
from random import shuffle

import tensorflow as tf
from numpy import array


def       A       (					lowercase    ,       lowercase )   ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase		=     int(lowercase )
		assert noofclusters < len(lowercase )

		# Find out the dimensionality
		UpperCamelCase		=     len(vectors[0] )

		# Will help select random centroids from among the available vectors
		UpperCamelCase		=     list(range(len(lowercase ) ) )
		shuffle(lowercase )

		# 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.

		UpperCamelCase		=     tf.Graph()

		with graph.as_default():
				# SESSION OF COMPUTATION

				UpperCamelCase		=     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
				UpperCamelCase		=     [
				    tf.Variable(vectors[vector_indices[i]] ) for i in range(lowercase )
				]
				##These nodes will assign the centroid Variables the appropriate
				##values
				UpperCamelCase		=     tf.placeholder('float64'    ,       [dim] )
				UpperCamelCase		=     []
				for centroid in centroids:
						cent_assigns.append(tf.assign(lowercase    ,       lowercase ) )

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

				##Now lets construct the node that will compute the mean
				# The placeholder for the input
				UpperCamelCase		=     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
				UpperCamelCase		=     tf.reduce_mean(lowercase    ,       0 )

				##Node for computing Euclidean distances
				# Placeholders for input
				UpperCamelCase		=     tf.placeholder('float'    ,       [dim] )
				UpperCamelCase		=     tf.placeholder('float'    ,       [dim] )
				UpperCamelCase		=     tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowercase    ,       lowercase )    ,       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
				UpperCamelCase		=     tf.placeholder('float'    ,       [noofclusters] )
				UpperCamelCase		=     tf.argmin(lowercase    ,       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.
				UpperCamelCase		=     tf.initialize_all_variables()

				# Initialize all variables
				sess.run(lowercase )

				##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.
				UpperCamelCase		=     100
				for _ in range(lowercase ):
						##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(lowercase ) ):
								UpperCamelCase		=     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.
								UpperCamelCase		=     [
								    sess.run(lowercase    ,       feed_dict={va: vect, va: sess.run(lowercase )} )
								    for centroid in centroids
								]
								# Now use the cluster assignment node, with the distances
								# as the input
								UpperCamelCase		=     sess.run(
								    lowercase    ,       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(lowercase ):
								# Collect all the vectors assigned to this cluster
								UpperCamelCase		=     [
								    vectors[i]
								    for i in range(len(lowercase ) )
								    if sess.run(assignments[i] ) == cluster_n
								]
								# Compute new centroid location
								UpperCamelCase		=     sess.run(
								    lowercase    ,       feed_dict={mean_input: array(lowercase )} )
								# Assign value to appropriate variable
								sess.run(
								    cent_assigns[cluster_n]    ,       feed_dict={centroid_value: new_location} )

        # Return centroids and assignments
				UpperCamelCase		=     sess.run(lowercase )
				UpperCamelCase		=     sess.run(lowercase )
				return centroids, assignments


from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters


def       A       (					)   ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase		=     {
		    'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
		    'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
		    'content': ['a ' * 20, 'a ' * 30, 'b ' * 7],
		}
		UpperCamelCase		=     Dataset.from_dict(lowercase )
		return dataset


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     get_dataset()
				UpperCamelCase		=     make_duplicate_clusters(A_				,      0.85       )
				self.assertEqual(len(duplicate_clusters[0]       )				,      2       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     get_dataset()
				UpperCamelCase	,  UpperCamelCase		=     deduplicate_dataset(A_       )
				self.assertEqual(len(A_       )				,      2       )
				print(A_       )
				self.assertEqual(duplicate_clusters[0][0]['copies']				,      2       )
				self.assertEqual(duplicate_clusters[0][0]['is_extreme']				,      A_       )
