
_SCREAMING_SNAKE_CASE     :   List[str]							 =      {
    '+': lambda x, y: x + y,
    '-': lambda x, y: x - y,
    '*': lambda x, y: x * y,
    '/': lambda x, y: x / y,
}

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    List[Any]							)			->							int:

				"""simple docstring"""







				return c.isdigit()

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Dict							)			->							Union[str, Any]:

				"""simple docstring"""







				A__	:						Dict								=				[]

				# iterate over the string in reverse order
				for c in expression.split()[::-1]:
								# push operand to stack
								if is_operand(__UpperCamelCase							):
												stack.append(int(__UpperCamelCase							)							)

								else:
												# pop values from stack can calculate the result
												# push the result onto the stack again
												A__	:						Dict								=				stack.pop()
												A__	:						str								=				stack.pop()
												stack.append(calc[c](__UpperCamelCase				,    __UpperCamelCase							)							)

				return stack.pop()


# Driver code
if __name__ == "__main__":
					_SCREAMING_SNAKE_CASE     :   List[str]							 =      '+ 9 * 2 6'
					print(evaluate(test_expression))

					_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      '/ * 10 2 + 4 1 '
					print(evaluate(test_expression))

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 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Any=2				,    __UpperCamelCase			:    str=True				,    __UpperCamelCase			:    Optional[Any]=False							)			->							Union[str, Any]:

				"""simple docstring"""







				A__	:						List[str]								=				Accelerator(step_scheduler_with_optimizer=__UpperCamelCase				,    split_batches=__UpperCamelCase							)
				A__	:						str								=				torch.nn.Linear(2				,    4							)
				A__	:						Any								=				torch.optim.AdamW(model.parameters()				,    lr=1.0							)
				A__	:						Tuple								=				torch.optim.lr_scheduler.OneCycleLR(__UpperCamelCase				,    max_lr=0.0_1				,    steps_per_epoch=2				,    epochs=1							)
				A__							,				A__							,				A__	:						str								=				accelerator.prepare(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)

				# 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 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Optional[Any]=2				,    __UpperCamelCase			:    Tuple=True				,    __UpperCamelCase			:    List[Any]=False							)			->							str:

				"""simple docstring"""







				A__	:						Union[str, Any]								=				Accelerator(step_scheduler_with_optimizer=__UpperCamelCase				,    split_batches=__UpperCamelCase							)
				A__	:						List[str]								=				torch.nn.Linear(2				,    4							)
				A__	:						int								=				torch.optim.AdamW(model.parameters()				,    lr=1.0							)
				A__	:						List[Any]								=				torch.optim.lr_scheduler.LambdaLR(__UpperCamelCase				,    lr_lambda=lambda __UpperCamelCase							: 1 - n / 10							)
				A__							,				A__							,				A__	:						int								=				accelerator.prepare(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)

				# Optimizer has stepped
				A__	:						List[Any]								=				False
				scheduler.step()
				A__	:						str								=				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
				A__	:						str								=				True
				scheduler.step()
				if not step_scheduler_with_optimizer:
								A__	:						Dict								=				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 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    int = 2							)			->							List[Any]:

				"""simple docstring"""







				from transformers import get_linear_schedule_with_warmup

				A__	:						str								=				[1, 2, 4]
				for num_steps in steps:
								A__	:						Any								=				GradientAccumulationPlugin(num_steps=__UpperCamelCase				,    adjust_scheduler=num_steps > 1							)
								A__	:						Tuple								=				Accelerator(gradient_accumulation_plugin=__UpperCamelCase							)
								A__	:						Optional[int]								=				torch.nn.Linear(2				,    4							)
								A__	:						Optional[int]								=				torch.optim.AdamW(model.parameters()				,    lr=1_0.0							)
								A__	:						Optional[int]								=				get_linear_schedule_with_warmup(optimizer=__UpperCamelCase				,    num_warmup_steps=0				,    num_training_steps=20							)

								A__							,				A__							,				A__	:						List[Any]								=				accelerator.prepare(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)

								for i in range(10 * num_steps							):
												with accelerator.accumulate(__UpperCamelCase							):
																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 			UpperCamelCase__		(     unittest.TestCase ):







	'''simple docstring'''





	def 	__snake_case					(      self		):
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1		)					,							num_processes=1		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							split_batches=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1		)					,							num_processes=1		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							split_batches=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							step_scheduler_with_optimizer=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							step_scheduler_with_optimizer=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							split_batches=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							split_batches=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							step_scheduler_with_optimizer=UpperCamelCase__		)		)





	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							step_scheduler_with_optimizer=UpperCamelCase__		)		)





	@require_huggingface_suite
	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1		)		)
					debug_launcher(UpperCamelCase__		)