


'''simple docstring'''







from random import shuffle

import tensorflow as tf
from numpy import array


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Optional[Any]    ,_UpperCAmelCase	:	Optional[Any]  )  ->			Optional[int]:
       _a    : Tuple     					=int(_UpperCAmelCase  )
       assert noofclusters < len(_UpperCAmelCase  )

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

       # Will help select random centroids from among the available vectors
       _a    : List[Any]     					=list(range(len(_UpperCAmelCase  )  )  )
       shuffle(_UpperCAmelCase  )

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

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

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

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

              # Initialize all variables
              sess.run(_UpperCAmelCase  )

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

        # Return centroids and assignments
              _a    : str     					=sess.run(_UpperCAmelCase  )
              _a    : Optional[int]     					=sess.run(_UpperCAmelCase  )
              return centroids, assignments






'''simple docstring'''







from __future__ import annotations







class A__       :






      def __init__( self			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:str	)       ->					Optional[int]:




             '''simple docstring'''
             _a					,    _a    : List[str]     					=text, pattern
             _a					,    _a    : Union[str, Any]     					=len(SCREAMING_SNAKE_CASE	), len(SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Optional[int]     ,						SCREAMING_SNAKE_CASE			:str	)       ->					int:




             '''simple docstring'''

             for i in range(self.patLen - 1     ,						-1     ,						-1	):
                    if char == self.pattern[i]:
                           return i
             return -1






      def __UpperCAmelCase       ( self			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:int	)       ->					int:




             '''simple docstring'''

             for i in range(self.patLen - 1     ,						-1     ,						-1	):
                    if self.pattern[i] != self.text[current_pos + i]:
                           return current_pos + i
             return -1






      def __UpperCAmelCase       ( self			:Union[str, Any]	)       ->					list[int]:




             '''simple docstring'''
             # searches pattern in text and returns index positions
             _a    : Union[str, Any]     					=[]
             for i in range(self.textLen - self.patLen + 1	):
                    _a    : Any     					=self.mismatch_in_text(SCREAMING_SNAKE_CASE	)
                    if mismatch_index == -1:
                           positions.append(SCREAMING_SNAKE_CASE	)
                    else:
                           _a    : int     					=self.match_in_pattern(self.text[mismatch_index]	)
                           _a    : List[str]     					=(
                               mismatch_index - match_index
                           )  # shifting index lgtm [py/multiple-definition]
             return positions

A__:    Any					    =       '''ABAABA'''
A__:    int					    =       '''AB'''
A__:    Optional[int]					    =       BoyerMooreSearch(text, pattern)
A__:    Optional[Any]					    =       bms.bad_character_heuristic()

if len(positions) == 0:
   print('''No match found''')
else:
   print('''Pattern found in following positions: ''')
   print(positions)


