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''')

lowerCAmelCase__					:       str		=       '''K-MEANS-CLUST/ '''
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__=None   )       -> Tuple:
				if seed is not None:  # useful for obtaining consistent results
								np.random.seed(A__   )
				snake_case__ :     Dict 							=				data.shape[0]  # number of data points

				# Pick K indices from range [0, N).
				snake_case__ :     List[str] 							=				np.random.randint(0      ,			A__      ,			A__   )

				# 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.
				snake_case__ :     Tuple 							=				data[rand_indices, :]

				return centroids
def 			UpperCamelCase__						(    A__      ,			A__   )       -> int:
				return pairwise_distances(A__      ,			A__      ,			metric='euclidean'   )
def 			UpperCamelCase__						(    A__      ,			A__   )       -> int:
				# Compute distances between each data point and the set of centroids:
				# Fill in the blank (RHS only)
				snake_case__ :     Optional[int] 							=				centroid_pairwise_dist(A__      ,			A__   )

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

				return cluster_assignment
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__   )       -> List[str]:
				snake_case__ :     Optional[Any] 							=				[]
				for i in range(A__   ):
								# Select all data points that belong to cluster i. Fill in the blank (RHS only)
								snake_case__ :     Any 							=				data[cluster_assignment == i]
								# Compute the mean of the data points. Fill in the blank (RHS only)
								snake_case__ :     Union[str, Any] 							=				member_data_points.mean(axis=0   )
								new_centroids.append(A__   )
				snake_case__ :     Optional[Any] 							=				np.array(A__   )

				return new_centroids
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__      ,			A__   )       -> List[str]:
				snake_case__ :     Dict 							=				0.0
				for i in range(A__   ):
								# Select all data points that belong to cluster i. Fill in the blank (RHS only)
								snake_case__ :     Tuple 							=				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)
												snake_case__ :     Optional[Any] 							=				pairwise_distances(
												    A__      ,			[centroids[i]]      ,			metric='euclidean'   )
												snake_case__ :     Any 							=				distances**2
												heterogeneity += np.sum(A__   )

				return heterogeneity
def 			UpperCamelCase__						(    A__      ,			A__   )       -> List[str]:
				plt.figure(figsize=(7, 4)   )
				plt.plot(A__      ,			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 			UpperCamelCase__						(    A__      ,			A__      ,			A__      ,			A__=500      ,			A__=None      ,			A__=False   )       -> int:
				snake_case__ :     int 							=				initial_centroids[:]
				snake_case__ :     List[Any] 							=				None

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

								# 1. Make cluster assignments using nearest centroids
								snake_case__ :     Any 							=				assign_clusters(A__      ,			A__   )

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

								# 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:
												snake_case__ :     int 							=				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
												snake_case__ :     Tuple 							=				compute_heterogeneity(A__      ,			A__      ,			A__      ,			A__   )
												record_heterogeneity.append(A__   )

								snake_case__ :     List[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

				lowerCAmelCase__					:       Union[str, Any]		=       ds.load_iris()
				lowerCAmelCase__					:       Tuple		=       3
				lowerCAmelCase__					:       Optional[int]		=       []
				lowerCAmelCase__					:       Optional[int]		=       get_initial_centroids(dataset['''data'''], k, seed=0)
				lowerCAmelCase__,					lowerCAmelCase__					:       Union[str, Any]		=       kmeans(
				    dataset['''data'''],
				    k,
				    initial_centroids,
				    maxiter=4_00,
				    record_heterogeneity=heterogeneity,
				    verbose=True,
				)
				plot_heterogeneity(heterogeneity, k)
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__=None   )       -> pd.DataFrame:
				# Fill missing values with given rules
				if fill_missing_report:
								snake_case__ :     str 							=				df.fillna(value=A__   )
				snake_case__ :     str 							=				1
				snake_case__ :     Optional[Any] 							=				df.select_dtypes(np.number   ).columns
				snake_case__ :     Any 							=				(
				    df.groupby(['Cluster']   )[  # construct report dataframe
				        numeric_cols
				    ]  # group by cluster number
				    .agg(
				        [
				            ('sum', np.sum),
				            ('mean_with_zeros', lambda A__   : np.mean(np.nan_to_num(A__   )   )),
				            ('mean_without_zeros', lambda A__   : x.replace(0      ,			np.NaN   ).mean()),
				            (
				                'mean_25-75',
				                lambda A__   : np.mean(
				                    np.nan_to_num(
				                        sorted(A__   )[
				                            round(len(A__   ) * 25 / 100   ) : round(len(A__   ) * 75 / 100   )
				                        ]   )   ),
				            ),
				            ('mean_with_na', np.mean),
				            ('min', lambda A__   : x.min()),
				            ('5%', lambda A__   : x.quantile(0.0_5   )),
				            ('25%', lambda A__   : x.quantile(0.2_5   )),
				            ('50%', lambda A__   : x.quantile(0.5_0   )),
				            ('75%', lambda A__   : x.quantile(0.7_5   )),
				            ('95%', lambda A__   : x.quantile(0.9_5   )),
				            ('max', lambda A__   : x.max()),
				            ('count', lambda A__   : x.count()),
				            ('stdev', lambda A__   : x.std()),
				            ('mode', lambda A__   : x.mode()[0]),
				            ('median', lambda A__   : x.median()),
				            ('# > 0', lambda A__   : (x > 0).sum()),
				        ]   )
				    .T.reset_index()
				    .rename(index=A__      ,			columns={'level_0': 'Features', 'level_1': 'Type'}   )
				)  # rename columns
				# calculate the size of cluster(count of clientID's)
				snake_case__ :     Tuple 							=				report[
				    (report['Features'] == 'dummy') & (report['Type'] == 'count')
				].copy()  # avoid SettingWithCopyWarning
				snake_case__ :     str 							=				(
				    'ClusterSize'  # rename created cluster df to match report column names
				)
				snake_case__ :     Any 							=				'# of Customers'
				snake_case__ :     int 							=				pd.DataFrame(
				    clustersize.iloc[:, 2:].values
				    / clustersize.iloc[:, 2:].values.sum()   )
				snake_case__ :     Tuple 							=				'% of Customers'  # rename created cluster df to match report column names
				snake_case__ :     Optional[Any] 							=				'ClusterProportion'
				snake_case__ :     Any 							=				clusterproportion.columns.tolist()
				snake_case__ :     List[str] 							=				cols[-2:] + cols[:-2]
				snake_case__ :     Dict 							=				clusterproportion[cols]  # rearrange columns to match report
				snake_case__ :     int 							=				report.columns
				snake_case__ :     List[Any] 							=				pd.DataFrame(
				    abs(
				        report[report['Type'] == 'count'].iloc[:, 2:].values
				        - clustersize.iloc[:, 2:].values   )   )  # generating df with count of nan values
				snake_case__ :     List[Any] 							=				0
				snake_case__ :     str 							=				'# of nan'
				snake_case__ :     int 							=				report[
				    report['Type'] == 'count'
				].Features.tolist()  # filling values in order to match report
				snake_case__ :     Any 							=				a.columns.tolist()
				snake_case__ :     List[str] 							=				cols[-2:] + cols[:-2]
				snake_case__ :     List[str] 							=				a[cols]  # rearrange columns to match report
				snake_case__ :     Optional[int] 							=				report.columns  # rename columns to match report
				snake_case__ :     Tuple 							=				report.drop(
				    report[report.Type == 'count'].index   )  # drop count values except cluster size
				snake_case__ :     List[str] 							=				pd.concat(
				    [report, a, clustersize, clusterproportion]      ,			axis=0   )  # concat report with clustert size and nan values
				snake_case__ :     Any 							=				report['Features'].isin(A__   )
				snake_case__ :     int 							=				report.columns.tolist()
				snake_case__ :     Union[str, Any] 							=				cols[0:2] + cols[-1:] + cols[2:-1]
				snake_case__ :     str 							=				report[cols]
				snake_case__ :     Tuple 							=				{
				    '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,
				}
				snake_case__ :     int 							=				(
				    report.assign(
				        Sortera=lambda A__   : x.Type.map(A__   )      ,			Sortera=lambda A__   : list(reversed(range(len(A__   )   )   )   )      ,			)
				    .sort_values(['Sorter1', 'Mark', 'Sorter2']      ,			ascending=A__   )
				    .drop(['Sorter1', 'Sorter2']      ,			axis=1   )
				)
				snake_case__ :     List[str] 							=				''
				snake_case__ :     List[Any] 							=				report.reset_index()
				snake_case__ :     Optional[Any] 							=				report.drop(columns=['index']   )
				return report


if __name__ == "__main__":
				import doctest

				doctest.testmod()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCAmelCase__					:       Optional[int]		=       {}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Tuple		=       ['''MBart50Tokenizer''']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Tuple		=       ['''MBart50TokenizerFast''']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa import MBartaaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
				import sys

				lowerCAmelCase__					:       Optional[int]		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


