
'''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       A   (		lowerCAmelCase__			):
	snake_case__					:UNetaDModel
	snake_case__					:PNDMScheduler





	def __init__(    self	:				Tuple					,							__magic_name__	:				Any					,							__magic_name__	:				str							):


				"""simple docstring"""

				super().__init__()

				lowerCAmelCase__									=							PNDMScheduler.from_config(scheduler.config							)

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







	@torch.no_grad()
	def __call__(    self	:				Optional[Any]					,							__magic_name__	:				Any = 1					,							__magic_name__	:				List[Any] = 50					,							__magic_name__	:				Any = None					,							__magic_name__	:				Any = "pil"					,							__magic_name__	:				Optional[Any] = True					,							**__magic_name__	:				Optional[int]					,							):


				"""simple docstring"""

				lowerCAmelCase__									=							randn_tensor(
				    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,							generator=a__					,							device=self.device					,							)

				self.scheduler.set_timesteps(a__							)
				for t in self.progress_bar(self.scheduler.timesteps							):
							lowerCAmelCase__									=							self.unet(a__					,							a__							).sample

							lowerCAmelCase__									=							self.scheduler.step(a__					,							a__					,							a__							).prev_sample

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

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=a__							)












"""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[str]								=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_     :      str								=KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_     :      Any								=[[1, 2, 1, 4], [2, 3, 4, 5]]

A_     :      int								=knn.predict(X_new)

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






