


"""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_     :     Optional[int]  		=						"""K-MEANS-CLUST/ """
def _A		(		lowercase ,					lowercase ,					lowercase=None	):







    """simple docstring"""
    if seed is not None:  # useful for obtaining consistent results
        np.random.seed(lowercase	)
    a					=data.shape[0]  # number of data points

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

    # 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.
    a					=data[rand_indices, :]

    return centroids
def _A		(		lowercase ,					lowercase	):







    """simple docstring"""
    return pairwise_distances(lowercase ,					lowercase ,					metric='''euclidean'''	)
def _A		(		lowercase ,					lowercase	):







    """simple docstring"""
    a					=centroid_pairwise_dist(lowercase ,					lowercase	)

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

    return cluster_assignment
def _A		(		lowercase ,					lowercase ,					lowercase	):







    """simple docstring"""
    a					=[]
    for i in range(lowercase	):
        # Select all data points that belong to cluster i. Fill in the blank (RHS only)
        a					=data[cluster_assignment == i]
        # Compute the mean of the data points. Fill in the blank (RHS only)
        a					=member_data_points.mean(axis=0	)
        new_centroids.append(lowercase	)
    a					=np.array(lowercase	)

    return new_centroids
def _A		(		lowercase ,					lowercase ,					lowercase ,					lowercase	):







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

    return heterogeneity
def _A		(		lowercase ,					lowercase	):







    """simple docstring"""
    plt.figure(figsize=(7, 4)	)
    plt.plot(lowercase ,					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		(		lowercase ,					lowercase ,					lowercase ,					lowercase=5_00 ,					lowercase=None ,					lowercase=False	):







    """simple docstring"""
    a					=initial_centroids[:]
    a					=None

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

        # 1. Make cluster assignments using nearest centroids
        a					=assign_clusters(lowercase ,					lowercase	)

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

        # 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:
            a					=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
            a					=compute_heterogeneity(lowercase ,					lowercase ,					lowercase ,					lowercase	)
            record_heterogeneity.append(lowercase	)

        a					=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_     :     Optional[Any]  		=						ds.load_iris()
       lowerCamelCase_     :     Optional[Any]  		=						3
       lowerCamelCase_     :     int  		=						[]
       lowerCamelCase_     :     int  		=						get_initial_centroids(dataset["""data"""], k, seed=0)
       lowerCamelCase_     :     Any  		=						kmeans(
           dataset["""data"""],
           k,
           initial_centroids,
           maxiter=4_0_0,
           record_heterogeneity=heterogeneity,
           verbose=True,
       )
       plot_heterogeneity(heterogeneity, k)
def _A		(		lowercase ,					lowercase ,					lowercase=None	):







    """simple docstring"""
    # Fill missing values with given rules
    if fill_missing_report:
        a					=df.fillna(value=lowercase	)
    a					=1
    a					=df.select_dtypes(np.number	).columns
    a					=(
        df.groupby(['''Cluster''']	)[  # construct report dataframe
            numeric_cols
        ]  # group by cluster number
        .agg(
            [
                ('''sum''', np.sum),
                ('''mean_with_zeros''', lambda lowercase	: np.mean(np.nan_to_num(lowercase	)	)),
                ('''mean_without_zeros''', lambda lowercase	: x.replace(0 ,					np.NaN	).mean()),
                (
                    '''mean_25-75''',
                    lambda lowercase	: np.mean(
                        np.nan_to_num(
                            sorted(lowercase	)[
                                round(len(lowercase	) * 25 / 1_00	) : round(len(lowercase	) * 75 / 1_00	)
                            ]	)	),
                ),
                ('''mean_with_na''', np.mean),
                ('''min''', lambda lowercase	: x.min()),
                ('''5%''', lambda lowercase	: x.quantile(0.05	)),
                ('''25%''', lambda lowercase	: x.quantile(0.25	)),
                ('''50%''', lambda lowercase	: x.quantile(0.50	)),
                ('''75%''', lambda lowercase	: x.quantile(0.75	)),
                ('''95%''', lambda lowercase	: x.quantile(0.95	)),
                ('''max''', lambda lowercase	: x.max()),
                ('''count''', lambda lowercase	: x.count()),
                ('''stdev''', lambda lowercase	: x.std()),
                ('''mode''', lambda lowercase	: x.mode()[0]),
                ('''median''', lambda lowercase	: x.median()),
                ('''# > 0''', lambda lowercase	: (x > 0).sum()),
            ]	)
        .T.reset_index()
        .rename(index=lowercase ,					columns={'''level_0''': '''Features''', '''level_1''': '''Type'''}	)
    )  # rename columns
    # calculate the size of cluster(count of clientID's)
    a					=report[
        (report['''Features'''] == '''dummy''') & (report['''Type'''] == '''count''')
    ].copy()  # avoid SettingWithCopyWarning
    a					=(
        '''ClusterSize'''  # rename created cluster df to match report column names
    )
    a					='''# of Customers'''
    a					=pd.DataFrame(
        clustersize.iloc[:, 2:].values
        / clustersize.iloc[:, 2:].values.sum()	)
    a					='''% of Customers'''  # rename created cluster df to match report column names
    a					='''ClusterProportion'''
    a					=clusterproportion.columns.tolist()
    a					=cols[-2:] + cols[:-2]
    a					=clusterproportion[cols]  # rearrange columns to match report
    a					=report.columns
    a					=pd.DataFrame(
        abs(
            report[report['''Type'''] == '''count'''].iloc[:, 2:].values
            - clustersize.iloc[:, 2:].values	)	)  # generating df with count of nan values
    a					=0
    a					='''# of nan'''
    a					=report[
        report['''Type'''] == '''count'''
    ].Features.tolist()  # filling values in order to match report
    a					=a.columns.tolist()
    a					=cols[-2:] + cols[:-2]
    a					=a[cols]  # rearrange columns to match report
    a					=report.columns  # rename columns to match report
    a					=report.drop(
        report[report.Type == '''count'''].index	)  # drop count values except cluster size
    a					=pd.concat(
        [report, a, clustersize, clusterproportion] ,					axis=0	)  # concat report with clustert size and nan values
    a					=report['''Features'''].isin(lowercase	)
    a					=report.columns.tolist()
    a					=cols[0:2] + cols[-1:] + cols[2:-1]
    a					=report[cols]
    a					={
        '''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,
    }
    a					=(
        report.assign(
            Sortera=lambda lowercase	: x.Type.map(lowercase	) ,					Sortera=lambda lowercase	: list(reversed(range(len(lowercase	)	)	)	) ,					)
        .sort_values(['''Sorter1''', '''Mark''', '''Sorter2'''] ,					ascending=lowercase	)
        .drop(['''Sorter1''', '''Sorter2'''] ,					axis=1	)
    )
    a					=''''''
    a					=report.reset_index()
    a					=report.drop(columns=['''index''']	)
    return report


if __name__ == "__main__":
       import doctest

       doctest.testmod()



"""simple docstring"""







import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


lowerCamelCase_     :     int    		=						logging.get_logger(__name__)






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 __lowerCAmelCase				      =						"hybrid-clip"
 __lowerCAmelCase				      =						True


 def __init__(	self				,					__A=512				,					**__A		)      ->  Any:
     super().__init__(**__A		)

     if "text_config" not in kwargs:
         raise ValueError('''`text_config` can not be `None`.'''		)

     if "vision_config" not in kwargs:
         raise ValueError('''`vision_config` can not be `None`.'''		)

     a					=kwargs.pop('''text_config'''		)
     a					=kwargs.pop('''vision_config'''		)

     a					=text_config.pop('''model_type'''		)
     a					=vision_config.pop('''model_type'''		)

     from transformers import AutoConfig

     a					=AutoConfig.for_model(__A				,					**__A		)

     if vision_model_type == "clip":
         a					=AutoConfig.for_model(__A				,					**__A		).vision_config
     elif vision_model_type == "clip_vision_model":
         from transformers import CLIPVisionConfig

         a					=CLIPVisionConfig(**__A		)
     else:
         a					=AutoConfig.for_model(__A				,					**__A		)

     a					=projection_dim
     a					=1.0


 @classmethod
 def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A				,					**__A		)      ->  Tuple:
     return cls(text_config=text_config.to_dict()				,					vision_config=vision_config.to_dict()				,					**__A		)





 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
     a					=copy.deepcopy(self.__dict__		)
     a					=self.text_config.to_dict()
     a					=self.vision_config.to_dict()
     a					=self.__class__.model_type
     return output