


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   A__							(						unittest.TestCase					):
						UpperCAmelCase     =  "hf-internal-testing/tiny-stable-diffusion-pipe"
						UpperCAmelCase     =  1
						UpperCAmelCase     =  77
						UpperCAmelCase     =  32
						UpperCAmelCase     =  4
						UpperCAmelCase     =  UpperCAmelCase     =  64
						UpperCAmelCase     =  torch.manual_seed(0					)




						def 							__UpperCamelCase			(    self   : Optional[int]			,      _a   : List[Any]=0.99_99					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=UNetaDConditionModel.from_pretrained(self.model_id			,      subfolder='''unet'''					)
										_SCREAMING_SNAKE_CASE     					=unet.to(_a					)
										_SCREAMING_SNAKE_CASE     					=EMAModel(unet.parameters()			,      decay=_a			,      model_cls=_a			,      model_config=unet.config					)
										return unet, ema_unet




						def 							__UpperCamelCase			(    self   : Dict					)							->       Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=torch.randn(
										    self.batch_size			,      self.num_in_channels			,      self.latent_height			,      self.latent_width			,      generator=self.generator					).to(_a					)
										_SCREAMING_SNAKE_CASE     					=torch.randint(0			,      1000			,      size=(self.batch_size,)			,      generator=self.generator					).to(_a					)
										_SCREAMING_SNAKE_CASE     					=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 							__UpperCamelCase			(    self   : Union[str, Any]			,      _a   : Any					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					={}
										for k, param in unet.state_dict().items():
														_SCREAMING_SNAKE_CASE     					=torch.randn_like(_a					) + (param * torch.randn_like(_a					))
														updated_state_dict.update({k: updated_param}					)
										unet.load_state_dict(_a					)
										return unet




						def 							__UpperCamelCase			(    self   : List[Any]					)							->       int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_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 							__UpperCamelCase			(    self   : Dict					)							->       Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_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()					)
										_SCREAMING_SNAKE_CASE     					=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 							__UpperCamelCase			(    self   : str					)							->       int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_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.
										_SCREAMING_SNAKE_CASE     					=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.
										_SCREAMING_SNAKE_CASE     					=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 							__UpperCamelCase			(    self   : int					)							->       Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.get_models()

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

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

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




						def 							__UpperCamelCase			(    self   : Any					)							->       Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.get_models(decay=0.0					)
										_SCREAMING_SNAKE_CASE     					=self.simulate_backprop(_a					)
										ema_unet.step(unet_step_one.parameters()					)
										_SCREAMING_SNAKE_CASE     					=ema_unet.shadow_params

										_SCREAMING_SNAKE_CASE     					=self.simulate_backprop(_a					)
										ema_unet.step(unet_step_two.parameters()					)
										_SCREAMING_SNAKE_CASE     					=ema_unet.shadow_params

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




						@skip_mps
						def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.get_models()
										_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.get_dummy_inputs()

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

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

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



from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class   A__							:




						def __init__(    self   : List[Any]			,      _a   : Any					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=data
										_SCREAMING_SNAKE_CASE     					=None




						def __str__(    self   : List[Any]					)							->       str:


										"""simple docstring"""





										return f"{self.data}"

class   A__							:




						def __init__(    self   : Optional[Any]					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=None
										_SCREAMING_SNAKE_CASE     					=None




						def __iter__(    self   : List[str]					)							->       Iterator[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.front
										while node:
														yield node.data
														_SCREAMING_SNAKE_CASE     					=node.next




						def __len__(    self   : List[Any]					)							->       int:


										"""simple docstring"""





										return len(tuple(iter(self					)					)					)




						def __str__(    self   : int					)							->       str:


										"""simple docstring"""





										return " <- ".join(str(_a					) for item in self					)




						def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       bool:


										"""simple docstring"""





										return len(self					) == 0




						def 							__UpperCamelCase			(    self   : int			,      _a   : Any					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=Node(_a					)
										if self.is_empty():
														_SCREAMING_SNAKE_CASE     					=_SCREAMING_SNAKE_CASE     					=node
										else:
														assert isinstance(self.rear			,      _a					)
														_SCREAMING_SNAKE_CASE     					=node
														_SCREAMING_SNAKE_CASE     					=node




						def 							__UpperCamelCase			(    self   : Dict					)							->       Any:


										"""simple docstring"""





										if self.is_empty():
														raise IndexError('''dequeue from empty queue'''					)
										assert isinstance(self.front			,      _a					)
										_SCREAMING_SNAKE_CASE     					=self.front
										_SCREAMING_SNAKE_CASE     					=self.front.next
										if self.front is None:
														_SCREAMING_SNAKE_CASE     					=None
										return node.data




						def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=_SCREAMING_SNAKE_CASE     					=None


if __name__ == "__main__":
							from doctest import testmod

							testmod()