




'''simple docstring'''



import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				Any        =						(IPNDMScheduler,)
       A						:				List[Any]        =						(('''num_inference_steps''', 50),)







       def        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{'num_train_timesteps': 1_000}
             config.update(**A  )
             return config







       def        UpperCamelCase_							(      self,							A=0,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						dict(self.forward_default_kwargs  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						kwargs.pop('num_inference_steps',							A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.dummy_sample
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						0.1 * sample
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

             for scheduler_class in self.scheduler_classes:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.get_scheduler_config(**A  )
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						scheduler_class(**A  )
                   scheduler.set_timesteps(A  )
                   # copy over dummy past residuals
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						dummy_past_residuals[:]

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

                   with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(A  )
                         SCREAMING_SNAKE_CASE   :      Any    					=						scheduler_class.from_pretrained(A  )
                         new_scheduler.set_timesteps(A  )
                         # copy over dummy past residuals
                         SCREAMING_SNAKE_CASE   :      Any    					=						dummy_past_residuals[:]

                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						scheduler.step(A,							A,							A,							**A  ).prev_sample
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						new_scheduler.step(A,							A,							A,							**A  ).prev_sample

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

                   SCREAMING_SNAKE_CASE   :      Dict    					=						scheduler.step(A,							A,							A,							**A  ).prev_sample
                   SCREAMING_SNAKE_CASE   :      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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self,							A=0,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						dict(self.forward_default_kwargs  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						kwargs.pop('num_inference_steps',							A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.dummy_sample
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						0.1 * sample
             SCREAMING_SNAKE_CASE   :      Any    					=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

             for scheduler_class in self.scheduler_classes:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.get_scheduler_config()
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						scheduler_class(**A  )
                   scheduler.set_timesteps(A  )

                   # copy over dummy past residuals (must be after setting timesteps)
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						dummy_past_residuals[:]

                   if time_step is None:
                         SCREAMING_SNAKE_CASE   :      Any    					=						scheduler.timesteps[len(scheduler.timesteps  ) // 2]

                   with tempfile.TemporaryDirectory() as tmpdirname:
                         scheduler.save_config(A  )
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						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)
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						dummy_past_residuals[:]

                   SCREAMING_SNAKE_CASE   :      str    					=						scheduler.step(A,							A,							A,							**A  ).prev_sample
                   SCREAMING_SNAKE_CASE   :      int    					=						new_scheduler.step(A,							A,							A,							**A  ).prev_sample

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

                   SCREAMING_SNAKE_CASE   :      str    					=						scheduler.step(A,							A,							A,							**A  ).prev_sample
                   SCREAMING_SNAKE_CASE   :      Optional[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        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.scheduler_classes[0]
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.get_scheduler_config(**A  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						scheduler_class(**A  )

             SCREAMING_SNAKE_CASE   :      int    					=						10
             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.dummy_model()
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.dummy_sample_deter
             scheduler.set_timesteps(A  )

             for i, t in enumerate(scheduler.timesteps  ):
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						model(A,							A  )
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						scheduler.step(A,							A,							A  ).prev_sample

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

             return sample







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						dict(self.forward_default_kwargs  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						kwargs.pop('num_inference_steps',							A  )

             for scheduler_class in self.scheduler_classes:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.get_scheduler_config()
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						scheduler_class(**A  )

                   SCREAMING_SNAKE_CASE   :      Any    					=						self.dummy_sample
                   SCREAMING_SNAKE_CASE   :      Any    					=						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'  ):
                         SCREAMING_SNAKE_CASE   :      Any    					=						num_inference_steps

                   # copy over dummy past residuals (must be done after set_timesteps)
                   SCREAMING_SNAKE_CASE   :      str    					=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						dummy_past_residuals[:]

                   SCREAMING_SNAKE_CASE   :      Any    					=						scheduler.timesteps[5]
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						scheduler.timesteps[6]

                   SCREAMING_SNAKE_CASE   :      int    					=						scheduler.step(A,							A,							A,							**A  ).prev_sample
                   SCREAMING_SNAKE_CASE   :      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  )

                   SCREAMING_SNAKE_CASE   :      str    					=						scheduler.step(A,							A,							A,							**A  ).prev_sample
                   SCREAMING_SNAKE_CASE   :      int    					=						scheduler.step(A,							A,							A,							**A  ).prev_sample

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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for timesteps in [100, 1_000]:
                   self.check_over_configs(num_train_timesteps=A,							time_step=A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.full_loop()
             SCREAMING_SNAKE_CASE   :      str    					=						torch.mean(torch.abs(A  )  )

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





from collections.abc import Callable







class 					lowercase							:




				def __init__(   self ,  snake_case = None			):
											# Stores actual heap items.
											snake_case_     							=       []
											# Stores indexes of each item for supporting updates and deletion.
											snake_case_     							=       {}
											# Stores current size of heap.
											snake_case_     							=       0
											# Stores function used to evaluate the score of an item on which basis ordering
											# will be done.
											snake_case_     							=       key or (lambda snake_case			: x)




				def        a				(   self ,  snake_case			):
											return int((i - 1) / 2			) if i > 0 else None




				def        a				(   self ,  snake_case			):
											snake_case_     							=       int(2 * i + 1			)
											return left if 0 < left < self.size else None




				def        a				(   self ,  snake_case			):
											snake_case_     							=       int(2 * i + 2			)
											return right if 0 < right < self.size else None




				def        a				(   self ,  snake_case ,  snake_case			):
											snake_case_							,       snake_case_     							=       (
											    self.pos_map[self.arr[j][0]],
											    self.pos_map[self.arr[i][0]],
											)
											# Then swap the items in the list.
											snake_case_							,       snake_case_     							=       self.arr[j], self.arr[i]




				def        a				(   self ,  snake_case ,  snake_case			):
											return self.arr[i][1] < self.arr[j][1]




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self._left(snake_case			)
											snake_case_     							=       self._right(snake_case			)
											snake_case_     							=       i

											if left is not None and not self._cmp(snake_case ,  snake_case			):
																		snake_case_     							=       left
											if right is not None and not self._cmp(snake_case ,  snake_case			):
																		snake_case_     							=       right

											return valid_parent




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self._parent(snake_case			)
											while parent is not None and not self._cmp(snake_case ,  snake_case			):
																		self._swap(snake_case ,  snake_case			)
																		snake_case_							,       snake_case_     							=       parent, self._parent(snake_case			)




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self._get_valid_parent(snake_case			)
											while valid_parent != index:
																		self._swap(snake_case ,  snake_case			)
																		snake_case_							,       snake_case_     							=       valid_parent, self._get_valid_parent(snake_case			)




				def        a				(   self ,  snake_case ,  snake_case			):
											if item not in self.pos_map:
																		return
											snake_case_     							=       self.pos_map[item]
											snake_case_     							=       [item, self.key(snake_case			)]
											# Make sure heap is right in both up and down direction.
											# Ideally only one of them will make any change.
											self._heapify_up(snake_case			)
											self._heapify_down(snake_case			)




				def        a				(   self ,  snake_case			):
											if item not in self.pos_map:
																		return
											snake_case_     							=       self.pos_map[item]
											del self.pos_map[item]
											snake_case_     							=       self.arr[self.size - 1]
											snake_case_     							=       index
											self.size -= 1
											# Make sure heap is right in both up and down direction. Ideally only one
											# of them will make any change- so no performance loss in calling both.
											if self.size > index:
																		self._heapify_up(snake_case			)
																		self._heapify_down(snake_case			)




				def        a				(   self ,  snake_case ,  snake_case			):
											snake_case_     							=       len(self.arr			)
											if arr_len == self.size:
																		self.arr.append([item, self.key(snake_case			)]			)
											else:
																		snake_case_     							=       [item, self.key(snake_case			)]
											snake_case_     							=       self.size
											self.size += 1
											self._heapify_up(self.size - 1			)




				def        a				(   self			):
											return self.arr[0] if self.size else None




				def        a				(   self			):
											snake_case_     							=       self.get_top()
											if top_item_tuple:
																		self.delete_item(top_item_tuple[0]			)
											return top_item_tuple





def    __lowerCamelCase      (					):







							'''simple docstring'''








if __name__ == "__main__":
					import doctest

					doctest.testmod()




