
import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
					print("Googling.....")
					_UpperCAmelCase      :				Optional[Any]          = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
					_UpperCAmelCase      :				List[str]          = requests.get(url, headers={"UserAgent": UserAgent().random})
					# res.raise_for_status()
					with open("project1a.html", "wb") as out_file:  # only for knowing the class
										for data in res.iter_content(10_000):
															out_file.write(data)
					_UpperCAmelCase      :				List[str]          = BeautifulSoup(res.text, "html.parser")
					_UpperCAmelCase      :				List[str]          = list(soup.select(".eZt8xd"))[:5]

					print(len(links))
					for link in links:
										if link.text == "Maps":
															webbrowser.open(link.get("href"))
										else:
															webbrowser.open(F'''https://google.com{link.get('href')}''')


from random import shuffle

import tensorflow as tf
from numpy import array


def    A (      lowercase					, lowercase				)     ->						Optional[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
