
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       =					"K-MEANS-CLUST/ "



def 		lowercase_     (  A__					,						A__					,						A__=None						)				->				Tuple:
    """simple docstring"""

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

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

    return centroids



def 		lowercase_     (  A__					,						A__						)				->				Optional[int]:
    """simple docstring"""

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



def 		lowercase_     (  A__					,						A__						)				->				str:
    """simple docstring"""

    snake_case						=   centroid_pairwise_dist(A__					,						A__						)

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

    return cluster_assignment



def 		lowercase_     (  A__					,						A__					,						A__						)				->				str:
    """simple docstring"""

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

    return new_centroids



def 		lowercase_     (  A__					,						A__					,						A__					,						A__						)				->				int:
    """simple docstring"""

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

    return heterogeneity



def 		lowercase_     (  A__					,						A__						)				->				int:
    """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 		lowercase_     (  A__					,						A__					,						A__					,						A__=500					,						A__=None					,						A__=False						)				->				Union[str, Any]:
    """simple docstring"""

    snake_case						=   initial_centroids[:]
    snake_case						=   None

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

        # 1. Make cluster assignments using nearest centroids
        snake_case						=   assign_clusters(A__					,						A__						)

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

        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

   _A       =					ds.load_iris()
   _A       =					3
   _A       =					[]
   _A       =					get_initial_centroids(dataset["data"], k, seed=0)
   _A   ,    _A       =					kmeans(
       dataset["data"],
       k,
       initial_centroids,
       maxiter=4_00,
       record_heterogeneity=heterogeneity,
       verbose=True,
   )
   plot_heterogeneity(heterogeneity, k)



def 		lowercase_     (  A__					,						A__					,						A__=None						)				->				pd.DataFrame:
    """simple docstring"""

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()



from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest







@custom_tokenizers
class 	lowerCamelCase						(    A_    ):





     def 						UpperCAmelCase(self :       Optional[Any]							,       _A :       Tuple							,       _A :       Tuple							,       _A :       str							,       _A :       Tuple							,       _A :       Any  )		->		Optional[int]:
         return True





     def 						UpperCAmelCase(self :       str  )		->		List[str]:
         snake_case						=   CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate"  )
         snake_case						=   "Hugging Face大法好，谁用谁知道。"
         snake_case						=   "Hugging Face大法好,谁用谁知道。<unk>"
         snake_case						=   "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split()

         snake_case						=   tokenizer.tokenize(_A  )
         self.assertListEqual(_A							,       _A  )

         snake_case						=   tokens + [tokenizer.unk_token]

         snake_case						=   [1_3_7_8_9, 1_3_2_8_3, 1_4_2_1, 8, 1_0, 1_1_6_4, 1_3_6_0_8, 1_6_5_2_8, 6_3, 8, 9, 4_4_0, 1_0_8, 4_4_0, 1_2_1, 9_0, 8, 1_2, 0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_A  )							,       _A  )

         snake_case						=   tokenizer.decode(_A  )
         self.assertEqual(_A							,       _A  )

