


"""simple docstring"""







import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				str   =(IPNDMScheduler,)
       a  :				int   =(("num_inference_steps", 50),)

       def        lowercase__							(						self		,     **snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							{"num_train_timesteps": 1_000}
         config.update(**snake_case__						)
         return config

       def        lowercase__							(						self		,     snake_case__=0		,     **snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							dict(self.forward_default_kwargs						)
         lowerCAmelCase     :     List[str]								=							kwargs.pop("num_inference_steps"		,     snake_case__						)
         lowerCAmelCase     :     Optional[int]								=							self.dummy_sample
         lowerCAmelCase     :     Dict								=							0.1 * sample
         lowerCAmelCase     :     int								=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

           lowerCAmelCase     :     Dict								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample
           lowerCAmelCase     :     List[str]								=							new_scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample

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

           lowerCAmelCase     :     Union[str, Any]								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample
           lowerCAmelCase     :     List[Any]								=							new_scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample

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

       def        lowercase__							(						self						):






         """simple docstring"""





         pass

       def        lowercase__							(						self		,     snake_case__=0		,     **snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							dict(self.forward_default_kwargs						)
         lowerCAmelCase     :     List[Any]								=							kwargs.pop("num_inference_steps"		,     snake_case__						)
         lowerCAmelCase     :     int								=							self.dummy_sample
         lowerCAmelCase     :     Optional[Any]								=							0.1 * sample
         lowerCAmelCase     :     List[str]								=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase     :     Any								=							self.get_scheduler_config()
           lowerCAmelCase     :     Tuple								=							scheduler_class(**snake_case__						)
           scheduler.set_timesteps(snake_case__						)

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

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

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(snake_case__						)
             lowerCAmelCase     :     Dict								=							scheduler_class.from_pretrained(snake_case__						)
             # copy over dummy past residuals
             new_scheduler.set_timesteps(snake_case__						)

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

           lowerCAmelCase     :     Dict								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample
           lowerCAmelCase     :     str								=							new_scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample

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

           lowerCAmelCase     :     List[str]								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample
           lowerCAmelCase     :     Any								=							new_scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample

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

       def        lowercase__							(						self		,     **snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							self.scheduler_classes[0]
         lowerCAmelCase     :     List[str]								=							self.get_scheduler_config(**snake_case__						)
         lowerCAmelCase     :     Any								=							scheduler_class(**snake_case__						)

         lowerCAmelCase     :     int								=							10
         lowerCAmelCase     :     List[Any]								=							self.dummy_model()
         lowerCAmelCase     :     Tuple								=							self.dummy_sample_deter
         scheduler.set_timesteps(snake_case__						)

         for i, t in enumerate(scheduler.timesteps						):
           lowerCAmelCase     :     List[Any]								=							model(snake_case__		,     snake_case__						)
           lowerCAmelCase     :     Union[str, Any]								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__						).prev_sample

         for i, t in enumerate(scheduler.timesteps						):
           lowerCAmelCase     :     Tuple								=							model(snake_case__		,     snake_case__						)
           lowerCAmelCase     :     List[Any]								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__						).prev_sample

         return sample

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							dict(self.forward_default_kwargs						)

         lowerCAmelCase     :     str								=							kwargs.pop("num_inference_steps"		,     snake_case__						)

         for scheduler_class in self.scheduler_classes:
           lowerCAmelCase     :     Dict								=							self.get_scheduler_config()
           lowerCAmelCase     :     Optional[Any]								=							scheduler_class(**snake_case__						)

           lowerCAmelCase     :     int								=							self.dummy_sample
           lowerCAmelCase     :     List[str]								=							0.1 * sample

           if num_inference_steps is not None and hasattr(snake_case__		,     "set_timesteps"						):
             scheduler.set_timesteps(snake_case__						)
           elif num_inference_steps is not None and not hasattr(snake_case__		,     "set_timesteps"						):
             lowerCAmelCase     :     List[Any]								=							num_inference_steps

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

           lowerCAmelCase     :     Union[str, Any]								=							scheduler.timesteps[5]
           lowerCAmelCase     :     List[Any]								=							scheduler.timesteps[6]

           lowerCAmelCase     :     Optional[Any]								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample
           lowerCAmelCase     :     Any								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample

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

           lowerCAmelCase     :     Union[str, Any]								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample
           lowerCAmelCase     :     int								=							scheduler.step(snake_case__		,     snake_case__		,     snake_case__		,     **snake_case__						).prev_sample

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

       def        lowercase__							(						self						):






         """simple docstring"""





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

       def        lowercase__							(						self						):






         """simple docstring"""





         for t, num_inference_steps in zip([1, 5, 10]		,     [10, 50, 100]						):
           self.check_over_forward(num_inference_steps=snake_case__		,     time_step=snake_case__						)







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							self.full_loop()
         lowerCAmelCase     :     Dict								=							torch.mean(torch.abs(snake_case__						)						)

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









"""simple docstring"""







class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self		,     snake_case__ = ""		,     snake_case__ = False						):






         """simple docstring"""





         lowerCAmelCase     :     dict[str, RadixNode]								=							{}

         # A node will be a leaf if the tree contains its word
         lowerCAmelCase     :     str								=							is_leaf

         lowerCAmelCase     :     str								=							prefix

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							0
         for q, w in zip(self.prefix		,     snake_case__						):
           if q != w:
             break

           x += 1

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

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         for word in words:
           self.insert(snake_case__						)

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         if self.prefix == word:
           lowerCAmelCase     :     Union[str, Any]								=							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:
           lowerCAmelCase     :     Optional[Any]								=							RadixNode(prefix=snake_case__		,     is_leaf=snake_case__						)

         else:
           lowerCAmelCase     :     Tuple								=							self.nodes[word[0]]
           lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     Optional[Any]								=							incoming_node.match(
               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(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:
             lowerCAmelCase     :     Optional[Any]								=							remaining_prefix

             lowerCAmelCase     :     int								=							self.nodes[matching_string[0]]
             lowerCAmelCase     :     List[Any]								=							RadixNode(snake_case__		,     snake_case__						)
             lowerCAmelCase     :     Optional[int]								=							aux_node

             if remaining_word == "":
               lowerCAmelCase     :     Optional[int]								=							True
             else:
               self.nodes[matching_string[0]].insert(snake_case__						)

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							self.nodes.get(word[0]		,     snake_case__						)
         if not incoming_node:
           return False
         else:
           lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     int								=							incoming_node.match(
               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(snake_case__						)

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							self.nodes.get(word[0]		,     snake_case__						)
         if not incoming_node:
           return False
         else:
           lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     Union[str, Any]								=							incoming_node.match(
               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(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:
                   lowerCAmelCase     :     List[str]								=							list(self.nodes.values()						)[0]
                   lowerCAmelCase     :     List[str]								=							merging_node.is_leaf
                   self.prefix += merging_node.prefix
                   lowerCAmelCase     :     Optional[int]								=							merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
               elif len(incoming_node.nodes						) > 1:
                 lowerCAmelCase     :     Optional[int]								=							False
               # If there is 1 edge, we merge it with its child
               else:
                 lowerCAmelCase     :     Optional[Any]								=							list(incoming_node.nodes.values()						)[0]
                 lowerCAmelCase     :     int								=							merging_node.is_leaf
                 incoming_node.prefix += merging_node.prefix
                 lowerCAmelCase     :     Tuple								=							merging_node.nodes

               return True







       def        lowercase__							(						self		,     snake_case__ = 0						):






         """simple docstring"""





         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 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							"banana bananas bandana band apple all beast".split()
  lowerCAmelCase     :     List[str]								=							RadixNode()
  root.insert_many(SCREAMING_SNAKE_CASE				)

  assert all(root.find(SCREAMING_SNAKE_CASE				) 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 					a__			(				):


  '''simple docstring'''







  assert test_trie()
def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     Dict								=							RadixNode()
  lowerCAmelCase     :     Optional[Any]								=							"banana bananas bandanas bandana band apple all beast".split()
  root.insert_many(SCREAMING_SNAKE_CASE				)

  print("Words:"				,      SCREAMING_SNAKE_CASE				)
  print("Tree:"				)
  root.print_tree()


if __name__ == "__main__":
     main()





