


'''simple docstring'''


import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowerCamelCase__    	=     False






class 	lowerCAmelCase__			(			unittest.TestCase	):




			def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     Optional[int]=32 )   ->str:



					'''simple docstring'''







					set_seed(0 )
					_UpperCAmelCase		:       int								=      UNetaDModel(sample_size=lowerCamelCase__	,  in_channels=3	,  out_channels=3 )
					_UpperCAmelCase		:       Union[str, Any]								=      torch.optim.SGD(model.parameters()	,  lr=0.0_0_0_1 )
					return model, optimizer






			@slow
			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      "cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
					_UpperCAmelCase		:       str								=      DDPMScheduler(
					    num_train_timesteps=10_00	,  beta_start=0.0_0_0_1	,  beta_end=0.0_2	,  beta_schedule="linear"	,  clip_sample=lowerCamelCase__	,  )
					_UpperCAmelCase		:       List[str]								=      DDIMScheduler(
					    num_train_timesteps=10_00	,  beta_start=0.0_0_0_1	,  beta_end=0.0_2	,  beta_schedule="linear"	,  clip_sample=lowerCamelCase__	,  )

					assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

					# shared batches for DDPM and DDIM
					set_seed(0 )
					_UpperCAmelCase		:       int								=      [torch.randn((4, 3, 32, 32) ).clip(-1	,  1 ).to(lowerCamelCase__ ) for _ in range(4 )]
					_UpperCAmelCase		:       int								=      [torch.randn((4, 3, 32, 32) ).to(lowerCamelCase__ ) for _ in range(4 )]
					_UpperCAmelCase		:       Any								=      [torch.randint(0	,  10_00	,  (4,) ).long().to(lowerCamelCase__ ) for _ in range(4 )]

					# train with a DDPM scheduler
					_UpperCAmelCase		:       Any								=      self.get_model_optimizer(resolution=32 )
					model.train().to(lowerCamelCase__ )
					for i in range(4 ):
							optimizer.zero_grad()
							_UpperCAmelCase		:       Union[str, Any]								=      ddpm_scheduler.add_noise(clean_images[i]	,  noise[i]	,  timesteps[i] )
							_UpperCAmelCase		:       Union[str, Any]								=      model(lowerCamelCase__	,  timesteps[i] ).sample
							_UpperCAmelCase		:       int								=      torch.nn.functional.mse_loss(lowerCamelCase__	,  noise[i] )
							loss.backward()
							optimizer.step()
					del model, optimizer

					# recreate the model and optimizer, and retry with DDIM
					_UpperCAmelCase		:       Any								=      self.get_model_optimizer(resolution=32 )
					model.train().to(lowerCamelCase__ )
					for i in range(4 ):
							optimizer.zero_grad()
							_UpperCAmelCase		:       Union[str, Any]								=      ddim_scheduler.add_noise(clean_images[i]	,  noise[i]	,  timesteps[i] )
							_UpperCAmelCase		:       Optional[Any]								=      model(lowerCamelCase__	,  timesteps[i] ).sample
							_UpperCAmelCase		:       Union[str, Any]								=      torch.nn.functional.mse_loss(lowerCamelCase__	,  noise[i] )
							loss.backward()
							optimizer.step()
					del model, optimizer

					self.assertTrue(torch.allclose(lowerCamelCase__	,  lowerCamelCase__	,  atol=1E-5 ) )
					self.assertTrue(torch.allclose(lowerCamelCase__	,  lowerCamelCase__	,  atol=1E-5 ) )







'''simple docstring'''


def  __lowerCAmelCase (__lowerCAmelCase   ):  # noqa: E741
		_UpperCAmelCase		:       List[str]								=      len(__lowerCAmelCase   )
		_UpperCAmelCase		:       str								=      0
		_UpperCAmelCase		:       List[str]								=      [0] * n
		_UpperCAmelCase		:       int								=      [False] * n
		_UpperCAmelCase		:       Dict								=      [False] * n

		def dfs(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
				if parent == root:
						out_edge_count += 1
				_UpperCAmelCase		:       List[Any]								=      True
				_UpperCAmelCase		:       str								=      at

				for to in l[at]:
						if to == parent:
								pass
						elif not visited[to]:
								_UpperCAmelCase		:       List[str]								=      dfs(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )
								_UpperCAmelCase		:       Tuple								=      min(low[at]      , low[to]   )

								# AP found via bridge
								if at < low[to]:
										_UpperCAmelCase		:       Dict								=      True
								# AP found via cycle
								if at == low[to]:
										_UpperCAmelCase		:       Dict								=      True
						else:
								_UpperCAmelCase		:       Optional[int]								=      min(low[at]      , __lowerCAmelCase   )
				return out_edge_count

		for i in range(__lowerCAmelCase   ):
				if not visited[i]:
						_UpperCAmelCase		:       str								=      0
						_UpperCAmelCase		:       Tuple								=      dfs(__lowerCAmelCase      , __lowerCAmelCase      , -1      , __lowerCAmelCase   )
						_UpperCAmelCase		:       Optional[int]								=      out_edge_count > 1

		for x in range(len(__lowerCAmelCase   )   ):
				if is_art[x] is True:
						print(__lowerCAmelCase   )


# Adjacency list of graph
lowerCamelCase__    	=     {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}





compute_ap(data)



