





'''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""")

lowercase_         =			"""K-MEANS-CLUST/ """





def lowerCamelCase						(					__lowerCamelCase			:   Tuple ,	__lowerCamelCase			:   Any ,	__lowerCamelCase			:   Union[str, Any]=None   )						->List[str]:
    if seed is not None:  # useful for obtaining consistent results
        np.random.seed(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      data.shape[0]  # number of data points

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

    # 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.
    _SCREAMING_SNAKE_CASE     =      data[rand_indices, :]

    return centroids





def lowerCamelCase						(					__lowerCamelCase			:   Any ,	__lowerCamelCase			:   Optional[Any]   )						->int:
    return pairwise_distances(__lowerCamelCase ,	__lowerCamelCase ,	metric="""euclidean"""   )





def lowerCamelCase						(					__lowerCamelCase			:   Dict ,	__lowerCamelCase			:   Any   )						->Optional[Any]:
    # Compute distances between each data point and the set of centroids:
    # Fill in the blank (RHS only)
    _SCREAMING_SNAKE_CASE     =      centroid_pairwise_dist(__lowerCamelCase ,	__lowerCamelCase   )

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

    return cluster_assignment





def lowerCamelCase						(					__lowerCamelCase			:   Optional[int] ,	__lowerCamelCase			:   List[str] ,	__lowerCamelCase			:   Union[str, Any]   )						->List[str]:
    _SCREAMING_SNAKE_CASE     =      []
    for i in range(__lowerCamelCase   ):
        # Select all data points that belong to cluster i. Fill in the blank (RHS only)
        _SCREAMING_SNAKE_CASE     =      data[cluster_assignment == i]
        # Compute the mean of the data points. Fill in the blank (RHS only)
        _SCREAMING_SNAKE_CASE     =      member_data_points.mean(axis=0   )
        new_centroids.append(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      np.array(__lowerCamelCase   )

    return new_centroids





def lowerCamelCase						(					__lowerCamelCase			:   Optional[Any] ,	__lowerCamelCase			:   List[str] ,	__lowerCamelCase			:   List[Any] ,	__lowerCamelCase			:   int   )						->List[Any]:
    _SCREAMING_SNAKE_CASE     =      0.0
    for i in range(__lowerCamelCase   ):
        # Select all data points that belong to cluster i. Fill in the blank (RHS only)
        _SCREAMING_SNAKE_CASE     =      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)
            _SCREAMING_SNAKE_CASE     =      pairwise_distances(
                __lowerCamelCase ,	[centroids[i]] ,	metric="""euclidean"""   )
            _SCREAMING_SNAKE_CASE     =      distances**2
            heterogeneity += np.sum(__lowerCamelCase   )

    return heterogeneity





def lowerCamelCase						(					__lowerCamelCase			:   Tuple ,	__lowerCamelCase			:   int   )						->Any:
    plt.figure(figsize=(7, 4)   )
    plt.plot(__lowerCamelCase ,	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						(					__lowerCamelCase			:   Optional[Any] ,	__lowerCamelCase			:   Optional[Any] ,	__lowerCamelCase			:   List[str] ,	__lowerCamelCase			:   str=500 ,	__lowerCamelCase			:   Any=None ,	__lowerCamelCase			:   Any=False   )						->Optional[int]:
    _SCREAMING_SNAKE_CASE     =      initial_centroids[:]
    _SCREAMING_SNAKE_CASE     =      None

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

        # 1. Make cluster assignments using nearest centroids
        _SCREAMING_SNAKE_CASE     =      assign_clusters(__lowerCamelCase ,	__lowerCamelCase   )

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

        # 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:
            _SCREAMING_SNAKE_CASE     =      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
            _SCREAMING_SNAKE_CASE     =      compute_heterogeneity(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )
            record_heterogeneity.append(__lowerCamelCase   )

        _SCREAMING_SNAKE_CASE     =      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

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





def lowerCamelCase						(					__lowerCamelCase			:   pd.DataFrame ,	__lowerCamelCase			:   np.ndarray ,	__lowerCamelCase			:   Union[str, Any]=None   )						->pd.DataFrame:
    # Fill missing values with given rules
    if fill_missing_report:
        _SCREAMING_SNAKE_CASE     =      df.fillna(value=__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      1
    _SCREAMING_SNAKE_CASE     =      df.select_dtypes(np.number   ).columns
    _SCREAMING_SNAKE_CASE     =      (
        df.groupby(["""Cluster"""]   )[  # construct report dataframe
            numeric_cols
        ]  # group by cluster number
        .agg(
            [
                ("""sum""", np.sum),
                ("""mean_with_zeros""", lambda __lowerCamelCase   : np.mean(np.nan_to_num(__lowerCamelCase   )   )),
                ("""mean_without_zeros""", lambda __lowerCamelCase   : x.replace(0 ,	np.NaN   ).mean()),
                (
                    """mean_25-75""",
                    lambda __lowerCamelCase   : np.mean(
                        np.nan_to_num(
                            sorted(__lowerCamelCase   )[
                                round(len(__lowerCamelCase   ) * 25 / 100   ) : round(len(__lowerCamelCase   ) * 75 / 100   )
                            ]   )   ),
                ),
                ("""mean_with_na""", np.mean),
                ("""min""", lambda __lowerCamelCase   : x.min()),
                ("""5%""", lambda __lowerCamelCase   : x.quantile(0.05   )),
                ("""25%""", lambda __lowerCamelCase   : x.quantile(0.25   )),
                ("""50%""", lambda __lowerCamelCase   : x.quantile(0.50   )),
                ("""75%""", lambda __lowerCamelCase   : x.quantile(0.75   )),
                ("""95%""", lambda __lowerCamelCase   : x.quantile(0.95   )),
                ("""max""", lambda __lowerCamelCase   : x.max()),
                ("""count""", lambda __lowerCamelCase   : x.count()),
                ("""stdev""", lambda __lowerCamelCase   : x.std()),
                ("""mode""", lambda __lowerCamelCase   : x.mode()[0]),
                ("""median""", lambda __lowerCamelCase   : x.median()),
                ("""# > 0""", lambda __lowerCamelCase   : (x > 0).sum()),
            ]   )
        .T.reset_index()
        .rename(index=__lowerCamelCase ,	columns={"""level_0""": """Features""", """level_1""": """Type"""}   )
    )  # rename columns
    # calculate the size of cluster(count of clientID's)
    _SCREAMING_SNAKE_CASE     =      report[
        (report["""Features"""] == """dummy""") & (report["""Type"""] == """count""")
    ].copy()  # avoid SettingWithCopyWarning
    _SCREAMING_SNAKE_CASE     =      (
        """ClusterSize"""  # rename created cluster df to match report column names
    )
    _SCREAMING_SNAKE_CASE     =      """# of Customers"""
    _SCREAMING_SNAKE_CASE     =      pd.DataFrame(
        clustersize.iloc[:, 2:].values
        / clustersize.iloc[:, 2:].values.sum()   )
    _SCREAMING_SNAKE_CASE     =      """% of Customers"""  # rename created cluster df to match report column names
    _SCREAMING_SNAKE_CASE     =      """ClusterProportion"""
    _SCREAMING_SNAKE_CASE     =      clusterproportion.columns.tolist()
    _SCREAMING_SNAKE_CASE     =      cols[-2:] + cols[:-2]
    _SCREAMING_SNAKE_CASE     =      clusterproportion[cols]  # rearrange columns to match report
    _SCREAMING_SNAKE_CASE     =      report.columns
    _SCREAMING_SNAKE_CASE     =      pd.DataFrame(
        abs(
            report[report["""Type"""] == """count"""].iloc[:, 2:].values
            - clustersize.iloc[:, 2:].values   )   )  # generating df with count of nan values
    _SCREAMING_SNAKE_CASE     =      0
    _SCREAMING_SNAKE_CASE     =      """# of nan"""
    _SCREAMING_SNAKE_CASE     =      report[
        report["""Type"""] == """count"""
    ].Features.tolist()  # filling values in order to match report
    _SCREAMING_SNAKE_CASE     =      a.columns.tolist()
    _SCREAMING_SNAKE_CASE     =      cols[-2:] + cols[:-2]
    _SCREAMING_SNAKE_CASE     =      a[cols]  # rearrange columns to match report
    _SCREAMING_SNAKE_CASE     =      report.columns  # rename columns to match report
    _SCREAMING_SNAKE_CASE     =      report.drop(
        report[report.Type == """count"""].index   )  # drop count values except cluster size
    _SCREAMING_SNAKE_CASE     =      pd.concat(
        [report, a, clustersize, clusterproportion] ,	axis=0   )  # concat report with clustert size and nan values
    _SCREAMING_SNAKE_CASE     =      report["""Features"""].isin(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      report.columns.tolist()
    _SCREAMING_SNAKE_CASE     =      cols[0:2] + cols[-1:] + cols[2:-1]
    _SCREAMING_SNAKE_CASE     =      report[cols]
    _SCREAMING_SNAKE_CASE     =      {
        """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,
    }
    _SCREAMING_SNAKE_CASE     =      (
        report.assign(
            Sortera=lambda __lowerCamelCase   : x.Type.map(__lowerCamelCase   ) ,	Sortera=lambda __lowerCamelCase   : list(reversed(range(len(__lowerCamelCase   )   )   )   ) ,	)
        .sort_values(["""Sorter1""", """Mark""", """Sorter2"""] ,	ascending=__lowerCamelCase   )
        .drop(["""Sorter1""", """Sorter2"""] ,	axis=1   )
    )
    _SCREAMING_SNAKE_CASE     =      """"""
    _SCREAMING_SNAKE_CASE     =      report.reset_index()
    _SCREAMING_SNAKE_CASE     =      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  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			(LMSDiscreteScheduler,)
    UpperCamelCase			   =			10


    def       snake_case_(		self			,		**A	)		->       str:
        _SCREAMING_SNAKE_CASE     =      {
            """num_train_timesteps""": 1100,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**A	)
        return config


    def       snake_case_(		self	)		->       str:
        for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=A	)


    def       snake_case_(		self	)		->       Optional[Any]:
        for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]			,		[0.0002, 0.002, 0.02]	):
            self.check_over_configs(beta_start=A			,		beta_end=A	)


    def       snake_case_(		self	)		->       Tuple:
        for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=A	)


    def       snake_case_(		self	)		->       List[str]:
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=A	)


    def       snake_case_(		self	)		->       Optional[int]:
        for t in [0, 500, 800]:
            self.check_over_forward(time_step=A	)


    def       snake_case_(		self	)		->       str:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps	)

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

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 1006.388	) < 1e-2
        assert abs(result_mean.item() - 1.31	) < 1e-3


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config(prediction_type="""v_prediction"""	)
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps	)

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

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 0.0017	) < 1e-2
        assert abs(result_mean.item() - 2.26_76e-06	) < 1e-3


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

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

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        _SCREAMING_SNAKE_CASE     =      sample.to(A	)

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 1006.388	) < 1e-2
        assert abs(result_mean.item() - 1.31	) < 1e-3





    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A			,		use_karras_sigmas=A	)

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

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter.to(A	) * scheduler.init_noise_sigma
        _SCREAMING_SNAKE_CASE     =      sample.to(A	)

        for t in scheduler.timesteps:
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 3812.9927	) < 2e-2
        assert abs(result_mean.item() - 4.9648	) < 1e-3



