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 						snake_case_ (	unittest.TestCase ):
   __A			:	str        =       "hf-internal-testing/tiny-stable-diffusion-pipe"
   __A			:	Union[str, Any]        =       1
   __A			:	Union[str, Any]        =       77
   __A			:	List[str]        =       32
   __A			:	List[Any]        =       4
   __A			:	Optional[Any]        =       64
   __A			:	Tuple        =       torch.manual_seed(0 )






   def    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  List[str]=0.99_99						)      ->		Tuple:
       lowercase__				:     Tuple					      =       UNetaDConditionModel.from_pretrained(self.model_id			,		subfolder="unet"						)
       lowercase__				:     int					      =       unet.to(lowercase_						)
       lowercase__				:     Optional[int]					      =       EMAModel(unet.parameters()			,		decay=lowercase_			,		model_cls=lowercase_			,		model_config=unet.config						)
       return unet, ema_unet






   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		Optional[int]:
       lowercase__				:     Optional[Any]					      =       torch.randn(
           self.batch_size			,		self.num_in_channels			,		self.latent_height			,		self.latent_width			,		generator=self.generator						).to(lowercase_						)
       lowercase__				:     str					      =       torch.randint(0			,		10_00			,		size=(self.batch_size,)			,		generator=self.generator						).to(lowercase_						)
       lowercase__				:     int					      =       torch.randn(
           self.batch_size			,		self.prompt_length			,		self.text_encoder_hidden_dim			,		generator=self.generator						).to(lowercase_						)
       return noisy_latents, timesteps, encoder_hidden_states






   def    __UpperCamelCase  (   self       :  Tuple			,		lowercase_       :  str						)      ->		Optional[int]:
       lowercase__				:     Any					      =       {}
       for k, param in unet.state_dict().items():
           lowercase__				:     Tuple					      =       torch.randn_like(lowercase_						) + (param * torch.randn_like(lowercase_						))
           updated_state_dict.update({k: updated_param}						)
       unet.load_state_dict(lowercase_						)
       return unet






   def    __UpperCamelCase  (   self       :  Any						)      ->		List[str]:
       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    __UpperCamelCase  (   self       :  List[str]						)      ->		Dict:
       lowercase__				:     int					      =       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__				:     Optional[Any]					      =       list(unet.parameters()						)
       for s_param, param in zip(ema_unet.shadow_params			,		lowercase_						):
           assert torch.allclose(lowercase_			,		lowercase_						)

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






   def    __UpperCamelCase  (   self       :  Tuple						)      ->		Optional[Any]:
       lowercase__				:     Tuple					      =       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__				:     Optional[Any]					      =       self.simulate_backprop(lowercase_						)

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

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






   def    __UpperCamelCase  (   self       :  Any						)      ->		Optional[Any]:
       lowercase__				:     Tuple					      =       self.get_models()

       # First backprop + EMA
       lowercase__				:     int					      =       self.simulate_backprop(lowercase_						)
       ema_unet.step(unet_step_one.parameters()						)
       lowercase__				:     str					      =       ema_unet.shadow_params

       # Second backprop + EMA
       lowercase__				:     Union[str, Any]					      =       self.simulate_backprop(lowercase_						)
       ema_unet.step(unet_step_two.parameters()						)
       lowercase__				:     Union[str, Any]					      =       ema_unet.shadow_params

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






   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		List[str]:
       lowercase__				:     Optional[int]					      =       self.get_models(decay=0.0						)
       lowercase__				:     Tuple					      =       self.simulate_backprop(lowercase_						)
       ema_unet.step(unet_step_one.parameters()						)
       lowercase__				:     Any					      =       ema_unet.shadow_params

       lowercase__				:     Optional[Any]					      =       self.simulate_backprop(lowercase_						)
       ema_unet.step(unet_step_two.parameters()						)
       lowercase__				:     Dict					      =       ema_unet.shadow_params

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







   @skip_mps
   def    __UpperCamelCase  (   self       :  List[Any]						)      ->		Union[str, Any]:
       lowercase__				:     Tuple					      =       self.get_models()
       lowercase__				:     str					      =       self.get_dummy_inputs()

       with tempfile.TemporaryDirectory() as tmpdir:
           ema_unet.save_pretrained(lowercase_						)
           lowercase__				:     int					      =       UNetaDConditionModel.from_pretrained(lowercase_			,		model_cls=lowercase_						)
           lowercase__				:     Tuple					      =       loaded_unet.to(unet.device						)

       # Since no EMA step has been performed the outputs should match.
       lowercase__				:     str					      =       unet(lowercase_			,		lowercase_			,		lowercase_						).sample
       lowercase__				:     int					      =       loaded_unet(lowercase_			,		lowercase_			,		lowercase_						).sample

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




from typing import Callable

def     lowercase_       (    _lowerCamelCase							: Callable):

    def wrapper(_lowerCamelCase							: str):
        lowercase__				:     Tuple					      =       function_with_docstring.__doc__
        return target_function

    return wrapper



