


'''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')

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







def _UpperCamelCase	(					__A				,      __A				,      __A=None   )							->		Optional[int]:


      '''simple docstring'''



      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 _UpperCamelCase	(					__A				,      __A   )							->		List[str]:


      '''simple docstring'''



      return pairwise_distances(__A				,      __A				,      metric="euclidean"   )







def _UpperCamelCase	(					__A				,      __A   )							->		Optional[Any]:


      '''simple docstring'''



      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 _UpperCamelCase	(					__A				,      __A				,      __A   )							->		Dict:


      '''simple docstring'''



      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 _UpperCamelCase	(					__A				,      __A				,      __A				,      __A   )							->		int:


      '''simple docstring'''



      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 _UpperCamelCase	(					__A				,      __A   )							->		str:


      '''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": 16}   )
      plt.show()







def _UpperCamelCase	(					__A				,      __A				,      __A				,      __A=500				,      __A=None				,      __A=False   )							->		Any:


      '''simple docstring'''



      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

  a__  : Union[str, Any]				=	ds.load_iris()
  a__  : Optional[Any]				=	3
  a__  : List[Any]				=	[]
  a__  : int				=	get_initial_centroids(dataset['data'], k, seed=0)
  a__			,			a__  : int				=	kmeans(
      dataset['data'],
      k,
      initial_centroids,
      maxiter=4_0_0,
      record_heterogeneity=heterogeneity,
      verbose=True,
  )
  plot_heterogeneity(heterogeneity, k)







def _UpperCamelCase	(					__A				,      __A				,      __A=None   )							->		pd.DataFrame:


      '''simple docstring'''



      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'''







import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class  lowercase_					(   a__       ):
    __UpperCAmelCase             =     (DPMSolverMultistepScheduler,)
    __UpperCAmelCase             =     (('num_inference_steps', 25),)


    def        __a							(					self  , **a   ):
          UpperCamelCase__  =    {
              "num_train_timesteps": 10_00,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "solver_order": 2,
              "prediction_type": "epsilon",
              "thresholding": False,
              "sample_max_value": 1.0,
              "algorithm_type": "dpmsolver++",
              "solver_type": "midpoint",
              "lower_order_final": False,
              "lambda_min_clipped": -float("inf"   ),
              "variance_type": None,
          }

          config.update(**a   )
          return config


    def        __a							(					self  , a=0  , **a   ):
          UpperCamelCase__  =    dict(self.forward_default_kwargs   )
          UpperCamelCase__  =    kwargs.pop("num_inference_steps"  , a   )
          UpperCamelCase__  =    self.dummy_sample
          UpperCamelCase__  =    0.1 * sample
          UpperCamelCase__  =    [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
                UpperCamelCase__  =    self.get_scheduler_config(**a   )
                UpperCamelCase__  =    scheduler_class(**a   )
                scheduler.set_timesteps(a   )
                # copy over dummy past residuals
                UpperCamelCase__  =    dummy_past_residuals[: scheduler.config.solver_order]

                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(a   )
                      UpperCamelCase__  =    scheduler_class.from_pretrained(a   )
                      new_scheduler.set_timesteps(a   )
                      # copy over dummy past residuals
                      UpperCamelCase__  =    dummy_past_residuals[: new_scheduler.config.solver_order]

                UpperCamelCase__ ,     UpperCamelCase__  =    sample, sample
                for t in range(a  , time_step + scheduler.config.solver_order + 1   ):
                      UpperCamelCase__  =    scheduler.step(a  , a  , a  , **a   ).prev_sample
                      UpperCamelCase__  =    new_scheduler.step(a  , a  , a  , **a   ).prev_sample

                      assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"


    def        __a							(					self   ):
          pass


    def        __a							(					self  , a=0  , **a   ):
          UpperCamelCase__  =    dict(self.forward_default_kwargs   )
          UpperCamelCase__  =    kwargs.pop("num_inference_steps"  , a   )
          UpperCamelCase__  =    self.dummy_sample
          UpperCamelCase__  =    0.1 * sample
          UpperCamelCase__  =    [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
                UpperCamelCase__  =    self.get_scheduler_config()
                UpperCamelCase__  =    scheduler_class(**a   )
                scheduler.set_timesteps(a   )

                # copy over dummy past residuals (must be after setting timesteps)
                UpperCamelCase__  =    dummy_past_residuals[: scheduler.config.solver_order]

                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(a   )
                      UpperCamelCase__  =    scheduler_class.from_pretrained(a   )
                      # copy over dummy past residuals
                      new_scheduler.set_timesteps(a   )

                      # copy over dummy past residual (must be after setting timesteps)
                      UpperCamelCase__  =    dummy_past_residuals[: new_scheduler.config.solver_order]

                UpperCamelCase__  =    scheduler.step(a  , a  , a  , **a   ).prev_sample
                UpperCamelCase__  =    new_scheduler.step(a  , a  , a  , **a   ).prev_sample

                assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"


    def        __a							(					self  , a=None  , **a   ):
          if scheduler is None:
                UpperCamelCase__  =    self.scheduler_classes[0]
                UpperCamelCase__  =    self.get_scheduler_config(**a   )
                UpperCamelCase__  =    scheduler_class(**a   )

          UpperCamelCase__  =    10
          UpperCamelCase__  =    self.dummy_model()
          UpperCamelCase__  =    self.dummy_sample_deter
          scheduler.set_timesteps(a   )

          for i, t in enumerate(scheduler.timesteps   ):
                UpperCamelCase__  =    model(a  , a   )
                UpperCamelCase__  =    scheduler.step(a  , a  , a   ).prev_sample

          return sample


    def        __a							(					self   ):
          UpperCamelCase__  =    dict(self.forward_default_kwargs   )

          UpperCamelCase__  =    kwargs.pop("num_inference_steps"  , a   )

          for scheduler_class in self.scheduler_classes:
                UpperCamelCase__  =    self.get_scheduler_config()
                UpperCamelCase__  =    scheduler_class(**a   )

                UpperCamelCase__  =    self.dummy_sample
                UpperCamelCase__  =    0.1 * sample

                if num_inference_steps is not None and hasattr(a  , "set_timesteps"   ):
                      scheduler.set_timesteps(a   )
                elif num_inference_steps is not None and not hasattr(a  , "set_timesteps"   ):
                      UpperCamelCase__  =    num_inference_steps

                # copy over dummy past residuals (must be done after set_timesteps)
                UpperCamelCase__  =    [residual + 0.2, residual + 0.15, residual + 0.10]
                UpperCamelCase__  =    dummy_past_residuals[: scheduler.config.solver_order]

                UpperCamelCase__  =    scheduler.timesteps[5]
                UpperCamelCase__  =    scheduler.timesteps[6]

                UpperCamelCase__  =    scheduler.step(a  , a  , a  , **a   ).prev_sample
                UpperCamelCase__  =    scheduler.step(a  , a  , a  , **a   ).prev_sample

                self.assertEqual(output_a.shape  , sample.shape   )
                self.assertEqual(output_a.shape  , output_a.shape   )


    def        __a							(					self   ):
          for timesteps in [25, 50, 1_00, 9_99, 10_00]:
                self.check_over_configs(num_train_timesteps=a   )


    def        __a							(					self   ):
          self.check_over_configs(thresholding=a   )
          for order in [1, 2, 3]:
                for solver_type in ["midpoint", "heun"]:
                      for threshold in [0.5, 1.0, 2.0]:
                            for prediction_type in ["epsilon", "sample"]:
                                  self.check_over_configs(
                                      thresholding=a  , prediction_type=a  , sample_max_value=a  , algorithm_type="dpmsolver++"  , solver_order=a  , solver_type=a  , )


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


    def        __a							(					self   ):
          for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
                for solver_type in ["midpoint", "heun"]:
                      for order in [1, 2, 3]:
                            for prediction_type in ["epsilon", "sample"]:
                                  if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                        if order == 3:
                                              continue
                                  else:
                                        self.check_over_configs(
                                            solver_order=a  , solver_type=a  , prediction_type=a  , algorithm_type=a  , )
                                  UpperCamelCase__  =    self.full_loop(
                                      solver_order=a  , solver_type=a  , prediction_type=a  , algorithm_type=a  , )
                                  assert not torch.isnan(a   ).any(), "Samples have nan numbers"


    def        __a							(					self   ):
          self.check_over_configs(lower_order_final=a   )
          self.check_over_configs(lower_order_final=a   )


    def        __a							(					self   ):
          self.check_over_configs(lambda_min_clipped=-float("inf"   )   )
          self.check_over_configs(lambda_min_clipped=-5.1   )


    def        __a							(					self   ):
          self.check_over_configs(variance_type=a   )
          self.check_over_configs(variance_type="learned_range"   )


    def        __a							(					self   ):
          for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
                self.check_over_forward(num_inference_steps=a  , time_step=0   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.full_loop()
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 0.3301   ) < 1e-3


    def        __a							(					self   ):
          UpperCamelCase__  =    self.full_loop(thresholding=a  , dynamic_thresholding_ratio=0.87  , sample_max_value=0.5   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 1.1364   ) < 1e-3


    def        __a							(					self   ):
          UpperCamelCase__  =    self.full_loop(prediction_type="v_prediction"   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 0.2251   ) < 1e-3


    def        __a							(					self   ):
          UpperCamelCase__  =    self.full_loop(prediction_type="v_prediction"  , use_karras_sigmas=a   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 0.2096   ) < 1e-3


    def        __a							(					self   ):
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          UpperCamelCase__  =    DPMSolverMultistepScheduler(**self.get_scheduler_config()   )
          UpperCamelCase__  =    self.full_loop(scheduler=a   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 0.3301   ) < 1e-3

          UpperCamelCase__  =    DPMSolverSinglestepScheduler.from_config(scheduler.config   )
          UpperCamelCase__  =    UniPCMultistepScheduler.from_config(scheduler.config   )
          UpperCamelCase__  =    DEISMultistepScheduler.from_config(scheduler.config   )
          UpperCamelCase__  =    DPMSolverMultistepScheduler.from_config(scheduler.config   )

          UpperCamelCase__  =    self.full_loop(scheduler=a   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 0.3301   ) < 1e-3


    def        __a							(					self   ):
          UpperCamelCase__  =    self.scheduler_classes[0]
          UpperCamelCase__  =    self.get_scheduler_config(thresholding=a  , dynamic_thresholding_ratio=0   )
          UpperCamelCase__  =    scheduler_class(**a   )

          UpperCamelCase__  =    10
          UpperCamelCase__  =    self.dummy_model()
          UpperCamelCase__  =    self.dummy_sample_deter.half()
          scheduler.set_timesteps(a   )

          for i, t in enumerate(scheduler.timesteps   ):
                UpperCamelCase__  =    model(a  , a   )
                UpperCamelCase__  =    scheduler.step(a  , a  , a   ).prev_sample

          assert sample.dtype == torch.floataa


    def        __a							(					self  , **a   ):
          for scheduler_class in self.scheduler_classes:
                UpperCamelCase__  =    self.get_scheduler_config(**a   )
                UpperCamelCase__  =    scheduler_class(**a   )

                scheduler.set_timesteps(scheduler.config.num_train_timesteps   )
                assert len(scheduler.timesteps.unique()   ) == scheduler.num_inference_steps





