
from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

lowercase_ 				=			TypeVar("""T""")





class     __UpperCamelCase (							Generic[T]       ):






     """simple docstring"""







     def __init__(				self							:							List[Any]     ,						_A							:							list[T]     ,						_A							:							Callable[[T, T], T] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any | T					=		None

       __SCREAMING_SNAKE_CASE      :	int					=		len(_A )
       __SCREAMING_SNAKE_CASE      :	list[T]					=		[any_type for _ in range(self.N )] + arr
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		fnc
       self.build()



     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       for p in range(self.N - 1     ,						0     ,						-1 ):
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.fn(self.st[p * 2]     ,						self.st[p * 2 + 1] )



     def  UpperCAmelCase__							(				self							:							str     ,						_A							:							int     ,						_A							:							T ):







       """simple docstring"""




       p += self.N
       __SCREAMING_SNAKE_CASE      :	Tuple					=		v
       while p > 1:
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		p // 2
         __SCREAMING_SNAKE_CASE      :	Any					=		self.fn(self.st[p * 2]     ,						self.st[p * 2 + 1] )




     def  UpperCAmelCase__							(				self							:							int     ,						_A							:							int     ,						_A							:							int ):  # noqa: E741







       """simple docstring"""




       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Tuple					=		l + self.N, r + self.N

       __SCREAMING_SNAKE_CASE      :	T | None					=		None
       while l <= r:
         if l % 2 == 1:
           __SCREAMING_SNAKE_CASE      :	Optional[int]					=		self.st[l] if res is None else self.fn(_A     ,						self.st[l] )
         if r % 2 == 0:
           __SCREAMING_SNAKE_CASE      :	int					=		self.st[r] if res is None else self.fn(_A     ,						self.st[r] )
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Any					=		(l + 1) // 2, (r - 1) // 2
       return res


if __name__ == "__main__":
   from functools import reduce

   lowercase_ 				=			[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

   lowercase_ 				=			{
       0: 7,
       1: 2,
       2: 6,
       3: -14,
       4: 5,
       5: 4,
       6: 7,
       7: -10,
       8: 9,
       9: 10,
       10: 12,
       11: 1,
   }

   lowercase_ 				=			SegmentTree(test_array, min)
   lowercase_ 				=			SegmentTree(test_array, max)
   lowercase_ 				=			SegmentTree(test_array, lambda a, b: a + b)


   def 							a__					(							):





     """simple docstring"""
     for i in range(len(snake_case							)							):
       for j in range(snake_case       ,			len(snake_case							)							):
         __SCREAMING_SNAKE_CASE      :	Any					=		reduce(snake_case       ,			test_array[i : j + 1]							)
         __SCREAMING_SNAKE_CASE      :	Tuple					=		reduce(snake_case       ,			test_array[i : j + 1]							)
         __SCREAMING_SNAKE_CASE      :	str					=		reduce(lambda snake_case       ,			snake_case							: a + b       ,			test_array[i : j + 1]							)
         assert min_range == min_segment_tree.query(snake_case       ,			snake_case							)
         assert max_range == max_segment_tree.query(snake_case       ,			snake_case							)
         assert sum_range == sum_segment_tree.query(snake_case       ,			snake_case							)

   test_all_segments()

   for index, value in test_updates.items():
      lowercase_ 				=			value
      min_segment_tree.update(index, value)
      max_segment_tree.update(index, value)
      sum_segment_tree.update(index, value)
      test_all_segments()

import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


SCREAMING_SNAKE_CASE		:     List[str]         =	False

class        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self			,     __UpperCamelCase=32						)				->				Optional[int]:
												set_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		UNetaDModel(sample_size=__UpperCamelCase			,     in_channels=3			,     out_channels=3						)
												UpperCamelCase_		:							Union[str, Any]  =		torch.optim.SGD(model.parameters()			,     lr=0.0_001						)
												return model, optimizer





						@slow
						def        A_							(self						)				->				str:
												UpperCamelCase_		:							str  =		"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												UpperCamelCase_		:							Tuple  =		DDPMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )
												UpperCamelCase_		:							List[Any]  =		DDIMScheduler(
												    num_train_timesteps=1_000			,     beta_start=0.0_001			,     beta_end=0.02			,     beta_schedule="""linear"""			,     clip_sample=__UpperCamelCase			,     )

												assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

												# shared batches for DDPM and DDIM
												set_seed(0						)
												UpperCamelCase_		:							Any  =		[torch.randn((4, 3, 32, 32)						).clip(-1			,     1						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							str  =		[torch.randn((4, 3, 32, 32)						).to(__UpperCamelCase						) for _ in range(4						)]
												UpperCamelCase_		:							Union[str, Any]  =		[torch.randint(0			,     1_000			,     (4,)						).long().to(__UpperCamelCase						) for _ in range(4						)]

												# train with a DDPM scheduler
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							Optional[Any]  =		ddpm_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							List[Any]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Dict  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												# recreate the model and optimizer, and retry with DDIM
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.get_model_optimizer(resolution=32						)
												model.train().to(__UpperCamelCase						)
												for i in range(4						):
																		optimizer.zero_grad()
																		UpperCamelCase_		:							str  =		ddim_scheduler.add_noise(clean_images[i]			,     noise[i]			,     timesteps[i]						)
																		UpperCamelCase_		:							Optional[int]  =		model(__UpperCamelCase			,     timesteps[i]						).sample
																		UpperCamelCase_		:							Union[str, Any]  =		torch.nn.functional.mse_loss(__UpperCamelCase			,     noise[i]						)
																		loss.backward()
																		optimizer.step()
												del model, optimizer

												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)
												self.assertTrue(torch.allclose(__UpperCamelCase			,     __UpperCamelCase			,     atol=1E-5						)						)






