





"""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''')

lowerCAmelCase_      =				'''K-MEANS-CLUST/ '''

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=None				)		->							str:






       """simple docstring"""




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

       # Pick K indices from range [0, N).
       _UpperCAmelCase			     =  np.random.randint(0,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)

       # 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     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							Optional[Any]:






       """simple docstring"""




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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							str:






       """simple docstring"""




       _UpperCAmelCase			     =  centroid_pairwise_dist(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)

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

       return cluster_assignment

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							Optional[int]:






       """simple docstring"""




       _UpperCAmelCase			     =  []
       for i in range(SCREAMING_SNAKE_CASE				):
              # 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(SCREAMING_SNAKE_CASE				)
       _UpperCAmelCase			     =  np.array(SCREAMING_SNAKE_CASE				)

       return new_centroids

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							Tuple:






       """simple docstring"""




       _UpperCAmelCase			     =  0.0
       for i in range(SCREAMING_SNAKE_CASE				):
              # 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(
                         SCREAMING_SNAKE_CASE,[centroids[i]],metric='euclidean'				)
                     _UpperCAmelCase			     =  distances**2
                     heterogeneity += np.sum(SCREAMING_SNAKE_CASE				)

       return heterogeneity

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							List[str]:






       """simple docstring"""




       plt.figure(figsize=(7, 4)				)
       plt.plot(SCREAMING_SNAKE_CASE,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     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=500,SCREAMING_SNAKE_CASE=None,SCREAMING_SNAKE_CASE=False				)		->							Optional[int]:






       """simple docstring"""




       _UpperCAmelCase			     =  initial_centroids[:]
       _UpperCAmelCase			     =  None

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

              # 1. Make cluster assignments using nearest centroids
              _UpperCAmelCase			     =  assign_clusters(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)

              # 2. Compute a new centroid for each of the k clusters, averaging all data
              #    points assigned to that cluster.
              _UpperCAmelCase			     =  revise_centroids(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)

              # 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(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)
                     record_heterogeneity.append(SCREAMING_SNAKE_CASE				)

              _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

    lowerCAmelCase_      =				ds.load_iris()
    lowerCAmelCase_      =				3
    lowerCAmelCase_      =				[]
    lowerCAmelCase_      =				get_initial_centroids(dataset['''data'''], k, seed=0)
    lowerCAmelCase_      ,					lowerCAmelCase_      =				kmeans(
        dataset['''data'''],
        k,
        initial_centroids,
        maxiter=400,
        record_heterogeneity=heterogeneity,
        verbose=True,
    )
    plot_heterogeneity(heterogeneity, k)

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=None				)		->							pd.DataFrame:






       """simple docstring"""




       if fill_missing_report:
              _UpperCAmelCase			     =  df.fillna(value=SCREAMING_SNAKE_CASE				)
       _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 SCREAMING_SNAKE_CASE				: np.mean(np.nan_to_num(SCREAMING_SNAKE_CASE				)				)),
                   ('mean_without_zeros', lambda SCREAMING_SNAKE_CASE				: x.replace(0,np.NaN				).mean()),
                   (
                       'mean_25-75',
                       lambda SCREAMING_SNAKE_CASE				: np.mean(
                           np.nan_to_num(
                               sorted(SCREAMING_SNAKE_CASE				)[
                                   round(len(SCREAMING_SNAKE_CASE				) * 25 / 100				) : round(len(SCREAMING_SNAKE_CASE				) * 75 / 100				)
                               ]				)				),
                   ),
                   ('mean_with_na', np.mean),
                   ('min', lambda SCREAMING_SNAKE_CASE				: x.min()),
                   ('5%', lambda SCREAMING_SNAKE_CASE				: x.quantile(0.05				)),
                   ('25%', lambda SCREAMING_SNAKE_CASE				: x.quantile(0.25				)),
                   ('50%', lambda SCREAMING_SNAKE_CASE				: x.quantile(0.50				)),
                   ('75%', lambda SCREAMING_SNAKE_CASE				: x.quantile(0.75				)),
                   ('95%', lambda SCREAMING_SNAKE_CASE				: x.quantile(0.95				)),
                   ('max', lambda SCREAMING_SNAKE_CASE				: x.max()),
                   ('count', lambda SCREAMING_SNAKE_CASE				: x.count()),
                   ('stdev', lambda SCREAMING_SNAKE_CASE				: x.std()),
                   ('mode', lambda SCREAMING_SNAKE_CASE				: x.mode()[0]),
                   ('median', lambda SCREAMING_SNAKE_CASE				: x.median()),
                   ('# > 0', lambda SCREAMING_SNAKE_CASE				: (x > 0).sum()),
               ]				)
           .T.reset_index()
           .rename(index=SCREAMING_SNAKE_CASE,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(SCREAMING_SNAKE_CASE				)
       _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 SCREAMING_SNAKE_CASE				: x.Type.map(SCREAMING_SNAKE_CASE				),Sortera=lambda SCREAMING_SNAKE_CASE				: list(reversed(range(len(SCREAMING_SNAKE_CASE				)				)				)				),)
           .sort_values(['Sorter1', 'Mark', 'Sorter2'],ascending=SCREAMING_SNAKE_CASE				)
           .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"""






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	(LMSDiscreteScheduler,)
       lowerCAmelCase__    			=	10






       def 				__A			( self     ,	**a__  ):
              _UpperCAmelCase			     =  {
                  'num_train_timesteps': 11_00,
                  'beta_start': 0.0_001,
                  'beta_end': 0.02,
                  'beta_schedule': 'linear',
              }

              config.update(**a__  )
              return config






       def 				__A			( self  ):
              for timesteps in [10, 50, 1_00, 10_00]:
                     self.check_over_configs(num_train_timesteps=a__  )






       def 				__A			( self  ):
              for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]     ,	[0.0_002, 0.002, 0.02]  ):
                     self.check_over_configs(beta_start=a__     ,	beta_end=a__  )






       def 				__A			( self  ):
              for schedule in ["linear", "scaled_linear"]:
                     self.check_over_configs(beta_schedule=a__  )






       def 				__A			( self  ):
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=a__  )






       def 				__A			( self  ):
              for t in [0, 5_00, 8_00]:
                     self.check_over_forward(time_step=a__  )






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config(prediction_type='v_prediction'  )
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 0.0_017  ) < 1E-2
              assert abs(result_mean.item() - 2.2676E-06  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__  )

              scheduler.set_timesteps(self.num_inference_steps     ,	device=a__  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              _UpperCAmelCase			     =  sample.to(a__  )

              for i, t in enumerate(scheduler.timesteps  ):
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 1_006.388  ) < 1E-2
              assert abs(result_mean.item() - 1.31  ) < 1E-3






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.scheduler_classes[0]
              _UpperCAmelCase			     =  self.get_scheduler_config()
              _UpperCAmelCase			     =  scheduler_class(**a__     ,	use_karras_sigmas=a__  )

              scheduler.set_timesteps(self.num_inference_steps     ,	device=a__  )

              _UpperCAmelCase			     =  self.dummy_model()
              _UpperCAmelCase			     =  self.dummy_sample_deter.to(a__  ) * scheduler.init_noise_sigma
              _UpperCAmelCase			     =  sample.to(a__  )

              for t in scheduler.timesteps:
                     _UpperCAmelCase			     =  scheduler.scale_model_input(a__     ,	a__  )

                     _UpperCAmelCase			     =  model(a__     ,	a__  )

                     _UpperCAmelCase			     =  scheduler.step(a__     ,	a__     ,	a__  )
                     _UpperCAmelCase			     =  output.prev_sample

              _UpperCAmelCase			     =  torch.sum(torch.abs(a__  )  )
              _UpperCAmelCase			     =  torch.mean(torch.abs(a__  )  )

              assert abs(result_sum.item() - 3_812.9_927  ) < 2E-2
              assert abs(result_mean.item() - 4.9_648  ) < 1E-3






