
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		:   Optional[Any]         =    'K-MEANS-CLUST/ '




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Optional[Any]		,      SCREAMING_SNAKE_CASE_  :  Optional[Any]		,      SCREAMING_SNAKE_CASE_  :  Union[str, Any]=None					)     ->    List[Any]:
						if seed is not None:  # useful for obtaining consistent results
												np.random.seed(lowercase__					)
						_lowercase									=				data.shape[0]  # number of data points

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

						return centroids




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Dict		,      SCREAMING_SNAKE_CASE_  :  List[Any]					)     ->    Union[str, Any]:
						return pairwise_distances(lowercase__		,      lowercase__		,      metric="""euclidean"""					)




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Dict		,      SCREAMING_SNAKE_CASE_  :  Optional[Any]					)     ->    List[str]:
						# Compute distances between each data point and the set of centroids:
						# Fill in the blank (RHS only)
						_lowercase									=				centroid_pairwise_dist(lowercase__		,      lowercase__					)

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

						return cluster_assignment




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

						return new_centroids




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

						return heterogeneity




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Optional[int]		,      SCREAMING_SNAKE_CASE_  :  Optional[int]					)     ->    Dict:
						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   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Dict		,      SCREAMING_SNAKE_CASE_  :  Optional[Any]		,      SCREAMING_SNAKE_CASE_  :  List[Any]		,      SCREAMING_SNAKE_CASE_  :  int=5_00		,      SCREAMING_SNAKE_CASE_  :  List[str]=None		,      SCREAMING_SNAKE_CASE_  :  Any=False					)     ->    List[str]:
						_lowercase									=				initial_centroids[:]
						_lowercase									=				None

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

												# 1. Make cluster assignments using nearest centroids
												_lowercase									=				assign_clusters(lowercase__		,      lowercase__					)

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

												_lowercase									=				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		:   str         =    ds.load_iris()
		A		:   Optional[Any]         =    3
		A		:   Tuple         =    []
		A		:   Any         =    get_initial_centroids(dataset['''data'''], k, seed=0)
		A		:   Any         =    kmeans(
		    dataset['''data'''],
		    k,
		    initial_centroids,
		    maxiter=400,
		    record_heterogeneity=heterogeneity,
		    verbose=True,
		)
		plot_heterogeneity(heterogeneity, k)




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  pd.DataFrame		,      SCREAMING_SNAKE_CASE_  :  np.ndarray		,      SCREAMING_SNAKE_CASE_  :  Optional[Any]=None					)     ->    Any:
						# Fill missing values with given rules
						if fill_missing_report:
												_lowercase									=				df.fillna(value=lowercase__					)
						_lowercase									=				1
						_lowercase									=				df.select_dtypes(np.number					).columns
						_lowercase									=				(
						    df.groupby(["""Cluster"""]					)[  # construct report dataframe
						        numeric_cols
						    ]  # group by cluster number
						    .agg(
						        [
						            ("""sum""", np.sum),
						            ("""mean_with_zeros""", lambda SCREAMING_SNAKE_CASE_					: np.mean(np.nan_to_num(lowercase__					)					)),
						            ("""mean_without_zeros""", lambda SCREAMING_SNAKE_CASE_					: x.replace(0		,      np.NaN					).mean()),
						            (
						                """mean_25-75""",
						                lambda SCREAMING_SNAKE_CASE_					: 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 SCREAMING_SNAKE_CASE_					: x.min()),
						            ("""5%""", lambda SCREAMING_SNAKE_CASE_					: x.quantile(0.05					)),
						            ("""25%""", lambda SCREAMING_SNAKE_CASE_					: x.quantile(0.25					)),
						            ("""50%""", lambda SCREAMING_SNAKE_CASE_					: x.quantile(0.50					)),
						            ("""75%""", lambda SCREAMING_SNAKE_CASE_					: x.quantile(0.75					)),
						            ("""95%""", lambda SCREAMING_SNAKE_CASE_					: x.quantile(0.95					)),
						            ("""max""", lambda SCREAMING_SNAKE_CASE_					: x.max()),
						            ("""count""", lambda SCREAMING_SNAKE_CASE_					: x.count()),
						            ("""stdev""", lambda SCREAMING_SNAKE_CASE_					: x.std()),
						            ("""mode""", lambda SCREAMING_SNAKE_CASE_					: x.mode()[0]),
						            ("""median""", lambda SCREAMING_SNAKE_CASE_					: x.median()),
						            ("""# > 0""", lambda SCREAMING_SNAKE_CASE_					: (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)
						_lowercase									=				report[
						    (report["""Features"""] == """dummy""") & (report["""Type"""] == """count""")
						].copy()  # avoid SettingWithCopyWarning
						_lowercase									=				(
						    """ClusterSize"""  # rename created cluster df to match report column names
						)
						_lowercase									=				"""# of Customers"""
						_lowercase									=				pd.DataFrame(
						    clustersize.iloc[:, 2:].values
						    / clustersize.iloc[:, 2:].values.sum()					)
						_lowercase									=				"""% of Customers"""  # rename created cluster df to match report column names
						_lowercase									=				"""ClusterProportion"""
						_lowercase									=				clusterproportion.columns.tolist()
						_lowercase									=				cols[-2:] + cols[:-2]
						_lowercase									=				clusterproportion[cols]  # rearrange columns to match report
						_lowercase									=				report.columns
						_lowercase									=				pd.DataFrame(
						    abs(
						        report[report["""Type"""] == """count"""].iloc[:, 2:].values
						        - clustersize.iloc[:, 2:].values					)					)  # generating df with count of nan values
						_lowercase									=				0
						_lowercase									=				"""# of nan"""
						_lowercase									=				report[
						    report["""Type"""] == """count"""
						].Features.tolist()  # filling values in order to match report
						_lowercase									=				a.columns.tolist()
						_lowercase									=				cols[-2:] + cols[:-2]
						_lowercase									=				a[cols]  # rearrange columns to match report
						_lowercase									=				report.columns  # rename columns to match report
						_lowercase									=				report.drop(
						    report[report.Type == """count"""].index					)  # drop count values except cluster size
						_lowercase									=				pd.concat(
						    [report, a, clustersize, clusterproportion]		,      axis=0					)  # concat report with clustert size and nan values
						_lowercase									=				report["""Features"""].isin(lowercase__					)
						_lowercase									=				report.columns.tolist()
						_lowercase									=				cols[0:2] + cols[-1:] + cols[2:-1]
						_lowercase									=				report[cols]
						_lowercase									=				{
						    """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,
						}
						_lowercase									=				(
						    report.assign(
						        Sortera=lambda SCREAMING_SNAKE_CASE_					: x.Type.map(lowercase__					)		,      Sortera=lambda SCREAMING_SNAKE_CASE_					: list(reversed(range(len(lowercase__					)					)					)					)		,      )
						    .sort_values(["""Sorter1""", """Mark""", """Sorter2"""]		,      ascending=lowercase__					)
						    .drop(["""Sorter1""", """Sorter2"""]		,      axis=1					)
						)
						_lowercase									=				""""""
						_lowercase									=				report.reset_index()
						_lowercase									=				report.drop(columns=["""index"""]					)
						return report


if __name__ == "__main__":
		import doctest

		doctest.testmod()

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A		:   Any         =    {'''configuration_bit''': ['''BIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BitConfig''', '''BitOnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		:   Any         =    [
		    '''BIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BitForImageClassification''',
		    '''BitModel''',
		    '''BitPreTrainedModel''',
		    '''BitBackbone''',
		]


try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		:   Tuple         =    ['''BitImageProcessor''']


if TYPE_CHECKING:
		from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bit import (
				    BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BitBackbone,
				    BitForImageClassification,
				    BitModel,
				    BitPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_bit import BitImageProcessor

else:
		import sys

		A		:   int         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)