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       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase=None    )							-> Tuple:




						'''simple docstring'''



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

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

						# 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.
						lowerCamelCase__ =data[rand_indices, :]

						return centroids





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Union[str, Any]:




						'''simple docstring'''



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





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[Any]:




						'''simple docstring'''



						lowerCamelCase__ =centroid_pairwise_dist(__lowerCAmelCase       ,   __lowerCAmelCase    )

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

						return cluster_assignment





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> int:




						'''simple docstring'''



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

						return new_centroids





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Dict:




						'''simple docstring'''



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

						return heterogeneity





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[Any]:




						'''simple docstring'''



						plt.figure(figsize=(7, 4)    )
						plt.plot(__lowerCAmelCase       ,   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_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase=500       ,   __lowerCAmelCase=None       ,   __lowerCAmelCase=False    )							-> List[Any]:




						'''simple docstring'''



						lowerCamelCase__ =initial_centroids[:]
						lowerCamelCase__ =None

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

												# 1. Make cluster assignments using nearest centroids
												lowerCamelCase__ =assign_clusters(__lowerCAmelCase       ,   __lowerCAmelCase    )

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

												# 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:
																		lowerCamelCase__ =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
																		lowerCamelCase__ =compute_heterogeneity(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )
																		record_heterogeneity.append(__lowerCAmelCase    )

												lowerCamelCase__ =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=400,
	    record_heterogeneity=heterogeneity,
	    verbose=True,
	)
	plot_heterogeneity(heterogeneity, k)





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase=None    )							-> pd.DataFrame:




						'''simple docstring'''



						if fill_missing_report:
												lowerCamelCase__ =df.fillna(value=__lowerCAmelCase    )
						lowerCamelCase__ =1
						lowerCamelCase__ =df.select_dtypes(np.number    ).columns
						lowerCamelCase__ =(
						    df.groupby(["Cluster"]    )[  # construct report dataframe
						        numeric_cols
						    ]  # group by cluster number
						    .agg(
						        [
						            ("sum", np.sum),
						            ("mean_with_zeros", lambda __lowerCAmelCase    : np.mean(np.nan_to_num(__lowerCAmelCase    )    )),
						            ("mean_without_zeros", lambda __lowerCAmelCase    : x.replace(0       ,   np.NaN    ).mean()),
						            (
						                "mean_25-75",
						                lambda __lowerCAmelCase    : np.mean(
						                    np.nan_to_num(
						                        sorted(__lowerCAmelCase    )[
						                            round(len(__lowerCAmelCase    ) * 25 / 100    ) : round(len(__lowerCAmelCase    ) * 75 / 100    )
						                        ]    )    ),
						            ),
						            ("mean_with_na", np.mean),
						            ("min", lambda __lowerCAmelCase    : x.min()),
						            ("5%", lambda __lowerCAmelCase    : x.quantile(0.05    )),
						            ("25%", lambda __lowerCAmelCase    : x.quantile(0.25    )),
						            ("50%", lambda __lowerCAmelCase    : x.quantile(0.50    )),
						            ("75%", lambda __lowerCAmelCase    : x.quantile(0.75    )),
						            ("95%", lambda __lowerCAmelCase    : x.quantile(0.95    )),
						            ("max", lambda __lowerCAmelCase    : x.max()),
						            ("count", lambda __lowerCAmelCase    : x.count()),
						            ("stdev", lambda __lowerCAmelCase    : x.std()),
						            ("mode", lambda __lowerCAmelCase    : x.mode()[0]),
						            ("median", lambda __lowerCAmelCase    : x.median()),
						            ("# > 0", lambda __lowerCAmelCase    : (x > 0).sum()),
						        ]    )
						    .T.reset_index()
						    .rename(index=__lowerCAmelCase       ,   columns={"level_0": "Features", "level_1": "Type"}    )
						)  # rename columns
						# calculate the size of cluster(count of clientID's)
						lowerCamelCase__ =report[
						    (report["Features"] == "dummy") & (report["Type"] == "count")
						].copy()  # avoid SettingWithCopyWarning
						lowerCamelCase__ =(
						    "ClusterSize"  # rename created cluster df to match report column names
						)
						lowerCamelCase__ ="# of Customers"
						lowerCamelCase__ =pd.DataFrame(
						    clustersize.iloc[:, 2:].values
						    / clustersize.iloc[:, 2:].values.sum()    )
						lowerCamelCase__ ="% of Customers"  # rename created cluster df to match report column names
						lowerCamelCase__ ="ClusterProportion"
						lowerCamelCase__ =clusterproportion.columns.tolist()
						lowerCamelCase__ =cols[-2:] + cols[:-2]
						lowerCamelCase__ =clusterproportion[cols]  # rearrange columns to match report
						lowerCamelCase__ =report.columns
						lowerCamelCase__ =pd.DataFrame(
						    abs(
						        report[report["Type"] == "count"].iloc[:, 2:].values
						        - clustersize.iloc[:, 2:].values    )    )  # generating df with count of nan values
						lowerCamelCase__ =0
						lowerCamelCase__ ="# of nan"
						lowerCamelCase__ =report[
						    report["Type"] == "count"
						].Features.tolist()  # filling values in order to match report
						lowerCamelCase__ =a.columns.tolist()
						lowerCamelCase__ =cols[-2:] + cols[:-2]
						lowerCamelCase__ =a[cols]  # rearrange columns to match report
						lowerCamelCase__ =report.columns  # rename columns to match report
						lowerCamelCase__ =report.drop(
						    report[report.Type == "count"].index    )  # drop count values except cluster size
						lowerCamelCase__ =pd.concat(
						    [report, a, clustersize, clusterproportion]       ,   axis=0    )  # concat report with clustert size and nan values
						lowerCamelCase__ =report["Features"].isin(__lowerCAmelCase    )
						lowerCamelCase__ =report.columns.tolist()
						lowerCamelCase__ =cols[0:2] + cols[-1:] + cols[2:-1]
						lowerCamelCase__ =report[cols]
						lowerCamelCase__ ={
						    "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,
						}
						lowerCamelCase__ =(
						    report.assign(
						        Sortera=lambda __lowerCAmelCase    : x.Type.map(__lowerCAmelCase    )       ,   Sortera=lambda __lowerCAmelCase    : list(reversed(range(len(__lowerCAmelCase    )    )    )    )       ,   )
						    .sort_values(["Sorter1", "Mark", "Sorter2"]       ,   ascending=__lowerCAmelCase    )
						    .drop(["Sorter1", "Sorter2"]       ,   axis=1    )
						)
						lowerCamelCase__ =""
						lowerCamelCase__ =report.reset_index()
						lowerCamelCase__ =report.drop(columns=["index"]    )
						return report


if __name__ == "__main__":
	import doctest

	doctest.testmod()



"""simple docstring"""


import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class        __UpperCAmelCase						(     __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase     ):
				A__				:       Any  							=	IFSuperResolutionPipeline
				A__				:       Tuple  							=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
				A__				:       Optional[int]  							=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				A__				:       List[Any]  							=	PipelineTesterMixin.required_optional_params - {'''latents'''}

				def 		_a     (				self				):
										return self._get_superresolution_dummy_components()

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase=0				):
										if str(_lowerCamelCase				).startswith("mps"				):
																lowerCamelCase__ =torch.manual_seed(_lowerCamelCase				)
										else:
																lowerCamelCase__ =torch.Generator(device=_lowerCamelCase				).manual_seed(_lowerCamelCase				)

										lowerCamelCase__ =floats_tensor((1, 3, 32, 32)     ,			rng=random.Random(_lowerCamelCase				)				).to(_lowerCamelCase				)

										lowerCamelCase__ ={
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": image,
										    "generator": generator,
										    "num_inference_steps": 2,
										    "output_type": "numpy",
										}

										return inputs

				@unittest.skipIf(
				    torch_device != "cuda" or not is_xformers_available()     ,			reason="XFormers attention is only available with CUDA and `xformers` installed"     ,			)
				def 		_a     (				self				):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)

				def 		_a     (				self				):
										self._test_save_load_optional_components()

				@unittest.skipIf(torch_device != "cuda"     ,			reason="float16 requires CUDA"				)
				def 		_a     (				self				):
										# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
										super().test_save_load_floataa(expected_max_diff=1E-1				)

				def 		_a     (				self				):
										self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)

				def 		_a     (				self				):
										self._test_save_load_local()






				def 		_a     (				self				):
										self._test_inference_batch_single_identical(
										    expected_max_diff=1E-2     ,			)


