

'''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 lowerCamelCase   (    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 					_lowerCamelCase    (       self,						lowercase_=0.9_999 )    ->							Optional[Any]:
					snake_case							=					UNetaDConditionModel.from_pretrained(self.model_id,						subfolder='unet' )
					snake_case							=					unet.to(lowercase_ )
					snake_case							=					EMAModel(unet.parameters(),						decay=lowercase_,						model_cls=lowercase_,						model_config=unet.config )
					return unet, ema_unet

				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case							=					torch.randn(
					    self.batch_size,						self.num_in_channels,						self.latent_height,						self.latent_width,						generator=self.generator ).to(lowercase_ )
					snake_case							=					torch.randint(0,						1000,						size=(self.batch_size,),						generator=self.generator ).to(lowercase_ )
					snake_case							=					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 					_lowerCamelCase    (       self,						lowercase_ )    ->							Union[str, Any]:
					snake_case							=					{}
					for k, param in unet.state_dict().items():
						snake_case							=					torch.randn_like(lowercase_ ) + (param * torch.randn_like(lowercase_ ))
						updated_state_dict.update({k: updated_param} )
					unet.load_state_dict(lowercase_ )
					return unet

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case			,			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 					_lowerCamelCase    (       self )    ->							Tuple:
					snake_case			,			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() )
					snake_case							=					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 					_lowerCamelCase    (       self )    ->							str:
					snake_case			,			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.
					snake_case							=					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.
					snake_case							=					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 					_lowerCamelCase    (       self )    ->							Dict:
					# If we call EMA step after a backpropagation consecutively for two times,
					# the shadow params from those two steps should be different.
					snake_case			,			snake_case							=					self.get_models()

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

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

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

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					# 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.
					snake_case			,			snake_case							=					self.get_models(decay=0.0 )
					snake_case							=					self.simulate_backprop(lowercase_ )
					ema_unet.step(unet_step_one.parameters() )
					snake_case							=					ema_unet.shadow_params

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

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






				@skip_mps
				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case			,			snake_case							=					self.get_models()
					snake_case			,			snake_case			,			snake_case							=					self.get_dummy_inputs()

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

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

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








'''simple docstring'''

from .balanced_parentheses import balanced_parentheses
from .stack import Stack

def      __magic_name__							(			A				)      ->		int:
	return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(A       , -1				)

def      __magic_name__							(			A				)      ->		str:
	if not balanced_parentheses(A				):
		raise ValueError('Mismatched parentheses'				)
	snake_case							=					Stack()
	snake_case							=					[]
	for char in expression_str:
		if char.isalpha() or char.isdigit():
			postfix.append(A				)
		elif char == "(":
			stack.push(A				)
		elif char == ")":
			while not stack.is_empty() and stack.peek() != "(":
				postfix.append(stack.pop()				)
			stack.pop()
		else:
			while not stack.is_empty() and precedence(A				) <= precedence(stack.peek()				):
				postfix.append(stack.pop()				)
			stack.push(A				)
	while not stack.is_empty():
		postfix.append(stack.pop()				)
	return " ".join(A				)


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						lowerCAmelCase_     =      "a+b*(c^d-e)^(f+g*h)-i"

						print("Infix to Postfix Notation demonstration:\n")
						print("Infix notation: " + expression)
						print("Postfix notation: " + infix_to_postfix(expression))





