


import random





class       _a							:




	def __init__(      self		:	int	,      _SCREAMING_SNAKE_CASE		:	list[list[float]]	,      _SCREAMING_SNAKE_CASE		:	list[int]	,      _SCREAMING_SNAKE_CASE		:	float = 0.01	,      _SCREAMING_SNAKE_CASE		:	int = 1000	,      _SCREAMING_SNAKE_CASE		:	float = -1	,      )-> None:
			lowerCAmelCase__     :    Union[str, Any]					  =      sample
			if len(self.sample	) == 0:
					raise ValueError('''Sample data can not be empty'''	)
			lowerCAmelCase__     :    Any					  =      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'''	)
			lowerCAmelCase__     :    str					  =      learning_rate
			lowerCAmelCase__     :    int					  =      epoch_number
			lowerCAmelCase__     :    List[str]					  =      bias
			lowerCAmelCase__     :    Union[str, Any]					  =      len(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Union[str, Any]					  =      len(sample[0]	)  # number of columns in dataset
			lowerCAmelCase__     :    list					  =      []




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	)-> None:
			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	)

			lowerCAmelCase__     :    Optional[Any]					  =      0

			while True:
					lowerCAmelCase__     :    List[Any]					  =      False
					for i in range(self.number_sample	):
							lowerCAmelCase__     :    Any					  =      0
							for j in range(self.col_sample + 1	):
									lowerCAmelCase__     :    Optional[Any]					  =      u + self.weight[j] * self.sample[i][j]
							lowerCAmelCase__     :    Dict					  =      self.sign(_SCREAMING_SNAKE_CASE	)
							if y != self.target[i]:
									for j in range(self.col_sample + 1	):
											lowerCAmelCase__     :    Optional[int]					  =      (
											    self.weight[j]
											    + self.learning_rate
											    * (self.target[i] - y)
											    * self.sample[i][j]
											)
									lowerCAmelCase__     :    Optional[Any]					  =      True
            # print('Epoch: \n',epoch_count)
					lowerCAmelCase__     :    str					  =      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 							UpperCAmelCase__(      self		:	List[Any]	,      _SCREAMING_SNAKE_CASE		:	list[float]	)-> None:
			if len(self.sample	) == 0:
					raise ValueError('''Sample data can not be empty'''	)
			sample.insert(0	,      self.bias	)
			lowerCAmelCase__     :    Union[str, Any]					  =      0
			for i in range(self.col_sample + 1	):
					lowerCAmelCase__     :    Tuple					  =      u + self.weight[i] * sample[i]

			lowerCAmelCase__     :    Dict					  =      self.sign(_SCREAMING_SNAKE_CASE	)

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




	def 							UpperCAmelCase__(      self		:	str	,      _SCREAMING_SNAKE_CASE		:	float	)-> int:
			return 1 if u >= 0 else -1
lowerCamelCase		=	[
    [-0.6_5_0_8, 0.1_0_9_7, 4.0_0_0_9],
    [-1.4_4_9_2, 0.8_8_9_6, 4.4_0_0_5],
    [2.0_8_5_0, 0.6_8_7_6, 1_2.0_7_1_0],
    [0.2_6_2_6, 1.1_4_7_6, 7.7_9_8_5],
    [0.6_4_1_8, 1.0_2_3_4, 7.0_4_2_7],
    [0.2_5_6_9, 0.6_7_3_0, 8.3_2_6_5],
    [1.1_1_5_5, 0.6_0_4_3, 7.4_4_4_6],
    [0.0_9_1_4, 0.3_3_9_9, 7.0_6_7_7],
    [0.0_1_2_1, 0.5_2_5_6, 4.6_3_1_6],
    [-0.0_4_2_9, 0.4_6_6_0, 5.4_3_2_3],
    [0.4_3_4_0, 0.6_8_7_0, 8.2_2_8_7],
    [0.2_7_3_5, 1.0_2_8_7, 7.1_9_3_4],
    [0.4_8_3_9, 0.4_8_5_1, 7.4_8_5_0],
    [0.4_0_8_9, -0.1_2_6_7, 5.5_0_1_9],
    [1.4_3_9_1, 0.1_6_1_4, 8.5_8_4_3],
    [-0.9_1_1_5, -0.1_9_7_3, 2.1_9_6_2],
    [0.3_6_5_4, 1.0_4_7_5, 7.4_8_5_8],
    [0.2_1_4_4, 0.7_5_1_5, 7.1_6_9_9],
    [0.2_0_1_3, 1.0_0_1_4, 6.5_4_8_9],
    [0.6_4_8_3, 0.2_1_8_3, 5.8_9_9_1],
    [-0.1_1_4_7, 0.2_2_4_2, 7.2_4_3_5],
    [-0.7_9_7_0, 0.8_7_9_5, 3.8_7_6_2],
    [-1.0_6_2_5, 0.6_3_6_6, 2.4_7_0_7],
    [0.5_3_0_7, 0.1_2_8_5, 5.6_8_8_3],
    [-1.2_2_0_0, 0.7_7_7_7, 1.7_2_5_2],
    [0.3_9_5_7, 0.1_0_7_6, 5.6_6_2_3],
    [-0.1_0_1_3, 0.5_9_8_9, 7.1_8_1_2],
    [2.4_4_8_2, 0.9_4_5_5, 1_1.2_0_9_5],
    [2.0_1_4_9, 0.6_1_9_2, 1_0.9_2_6_3],
    [0.2_0_1_2, 0.2_6_1_1, 5.4_6_3_1],
]

lowerCamelCase		=	[
    -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		=	Perceptron(
		    sample=samples, target=target, learning_rate=0.0_1, epoch_number=1000, bias=-1
		)
		network.training()
		print('''Finished training perceptron''')
		print('''Enter values to predict or q to exit''')
		while True:
				lowerCamelCase		=	[]
				for i in range(len(samples[0])):
						lowerCamelCase		=	input('''value: ''').strip()
						if user_input == "q":
								break
						lowerCamelCase		=	float(user_input)
						sample.insert(i, observation)
				network.sort(sample)








import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()





class       _a							(       unittest.TestCase):




	@property
	def 							UpperCAmelCase__(      self		:	Dict	)-> int:
			torch.manual_seed(0	)
			lowerCAmelCase__     :    int					  =      UNetaDModel(
			    block_out_channels=(32, 64)	,      layers_per_block=2	,      sample_size=32	,      in_channels=3	,      out_channels=3	,      down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')	,      up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')	,      )
			return model




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	)-> Dict:
			lowerCAmelCase__     :    Union[str, Any]					  =      '''cpu'''
			lowerCAmelCase__     :    List[Any]					  =      self.dummy_uncond_unet
			lowerCAmelCase__     :    Optional[Any]					  =      DDPMScheduler()

			lowerCAmelCase__     :    List[str]					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    Optional[Any]					  =      torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)
			lowerCAmelCase__     :    int					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    str					  =      torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)
			lowerCAmelCase__     :    Optional[int]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	,      return_dict=_SCREAMING_SNAKE_CASE	)[0]

			lowerCAmelCase__     :    Tuple					  =      image[0, -3:, -3:, -1]
			lowerCAmelCase__     :    List[str]					  =      image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    int					  =      np.array(
			    [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04]	)

			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2




	def 							UpperCAmelCase__(      self		:	List[str]	)-> Any:
			lowerCAmelCase__     :    int					  =      self.dummy_uncond_unet
			lowerCAmelCase__     :    List[Any]					  =      DDPMScheduler(prediction_type='''sample'''	)

			lowerCAmelCase__     :    int					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    Optional[int]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    List[Any]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    Optional[Any]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    Union[str, Any]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	)[0]

			lowerCAmelCase__     :    str					  =      image[0, -3:, -3:, -1]
			lowerCAmelCase__     :    Optional[Any]					  =      image_eps[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    Any					  =      1E-2 if torch_device != '''mps''' else 3E-2
			assert np.abs(image_slice.flatten() - image_eps_slice.flatten()	).max() < tolerance





@slow
@require_torch_gpu
class       _a							(       unittest.TestCase):




	def 							UpperCAmelCase__(      self		:	Any	)-> List[Any]:
			lowerCAmelCase__     :    str					  =      '''google/ddpm-cifar10-32'''

			lowerCAmelCase__     :    List[Any]					  =      UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Any					  =      DDPMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    List[str]					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    List[Any]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    Optional[int]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    List[Any]					  =      image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    List[str]					  =      np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]	)
			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2




