

import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()





class 			snake_case_     (	unittest.TestCase      ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE  :     List[Any]		    =       "hf-internal-testing/tiny-stable-diffusion-pipe"
			SCREAMING_SNAKE_CASE  :     List[Any]		    =       1
			SCREAMING_SNAKE_CASE  :     Tuple		    =       77
			SCREAMING_SNAKE_CASE  :     str		    =       32
			SCREAMING_SNAKE_CASE  :     List[str]		    =       4
			SCREAMING_SNAKE_CASE  :     Tuple		    =       64
			SCREAMING_SNAKE_CASE  :     str		    =       torch.manual_seed(0      )







			def        snake_case__(  self				:       List[Any]							,     _UpperCamelCase				:       int=0.9999							)   ->Optional[Any]:
									snake_case_     =			UNetaDConditionModel.from_pretrained(self.model_id							,     subfolder='''unet'''							)
									snake_case_     =			unet.to(_UpperCamelCase							)
									snake_case_     =			EMAModel(unet.parameters()							,     decay=_UpperCamelCase							,     model_cls=_UpperCamelCase							,     model_config=unet.config							)
									return unet, ema_unet







			def        snake_case__(  self				:       Optional[Any]							)   ->Union[str, Any]:
									snake_case_     =			torch.randn(
									    self.batch_size							,     self.num_in_channels							,     self.latent_height							,     self.latent_width							,     generator=self.generator							).to(_UpperCamelCase							)
									snake_case_     =			torch.randint(0							,     1_0_0_0							,     size=(self.batch_size,)							,     generator=self.generator							).to(_UpperCamelCase							)
									snake_case_     =			torch.randn(
									    self.batch_size							,     self.prompt_length							,     self.text_encoder_hidden_dim							,     generator=self.generator							).to(_UpperCamelCase							)
									return noisy_latents, timesteps, encoder_hidden_states







			def        snake_case__(  self				:       Dict							,     _UpperCamelCase				:       int							)   ->int:
									snake_case_     =			{}
									for k, param in unet.state_dict().items():
															snake_case_     =			torch.randn_like(_UpperCamelCase							) + (param * torch.randn_like(_UpperCamelCase							))
															updated_state_dict.update({k: updated_param}							)
									unet.load_state_dict(_UpperCamelCase							)
									return unet







			def        snake_case__(  self				:       Dict							)   ->Any:
									snake_case_,					snake_case_     =			self.get_models()
									# Take the first (hypothetical) EMA step.
									ema_unet.step(unet.parameters()							)
									assert ema_unet.optimization_step == 1

									# Take two more.
									for _ in range(2							):
															ema_unet.step(unet.parameters()							)
									assert ema_unet.optimization_step == 3







			def        snake_case__(  self				:       Optional[int]							)   ->Union[str, Any]:
									snake_case_,					snake_case_     =			self.get_models()
									# Since the `unet` is not being updated (i.e., backprop'd)
									# there won't be any difference between the `params` of `unet`
									# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
									ema_unet.step(unet.parameters()							)
									snake_case_     =			list(unet.parameters()							)
									for s_param, param in zip(ema_unet.shadow_params							,     _UpperCamelCase							):
															assert torch.allclose(_UpperCamelCase							,     _UpperCamelCase							)

									# The above holds true even if we call `ema.step()` multiple times since
									# `unet` params are still not being updated.
									for _ in range(4							):
															ema_unet.step(unet.parameters()							)
									for s_param, param in zip(ema_unet.shadow_params							,     _UpperCamelCase							):
															assert torch.allclose(_UpperCamelCase							,     _UpperCamelCase							)







			def        snake_case__(  self				:       Any							)   ->Any:
									snake_case_,					snake_case_     =			self.get_models()
									# Here we simulate the parameter updates for `unet`. Since there might
									# be some parameters which are initialized to zero we take extra care to
									# initialize their values to something non-zero before the multiplication.
									snake_case_     =			self.simulate_backprop(_UpperCamelCase							)

									# Take the EMA step.
									ema_unet.step(unet_pseudo_updated_step_one.parameters()							)

									# Now the EMA'd parameters won't be equal to the original model parameters.
									snake_case_     =			list(unet_pseudo_updated_step_one.parameters()							)
									for s_param, param in zip(ema_unet.shadow_params							,     _UpperCamelCase							):
															assert ~torch.allclose(_UpperCamelCase							,     _UpperCamelCase							)

									# Ensure this is the case when we take multiple EMA steps.
									for _ in range(4							):
															ema_unet.step(unet.parameters()							)
									for s_param, param in zip(ema_unet.shadow_params							,     _UpperCamelCase							):
															assert ~torch.allclose(_UpperCamelCase							,     _UpperCamelCase							)







			def        snake_case__(  self				:       List[Any]							)   ->Any:
									snake_case_,					snake_case_     =			self.get_models()

									# First backprop + EMA
									snake_case_     =			self.simulate_backprop(_UpperCamelCase							)
									ema_unet.step(unet_step_one.parameters()							)
									snake_case_     =			ema_unet.shadow_params

									# Second backprop + EMA
									snake_case_     =			self.simulate_backprop(_UpperCamelCase							)
									ema_unet.step(unet_step_two.parameters()							)
									snake_case_     =			ema_unet.shadow_params

									for step_one, step_two in zip(_UpperCamelCase							,     _UpperCamelCase							):
															assert ~torch.allclose(_UpperCamelCase							,     _UpperCamelCase							)







			def        snake_case__(  self				:       int							)   ->List[Any]:
									snake_case_,					snake_case_     =			self.get_models(decay=0.0							)
									snake_case_     =			self.simulate_backprop(_UpperCamelCase							)
									ema_unet.step(unet_step_one.parameters()							)
									snake_case_     =			ema_unet.shadow_params

									snake_case_     =			self.simulate_backprop(_UpperCamelCase							)
									ema_unet.step(unet_step_two.parameters()							)
									snake_case_     =			ema_unet.shadow_params

									for step_one, step_two in zip(_UpperCamelCase							,     _UpperCamelCase							):
															assert torch.allclose(_UpperCamelCase							,     _UpperCamelCase							)







			@skip_mps
			def        snake_case__(  self				:       Union[str, Any]							)   ->int:
									snake_case_,					snake_case_     =			self.get_models()
									snake_case_,					snake_case_,					snake_case_     =			self.get_dummy_inputs()

									with tempfile.TemporaryDirectory() as tmpdir:
															ema_unet.save_pretrained(_UpperCamelCase							)
															snake_case_     =			UNetaDConditionModel.from_pretrained(_UpperCamelCase							,     model_cls=_UpperCamelCase							)
															snake_case_     =			loaded_unet.to(unet.device							)

									# Since no EMA step has been performed the outputs should match.
									snake_case_     =			unet(_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							).sample
									snake_case_     =			loaded_unet(_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							).sample

									assert torch.allclose(_UpperCamelCase							,     _UpperCamelCase							,     atol=1e-4							)


import random





class 			snake_case_     :







			'''simple docstring'''

			def __init__(  self				:       List[str]							,     _UpperCamelCase				:       list[list[float]]							,     _UpperCamelCase				:       list[int]							,     _UpperCamelCase				:       float = 0.01							,     _UpperCamelCase				:       int = 1_0_0_0							,     _UpperCamelCase				:       float = -1							,     )   ->None:
									snake_case_     =			sample
									if len(self.sample							) == 0:
															raise ValueError('''Sample data can not be empty'''							)
									snake_case_     =			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'''							)
									snake_case_     =			learning_rate
									snake_case_     =			epoch_number
									snake_case_     =			bias
									snake_case_     =			len(_UpperCamelCase							)
									snake_case_     =			len(sample[0]							)  # number of columns in dataset
									snake_case_     =			[]







			def        snake_case__(  self				:       List[str]							)   ->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							)

									snake_case_     =			0

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

									snake_case_     =			self.sign(_UpperCamelCase							)

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







			def        snake_case__(  self				:       str							,     _UpperCamelCase				:       float							)   ->int:
									return 1 if u >= 0 else -1






lowerCAmelCase_ 			=			[
    [-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_ 			=			[
    -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=0.01, epoch_number=10_00, 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)