
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""")

A__       :   Optional[Any]   						=					"""K-MEANS-CLUST/ """



def      _a     (   __UpperCamelCase       :  Any	,__UpperCamelCase       :  Union[str, Any]	,__UpperCamelCase       :  List[Any]=None    ):
			if seed is not None:  # useful for obtaining consistent results
						np.random.seed(__UpperCamelCase    )
			lowerCAmelCase__ :    List[Any]				      =			data.shape[0]  # number of data points

			# Pick K indices from range [0, N).
			lowerCAmelCase__ :    int				      =			np.random.randint(0	,__UpperCamelCase	,__UpperCamelCase    )

			# 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__ :    List[str]				      =			data[rand_indices, :]

			return centroids



def      _a     (   __UpperCamelCase       :  Optional[Any]	,__UpperCamelCase       :  Any    ):
			return pairwise_distances(__UpperCamelCase	,__UpperCamelCase	,metric='''euclidean'''    )



def      _a     (   __UpperCamelCase       :  int	,__UpperCamelCase       :  Tuple    ):
			# Compute distances between each data point and the set of centroids:
			# Fill in the blank (RHS only)
			lowerCAmelCase__ :    Tuple				      =			centroid_pairwise_dist(__UpperCamelCase	,__UpperCamelCase    )

			# Compute cluster assignments for each data point:
			# Fill in the blank (RHS only)
			lowerCAmelCase__ :    Optional[int]				      =			np.argmin(__UpperCamelCase	,axis=1    )

			return cluster_assignment



def      _a     (   __UpperCamelCase       :  Tuple	,__UpperCamelCase       :  Optional[int]	,__UpperCamelCase       :  List[Any]    ):
			lowerCAmelCase__ :    Optional[Any]				      =			[]
			for i in range(__UpperCamelCase    ):
						# Select all data points that belong to cluster i. Fill in the blank (RHS only)
						lowerCAmelCase__ :    Any				      =			data[cluster_assignment == i]
						# Compute the mean of the data points. Fill in the blank (RHS only)
						lowerCAmelCase__ :    int				      =			member_data_points.mean(axis=0    )
						new_centroids.append(__UpperCamelCase    )
			lowerCAmelCase__ :    Union[str, Any]				      =			np.array(__UpperCamelCase    )

			return new_centroids



def      _a     (   __UpperCamelCase       :  int	,__UpperCamelCase       :  List[Any]	,__UpperCamelCase       :  List[Any]	,__UpperCamelCase       :  Optional[Any]    ):
			lowerCAmelCase__ :    Union[str, Any]				      =			0.0
			for i in range(__UpperCamelCase    ):
						# Select all data points that belong to cluster i. Fill in the blank (RHS only)
						lowerCAmelCase__ :    Union[str, Any]				      =			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__ :    Tuple				      =			pairwise_distances(
									    __UpperCamelCase	,[centroids[i]]	,metric='''euclidean'''    )
									lowerCAmelCase__ :    Any				      =			distances**2
									heterogeneity += np.sum(__UpperCamelCase    )

			return heterogeneity



def      _a     (   __UpperCamelCase       :  Any	,__UpperCamelCase       :  Optional[Any]    ):
			plt.figure(figsize=(7, 4)    )
			plt.plot(__UpperCamelCase	,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      _a     (   __UpperCamelCase       :  Any	,__UpperCamelCase       :  Tuple	,__UpperCamelCase       :  Optional[int]	,__UpperCamelCase       :  List[str]=500	,__UpperCamelCase       :  int=None	,__UpperCamelCase       :  Union[str, Any]=False    ):
			lowerCAmelCase__ :    Optional[int]				      =			initial_centroids[:]
			lowerCAmelCase__ :    Union[str, Any]				      =			None

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

						# 1. Make cluster assignments using nearest centroids
						lowerCAmelCase__ :    Tuple				      =			assign_clusters(__UpperCamelCase	,__UpperCamelCase    )

						# 2. Compute a new centroid for each of the k clusters, averaging all data
						#    points assigned to that cluster.
						lowerCAmelCase__ :    Optional[Any]				      =			revise_centroids(__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase    )

						# 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__ :    Any				      =			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__ :    Optional[int]				      =			compute_heterogeneity(__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase    )
									record_heterogeneity.append(__UpperCamelCase    )

						lowerCAmelCase__ :    Any				      =			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

							A__       :   str   						=					ds.load_iris()
							A__       :   List[str]   						=					3
							A__       :   str   						=					[]
							A__       :   Dict   						=					get_initial_centroids(dataset["""data"""], k, seed=0)
							A__						,  A__       :   List[Any]   						=					kmeans(
							    dataset["""data"""],
							    k,
							    initial_centroids,
							    maxiter=4_0_0,
							    record_heterogeneity=heterogeneity,
							    verbose=True,
							)
							plot_heterogeneity(heterogeneity, k)



def      _a     (   __UpperCamelCase       :  pd.DataFrame	,__UpperCamelCase       :  np.ndarray	,__UpperCamelCase       :  Union[str, Any]=None    ):
			# Fill missing values with given rules
			if fill_missing_report:
						lowerCAmelCase__ :    Optional[Any]				      =			df.fillna(value=__UpperCamelCase    )
			lowerCAmelCase__ :    Dict				      =			1
			lowerCAmelCase__ :    Dict				      =			df.select_dtypes(np.number    ).columns
			lowerCAmelCase__ :    Optional[Any]				      =			(
			    df.groupby(['''Cluster''']    )[  # construct report dataframe
			        numeric_cols
			    ]  # group by cluster number
			    .agg(
			        [
			            ('''sum''', np.sum),
			            ('''mean_with_zeros''', lambda __UpperCamelCase    : np.mean(np.nan_to_num(__UpperCamelCase    )    )),
			            ('''mean_without_zeros''', lambda __UpperCamelCase    : x.replace(0	,np.NaN    ).mean()),
			            (
			                '''mean_25-75''',
			                lambda __UpperCamelCase    : np.mean(
			                    np.nan_to_num(
			                        sorted(__UpperCamelCase    )[
			                            round(len(__UpperCamelCase    ) * 25 / 100    ) : round(len(__UpperCamelCase    ) * 75 / 100    )
			                        ]    )    ),
			            ),
			            ('''mean_with_na''', np.mean),
			            ('''min''', lambda __UpperCamelCase    : x.min()),
			            ('''5%''', lambda __UpperCamelCase    : x.quantile(0.05    )),
			            ('''25%''', lambda __UpperCamelCase    : x.quantile(0.25    )),
			            ('''50%''', lambda __UpperCamelCase    : x.quantile(0.50    )),
			            ('''75%''', lambda __UpperCamelCase    : x.quantile(0.75    )),
			            ('''95%''', lambda __UpperCamelCase    : x.quantile(0.95    )),
			            ('''max''', lambda __UpperCamelCase    : x.max()),
			            ('''count''', lambda __UpperCamelCase    : x.count()),
			            ('''stdev''', lambda __UpperCamelCase    : x.std()),
			            ('''mode''', lambda __UpperCamelCase    : x.mode()[0]),
			            ('''median''', lambda __UpperCamelCase    : x.median()),
			            ('''# > 0''', lambda __UpperCamelCase    : (x > 0).sum()),
			        ]    )
			    .T.reset_index()
			    .rename(index=__UpperCamelCase	,columns={'''level_0''': '''Features''', '''level_1''': '''Type'''}    )
			)  # rename columns
			# calculate the size of cluster(count of clientID's)
			lowerCAmelCase__ :    List[Any]				      =			report[
			    (report['''Features'''] == '''dummy''') & (report['''Type'''] == '''count''')
			].copy()  # avoid SettingWithCopyWarning
			lowerCAmelCase__ :    int				      =			(
			    '''ClusterSize'''  # rename created cluster df to match report column names
			)
			lowerCAmelCase__ :    Dict				      =			'''# of Customers'''
			lowerCAmelCase__ :    Optional[int]				      =			pd.DataFrame(
			    clustersize.iloc[:, 2:].values
			    / clustersize.iloc[:, 2:].values.sum()    )
			lowerCAmelCase__ :    Dict				      =			'''% of Customers'''  # rename created cluster df to match report column names
			lowerCAmelCase__ :    int				      =			'''ClusterProportion'''
			lowerCAmelCase__ :    Optional[int]				      =			clusterproportion.columns.tolist()
			lowerCAmelCase__ :    List[str]				      =			cols[-2:] + cols[:-2]
			lowerCAmelCase__ :    str				      =			clusterproportion[cols]  # rearrange columns to match report
			lowerCAmelCase__ :    Any				      =			report.columns
			lowerCAmelCase__ :    List[str]				      =			pd.DataFrame(
			    abs(
			        report[report['''Type'''] == '''count'''].iloc[:, 2:].values
			        - clustersize.iloc[:, 2:].values    )    )  # generating df with count of nan values
			lowerCAmelCase__ :    str				      =			0
			lowerCAmelCase__ :    Any				      =			'''# of nan'''
			lowerCAmelCase__ :    Tuple				      =			report[
			    report['''Type'''] == '''count'''
			].Features.tolist()  # filling values in order to match report
			lowerCAmelCase__ :    Optional[int]				      =			a.columns.tolist()
			lowerCAmelCase__ :    Optional[Any]				      =			cols[-2:] + cols[:-2]
			lowerCAmelCase__ :    Dict				      =			a[cols]  # rearrange columns to match report
			lowerCAmelCase__ :    str				      =			report.columns  # rename columns to match report
			lowerCAmelCase__ :    Union[str, Any]				      =			report.drop(
			    report[report.Type == '''count'''].index    )  # drop count values except cluster size
			lowerCAmelCase__ :    str				      =			pd.concat(
			    [report, a, clustersize, clusterproportion]	,axis=0    )  # concat report with clustert size and nan values
			lowerCAmelCase__ :    Any				      =			report['''Features'''].isin(__UpperCamelCase    )
			lowerCAmelCase__ :    List[Any]				      =			report.columns.tolist()
			lowerCAmelCase__ :    Union[str, Any]				      =			cols[0:2] + cols[-1:] + cols[2:-1]
			lowerCAmelCase__ :    List[Any]				      =			report[cols]
			lowerCAmelCase__ :    str				      =			{
			    '''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__ :    List[Any]				      =			(
			    report.assign(
			        Sortera=lambda __UpperCamelCase    : x.Type.map(__UpperCamelCase    )	,Sortera=lambda __UpperCamelCase    : list(reversed(range(len(__UpperCamelCase    )    )    )    )	,)
			    .sort_values(['''Sorter1''', '''Mark''', '''Sorter2''']	,ascending=__UpperCamelCase    )
			    .drop(['''Sorter1''', '''Sorter2''']	,axis=1    )
			)
			lowerCAmelCase__ :    str				      =			''''''
			lowerCAmelCase__ :    Optional[int]				      =			report.reset_index()
			lowerCAmelCase__ :    str				      =			report.drop(columns=['''index''']    )
			return report


if __name__ == "__main__":
							import doctest

							doctest.testmod()





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils





class 						lowercase       (  unittest.TestCase					):
					@patch('''diffusers.utils.hub_utils.get_full_repo_name'''    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    str				      =			'''full_repo_name'''
								with TemporaryDirectory() as tmpdir:
											# Dummy args values
											lowerCAmelCase__ :    Optional[int]				      =			Mock()
											lowerCAmelCase__ :    Optional[int]				      =			tmpdir
											lowerCAmelCase__ :    Optional[Any]				      =			0
											lowerCAmelCase__ :    Optional[int]				      =			'''hub_token'''
											lowerCAmelCase__ :    Tuple				      =			'''dataset_name'''
											lowerCAmelCase__ :    int				      =			0.01
											lowerCAmelCase__ :    Optional[int]				      =			100000
											lowerCAmelCase__ :    Any				      =			10000
											lowerCAmelCase__ :    Union[str, Any]				      =			0.01
											lowerCAmelCase__ :    int				      =			0.02
											lowerCAmelCase__ :    Union[str, Any]				      =			0.03
											lowerCAmelCase__ :    str				      =			0.0_005
											lowerCAmelCase__ :    Dict				      =			0.000_001
											lowerCAmelCase__ :    Tuple				      =			1
											lowerCAmelCase__ :    Optional[int]				      =			10
											lowerCAmelCase__ :    int				      =			0.001
											lowerCAmelCase__ :    Union[str, Any]				      =			0.1
											lowerCAmelCase__ :    Tuple				      =			0.2
											lowerCAmelCase__ :    Optional[int]				      =			True

											# Model card mush be rendered and saved
											diffusers.utils.hub_utils.create_model_card(SCREAMING_SNAKE_CASE__ ,	model_name='''model_name'''    )
											self.assertTrue((Path(SCREAMING_SNAKE_CASE__    ) / '''README.md''').is_file()    )



