code: 
  
  
  from scipy.constants import g
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		str:
  
  
  
  
  
  
  
       '''simple docstring'''
  
  
  
       if mass < 0:
            # handling of negative values of mass
            raise ValueError('''The mass of a body cannot be negative'''      )
       if height < 0:
            # handling of negative values of height
            raise ValueError('''The height above the ground cannot be negative'''      )
       return mass * g * height
  
  
  if __name__ == "__main__":
    from doctest import testmod
  
    testmod(name="potential_energy")
  
  
  
style_context: 
  
  
  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					)
  
  
  