



'''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 _A  (    unittest.TestCase    ):
		_SCREAMING_SNAKE_CASE					:    str						      =			"""hf-internal-testing/tiny-stable-diffusion-pipe"""
		_SCREAMING_SNAKE_CASE					:    Optional[int]						      =			1
		_SCREAMING_SNAKE_CASE					:    List[str]						      =			77
		_SCREAMING_SNAKE_CASE					:    int						      =			32
		_SCREAMING_SNAKE_CASE					:    Dict						      =			4
		_SCREAMING_SNAKE_CASE					:    str						      =			64
		_SCREAMING_SNAKE_CASE					:    Optional[Any]						      =			torch.manual_seed(0    )





		def 							__A       (     self ,       __UpperCAmelCase=0.9999				)       ->     Tuple:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     UNetaDConditionModel.from_pretrained(self.model_id ,       subfolder="""unet"""				)
					__UpperCAmelCase   :      Any										=     unet.to(lowerCAmelCase__				)
					__UpperCAmelCase   :      int										=     EMAModel(unet.parameters() ,       decay=lowerCAmelCase__ ,       model_cls=lowerCAmelCase__ ,       model_config=unet.config				)
					return unet, ema_unet





		def 							__A       (     self				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      str										=     torch.randn(
					    self.batch_size ,       self.num_in_channels ,       self.latent_height ,       self.latent_width ,       generator=self.generator				).to(lowerCAmelCase__				)
					__UpperCAmelCase   :      Any										=     torch.randint(0 ,       1_000 ,       size=(self.batch_size,) ,       generator=self.generator				).to(lowerCAmelCase__				)
					__UpperCAmelCase   :      Tuple										=     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 							__A       (     self ,       __UpperCAmelCase				)       ->     Any:

					'''simple docstring'''
					__UpperCAmelCase   :      Any										=     {}
					for k, param in unet.state_dict().items():
								__UpperCAmelCase   :      Tuple										=     torch.randn_like(lowerCAmelCase__				) + (param * torch.randn_like(lowerCAmelCase__				))
								updated_state_dict.update({k: updated_param}				)
					unet.load_state_dict(lowerCAmelCase__				)
					return unet





		def 							__A       (     self				)       ->     Tuple:

					'''simple docstring'''
					__UpperCAmelCase   :      List[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 							__A       (     self				)       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      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()				)
					__UpperCAmelCase   :      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 							__A       (     self				)       ->     List[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     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.
					__UpperCAmelCase   :      List[str]										=     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.
					__UpperCAmelCase   :      Optional[int]										=     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 							__A       (     self				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Dict										=     self.get_models()

					# First backprop + EMA
					__UpperCAmelCase   :      Tuple										=     self.simulate_backprop(lowerCAmelCase__				)
					ema_unet.step(unet_step_one.parameters()				)
					__UpperCAmelCase   :      Dict										=     ema_unet.shadow_params

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

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





		def 							__A       (     self				)       ->     List[str]:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     self.get_models(decay=0.0				)
					__UpperCAmelCase   :      Optional[int]										=     self.simulate_backprop(lowerCAmelCase__				)
					ema_unet.step(unet_step_one.parameters()				)
					__UpperCAmelCase   :      List[Any]										=     ema_unet.shadow_params

					__UpperCAmelCase   :      Tuple										=     self.simulate_backprop(lowerCAmelCase__				)
					ema_unet.step(unet_step_two.parameters()				)
					__UpperCAmelCase   :      List[Any]										=     ema_unet.shadow_params

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





		@skip_mps
		def 							__A       (     self				)       ->     Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Union[str, Any]										=     self.get_models()
					__UpperCAmelCase   :      Tuple										=     self.get_dummy_inputs()

					with tempfile.TemporaryDirectory() as tmpdir:
								ema_unet.save_pretrained(lowerCAmelCase__				)
								__UpperCAmelCase   :      List[str]										=     UNetaDConditionModel.from_pretrained(lowerCAmelCase__ ,       model_cls=lowerCAmelCase__				)
								__UpperCAmelCase   :      Tuple										=     loaded_unet.to(unet.device				)

					# Since no EMA step has been performed the outputs should match.
					__UpperCAmelCase   :      Optional[Any]										=     unet(lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__				).sample
					__UpperCAmelCase   :      Optional[int]										=     loaded_unet(lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__				).sample

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









'''simple docstring'''
from __future__ import annotations

from typing import Any



class _A  :





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     None:  # noqa: A002

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     item
					__UpperCAmelCase   :      str										=     next



class _A  :





		def __init__(     self				)       ->     None:

					'''simple docstring'''
					__UpperCAmelCase   :      Node | None										=     None
					__UpperCAmelCase   :      Optional[int]										=     0





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     None:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     Node(__UpperCAmelCase ,       self.head				)
					self.size += 1





		def 							__A       (     self				)       ->     Any:

					'''simple docstring'''
					# Switched 'self.is_empty()' to 'self.head is None'
					# because mypy was considering the possibility that 'self.head'
					# can be None in below else part and giving error
					if self.head is None:
								return None
					else:
								__UpperCAmelCase   :      str										=     self.head.item
								__UpperCAmelCase   :      Tuple										=     self.head.next
								self.size -= 1
								return item





		def 							__A       (     self				)       ->     bool:

					'''simple docstring'''
					return self.head is None





		def __str__(     self				)       ->     str:

					'''simple docstring'''
					if self.is_empty():
								return ""
					else:
								__UpperCAmelCase   :      Union[str, Any]										=     self.head
								__UpperCAmelCase   :      List[Any]										=     """"""
								__UpperCAmelCase   :      list[str]										=     []
								while iterate:
											item_list.append(str(iterate.item				)				)
											__UpperCAmelCase   :      Any										=     iterate.next

								__UpperCAmelCase   :      Dict										=     """ --> """.join(__UpperCAmelCase				)

								return item_str





		def __len__(     self				)       ->     int:

					'''simple docstring'''
					return self.size




