code: 
  
  
  
  
  '''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				)
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''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()
  
  
  
  
  
  