"""simple docstring"""



from random import shuffle

import tensorflow as tf
from numpy import array



def      _lowerCamelCase(     a					,  a							):
       __a	      =  int(__snake_case							)
       assert noofclusters < len(__snake_case							)

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

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

       # 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(__snake_case							)
              ]
              ##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(__snake_case					,  __snake_case							)							)

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

              ##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(__snake_case					,  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(__snake_case					,  __snake_case							)					,  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(__snake_case					,  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(__snake_case							)

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

        # Return centroids and assignments
              __a	      =  sess.run(__snake_case							)
              __a	      =  sess.run(__snake_case							)
              return centroids, assignments





"""simple docstring"""






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__snake_case							:   Optional[int]      =    50_000
__snake_case							:   Dict      =    5_000

__snake_case      ,		__snake_case							:   Union[str, Any]      =    os.path.split(__file__)
__snake_case							:   Any      =    os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			for i in range(__snake_case	):
						__lowerCAmelCase      :  Union[str, Any]												=       dataset[i]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Dict:
			for i in range(0					,len(__snake_case	)					,__snake_case	):
						__lowerCAmelCase      :  List[str]												=       dataset[i : i + batch_size]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Dict:
			with dataset.formatted_as(type=__snake_case	):
						for i in range(__snake_case	):
									__lowerCAmelCase      :  Union[str, Any]												=       dataset[i]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case					,__snake_case	)						->   str:
			with dataset.formatted_as(type=__snake_case	):
						for i in range(0					,__snake_case					,__snake_case	):
									__lowerCAmelCase      :  Optional[int]												=       dataset[i : i + batch_size]


def 			_lowercase				(  )						->   Union[str, Any]:
			__lowerCAmelCase      :  Optional[int]												=       {"num examples": SPEED_TEST_N_EXAMPLES}
			__lowerCAmelCase      :  Optional[int]												=       [
			    (read, {"length": SMALL_TEST}),
			    (read, {"length": SPEED_TEST_N_EXAMPLES}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
			    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
			    (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
			    (read_formatted, {"type": "torch", "length": SMALL_TEST}),
			    (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_000}),
			]

			__lowerCAmelCase      :  Any												=       [
			    (read, {"length": SMALL_TEST}),
			    (read, {"length": SPEED_TEST_N_EXAMPLES}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
			    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_000}),
			]
			with tempfile.TemporaryDirectory() as tmp_dir:
						print("generating dataset"	)
						__lowerCAmelCase      :  int												=       datasets.Features(
						    {"list": datasets.Sequence(datasets.Value("float32"	)	), "numbers": datasets.Value("float32"	)}	)
						__lowerCAmelCase      :  str												=       generate_example_dataset(
						    os.path.join(__snake_case					,"dataset.arrow"	)					,__snake_case					,num_examples=__snake_case					,seq_shapes={"list": (100,)}					,)
						print("first set of iterations"	)
						for func, kwargs in functions:
									print(func.__name__					,str(__snake_case	)	)
									__lowerCAmelCase      :  str												=       func(__snake_case					,**__snake_case	)

						print("shuffling dataset"	)
						__lowerCAmelCase      :  Optional[int]												=       dataset.shuffle()
						print("Second set of iterations (after shuffling"	)
						for func, kwargs in functions_shuffled:
									print("shuffled "					,func.__name__					,str(__snake_case	)	)
									__lowerCAmelCase      :  List[str]												=       func(
									    __snake_case					,**__snake_case	)

			with open(__snake_case					,"wb"	) as f:
						f.write(json.dumps(__snake_case	).encode("utf-8"	)	)


if __name__ == "__main__":  # useful to run the profiler
	benchmark_iterating()