




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__				:     List[str]			       =							'K-MEANS-CLUST/ '

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Optional[int]    ,    __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			Optional[Any]=None							)      ->				List[str]:
      if seed is not None:  # useful for obtaining consistent results
            np.random.seed(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    data.shape[0]  # number of data points

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

      return centroids

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Optional[int]    ,    __UpperCAmelCase		:			Any							)      ->				List[str]:
      return pairwise_distances(__UpperCAmelCase    ,    __UpperCAmelCase    ,    metric='euclidean'							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Tuple    ,    __UpperCAmelCase		:			str							)      ->				Optional[int]:
      # Compute distances between each data point and the set of centroids:
      # Fill in the blank (RHS only)
      SCREAMING_SNAKE_CASE_					=    centroid_pairwise_dist(__UpperCAmelCase    ,    __UpperCAmelCase							)

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

      return cluster_assignment

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

      return new_centroids

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

      return heterogeneity

def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[Any]    ,    __UpperCAmelCase		:			List[str]							)      ->				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       UpperCAmelCase_							(   __UpperCAmelCase		:			Tuple    ,    __UpperCAmelCase		:			Optional[Any]    ,    __UpperCAmelCase		:			List[Any]    ,    __UpperCAmelCase		:			Optional[int]=5_00    ,    __UpperCAmelCase		:			Dict=None    ,    __UpperCAmelCase		:			Optional[Any]=False							)      ->				List[Any]:
      SCREAMING_SNAKE_CASE_					=    initial_centroids[:]
      SCREAMING_SNAKE_CASE_					=    None

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

            # 1. Make cluster assignments using nearest centroids
            SCREAMING_SNAKE_CASE_					=    assign_clusters(__UpperCAmelCase    ,    __UpperCAmelCase							)

            # 2. Compute a new centroid for each of the k clusters, averaging all data
            #    points assigned to that cluster.
            SCREAMING_SNAKE_CASE_					=    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:
                  SCREAMING_SNAKE_CASE_					=    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
                  SCREAMING_SNAKE_CASE_					=    compute_heterogeneity(__UpperCAmelCase    ,    __UpperCAmelCase    ,    __UpperCAmelCase    ,    __UpperCAmelCase							)
                  record_heterogeneity.append(__UpperCAmelCase							)

            SCREAMING_SNAKE_CASE_					=    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__				:     Optional[Any]			       =							ds.load_iris()
   lowerCamelCase__				:     Optional[Any]			       =							3
   lowerCamelCase__				:     List[Any]			       =							[]
   lowerCamelCase__				:     Optional[Any]			       =							get_initial_centroids(dataset['data'], k, seed=0)
   lowerCamelCase__       ,							lowerCamelCase__				:     List[str]			       =							kmeans(
       dataset['data'],
       k,
       initial_centroids,
       maxiter=400,
       record_heterogeneity=heterogeneity,
       verbose=True,
   )
   plot_heterogeneity(heterogeneity, k)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			pd.DataFrame    ,    __UpperCAmelCase		:			np.ndarray    ,    __UpperCAmelCase		:			Optional[int]=None							)      ->				pd.DataFrame:
      # Fill missing values with given rules
      if fill_missing_report:
            SCREAMING_SNAKE_CASE_					=    df.fillna(value=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    1
      SCREAMING_SNAKE_CASE_					=    df.select_dtypes(np.number							).columns
      SCREAMING_SNAKE_CASE_					=    (
          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 / 1_00							) : round(len(__UpperCAmelCase							) * 75 / 1_00							)
                              ]							)							),
                  ),
                  ('mean_with_na', np.mean),
                  ('min', lambda __UpperCAmelCase							: x.min()),
                  ('5%', lambda __UpperCAmelCase							: x.quantile(0.0_5							)),
                  ('25%', lambda __UpperCAmelCase							: x.quantile(0.2_5							)),
                  ('50%', lambda __UpperCAmelCase							: x.quantile(0.5_0							)),
                  ('75%', lambda __UpperCAmelCase							: x.quantile(0.7_5							)),
                  ('95%', lambda __UpperCAmelCase							: x.quantile(0.9_5							)),
                  ('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)
      SCREAMING_SNAKE_CASE_					=    report[
          (report['Features'] == 'dummy') & (report['Type'] == 'count')
      ].copy()  # avoid SettingWithCopyWarning
      SCREAMING_SNAKE_CASE_					=    (
          'ClusterSize'  # rename created cluster df to match report column names
      )
      SCREAMING_SNAKE_CASE_					=    '# of Customers'
      SCREAMING_SNAKE_CASE_					=    pd.DataFrame(
          clustersize.iloc[:, 2:].values
          / clustersize.iloc[:, 2:].values.sum()							)
      SCREAMING_SNAKE_CASE_					=    '% of Customers'  # rename created cluster df to match report column names
      SCREAMING_SNAKE_CASE_					=    'ClusterProportion'
      SCREAMING_SNAKE_CASE_					=    clusterproportion.columns.tolist()
      SCREAMING_SNAKE_CASE_					=    cols[-2:] + cols[:-2]
      SCREAMING_SNAKE_CASE_					=    clusterproportion[cols]  # rearrange columns to match report
      SCREAMING_SNAKE_CASE_					=    report.columns
      SCREAMING_SNAKE_CASE_					=    pd.DataFrame(
          abs(
              report[report['Type'] == 'count'].iloc[:, 2:].values
              - clustersize.iloc[:, 2:].values							)							)  # generating df with count of nan values
      SCREAMING_SNAKE_CASE_					=    0
      SCREAMING_SNAKE_CASE_					=    '# of nan'
      SCREAMING_SNAKE_CASE_					=    report[
          report['Type'] == 'count'
      ].Features.tolist()  # filling values in order to match report
      SCREAMING_SNAKE_CASE_					=    a.columns.tolist()
      SCREAMING_SNAKE_CASE_					=    cols[-2:] + cols[:-2]
      SCREAMING_SNAKE_CASE_					=    a[cols]  # rearrange columns to match report
      SCREAMING_SNAKE_CASE_					=    report.columns  # rename columns to match report
      SCREAMING_SNAKE_CASE_					=    report.drop(
          report[report.Type == 'count'].index							)  # drop count values except cluster size
      SCREAMING_SNAKE_CASE_					=    pd.concat(
          [report, a, clustersize, clusterproportion]    ,    axis=0							)  # concat report with clustert size and nan values
      SCREAMING_SNAKE_CASE_					=    report['Features'].isin(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    report.columns.tolist()
      SCREAMING_SNAKE_CASE_					=    cols[0:2] + cols[-1:] + cols[2:-1]
      SCREAMING_SNAKE_CASE_					=    report[cols]
      SCREAMING_SNAKE_CASE_					=    {
          '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,
      }
      SCREAMING_SNAKE_CASE_					=    (
          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							)
      )
      SCREAMING_SNAKE_CASE_					=    ''
      SCREAMING_SNAKE_CASE_					=    report.reset_index()
      SCREAMING_SNAKE_CASE_					=    report.drop(columns=['index']							)
      return report


if __name__ == "__main__":
   import doctest

   doctest.testmod()





import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


lowerCamelCase__				:     Optional[Any]			       =							logging.get_logger(__name__)





class  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE      ):


 '''simple docstring'''







 def __init__(							self						:	Dict       ,       *_lowerCAmelCase						:	str       ,       **_lowerCAmelCase						:	List[str]		):
       warnings.warn(
           'The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use DetrImageProcessor instead.'       ,       _lowerCAmelCase       ,       )
       super().__init__(*_lowerCAmelCase       ,       **_lowerCAmelCase		)