'''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''')

__snake_case       :   Any  		=    '''K-MEANS-CLUST/ '''



def lowerCamelCase__						(       A_       ,			A_       ,			A_=None       ):
 if seed is not None:  # useful for obtaining consistent results
  np.random.seed(A_       )
 UpperCAmelCase_				       =					data.shape[0]  # number of data points

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

 return centroids



def lowerCamelCase__						(       A_       ,			A_       ):
 return pairwise_distances(A_       ,			A_       ,			metric="euclidean"       )



def lowerCamelCase__						(       A_       ,			A_       ):
 # Compute distances between each data point and the set of centroids:
 # Fill in the blank (RHS only)
 UpperCAmelCase_				       =					centroid_pairwise_dist(A_       ,			A_       )

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

 return cluster_assignment



def lowerCamelCase__						(       A_       ,			A_       ,			A_       ):
 UpperCAmelCase_				       =					[]
 for i in range(A_       ):
  # Select all data points that belong to cluster i. Fill in the blank (RHS only)
  UpperCAmelCase_				       =					data[cluster_assignment == i]
  # Compute the mean of the data points. Fill in the blank (RHS only)
  UpperCAmelCase_				       =					member_data_points.mean(axis=0       )
  new_centroids.append(A_       )
 UpperCAmelCase_				       =					np.array(A_       )

 return new_centroids



def lowerCamelCase__						(       A_       ,			A_       ,			A_       ,			A_       ):
 UpperCAmelCase_				       =					0.0
 for i in range(A_       ):
  # Select all data points that belong to cluster i. Fill in the blank (RHS only)
  UpperCAmelCase_				       =					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)
   UpperCAmelCase_				       =					pairwise_distances(
       A_       ,			[centroids[i]]       ,			metric="euclidean"       )
   UpperCAmelCase_				       =					distances**2
   heterogeneity += np.sum(A_       )

 return heterogeneity



def lowerCamelCase__						(       A_       ,			A_       ):
 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 lowerCamelCase__						(       A_       ,			A_       ,			A_       ,			A_=500       ,			A_=None       ,			A_=False       ):
 UpperCAmelCase_				       =					initial_centroids[:]
 UpperCAmelCase_				       =					None

 for itr in range(A_       ):
  if verbose:
   print(A_       ,			end=""       )

  # 1. Make cluster assignments using nearest centroids
  UpperCAmelCase_				       =					assign_clusters(A_       ,			A_       )

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

  UpperCAmelCase_				       =					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       :   Optional[int]  		=    ds.load_iris()
      __snake_case       :   Optional[int]  		=    3
      __snake_case       :   Union[str, Any]  		=    []
      __snake_case       :   int  		=    get_initial_centroids(dataset['''data'''], k, seed=0)
      __snake_case		,					__snake_case       :   Dict  		=    kmeans(
          dataset['''data'''],
          k,
          initial_centroids,
          maxiter=4_00,
          record_heterogeneity=heterogeneity,
          verbose=True,
      )
      plot_heterogeneity(heterogeneity, k)



def lowerCamelCase__						(       A_       ,			A_       ,			A_=None       ):
 # Fill missing values with given rules
 if fill_missing_report:
  UpperCAmelCase_				       =					df.fillna(value=A_       )
 UpperCAmelCase_				       =					1
 UpperCAmelCase_				       =					df.select_dtypes(np.number       ).columns
 UpperCAmelCase_				       =					(
     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(0.05       )),
             ("25%", lambda A_       : x.quantile(0.25       )),
             ("50%", lambda A_       : x.quantile(0.50       )),
             ("75%", lambda A_       : x.quantile(0.75       )),
             ("95%", lambda A_       : x.quantile(0.95       )),
             ("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)
 UpperCAmelCase_				       =					report[
     (report["Features"] == "dummy") & (report["Type"] == "count")
 ].copy()  # avoid SettingWithCopyWarning
 UpperCAmelCase_				       =					(
     "ClusterSize"  # rename created cluster df to match report column names
 )
 UpperCAmelCase_				       =					"# of Customers"
 UpperCAmelCase_				       =					pd.DataFrame(
     clustersize.iloc[:, 2:].values
     / clustersize.iloc[:, 2:].values.sum()       )
 UpperCAmelCase_				       =					"% of Customers"  # rename created cluster df to match report column names
 UpperCAmelCase_				       =					"ClusterProportion"
 UpperCAmelCase_				       =					clusterproportion.columns.tolist()
 UpperCAmelCase_				       =					cols[-2:] + cols[:-2]
 UpperCAmelCase_				       =					clusterproportion[cols]  # rearrange columns to match report
 UpperCAmelCase_				       =					report.columns
 UpperCAmelCase_				       =					pd.DataFrame(
     abs(
         report[report["Type"] == "count"].iloc[:, 2:].values
         - clustersize.iloc[:, 2:].values       )       )  # generating df with count of nan values
 UpperCAmelCase_				       =					0
 UpperCAmelCase_				       =					"# of nan"
 UpperCAmelCase_				       =					report[
     report["Type"] == "count"
 ].Features.tolist()  # filling values in order to match report
 UpperCAmelCase_				       =					a.columns.tolist()
 UpperCAmelCase_				       =					cols[-2:] + cols[:-2]
 UpperCAmelCase_				       =					a[cols]  # rearrange columns to match report
 UpperCAmelCase_				       =					report.columns  # rename columns to match report
 UpperCAmelCase_				       =					report.drop(
     report[report.Type == "count"].index       )  # drop count values except cluster size
 UpperCAmelCase_				       =					pd.concat(
     [report, a, clustersize, clusterproportion]       ,			axis=0       )  # concat report with clustert size and nan values
 UpperCAmelCase_				       =					report["Features"].isin(A_       )
 UpperCAmelCase_				       =					report.columns.tolist()
 UpperCAmelCase_				       =					cols[0:2] + cols[-1:] + cols[2:-1]
 UpperCAmelCase_				       =					report[cols]
 UpperCAmelCase_				       =					{
     "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,
 }
 UpperCAmelCase_				       =					(
     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       )
 )
 UpperCAmelCase_				       =					""
 UpperCAmelCase_				       =					report.reset_index()
 UpperCAmelCase_				       =					report.drop(columns=["index"]       )
 return report


if __name__ == "__main__":
      import doctest

      doctest.testmod()

'''simple docstring'''

from typing import TYPE_CHECKING

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


__snake_case       :   int  		=    {
    '''configuration_videomae''': ['''VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VideoMAEConfig'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __snake_case       :   Optional[Any]  		=    [
          '''VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''VideoMAEForPreTraining''',
          '''VideoMAEModel''',
          '''VideoMAEPreTrainedModel''',
          '''VideoMAEForVideoClassification''',
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __snake_case       :   str  		=    ['''VideoMAEFeatureExtractor''']
      __snake_case       :   Any  		=    ['''VideoMAEImageProcessor''']

if TYPE_CHECKING:
      from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_videomae import (
                VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
                VideoMAEForPreTraining,
                VideoMAEForVideoClassification,
                VideoMAEModel,
                VideoMAEPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_videomae import VideoMAEFeatureExtractor
            from .image_processing_videomae import VideoMAEImageProcessor

else:
      import sys

      __snake_case       :   List[str]  		=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
