





import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					def __init__(			self						,	_lowerCAmelCase						,	_lowerCAmelCase=sys.maxsize						):
										lowerCamelCase__      =							"bilinear"
										lowerCamelCase__      =							max_size
										lowerCamelCase__      =							short_edge_length






					def __call__(			self						,	_lowerCAmelCase						):
										lowerCamelCase__      =							[]
										for img in imgs:
															lowerCamelCase__     ,		lowerCamelCase__      =							img.shape[:2]
															# later: provide list and randomly choose index for resize
															lowerCamelCase__      =							np.random.randint(self.short_edge_length[0]						,	self.short_edge_length[1] + 1						)
															if size == 0:
																				return img
															lowerCamelCase__      =							size * 1.0 / min(_lowerCAmelCase						,	_lowerCAmelCase						)
															if h < w:
																				lowerCamelCase__     ,		lowerCamelCase__      =							size, scale * w
															else:
																				lowerCamelCase__     ,		lowerCamelCase__      =							scale * h, size
															if max(_lowerCAmelCase						,	_lowerCAmelCase						) > self.max_size:
																				lowerCamelCase__      =							self.max_size * 1.0 / max(_lowerCAmelCase						,	_lowerCAmelCase						)
																				lowerCamelCase__      =							newh * scale
																				lowerCamelCase__      =							neww * scale
															lowerCamelCase__      =							int(neww + 0.5						)
															lowerCamelCase__      =							int(newh + 0.5						)

															if img.dtype == np.uinta:
																				lowerCamelCase__      =							Image.fromarray(_lowerCAmelCase						)
																				lowerCamelCase__      =							pil_image.resize((neww, newh)						,	PILImageResampling.BILINEAR						)
																				lowerCamelCase__      =							np.asarray(_lowerCAmelCase						)
															else:
																				lowerCamelCase__      =							img.permute(2						,	0						,	1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
																				lowerCamelCase__      =							nn.functional.interpolate(
																				    _lowerCAmelCase						,	(newh, neww)						,	mode=self.interp_method						,	align_corners=_lowerCAmelCase						).squeeze(0						)
															img_augs.append(_lowerCAmelCase						)

										return img_augs

class SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					def __init__(			self						,	_lowerCAmelCase						):
										lowerCamelCase__      =							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]						,	cfg.INPUT.MAX_SIZE_TEST						)
										lowerCamelCase__      =							cfg.INPUT.FORMAT
										lowerCamelCase__      =							cfg.SIZE_DIVISIBILITY
										lowerCamelCase__      =							cfg.PAD_VALUE
										lowerCamelCase__      =							cfg.INPUT.MAX_SIZE_TEST
										lowerCamelCase__      =							cfg.MODEL.DEVICE
										lowerCamelCase__      =							torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)						,	1						,	1						)
										lowerCamelCase__      =							torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)						,	1						,	1						)
										lowerCamelCase__      =							lambda _lowerCAmelCase						: (x - self.pixel_mean) / self.pixel_std





					def        __magic_name__			(			self						,	_lowerCAmelCase						):
										lowerCamelCase__      =							tuple(max(_lowerCAmelCase						) for s in zip(*[img.shape for img in images]						)						)
										lowerCamelCase__      =							[im.shape[-2:] for im in images]
										lowerCamelCase__      =							[
										    nn.functional.pad(
										        _lowerCAmelCase						,	[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]						,	value=self.pad_value						,	)
										    for size, im in zip(_lowerCAmelCase						,	_lowerCAmelCase						)
										]

										return torch.stack(_lowerCAmelCase						), torch.tensor(_lowerCAmelCase						)






					def __call__(			self						,	_lowerCAmelCase						,	_lowerCAmelCase=False						):






										with torch.no_grad():
															if not isinstance(_lowerCAmelCase						,	_lowerCAmelCase						):
																				lowerCamelCase__      =							[images]
															if single_image:
																				assert len(_lowerCAmelCase						) == 1
															for i in range(len(_lowerCAmelCase						)						):
																				if isinstance(images[i]						,	torch.Tensor						):
																									images.insert(_lowerCAmelCase						,	images.pop(_lowerCAmelCase						).to(self.device						).float()						)
																				elif not isinstance(images[i]						,	torch.Tensor						):
																									images.insert(
																									    _lowerCAmelCase						,	torch.as_tensor(img_tensorize(images.pop(_lowerCAmelCase						)						,	input_format=self.input_format						)						)
																									    .to(self.device						)
																									    .float()						,	)
            # resize smallest edge
															lowerCamelCase__      =							torch.tensor([im.shape[:2] for im in images]						)
															lowerCamelCase__      =							self.aug(_lowerCAmelCase						)
															# transpose images and convert to torch tensors
															# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
															# now normalize before pad to avoid useless arithmetic
															lowerCamelCase__      =							[self.normalizer(_lowerCAmelCase						) for x in images]
															# now pad them to do the following operations
															lowerCamelCase__     ,		lowerCamelCase__      =							self.pad(_lowerCAmelCase						)
															# Normalize

															if self.size_divisibility > 0:
																				raise NotImplementedError()
															# pad
															lowerCamelCase__      =							torch.true_divide(_lowerCAmelCase						,	_lowerCAmelCase						)
															if single_image:
																				return images[0], sizes[0], scales_yx[0]
															else:
																				return images, sizes, scales_yx





def  __UpperCamelCase   (     a,  a)			->Optional[int]:
					boxes[:, 0::2] *= scale_yx[:, 1]
					boxes[:, 1::2] *= scale_yx[:, 0]
					return boxes







def  __UpperCamelCase   (     a,  a)			->Tuple:
					assert torch.isfinite(__lowerCamelCase).all(), "Box tensor contains infinite or NaN!"
					lowerCamelCase__     ,		lowerCamelCase__      =							box_size
					tensor[:, 0].clamp_(min=0,  max=__lowerCamelCase)
					tensor[:, 1].clamp_(min=0,  max=__lowerCamelCase)
					tensor[:, 2].clamp_(min=0,  max=__lowerCamelCase)
					tensor[:, 3].clamp_(min=0,  max=__lowerCamelCase)





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""")

SCREAMING_SNAKE_CASE__       			=				"""K-MEANS-CLUST/ """




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[str]  ,			__lowerCamelCase:		List[Any]  ,			__lowerCamelCase:		Tuple=None  ):







	'''simple docstring'''



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

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

	return centroids




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Union[str, Any]  ,			__lowerCamelCase:		Union[str, Any]  ):







	'''simple docstring'''



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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[str]  ,			__lowerCamelCase:		int  ):







	'''simple docstring'''



	lowercase_			 = centroid_pairwise_dist(__lowerCamelCase  ,			__lowerCamelCase  )

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

	return cluster_assignment




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Dict  ,			__lowerCamelCase:		str  ,			__lowerCamelCase:		str  ):







	'''simple docstring'''



	lowercase_			 = []
	for i in range(__lowerCamelCase  ):
		# 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(__lowerCamelCase  )
	lowercase_			 = np.array(__lowerCamelCase  )

	return new_centroids




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		str  ,			__lowerCamelCase:		str  ,			__lowerCamelCase:		List[Any]  ,			__lowerCamelCase:		Optional[Any]  ):







	'''simple docstring'''



	lowercase_			 = 0.0
	for i in range(__lowerCamelCase  ):
		# 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(
			    __lowerCamelCase  ,			[centroids[i]]  ,			metric="euclidean"  )
			lowercase_			 = distances**2
			heterogeneity += np.sum(__lowerCamelCase  )

	return heterogeneity




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[Any]  ,			__lowerCamelCase:		Union[str, 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 SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		int  ,			__lowerCamelCase:		Dict  ,			__lowerCamelCase:		Any  ,			__lowerCamelCase:		str=500  ,			__lowerCamelCase:		Union[str, Any]=None  ,			__lowerCamelCase:		Optional[int]=False  ):







	'''simple docstring'''



	lowercase_			 = initial_centroids[:]
	lowercase_			 = None

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

		# 1. Make cluster assignments using nearest centroids
		lowercase_			 = assign_clusters(__lowerCamelCase  ,			__lowerCamelCase  )

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

		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

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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		pd.DataFrame  ,			__lowerCamelCase:		np.ndarray  ,			__lowerCamelCase:		str=None  ):







	'''simple docstring'''



	if fill_missing_report:
		lowercase_			 = df.fillna(value=__lowerCamelCase  )
	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 __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)
	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(__lowerCamelCase  )
	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 __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  )
	)
	lowercase_			 = ""
	lowercase_			 = report.reset_index()
	lowercase_			 = report.drop(columns=["index"]  )
	return report


if __name__ == "__main__":
							import doctest

							doctest.testmod()

