




'''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   lowerCamelCase_							( unittest.TestCase							):




					'''simple docstring'''






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






					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase=0.99_99				):
							_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        __SCREAMING_SNAKE_CASE			(					self				):
							_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_000,						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        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
							_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        __SCREAMING_SNAKE_CASE			(					self				):
							_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        __SCREAMING_SNAKE_CASE			(					self				):
							_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        __SCREAMING_SNAKE_CASE			(					self				):
							_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        __SCREAMING_SNAKE_CASE			(					self				):
							# If we call EMA step after a backpropagation consecutively for two times,
							# the shadow params from those two steps should be different.
							_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        __SCREAMING_SNAKE_CASE			(					self				):
							# 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.
							_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        __SCREAMING_SNAKE_CASE			(					self				):
							_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				)











'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

_lowerCamelCase							:		List[str]										=						TypeVar('T', bound=bool)





class   lowerCamelCase_							( Generic[T]							):




					'''simple docstring'''






					def __init__(					self,						__UpperCamelCase				):
							_snake_case			 = value
							_snake_case			 = None
							_snake_case			 = None






					@property
					def        __SCREAMING_SNAKE_CASE			(					self				):
							return self._value






					@staticmethod
					def        __SCREAMING_SNAKE_CASE			(					__UpperCamelCase,						__UpperCamelCase				):
							if not roota:
									return roota

							if not roota:
									return roota

							if roota.value > roota.value:
									_snake_case ,					_snake_case			 = roota, roota

							_snake_case			 = roota
							_snake_case			 = roota.right
							_snake_case			 = roota.left
							_snake_case			 = SkewNode.merge(__UpperCamelCase,						__UpperCamelCase				)

							return result





class   lowerCamelCase_							( Generic[T]							):




					'''simple docstring'''






					def __init__(					self,						__UpperCamelCase = ()				):
							_snake_case			 = None
							if data:
									for item in data:
											self.insert(__UpperCamelCase				)






					def __bool__(					self				):
							return self._root is not None






					def __iter__(					self				):
							_snake_case			 = []
							while self:
									result.append(self.pop()				)

							# Pushing items back to the heap not to clear it.
							for item in result:
									self.insert(__UpperCamelCase				)

							return iter(__UpperCamelCase				)






					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
							_snake_case			 = SkewNode.merge(self._root,						SkewNode(__UpperCamelCase				)				)






					def        __SCREAMING_SNAKE_CASE			(					self				):
							_snake_case			 = self.top()
							_snake_case			 = (
							    SkewNode.merge(self._root.left,						self._root.right				) if self._root else None
							)

							return result






					def        __SCREAMING_SNAKE_CASE			(					self				):
							if not self._root:
									raise IndexError("Can't get top element for the empty heap."				)
							return self._root.value






					def        __SCREAMING_SNAKE_CASE			(					self				):
							_snake_case			 = None


if __name__ == "__main__":
						import doctest

						doctest.testmod()





