

"""simple docstring"""






import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       (KDPMaDiscreteScheduler,)
     _lowerCamelCase     =       10







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      __magic_name__     =      {
          '''num_train_timesteps''': 1100,
          '''beta_start''': 0.0_0_0_1,
          '''beta_end''': 0.0_2,
          '''beta_schedule''': '''linear''',
      }

      config.update(**UpperCamelCase_       )
      return config







     def 	lowerCAmelCase__    (      self       ):
      for timesteps in [10, 50, 100, 1000]:
       self.check_over_configs(num_train_timesteps=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1] ,						[0.0_0_0_2, 0.0_0_2, 0.0_2]       ):
       self.check_over_configs(beta_start=UpperCamelCase_ ,						beta_end=UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=UpperCamelCase_       )







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







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config(prediction_type='''v_prediction'''       )
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for i, t in enumerate(scheduler.timesteps       ):
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      if torch_device in ["cpu", "mps"]:
       assert abs(result_sum.item() - 4.6_9_3_4E-0_7       ) < 1E-2
       assert abs(result_mean.item() - 6.1_1_1_2E-1_0       ) < 1E-3
      else:
       # CUDA
       assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7       ) < 1E-2
       assert abs(result_mean.item() - 0.0_0_0_2       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      if torch_device == "mps":
       return
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter * scheduler.init_noise_sigma
      __magic_name__     =      sample.to(UpperCamelCase_       )

      for i, t in enumerate(scheduler.timesteps       ):
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      if torch_device in ["cpu", "mps"]:
       assert abs(result_sum.item() - 2_0.4_1_2_5       ) < 1E-2
       assert abs(result_mean.item() - 0.0_2_6_6       ) < 1E-3
      else:
       # CUDA
       assert abs(result_sum.item() - 2_0.4_1_2_5       ) < 1E-2
       assert abs(result_mean.item() - 0.0_2_6_6       ) < 1E-3







     def 	lowerCAmelCase__    (      self       ):
      if torch_device == "mps":
       return
      __magic_name__     =      self.scheduler_classes[0]
      __magic_name__     =      self.get_scheduler_config()
      __magic_name__     =      scheduler_class(**UpperCamelCase_       )

      scheduler.set_timesteps(self.num_inference_steps ,						device=UpperCamelCase_       )

      __magic_name__     =      self.dummy_model()
      __magic_name__     =      self.dummy_sample_deter.to(UpperCamelCase_       ) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
       __magic_name__     =      scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      model(UpperCamelCase_ ,						UpperCamelCase_       )

       __magic_name__     =      scheduler.step(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )
       __magic_name__     =      output.prev_sample

      __magic_name__     =      torch.sum(torch.abs(UpperCamelCase_       )       )
      __magic_name__     =      torch.mean(torch.abs(UpperCamelCase_       )       )

      if str(UpperCamelCase_       ).startswith('''cpu'''       ):
       # The following sum varies between 148 and 156 on mps. Why?
       assert abs(result_sum.item() - 2_0.4_1_2_5       ) < 1E-2
       assert abs(result_mean.item() - 0.0_2_6_6       ) < 1E-3
      else:
       # CUDA
       assert abs(result_sum.item() - 2_0.4_1_2_5       ) < 1E-2
       assert abs(result_mean.item() - 0.0_2_6_6       ) < 1E-3




"""simple docstring"""






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







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_=None ,						UpperCamelCase_=None       ):
      __magic_name__     =      start
      __magic_name__     =      end
      __magic_name__     =      val
      __magic_name__     =      (start + end) // 2
      __magic_name__     =      left
      __magic_name__     =      right







     def __repr__(      self       ):
      return f'''SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'''


class        _lowercase					:







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      collection
      __magic_name__     =      function
      if self.collection:
       __magic_name__     =      self._build_tree(0 ,						len(UpperCamelCase_       ) - 1       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      self._update_tree(self.root ,						UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      return self._query_range(self.root ,						UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      if start == end:
       return SegmentTreeNode(UpperCamelCase_ ,						UpperCamelCase_ ,						self.collection[start]       )
      __magic_name__     =      (start + end) // 2
      __magic_name__     =      self._build_tree(UpperCamelCase_ ,						UpperCamelCase_       )
      __magic_name__     =      self._build_tree(mid + 1 ,						UpperCamelCase_       )
      return SegmentTreeNode(UpperCamelCase_ ,						UpperCamelCase_ ,						self.fn(left.val ,						right.val       ) ,						UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      if node.start == i and node.end == i:
       __magic_name__     =      val
       return
      if i <= node.mid:
       self._update_tree(node.left ,						UpperCamelCase_ ,						UpperCamelCase_       )
      else:
       self._update_tree(node.right ,						UpperCamelCase_ ,						UpperCamelCase_       )
      __magic_name__     =      self.fn(node.left.val ,						node.right.val       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      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 ,						UpperCamelCase_ ,						UpperCamelCase_       )
       else:
        # range in left child tree and right child tree
        return self.fn(
            self._query_range(node.left ,						UpperCamelCase_ ,						node.mid       ) ,						self._query_range(node.right ,						node.mid + 1 ,						UpperCamelCase_       ) ,						)
      else:
       # range in right child tree
       return self._query_range(node.right ,						UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      if self.root is not None:
       __magic_name__     =      Queue()
       queue.put(self.root       )
       while not queue.empty():
        __magic_name__     =      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("*" * 50)
  __lowerCamelCase										=		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()

