



import inspect
from typing import List, Optional, Tuple, Union

import torch

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



class        lowerCAmelCase__					(  _lowerCamelCase       ):


				def __init__(							self      :  int						, __UpperCamelCase      :  VQModel						, __UpperCamelCase      :  UNetaDModel						, __UpperCamelCase      :  DDIMScheduler       )		->     Any:
											super().__init__()
											self.register_modules(vqvae=__UpperCamelCase						, unet=__UpperCamelCase						, scheduler=__UpperCamelCase       )


				@torch.no_grad()
				def __call__(							self      :  int						, __UpperCamelCase      :  int = 1						, __UpperCamelCase      :  Optional[Union[torch.Generator, List[torch.Generator]]] = None						, __UpperCamelCase      :  float = 0.0						, __UpperCamelCase      :  int = 50						, __UpperCamelCase      :  Optional[str] = "pil"						, __UpperCamelCase      :  bool = True						, **__UpperCamelCase      :  Tuple						, )		->     Union[Tuple, ImagePipelineOutput]:
											A      						=    randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						, generator=__UpperCamelCase						, )
											A      						=    latents.to(self.device       )

											# scale the initial noise by the standard deviation required by the scheduler
											A      						=    latents * self.scheduler.init_noise_sigma

											self.scheduler.set_timesteps(__UpperCamelCase       )

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											A      						=    'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

											A      						=    {}
											if accepts_eta:
																		A      						=    eta

											for t in self.progress_bar(self.scheduler.timesteps       ):
																		A      						=    self.scheduler.scale_model_input(__UpperCamelCase						, __UpperCamelCase       )
																		# predict the noise residual
																		A      						=    self.unet(__UpperCamelCase						, __UpperCamelCase       ).sample
																		# compute the previous noisy sample x_t -> x_t-1
																		A      						=    self.scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, **__UpperCamelCase       ).prev_sample

											# decode the image latents with the VAE
											A      						=    self.vqvae.decode(__UpperCamelCase       ).sample

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

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=__UpperCamelCase       )




from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

lowercase_													=      datasets.load_iris()

lowercase_													=      np.array(data['''data'''])
lowercase_													=      np.array(data['''target'''])
lowercase_													=      data['''target_names''']

lowercase_							,							lowercase_							,							lowercase_							,							lowercase_													=      train_test_split(X, y)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Optional[Any]						)	->    Optional[int]:
					return np.linalg.norm(np.array(__lowerCamelCase						) - np.array(__lowerCamelCase						)						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[str]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     int=5						)	->    str:
					__lowerCAmelCase						 =zip(__lowerCamelCase					, __lowerCamelCase						)
					# List of distances of all points from the point to be classified
					__lowerCAmelCase						 =[]
					for data_point in data:
										__lowerCAmelCase						 =euclidean_distance(data_point[0]					, __lowerCamelCase						)
										distances.append((distance, data_point[1])						)
					# Choosing 'k' points with the least distances.
					__lowerCAmelCase						 =[i[1] for i in sorted(__lowerCamelCase						)[:k]]
					# Most commonly occurring class among them
					# is the class into which the point is classified
					__lowerCAmelCase						 =Counter(__lowerCamelCase						).most_common(1						)[0][0]
					return classes[result]


if __name__ == "__main__":
				print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))




