


"""simple docstring"""
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      _UpperCAmelCase					(				unittest.TestCase							):






		'''simple docstring'''

		lowerCamelCase__           ='hf-internal-testing/tiny-stable-diffusion-pipe'
		lowerCamelCase__           =1
		lowerCamelCase__           =77
		lowerCamelCase__           =32
		lowerCamelCase__           =4
		lowerCamelCase__           =lowerCamelCase__           =64
		lowerCamelCase__           =torch.manual_seed(0							)



		def 							SCREAMING_SNAKE_CASE    (self							,			a_=0.9999   ):





			'''simple docstring'''

			__snake_case      :		Optional[Any]         =							UNetaDConditionModel.from_pretrained(self.model_id							,			subfolder='''unet'''   )
			__snake_case      :		Union[str, Any]         =							unet.to(a_   )
			__snake_case      :		Tuple         =							EMAModel(unet.parameters()							,			decay=a_							,			model_cls=a_							,			model_config=unet.config   )
			return unet, ema_unet



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							torch.randn(
			    self.batch_size							,			self.num_in_channels							,			self.latent_height							,			self.latent_width							,			generator=self.generator   ).to(a_   )
			__snake_case      :		int         =							torch.randint(0							,			10_00							,			size=(self.batch_size,)							,			generator=self.generator   ).to(a_   )
			__snake_case      :		int         =							torch.randn(
			    self.batch_size							,			self.prompt_length							,			self.text_encoder_hidden_dim							,			generator=self.generator   ).to(a_   )
			return noisy_latents, timesteps, encoder_hidden_states



		def 							SCREAMING_SNAKE_CASE    (self							,			a_   ):





			'''simple docstring'''

			__snake_case      :		List[Any]         =							{}
			for k, param in unet.state_dict().items():
				__snake_case      :		int         =							torch.randn_like(a_   ) + (param * torch.randn_like(a_   ))
				updated_state_dict.update({k: updated_param}   )
			unet.load_state_dict(a_   )
			return unet



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		Optional[Any]         =							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 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		Optional[Any]         =							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      :		Optional[Any]         =							list(unet.parameters()   )
			for s_param, param in zip(ema_unet.shadow_params							,			a_   ):
				assert torch.allclose(a_							,			a_   )

			# 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							,			a_   ):
				assert torch.allclose(a_							,			a_   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		List[str]         =							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      :		Union[str, Any]         =							self.simulate_backprop(a_   )

			# 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      :		Any         =							list(unet_pseudo_updated_step_one.parameters()   )
			for s_param, param in zip(ema_unet.shadow_params							,			a_   ):
				assert ~torch.allclose(a_							,			a_   )

			# 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							,			a_   ):
				assert ~torch.allclose(a_							,			a_   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		Dict         =							self.get_models()

			# First backprop + EMA
			__snake_case      :		Optional[int]         =							self.simulate_backprop(a_   )
			ema_unet.step(unet_step_one.parameters()   )
			__snake_case      :		Union[str, Any]         =							ema_unet.shadow_params

			# Second backprop + EMA
			__snake_case      :		List[str]         =							self.simulate_backprop(a_   )
			ema_unet.step(unet_step_two.parameters()   )
			__snake_case      :		Tuple         =							ema_unet.shadow_params

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



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		Optional[int]         =							self.get_models(decay=0.0   )
			__snake_case      :		Any         =							self.simulate_backprop(a_   )
			ema_unet.step(unet_step_one.parameters()   )
			__snake_case      :		Any         =							ema_unet.shadow_params

			__snake_case      :		Optional[Any]         =							self.simulate_backprop(a_   )
			ema_unet.step(unet_step_two.parameters()   )
			__snake_case      :		List[str]         =							ema_unet.shadow_params

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



		@skip_mps
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		Optional[int]         =							self.get_models()
			__snake_case      ,				__snake_case      ,				__snake_case      :		Any         =							self.get_dummy_inputs()

			with tempfile.TemporaryDirectory() as tmpdir:
				ema_unet.save_pretrained(a_   )
				__snake_case      :		int         =							UNetaDConditionModel.from_pretrained(a_							,			model_cls=a_   )
				__snake_case      :		Union[str, Any]         =							loaded_unet.to(unet.device   )

			# Since no EMA step has been performed the outputs should match.
			__snake_case      :		List[str]         =							unet(a_							,			a_							,			a_   ).sample
			__snake_case      :		Tuple         =							loaded_unet(a_							,			a_							,			a_   ).sample

			assert torch.allclose(a_							,			a_							,			atol=1E-4   )









"""simple docstring"""
from __future__ import annotations

import math
import random






def 						lowercase				(	_snake_case		: int	,  _snake_case		: int	,  _snake_case		: int	)	->list[int]:
	"""simple docstring"""





	__snake_case      :		Tuple         =							d * e - 1
	__snake_case      :		List[str]         =							0
	__snake_case      :		Dict         =							0
	while p == 0:
		__snake_case      :		str         =							random.randint(2	,  n - 1	)
		__snake_case      :		int         =							k
		while True:
			if t % 2 == 0:
				__snake_case      :		int         =							t // 2
				__snake_case      :		int         =							(g**t) % n
				__snake_case      :		Optional[Any]         =							math.gcd(x - 1	,  _snake_case	)
				if x > 1 and y > 1:
					__snake_case      :		Tuple         =							y
					__snake_case      :		int         =							n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]	)


if __name__ == "__main__":
			import doctest

			doctest.testmod()





