






"""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')

_UpperCamelCase							:       List[Any]       					=		'K-MEANS-CLUST/ '




def      snake_case    (A_ :Dict      ,     A_ :Optional[Any]      ,     A_ :List[Any]=None ):







  '''simple docstring'''

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

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

  return centroids




def      snake_case    (A_ :Union[str, Any]      ,     A_ :str ):







  '''simple docstring'''

  return pairwise_distances(A_      ,     A_      ,     metric='euclidean' )




def      snake_case    (A_ :Tuple      ,     A_ :int ):







  '''simple docstring'''

  a	:     Optional[Any]							=  centroid_pairwise_dist(A_      ,     A_ )

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

  return cluster_assignment




def      snake_case    (A_ :Optional[Any]      ,     A_ :List[Any]      ,     A_ :Optional[int] ):







  '''simple docstring'''

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

  return new_centroids




def      snake_case    (A_ :str      ,     A_ :Union[str, Any]      ,     A_ :Optional[Any]      ,     A_ :Dict ):







  '''simple docstring'''

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

  return heterogeneity




def      snake_case    (A_ :Any      ,     A_ :Any ):







  '''simple docstring'''

  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': 1_6} )
  plt.show()




def      snake_case    (A_ :Any      ,     A_ :Tuple      ,     A_ :List[Any]      ,     A_ :Any=5_0_0      ,     A_ :str=None      ,     A_ :int=False ):







  '''simple docstring'''

  a	:     Any							=  initial_centroids[:]
  a	:     Optional[Any]							=  None

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

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

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

    a	:     str							=  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

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




def      snake_case    (A_ :pd.DataFrame      ,     A_ :np.ndarray      ,     A_ :Any=None ):







  '''simple docstring'''

  if fill_missing_report:
    a	:     Tuple							=  df.fillna(value=A_ )
  a	:     str							=  1
  a	:     int							=  df.select_dtypes(np.number ).columns
  a	:     List[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_ ) * 2_5 / 1_0_0 ) : round(len(A_ ) * 7_5 / 1_0_0 )
                          ] ) ),
              ),
              ('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)
  a	:     Optional[Any]							=  report[
      (report['Features'] == 'dummy') & (report['Type'] == 'count')
  ].copy()  # avoid SettingWithCopyWarning
  a	:     Dict							=  (
      'ClusterSize'  # rename created cluster df to match report column names
  )
  a	:     Union[str, Any]							=  '# of Customers'
  a	:     List[str]							=  pd.DataFrame(
      clustersize.iloc[:, 2:].values
      / clustersize.iloc[:, 2:].values.sum() )
  a	:     Optional[Any]							=  '% of Customers'  # rename created cluster df to match report column names
  a	:     str							=  'ClusterProportion'
  a	:     List[str]							=  clusterproportion.columns.tolist()
  a	:     Dict							=  cols[-2:] + cols[:-2]
  a	:     Tuple							=  clusterproportion[cols]  # rearrange columns to match report
  a	:     List[str]							=  report.columns
  a	:     Union[str, Any]							=  pd.DataFrame(
      abs(
          report[report['Type'] == 'count'].iloc[:, 2:].values
          - clustersize.iloc[:, 2:].values ) )  # generating df with count of nan values
  a	:     Any							=  0
  a	:     List[Any]							=  '# of nan'
  a	:     str							=  report[
      report['Type'] == 'count'
  ].Features.tolist()  # filling values in order to match report
  a	:     str							=  a.columns.tolist()
  a	:     List[str]							=  cols[-2:] + cols[:-2]
  a	:     List[str]							=  a[cols]  # rearrange columns to match report
  a	:     Optional[Any]							=  report.columns  # rename columns to match report
  a	:     Union[str, Any]							=  report.drop(
      report[report.Type == 'count'].index )  # drop count values except cluster size
  a	:     Dict							=  pd.concat(
      [report, a, clustersize, clusterproportion]      ,     axis=0 )  # concat report with clustert size and nan values
  a	:     Dict							=  report['Features'].isin(A_ )
  a	:     List[Any]							=  report.columns.tolist()
  a	:     List[Any]							=  cols[0:2] + cols[-1:] + cols[2:-1]
  a	:     Optional[int]							=  report[cols]
  a	:     Any							=  {
      '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,
  }
  a	:     Union[str, Any]							=  (
      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 )
  )
  a	:     Union[str, Any]							=  ''
  a	:     int							=  report.reset_index()
  a	:     List[str]							=  report.drop(columns=['index'] )
  return report


if __name__ == "__main__":
   import doctest

   doctest.testmod()












"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

_UpperCamelCase							:       int       					=		{
    'apple/mobilevitv2-1.0': 'https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json',
}






class 		snake_case    (    UpperCAmelCase			):
    __magic_name__       	=	'''mobilevitv2'''





    def __init__(				self      :   Any							,						A      :   Any=3							,						A      :   Any=2_5_6							,						A      :   Optional[int]=2							,						A      :   str=2.0							,						A      :   Union[str, Any]="swish"							,						A      :   str=3							,						A      :   Tuple=3_2							,						A      :   Optional[int]=0.1							,						A      :   str=0.02							,						A      :   int=1E-5							,						A      :   List[Any]=5_1_2							,						A      :   str=[6, 1_2, 1_8]							,						A      :   Any=0.1							,						A      :   Union[str, Any]=2_5_5							,						A      :   Optional[Any]=[2, 4, 3]							,						A      :   List[Any]=[1_2_8, 1_9_2, 2_5_6]							,						A      :   Tuple=1.0							,						A      :   Union[str, Any]=2							,						A      :   List[Any]=0.0							,						A      :   int=0.0							,						**A      :   Optional[Any]							,						):


      '''simple docstring'''




      super().__init__(**A     )

      a	:     Optional[Any]							=  num_channels
      a	:     List[Any]							=  image_size
      a	:     Tuple							=  patch_size
      a	:     Optional[Any]							=  expand_ratio
      a	:     Any							=  hidden_act
      a	:     Dict							=  conv_kernel_size
      a	:     Optional[int]							=  output_stride
      a	:     Optional[Any]							=  initializer_range
      a	:     Tuple							=  layer_norm_eps
      a	:     Optional[int]							=  n_attn_blocks
      a	:     Tuple							=  base_attn_unit_dims
      a	:     List[str]							=  width_multiplier
      a	:     Dict							=  ffn_multiplier
      a	:     Dict							=  ffn_dropout
      a	:     Optional[int]							=  attn_dropout
      a	:     Tuple							=  classifier_dropout_prob

      # decode head attributes for semantic segmentation
      a	:     str							=  aspp_out_channels
      a	:     List[Any]							=  atrous_rates
      a	:     Optional[int]							=  aspp_dropout_prob
      a	:     Optional[Any]							=  semantic_loss_ignore_index






class 		snake_case    (    UpperCAmelCase			):
    __magic_name__       	=	version.parse('''1.11'''			)





    @property
    def 							lowerCamelCase__ (				self      :   List[str]     ):


      '''simple docstring'''




      return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]     )





    @property
    def 							lowerCamelCase__ (				self      :   int     ):


      '''simple docstring'''




      if self.task == "image-classification":
        return OrderedDict([('logits', {0: 'batch'})]     )
      else:
        return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]     )





    @property
    def 							lowerCamelCase__ (				self      :   Union[str, Any]     ):


      '''simple docstring'''




      return 1E-4




