



'''simple docstring'''






class 							lowercase       :


 """simple docstring"""


 def __init__(							self  ,a_							)		->   List[str]:
        # we need a list not a string, so do something to change the type
        _UpperCAmelCase  :						List[Any]			     = arr.split(""","""							)







 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						str			     = [int(self.array[0]							)] * len(self.array							)
        _UpperCAmelCase  :						List[Any]			     = [int(self.array[0]							)] * len(self.array							)
        for i in range(1  ,len(self.array							)							):
               _UpperCAmelCase  :						Any			     = max(
                   int(self.array[i]							) + sum_value[i - 1]  ,int(self.array[i]							)							)
               _UpperCAmelCase  :						Any			     = max(sum_value[i]  ,rear[i - 1]							)
        return rear[len(self.array							) - 1]


if __name__ == "__main__":
   A_							: Union[str, Any]						 =	input("""please input some numbers:""")
   A_							: Any						 =	SubArray(whole_array)
   A_							: Optional[Any]						 =	array.solve_sub_array()
   print(("""the results is:""", re))








'''simple docstring'''






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  (IPNDMScheduler,)
 UpperCAmelCase							=  (("""num_inference_steps""", 50),)







 def _snake_case (							self  ,**a_							)		->   Optional[int]:
        _UpperCAmelCase  :						str			     = {"""num_train_timesteps""": 1_000}
        config.update(**a_							)
        return config







 def _snake_case (							self  ,a_=0  ,**a_							)		->   List[str]:
        _UpperCAmelCase  :						Any			     = dict(self.forward_default_kwargs							)
        _UpperCAmelCase  :						Any			     = kwargs.pop("""num_inference_steps"""  ,a_							)
        _UpperCAmelCase  :						Union[str, Any]			     = self.dummy_sample
        _UpperCAmelCase  :						Union[str, Any]			     = 0.1 * sample
        _UpperCAmelCase  :						List[str]			     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase  :						str			     = self.get_scheduler_config(**a_							)
               _UpperCAmelCase  :						List[str]			     = scheduler_class(**a_							)
               scheduler.set_timesteps(a_							)
               # copy over dummy past residuals
               _UpperCAmelCase  :						str			     = dummy_past_residuals[:]

               if time_step is None:
                      _UpperCAmelCase  :						List[str]			     = scheduler.timesteps[len(scheduler.timesteps							) // 2]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(a_							)
                      _UpperCAmelCase  :						List[str]			     = scheduler_class.from_pretrained(a_							)
                      new_scheduler.set_timesteps(a_							)
                      # copy over dummy past residuals
                      _UpperCAmelCase  :						Tuple			     = dummy_past_residuals[:]

               _UpperCAmelCase  :						Dict			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Any			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

               _UpperCAmelCase  :						str			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Any			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"







 def _snake_case (							self							)		->   Any:
        pass







 def _snake_case (							self  ,a_=0  ,**a_							)		->   Dict:
        _UpperCAmelCase  :						str			     = dict(self.forward_default_kwargs							)
        _UpperCAmelCase  :						Union[str, Any]			     = kwargs.pop("""num_inference_steps"""  ,a_							)
        _UpperCAmelCase  :						Optional[int]			     = self.dummy_sample
        _UpperCAmelCase  :						Tuple			     = 0.1 * sample
        _UpperCAmelCase  :						Optional[Any]			     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase  :						Optional[Any]			     = self.get_scheduler_config()
               _UpperCAmelCase  :						Dict			     = scheduler_class(**a_							)
               scheduler.set_timesteps(a_							)

               # copy over dummy past residuals (must be after setting timesteps)
               _UpperCAmelCase  :						Any			     = dummy_past_residuals[:]

               if time_step is None:
                      _UpperCAmelCase  :						Optional[int]			     = scheduler.timesteps[len(scheduler.timesteps							) // 2]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(a_							)
                      _UpperCAmelCase  :						str			     = scheduler_class.from_pretrained(a_							)
                      # copy over dummy past residuals
                      new_scheduler.set_timesteps(a_							)

                      # copy over dummy past residual (must be after setting timesteps)
                      _UpperCAmelCase  :						Dict			     = dummy_past_residuals[:]

               _UpperCAmelCase  :						Tuple			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						str			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

               _UpperCAmelCase  :						str			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						str			     = new_scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"







 def _snake_case (							self  ,**a_							)		->   List[Any]:
        _UpperCAmelCase  :						int			     = self.scheduler_classes[0]
        _UpperCAmelCase  :						List[str]			     = self.get_scheduler_config(**a_							)
        _UpperCAmelCase  :						List[Any]			     = scheduler_class(**a_							)

        _UpperCAmelCase  :						List[Any]			     = 10
        _UpperCAmelCase  :						Dict			     = self.dummy_model()
        _UpperCAmelCase  :						Dict			     = self.dummy_sample_deter
        scheduler.set_timesteps(a_							)

        for i, t in enumerate(scheduler.timesteps							):
               _UpperCAmelCase  :						Optional[Any]			     = model(a_  ,a_							)
               _UpperCAmelCase  :						Tuple			     = scheduler.step(a_  ,a_  ,a_							).prev_sample

        for i, t in enumerate(scheduler.timesteps							):
               _UpperCAmelCase  :						List[str]			     = model(a_  ,a_							)
               _UpperCAmelCase  :						int			     = scheduler.step(a_  ,a_  ,a_							).prev_sample

        return sample







 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						int			     = dict(self.forward_default_kwargs							)

        _UpperCAmelCase  :						Optional[Any]			     = kwargs.pop("""num_inference_steps"""  ,a_							)

        for scheduler_class in self.scheduler_classes:
               _UpperCAmelCase  :						int			     = self.get_scheduler_config()
               _UpperCAmelCase  :						List[str]			     = scheduler_class(**a_							)

               _UpperCAmelCase  :						Optional[int]			     = self.dummy_sample
               _UpperCAmelCase  :						Tuple			     = 0.1 * sample

               if num_inference_steps is not None and hasattr(a_  ,"""set_timesteps"""							):
                      scheduler.set_timesteps(a_							)
               elif num_inference_steps is not None and not hasattr(a_  ,"""set_timesteps"""							):
                      _UpperCAmelCase  :						Optional[Any]			     = num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               _UpperCAmelCase  :						Union[str, Any]			     = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
               _UpperCAmelCase  :						Optional[int]			     = dummy_past_residuals[:]

               _UpperCAmelCase  :						Tuple			     = scheduler.timesteps[5]
               _UpperCAmelCase  :						Optional[Any]			     = scheduler.timesteps[6]

               _UpperCAmelCase  :						Optional[Any]			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Optional[int]			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               self.assertEqual(output_a.shape  ,sample.shape							)
               self.assertEqual(output_a.shape  ,output_a.shape							)

               _UpperCAmelCase  :						Dict			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample
               _UpperCAmelCase  :						Any			     = scheduler.step(a_  ,a_  ,a_  ,**a_							).prev_sample

               self.assertEqual(output_a.shape  ,sample.shape							)
               self.assertEqual(output_a.shape  ,output_a.shape							)







 def _snake_case (							self							)		->   List[Any]:
        for timesteps in [100, 1_000]:
               self.check_over_configs(num_train_timesteps=a_  ,time_step=a_							)







 def _snake_case (							self							)		->   int:
        for t, num_inference_steps in zip([1, 5, 10]  ,[10, 50, 100]							):
               self.check_over_forward(num_inference_steps=a_  ,time_step=a_							)







 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						Union[str, Any]			     = self.full_loop()
        _UpperCAmelCase  :						Any			     = torch.mean(torch.abs(a_							)							)

        assert abs(result_mean.item() - 2_540_529							) < 10



