



"""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 			__SCREAMING_SNAKE_CASE      ( unittest.TestCase  ):


					'''simple docstring'''





					_a       =				'hf-internal-testing/tiny-stable-diffusion-pipe'
					_a       =				1
					_a       =				7_7
					_a       =				3_2
					_a       =				4
					_a       =				_a       =				6_4
					_a       =				torch.manual_seed(0  )



					def 						snake_case			(			self				:    Optional[Any],    lowerCamelCase				:    List[str]=0.9_999	)->   Union[str, Any]:
								lowerCamelCase__						:						str						      =UNetaDConditionModel.from_pretrained(self.model_id,    subfolder='''unet'''	)
								lowerCamelCase__						:						Optional[int]						      =unet.to(lowerCamelCase	)
								lowerCamelCase__						:						Optional[Any]						      =EMAModel(unet.parameters(),    decay=lowerCamelCase,    model_cls=lowerCamelCase,    model_config=unet.config	)
								return unet, ema_unet



					def 						snake_case			(			self				:    Any	)->   Dict:
								lowerCamelCase__						:						List[Any]						      =torch.randn(
								    self.batch_size,    self.num_in_channels,    self.latent_height,    self.latent_width,    generator=self.generator	).to(lowerCamelCase	)
								lowerCamelCase__						:						str						      =torch.randint(0,    1000,    size=(self.batch_size,),    generator=self.generator	).to(lowerCamelCase	)
								lowerCamelCase__						:						Any						      =torch.randn(
								    self.batch_size,    self.prompt_length,    self.text_encoder_hidden_dim,    generator=self.generator	).to(lowerCamelCase	)
								return noisy_latents, timesteps, encoder_hidden_states



					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    Optional[int]	)->   int:
								lowerCamelCase__						:						Optional[int]						      ={}
								for k, param in unet.state_dict().items():
											lowerCamelCase__						:						List[Any]						      =torch.randn_like(lowerCamelCase	) + (param * torch.randn_like(lowerCamelCase	))
											updated_state_dict.update({k: updated_param}	)
								unet.load_state_dict(lowerCamelCase	)
								return unet



					def 						snake_case			(			self				:    int	)->   int:
								lowerCamelCase__      ,  lowerCamelCase__						:						List[str]						      =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				:    str	)->   Union[str, Any]:
								lowerCamelCase__      ,  lowerCamelCase__						:						str						      =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()	)
								lowerCamelCase__						:						str						      =list(unet.parameters()	)
								for s_param, param in zip(ema_unet.shadow_params,    lowerCamelCase	):
											assert torch.allclose(lowerCamelCase,    lowerCamelCase	)

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



					def 						snake_case			(			self				:    List[str]	)->   str:
								lowerCamelCase__      ,  lowerCamelCase__						:						Tuple						      =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.
								lowerCamelCase__						:						Dict						      =self.simulate_backprop(lowerCamelCase	)

								# 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.
								lowerCamelCase__						:						Union[str, Any]						      =list(unet_pseudo_updated_step_one.parameters()	)
								for s_param, param in zip(ema_unet.shadow_params,    lowerCamelCase	):
											assert ~torch.allclose(lowerCamelCase,    lowerCamelCase	)

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



					def 						snake_case			(			self				:    List[str]	)->   Tuple:
								# If we call EMA step after a backpropagation consecutively for two times,
								# the shadow params from those two steps should be different.
								lowerCamelCase__      ,  lowerCamelCase__						:						Optional[Any]						      =self.get_models()

								# First backprop + EMA
								lowerCamelCase__						:						Optional[int]						      =self.simulate_backprop(lowerCamelCase	)
								ema_unet.step(unet_step_one.parameters()	)
								lowerCamelCase__						:						Any						      =ema_unet.shadow_params

								# Second backprop + EMA
								lowerCamelCase__						:						int						      =self.simulate_backprop(lowerCamelCase	)
								ema_unet.step(unet_step_two.parameters()	)
								lowerCamelCase__						:						Dict						      =ema_unet.shadow_params

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



					def 						snake_case			(			self				:    Any	)->   int:
								# If there's no decay even if there are backprops, EMA steps
								# won't take any effect i.e., the shadow params would remain the
								# same.
								lowerCamelCase__      ,  lowerCamelCase__						:						Optional[int]						      =self.get_models(decay=0.0	)
								lowerCamelCase__						:						Union[str, Any]						      =self.simulate_backprop(lowerCamelCase	)
								ema_unet.step(unet_step_one.parameters()	)
								lowerCamelCase__						:						Union[str, Any]						      =ema_unet.shadow_params

								lowerCamelCase__						:						List[Any]						      =self.simulate_backprop(lowerCamelCase	)
								ema_unet.step(unet_step_two.parameters()	)
								lowerCamelCase__						:						List[str]						      =ema_unet.shadow_params

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




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

								with tempfile.TemporaryDirectory() as tmpdir:
											ema_unet.save_pretrained(lowerCamelCase	)
											lowerCamelCase__						:						Tuple						      =UNetaDConditionModel.from_pretrained(lowerCamelCase,    model_cls=lowerCamelCase	)
											lowerCamelCase__						:						Dict						      =loaded_unet.to(unet.device	)

								# Since no EMA step has been performed the outputs should match.
								lowerCamelCase__						:						Tuple						      =unet(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	).sample
								lowerCamelCase__						:						int						      =loaded_unet(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	).sample

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











def  A_(      A = 1000 ):
	UpperCAmelCase_		    =  0
	for i in range(A ):
		if i % 3 == 0 or i % 5 == 0:
			result += i
	return result


if __name__ == "__main__":
				print(f"{solution() = }")


