




'''simple docstring'''







from typing import List, Optional, Union

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

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowercase			:      List[Any]			=					logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase			:      List[Any]			=					"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")

        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase=8							):
     snake_case					   = height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     snake_case					   = width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase=512    ,      __lowercase=512							):
     snake_case					   = pil_image.resize((w, h)    ,      resample=Image.BICUBIC    ,      reducing_gap=1							)
     snake_case					   = np.array(pil_image.convert("""RGB"""							)							)
     snake_case					   = arr.astype(np.floataa							) / 127.5 - 1
     snake_case					   = np.transpose(__lowercase    ,      [2, 0, 1]							)
     snake_case					   = torch.from_numpy(__lowercase							).unsqueeze(0							)
     return image







class 				_a   (a__  ):





     '''simple docstring'''



     def __init__(							self       ,__a       ,__a       ,__a       ,)			->							Optional[Any]:
          super().__init__()

          self.register_modules(
              unet=__a       ,scheduler=__a       ,movq=__a       ,)
          snake_case					   = 2 ** (len(self.movq.config.block_out_channels       ) - 1)







     def  snake_case_			(							self       ,__a       ,__a       ,__a       )			->							List[Any]:
          # get the original timestep using init_timestep
          snake_case					   = min(int(num_inference_steps * strength       )       ,__a       )

          snake_case					   = max(num_inference_steps - init_timestep       ,0       )
          snake_case					   = self.scheduler.timesteps[t_start:]

          return timesteps, num_inference_steps - t_start







     def  snake_case_			(							self       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a       ,__a=None       )			->							Any:
          if not isinstance(__a       ,(torch.Tensor, PIL.Image.Image, list)       ):
               raise ValueError(
                   F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__a       )}'''       )

          snake_case					   = image.to(device=__a       ,dtype=__a       )

          snake_case					   = batch_size * num_images_per_prompt

          if image.shape[1] == 4:
               snake_case					   = image

          else:
               if isinstance(__a       ,__a       ) and len(__a       ) != batch_size:
                    raise ValueError(
                        F'''You have passed a list of generators of length {len(__a       )}, but requested an effective batch'''
                        F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''       )

               elif isinstance(__a       ,__a       ):
                    snake_case					   = [
                        self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(__a       )
                    ]
                    snake_case					   = torch.cat(__a       ,dim=0       )
               else:
                    snake_case					   = self.movq.encode(__a       ).latent_dist.sample(__a       )

               snake_case					   = self.movq.config.scaling_factor * init_latents

          snake_case					   = torch.cat([init_latents]       ,dim=0       )

          snake_case					   = init_latents.shape
          snake_case					   = randn_tensor(__a       ,generator=__a       ,device=__a       ,dtype=__a       )

          # get latents
          snake_case					   = self.scheduler.add_noise(__a       ,__a       ,__a       )

          snake_case					   = init_latents

          return latents







     def  snake_case_			(							self       ,__a=0       )			->							int:
          if is_accelerate_available():
               from accelerate import cpu_offload
          else:
               raise ImportError("""Please install accelerate via `pip install accelerate`"""       )

          snake_case					   = torch.device(F'''cuda:{gpu_id}'''       )

          snake_case					   = [
              self.unet,
              self.movq,
          ]
          for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                    cpu_offload(__a       ,__a       )







     def  snake_case_			(							self       ,__a=0       )			->							int:
          if is_accelerate_available() and is_accelerate_version(""">="""       ,"""0.17.0.dev0"""       ):
               from accelerate import cpu_offload_with_hook
          else:
               raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""       )

          snake_case					   = torch.device(F'''cuda:{gpu_id}'''       )

          if self.device.type != "cpu":
               self.to("""cpu"""       ,silence_dtype_warnings=__a       )
               torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

          snake_case					   = None
          for cpu_offloaded_model in [self.unet, self.movq]:
               snake_case					   = cpu_offload_with_hook(__a       ,__a       ,prev_module_hook=__a       )

          # We'll offload the last model manually.
          snake_case					   = hook







     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def  snake_case_			(							self       )			->							Union[str, Any]:
          if not hasattr(self.unet       ,"""_hf_hook"""       ):
               return self.device
          for module in self.unet.modules():
               if (
                   hasattr(__a       ,"""_hf_hook"""       )
                   and hasattr(module._hf_hook       ,"""execution_device"""       )
                   and module._hf_hook.execution_device is not None
               ):
                    return torch.device(module._hf_hook.execution_device       )
          return self.device







     @torch.no_grad()
     @replace_example_docstring(__a       )
     def __call__(							self       ,__a       ,__a       ,__a       ,__a       ,__a = 512       ,__a = 512       ,__a = 100       ,__a = 4.0       ,__a = 0.3       ,__a = 1       ,__a = None       ,__a = "pil"       ,__a = True       ,)			->							List[str]:
          snake_case					   = self._execution_device

          snake_case					   = guidance_scale > 1.0

          if isinstance(__a       ,__a       ):
               snake_case					   = torch.cat(__a       ,dim=0       )
          if isinstance(__a       ,__a       ):
               snake_case					   = torch.cat(__a       ,dim=0       )
          if isinstance(__a       ,__a       ):
               snake_case					   = torch.cat(__a       ,dim=0       )

          snake_case					   = image_embeds.shape[0]

          if do_classifier_free_guidance:
               snake_case					   = image_embeds.repeat_interleave(__a       ,dim=0       )
               snake_case					   = negative_image_embeds.repeat_interleave(__a       ,dim=0       )
               snake_case					   = hint.repeat_interleave(__a       ,dim=0       )

          snake_case					   = torch.cat([negative_image_embeds, image_embeds]       ,dim=0       ).to(dtype=self.unet.dtype       ,device=__a       )
          snake_case					   = torch.cat([hint, hint]       ,dim=0       ).to(dtype=self.unet.dtype       ,device=__a       )

          if not isinstance(__a       ,__a       ):
               snake_case					   = [image]
          if not all(isinstance(__a       ,(PIL.Image.Image, torch.Tensor)       ) for i in image       ):
               raise ValueError(
                   F'''Input is in incorrect format: {[type(__a       ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''       )

          snake_case					   = torch.cat([prepare_image(__a       ,__a       ,__a       ) for i in image]       ,dim=0       )
          snake_case					   = image.to(dtype=image_embeds.dtype       ,device=__a       )

          snake_case					   = self.movq.encode(__a       )["""latents"""]
          snake_case					   = latents.repeat_interleave(__a       ,dim=0       )
          self.scheduler.set_timesteps(__a       ,device=__a       )
          snake_case					   = self.get_timesteps(__a       ,__a       ,__a       )
          snake_case					   = timesteps[:1].repeat(batch_size * num_images_per_prompt       )
          snake_case					   = downscale_height_and_width(__a       ,__a       ,self.movq_scale_factor       )
          snake_case					   = self.prepare_latents(
              __a       ,__a       ,__a       ,__a       ,image_embeds.dtype       ,__a       ,__a       )
          for i, t in enumerate(self.progress_bar(__a       )       ):
               # expand the latents if we are doing classifier free guidance
               snake_case					   = torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

               snake_case					   = {"""image_embeds""": image_embeds, """hint""": hint}
               snake_case					   = self.unet(
                   sample=__a       ,timestep=__a       ,encoder_hidden_states=__a       ,added_cond_kwargs=__a       ,return_dict=__a       ,)[0]

               if do_classifier_free_guidance:
                    snake_case					   = noise_pred.split(latents.shape[1]       ,dim=1       )
                    snake_case					   = noise_pred.chunk(2       )
                    snake_case					   = variance_pred.chunk(2       )
                    snake_case					   = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                    snake_case					   = torch.cat([noise_pred, variance_pred_text]       ,dim=1       )

               if not (
                   hasattr(self.scheduler.config       ,"""variance_type"""       )
                   and self.scheduler.config.variance_type in ["learned", "learned_range"]
               ):
                    snake_case					   = noise_pred.split(latents.shape[1]       ,dim=1       )

               # compute the previous noisy sample x_t -> x_t-1

               snake_case					   = self.scheduler.step(
                   __a       ,__a       ,__a       ,generator=__a       ,)[0]

          # post-processing
          snake_case					   = self.movq.decode(__a       ,force_not_quantize=__a       )["""sample"""]

          if output_type not in ["pt", "np", "pil"]:
               raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''       )

          if output_type in ["np", "pil"]:
               snake_case					   = image * 0.5 + 0.5
               snake_case					   = image.clamp(0       ,1       )
               snake_case					   = image.cpu().permute(0       ,2       ,3       ,1       ).float().numpy()

          if output_type == "pil":
               snake_case					   = self.numpy_to_pil(__a       )

          if not return_dict:
               return (image,)

          return ImagePipelineOutput(images=__a       )









'''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""")

lowercase			:      Any			=					"""K-MEANS-CLUST/ """



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase=None							):
     if seed is not None:  # useful for obtaining consistent results
          np.random.seed(__lowercase							)
     snake_case :			Optional[int]					   = data.shape[0]  # number of data points

     # Pick K indices from range [0, N).
     snake_case :			Tuple					   = np.random.randint(0    ,      __lowercase    ,      __lowercase							)

     # 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.
     snake_case :			List[Any]					   = data[rand_indices, :]

     return centroids



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     return pairwise_distances(__lowercase    ,      __lowercase    ,      metric="""euclidean"""							)



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

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

     return cluster_assignment



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

     return new_centroids



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

     return heterogeneity



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     plt.figure(figsize=(7, 4)							)
     plt.plot(__lowercase    ,      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__							(			__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase=500    ,      __lowercase=None    ,      __lowercase=False							):
     snake_case :			Tuple					   = initial_centroids[:]
     snake_case :			Dict					   = None

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

          # 1. Make cluster assignments using nearest centroids
          snake_case :			List[Any]					   = assign_clusters(__lowercase    ,      __lowercase							)

          # 2. Compute a new centroid for each of the k clusters, averaging all data
          #    points assigned to that cluster.
          snake_case :			Optional[int]					   = revise_centroids(__lowercase    ,      __lowercase    ,      __lowercase							)

          # 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:
               snake_case :			Tuple					   = 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
               snake_case :			Any					   = compute_heterogeneity(__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase							)
               record_heterogeneity.append(__lowercase							)

          snake_case :			Any					   = 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

      lowercase			:      Optional[int]			=					ds.load_iris()
      lowercase			:      str			=					3
      lowercase			:      Any			=					[]
      lowercase			:      Dict			=					get_initial_centroids(dataset["""data"""], k, seed=0)
      lowercase,  lowercase			:      str			=					kmeans(
          dataset["""data"""],
          k,
          initial_centroids,
          maxiter=400,
          record_heterogeneity=heterogeneity,
          verbose=True,
      )
      plot_heterogeneity(heterogeneity, k)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase=None							):
     # Fill missing values with given rules
     if fill_missing_report:
          snake_case :			List[str]					   = df.fillna(value=__lowercase							)
     snake_case :			List[Any]					   = 1
     snake_case :			Optional[int]					   = df.select_dtypes(np.number							).columns
     snake_case :			int					   = (
         df.groupby(["""Cluster"""]							)[  # construct report dataframe
             numeric_cols
         ]  # group by cluster number
         .agg(
             [
                 ("""sum""", np.sum),
                 ("""mean_with_zeros""", lambda __lowercase							: np.mean(np.nan_to_num(__lowercase							)							)),
                 ("""mean_without_zeros""", lambda __lowercase							: x.replace(0    ,      np.NaN							).mean()),
                 (
                     """mean_25-75""",
                     lambda __lowercase							: np.mean(
                         np.nan_to_num(
                             sorted(__lowercase							)[
                                 round(len(__lowercase							) * 25 / 100							) : round(len(__lowercase							) * 75 / 100							)
                             ]							)							),
                 ),
                 ("""mean_with_na""", np.mean),
                 ("""min""", lambda __lowercase							: x.min()),
                 ("""5%""", lambda __lowercase							: x.quantile(0.05							)),
                 ("""25%""", lambda __lowercase							: x.quantile(0.25							)),
                 ("""50%""", lambda __lowercase							: x.quantile(0.50							)),
                 ("""75%""", lambda __lowercase							: x.quantile(0.75							)),
                 ("""95%""", lambda __lowercase							: x.quantile(0.95							)),
                 ("""max""", lambda __lowercase							: x.max()),
                 ("""count""", lambda __lowercase							: x.count()),
                 ("""stdev""", lambda __lowercase							: x.std()),
                 ("""mode""", lambda __lowercase							: x.mode()[0]),
                 ("""median""", lambda __lowercase							: x.median()),
                 ("""# > 0""", lambda __lowercase							: (x > 0).sum()),
             ]							)
         .T.reset_index()
         .rename(index=__lowercase    ,      columns={"""level_0""": """Features""", """level_1""": """Type"""}							)
     )  # rename columns
     # calculate the size of cluster(count of clientID's)
     snake_case :			List[str]					   = report[
         (report["""Features"""] == """dummy""") & (report["""Type"""] == """count""")
     ].copy()  # avoid SettingWithCopyWarning
     snake_case :			Optional[Any]					   = (
         """ClusterSize"""  # rename created cluster df to match report column names
     )
     snake_case :			Optional[int]					   = """# of Customers"""
     snake_case :			List[str]					   = pd.DataFrame(
         clustersize.iloc[:, 2:].values
         / clustersize.iloc[:, 2:].values.sum()							)
     snake_case :			List[str]					   = """% of Customers"""  # rename created cluster df to match report column names
     snake_case :			List[Any]					   = """ClusterProportion"""
     snake_case :			Tuple					   = clusterproportion.columns.tolist()
     snake_case :			Optional[int]					   = cols[-2:] + cols[:-2]
     snake_case :			Tuple					   = clusterproportion[cols]  # rearrange columns to match report
     snake_case :			Tuple					   = report.columns
     snake_case :			List[str]					   = pd.DataFrame(
         abs(
             report[report["""Type"""] == """count"""].iloc[:, 2:].values
             - clustersize.iloc[:, 2:].values							)							)  # generating df with count of nan values
     snake_case :			Tuple					   = 0
     snake_case :			int					   = """# of nan"""
     snake_case :			int					   = report[
         report["""Type"""] == """count"""
     ].Features.tolist()  # filling values in order to match report
     snake_case :			Optional[Any]					   = a.columns.tolist()
     snake_case :			Dict					   = cols[-2:] + cols[:-2]
     snake_case :			Optional[Any]					   = a[cols]  # rearrange columns to match report
     snake_case :			Tuple					   = report.columns  # rename columns to match report
     snake_case :			Optional[Any]					   = report.drop(
         report[report.Type == """count"""].index							)  # drop count values except cluster size
     snake_case :			List[Any]					   = pd.concat(
         [report, a, clustersize, clusterproportion]    ,      axis=0							)  # concat report with clustert size and nan values
     snake_case :			Optional[int]					   = report["""Features"""].isin(__lowercase							)
     snake_case :			Any					   = report.columns.tolist()
     snake_case :			Union[str, Any]					   = cols[0:2] + cols[-1:] + cols[2:-1]
     snake_case :			List[Any]					   = report[cols]
     snake_case :			Any					   = {
         """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,
     }
     snake_case :			List[Any]					   = (
         report.assign(
             Sortera=lambda __lowercase							: x.Type.map(__lowercase							)    ,      Sortera=lambda __lowercase							: list(reversed(range(len(__lowercase							)							)							)							)    ,      )
         .sort_values(["""Sorter1""", """Mark""", """Sorter2"""]    ,      ascending=__lowercase							)
         .drop(["""Sorter1""", """Sorter2"""]    ,      axis=1							)
     )
     snake_case :			str					   = """"""
     snake_case :			Dict					   = report.reset_index()
     snake_case :			Optional[int]					   = report.drop(columns=["""index"""]							)
     return report


if __name__ == "__main__":
      import doctest

      doctest.testmod()



