






'''simple docstring'''







from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_A			:    Optional[int]					  =10





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> int:
							for i in range(UpperCamelCase       ,    UpperCamelCase   ):
														if array[i] == target:
																					return i
							return -1





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> int:
							lowerCamelCase__      :			Optional[Any] 						= 0
							lowerCamelCase__      :			Dict 						= len(UpperCamelCase   )
							while left <= right:
														if right - left < precision:
																					return lin_search(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )

														lowerCamelCase__      :			int 						= (left + right) // 3 + 1
														lowerCamelCase__      :			Dict 						= 2 * (left + right) // 3 + 1

														if array[one_third] == target:
																					return one_third
														elif array[two_third] == target:
																					return two_third

														elif target < array[one_third]:
																					lowerCamelCase__      :			Dict 						= one_third - 1
														elif array[two_third] < target:
																					lowerCamelCase__      :			Union[str, Any] 						= two_third + 1

														else:
																					lowerCamelCase__      :			Any 						= one_third + 1
																					lowerCamelCase__      :			Optional[Any] 						= two_third - 1
							else:
														return -1





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> int:
							if left < right:
														if right - left < precision:
																					return lin_search(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )
														lowerCamelCase__      :			Dict 						= (left + right) // 3 + 1
														lowerCamelCase__      :			int 						= 2 * (left + right) // 3 + 1

														if array[one_third] == target:
																					return one_third
														elif array[two_third] == target:
																					return two_third

														elif target < array[one_third]:
																					return rec_ternary_search(UpperCamelCase       ,    one_third - 1       ,    UpperCamelCase       ,    UpperCamelCase   )
														elif array[two_third] < target:
																					return rec_ternary_search(two_third + 1       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )
														else:
																					return rec_ternary_search(one_third + 1       ,    two_third - 1       ,    UpperCamelCase       ,    UpperCamelCase   )
							else:
														return -1


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			_A			:    Tuple					  =input('''Enter numbers separated by comma:\n''').strip()
			_A			:    Any					  =[int(item.strip()) for item in user_input.split(''',''')]
			assert collection == sorted(collection), F"List must be ordered.\n{collection}."
			_A			:    List[str]					  =int(input('''Enter the number to be found in the list:\n''').strip())
			_A			:    Optional[int]					  =ite_ternary_search(collection, target)
			_A			:    List[Any]					  =rec_ternary_search(0, len(collection) - 1, collection, target)
			if resulta != -1:
						print(F'Iterative search: {target} found at positions: {resulta}')
						print(F'Recursive search: {target} found at positions: {resulta}')
			else:
						print('''Not found''')









'''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


_A			:    Union[str, Any]					  =False

class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       str=32  ):
														set_seed(0  )
														lowerCamelCase__      :			Optional[int] 						= UNetaDModel(sample_size=UpperCamelCase__    ,							in_channels=3    ,							out_channels=3  )
														lowerCamelCase__      :			List[Any] 						= torch.optim.SGD(model.parameters()    ,							lr=0.0_001  )
														return model, optimizer




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Optional[Any] 						= """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
														lowerCamelCase__      :			List[Any] 						= DDPMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Any 						= DDIMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)

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

														# shared batches for DDPM and DDIM
														set_seed(0  )
														lowerCamelCase__      :			str 						= [torch.randn((4, 3, 32, 32)  ).clip(-1    ,							1  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randn((4, 3, 32, 32)  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randint(0    ,							1_000    ,							(4,)  ).long().to(UpperCamelCase__  ) for _ in range(4  )]

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

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

														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )
														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )

