

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"""






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





						def 	UpperCamelCase__			(  self       ,     _lowercase=0.9_9_9_9  )      ->				Dict:
									a_		:		List[Any]				    =				UNetaDConditionModel.from_pretrained(self.model_id       ,     subfolder="""unet"""  )
									a_		:		Optional[Any]				    =				unet.to(_lowercase  )
									a_		:		Tuple				    =				EMAModel(unet.parameters()       ,     decay=_lowercase       ,     model_cls=_lowercase       ,     model_config=unet.config  )
									return unet, ema_unet





						def 	UpperCamelCase__			(  self  )      ->				Union[str, Any]:
									a_		:		int				    =				torch.randn(
									    self.batch_size       ,     self.num_in_channels       ,     self.latent_height       ,     self.latent_width       ,     generator=self.generator  ).to(_lowercase  )
									a_		:		Optional[int]				    =				torch.randint(0       ,     1_000       ,     size=(self.batch_size,)       ,     generator=self.generator  ).to(_lowercase  )
									a_		:		List[str]				    =				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       ,     _lowercase  )      ->				List[str]:
									a_		:		str				    =				{}
									for k, param in unet.state_dict().items():
												a_		:		List[str]				    =				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  )      ->				Union[str, Any]:
									a_		:		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]:
									a_		:		Union[str, 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()  )
									a_		:		List[str]				    =				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  )      ->				List[str]:
									a_		:		str				    =				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.
									a_		:		Tuple				    =				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.
									a_		:		Dict				    =				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  )      ->				int:
									# If we call EMA step after a backpropagation consecutively for two times,
									# the shadow params from those two steps should be different.
									a_		:		Dict				    =				self.get_models()

									# First backprop + EMA
									a_		:		List[Any]				    =				self.simulate_backprop(_lowercase  )
									ema_unet.step(unet_step_one.parameters()  )
									a_		:		List[str]				    =				ema_unet.shadow_params

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

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





						def 	UpperCamelCase__			(  self  )      ->				List[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.
									a_		:		Union[str, Any]				    =				self.get_models(decay=0.0  )
									a_		:		str				    =				self.simulate_backprop(_lowercase  )
									ema_unet.step(unet_step_one.parameters()  )
									a_		:		str				    =				ema_unet.shadow_params

									a_		:		Any				    =				self.simulate_backprop(_lowercase  )
									ema_unet.step(unet_step_two.parameters()  )
									a_		:		Optional[int]				    =				ema_unet.shadow_params

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





						@skip_mps
						def 	UpperCamelCase__			(  self  )      ->				Any:
									a_		:		Tuple				    =				self.get_models()
									a_		:		Optional[Any]				    =				self.get_dummy_inputs()

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

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

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




from collections import namedtuple

__snake_case							:     Optional[Any] 						=	namedtuple("""from_to""", """from_ to""")

__snake_case							:     Tuple 						=	{
    """millimeter""": """mm""",
    """centimeter""": """cm""",
    """meter""": """m""",
    """kilometer""": """km""",
    """inch""": """in""",
    """inche""": """in""",  # Trailing 's' has been stripped off
    """feet""": """ft""",
    """foot""": """ft""",
    """yard""": """yd""",
    """mile""": """mi""",
}

__snake_case							:     Optional[int] 						=	{
    """mm""": from_to(0.001, 10_00),
    """cm""": from_to(0.01, 1_00),
    """m""": from_to(1, 1),
    """km""": from_to(10_00, 0.001),
    """in""": from_to(0.0254, 39.3701),
    """ft""": from_to(0.3048, 3.2_8084),
    """yd""": from_to(0.9144, 1.0_9361),
    """mi""": from_to(1609.34, 0.0_0062_1371),
}


def 			_UpperCAmelCase							(		a__       ,					a__       ,					a__):

			'''simple docstring'''



			a_		:		List[str]				    =				from_type.lower().rstrip("""s""")
			a_		:		Union[str, Any]				    =				TYPE_CONVERSION.get(a__       ,					a__)
			a_		:		Optional[int]				    =				to_type.lower().rstrip("""s""")
			a_		:		Optional[int]				    =				TYPE_CONVERSION.get(a__       ,					a__)
			if new_from not in METRIC_CONVERSION:
						a_		:		Any				    =				(
						    f'''Invalid \'from_type\' value: {from_type!r}.\n'''
						    f'''Conversion abbreviations are: {", ".join(a__)}'''
						)
						raise ValueError(a__)
			if new_to not in METRIC_CONVERSION:
						a_		:		Union[str, Any]				    =				(
						    f'''Invalid \'to_type\' value: {to_type!r}.\n'''
						    f'''Conversion abbreviations are: {", ".join(a__)}'''
						)
						raise ValueError(a__)
			return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
	import doctest

	doctest.testmod()


