




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'''





     lowerCAmelCase				:       Union[str, Any]   		=  'hf-internal-testing/tiny-stable-diffusion-pipe'
     lowerCAmelCase				:       Optional[int]   		=  1
     lowerCAmelCase				:       Union[str, Any]   		=  77
     lowerCAmelCase				:       int   		=  32
     lowerCAmelCase				:       str   		=  4
     lowerCAmelCase				:       str   		=  64
     lowerCAmelCase				:       Optional[Any]   		=  torch.manual_seed(0			)
     def   lowerCamelCase_   (	self   :			List[Any]					,    __snake_case   :			Optional[int]=0.9_999				):
         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   lowerCamelCase_   (	self   :			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					,    10_00					,    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   lowerCamelCase_   (	self   :			Union[str, Any]					,    __snake_case   :			List[Any]				):
         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   lowerCamelCase_   (	self   :			Optional[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   lowerCamelCase_   (	self   :			Union[str, Any]				):
         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   lowerCamelCase_   (	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   lowerCamelCase_   (	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   lowerCamelCase_   (	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   lowerCamelCase_   (	self   :			Optional[int]				):
         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				)








'''simple docstring'''
from transformers import PretrainedConfig
class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Optional[Any]          ="custom"






			def __init__(   self       ,_snake_case=1       ,**_snake_case			):
				UpperCAmelCase_			:	Optional[int]      =   attribute
				super().__init__(**_snake_case			)




class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Dict          ="custom"






			def __init__(   self       ,_snake_case=1       ,**_snake_case			):
				UpperCAmelCase_			:	Optional[int]      =   attribute




