



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_	       =   "K-MEANS-CLUST/ "







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase=None	)       ->       Any:



						'''simple docstring'''






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

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

						return centroids







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase	)       ->       Optional[Any]:



						'''simple docstring'''






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







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase	)       ->       str:



						'''simple docstring'''






						UpperCAmelCase_     							=     centroid_pairwise_dist(__UpperCAmelCase							,					__UpperCAmelCase	)

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

						return cluster_assignment







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       List[str]:



						'''simple docstring'''






						UpperCAmelCase_     							=     []
						for i in range(__UpperCAmelCase	):
												# 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(__UpperCAmelCase	)
						UpperCAmelCase_     							=     np.array(__UpperCAmelCase	)

						return new_centroids







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       List[Any]:



						'''simple docstring'''






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

						return heterogeneity







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase	)       ->       List[str]:



						'''simple docstring'''






						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 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase=500							,					__UpperCAmelCase=None							,					__UpperCAmelCase=False	)       ->       Dict:



						'''simple docstring'''






						UpperCAmelCase_     							=     initial_centroids[:]
						UpperCAmelCase_     							=     None

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

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

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

												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

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







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase=None	)       ->       pd.DataFrame:



						'''simple docstring'''






						if fill_missing_report:
												UpperCAmelCase_     							=     df.fillna(value=__UpperCAmelCase	)
						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 __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 / 100	) : round(len(__UpperCAmelCase	) * 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=__UpperCAmelCase							,					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(__UpperCAmelCase	)
						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 __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	)
						)
						UpperCAmelCase_     							=     ''''''
						UpperCAmelCase_     							=     report.reset_index()
						UpperCAmelCase_     							=     report.drop(columns=['''index''']	)
						return report


if __name__ == "__main__":
							import doctest

							doctest.testmod()








import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Tuple          =					(DDIMScheduler,)
      lowerCamelCase_	:      Union[str, Any]          =					(('eta', 0.0), ('num_inference_steps', 50))







      def 	lowerCAmelCase_    (     self							,  **lowerCamelCase   )						->       Dict:
        snake_case_       						=      {
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """clip_sample""": True,
        }

        config.update(**lowerCamelCase   )
        return config







      def 	lowerCAmelCase_    (     self							,  **lowerCamelCase   )						->       List[str]:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config(**lowerCamelCase   )
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        snake_case_						,				snake_case_       						=      10, 0.0

        snake_case_       						=      self.dummy_model()
        snake_case_       						=      self.dummy_sample_deter

        scheduler.set_timesteps(lowerCamelCase   )

        for t in scheduler.timesteps:
          snake_case_       						=      model(lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      scheduler.step(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   ).prev_sample

        return sample







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        for timesteps in [100, 500, 1000]:
          self.check_over_configs(num_train_timesteps=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=lowerCamelCase   )

        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config(steps_offset=1   )
        snake_case_       						=      scheduler_class(**lowerCamelCase   )
        scheduler.set_timesteps(5   )
        assert torch.equal(scheduler.timesteps							,  torch.LongTensor([801, 601, 401, 201, 1]   )   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]							,  [0.002, 0.02, 0.2, 2]   ):
          self.check_over_configs(beta_start=lowerCamelCase							,  beta_end=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Any:
        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for timestep_spacing in ["trailing", "leading"]:
          self.check_over_configs(timestep_spacing=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for rescale_betas_zero_snr in [True, False]:
          self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        self.check_over_configs(thresholding=lowerCamelCase   )
        for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(
                thresholding=lowerCamelCase							,  prediction_type=lowerCamelCase							,  sample_max_value=lowerCamelCase							,  )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for t in [1, 10, 49]:
          self.check_over_forward(time_step=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        for t, num_inference_steps in zip([1, 10, 50]							,  [10, 50, 500]   ):
          self.check_over_forward(time_step=lowerCamelCase							,  num_inference_steps=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        for t, eta in zip([1, 10, 49]							,  [0.0, 0.5, 1.0]   ):
          self.check_over_forward(time_step=lowerCamelCase							,  eta=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config()
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        assert torch.sum(torch.abs(scheduler._get_variance(0							,  0   ) - 0.0   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(420							,  400   ) - 0.1_4771   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(980							,  960   ) - 0.3_2460   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(0							,  0   ) - 0.0   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487							,  486   ) - 0.0_0979   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999							,  998   ) - 0.02   )   ) < 1e-5







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_       						=      self.full_loop()

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 172.0067   ) < 1e-2
        assert abs(result_mean.item() - 0.22_3967   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      self.full_loop(prediction_type="""v_prediction"""   )

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 52.5302   ) < 1e-2
        assert abs(result_mean.item() - 0.0684   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        # We specify different beta, so that the first alpha is 0.99
        snake_case_       						=      self.full_loop(set_alpha_to_one=lowerCamelCase							,  beta_start=0.01   )
        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 149.8295   ) < 1e-2
        assert abs(result_mean.item() - 0.1951   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        # We specify different beta, so that the first alpha is 0.99
        snake_case_       						=      self.full_loop(set_alpha_to_one=lowerCamelCase							,  beta_start=0.01   )
        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 149.0784   ) < 1e-2
        assert abs(result_mean.item() - 0.1941   ) < 1e-3