


def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase      )->		float:







					'''simple docstring'''



					if mass < 0:
										raise ValueError('''The mass of the body cannot be negative'''      )
					if radius <= 0:
										raise ValueError('''The radius is always a positive non zero integer'''      )
					return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)






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	):
			'''simple docstring'''

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

			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     str=0.9_999					)					->					int:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       UNetaDConditionModel.from_pretrained(self.model_id							,							subfolder='''unet'''					)
								SCREAMING_SNAKE_CASE_	      =       unet.to(lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       EMAModel(unet.parameters()							,							decay=lowerCAmelCase_							,							model_cls=lowerCAmelCase_							,							model_config=unet.config					)
								return unet, ema_unet

			def _lowercase (	self     :     str					)					->					List[str]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       torch.randn(
								    self.batch_size							,							self.num_in_channels							,							self.latent_height							,							self.latent_width							,							generator=self.generator					).to(lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       torch.randint(0							,							1_000							,							size=(self.batch_size,)							,							generator=self.generator					).to(lowerCAmelCase_					)
								SCREAMING_SNAKE_CASE_	      =       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 _lowercase (	self     :     str							,							lowerCAmelCase_     :     Tuple					)					->					str:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       {}
								for k, param in unet.state_dict().items():
													SCREAMING_SNAKE_CASE_	      =       torch.randn_like(lowerCAmelCase_					) + (param * torch.randn_like(lowerCAmelCase_					))
													updated_state_dict.update({k: updated_param}					)
								unet.load_state_dict(lowerCAmelCase_					)
								return unet

			def _lowercase (	self     :     Dict					)					->					str:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       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 _lowercase (	self     :     Union[str, Any]					)					->					List[str]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       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()					)
								SCREAMING_SNAKE_CASE_	      =       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 _lowercase (	self     :     Any					)					->					Union[str, Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       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.
								SCREAMING_SNAKE_CASE_	      =       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.
								SCREAMING_SNAKE_CASE_	      =       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 _lowercase (	self     :     Any					)					->					Tuple:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models()

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

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

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

			def _lowercase (	self     :     str					)					->					Optional[Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models(decay=0.0					)
								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
								ema_unet.step(unet_step_one.parameters()					)
								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params

								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
								ema_unet.step(unet_step_two.parameters()					)
								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params

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




			@skip_mps
			def _lowercase (	self     :     str					)					->					List[Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models()
								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_dummy_inputs()

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

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

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


