


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')

snake_case__     							= 'K-MEANS-CLUST/ '
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple      ,						lowercase__  :List[Any]      ,						lowercase__  :Tuple=None				)       ->  Any:
						if seed is not None:  # useful for obtaining consistent results
												np.random.seed(lowercase__				)
						__magic_name__				:							Tuple				=      data.shape[0]  # number of data points

						# Pick K indices from range [0, N).
						__magic_name__				:							Dict				=      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.
						__magic_name__				:							int				=      data[rand_indices, :]

						return centroids
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Dict      ,						lowercase__  :Union[str, Any]				)       ->  List[str]:
						return pairwise_distances(lowercase__      ,						lowercase__      ,						metric='euclidean'				)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[Any]      ,						lowercase__  :Optional[Any]				)       ->  Any:
						# Compute distances between each data point and the set of centroids:
						# Fill in the blank (RHS only)
						__magic_name__				:							List[str]				=      centroid_pairwise_dist(lowercase__      ,						lowercase__				)

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

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

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

						return heterogeneity
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Union[str, Any]      ,						lowercase__  :Union[str, Any]				)       ->  Optional[Any]:
						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': 1_6}				)
						plt.show()
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple      ,						lowercase__  :Any      ,						lowercase__  :Tuple      ,						lowercase__  :Optional[int]=5_0_0      ,						lowercase__  :Optional[Any]=None      ,						lowercase__  :Any=False				)       ->  Optional[Any]:
						__magic_name__				:							List[Any]				=      initial_centroids[:]
						__magic_name__				:							Dict				=      None

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

												# 1. Make cluster assignments using nearest centroids
												__magic_name__				:							Dict				=      assign_clusters(lowercase__      ,						lowercase__				)

												# 2. Compute a new centroid for each of the k clusters, averaging all data
												#    points assigned to that cluster.
												__magic_name__				:							int				=      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:
																		__magic_name__				:							Union[str, 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
																		__magic_name__				:							Any				=      compute_heterogeneity(lowercase__      ,						lowercase__      ,						lowercase__      ,						lowercase__				)
																		record_heterogeneity.append(lowercase__				)

												__magic_name__				:							Tuple				=      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

			snake_case__     							= ds.load_iris()
			snake_case__     							= 3
			snake_case__     							= []
			snake_case__     							= get_initial_centroids(dataset['data'], k, seed=0)
			snake_case__							,     snake_case__     							= kmeans(
			    dataset['data'],
			    k,
			    initial_centroids,
			    maxiter=400,
			    record_heterogeneity=heterogeneity,
			    verbose=True,
			)
			plot_heterogeneity(heterogeneity, k)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :pd.DataFrame      ,						lowercase__  :np.ndarray      ,						lowercase__  :Optional[int]=None				)       ->  pd.DataFrame:
						# Fill missing values with given rules
						if fill_missing_report:
												__magic_name__				:							Optional[int]				=      df.fillna(value=lowercase__				)
						__magic_name__				:							Union[str, Any]				=      1
						__magic_name__				:							Any				=      df.select_dtypes(np.number				).columns
						__magic_name__				:							Optional[int]				=      (
						    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__				) * 2_5 / 1_0_0				) : round(len(lowercase__				) * 7_5 / 1_0_0				)
						                        ]				)				),
						            ),
						            ('mean_with_na', np.mean),
						            ('min', lambda lowercase__				: x.min()),
						            ('5%', lambda lowercase__				: x.quantile(0.0_5				)),
						            ('25%', lambda lowercase__				: x.quantile(0.2_5				)),
						            ('50%', lambda lowercase__				: x.quantile(0.5_0				)),
						            ('75%', lambda lowercase__				: x.quantile(0.7_5				)),
						            ('95%', lambda lowercase__				: x.quantile(0.9_5				)),
						            ('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)
						__magic_name__				:							List[Any]				=      report[
						    (report['Features'] == 'dummy') & (report['Type'] == 'count')
						].copy()  # avoid SettingWithCopyWarning
						__magic_name__				:							List[str]				=      (
						    'ClusterSize'  # rename created cluster df to match report column names
						)
						__magic_name__				:							Optional[Any]				=      '# of Customers'
						__magic_name__				:							int				=      pd.DataFrame(
						    clustersize.iloc[:, 2:].values
						    / clustersize.iloc[:, 2:].values.sum()				)
						__magic_name__				:							Tuple				=      '% of Customers'  # rename created cluster df to match report column names
						__magic_name__				:							Any				=      'ClusterProportion'
						__magic_name__				:							List[str]				=      clusterproportion.columns.tolist()
						__magic_name__				:							List[Any]				=      cols[-2:] + cols[:-2]
						__magic_name__				:							str				=      clusterproportion[cols]  # rearrange columns to match report
						__magic_name__				:							Optional[int]				=      report.columns
						__magic_name__				:							Tuple				=      pd.DataFrame(
						    abs(
						        report[report['Type'] == 'count'].iloc[:, 2:].values
						        - clustersize.iloc[:, 2:].values				)				)  # generating df with count of nan values
						__magic_name__				:							List[str]				=      0
						__magic_name__				:							Tuple				=      '# of nan'
						__magic_name__				:							Tuple				=      report[
						    report['Type'] == 'count'
						].Features.tolist()  # filling values in order to match report
						__magic_name__				:							int				=      a.columns.tolist()
						__magic_name__				:							str				=      cols[-2:] + cols[:-2]
						__magic_name__				:							str				=      a[cols]  # rearrange columns to match report
						__magic_name__				:							Optional[int]				=      report.columns  # rename columns to match report
						__magic_name__				:							List[str]				=      report.drop(
						    report[report.Type == 'count'].index				)  # drop count values except cluster size
						__magic_name__				:							List[Any]				=      pd.concat(
						    [report, a, clustersize, clusterproportion]      ,						axis=0				)  # concat report with clustert size and nan values
						__magic_name__				:							int				=      report['Features'].isin(lowercase__				)
						__magic_name__				:							Union[str, Any]				=      report.columns.tolist()
						__magic_name__				:							List[str]				=      cols[0:2] + cols[-1:] + cols[2:-1]
						__magic_name__				:							Optional[int]				=      report[cols]
						__magic_name__				:							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,
						}
						__magic_name__				:							List[str]				=      (
						    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				)
						)
						__magic_name__				:							List[Any]				=      ''
						__magic_name__				:							Union[str, Any]				=      report.reset_index()
						__magic_name__				:							List[str]				=      report.drop(columns=['index']				)
						return report


if __name__ == "__main__":
			import doctest

			doctest.testmod()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case__     							= {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'FlaubertForMultipleChoice',
			    'FlaubertForQuestionAnswering',
			    'FlaubertForQuestionAnsweringSimple',
			    'FlaubertForSequenceClassification',
			    'FlaubertForTokenClassification',
			    'FlaubertModel',
			    'FlaubertWithLMHeadModel',
			    'FlaubertPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFFlaubertForMultipleChoice',
			    'TFFlaubertForQuestionAnsweringSimple',
			    'TFFlaubertForSequenceClassification',
			    'TFFlaubertForTokenClassification',
			    'TFFlaubertModel',
			    'TFFlaubertPreTrainedModel',
			    'TFFlaubertWithLMHeadModel',
			]


if TYPE_CHECKING:
			from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
			from .tokenization_flaubert import FlaubertTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flaubert import (
						    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaubertForMultipleChoice,
						    FlaubertForQuestionAnswering,
						    FlaubertForQuestionAnsweringSimple,
						    FlaubertForSequenceClassification,
						    FlaubertForTokenClassification,
						    FlaubertModel,
						    FlaubertPreTrainedModel,
						    FlaubertWithLMHeadModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_flaubert import (
						    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFlaubertForMultipleChoice,
						    TFFlaubertForQuestionAnsweringSimple,
						    TFFlaubertForSequenceClassification,
						    TFFlaubertForTokenClassification,
						    TFFlaubertModel,
						    TFFlaubertPreTrainedModel,
						    TFFlaubertWithLMHeadModel,
						)

else:
			import sys

			snake_case__     							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


