


from collections.abc import Sequence
from queue import Queue
class 		__magic_name__						:





     '''simple docstring'''



     def __init__(					self:str     ,		_a:List[Any]     ,		_a:Optional[Any]     ,		_a:List[Any]     ,		_a:Dict=None     ,		_a:List[Any]=None   ):
       snake_case__						= start
       snake_case__						= end
       snake_case__						= val
       snake_case__						= (start + end) // 2
       snake_case__						= left
       snake_case__						= right







     def __repr__(					self:Dict   ):
       return F"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""




class 		__magic_name__						:





     '''simple docstring'''



     def __init__(					self:Dict     ,		_a:Sequence     ,		_a:Dict   ):
       snake_case__						= collection
       snake_case__						= function
       if self.collection:
         snake_case__						= self._build_tree(0     ,		len(_a   ) - 1   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:Optional[Any]     ,		_a:Optional[Any]   ):
       self._update_tree(self.root     ,		_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:str     ,		_a:str   ):
       return self._query_range(self.root     ,		_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:str     ,		_a:List[Any]   ):
       if start == end:
         return SegmentTreeNode(_a     ,		_a     ,		self.collection[start]   )
       snake_case__						= (start + end) // 2
       snake_case__						= self._build_tree(_a     ,		_a   )
       snake_case__						= self._build_tree(mid + 1     ,		_a   )
       return SegmentTreeNode(_a     ,		_a     ,		self.fn(left.val     ,		right.val   )     ,		_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:Any     ,		_a:Union[str, Any]     ,		_a:Optional[Any]   ):
       if node.start == i and node.end == i:
         snake_case__						= val
         return
       if i <= node.mid:
         self._update_tree(node.left     ,		_a     ,		_a   )
       else:
         self._update_tree(node.right     ,		_a     ,		_a   )
       snake_case__						= self.fn(node.left.val     ,		node.right.val   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		_a:List[str]     ,		_a:Any     ,		_a:Any   ):
       if node.start == i and node.end == j:
         return node.val

       if i <= node.mid:
         if j <= node.mid:
           # range in left child tree
           return self._query_range(node.left     ,		_a     ,		_a   )
         else:
           # range in left child tree and right child tree
           return self.fn(
               self._query_range(node.left     ,		_a     ,		node.mid   )     ,		self._query_range(node.right     ,		node.mid + 1     ,		_a   )     ,		)
       else:
         # range in right child tree
         return self._query_range(node.right     ,		_a     ,		_a   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       if self.root is not None:
         snake_case__						= Queue()
         queue.put(self.root   )
         while not queue.empty():
           snake_case__						= queue.get()
           yield node

           if node.left is not None:
             queue.put(node.left   )

           if node.right is not None:
             queue.put(node.right   )


if __name__ == "__main__":
   import operator

   for fn in [operator.add, max, min]:
      print("""*""" * 5_0)
      lowerCamelCase__							:						int							     =   SegmentTree([2, 1, 5, 3, 4], fn)
      for node in arr.traverse():
         print(node)
      print()

      arr.update(1, 5)
      for node in arr.traverse():
         print(node)
      print()

      print(arr.query_range(3, 4))  # 7
      print(arr.query_range(2, 2))  # 5
      print(arr.query_range(1, 3))  # 13
      print()





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							str	   =       (CMStochasticIterativeScheduler,)
     __lowercase				:							List[str]	   =       10

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		**_a:Optional[int]   ):
       snake_case__						= {
           '''num_train_timesteps''': 2_01,
           '''sigma_min''': 0.002,
           '''sigma_max''': 80.0,
       }

       config.update(**_a   )
       return config

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= 10

       snake_case__						= self.get_scheduler_config()
       snake_case__						= self.scheduler_classes[0](**_a   )

       scheduler.set_timesteps(_a   )

       snake_case__						= scheduler.timesteps[0]
       snake_case__						= scheduler.timesteps[1]

       snake_case__						= self.dummy_sample
       snake_case__						= 0.1 * sample

       snake_case__						= scheduler.step(_a     ,		_a     ,		_a   ).prev_sample
       snake_case__						= scheduler.step(_a     ,		_a     ,		_a   ).prev_sample

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       for timesteps in [10, 50, 1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       for clip_denoised in [True, False]:
         self.check_over_configs(clip_denoised=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= 1
       scheduler.set_timesteps(_a   )
       snake_case__						= scheduler.timesteps

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(_a   ):
         # 1. scale model input
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         # 2. predict noise residual
         snake_case__						= model(_a     ,		_a   )

         # 3. predict previous sample x_t-1
         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   ).prev_sample

         snake_case__						= pred_prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_sum.item() - 192.7614   ) < 1e-2
       assert abs(result_mean.item() - 0.2510   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [1_06, 0]
       scheduler.set_timesteps(timesteps=_a   )
       snake_case__						= scheduler.timesteps

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma

       for t in timesteps:
         # 1. scale model input
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         # 2. predict noise residual
         snake_case__						= model(_a     ,		_a   )

         # 3. predict previous sample x_t-1
         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   ).prev_sample

         snake_case__						= pred_prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_sum.item() - 347.6357   ) < 1e-2
       assert abs(result_mean.item() - 0.4527   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [39, 30, 12, 15, 0]

       with self.assertRaises(_a     ,		msg='''`timesteps` must be in descending order.'''   ):
         scheduler.set_timesteps(timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [39, 30, 12, 1, 0]
       snake_case__						= len(_a   )

       with self.assertRaises(_a     ,		msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''   ):
         scheduler.set_timesteps(num_inference_steps=_a     ,		timesteps=_a   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [scheduler.config.num_train_timesteps]

       with self.assertRaises(
           _a     ,		msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''     ,		):
         scheduler.set_timesteps(timesteps=_a   )

