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__  	=      "K-MEANS-CLUST/ "

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

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

	return centroids

def 	__lowerCamelCase							(				__a    : List[Any]      ,							__a    : Optional[int]		)      ->       int:
	return pairwise_distances(__a      ,							__a      ,							metric="euclidean"		)

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

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

	return cluster_assignment

def 	__lowerCamelCase							(				__a    : Union[str, Any]      ,							__a    : List[Any]      ,							__a    : Optional[int]		)      ->       Tuple:
	_lowercase   =[]
	for i in range(__a		):
		# 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(__a		)
	_lowercase   =np.array(__a		)

	return new_centroids

def 	__lowerCamelCase							(				__a    : Optional[int]      ,							__a    : Optional[Any]      ,							__a    : Optional[int]      ,							__a    : Union[str, Any]		)      ->       List[str]:
	_lowercase   =0.0
	for i in range(__a		):
		# 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(
			    __a      ,							[centroids[i]]      ,							metric="euclidean"		)
			_lowercase   =distances**2
			heterogeneity += np.sum(__a		)

	return heterogeneity

def 	__lowerCamelCase							(				__a    : List[Any]      ,							__a    : List[Any]		)      ->       str:
	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 	__lowerCamelCase							(				__a    : Dict      ,							__a    : List[str]      ,							__a    : List[Any]      ,							__a    : Union[str, Any]=500      ,							__a    : List[str]=None      ,							__a    : List[str]=False		)      ->       Optional[Any]:
	_lowercase   =initial_centroids[:]
	_lowercase   =None

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

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

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

		_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

						lowerCAmelCase__  	=      ds.load_iris()
						lowerCAmelCase__  	=      3
						lowerCAmelCase__  	=      []
						lowerCAmelCase__  	=      get_initial_centroids(dataset["data"], k, seed=0)
						lowerCAmelCase__	,					lowerCAmelCase__  	=      kmeans(
						    dataset["data"],
						    k,
						    initial_centroids,
						    maxiter=4_0_0,
						    record_heterogeneity=heterogeneity,
						    verbose=True,
						)
						plot_heterogeneity(heterogeneity, k)

def 	__lowerCamelCase							(				__a    : pd.DataFrame      ,							__a    : np.ndarray      ,							__a    : Union[str, Any]=None		)      ->       pd.DataFrame:
	# Fill missing values with given rules
	if fill_missing_report:
		_lowercase   =df.fillna(value=__a		)
	_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 __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)
	_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(__a		)
	_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 __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		)
	)
	_lowercase   =""
	_lowercase   =report.reset_index()
	_lowercase   =report.drop(columns=["index"]		)
	return report


if __name__ == "__main__":
						import doctest

						doctest.testmod()






"""simple docstring"""






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import TvltImageProcessor
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase=10 ,   UpperCAmelCase=10 ,   UpperCAmelCase=False ,   UpperCAmelCase=False     )						->Union[str, Any]:

   """simple docstring"""



   a_						   =     []
   for i in range(image_processor_tester.num_frames     ):
      video.append(np.random.randint(255 ,   size=(image_processor_tester.num_channels, width, height) ,   dtype=np.uinta     )     )

   if not numpify and not torchify:
      # PIL expects the channel dimension as last dimension
      a_						   =     [Image.fromarray(np.moveaxis(UpperCAmelCase ,   0 ,   -1     )     ) for frame in video]

   if torchify:
      a_						   =     [torch.from_numpy(UpperCAmelCase     ) for frame in video]

   return video
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase=False ,   UpperCAmelCase=False ,   UpperCAmelCase=False     )						->Optional[Any]:

   """simple docstring"""



   assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

   a_						   =     []
   for i in range(image_processor_tester.batch_size     ):
      if equal_resolution:
         a_						   =     a_						   =     image_processor_tester.max_resolution
      else:
         a_   ,			a_						   =     np.random.choice(
             np.arange(image_processor_tester.min_resolution ,   image_processor_tester.max_resolution     ) ,   2     )
         a_						   =     prepare_video(
             image_processor_tester=UpperCAmelCase ,   width=UpperCAmelCase ,   height=UpperCAmelCase ,   numpify=UpperCAmelCase ,   torchify=UpperCAmelCase ,   )
      video_inputs.append(UpperCAmelCase     )

   return video_inputs





class 							snake_case       (     unittest.TestCase		):







   def __init__( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase=7       ,							__UpperCAmelCase=3       ,							__UpperCAmelCase=4       ,							__UpperCAmelCase=18       ,							__UpperCAmelCase=30       ,							__UpperCAmelCase=4_00       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=None       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=[0.5, 0.5, 0.5]       ,							__UpperCAmelCase=[0.5, 0.5, 0.5]       ,							__UpperCAmelCase=True       ,							__UpperCAmelCase=None       ,							)   ->str:
      a_						   =     size if size is not None else {"shortest_edge": 18}
      a_						   =     crop_size if crop_size is not None else {"height": 18, "width": 18}

      a_						   =     parent
      a_						   =     batch_size
      a_						   =     num_channels
      a_						   =     num_frames
      a_						   =     image_size
      a_						   =     min_resolution
      a_						   =     max_resolution
      a_						   =     do_resize
      a_						   =     size
      a_						   =     do_normalize
      a_						   =     image_mean
      a_						   =     image_std
      a_						   =     do_center_crop
      a_						   =     crop_size







   def    UpperCAmelCase__	( self)   ->Tuple:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }






@require_torch
@require_vision
class 							snake_case       (     SCREAMING_SNAKE_CASE_							,       unittest.TestCase		):
   a_					:		int     					=     TvltImageProcessor if is_vision_available() else None







   def    UpperCAmelCase__	( self)   ->Optional[Any]:
      a_						   =     TvltImageProcessorTester(self)







   @property
   def    UpperCAmelCase__	( self)   ->int:
      return self.image_processor_tester.prepare_image_processor_dict()







   def    UpperCAmelCase__	( self)   ->int:
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"image_mean"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"image_std"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"do_normalize"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"do_resize"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"do_center_crop"))
      self.assertTrue(hasattr(__UpperCAmelCase       ,							"size"))







   def    UpperCAmelCase__	( self)   ->Any:
      # Initialize image_processor
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random PIL videos
      a_						   =     prepare_video_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase)
      for video in video_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							__UpperCAmelCase)
         self.assertIsInstance(video[0]       ,							Image.Image)

      # Test not batched input
      a_						   =     image_processor(video_inputs[0]       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)

      # Test batched
      a_						   =     image_processor(__UpperCAmelCase       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)







   def    UpperCAmelCase__	( self)   ->int:
      # Initialize image_processor
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      a_						   =     prepare_video_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase       ,							numpify=__UpperCAmelCase)
      for video in video_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							__UpperCAmelCase)
         self.assertIsInstance(video[0]       ,							np.ndarray)

      # Test not batched input
      a_						   =     image_processor(video_inputs[0]       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)

      # Test batched
      a_						   =     image_processor(__UpperCAmelCase       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)







   def    UpperCAmelCase__	( self)   ->Tuple:
      # Initialize image_processor
      a_						   =     self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      a_						   =     prepare_video_inputs(self.image_processor_tester       ,							equal_resolution=__UpperCAmelCase       ,							torchify=__UpperCAmelCase)
      for video in video_inputs:
         self.assertIsInstance(__UpperCAmelCase       ,							__UpperCAmelCase)
         self.assertIsInstance(video[0]       ,							torch.Tensor)

      # Test not batched input
      a_						   =     image_processor(video_inputs[0]       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)

      # Test batched
      a_						   =     image_processor(__UpperCAmelCase       ,							return_tensors="pt").pixel_values
      self.assertEqual(
          encoded_videos.shape       ,							(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,							)