


"""simple docstring"""




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 						__a      (  a    ,		a    ,		a=None					):





       """simple docstring"""







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

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

       return centroids



def 						__a      (  a    ,		a					):





       """simple docstring"""







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



def 						__a      (  a    ,		a					):





       """simple docstring"""







       _a          =				centroid_pairwise_dist(a    ,		a					)

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

       return cluster_assignment



def 						__a      (  a    ,		a    ,		a					):





       """simple docstring"""







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

       return new_centroids



def 						__a      (  a    ,		a    ,		a    ,		a					):





       """simple docstring"""







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

       return heterogeneity



def 						__a      (  a    ,		a					):





       """simple docstring"""







       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": 1_6}					)
       plt.show()



def 						__a      (  a    ,		a    ,		a    ,		a=5_0_0    ,		a=None    ,		a=False					):





       """simple docstring"""







       _a          =				initial_centroids[:]
       _a          =				None

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

              # 1. Make cluster assignments using nearest centroids
              _a          =				assign_clusters(a    ,		a					)

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

              _a          =				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_00,
       record_heterogeneity=heterogeneity,
       verbose=True,
   )
   plot_heterogeneity(heterogeneity, k)



def 						__a      (  a    ,		a    ,		a=None					):





       """simple docstring"""







       if fill_missing_report:
              _a          =				df.fillna(value=a					)
       _a          =				1
       _a          =				df.select_dtypes(np.number					).columns
       _a          =				(
           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					) * 2_5 / 1_0_0					) : round(len(a					) * 7_5 / 1_0_0					)
                               ]					)					),
                   ),
                   ("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)
       _a          =				report[
           (report["Features"] == "dummy") & (report["Type"] == "count")
       ].copy()  # avoid SettingWithCopyWarning
       _a          =				(
           "ClusterSize"  # rename created cluster df to match report column names
       )
       _a          =				"# of Customers"
       _a          =				pd.DataFrame(
           clustersize.iloc[:, 2:].values
           / clustersize.iloc[:, 2:].values.sum()					)
       _a          =				"% of Customers"  # rename created cluster df to match report column names
       _a          =				"ClusterProportion"
       _a          =				clusterproportion.columns.tolist()
       _a          =				cols[-2:] + cols[:-2]
       _a          =				clusterproportion[cols]  # rearrange columns to match report
       _a          =				report.columns
       _a          =				pd.DataFrame(
           abs(
               report[report["Type"] == "count"].iloc[:, 2:].values
               - clustersize.iloc[:, 2:].values					)					)  # generating df with count of nan values
       _a          =				0
       _a          =				"# of nan"
       _a          =				report[
           report["Type"] == "count"
       ].Features.tolist()  # filling values in order to match report
       _a          =				a.columns.tolist()
       _a          =				cols[-2:] + cols[:-2]
       _a          =				a[cols]  # rearrange columns to match report
       _a          =				report.columns  # rename columns to match report
       _a          =				report.drop(
           report[report.Type == "count"].index					)  # drop count values except cluster size
       _a          =				pd.concat(
           [report, a, clustersize, clusterproportion]    ,		axis=0					)  # concat report with clustert size and nan values
       _a          =				report["Features"].isin(a					)
       _a          =				report.columns.tolist()
       _a          =				cols[0:2] + cols[-1:] + cols[2:-1]
       _a          =				report[cols]
       _a          =				{
           "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,
       }
       _a          =				(
           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					)
       )
       _a          =				""
       _a          =				report.reset_index()
       _a          =				report.drop(columns=["index"]					)
       return report


if __name__ == "__main__":
   import doctest

   doctest.testmod()




"""simple docstring"""




from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)





class __snake_case		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




   def __init__(		self							:List[Any]				,  UpperCamelCase__							:AutoencoderKL				,  UpperCamelCase__							:CLIPTextModel				,  UpperCamelCase__							:CLIPTokenizer				,  UpperCamelCase__							:UNetaDConditionModel				,  UpperCamelCase__							:Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]				,  ):
          super().__init__()

          self.register_modules(vae=UpperCamelCase__				,  text_encoder=UpperCamelCase__				,  tokenizer=UpperCamelCase__				,  unet=UpperCamelCase__				,  scheduler=UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:str       ):
          with torch.no_grad():
                 _a          =				self.vae.encode(tfms.ToTensor()(UpperCamelCase__       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
                 _a          =				0.18215 * latent.latent_dist.sample()
          return latent




   def SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:Any       ):
          _a          =				(1 / 0.18215) * latent
          with torch.no_grad():
                 _a          =				self.vae.decode(UpperCamelCase__       ).sample
          _a          =				(img / 2 + 0.5).clamp(0				,  1       )
          _a          =				img.detach().cpu().permute(0				,  2				,  3				,  1       ).numpy()
          _a          =				(img * 255).round().astype("uint8"       )
          return Image.fromarray(img[0]       )




   def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:List[Any]       ):
          _a          =				self.tokenizer(
              UpperCamelCase__				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  return_tensors="pt"				,  )

          _a          =				self.text_encoder(text_input.input_ids.to(self.device       )       )[0]

          _a          =				self.tokenizer(
              ""				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  return_tensors="pt"				,  )

          _a          =				self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

          return torch.cat([uncond_embedding, text_embedding]       )




   def __call__(		self							:Tuple				,  UpperCamelCase__							:Image.Image				,  UpperCamelCase__							:str				,  UpperCamelCase__							:float = 0.3				,  UpperCamelCase__							:float = 0.6				,  UpperCamelCase__							:float = 0.5				,  UpperCamelCase__							:int = 42				,  UpperCamelCase__							:int = 50				,  UpperCamelCase__							:float = 7.5				,  ):
          _a          =				steps - int(kmin * steps       )
          _a          =				steps - int(kmax * steps       )

          _a          =				self.prep_text(UpperCamelCase__       )

          self.scheduler.set_timesteps(UpperCamelCase__       )

          _a   , _a          =				img.size
          _a          =				self.encode(UpperCamelCase__       )

          torch.manual_seed(UpperCamelCase__       )
          _a          =				torch.randn(
              (1, self.unet.config.in_channels, height // 8, width // 8)				,  ).to(self.device       )

          _a          =				self.scheduler.add_noise(
              UpperCamelCase__				,  UpperCamelCase__				,  timesteps=self.scheduler.timesteps[tmax]				,  )

          _a          =				torch.cat([latents] * 2       )

          _a          =				self.scheduler.scale_model_input(UpperCamelCase__				,  self.scheduler.timesteps[tmax]       )

          with torch.no_grad():
                 _a          =				self.unet(
                     UpperCamelCase__				,  self.scheduler.timesteps[tmax]				,  encoder_hidden_states=UpperCamelCase__				,  ).sample

          _a   , _a          =				pred.chunk(2       )
          _a          =				pred_uncond + guidance_scale * (pred_text - pred_uncond)

          _a          =				self.scheduler.step(UpperCamelCase__				,  self.scheduler.timesteps[tmax]				,  UpperCamelCase__       ).prev_sample

          for i, t in enumerate(tqdm(self.scheduler.timesteps       )       ):
                 if i > tmax:
                        if i < tmin:  # layout generation phase
                               _a          =				self.scheduler.add_noise(
                                   UpperCamelCase__				,  UpperCamelCase__				,  timesteps=UpperCamelCase__				,  )

                               _a          =				(mix_factor * latents) + (
                                   1 - mix_factor
                               ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                               _a          =				torch.cat([input] * 2       )

                        else:  # content generation phase
                               _a          =				torch.cat([latents] * 2       )

                        _a          =				self.scheduler.scale_model_input(UpperCamelCase__				,  UpperCamelCase__       )

                        with torch.no_grad():
                               _a          =				self.unet(
                                   UpperCamelCase__				,  UpperCamelCase__				,  encoder_hidden_states=UpperCamelCase__				,  ).sample

                        _a   , _a          =				pred.chunk(2       )
                        _a          =				pred_uncond + guidance_scale * (pred_text - pred_uncond)

                        _a          =				self.scheduler.step(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       ).prev_sample

          return self.decode(UpperCamelCase__       )
