




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							:						int					       =  False







class     A(		unittest.TestCase  ):



 '''simple docstring'''


 def 							a__					(							self   :    int		,				A_   :    Dict=32    )  ->      Any:

       """simple docstring"""






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





 @slow
 def 							a__					(							self   :    int    )  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
       lowerCamelCase_							=       DDPMScheduler(
           num_train_timesteps=1000		,				beta_start=0.0001		,				beta_end=0.02		,				beta_schedule='linear'		,				clip_sample=A_		,				)
       lowerCamelCase_							=       DDIMScheduler(
           num_train_timesteps=1000		,				beta_start=0.0001		,				beta_end=0.02		,				beta_schedule='linear'		,				clip_sample=A_		,				)

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

       # shared batches for DDPM and DDIM
       set_seed(0    )
       lowerCamelCase_							=       [torch.randn((4, 3, 32, 32)    ).clip(-1		,				1    ).to(A_    ) for _ in range(4    )]
       lowerCamelCase_							=       [torch.randn((4, 3, 32, 32)    ).to(A_    ) for _ in range(4    )]
       lowerCamelCase_							=       [torch.randint(0		,				1000		,				(4,)    ).long().to(A_    ) for _ in range(4    )]

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

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

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







def 						a__			(				A__,      A__				):

			def get_matched_characters(A__,      A__				) -> str:
						SCREAMING_SNAKE_CASE_		:     Dict					=					[]
						SCREAMING_SNAKE_CASE_		:     Any					=					min(len(_stra				),      len(_stra				)				) // 2
						for i, l in enumerate(_stra				):
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					int(max(0,      i - limit				)				)
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					int(min(i + limit + 1,      len(_stra				)				)				)
									if l in _stra[left:right]:
												matched.append(A__				)
												SCREAMING_SNAKE_CASE_		:     List[str]					=					F'''{_stra[0:_stra.index(A__				)]} {_stra[_stra.index(A__				) + 1:]}'''

						return "".join(A__				)

			# matching characters
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					get_matched_characters(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					get_matched_characters(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					len(A__				)

			# transposition
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					(
			    len([(ca, ca) for ca, ca in zip(A__,      A__				) if ca != ca]				) // 2
			)

			if not match_count:
						SCREAMING_SNAKE_CASE_		:     Dict					=					0.0
			else:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					(
						    1
						    / 3
						    * (
						        match_count / len(A__				)
						        + match_count / len(A__				)
						        + (match_count - transpositions) / match_count
						    )
						)

			# common prefix up to 4 characters
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					0
			for ca, ca in zip(stra[:4],      stra[:4]				):
						if ca == ca:
									prefix_len += 1
						else:
									break

			return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(jaro_winkler('hello', 'world'))





