




def    UpperCAmelCase__	(  lowerCamelCase ):
  lowercase     :Optional[int]   						=    len(lowerCamelCase )
  if n in (0, 1):
    return 0
  return sum(query * (n - i - 1) for i, query in enumerate(sorted(lowerCamelCase ) ) )


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






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       int=0.99_99					):
         lowercase     :Optional[int]   						=    UNetaDConditionModel.from_pretrained(self.model_id							,       subfolder="unet"					)
         lowercase     :Union[str, Any]   						=    unet.to(_lowerCAmelCase					)
         lowercase     :Any   						=    EMAModel(unet.parameters()							,       decay=_lowerCAmelCase							,       model_cls=_lowerCAmelCase							,       model_config=unet.config					)
         return unet, ema_unet






       def        SCREAMING_SNAKE_CASE     ( self:       List[str]					):
         lowercase     :Optional[Any]   						=    torch.randn(
             self.batch_size							,       self.num_in_channels							,       self.latent_height							,       self.latent_width							,       generator=self.generator					).to(_lowerCAmelCase					)
         lowercase     :Dict   						=    torch.randint(0							,       10_00							,       size=(self.batch_size,)							,       generator=self.generator					).to(_lowerCAmelCase					)
         lowercase     :List[Any]   						=    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        SCREAMING_SNAKE_CASE     ( self:       Optional[int]							,       _lowerCAmelCase:       Optional[Any]					):
         lowercase     :Any   						=    {}
         for k, param in unet.state_dict().items():
           lowercase     :Optional[int]   						=    torch.randn_like(_lowerCAmelCase					) + (param * torch.randn_like(_lowerCAmelCase					))
           updated_state_dict.update({k: updated_param}					)
         unet.load_state_dict(_lowerCAmelCase					)
         return unet






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

         # First backprop + EMA
         lowercase     :Union[str, Any]   						=    self.simulate_backprop(_lowerCAmelCase					)
         ema_unet.step(unet_step_one.parameters()					)
         lowercase     :int   						=    ema_unet.shadow_params

         # Second backprop + EMA
         lowercase     :Optional[int]   						=    self.simulate_backprop(_lowerCAmelCase					)
         ema_unet.step(unet_step_two.parameters()					)
         lowercase     :Optional[int]   						=    ema_unet.shadow_params

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






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]					):
         # 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.
         lowercase						,     lowercase     :Any   						=    self.get_models(decay=0.0					)
         lowercase     :List[Any]   						=    self.simulate_backprop(_lowerCAmelCase					)
         ema_unet.step(unet_step_one.parameters()					)
         lowercase     :List[str]   						=    ema_unet.shadow_params

         lowercase     :str   						=    self.simulate_backprop(_lowerCAmelCase					)
         ema_unet.step(unet_step_two.parameters()					)
         lowercase     :List[str]   						=    ema_unet.shadow_params

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






       @skip_mps
       def        SCREAMING_SNAKE_CASE     ( self:       List[Any]					):
         lowercase						,     lowercase     :Optional[Any]   						=    self.get_models()
         lowercase						,     lowercase						,     lowercase     :str   						=    self.get_dummy_inputs()

         with tempfile.TemporaryDirectory() as tmpdir:
           ema_unet.save_pretrained(_lowerCAmelCase					)
           lowercase     :Optional[int]   						=    UNetaDConditionModel.from_pretrained(_lowerCAmelCase							,       model_cls=_lowerCAmelCase					)
           lowercase     :Tuple   						=    loaded_unet.to(unet.device					)

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

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



