



import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin






def        lowerCamelCase__    (    __lowerCAmelCase  :  Optional[int]=2   ,							__lowerCAmelCase  :  List[str]=True   ,							__lowerCAmelCase  :  Any=False    ):




			"""simple docstring"""


			lowerCAmelCase_      =     Accelerator(step_scheduler_with_optimizer=_A   ,							split_batches=_A    )
			lowerCAmelCase_      =     torch.nn.Linear(2   ,							4    )
			lowerCAmelCase_      =     torch.optim.AdamW(model.parameters()   ,							lr=1.0    )
			lowerCAmelCase_      =     torch.optim.lr_scheduler.OneCycleLR(_A   ,							max_lr=0.01   ,							steps_per_epoch=2   ,							epochs=1    )
			lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_      =     accelerator.prepare(_A   ,							_A   ,							_A    )

			# Optimizer has stepped
			scheduler.step()
			if step_scheduler_with_optimizer or (num_processes == 1):
						assert (
						    scheduler.scheduler.last_epoch == num_processes
						), F"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
			else:
						assert (
						    scheduler.scheduler.last_epoch != num_processes
						), F"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""






def        lowerCamelCase__    (    __lowerCAmelCase  :  Tuple=2   ,							__lowerCAmelCase  :  Dict=True   ,							__lowerCAmelCase  :  Optional[Any]=False    ):




			"""simple docstring"""


			lowerCAmelCase_      =     Accelerator(step_scheduler_with_optimizer=_A   ,							split_batches=_A    )
			lowerCAmelCase_      =     torch.nn.Linear(2   ,							4    )
			lowerCAmelCase_      =     torch.optim.AdamW(model.parameters()   ,							lr=1.0    )
			lowerCAmelCase_      =     torch.optim.lr_scheduler.LambdaLR(_A   ,							lr_lambda=lambda __lowerCAmelCase    : 1 - n / 10    )
			lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_      =     accelerator.prepare(_A   ,							_A   ,							_A    )

			# Optimizer has stepped
			lowerCAmelCase_      =     False
			scheduler.step()
			lowerCAmelCase_      =     1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
			assert (
			    scheduler.get_last_lr()[0] == expected_lr
			), F"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

			# Optimizer has not stepped
			lowerCAmelCase_      =     True
			scheduler.step()
			if not step_scheduler_with_optimizer:
						lowerCAmelCase_      =     1 - 2 / 10
			assert (
			    scheduler.get_last_lr()[0] == expected_lr
			), F"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""






def        lowerCamelCase__    (    __lowerCAmelCase  :  Union[str, Any] = 2    ):




			"""simple docstring"""


			from transformers import get_linear_schedule_with_warmup

			lowerCAmelCase_      =     [1, 2, 4]
			for num_steps in steps:
						lowerCAmelCase_      =     GradientAccumulationPlugin(num_steps=_A   ,							adjust_scheduler=num_steps > 1    )
						lowerCAmelCase_      =     Accelerator(gradient_accumulation_plugin=_A    )
						lowerCAmelCase_      =     torch.nn.Linear(2   ,							4    )
						lowerCAmelCase_      =     torch.optim.AdamW(model.parameters()   ,							lr=10.0    )
						lowerCAmelCase_      =     get_linear_schedule_with_warmup(optimizer=_A   ,							num_warmup_steps=0   ,							num_training_steps=20    )

						lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_      =     accelerator.prepare(_A   ,							_A   ,							_A    )

						for i in range(10 * num_steps    ):
									with accelerator.accumulate(_A    ):
												optimizer.step()
												scheduler.step()

									if i == (10 * num_steps - 2):
												assert (
												    scheduler.get_last_lr()[0] != 0
												), F"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
						assert (
						    scheduler.get_last_lr()[0] == 0
						), F"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
						GradientState._reset_state()





@require_cpu
class      _lowerCAmelCase (     unittest.TestCase	):




				def 					__a						( self					)    ->				int:
							debug_launcher(partial(__lowercase    ,    num_processes=1					)    ,    num_processes=1					)
							debug_launcher(partial(__lowercase    ,    num_processes=1    ,    split_batches=__lowercase					)    ,    num_processes=1					)




				def 					__a						( self					)    ->				str:
							debug_launcher(partial(__lowercase    ,    num_processes=1					)    ,    num_processes=1					)
							debug_launcher(partial(__lowercase    ,    num_processes=1    ,    split_batches=__lowercase					)    ,    num_processes=1					)




				def 					__a						( self					)    ->				List[str]:
							debug_launcher(partial(__lowercase    ,    num_processes=1    ,    step_scheduler_with_optimizer=__lowercase					)    ,    num_processes=1					)




				def 					__a						( self					)    ->				List[Any]:
							debug_launcher(partial(__lowercase    ,    num_processes=1    ,    step_scheduler_with_optimizer=__lowercase					)    ,    num_processes=1					)




				def 					__a						( self					)    ->				List[str]:
							AcceleratorState._reset_state(__lowercase					)
							debug_launcher(__lowercase					)
							debug_launcher(partial(__lowercase    ,    num_processes=1    ,    split_batches=__lowercase					)    ,    num_processes=1					)




				def 					__a						( self					)    ->				str:
							AcceleratorState._reset_state(__lowercase					)
							debug_launcher(__lowercase					)
							debug_launcher(partial(__lowercase    ,    num_processes=1    ,    split_batches=__lowercase					)    ,    num_processes=1					)




				def 					__a						( self					)    ->				List[Any]:
							AcceleratorState._reset_state(__lowercase					)
							debug_launcher(partial(__lowercase    ,    step_scheduler_with_optimizer=__lowercase					)					)




				def 					__a						( self					)    ->				Tuple:
							AcceleratorState._reset_state(__lowercase					)
							debug_launcher(partial(__lowercase    ,    step_scheduler_with_optimizer=__lowercase					)					)




				@require_huggingface_suite
				def 					__a						( self					)    ->				Optional[int]:
							AcceleratorState._reset_state(__lowercase					)
							debug_launcher(partial(__lowercase    ,    num_processes=1					)					)
							debug_launcher(__lowercase					)





import math





class      _lowerCAmelCase :




				def __init__( self    ,    _UpperCamelCase					)    ->				Dict:
							lowerCAmelCase_      =     len(_UpperCamelCase					)
							lowerCAmelCase_      =     [0] * (
							    4 * self.N
							)  # approximate the overall size of segment tree with array N
							self.build(1    ,    0    ,    self.N - 1					)




				def 					__a						( self    ,    _UpperCamelCase					)    ->				List[str]:
							return idx * 2




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Tuple:
							return idx * 2 + 1




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				int:  # noqa: E741
							if l == r:
										lowerCAmelCase_      =     A[l]
							else:
										lowerCAmelCase_      =     (l + r) // 2
										self.build(self.left(_UpperCamelCase					)    ,    _UpperCamelCase    ,    _UpperCamelCase					)
										self.build(self.right(_UpperCamelCase					)    ,    mid + 1    ,    _UpperCamelCase					)
										lowerCAmelCase_      =     max(self.st[self.left(_UpperCamelCase					)]    ,    self.st[self.right(_UpperCamelCase					)]					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				int:
							return self.update_recursive(1    ,    0    ,    self.N - 1    ,    a - 1    ,    b - 1    ,    _UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				str:  # noqa: E741
							if r < a or l > b:
										return True
							if l == r:
										lowerCAmelCase_      =     val
										return True
							lowerCAmelCase_      =     (l + r) // 2
							self.update_recursive(self.left(_UpperCamelCase					)    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							self.update_recursive(self.right(_UpperCamelCase					)    ,    mid + 1    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     max(self.st[self.left(_UpperCamelCase					)]    ,    self.st[self.right(_UpperCamelCase					)]					)
							return True




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Optional[Any]:
							return self.query_recursive(1    ,    0    ,    self.N - 1    ,    a - 1    ,    b - 1					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Any:  # noqa: E741
							if r < a or l > b:
										return -math.inf
							if l >= a and r <= b:
										return self.st[idx]
							lowerCAmelCase_      =     (l + r) // 2
							lowerCAmelCase_      =     self.query_recursive(self.left(_UpperCamelCase					)    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     self.query_recursive(self.right(_UpperCamelCase					)    ,    mid + 1    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							return max(_UpperCamelCase    ,    _UpperCamelCase					)




				def 					__a						( self					)    ->				int:
							lowerCAmelCase_      =     []
							for i in range(1    ,    N + 1					):
										show_list += [self.query(_UpperCamelCase    ,    _UpperCamelCase					)]
							print(_UpperCamelCase					)


if __name__ == "__main__":
			_A       =					[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
			_A       =					15
			_A       =					SegmentTree(A)
			print(segt.query(4, 6))
			print(segt.query(7, 11))
			print(segt.query(7, 12))
			segt.update(1, 3, 1_11)
			print(segt.query(1, 15))
			segt.update(7, 8, 2_35)
			segt.show_data()



