

'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class   UpperCamelCase__   (       lowercase__       ):


  """simple docstring"""

  SCREAMING_SNAKE_CASE__  :     UNetaDModel
  SCREAMING_SNAKE_CASE__  :     PNDMScheduler







  def __init__(			self   ,      snake_case   ,      snake_case      ):


       '''simple docstring'''

       super().__init__()

       UpperCAmelCase		:							Union[str, Any]        =       PNDMScheduler.from_config(scheduler.config      )

       self.register_modules(unet=lowercase__   ,      scheduler=lowercase__      )







  @torch.no_grad()
  def __call__(			self   ,      snake_case = 1   ,      snake_case = 5_0   ,      snake_case = None   ,      snake_case = "pil"   ,      snake_case = True   ,      **snake_case   ,      ):


       '''simple docstring'''

       UpperCAmelCase		:							int        =       randn_tensor(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,      generator=lowercase__   ,      device=self.device   ,      )

       self.scheduler.set_timesteps(lowercase__      )
       for t in self.progress_bar(self.scheduler.timesteps      ):
            UpperCAmelCase		:							Optional[int]        =       self.unet(lowercase__   ,      lowercase__      ).sample

            UpperCAmelCase		:							Optional[Any]        =       self.scheduler.step(lowercase__   ,      lowercase__   ,      lowercase__      ).prev_sample

       UpperCAmelCase		:							Any        =       (image / 2 + 0.5).clamp(0   ,      1      )
       UpperCAmelCase		:							Dict        =       image.cpu().permute(0   ,      2   ,      3   ,      1      ).numpy()
       if output_type == "pil":
            UpperCAmelCase		:							Any        =       self.numpy_to_pil(lowercase__      )

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=lowercase__      )



'''simple docstring'''

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
a       :      List[Any]  	=			load_iris()
iris.keys()


print(F'Target names: \n {iris.target_names} ')
print(F'\n Features: \n {iris.feature_names}')

# Train set e Test set
a	,						a	,						a	,						a       :      List[Any]  	=			train_test_split(
    iris["data"], iris["target"], random_state=4
)

# KNN

a       :      Tuple  	=			KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
a       :      Optional[int]  	=			[[1, 2, 1, 4], [2, 3, 4, 5]]

a       :      Tuple  	=			knn.predict(X_new)

print(
    F'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    F' {iris["target_names"][prediction]}'
)
