
"""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 			_lowercase  (							unittest.TestCase						):
	lowercase_								=      'hf-internal-testing/tiny-stable-diffusion-pipe'
	lowercase_								=      1
	lowercase_								=      7_7
	lowercase_								=      3_2
	lowercase_								=      4
	lowercase_								=      lowercase_								=      6_4
	lowercase_								=      torch.manual_seed(0						)




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_=0.9999   )						->				int:
			lowerCamelCase	:     str            =       UNetaDConditionModel.from_pretrained(self.model_id						,					subfolder='unet'   )
			lowerCamelCase	:     Tuple            =       unet.to(UpperCAmelCase_   )
			lowerCamelCase	:     Dict            =       EMAModel(unet.parameters()						,					decay=UpperCAmelCase_						,					model_cls=UpperCAmelCase_						,					model_config=unet.config   )
			return unet, ema_unet




	def      _UpperCamelCase       (					self   )						->				List[Any]:
			lowerCamelCase	:     List[Any]            =       torch.randn(
			    self.batch_size						,					self.num_in_channels						,					self.latent_height						,					self.latent_width						,					generator=self.generator   ).to(UpperCAmelCase_   )
			lowerCamelCase	:     Optional[Any]            =       torch.randint(0						,					1000						,					size=(self.batch_size,)						,					generator=self.generator   ).to(UpperCAmelCase_   )
			lowerCamelCase	:     Optional[int]            =       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      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				str:
			lowerCamelCase	:     List[str]            =       {}
			for k, param in unet.state_dict().items():
					lowerCamelCase	:     Optional[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      _UpperCamelCase       (					self   )						->				Dict:
			lowerCamelCase      ,       lowerCamelCase	:     Dict            =       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   )						->				Union[str, Any]:
			lowerCamelCase      ,       lowerCamelCase	:     Dict            =       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()   )
			lowerCamelCase	:     int            =       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      _UpperCamelCase       (					self   )						->				Dict:
			lowerCamelCase      ,       lowerCamelCase	:     str            =       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.
			lowerCamelCase	:     Optional[int]            =       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.
			lowerCamelCase	:     int            =       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      _UpperCamelCase       (					self   )						->				List[str]:
			# If we call EMA step after a backpropagation consecutively for two times,
			# the shadow params from those two steps should be different.
			lowerCamelCase      ,       lowerCamelCase	:     Optional[int]            =       self.get_models()

			# First backprop + EMA
			lowerCamelCase	:     Dict            =       self.simulate_backprop(UpperCAmelCase_   )
			ema_unet.step(unet_step_one.parameters()   )
			lowerCamelCase	:     Tuple            =       ema_unet.shadow_params

			# Second backprop + EMA
			lowerCamelCase	:     List[str]            =       self.simulate_backprop(UpperCAmelCase_   )
			ema_unet.step(unet_step_two.parameters()   )
			lowerCamelCase	:     Optional[Any]            =       ema_unet.shadow_params

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




	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.
			lowerCamelCase      ,       lowerCamelCase	:     int            =       self.get_models(decay=0.0   )
			lowerCamelCase	:     Any            =       self.simulate_backprop(UpperCAmelCase_   )
			ema_unet.step(unet_step_one.parameters()   )
			lowerCamelCase	:     List[Any]            =       ema_unet.shadow_params

			lowerCamelCase	:     List[str]            =       self.simulate_backprop(UpperCAmelCase_   )
			ema_unet.step(unet_step_two.parameters()   )
			lowerCamelCase	:     Union[str, Any]            =       ema_unet.shadow_params

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




	@skip_mps
	def      _UpperCamelCase       (					self   )						->				str:
			lowerCamelCase      ,       lowerCamelCase	:     Union[str, Any]            =       self.get_models()
			lowerCamelCase      ,       lowerCamelCase      ,       lowerCamelCase	:     Optional[int]            =       self.get_dummy_inputs()

			with tempfile.TemporaryDirectory() as tmpdir:
					ema_unet.save_pretrained(UpperCAmelCase_   )
					lowerCamelCase	:     Tuple            =       UNetaDConditionModel.from_pretrained(UpperCAmelCase_						,					model_cls=UpperCAmelCase_   )
					lowerCamelCase	:     Any            =       loaded_unet.to(unet.device   )

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

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








"""simple docstring"""

from __future__ import annotations

from queue import Queue
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		a ^= b
		b ^= a
		a ^= b
		return a, b
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		lowerCamelCase	:     int            =       1
		while (1 << j) < max_node:
				for i in range(1,		max_node + 1     ):
						lowerCamelCase	:     Tuple            =       parent[j - 1][parent[j - 1][i]]
				j += 1
		return parent
def 						UpperCAmelCase   (						a_,		a_,		a_,		a_     ):





		'''simple docstring'''


		if level[u] < level[v]:
				lowerCamelCase      ,       lowerCamelCase	:     List[Any]            =       swap(a_,		a_     )
		# making depth of u same as depth of v
		for i in range(18,		-1,		-1     ):
				if level[u] - (1 << i) >= level[v]:
						lowerCamelCase	:     int            =       parent[i][u]
    # at the same depth if u==v that mean lca is found
		if u == v:
				return u
		# moving both nodes upwards till lca in found
		for i in range(18,		-1,		-1     ):
				if parent[i][u] not in [0, parent[i][v]]:
						lowerCamelCase      ,       lowerCamelCase	:     str            =       parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
		return parent[0][u]
def 						UpperCAmelCase   (						a_,		a_,		a_,		a_,		a_ = 1,		):





		'''simple docstring'''


		lowerCamelCase	:     Any            =       0
		lowerCamelCase	:     Queue[int]            =       Queue(maxsize=a_     )
		q.put(a_     )
		while q.qsize() != 0:
				lowerCamelCase	:     Tuple            =       q.get()
				for v in graph[u]:
						if level[v] == -1:
								lowerCamelCase	:     Optional[Any]            =       level[u] + 1
								q.put(a_     )
								lowerCamelCase	:     str            =       u
		return level, parent
def 						UpperCAmelCase   (						):





		'''simple docstring'''


		lowerCamelCase	:     Any            =       13
		# initializing with 0
		lowerCamelCase	:     Dict            =       [[0 for _ in range(max_node + 10     )] for _ in range(20     )]
		# initializing with -1 which means every node is unvisited
		lowerCamelCase	:     List[Any]            =       [-1 for _ in range(max_node + 10     )]
		lowerCamelCase	:     dict[int, list[int]]            =       {
		    1: [2, 3, 4],
		    2: [5],
		    3: [6, 7],
		    4: [8],
		    5: [9, 10],
		    6: [11],
		    7: [],
		    8: [12, 13],
		    9: [],
		    10: [],
		    11: [],
		    12: [],
		    13: [],
		}
		lowerCamelCase      ,       lowerCamelCase	:     Dict            =       breadth_first_search(a_,		a_,		a_,		a_,		1     )
		lowerCamelCase	:     List[Any]            =       create_sparse(a_,		a_     )
		print('LCA of node 1 and 3 is: ',		lowest_common_ancestor(1,		3,		a_,		a_     )     )
		print('LCA of node 5 and 6 is: ',		lowest_common_ancestor(5,		6,		a_,		a_     )     )
		print('LCA of node 7 and 11 is: ',		lowest_common_ancestor(7,		11,		a_,		a_     )     )
		print('LCA of node 6 and 7 is: ',		lowest_common_ancestor(6,		7,		a_,		a_     )     )
		print('LCA of node 4 and 12 is: ',		lowest_common_ancestor(4,		12,		a_,		a_     )     )
		print('LCA of node 8 and 8 is: ',		lowest_common_ancestor(8,		8,		a_,		a_     )     )


if __name__ == "__main__":
					main()






