





import warnings

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics import pairwise_distances

warnings.filterwarnings("""ignore""")

__lowercase													=			"""K-MEANS-CLUST/ """


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Any     ,							__lowercase:		Dict     ,							__lowercase:		Any=None):



		'''simple docstring'''






		if seed is not None:  # useful for obtaining consistent results
				np.random.seed(__lowercase)
		__lowerCAmelCase			=				data.shape[0]  # number of data points

		# Pick K indices from range [0, N).
		__lowerCAmelCase			=				np.random.randint(0     ,							__lowercase     ,							__lowercase)

		# Keep centroids as dense format, as many entries will be nonzero due to averaging.
		# As long as at least one document in a cluster contains a word,
		# it will carry a nonzero weight in the TF-IDF vector of the centroid.
		__lowerCAmelCase			=				data[rand_indices, :]

		return centroids


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Dict     ,							__lowercase:		Optional[Any]):



		'''simple docstring'''






		return pairwise_distances(__lowercase     ,							__lowercase     ,							metric='''euclidean''')


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Dict     ,							__lowercase:		Optional[int]):



		'''simple docstring'''






		__lowerCAmelCase			=				centroid_pairwise_dist(__lowercase     ,							__lowercase)

		# Compute cluster assignments for each data point:
		# Fill in the blank (RHS only)
		__lowerCAmelCase			=				np.argmin(__lowercase     ,							axis=1)

		return cluster_assignment


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		int     ,							__lowercase:		Any     ,							__lowercase:		str):



		'''simple docstring'''






		__lowerCAmelCase			=				[]
		for i in range(__lowercase):
				# Select all data points that belong to cluster i. Fill in the blank (RHS only)
				__lowerCAmelCase			=				data[cluster_assignment == i]
				# Compute the mean of the data points. Fill in the blank (RHS only)
				__lowerCAmelCase			=				member_data_points.mean(axis=0)
				new_centroids.append(__lowercase)
		__lowerCAmelCase			=				np.array(__lowercase)

		return new_centroids


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		List[Any]     ,							__lowercase:		Any     ,							__lowercase:		Optional[Any]     ,							__lowercase:		Optional[int]):



		'''simple docstring'''






		__lowerCAmelCase			=				0.0
		for i in range(__lowercase):
				# Select all data points that belong to cluster i. Fill in the blank (RHS only)
				__lowerCAmelCase			=				data[cluster_assignment == i, :]

				if member_data_points.shape[0] > 0:  # check if i-th cluster is non-empty
						# Compute distances from centroid to data points (RHS only)
						__lowerCAmelCase			=				pairwise_distances(
						    __lowercase     ,							[centroids[i]]     ,							metric='''euclidean''')
						__lowerCAmelCase			=				distances**2
						heterogeneity += np.sum(__lowercase)

		return heterogeneity


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		List[Any]     ,							__lowercase:		Optional[int]):



		'''simple docstring'''






		plt.figure(figsize=(7, 4))
		plt.plot(__lowercase     ,							linewidth=4)
		plt.xlabel('''# Iterations''')
		plt.ylabel('''Heterogeneity''')
		plt.title(F'''Heterogeneity of clustering over time, K={k:d}''')
		plt.rcParams.update({'''font.size''': 16})
		plt.show()


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		str     ,							__lowercase:		List[str]     ,							__lowercase:		str     ,							__lowercase:		Optional[int]=5_00     ,							__lowercase:		int=None     ,							__lowercase:		Dict=False):



		'''simple docstring'''






		__lowerCAmelCase			=				initial_centroids[:]
		__lowerCAmelCase			=				None

		for itr in range(__lowercase):
				if verbose:
						print(__lowercase     ,							end='''''')

				# 1. Make cluster assignments using nearest centroids
				__lowerCAmelCase			=				assign_clusters(__lowercase     ,							__lowercase)

				# 2. Compute a new centroid for each of the k clusters, averaging all data
				#    points assigned to that cluster.
				__lowerCAmelCase			=				revise_centroids(__lowercase     ,							__lowercase     ,							__lowercase)

				# Check for convergence: if none of the assignments changed, stop
				if (
				    prev_cluster_assignment is not None
				    and (prev_cluster_assignment == cluster_assignment).all()
				):
						break

				# Print number of new assignments
				if prev_cluster_assignment is not None:
						__lowerCAmelCase			=				np.sum(prev_cluster_assignment != cluster_assignment)
						if verbose:
								print(
								    F'''    {num_changed:5d} elements changed their cluster assignment.''')

        # Record heterogeneity convergence metric
				if record_heterogeneity is not None:
						# YOUR CODE HERE
						__lowerCAmelCase			=				compute_heterogeneity(__lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase)
						record_heterogeneity.append(__lowercase)

				__lowerCAmelCase			=				cluster_assignment[:]

		return centroids, cluster_assignment


# Mock test below
if False:  # change to true to run this test case.
						from sklearn import datasets as ds

						__lowercase													=			ds.load_iris()
						__lowercase													=			3
						__lowercase													=			[]
						__lowercase													=			get_initial_centroids(dataset["""data"""], k, seed=0)
						__lowercase						,       __lowercase													=			kmeans(
						    dataset["""data"""],
						    k,
						    initial_centroids,
						    maxiter=400,
						    record_heterogeneity=heterogeneity,
						    verbose=True,
						)
						plot_heterogeneity(heterogeneity, k)


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		List[str]     ,							__lowercase:		Union[str, Any]     ,							__lowercase:		int=None):



		'''simple docstring'''






		if fill_missing_report:
				__lowerCAmelCase			=				df.fillna(value=__lowercase)
		__lowerCAmelCase			=				1
		__lowerCAmelCase			=				df.select_dtypes(np.number).columns
		__lowerCAmelCase			=				(
		    df.groupby(['''Cluster'''])[  # construct report dataframe
		        numeric_cols
		    ]  # group by cluster number
		    .agg(
		        [
		            ('''sum''', np.sum),
		            ('''mean_with_zeros''', lambda __lowercase: np.mean(np.nan_to_num(__lowercase))),
		            ('''mean_without_zeros''', lambda __lowercase: x.replace(0     ,							np.NaN).mean()),
		            (
		                '''mean_25-75''',
		                lambda __lowercase: np.mean(
		                    np.nan_to_num(
		                        sorted(__lowercase)[
		                            round(len(__lowercase) * 25 / 1_00) : round(len(__lowercase) * 75 / 1_00)
		                        ])),
		            ),
		            ('''mean_with_na''', np.mean),
		            ('''min''', lambda __lowercase: x.min()),
		            ('''5%''', lambda __lowercase: x.quantile(0.05)),
		            ('''25%''', lambda __lowercase: x.quantile(0.25)),
		            ('''50%''', lambda __lowercase: x.quantile(0.50)),
		            ('''75%''', lambda __lowercase: x.quantile(0.75)),
		            ('''95%''', lambda __lowercase: x.quantile(0.95)),
		            ('''max''', lambda __lowercase: x.max()),
		            ('''count''', lambda __lowercase: x.count()),
		            ('''stdev''', lambda __lowercase: x.std()),
		            ('''mode''', lambda __lowercase: x.mode()[0]),
		            ('''median''', lambda __lowercase: x.median()),
		            ('''# > 0''', lambda __lowercase: (x > 0).sum()),
		        ])
		    .T.reset_index()
		    .rename(index=__lowercase     ,							columns={'''level_0''': '''Features''', '''level_1''': '''Type'''})
		)  # rename columns
		# calculate the size of cluster(count of clientID's)
		__lowerCAmelCase			=				report[
		    (report['''Features'''] == '''dummy''') & (report['''Type'''] == '''count''')
		].copy()  # avoid SettingWithCopyWarning
		__lowerCAmelCase			=				(
		    '''ClusterSize'''  # rename created cluster df to match report column names
		)
		__lowerCAmelCase			=				'''# of Customers'''
		__lowerCAmelCase			=				pd.DataFrame(
		    clustersize.iloc[:, 2:].values
		    / clustersize.iloc[:, 2:].values.sum())
		__lowerCAmelCase			=				'''% of Customers'''  # rename created cluster df to match report column names
		__lowerCAmelCase			=				'''ClusterProportion'''
		__lowerCAmelCase			=				clusterproportion.columns.tolist()
		__lowerCAmelCase			=				cols[-2:] + cols[:-2]
		__lowerCAmelCase			=				clusterproportion[cols]  # rearrange columns to match report
		__lowerCAmelCase			=				report.columns
		__lowerCAmelCase			=				pd.DataFrame(
		    abs(
		        report[report['''Type'''] == '''count'''].iloc[:, 2:].values
		        - clustersize.iloc[:, 2:].values))  # generating df with count of nan values
		__lowerCAmelCase			=				0
		__lowerCAmelCase			=				'''# of nan'''
		__lowerCAmelCase			=				report[
		    report['''Type'''] == '''count'''
		].Features.tolist()  # filling values in order to match report
		__lowerCAmelCase			=				a.columns.tolist()
		__lowerCAmelCase			=				cols[-2:] + cols[:-2]
		__lowerCAmelCase			=				a[cols]  # rearrange columns to match report
		__lowerCAmelCase			=				report.columns  # rename columns to match report
		__lowerCAmelCase			=				report.drop(
		    report[report.Type == '''count'''].index)  # drop count values except cluster size
		__lowerCAmelCase			=				pd.concat(
		    [report, a, clustersize, clusterproportion]     ,							axis=0)  # concat report with clustert size and nan values
		__lowerCAmelCase			=				report['''Features'''].isin(__lowercase)
		__lowerCAmelCase			=				report.columns.tolist()
		__lowerCAmelCase			=				cols[0:2] + cols[-1:] + cols[2:-1]
		__lowerCAmelCase			=				report[cols]
		__lowerCAmelCase			=				{
		    '''ClusterSize''': 9,
		    '''ClusterProportion''': 8,
		    '''mean_with_zeros''': 7,
		    '''mean_with_na''': 6,
		    '''max''': 5,
		    '''50%''': 4,
		    '''min''': 3,
		    '''25%''': 2,
		    '''75%''': 1,
		    '''# of nan''': 0,
		    '''# > 0''': -1,
		    '''sum_with_na''': -2,
		}
		__lowerCAmelCase			=				(
		    report.assign(
		        Sortera=lambda __lowercase: x.Type.map(__lowercase)     ,							Sortera=lambda __lowercase: list(reversed(range(len(__lowercase))))     ,							)
		    .sort_values(['''Sorter1''', '''Mark''', '''Sorter2''']     ,							ascending=__lowercase)
		    .drop(['''Sorter1''', '''Sorter2''']     ,							axis=1)
		)
		__lowerCAmelCase			=				''''''
		__lowerCAmelCase			=				report.reset_index()
		__lowerCAmelCase			=				report.drop(columns=['''index'''])
		return report


if __name__ == "__main__":
						import doctest

						doctest.testmod()







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment

class   __snake_case				(			unittest.TestCase   ):






    '''simple docstring'''





    def 	A		(   self						:		int							):

      """simple docstring"""





      __lowerCAmelCase			=				inspect.getfile(accelerate.test_utils							)
      __lowerCAmelCase			=				os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ['''scripts''', '''test_sync.py''']							)





    @require_cpu
    def 	A		(   self						:		str							):

      """simple docstring"""





      debug_launcher(test_sync.main			,						num_processes=1							)





    @require_cpu
    def 	A		(   self						:		List[str]							):

      """simple docstring"""





      debug_launcher(test_sync.main							)





    @require_single_gpu
    def 	A		(   self						:		Optional[Any]							):

      """simple docstring"""





      test_sync.main()





    @require_multi_gpu
    def 	A		(   self						:		Union[str, Any]							):

      """simple docstring"""





      print(f'''Found {torch.cuda.device_count()} devices.'''							)
      __lowerCAmelCase			=				['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
      with patch_environment(omp_num_threads=1							):
        execute_subprocess_async(snake_case			,						env=os.environ.copy()							)
