



"""simple docstring"""







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    UpperCamelCase     (			unittest.TestCase    ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_				:    Optional[Any]		    =   "hf-internal-testing/tiny-stable-diffusion-pipe"
	SCREAMING_SNAKE_CASE_				:    Tuple		    =   1
	SCREAMING_SNAKE_CASE_				:    Union[str, Any]		    =   7_7
	SCREAMING_SNAKE_CASE_				:    Dict		    =   3_2
	SCREAMING_SNAKE_CASE_				:    Tuple		    =   4
	SCREAMING_SNAKE_CASE_				:    Union[str, Any]		    =   6_4
	SCREAMING_SNAKE_CASE_				:    List[str]		    =   torch.manual_seed(0    )






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_=0.9999					):
			_lowercase							:      str			=					UNetaDConditionModel.from_pretrained(self.model_id							,subfolder="""unet"""					)
			_lowercase							:      List[Any]			=					unet.to(UpperCAmelCase_					)
			_lowercase							:      List[Any]			=					EMAModel(unet.parameters()							,decay=UpperCAmelCase_							,model_cls=UpperCAmelCase_							,model_config=unet.config					)
			return unet, ema_unet






	def 					lowerCamelCase__ (			self					):
			_lowercase							:      List[str]			=					torch.randn(
			    self.batch_size							,self.num_in_channels							,self.latent_height							,self.latent_width							,generator=self.generator					).to(UpperCAmelCase_					)
			_lowercase							:      Tuple			=					torch.randint(0							,10_00							,size=(self.batch_size,)							,generator=self.generator					).to(UpperCAmelCase_					)
			_lowercase							:      Tuple			=					torch.randn(
			    self.batch_size							,self.prompt_length							,self.text_encoder_hidden_dim							,generator=self.generator					).to(UpperCAmelCase_					)
			return noisy_latents, timesteps, encoder_hidden_states






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_					):
			_lowercase							:      int			=					{}
			for k, param in unet.state_dict().items():
					_lowercase							:      Any			=					torch.randn_like(UpperCAmelCase_					) + (param * torch.randn_like(UpperCAmelCase_					))
					updated_state_dict.update({k: updated_param}					)
			unet.load_state_dict(UpperCAmelCase_					)
			return unet






	def 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase							:      List[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 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase							:      Tuple			=					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()					)
			_lowercase							:      Any			=					list(unet.parameters()					)
			for s_param, param in zip(ema_unet.shadow_params							,UpperCAmelCase_					):
					assert torch.allclose(UpperCAmelCase_							,UpperCAmelCase_					)

			# 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							,UpperCAmelCase_					):
					assert torch.allclose(UpperCAmelCase_							,UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase							:      Tuple			=					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.
			_lowercase							:      Any			=					self.simulate_backprop(UpperCAmelCase_					)

			# 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.
			_lowercase							:      Tuple			=					list(unet_pseudo_updated_step_one.parameters()					)
			for s_param, param in zip(ema_unet.shadow_params							,UpperCAmelCase_					):
					assert ~torch.allclose(UpperCAmelCase_							,UpperCAmelCase_					)

			# 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							,UpperCAmelCase_					):
					assert ~torch.allclose(UpperCAmelCase_							,UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self					):
			# If we call EMA step after a backpropagation consecutively for two times,
			# the shadow params from those two steps should be different.
			_lowercase			,   _lowercase							:      Any			=					self.get_models()

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

			# Second backprop + EMA
			_lowercase							:      int			=					self.simulate_backprop(UpperCAmelCase_					)
			ema_unet.step(unet_step_two.parameters()					)
			_lowercase							:      Any			=					ema_unet.shadow_params

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






	def 					lowerCamelCase__ (			self					):
			# 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.
			_lowercase			,   _lowercase							:      Dict			=					self.get_models(decay=0.0					)
			_lowercase							:      str			=					self.simulate_backprop(UpperCAmelCase_					)
			ema_unet.step(unet_step_one.parameters()					)
			_lowercase							:      int			=					ema_unet.shadow_params

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

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






	@skip_mps
	def 					lowerCamelCase__ (			self					):
			_lowercase			,   _lowercase							:      Union[str, Any]			=					self.get_models()
			_lowercase			,   _lowercase			,   _lowercase							:      Any			=					self.get_dummy_inputs()

			with tempfile.TemporaryDirectory() as tmpdir:
					ema_unet.save_pretrained(UpperCAmelCase_					)
					_lowercase							:      List[str]			=					UNetaDConditionModel.from_pretrained(UpperCAmelCase_							,model_cls=UpperCAmelCase_					)
					_lowercase							:      List[str]			=					loaded_unet.to(unet.device					)

			# Since no EMA step has been performed the outputs should match.
			_lowercase							:      Any			=					unet(UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					).sample
			_lowercase							:      List[str]			=					loaded_unet(UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					).sample

			assert torch.allclose(UpperCAmelCase_							,UpperCAmelCase_							,atol=1E-4					)










from itertools import permutations






def 			lowerCamelCase__	():
     _SCREAMING_SNAKE_CASE				:		Dict  			=							list(map("".join,				permutations("0123456789"		)		)		)
     return result[999999]


if __name__ == "__main__":
       print(solution())