def SCREAMING_SNAKE_CASE				(     lowercase_:	int				):
       if not isinstance(lowercase_		,						lowercase_				):
              _UpperCamelCase     :       List[Any]  						=  F"Input value of [number={number}] must be an integer"
              raise TypeError(lowercase_				)
       if number < 0 or number % 2 == 1:
              return False
       _UpperCamelCase     :       Dict  						=  int(number**0.5				)
       return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




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

 def 					__snake_case					(			self       :		Any       ,			lowerCamelCase       :		Union[str, Any]=0.9_9_9_9  ) ->							Dict:
        _UpperCamelCase     :       int  						=  UNetaDConditionModel.from_pretrained(self.model_id       ,			subfolder='unet'  )
        _UpperCamelCase     :       Any  						=  unet.to(lowerCamelCase  )
        _UpperCamelCase     :       Union[str, Any]  						=  EMAModel(unet.parameters()       ,			decay=lowerCamelCase       ,			model_cls=lowerCamelCase       ,			model_config=unet.config  )
        return unet, ema_unet

 def 					__snake_case					(			self       :		Tuple  ) ->							Union[str, Any]:
        _UpperCamelCase     :       Tuple  						=  torch.randn(
            self.batch_size       ,			self.num_in_channels       ,			self.latent_height       ,			self.latent_width       ,			generator=self.generator  ).to(lowerCamelCase  )
        _UpperCamelCase     :       Union[str, Any]  						=  torch.randint(0       ,			1_0_0_0       ,			size=(self.batch_size,)       ,			generator=self.generator  ).to(lowerCamelCase  )
        _UpperCamelCase     :       int  						=  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 					__snake_case					(			self       :		str       ,			lowerCamelCase       :		List[Any]  ) ->							List[Any]:
        _UpperCamelCase     :       int  						=  {}
        for k, param in unet.state_dict().items():
               _UpperCamelCase     :       Optional[Any]  						=  torch.randn_like(lowerCamelCase  ) + (param * torch.randn_like(lowerCamelCase  ))
               updated_state_dict.update({k: updated_param}  )
        unet.load_state_dict(lowerCamelCase  )
        return unet

 def 					__snake_case					(			self       :		List[Any]  ) ->							Tuple:
        _UpperCamelCase						,      _UpperCamelCase     :       Union[str, 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 					__snake_case					(			self       :		int  ) ->							Any:
        _UpperCamelCase						,      _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     :       List[Any]  						=  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 					__snake_case					(			self       :		Dict  ) ->							Dict:
        _UpperCamelCase						,      _UpperCamelCase     :       Dict  						=  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     :       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[Any]  						=  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 					__snake_case					(			self       :		Union[str, Any]  ) ->							Tuple:
        # If we call EMA step after a backpropagation consecutively for two times,
        # the shadow params from those two steps should be different.
        _UpperCamelCase						,      _UpperCamelCase     :       Dict  						=  self.get_models()

        # First backprop + EMA
        _UpperCamelCase     :       Optional[Any]  						=  self.simulate_backprop(lowerCamelCase  )
        ema_unet.step(unet_step_one.parameters()  )
        _UpperCamelCase     :       Optional[Any]  						=  ema_unet.shadow_params

        # Second backprop + EMA
        _UpperCamelCase     :       Optional[int]  						=  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 					__snake_case					(			self       :		str  ) ->							int:
        # 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.
        _UpperCamelCase						,      _UpperCamelCase     :       List[str]  						=  self.get_models(decay=0.0  )
        _UpperCamelCase     :       Optional[Any]  						=  self.simulate_backprop(lowerCamelCase  )
        ema_unet.step(unet_step_one.parameters()  )
        _UpperCamelCase     :       Dict  						=  ema_unet.shadow_params

        _UpperCamelCase     :       List[str]  						=  self.simulate_backprop(lowerCamelCase  )
        ema_unet.step(unet_step_two.parameters()  )
        _UpperCamelCase     :       Union[str, Any]  						=  ema_unet.shadow_params

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

 @skip_mps
 def 					__snake_case					(			self       :		Dict  ) ->							str:
        _UpperCamelCase						,      _UpperCamelCase     :       Optional[Any]  						=  self.get_models()
        _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase     :       Any  						=  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     :       Dict  						=  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     :       List[str]  						=  loaded_unet(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  ).sample

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



