



'''simple docstring'''


from random import shuffle

import tensorflow as tf
from numpy import array






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=int(lowercase__       )
     assert noofclusters < len(lowercase__       )

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

     # Will help select random centroids from among the available vectors
     a_       		=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.

     a_       		=tf.Graph()

     with graph.as_default():
          # SESSION OF COMPUTATION

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

          ##Variables for cluster assignments of individual vectors(initialized
          ##to 0 at first)
          a_       		=[tf.Variable(0       ) for i in range(len(lowercase__       )       )]
          ##These nodes will assign an assignment Variable the appropriate
          ##value
          a_       		=tf.placeholder("int32"       )
          a_       		=[]
          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
          a_       		=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_       		=tf.reduce_mean(lowercase__       ,	0       )

          ##Node for computing Euclidean distances
          # Placeholders for input
          a_       		=tf.placeholder("float"       ,	[dim]       )
          a_       		=tf.placeholder("float"       ,	[dim]       )
          a_       		=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
          a_       		=tf.placeholder("float"       ,	[noofclusters]       )
          a_       		=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.
          a_       		=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.
          a_       		=1_0_0
          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__       )       ):
                    a_       		=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_       		=[
                        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
                    a_       		=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
                    a_       		=[
                        vectors[i]
                        for i in range(len(lowercase__       )       )
                        if sess.run(assignments[i]       ) == cluster_n
                    ]
                    # Compute new centroid location
                    a_       		=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
          a_       		=sess.run(lowercase__       )
          a_       		=sess.run(lowercase__       )
          return centroids, assignments







'''simple docstring'''


from collections.abc import Sequence






def  UpperCAmelCase_       (		lowercase__ = None       ):


     '''simple docstring'''







     if nums is None or not nums:
          raise ValueError("Input sequence should not be empty"       )

     a_       		=nums[0]
     for i in range(1       ,	len(lowercase__       )       ):
          a_       		=nums[i]
          a_       		=max(lowercase__       ,	ans + num       ,	lowercase__       )

     return ans


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # Try on a sample input from the user
  lowercase							       =			int(input('''Enter number of elements : ''').strip())
  lowercase							       =			list(map(int, input('''\nEnter the numbers : ''').strip().split()))[:n]
  print(max_subsequence_sum(array))


