




'''simple docstring'''







from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available







@dataclass
class 				_a   (a__  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Optional[Any]									=				{
         """sld_warmup_steps""": 15,
         """sld_guidance_scale""": 20,
         """sld_threshold""": 0.0,
         """sld_momentum_scale""": 0.0,
         """sld_mom_beta""": 0.0,
     }
     lowerCAmelCase_     :    Any									=				{
         """sld_warmup_steps""": 10,
         """sld_guidance_scale""": 1000,
         """sld_threshold""": 0.01,
         """sld_momentum_scale""": 0.3,
         """sld_mom_beta""": 0.4,
     }
     lowerCAmelCase_     :    Dict									=				{
         """sld_warmup_steps""": 7,
         """sld_guidance_scale""": 2000,
         """sld_threshold""": 0.025,
         """sld_momentum_scale""": 0.5,
         """sld_mom_beta""": 0.7,
     }
     lowerCAmelCase_     :    List[Any]									=				{
         """sld_warmup_steps""": 0,
         """sld_guidance_scale""": 5000,
         """sld_threshold""": 1.0,
         """sld_momentum_scale""": 0.5,
         """sld_mom_beta""": 0.7,
     }







@dataclass
class 				_a   (a__  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Union[List[PIL.Image.Image], np.ndarray]
     lowerCAmelCase_     :    Optional[List[bool]]
     lowerCAmelCase_     :    Optional[Union[List[PIL.Image.Image], np.ndarray]]
     lowerCAmelCase_     :    Optional[str]


if is_transformers_available() and is_torch_available():
      from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
      from .safety_checker import SafeStableDiffusionSafetyChecker









'''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			:      Any			=					"""K-MEANS-CLUST/ """



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase=None							):
     if seed is not None:  # useful for obtaining consistent results
          np.random.seed(__lowercase							)
     snake_case :			Optional[int]					   = data.shape[0]  # number of data points

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

     return centroids



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     return pairwise_distances(__lowercase    ,      __lowercase    ,      metric="""euclidean"""							)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     # Compute distances between each data point and the set of centroids:
     # Fill in the blank (RHS only)
     snake_case :			Union[str, Any]					   = centroid_pairwise_dist(__lowercase    ,      __lowercase							)

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

     return cluster_assignment



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

     return new_centroids



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

     return heterogeneity



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     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 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase=500    ,      __lowercase=None    ,      __lowercase=False							):
     snake_case :			Tuple					   = initial_centroids[:]
     snake_case :			Dict					   = None

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

          # 1. Make cluster assignments using nearest centroids
          snake_case :			List[Any]					   = assign_clusters(__lowercase    ,      __lowercase							)

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

          snake_case :			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

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



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase=None							):
     # Fill missing values with given rules
     if fill_missing_report:
          snake_case :			List[str]					   = df.fillna(value=__lowercase							)
     snake_case :			List[Any]					   = 1
     snake_case :			Optional[int]					   = df.select_dtypes(np.number							).columns
     snake_case :			int					   = (
         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 / 100							) : round(len(__lowercase							) * 75 / 100							)
                             ]							)							),
                 ),
                 ("""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)
     snake_case :			List[str]					   = report[
         (report["""Features"""] == """dummy""") & (report["""Type"""] == """count""")
     ].copy()  # avoid SettingWithCopyWarning
     snake_case :			Optional[Any]					   = (
         """ClusterSize"""  # rename created cluster df to match report column names
     )
     snake_case :			Optional[int]					   = """# of Customers"""
     snake_case :			List[str]					   = pd.DataFrame(
         clustersize.iloc[:, 2:].values
         / clustersize.iloc[:, 2:].values.sum()							)
     snake_case :			List[str]					   = """% of Customers"""  # rename created cluster df to match report column names
     snake_case :			List[Any]					   = """ClusterProportion"""
     snake_case :			Tuple					   = clusterproportion.columns.tolist()
     snake_case :			Optional[int]					   = cols[-2:] + cols[:-2]
     snake_case :			Tuple					   = clusterproportion[cols]  # rearrange columns to match report
     snake_case :			Tuple					   = report.columns
     snake_case :			List[str]					   = pd.DataFrame(
         abs(
             report[report["""Type"""] == """count"""].iloc[:, 2:].values
             - clustersize.iloc[:, 2:].values							)							)  # generating df with count of nan values
     snake_case :			Tuple					   = 0
     snake_case :			int					   = """# of nan"""
     snake_case :			int					   = report[
         report["""Type"""] == """count"""
     ].Features.tolist()  # filling values in order to match report
     snake_case :			Optional[Any]					   = a.columns.tolist()
     snake_case :			Dict					   = cols[-2:] + cols[:-2]
     snake_case :			Optional[Any]					   = a[cols]  # rearrange columns to match report
     snake_case :			Tuple					   = report.columns  # rename columns to match report
     snake_case :			Optional[Any]					   = report.drop(
         report[report.Type == """count"""].index							)  # drop count values except cluster size
     snake_case :			List[Any]					   = pd.concat(
         [report, a, clustersize, clusterproportion]    ,      axis=0							)  # concat report with clustert size and nan values
     snake_case :			Optional[int]					   = report["""Features"""].isin(__lowercase							)
     snake_case :			Any					   = report.columns.tolist()
     snake_case :			Union[str, Any]					   = cols[0:2] + cols[-1:] + cols[2:-1]
     snake_case :			List[Any]					   = report[cols]
     snake_case :			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,
     }
     snake_case :			List[Any]					   = (
         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							)
     )
     snake_case :			str					   = """"""
     snake_case :			Dict					   = report.reset_index()
     snake_case :			Optional[int]					   = report.drop(columns=["""index"""]							)
     return report


if __name__ == "__main__":
      import doctest

      doctest.testmod()



