



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







   def 							A__						(self       ,	lowerCamelCase=0.9999				):




      '''simple docstring'''




      _lowerCAmelCase					     =		UNetaDConditionModel.from_pretrained(self.model_id       ,	subfolder="""unet"""				)
      _lowerCAmelCase					     =		unet.to(lowerCAmelCase_				)
      _lowerCAmelCase					     =		EMAModel(unet.parameters()       ,	decay=lowerCAmelCase_       ,	model_cls=lowerCAmelCase_       ,	model_config=unet.config				)
      return unet, ema_unet







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		torch.randn(
          self.batch_size       ,	self.num_in_channels       ,	self.latent_height       ,	self.latent_width       ,	generator=self.generator				).to(lowerCAmelCase_				)
      _lowerCAmelCase					     =		torch.randint(0       ,	1_000       ,	size=(self.batch_size,)       ,	generator=self.generator				).to(lowerCAmelCase_				)
      _lowerCAmelCase					     =		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       ,	lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		{}
      for k, param in unet.state_dict().items():
         _lowerCAmelCase					     =		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				):




      '''simple docstring'''




      _lowerCAmelCase     ,						_lowerCAmelCase					     =		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				):




      '''simple docstring'''




      _lowerCAmelCase     ,						_lowerCAmelCase					     =		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					     =		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				):




      '''simple docstring'''




      _lowerCAmelCase     ,						_lowerCAmelCase					     =		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					     =		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					     =		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				):




      '''simple docstring'''




      _lowerCAmelCase     ,						_lowerCAmelCase					     =		self.get_models()

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

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

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







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase     ,						_lowerCAmelCase					     =		self.get_models(decay=0.0				)
      _lowerCAmelCase					     =		self.simulate_backprop(lowerCAmelCase_				)
      ema_unet.step(unet_step_one.parameters()				)
      _lowerCAmelCase					     =		ema_unet.shadow_params

      _lowerCAmelCase					     =		self.simulate_backprop(lowerCAmelCase_				)
      ema_unet.step(unet_step_two.parameters()				)
      _lowerCAmelCase					     =		ema_unet.shadow_params

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







   @skip_mps
   def 							A__						(self				):




      '''simple docstring'''




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

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

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

      assert torch.allclose(lowerCAmelCase_       ,	lowerCAmelCase_       ,	atol=1e-4				)




"""simple docstring"""





def 	__UpperCAmelCase					(       snake_case_  :  float   )							->	int:






   """simple docstring"""


   return int(snake_case_   ) if x - int(snake_case_   ) <= 0 else int(snake_case_   ) + 1


if __name__ == "__main__":
  import doctest

  doctest.testmod()