



'''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


lowercase_		       =  False






class __A			(  unittest.TestCase						):



 '''simple docstring'''







 def 					a__  (self ,   A=32       )    ->     Optional[Any]:


      """simple docstring"""



      set_seed(0       )
      _a      =  UNetaDModel(sample_size=UpperCamelCase__ ,   in_channels=3 ,   out_channels=3       )
      _a      =  torch.optim.SGD(model.parameters() ,   lr=0.0001       )
      return model, optimizer



 @slow
 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a      =  """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
      _a      =  DDPMScheduler(
          num_train_timesteps=1_000 ,   beta_start=0.0001 ,   beta_end=0.02 ,   beta_schedule='''linear''' ,   clip_sample=UpperCamelCase__ ,   )
      _a      =  DDIMScheduler(
          num_train_timesteps=1_000 ,   beta_start=0.0001 ,   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       )
      _a      =  [torch.randn((4, 3, 32, 32)       ).clip(-1 ,   1       ).to(UpperCamelCase__       ) for _ in range(4       )]
      _a      =  [torch.randn((4, 3, 32, 32)       ).to(UpperCamelCase__       ) for _ in range(4       )]
      _a      =  [torch.randint(0 ,   1_000 ,   (4,)       ).long().to(UpperCamelCase__       ) for _ in range(4       )]

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

      # recreate the model and optimizer, and retry with DDIM
      _a      =  self.get_model_optimizer(resolution=32       )
      model.train().to(UpperCamelCase__       )
      for i in range(4       ):
           optimizer.zero_grad()
           _a      =  ddim_scheduler.add_noise(clean_images[i] ,   noise[i] ,   timesteps[i]       )
           _a      =  model(UpperCamelCase__ ,   timesteps[i]       ).sample
           _a      =  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       )       )











'''simple docstring'''







class _lowercase	:




							def __init__(							self:				Optional[Any]     ):
														lowerCamelCase__					:	dict[str, TrieNode]         =       {}  # Mapping from char to TrieNode
														lowerCamelCase__					:	List[str]         =       False




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				list[str]     ):
														for word in words:
																					self.insert(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				List[str]			,							UpperCamelCase__:				str     ):
														lowerCamelCase__					:	List[Any]         =       self
														for char in word:
																					if char not in curr.nodes:
																												lowerCamelCase__					:	Tuple         =       TrieNode()
																					lowerCamelCase__					:	List[Any]         =       curr.nodes[char]
														lowerCamelCase__					:	Any         =       True




							def 		lowerCamelCase_				(							self:				Union[str, Any]			,							UpperCamelCase__:				str     ):
														lowerCamelCase__					:	Union[str, Any]         =       self
														for char in word:
																					if char not in curr.nodes:
																												return False
																					lowerCamelCase__					:	Any         =       curr.nodes[char]
														return curr.is_leaf




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				str     ):

														def _delete(UpperCamelCase__:				TrieNode			,							UpperCamelCase__:				str			,							UpperCamelCase__:				int     ) -> bool:
																					if index == len(UpperCamelCase__     ):
																												# If word does not exist
																												if not curr.is_leaf:
																																			return False
																												lowerCamelCase__					:	str         =       False
																												return len(curr.nodes     ) == 0
																					lowerCamelCase__					:	List[str]         =       word[index]
																					lowerCamelCase__					:	Dict         =       curr.nodes.get(UpperCamelCase__     )
																					# If char not in current trie node
																					if not char_node:
																												return False
																					# Flag to check if node can be deleted
																					lowerCamelCase__					:	List[Any]         =       _delete(UpperCamelCase__			,							UpperCamelCase__			,							index + 1     )
																					if delete_curr:
																												del curr.nodes[char]
																												return len(curr.nodes     ) == 0
																					return delete_curr

														_delete(self			,							UpperCamelCase__			,							0     )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       None:
							if node.is_leaf:
														print(UpperCamelCase				,			end=""" """      )

							for key, value in node.nodes.items():
														print_words(UpperCamelCase				,			word + key      )





def  SCREAMING_SNAKE_CASE_       ()      ->       bool:
							lowerCamelCase__					:	str         =       """banana bananas bandana band apple all beast""".split()
							lowerCamelCase__					:	Union[str, Any]         =       TrieNode()
							root.insert_many(UpperCamelCase      )
							# print_words(root, "")
							assert all(root.find(UpperCamelCase      ) for word in words      )
							assert root.find("""banana"""      )
							assert not root.find("""bandanas"""      )
							assert not root.find("""apps"""      )
							assert root.find("""apple"""      )
							assert root.find("""all"""      )
							root.delete("""all"""      )
							assert not root.find("""all"""      )
							root.delete("""banana"""      )
							assert not root.find("""banana"""      )
							assert root.find("""bananas"""      )
							return True





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       None:
							print(str(UpperCamelCase      )				,			"""works!""" if passes else """doesn't work :("""      )





def  SCREAMING_SNAKE_CASE_       ()      ->       None:
							assert test_trie()





def  SCREAMING_SNAKE_CASE_       ()      ->       None:
							print_results("""Testing trie functionality"""				,			test_trie()      )


if __name__ == "__main__":
			main()

