




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__	:       List[str]			=    1
      lowerCAmelCase__	:       str			=    77
      lowerCAmelCase__	:       Union[str, Any]			=    32
      lowerCAmelCase__	:       int			=    4
      lowerCAmelCase__	:       Tuple			=    64
      lowerCAmelCase__	:       Optional[Any]			=    torch.manual_seed(0      )







      def 					snake_case		(   self    :       Union[str, Any]       ,				snake_case    :       Dict=0.99_99 ):
          __UpperCamelCase     						=  UNetaDConditionModel.from_pretrained(self.model_id       ,				subfolder='''unet''' )
          __UpperCamelCase     						=  unet.to(snake_case )
          __UpperCamelCase     						=  EMAModel(unet.parameters()       ,				decay=snake_case       ,				model_cls=snake_case       ,				model_config=unet.config )
          return unet, ema_unet







      def 					snake_case		(   self    :       Optional[int] ):
          __UpperCamelCase     						=  torch.randn(
              self.batch_size       ,				self.num_in_channels       ,				self.latent_height       ,				self.latent_width       ,				generator=self.generator ).to(snake_case )
          __UpperCamelCase     						=  torch.randint(0       ,				1000       ,				size=(self.batch_size,)       ,				generator=self.generator ).to(snake_case )
          __UpperCamelCase     						=  torch.randn(
              self.batch_size       ,				self.prompt_length       ,				self.text_encoder_hidden_dim       ,				generator=self.generator ).to(snake_case )
          return noisy_latents, timesteps, encoder_hidden_states







      def 					snake_case		(   self    :       Dict       ,				snake_case    :       Dict ):
          __UpperCamelCase     						=  {}
          for k, param in unet.state_dict().items():
              __UpperCamelCase     						=  torch.randn_like(snake_case ) + (param * torch.randn_like(snake_case ))
              updated_state_dict.update({k: updated_param} )
          unet.load_state_dict(snake_case )
          return unet







      def 					snake_case		(   self    :       int ):
          __UpperCamelCase						,					__UpperCamelCase     						=  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    :       Tuple ):
          __UpperCamelCase						,					__UpperCamelCase     						=  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(unet.parameters() )
          for s_param, param in zip(ema_unet.shadow_params       ,				snake_case ):
              assert torch.allclose(snake_case       ,				snake_case )

          # 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       ,				snake_case ):
              assert torch.allclose(snake_case       ,				snake_case )







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase						,					__UpperCamelCase     						=  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     						=  self.simulate_backprop(snake_case )

          # 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     						=  list(unet_pseudo_updated_step_one.parameters() )
          for s_param, param in zip(ema_unet.shadow_params       ,				snake_case ):
              assert ~torch.allclose(snake_case       ,				snake_case )

          # 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       ,				snake_case ):
              assert ~torch.allclose(snake_case       ,				snake_case )







      def 					snake_case		(   self    :       List[str] ):
          # If we call EMA step after a backpropagation consecutively for two times,
          # the shadow params from those two steps should be different.
          __UpperCamelCase						,					__UpperCamelCase     						=  self.get_models()

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

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

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







      def 					snake_case		(   self    :       Optional[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     						=  self.get_models(decay=0.0 )
          __UpperCamelCase     						=  self.simulate_backprop(snake_case )
          ema_unet.step(unet_step_one.parameters() )
          __UpperCamelCase     						=  ema_unet.shadow_params

          __UpperCamelCase     						=  self.simulate_backprop(snake_case )
          ema_unet.step(unet_step_two.parameters() )
          __UpperCamelCase     						=  ema_unet.shadow_params

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







      @skip_mps
      def 					snake_case		(   self    :       Optional[Any] ):
          __UpperCamelCase						,					__UpperCamelCase     						=  self.get_models()
          __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  self.get_dummy_inputs()

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

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

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












def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		bool:

    """simple docstring"""


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


if __name__ == "__main__":
 import doctest

 doctest.testmod()






