

'''simple docstring'''





import random



class 						__lowercase       :




			"""simple docstring"""







			def __init__(				self						:  Dict     ,		lowerCAmelCase__						:  list[list[float]]     ,		lowerCAmelCase__						:  list[int]     ,		lowerCAmelCase__						:  float = 0.01     ,		lowerCAmelCase__						:  int = 1000     ,		lowerCAmelCase__						:  float = -1     ,		):
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= sample
									if len(self.sample) == 0:
															raise ValueError("Sample data can not be empty")
									SCREAMING_SNAKE_CASE_:				int							= target
									if len(self.target) == 0:
															raise ValueError("Target data can not be empty")
									if len(self.sample) != len(self.target):
															raise ValueError("Sample data and Target data do not have matching lengths")
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= learning_rate
									SCREAMING_SNAKE_CASE_:				List[str]							= epoch_number
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= bias
									SCREAMING_SNAKE_CASE_:				List[str]							= len(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Any							= len(sample[0])  # number of columns in dataset
									SCREAMING_SNAKE_CASE_:				list							= []



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]):
									for sample in self.sample:
															sample.insert(0     ,		self.bias)

									for _ in range(self.col_sample):
															self.weight.append(random.random())

									self.weight.insert(0     ,		self.bias)

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= 0

									while True:
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= False
															for i in range(self.number_sample):
																					SCREAMING_SNAKE_CASE_:				Dict							= 0
																					for j in range(self.col_sample + 1):
																											SCREAMING_SNAKE_CASE_:				Union[str, Any]							= u + self.weight[j] * self.sample[i][j]
																					SCREAMING_SNAKE_CASE_:				List[str]							= self.sign(lowerCAmelCase__)
																					if y != self.target[i]:
																											for j in range(self.col_sample + 1):
																																	SCREAMING_SNAKE_CASE_:				Tuple							= (
																																	    self.weight[j]
																																	    + self.learning_rate
																																	    * (self.target[i] - y)
																																	    * self.sample[i][j]
																																	)
																											SCREAMING_SNAKE_CASE_:				int							= True
            # print('Epoch: \n',epoch_count)
															SCREAMING_SNAKE_CASE_:				int							= epoch_count + 1
															# if you want control the epoch or just by error
															if not has_misclassified:
																					print(("\nEpoch:\n", epoch_count))
																					print("------------------------\n")
																					# if epoch_count > self.epoch_number or not error:
																					break



			def  _SCREAMING_SNAKE_CASE    (				self						:  str     ,		lowerCAmelCase__						:  list[float]):
									if len(self.sample) == 0:
															raise ValueError("Sample data can not be empty")
									sample.insert(0     ,		self.bias)
									SCREAMING_SNAKE_CASE_:				int							= 0
									for i in range(self.col_sample + 1):
															SCREAMING_SNAKE_CASE_:				str							= u + self.weight[i] * sample[i]

									SCREAMING_SNAKE_CASE_:				Dict							= self.sign(lowerCAmelCase__)

									if y == -1:
															print(("Sample: ", sample))
															print("classification: P1")
									else:
															print(("Sample: ", sample))
															print("classification: P2")




			def  _SCREAMING_SNAKE_CASE    (				self						:  int     ,		lowerCAmelCase__						:  float):
									return 1 if u >= 0 else -1







lowerCAmelCase							:						List[str]     			=		[
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

lowerCAmelCase							:						List[Any]     			=		[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						lowerCAmelCase							:						Tuple     			=		Perceptron(
						    sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
						)
						network.training()
						print("""Finished training perceptron""")
						print("""Enter values to predict or q to exit""")
						while True:
												lowerCAmelCase							:						list     			=		[]
												for i in range(len(samples[0])):
																		lowerCAmelCase							:						int     			=		input("""value: """).strip()
																		if user_input == "q":
																								break
																		lowerCAmelCase							:						Union[str, Any]     			=		float(user_input)
																		sample.insert(i, observation)
												network.sort(sample)









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 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							UNetaDModel
			_UpperCAmelCase     :							PNDMScheduler



			def __init__(				self						:  Optional[int]     ,		lowerCAmelCase__						:  UNetaDModel     ,		lowerCAmelCase__						:  PNDMScheduler):
									super().__init__()

									SCREAMING_SNAKE_CASE_:				Dict							= PNDMScheduler.from_config(scheduler.config)

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




			@torch.no_grad()
			def __call__(				self						:  int     ,		lowerCAmelCase__						:  int = 1     ,		lowerCAmelCase__						:  int = 50     ,		lowerCAmelCase__						:  Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		lowerCAmelCase__						:  Optional[str] = "pil"     ,		lowerCAmelCase__						:  bool = True     ,		**lowerCAmelCase__						:  str     ,		):
									SCREAMING_SNAKE_CASE_:				Dict							= randn_tensor(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,		generator=lowerCAmelCase__     ,		device=self.device     ,		)

									self.scheduler.set_timesteps(lowerCAmelCase__)
									for t in self.progress_bar(self.scheduler.timesteps):
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.unet(lowerCAmelCase__     ,		lowerCAmelCase__).sample

															SCREAMING_SNAKE_CASE_:				Any							= self.scheduler.step(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__).prev_sample

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= (image / 2 + 0.5).clamp(0     ,		1)
									SCREAMING_SNAKE_CASE_:				Dict							= image.cpu().permute(0     ,		2     ,		3     ,		1).numpy()
									if output_type == "pil":
															SCREAMING_SNAKE_CASE_:				Any							= self.numpy_to_pil(lowerCAmelCase__)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowerCAmelCase__)






