
"""simple docstring"""



class  lowerCAmelCase__							:







     def __init__(				self     :     Optional[int]       , _lowerCamelCase     :     str = ""       , _lowerCamelCase     :     bool = False     ):
       _snake_case   	=			{}

       # A node will be a leaf if the tree contains its word
       _snake_case   	=			is_leaf

       _snake_case   	=			prefix







     def 	lowercase      (				self     :     Tuple       , _lowerCamelCase     :     str     ):
       _snake_case   	=			0
       for q, w in zip(self.prefix       , _SCREAMING_SNAKE_CASE     ):
         if q != w:
           break

         x += 1

       return self.prefix[:x], self.prefix[x:], word[x:]







     def 	lowercase      (				self     :     int       , _lowerCamelCase     :     list[str]     ):


       for word in words:
         self.insert(_SCREAMING_SNAKE_CASE     )







     def 	lowercase      (				self     :     Union[str, Any]       , _lowerCamelCase     :     str     ):


       if self.prefix == word:
         _snake_case   	=			True

       # Case 2: The node has no edges that have a prefix to the word
       # Solution: We create an edge from the current node to a new one
       # containing the word
       elif word[0] not in self.nodes:
         _snake_case   	=			RadixNode(prefix=_SCREAMING_SNAKE_CASE       , is_leaf=_SCREAMING_SNAKE_CASE     )

       else:
         _snake_case   	=			self.nodes[word[0]]
         _snake_case   	=			incoming_node.match(
             _SCREAMING_SNAKE_CASE     )

         # Case 3: The node prefix is equal to the matching
         # Solution: We insert remaining word on the next node
         if remaining_prefix == "":
           self.nodes[matching_string[0]].insert(_SCREAMING_SNAKE_CASE     )

         # Case 4: The word is greater equal to the matching
         # Solution: Create a node in between both nodes, change
         # prefixes and add the new node for the remaining word
         else:
           _snake_case   	=			remaining_prefix

           _snake_case   	=			self.nodes[matching_string[0]]
           _snake_case   	=			RadixNode(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE     )
           _snake_case   	=			aux_node

           if remaining_word == "":
             _snake_case   	=			True
           else:
             self.nodes[matching_string[0]].insert(_SCREAMING_SNAKE_CASE     )







     def 	lowercase      (				self     :     str       , _lowerCamelCase     :     str     ):
       _snake_case   	=			self.nodes.get(word[0]       , _SCREAMING_SNAKE_CASE     )
       if not incoming_node:
         return False
       else:
         _snake_case   	=			incoming_node.match(
             _SCREAMING_SNAKE_CASE     )
         # If there is remaining prefix, the word can't be on the tree
         if remaining_prefix != "":
           return False
         # This applies when the word and the prefix are equal
         elif remaining_word == "":
           return incoming_node.is_leaf
         # We have word remaining so we check the next node
         else:
           return incoming_node.find(_SCREAMING_SNAKE_CASE     )







     def 	lowercase      (				self     :     Optional[Any]       , _lowerCamelCase     :     str     ):
       _snake_case   	=			self.nodes.get(word[0]       , _SCREAMING_SNAKE_CASE     )
       if not incoming_node:
         return False
       else:
         _snake_case   	=			incoming_node.match(
             _SCREAMING_SNAKE_CASE     )
         # If there is remaining prefix, the word can't be on the tree
         if remaining_prefix != "":
           return False
         # We have word remaining so we check the next node
         elif remaining_word != "":
           return incoming_node.delete(_SCREAMING_SNAKE_CASE     )
         else:
           # If it is not a leaf, we don't have to delete
           if not incoming_node.is_leaf:
             return False
           else:
             # We delete the nodes if no edges go from it
             if len(incoming_node.nodes     ) == 0:
               del self.nodes[word[0]]
               # We merge the current node with its only child
               if len(self.nodes     ) == 1 and not self.is_leaf:
                 _snake_case   	=			list(self.nodes.values()     )[0]
                 _snake_case   	=			merging_node.is_leaf
                 self.prefix += merging_node.prefix
                 _snake_case   	=			merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
             elif len(incoming_node.nodes     ) > 1:
               _snake_case   	=			False
             # If there is 1 edge, we merge it with its child
             else:
               _snake_case   	=			list(incoming_node.nodes.values()     )[0]
               _snake_case   	=			merging_node.is_leaf
               incoming_node.prefix += merging_node.prefix
               _snake_case   	=			merging_node.nodes

             return True







     def 	lowercase      (				self     :     Any       , _lowerCamelCase     :     int = 0     ):


       if self.prefix != "":
         print('''-''' * height       , self.prefix       , '''  (leaf)''' if self.is_leaf else ''''''     )

       for value in self.nodes.values():
         value.print_tree(height + 1     )






def    _UpperCAmelCase (					)	->   bool:
  _snake_case   	=			'banana bananas bandana band apple all beast'.split()
  _snake_case   	=			RadixNode()
  root.insert_many(__A						)

  assert all(root.find(__A						) for word in words						)
  assert not root.find('''bandanas'''						)
  assert not root.find('''apps'''						)
  root.delete('''all'''						)
  assert not root.find('''all'''						)
  root.delete('''banana'''						)
  assert not root.find('''banana'''						)
  assert root.find('''bananas'''						)

  return True
def    _UpperCAmelCase (					)	->   None:
  assert test_trie()
def    _UpperCAmelCase (					)	->   None:
  _snake_case   	=			RadixNode()
  _snake_case   	=			'banana bananas bandanas bandana band apple all beast'.split()
  root.insert_many(__A						)

  print('''Words:'''					,   __A						)
  print('''Tree:'''						)
  root.print_tree()


if __name__ == "__main__":
 main()








"""simple docstring"""

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= (DEISMultistepScheduler,)
	_SCREAMING_SNAKE_CASE	:		str  						= (('''num_inference_steps''', 25),)





	def 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       **_SCREAMING_SNAKE_CASE				:       int    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     {
					    'num_train_timesteps': 1_000,
					    'beta_start': 0.0_0_0_1,
					    'beta_end': 0.0_2,
					    'beta_schedule': 'linear',
					    'solver_order': 2,
					}

					config.update(**_SCREAMING_SNAKE_CASE    )
					return config





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       int=0     ,       **_SCREAMING_SNAKE_CASE				:       Optional[int]    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     dict(self.forward_default_kwargs    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     kwargs.pop('num_inference_steps'     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.dummy_sample
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     0.1 * sample
					SCREAMING_SNAKE_CASE						:							Any							=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

					for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE						:							List[str]							=     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
									# copy over dummy past residuals
									SCREAMING_SNAKE_CASE						:							Dict							=     dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE    )
													new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
													# copy over dummy past residuals
													SCREAMING_SNAKE_CASE						:							List[str]							=     dummy_past_residuals[: new_scheduler.config.solver_order]

									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Tuple							=     sample, sample
									for t in range(_SCREAMING_SNAKE_CASE     ,       time_step + scheduler.config.solver_order + 1    ):
													SCREAMING_SNAKE_CASE						:							Tuple							=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    ).prev_sample
													SCREAMING_SNAKE_CASE						:							Tuple							=     new_scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    ).prev_sample

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





	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	Any:





					"""simple docstring"""


					pass





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict=0     ,       **_SCREAMING_SNAKE_CASE				:       Dict    )						->	List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     dict(self.forward_default_kwargs    )
					SCREAMING_SNAKE_CASE						:							Dict							=     kwargs.pop('num_inference_steps'     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.dummy_sample
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     0.1 * sample
					SCREAMING_SNAKE_CASE						:							int							=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

					for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE						:							Any							=     self.get_scheduler_config()
									SCREAMING_SNAKE_CASE						:							List[str]							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

									# copy over dummy past residuals (must be after setting timesteps)
									SCREAMING_SNAKE_CASE						:							Tuple							=     dummy_past_residuals[: scheduler.config.solver_order]

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

													# copy over dummy past residual (must be after setting timesteps)
													SCREAMING_SNAKE_CASE						:							Optional[int]							=     dummy_past_residuals[: new_scheduler.config.solver_order]

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

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





	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=None     ,       **_SCREAMING_SNAKE_CASE				:       List[str]    )						->	Any:





					"""simple docstring"""


					if scheduler is None:
									SCREAMING_SNAKE_CASE						:							int							=     self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							List[str]							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Any							=     10
					SCREAMING_SNAKE_CASE						:							Any							=     self.dummy_model()
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.dummy_sample_deter
					scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

					for i, t in enumerate(scheduler.timesteps    ):
									SCREAMING_SNAKE_CASE						:							int							=     model(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Tuple							=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ).prev_sample

					return sample





	def 			_lowerCAmelCase     (      self				:       str    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     dict(self.forward_default_kwargs    )

					SCREAMING_SNAKE_CASE						:							List[str]							=     kwargs.pop('num_inference_steps'     ,       _SCREAMING_SNAKE_CASE    )

					for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE						:							int							=     self.get_scheduler_config()
									SCREAMING_SNAKE_CASE						:							Dict							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )

									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.dummy_sample
									SCREAMING_SNAKE_CASE						:							Tuple							=     0.1 * sample

									if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE     ,       'set_timesteps'    ):
													scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
									elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE     ,       'set_timesteps'    ):
													SCREAMING_SNAKE_CASE						:							List[str]							=     num_inference_steps

									# copy over dummy past residuals (must be done after set_timesteps)
									SCREAMING_SNAKE_CASE						:							Tuple							=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     dummy_past_residuals[: scheduler.config.solver_order]

									SCREAMING_SNAKE_CASE						:							Tuple							=     scheduler.timesteps[5]
									SCREAMING_SNAKE_CASE						:							Tuple							=     scheduler.timesteps[6]

									SCREAMING_SNAKE_CASE						:							str							=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    ).prev_sample
									SCREAMING_SNAKE_CASE						:							List[Any]							=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    ).prev_sample

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





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     DEISMultistepScheduler(**self.get_scheduler_config()    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     self.full_loop(scheduler=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

					assert abs(result_mean.item() - 0.2_3_9_1_6    ) < 1E-3

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     DPMSolverSinglestepScheduler.from_config(scheduler.config    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     DPMSolverMultistepScheduler.from_config(scheduler.config    )
					SCREAMING_SNAKE_CASE						:							Any							=     UniPCMultistepScheduler.from_config(scheduler.config    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     DEISMultistepScheduler.from_config(scheduler.config    )

					SCREAMING_SNAKE_CASE						:							str							=     self.full_loop(scheduler=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Any							=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

					assert abs(result_mean.item() - 0.2_3_9_1_6    ) < 1E-3





	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Optional[int]:





					"""simple docstring"""


					for timesteps in [25, 50, 100, 999, 1_000]:
									self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       str    )						->	List[str]:





					"""simple docstring"""


					self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE    )
					for order in [1, 2, 3]:
									for solver_type in ["logrho"]:
													for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample"]:
																					self.check_over_configs(
																					    thresholding=_SCREAMING_SNAKE_CASE     ,       prediction_type=_SCREAMING_SNAKE_CASE     ,       sample_max_value=_SCREAMING_SNAKE_CASE     ,       algorithm_type='deis'     ,       solver_order=_SCREAMING_SNAKE_CASE     ,       solver_type=_SCREAMING_SNAKE_CASE     ,       )





	def 			_lowerCAmelCase     (      self				:       str    )						->	Optional[int]:





					"""simple docstring"""


					for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	Tuple:





					"""simple docstring"""


					for algorithm_type in ["deis"]:
									for solver_type in ["logrho"]:
													for order in [1, 2, 3]:
																	for prediction_type in ["epsilon", "sample"]:
																					self.check_over_configs(
																					    solver_order=_SCREAMING_SNAKE_CASE     ,       solver_type=_SCREAMING_SNAKE_CASE     ,       prediction_type=_SCREAMING_SNAKE_CASE     ,       algorithm_type=_SCREAMING_SNAKE_CASE     ,       )
																					SCREAMING_SNAKE_CASE						:							Dict							=     self.full_loop(
																					    solver_order=_SCREAMING_SNAKE_CASE     ,       solver_type=_SCREAMING_SNAKE_CASE     ,       prediction_type=_SCREAMING_SNAKE_CASE     ,       algorithm_type=_SCREAMING_SNAKE_CASE     ,       )
																					assert not torch.isnan(_SCREAMING_SNAKE_CASE    ).any(), "Samples have nan numbers"





	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	str:





					"""simple docstring"""


					self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE    )
					self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Any    )						->	List[Any]:





					"""simple docstring"""


					for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
									self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE     ,       time_step=0    )





	def 			_lowerCAmelCase     (      self				:       int    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.full_loop()
					SCREAMING_SNAKE_CASE						:							Tuple							=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

					assert abs(result_mean.item() - 0.2_3_9_1_6    ) < 1E-3





	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     self.full_loop(prediction_type='v_prediction'    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

					assert abs(result_mean.item() - 0.0_9_1    ) < 1E-3







	def 			_lowerCAmelCase     (      self				:       str    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE						:							Tuple							=     self.get_scheduler_config(thresholding=_SCREAMING_SNAKE_CASE     ,       dynamic_thresholding_ratio=0    )
					SCREAMING_SNAKE_CASE						:							Any							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Any							=     10
					SCREAMING_SNAKE_CASE						:							Dict							=     self.dummy_model()
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.dummy_sample_deter.half()
					scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

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

					assert sample.dtype == torch.floataa





