"""simple docstring"""




class 		__A							:






     def __init__(	self						):
       _lowerCAmelCase		: dict[str, TrieNode]						    =  {}  # Mapping from char to TrieNode
       _lowerCAmelCase		: Tuple						    =  False






     def 					__A						(	self       ,  a__						):
       for word in words:
         self.insert(a__						)






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: Optional[Any]						    =  self
       for char in word:
         if char not in curr.nodes:
           _lowerCAmelCase		: List[str]						    =  TrieNode()
         _lowerCAmelCase		: str						    =  curr.nodes[char]
       _lowerCAmelCase		: int						    =  True






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: Optional[int]						    =  self
       for char in word:
         if char not in curr.nodes:
           return False
         _lowerCAmelCase		: Tuple						    =  curr.nodes[char]
       return curr.is_leaf






     def 					__A						(	self       ,  a__						):

       def _delete(a__       ,  a__       ,  a__						) -> bool:
         if index == len(a__						):
           # If word does not exist
           if not curr.is_leaf:
             return False
           _lowerCAmelCase		: Optional[Any]						    =  False
           return len(curr.nodes						) == 0
         _lowerCAmelCase		: Union[str, Any]						    =  word[index]
         _lowerCAmelCase		: Tuple						    =  curr.nodes.get(a__						)
         # If char not in current trie node
         if not char_node:
           return False
         # Flag to check if node can be deleted
         _lowerCAmelCase		: Dict						    =  _delete(a__       ,  a__       ,  index + 1						)
         if delete_curr:
           del curr.nodes[char]
           return len(curr.nodes						) == 0
         return delete_curr

       _delete(self       ,  a__       ,  0						)







def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: TrieNode    ,_lowerCamelCase		: str			) ->   None:
  if node.is_leaf:
    print(_lowerCamelCase    ,end=""" """			)

  for key, value in node.nodes.items():
    print_words(_lowerCamelCase    ,word + key			)





def 							SCREAMING_SNAKE_CASE  (    ) ->   bool:
  _lowerCAmelCase		: Dict						    =  """banana bananas bandana band apple all beast""".split()
  _lowerCAmelCase		: Dict						    =  TrieNode()
  root.insert_many(_lowerCamelCase			)
  # print_words(root, "")
  assert all(root.find(_lowerCamelCase			) for word in words			)
  assert root.find("""banana"""			)
  assert not root.find("""bandanas"""			)
  assert not root.find("""apps"""			)
  assert root.find("""apple"""			)
  assert root.find("""all"""			)
  root.delete("""all"""			)
  assert not root.find("""all"""			)
  root.delete("""banana"""			)
  assert not root.find("""banana"""			)
  assert root.find("""bananas"""			)
  return True





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: bool			) ->   None:
  print(str(_lowerCamelCase			)    ,"""works!""" if passes else """doesn't work :("""			)





def 							SCREAMING_SNAKE_CASE  (    ) ->   None:
  assert test_trie()





def 							SCREAMING_SNAKE_CASE  (    ) ->   None:
  print_results("""Testing trie functionality"""    ,test_trie()			)


if __name__ == "__main__":
     main()





"""simple docstring"""




from random import shuffle

import tensorflow as tf
from numpy import array





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: Tuple			) ->   Dict:
  _lowerCAmelCase		: List[str]						    =  int(_lowerCamelCase			)
  assert noofclusters < len(_lowerCamelCase			)

  # Find out the dimensionality
  _lowerCAmelCase		: Any						    =  len(vectors[0]			)

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

  # 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.

  _lowerCAmelCase		: List[Any]						    =  tf.Graph()

  with graph.as_default():
    # SESSION OF COMPUTATION

    _lowerCAmelCase		: str						    =  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
    _lowerCAmelCase		: List[str]						    =  [
        tf.Variable(vectors[vector_indices[i]]			) for i in range(_lowerCamelCase			)
    ]
    ##These nodes will assign the centroid Variables the appropriate
    ##values
    _lowerCAmelCase		: Union[str, Any]						    =  tf.placeholder("""float64"""    ,[dim]			)
    _lowerCAmelCase		: Optional[int]						    =  []
    for centroid in centroids:
      cent_assigns.append(tf.assign(_lowerCamelCase    ,_lowerCamelCase			)			)

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

    ##Now lets construct the node that will compute the mean
    # The placeholder for the input
    _lowerCAmelCase		: Union[str, Any]						    =  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
    _lowerCAmelCase		: Optional[int]						    =  tf.reduce_mean(_lowerCamelCase    ,0			)

    ##Node for computing Euclidean distances
    # Placeholders for input
    _lowerCAmelCase		: Dict						    =  tf.placeholder("""float"""    ,[dim]			)
    _lowerCAmelCase		: Any						    =  tf.placeholder("""float"""    ,[dim]			)
    _lowerCAmelCase		: List[Any]						    =  tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_lowerCamelCase    ,_lowerCamelCase			)    ,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
    _lowerCAmelCase		: Any						    =  tf.placeholder("""float"""    ,[noofclusters]			)
    _lowerCAmelCase		: str						    =  tf.argmin(_lowerCamelCase    ,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.
    _lowerCAmelCase		: Optional[Any]						    =  tf.initialize_all_variables()

    # Initialize all variables
    sess.run(_lowerCamelCase			)

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

        # Return centroids and assignments
    _lowerCAmelCase		: Optional[int]						    =  sess.run(_lowerCamelCase			)
    _lowerCAmelCase		: List[Any]						    =  sess.run(_lowerCamelCase			)
    return centroids, assignments




