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      :				Any     			="K-MEANS-CLUST/ "







def     A__	(       __A ,			__A ,			__A=None						):







    '''simple docstring'''
    if seed is not None:  # useful for obtaining consistent results
        np.random.seed(__A						)
    _lowerCamelCase  :		List[str]			    =    data.shape[0]  # number of data points

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

    return centroids







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    return pairwise_distances(__A ,			__A ,			metric="""euclidean"""						)







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    # Compute distances between each data point and the set of centroids:
    # Fill in the blank (RHS only)
    _lowerCamelCase  :		int			    =    centroid_pairwise_dist(__A ,			__A						)

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

    return cluster_assignment







def     A__	(       __A ,			__A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Optional[int]			    =    []
    for i in range(__A						):
        # Select all data points that belong to cluster i. Fill in the blank (RHS only)
        _lowerCamelCase  :		Dict			    =    data[cluster_assignment == i]
        # Compute the mean of the data points. Fill in the blank (RHS only)
        _lowerCamelCase  :		Any			    =    member_data_points.mean(axis=0						)
        new_centroids.append(__A						)
    _lowerCamelCase  :		Optional[Any]			    =    np.array(__A						)

    return new_centroids







def     A__	(       __A ,			__A ,			__A ,			__A						):







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

    return heterogeneity







def     A__	(       __A ,			__A						):







    '''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     A__	(       __A ,			__A ,			__A ,			__A=500 ,			__A=None ,			__A=False						):







    '''simple docstring'''
    _lowerCamelCase  :		List[Any]			    =    initial_centroids[:]
    _lowerCamelCase  :		Optional[int]			    =    None

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

        # 1. Make cluster assignments using nearest centroids
        _lowerCamelCase  :		Union[str, Any]			    =    assign_clusters(__A ,			__A						)

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

        _lowerCamelCase  :		List[Any]			    =    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      :				List[str]     			=ds.load_iris()
    lowerCAmelCase      :				int     			=3
    lowerCAmelCase      :				List[Any]     			=[]
    lowerCAmelCase      :				Optional[Any]     			=get_initial_centroids(dataset["data"], k, seed=0)
    lowerCAmelCase			,			lowerCAmelCase      :				str     			=kmeans(
        dataset["data"],
        k,
        initial_centroids,
        maxiter=400,
        record_heterogeneity=heterogeneity,
        verbose=True,
    )
    plot_heterogeneity(heterogeneity, k)







def     A__	(       __A ,			__A ,			__A=None						):







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


if __name__ == "__main__":
    import doctest

    doctest.testmod()





import torch

from accelerate import Accelerator, DistributedType




class 						__snake_case						:





      '''simple docstring'''






      def __enter__(       self  :					Union[str, Any])					->Dict:


          """simple docstring"""


          if self.enabled:
              _lowerCamelCase  :		str			    =    self.model.no_sync()
              self.model_sync_obj.__enter__()

          return self



      def __exit__(       self  :					List[str]     ,	_UpperCamelCase  :					Any     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					int)					->Optional[Any]:


          """simple docstring"""


          if self.enabled:
              # Average all models on exit
              self._sync_and_avg_model_params()
              self.model_sync_obj.__exit__(_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase)



      def __init__(       self  :					Union[str, Any]     ,	_UpperCamelCase  :					Accelerator     ,	_UpperCamelCase  :					torch.nn.Module     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					bool = True)					->int:


          """simple docstring"""


          if accelerator.distributed_type not in [
              DistributedType.NO,
              DistributedType.MULTI_CPU,
              DistributedType.MULTI_GPU,
          ]:
              raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""")
          _lowerCamelCase  :		str			    =    enabled and accelerator.distributed_type != DistributedType.NO
          _lowerCamelCase  :		Optional[int]			    =    0
          if self.enabled:
              _lowerCamelCase  :		Union[str, Any]			    =    accelerator
              _lowerCamelCase  :		int			    =    model
              _lowerCamelCase  :		int			    =    local_sgd_steps



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->Union[str, Any]:


          """simple docstring"""


          self.num_steps += 1
          if not self.enabled:
              return

          if self.num_steps % self.local_sgd_steps == 0:
              self._sync_and_avg_model_params()




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->List[str]:


          """simple docstring"""


          self.accelerator.wait_for_everyone()
          with self.accelerator.autocast():
              for param in self.model.parameters():
                  _lowerCamelCase  :		Union[str, Any]			    =    self.accelerator.reduce(param.data     ,	reduction="""mean""")




