

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase     :			Optional[Any]    ={"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase     :			Optional[int]    =[
					    """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """SEWDForCTC""",
					    """SEWDForSequenceClassification""",
					    """SEWDModel""",
					    """SEWDPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sew_d import (
										    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SEWDForCTC,
										    SEWDForSequenceClassification,
										    SEWDModel,
										    SEWDPreTrainedModel,
										)


else:
					import sys

					_lowerCAmelCase     :			int    =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


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     :			str    ="""K-MEANS-CLUST/ """



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE=None			):
	if seed is not None:  # useful for obtaining consistent results
		np.random.seed(SCREAMING_SNAKE_CASE			)
	UpperCAmelCase__:  Optional[Any]		       =   data.shape[0]  # number of data points

	# Pick K indices from range [0, N).
	UpperCAmelCase__:  Optional[int]		       =   np.random.randint(0  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)

	# 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.
	UpperCAmelCase__:  Tuple		       =   data[rand_indices, :]

	return centroids



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	return pairwise_distances(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,metric="euclidean"			)



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

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

	return cluster_assignment



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

	return new_centroids



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

	return heterogeneity



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
	plt.figure(figsize=(7, 4)			)
	plt.plot(SCREAMING_SNAKE_CASE  ,linewidth=4			)
	plt.xlabel("# Iterations"			)
	plt.ylabel("Heterogeneity"			)
	plt.title(f"Heterogeneity of clustering over time, K={k:d}"			)
	plt.rcParams.update({"font.size": 1_6}			)
	plt.show()



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE=5_0_0  ,SCREAMING_SNAKE_CASE=None  ,SCREAMING_SNAKE_CASE=False			):
	UpperCAmelCase__:  Optional[int]		       =   initial_centroids[:]
	UpperCAmelCase__:  List[str]		       =   None

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

		# 1. Make cluster assignments using nearest centroids
		UpperCAmelCase__:  Optional[Any]		       =   assign_clusters(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)

		# 2. Compute a new centroid for each of the k clusters, averaging all data
		#    points assigned to that cluster.
		UpperCAmelCase__:  Union[str, Any]		       =   revise_centroids(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)

		# 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:
			UpperCAmelCase__:  Union[str, Any]		       =   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
			UpperCAmelCase__:  str		       =   compute_heterogeneity(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)
			record_heterogeneity.append(SCREAMING_SNAKE_CASE			)

		UpperCAmelCase__:  str		       =   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     :			int    =3
					_lowerCAmelCase     :			List[str]    =[]
					_lowerCAmelCase     :			int    =get_initial_centroids(dataset["""data"""], k, seed=0)
					_lowerCAmelCase   ,  _lowerCAmelCase     :			Union[str, Any]    =kmeans(
					    dataset["""data"""],
					    k,
					    initial_centroids,
					    maxiter=4_00,
					    record_heterogeneity=heterogeneity,
					    verbose=True,
					)
					plot_heterogeneity(heterogeneity, k)



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE=None			):
	# Fill missing values with given rules
	if fill_missing_report:
		UpperCAmelCase__:  int		       =   df.fillna(value=SCREAMING_SNAKE_CASE			)
	UpperCAmelCase__:  Any		       =   1
	UpperCAmelCase__:  Any		       =   df.select_dtypes(np.number			).columns
	UpperCAmelCase__:  List[Any]		       =   (
	    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(SCREAMING_SNAKE_CASE			)			)),
	            ("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(SCREAMING_SNAKE_CASE			)[
	                            round(len(SCREAMING_SNAKE_CASE			) * 2_5 / 1_0_0			) : round(len(SCREAMING_SNAKE_CASE			) * 7_5 / 1_0_0			)
	                        ]			)			),
	            ),
	            ("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=SCREAMING_SNAKE_CASE  ,columns={"level_0": "Features", "level_1": "Type"}			)
	)  # rename columns
	# calculate the size of cluster(count of clientID's)
	UpperCAmelCase__:  str		       =   report[
	    (report["Features"] == "dummy") & (report["Type"] == "count")
	].copy()  # avoid SettingWithCopyWarning
	UpperCAmelCase__:  int		       =   (
	    "ClusterSize"  # rename created cluster df to match report column names
	)
	UpperCAmelCase__:  List[str]		       =   "# of Customers"
	UpperCAmelCase__:  int		       =   pd.DataFrame(
	    clustersize.iloc[:, 2:].values
	    / clustersize.iloc[:, 2:].values.sum()			)
	UpperCAmelCase__:  Dict		       =   "% of Customers"  # rename created cluster df to match report column names
	UpperCAmelCase__:  Any		       =   "ClusterProportion"
	UpperCAmelCase__:  int		       =   clusterproportion.columns.tolist()
	UpperCAmelCase__:  int		       =   cols[-2:] + cols[:-2]
	UpperCAmelCase__:  Optional[int]		       =   clusterproportion[cols]  # rearrange columns to match report
	UpperCAmelCase__:  Union[str, Any]		       =   report.columns
	UpperCAmelCase__:  int		       =   pd.DataFrame(
	    abs(
	        report[report["Type"] == "count"].iloc[:, 2:].values
	        - clustersize.iloc[:, 2:].values			)			)  # generating df with count of nan values
	UpperCAmelCase__:  Optional[int]		       =   0
	UpperCAmelCase__:  Tuple		       =   "# of nan"
	UpperCAmelCase__:  Any		       =   report[
	    report["Type"] == "count"
	].Features.tolist()  # filling values in order to match report
	UpperCAmelCase__:  int		       =   a.columns.tolist()
	UpperCAmelCase__:  Tuple		       =   cols[-2:] + cols[:-2]
	UpperCAmelCase__:  List[str]		       =   a[cols]  # rearrange columns to match report
	UpperCAmelCase__:  int		       =   report.columns  # rename columns to match report
	UpperCAmelCase__:  List[Any]		       =   report.drop(
	    report[report.Type == "count"].index			)  # drop count values except cluster size
	UpperCAmelCase__:  Optional[Any]		       =   pd.concat(
	    [report, a, clustersize, clusterproportion]  ,axis=0			)  # concat report with clustert size and nan values
	UpperCAmelCase__:  Any		       =   report["Features"].isin(SCREAMING_SNAKE_CASE			)
	UpperCAmelCase__:  Tuple		       =   report.columns.tolist()
	UpperCAmelCase__:  str		       =   cols[0:2] + cols[-1:] + cols[2:-1]
	UpperCAmelCase__:  int		       =   report[cols]
	UpperCAmelCase__:  str		       =   {
	    "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,
	}
	UpperCAmelCase__:  int		       =   (
	    report.assign(
	        Sortera=lambda SCREAMING_SNAKE_CASE			: x.Type.map(SCREAMING_SNAKE_CASE			)  ,Sortera=lambda SCREAMING_SNAKE_CASE			: list(reversed(range(len(SCREAMING_SNAKE_CASE			)			)			)			)  ,)
	    .sort_values(["Sorter1", "Mark", "Sorter2"]  ,ascending=SCREAMING_SNAKE_CASE			)
	    .drop(["Sorter1", "Sorter2"]  ,axis=1			)
	)
	UpperCAmelCase__:  List[str]		       =   ""
	UpperCAmelCase__:  Any		       =   report.reset_index()
	UpperCAmelCase__:  Any		       =   report.drop(columns=["index"]			)
	return report


if __name__ == "__main__":
					import doctest

					doctest.testmod()