

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    ):







  """simple docstring"""




  UpperCAmelCase__ :	int         ="""hf-internal-testing/tiny-stable-diffusion-pipe"""
  UpperCAmelCase__ :	Optional[Any]         =1
  UpperCAmelCase__ :	List[Any]         =7_7
  UpperCAmelCase__ :	str         =3_2
  UpperCAmelCase__ :	str         =4
  UpperCAmelCase__ :	str         =6_4
  UpperCAmelCase__ :	List[str]         =torch.manual_seed(0    )


  def 	_lowercase (     self      :	Any			,			UpperCAmelCase__      :	int=0.99_99						)   ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				UNetaDConditionModel.from_pretrained(self.model_id			,			subfolder="""unet"""						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				unet.to(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				EMAModel(unet.parameters()			,			decay=UpperCAmelCase__			,			model_cls=UpperCAmelCase__			,			model_config=unet.config						)
        return unet, ema_unet


  def 	_lowercase (     self      :	List[Any]						)   ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[Any]					       =				torch.randn(
            self.batch_size			,			self.num_in_channels			,			self.latent_height			,			self.latent_width			,			generator=self.generator						).to(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				torch.randint(0			,			1_0_0_0			,			size=(self.batch_size,)			,			generator=self.generator						).to(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				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 	_lowercase (     self      :	str			,			UpperCAmelCase__      :	Tuple						)   ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[Any]					       =				{}
        for k, param in unet.state_dict().items():
              SCREAMING_SNAKE_CASE	: int					       =				torch.randn_like(UpperCAmelCase__						) + (param * torch.randn_like(UpperCAmelCase__						))
              updated_state_dict.update({k: updated_param}						)
        unet.load_state_dict(UpperCAmelCase__						)
        return unet


  def 	_lowercase (     self      :	str						)   ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: str					       =				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 	_lowercase (     self      :	Union[str, Any]						)   ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: List[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()						)
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				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 	_lowercase (     self      :	Dict						)   ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Union[str, 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.
        SCREAMING_SNAKE_CASE	: str					       =				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.
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				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 	_lowercase (     self      :	Tuple						)   ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Tuple					       =				self.get_models()

        # First backprop + EMA
        SCREAMING_SNAKE_CASE	: int					       =				self.simulate_backprop(UpperCAmelCase__						)
        ema_unet.step(unet_step_one.parameters()						)
        SCREAMING_SNAKE_CASE	: List[str]					       =				ema_unet.shadow_params

        # Second backprop + EMA
        SCREAMING_SNAKE_CASE	: List[Any]					       =				self.simulate_backprop(UpperCAmelCase__						)
        ema_unet.step(unet_step_two.parameters()						)
        SCREAMING_SNAKE_CASE	: Dict					       =				ema_unet.shadow_params

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


  def 	_lowercase (     self      :	Optional[int]						)   ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.get_models(decay=0.0						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				self.simulate_backprop(UpperCAmelCase__						)
        ema_unet.step(unet_step_one.parameters()						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				ema_unet.shadow_params

        SCREAMING_SNAKE_CASE	: Dict					       =				self.simulate_backprop(UpperCAmelCase__						)
        ema_unet.step(unet_step_two.parameters()						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				ema_unet.shadow_params

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




  @skip_mps
  def 	_lowercase (     self      :	Tuple						)   ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Tuple					       =				self.get_models()
        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: int					       =				self.get_dummy_inputs()

        with tempfile.TemporaryDirectory() as tmpdir:
              ema_unet.save_pretrained(UpperCAmelCase__						)
              SCREAMING_SNAKE_CASE	: Any					       =				UNetaDConditionModel.from_pretrained(UpperCAmelCase__			,			model_cls=UpperCAmelCase__						)
              SCREAMING_SNAKE_CASE	: int					       =				loaded_unet.to(unet.device						)

        # Since no EMA step has been performed the outputs should match.
        SCREAMING_SNAKE_CASE	: Dict					       =				unet(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__						).sample
        SCREAMING_SNAKE_CASE	: List[Any]					       =				loaded_unet(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__						).sample

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







import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

UpperCAmelCase__	:       Dict			      =      [(1,)]






def    __lowercase (       )       ->  Tuple:
      SCREAMING_SNAKE_CASE	: Optional[Any]					       =				bool(os.getenv("""RUN_SLOW"""  ,      0     )     )
      if run_slow:
            assert True
      else:
            assert False, "should have been skipped"






def    __lowercase (       )       ->  Optional[Any]:
      SCREAMING_SNAKE_CASE	: Dict					       =				bool(os.getenv("""RUN_SLOW"""  ,      0     )     )
      if run_slow and torch_device == "cuda":
            assert True
      else:
            assert False, "should have been skipped"







@require_torch
class     a__    (				unittest.TestCase    ):







  """simple docstring"""




  @slow
  @require_torch_gpu
  def 	_lowercase (     self      :	Any						)   ->Optional[Any]:
        """simple docstring"""







        check_slow_torch_cuda()


  @require_torch_gpu
  @slow
  def 	_lowercase (     self      :	Optional[Any]						)   ->Any:
        """simple docstring"""







        check_slow_torch_cuda()




  @parameterized.expand(UpperCAmelCase__						)
  @slow
  def 	_lowercase (     self      :	Optional[int]			,			UpperCAmelCase__      :	Dict=None						)   ->Optional[Any]:
        """simple docstring"""







        check_slow()






@slow
@require_torch_gpu
def    __lowercase (       )       ->  Optional[int]:
      check_slow_torch_cuda()






@require_torch_gpu
@slow
def    __lowercase (       )       ->  int:
      check_slow_torch_cuda()






@slow
@pytest.mark.parametrize("""param"""  ,      [1]     )
def    __lowercase (       _A     )       ->  Optional[int]:
      check_slow()







@pytest.mark.parametrize("""param"""  ,      [1]     )
@slow
def    __lowercase (       _A     )       ->  str:
      check_slow()




