



'''simple docstring'''






class 							lowercase       :


 """simple docstring"""


 def __init__(							self  ,a_  ,a_							)		->   Any:
        _UpperCAmelCase  :						Dict			     = name
        _UpperCAmelCase  :						Optional[Any]			     = val







 def __str__(							self							)		->   int:
        return f'''{self.__class__.__name__}({self.name}, {self.val})'''







 def __lt__(							self  ,a_							)		->   Tuple:
        return self.val < other.val







class 							lowercase       :


 """simple docstring"""


 def __init__(							self  ,a_							)		->   List[Any]:
        _UpperCAmelCase  :						List[str]			     = {}
        _UpperCAmelCase  :						Any			     = {}
        _UpperCAmelCase  :						Optional[Any]			     = self.build_heap(a_							)







 def __getitem__(							self  ,a_							)		->   Dict:
        return self.get_value(a_							)







 def _snake_case (							self  ,a_							)		->   Any:
        return (idx - 1) // 2







 def _snake_case (							self  ,a_							)		->   Any:
        return idx * 2 + 1







 def _snake_case (							self  ,a_							)		->   Optional[Any]:
        return idx * 2 + 2







 def _snake_case (							self  ,a_							)		->   Union[str, Any]:
        return self.heap_dict[key]







 def _snake_case (							self  ,a_							)		->   str:
        _UpperCAmelCase  :						Optional[Any]			     = len(a_							) - 1
        _UpperCAmelCase  :						List[Any]			     = self.get_parent_idx(a_							)

        for idx, i in enumerate(a_							):
               _UpperCAmelCase  :						Optional[int]			     = idx
               _UpperCAmelCase  :						List[Any]			     = i.val

        for i in range(a_  ,-1  ,-1							):
               self.sift_down(a_  ,a_							)
        return array







 def _snake_case (							self  ,a_  ,a_							)		->   Union[str, Any]:
        while True:
               _UpperCAmelCase  :						Dict			     = self.get_left_child_idx(a_							)  # noqa: E741
               _UpperCAmelCase  :						Tuple			     = self.get_right_child_idx(a_							)

               _UpperCAmelCase  :						List[Any]			     = idx
               if l < len(a_							) and array[l] < array[idx]:
                      _UpperCAmelCase  :						str			     = l
               if r < len(a_							) and array[r] < array[smallest]:
                      _UpperCAmelCase  :						Union[str, Any]			     = r

               if smallest != idx:
                      _UpperCAmelCase     ,_UpperCAmelCase  :						Union[str, Any]			     = array[smallest], array[idx]
                      (
                          (
                          _UpperCAmelCase
                      )     ,(
                          _UpperCAmelCase
                      )     ,
                      )  :						int			     = (
                          self.idx_of_element[array[smallest]],
                          self.idx_of_element[array[idx]],
                      )
                      _UpperCAmelCase  :						Union[str, Any]			     = smallest
               else:
                      break







 def _snake_case (							self  ,a_							)		->   Tuple:
        _UpperCAmelCase  :						Union[str, Any]			     = self.get_parent_idx(a_							)
        while p >= 0 and self.heap[p] > self.heap[idx]:
               _UpperCAmelCase     ,_UpperCAmelCase  :						Union[str, Any]			     = self.heap[idx], self.heap[p]
               _UpperCAmelCase     ,_UpperCAmelCase  :						Optional[Any]			     = (
                   self.idx_of_element[self.heap[idx]],
                   self.idx_of_element[self.heap[p]],
               )
               _UpperCAmelCase  :						List[str]			     = p
               _UpperCAmelCase  :						List[str]			     = self.get_parent_idx(a_							)







 def _snake_case (							self							)		->   List[str]:
        return self.heap[0]







 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase     ,_UpperCAmelCase  :						Tuple			     = self.heap[-1], self.heap[0]
        _UpperCAmelCase     ,_UpperCAmelCase  :						List[Any]			     = (
            self.idx_of_element[self.heap[-1]],
            self.idx_of_element[self.heap[0]],
        )

        _UpperCAmelCase  :						Any			     = self.heap.pop()
        del self.idx_of_element[x]
        self.sift_down(0  ,self.heap							)
        return x







 def _snake_case (							self  ,a_							)		->   Optional[int]:
        self.heap.append(a_							)
        _UpperCAmelCase  :						Dict			     = len(self.heap							) - 1
        _UpperCAmelCase  :						Any			     = node.val
        self.sift_up(len(self.heap							) - 1							)







 def _snake_case (							self							)		->   str:
        return len(self.heap							) == 0







 def _snake_case (							self  ,a_  ,a_							)		->   str:
        assert (
            self.heap[self.idx_of_element[node]].val > new_value
        ), "newValue must be less that current value"
        _UpperCAmelCase  :						str			     = new_value
        _UpperCAmelCase  :						Dict			     = new_value
        self.sift_up(self.idx_of_element[node]							)







A_							: Union[str, Any]						 =	Node("""R""", -1)
A_							: List[Any]						 =	Node("""B""", 6)
A_							: Optional[int]						 =	Node("""A""", 3)
A_							: int						 =	Node("""X""", 1)
A_							: Tuple						 =	Node("""E""", 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
A_							: int						 =	MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print("""Min Heap - before decrease key""")
for i in my_min_heap.heap:
   print(i)

print("""Min Heap - After decrease key of node [B -> -17]""")
my_min_heap.decrease_key(b, -1_7)

# After
for i in my_min_heap.heap:
   print(i)

if __name__ == "__main__":
   import doctest

   doctest.testmod()








'''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



