropriate
										##values
										__a     :			int          =    tf.placeholder('float64'		, [dim]  )
										__a     :			List[Any]          =    []
										for centroid in centroids:
															cent_assigns.append(tf.assign(A__		, A__  )  )

										##Variables for cluster assignments of individual vectors(initialized
										##to 0 at first)
										__a     :			Union[str, Any]          =    [tf.Variable(0  ) for i in range(len(A__  )  )]
										##These nodes will assign an assignment Variable the appropriate
										##value
										__a     :			List[Any]          =    tf.placeholder('int32'  )
										__a     :			str          =    []
										for assignment in assignments:
															cluster_assigns.append(tf.assign(A__		, A__  )  )

										##Now lets construct the node that will compute the mean
										# The placeholder for the input
										__a     :			List[str]          =    tf.placeholder('float'		, [None, dim]  )
										# The Node/op takes the input and computes a mean along the 0th
										# dimension, i.e. the list of input vectors
										__a     :			Union[str, Any]          =    tf.reduce_mean(A__		, 0  )

										##Node for computing Euclidean distances
										# Placeholders for input
										__a     :			List[str]          =    tf.placeholder('float'		, [dim]  )
										__a     :			Any          =    tf.placeholder('float'		, [dim]  )
										__a     :			Optional[int]          =    tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(A__		, A__  )		, 2  )  )  )

										##This node will figure out which cluster to assign a vector to,
										##based on Euclidean distances of the vector from the centroids.
										# Placeholder for input
										__a     :			Union[str, Any]          =    tf.placeholder('float'		, [noofclusters]  )
										__a     :			Any          =    tf.argmin(A__		, 0  )

										##INITIALIZING STATE VARIABLES

										##This will help initialization of all Variables defined with respect
										##to the graph. The Variable-initializer should be defined after
										##all the Variables have been constructed, so that each of them
										##will be included in the initialization.
										__a     :			Optional[Any]          =    tf.initialize_all_variables()

										# Initialize all variables
										sess.run(A__  )

										##CLUSTERING ITERATIONS

										# Now perform the Expectation-Maximization steps of K-Means clustering
										# iterations. To keep things simple, we will only do a set number of
										# iterations, instead of using a Stopping Criterion.
										__a     :			List[Any]          =    100
										for _ in range(A__  ):
															##EXPECTATION STEP
															##Based on the centroid locations till last iteration, compute
															##the _expected_ centroid assignments.
															# Iterate over each vector
															for vector_n in range(len(A__  )  ):
																				__a     :			Tuple          =    vectors[vector_n]
																				# Compute Euclidean distance between this vector and each
																				# centroid. Remember that this list cannot be named
																				#'centroid_distances', since that is the input to the
																				# cluster assignment node.
																				__a     :			Dict          =    [
																				    sess.run(A__		, feed_dict={va: vect, va: sess.run(A__  )}  )
																				    for centroid in centroids
																				]
																				# Now use the cluster assignment node, with the distances
																				# as the input
																				__a     :			int          =    sess.run(
																				    A__		, feed_dict={centroid_distances: distances}  )
																				# Now assign the value to the appropriate state variable
																				sess.run(
																				    cluster_assigns[vector_n]		, feed_dict={assignment_value: assignment}  )

															##MAXIMIZATION STEP
															# Based on the expected state computed from the Expectation Step,
															# compute the locations of the centroids so as to maximize the
															# overall objective of minimizing within-cluster Sum-of-Squares
															for cluster_n in range(A__  ):
																				# Collect all the vectors assigned to this cluster
																				__a     :			Optional[int]          =    [
																				    vectors[i]
																				    for i in range(len(A__  )  )
																				    if sess.run(assignments[i]  ) == cluster_n
																				]
																				# Compute new centroid location
																				__a     :			Any          =    sess.run(
																				    A__		, feed_dict={mean_input: array(A__  )}  )
																				# Assign value to appropriate variable
																				sess.run(
																				    cent_assigns[cluster_n]		, feed_dict={centroid_value: new_location}  )

        # Return centroids and assignments
										__a     :			Optional[int]          =    sess.run(A__  )
										__a     :			Union[str, Any]          =    sess.run(A__  )
										return centroids, assignments





'''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      UpperCamelCase_				(      A__  ,      A__						):
	a_      				=						[]
	for part_id in partition_order:
		a_      				=						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      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(1_00						).repartition(1						)
	a_      				=						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      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(10						).repartition(2						)
	a_      				=						[1, 0]
	a_      				=						_generate_iterable_examples(A__  ,      A__						)  # Reverse the partitions.
	a_      				=						_get_expected_row_ids_and_row_dicts_for_partition_order(A__  ,      A__						)

	for i, (row_id, row_dict) in enumerate(generate_fn()						):
		a_				,  a_      				=						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      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(10						).repartition(1						)
	a_      				=						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      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(30						).repartition(3						)
	# Mock the generator so that shuffle reverses the partition indices.
	with patch("""numpy.random.Generator"""						) as generator_mock:
		a_      				=						lambda A__						: x.reverse()
		a_      				=						_get_expected_row_ids_and_row_dicts_for_partition_order(A__  ,      [2, 1, 0]						)

		a_      				=						SparkExamplesIterable(A__						).shuffle_data_sources(A__						)
		assert shuffled_it.n_shards == 3
		for i, (row_id, row_dict) in enumerate(A__						):
			a_				,  a_      				=						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      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(20						).repartition(4						)

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

	# Partitions 1 and 3
	a_      				=						SparkExamplesIterable(A__						).shard_data_sources(worker_id=1  ,      num_workers=2						)
	assert shard_it_a.n_shards == 2
	a_      				=						_get_expected_row_ids_and_row_dicts_for_partition_order(A__  ,      [1, 3]						)
	for i, (row_id, row_dict) in enumerate(A__						):
		a_				,  a_      				=						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      UpperCamelCase_				(      ):
	a_      				=						pyspark.sql.SparkSession.builder.master("""local[*]"""						).appName("""pyspark"""						).getOrCreate()
	a_      				=						spark.range(1_00						).repartition(1						)
	a_      				=						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() == 1_00
