


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 			lowercase_				(unittest.TestCase      ):
				snake_case  						='hf-internal-testing/tiny-stable-diffusion-pipe'
				snake_case  						=1
				snake_case  						=77
				snake_case  						=32
				snake_case  						=4
				snake_case  						=snake_case  						=64
				snake_case  						=torch.manual_seed(0      )





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





				def 	__UpperCamelCase						(							self)     ->   List[str]:
											a__     						=torch.randn(
											    self.batch_size					,     self.num_in_channels					,     self.latent_height					,     self.latent_width					,     generator=self.generator).to(lowercase_)
											a__     						=torch.randint(0					,     1000					,     size=(self.batch_size,)					,     generator=self.generator).to(lowercase_)
											a__     						=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_)     ->   Any:
											a__     						={}
											for k, param in unet.state_dict().items():
																		a__     						=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)     ->   int:
											a__						,    a__     						=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)     ->   str:
											a__						,    a__     						=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(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)     ->   Dict:
											a__						,    a__     						=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__     						=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__     						=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__						,    a__     						=self.get_models()

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

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

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





				def 	__UpperCamelCase						(							self)     ->   Union[str, 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__						,    a__     						=self.get_models(decay=0.0)
											a__     						=self.simulate_backprop(lowercase_)
											ema_unet.step(unet_step_one.parameters())
											a__     						=ema_unet.shadow_params

											a__     						=self.simulate_backprop(lowercase_)
											ema_unet.step(unet_step_two.parameters())
											a__     						=ema_unet.shadow_params

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





				@skip_mps
				def 	__UpperCamelCase						(							self)     ->   Optional[Any]:
											a__						,    a__     						=self.get_models()
											a__						,    a__						,    a__     						=self.get_dummy_inputs()

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

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

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





from __future__ import annotations






def      _lowercase(				__a							:	float | int						,    __a							:	float | int						,    __a							:	float | int						,    ):
							if not all((nth_term, start_term_a, common_ratio_r)    ):
														return []
							a__     						=[]
							a__     						=1
							a__     						=common_ratio_r
							for _ in range(int(__a    )    ):
														if not series:
																					series.append(__a    )
														else:
																					power += 1
																					series.append(float(start_term_a * multiple    )    )
																					a__     						=pow(float(__a    )						,    __a    )
							return series


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowerCAmelCase:	Tuple      							=					float(input('Enter the last number (n term) of the Geometric Series'))
	_lowerCAmelCase:	Optional[int]      							=					float(input('Enter the starting term (a) of the Geometric Series'))
	_lowerCAmelCase:	List[Any]      							=					float(
	    input('Enter the common ratio between two terms (r) of the Geometric Series')
	)
	print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
	print(geometric_series(nth_term, start_term_a, common_ratio_r))

