'''simple docstring'''
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 							__UpperCamelCase   (				__UpperCAmelCase     :				Optional[Any]   ,      __UpperCAmelCase     :				Optional[Any]   ,      __UpperCAmelCase     :				Union[str, Any]=None)       ->  Any:
  if seed is not None:  # useful for obtaining consistent results
    np.random.seed(lowercase__)
  lowercase							:       Dict											=      data.shape[0]  # number of data points

  # Pick K indices from range [0, N).
  lowercase							:       Any											=      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.
  lowercase							:       str											=      data[rand_indices, :]

  return centroids







def 							__UpperCamelCase   (				__UpperCAmelCase     :				Dict   ,      __UpperCAmelCase     :				List[Any])       ->  int:
  return pairwise_distances(lowercase__   ,      lowercase__   ,      metric='''euclidean''')







def 							__UpperCamelCase   (				__UpperCAmelCase     :				Dict   ,      __UpperCAmelCase     :				Optional[Any])       ->  Any:
  # Compute distances between each data point and the set of centroids:
  # Fill in the blank (RHS only)
  lowercase							:       List[Any]											=      centroid_pairwise_dist(lowercase__   ,      lowercase__)

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

  return cluster_assignment







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

  return new_centroids







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

  return heterogeneity







def 							__UpperCamelCase   (				__UpperCAmelCase     :				Optional[int]   ,      __UpperCAmelCase     :				Optional[int])       ->  Union[str, 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''': 16})
  plt.show()







def 							__UpperCamelCase   (				__UpperCAmelCase     :				Dict   ,      __UpperCAmelCase     :				Optional[Any]   ,      __UpperCAmelCase     :				List[Any]   ,      __UpperCAmelCase     :				int=500   ,      __UpperCAmelCase     :				List[str]=None   ,      __UpperCAmelCase     :				Any=False)       ->  int:
  lowercase							:       Any											=      initial_centroids[:]
  lowercase							:       Any											=      None

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

    # 1. Make cluster assignments using nearest centroids
    lowercase							:       Optional[int]											=      assign_clusters(lowercase__   ,      lowercase__)

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

    lowercase							:       Union[str, 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		:			Optional[Any]	      =3
 _A		:			Tuple	      =[]
 _A		:			Any	      =get_initial_centroids(dataset['''data'''], k, seed=0)
 _A		:			Any	      =kmeans(
     dataset['''data'''],
     k,
     initial_centroids,
     maxiter=4_0_0,
     record_heterogeneity=heterogeneity,
     verbose=True,
 )
 plot_heterogeneity(heterogeneity, k)







def 							__UpperCamelCase   (				__UpperCAmelCase     :				pd.DataFrame   ,      __UpperCAmelCase     :				np.ndarray   ,      __UpperCAmelCase     :				Optional[Any]=None)       ->  Any:
  # Fill missing values with given rules
  if fill_missing_report:
    lowercase							:       Optional[int]											=      df.fillna(value=lowercase__)
  lowercase							:       int											=      1
  lowercase							:       Dict											=      df.select_dtypes(np.number).columns
  lowercase							:       Union[str, 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(lowercase__))),
              ('''mean_without_zeros''', lambda __UpperCAmelCase: x.replace(0   ,      np.NaN).mean()),
              (
                  '''mean_25-75''',
                  lambda __UpperCAmelCase: np.mean(
                      np.nan_to_num(
                          sorted(lowercase__)[
                              round(len(lowercase__) * 25 / 100) : round(len(lowercase__) * 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=lowercase__   ,      columns={'''level_0''': '''Features''', '''level_1''': '''Type'''})
  )  # rename columns
  # calculate the size of cluster(count of clientID's)
  lowercase							:       List[Any]											=      report[
      (report['''Features'''] == '''dummy''') & (report['''Type'''] == '''count''')
  ].copy()  # avoid SettingWithCopyWarning
  lowercase							:       Optional[int]											=      (
      '''ClusterSize'''  # rename created cluster df to match report column names
  )
  lowercase							:       Optional[Any]											=      '''# of Customers'''
  lowercase							:       List[str]											=      pd.DataFrame(
      clustersize.iloc[:, 2:].values
      / clustersize.iloc[:, 2:].values.sum())
  lowercase							:       List[str]											=      '''% of Customers'''  # rename created cluster df to match report column names
  lowercase							:       Dict											=      '''ClusterProportion'''
  lowercase							:       List[Any]											=      clusterproportion.columns.tolist()
  lowercase							:       Dict											=      cols[-2:] + cols[:-2]
  lowercase							:       Dict											=      clusterproportion[cols]  # rearrange columns to match report
  lowercase							:       List[str]											=      report.columns
  lowercase							:       List[Any]											=      pd.DataFrame(
      abs(
          report[report['''Type'''] == '''count'''].iloc[:, 2:].values
          - clustersize.iloc[:, 2:].values))  # generating df with count of nan values
  lowercase							:       str											=      0
  lowercase							:       Dict											=      '''# of nan'''
  lowercase							:       List[str]											=      report[
      report['''Type'''] == '''count'''
  ].Features.tolist()  # filling values in order to match report
  lowercase							:       str											=      a.columns.tolist()
  lowercase							:       Union[str, Any]											=      cols[-2:] + cols[:-2]
  lowercase							:       int											=      a[cols]  # rearrange columns to match report
  lowercase							:       Tuple											=      report.columns  # rename columns to match report
  lowercase							:       Optional[int]											=      report.drop(
      report[report.Type == '''count'''].index)  # drop count values except cluster size
  lowercase							:       Optional[int]											=      pd.concat(
      [report, a, clustersize, clusterproportion]   ,      axis=0)  # concat report with clustert size and nan values
  lowercase							:       Optional[Any]											=      report['''Features'''].isin(lowercase__)
  lowercase							:       Union[str, Any]											=      report.columns.tolist()
  lowercase							:       Optional[Any]											=      cols[0:2] + cols[-1:] + cols[2:-1]
  lowercase							:       str											=      report[cols]
  lowercase							:       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,
  }
  lowercase							:       Dict											=      (
      report.assign(
          Sortera=lambda __UpperCAmelCase: x.Type.map(lowercase__)   ,      Sortera=lambda __UpperCAmelCase: list(reversed(range(len(lowercase__))))   ,      )
      .sort_values(['''Sorter1''', '''Mark''', '''Sorter2''']   ,      ascending=lowercase__)
      .drop(['''Sorter1''', '''Sorter2''']   ,      axis=1)
  )
  lowercase							:       Optional[int]											=      ''''''
  lowercase							:       int											=      report.reset_index()
  lowercase							:       Optional[Any]											=      report.drop(columns=['''index'''])
  return report


if __name__ == "__main__":
 import doctest

 doctest.testmod()
'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


_A		:			Optional[int]	      =logging.get_logger(__name__)




class _lowerCAmelCase (  snake_case				):







      def __init__(     self     ,						*lowercase     ,						**lowercase    ):
        warnings.warn(
            '''The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use LayoutLMv3ImageProcessor instead.'''     ,						lowercase     ,						)
        super().__init__(*lowercase     ,						**lowercase    )