





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 			__A       (      unittest.TestCase  ):




      """simple docstring"""







      def 				__snake_case			(		self      ,							a__=32):



             """simple docstring"""





             set_seed(0)
             _lowerCamelCase							:     Optional[int]	=		UNetaDModel(sample_size=a__      ,							in_channels=3      ,							out_channels=3)
             _lowerCamelCase							:     Any	=		torch.optim.SGD(model.parameters()      ,							lr=0.0001)
             return model, optimizer



      @slow
      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		'''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
             _lowerCamelCase							:     int	=		DDPMScheduler(
                 num_train_timesteps=1000      ,							beta_start=0.0001      ,							beta_end=0.02      ,							beta_schedule='''linear'''      ,							clip_sample=a__      ,							)
             _lowerCamelCase							:     Tuple	=		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							:     Optional[Any]	=		[torch.randn((4, 3, 32, 32)).clip(-1      ,							1).to(a__) for _ in range(4)]
             _lowerCamelCase							:     Optional[Any]	=		[torch.randn((4, 3, 32, 32)).to(a__) for _ in range(4)]
             _lowerCamelCase							:     List[str]	=		[torch.randint(0      ,							1000      ,							(4,)).long().to(a__) for _ in range(4)]

             # train with a DDPM scheduler
             _lowerCamelCase,  _lowerCamelCase							:     List[str]	=		self.get_model_optimizer(resolution=32)
             model.train().to(a__)
             for i in range(4):
                    optimizer.zero_grad()
                    _lowerCamelCase							:     Any	=		ddpm_scheduler.add_noise(clean_images[i]      ,							noise[i]      ,							timesteps[i])
                    _lowerCamelCase							:     Dict	=		model(a__      ,							timesteps[i]).sample
                    _lowerCamelCase							:     Union[str, Any]	=		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							:     Any	=		self.get_model_optimizer(resolution=32)
             model.train().to(a__)
             for i in range(4):
                    optimizer.zero_grad()
                    _lowerCamelCase							:     Optional[Any]	=		ddim_scheduler.add_noise(clean_images[i]      ,							noise[i]      ,							timesteps[i])
                    _lowerCamelCase							:     str	=		model(a__      ,							timesteps[i]).sample
                    _lowerCamelCase							:     Optional[int]	=		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))








import math




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     List[str]	=		len(lowercase_      )
       _lowerCamelCase							:     Optional[int]	=		int(math.floor(math.sqrt(lowercase_      )      )      )
       _lowerCamelCase							:     Tuple	=		0
       while arr[min(lowercase_ ,    lowercase_      ) - 1] < x:
              _lowerCamelCase							:     Optional[Any]	=		step
              step += int(math.floor(math.sqrt(lowercase_      )      )      )
              if prev >= n:
                     return -1

       while arr[prev] < x:
              _lowerCamelCase							:     int	=		prev + 1
              if prev == min(lowercase_ ,    lowercase_      ):
                     return -1
       if arr[prev] == x:
              return prev
       return -1


if __name__ == "__main__":
       _lowerCamelCase			       =      input('Enter numbers separated by a comma:\n').strip()
       _lowerCamelCase			       =      [int(item) for item in user_input.split(',')]
       _lowerCamelCase			       =      int(input('Enter the number to be searched:\n'))
       _lowerCamelCase			       =      jump_search(arr, x)
       if res == -1:
              print('Number not found!')
       else:
              print(F'''Number {x} is at index {res}''')

