"""simple docstring"""







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 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				List[str]=2  ,       _SCREAMING_SNAKE_CASE				:				str=True  ,       _SCREAMING_SNAKE_CASE				:				Dict=False						)    ->     str:







     '''simple docstring'''


     __A					:					Tuple								=		Accelerator(step_scheduler_with_optimizer=_SCREAMING_SNAKE_CASE  ,       split_batches=_SCREAMING_SNAKE_CASE						)
     __A					:					Union[str, Any]								=		torch.nn.Linear(2  ,       4						)
     __A					:					Any								=		torch.optim.AdamW(model.parameters()  ,       lr=1.0						)
     __A					:					Any								=		torch.optim.lr_scheduler.OneCycleLR(_SCREAMING_SNAKE_CASE  ,       max_lr=0.01  ,       steps_per_epoch=2  ,       epochs=1						)
     __A						,		__A						,		__A					:					Any								=		accelerator.prepare(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						)

     # 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 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Any=2  ,       _SCREAMING_SNAKE_CASE				:				List[str]=True  ,       _SCREAMING_SNAKE_CASE				:				Optional[int]=False						)    ->     Optional[int]:







     '''simple docstring'''


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

     # Optimizer has stepped
     __A					:					Tuple								=		False
     scheduler.step()
     __A					:					Dict								=		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					:					List[str]								=		True
     scheduler.step()
     if not step_scheduler_with_optimizer:
          __A					:					Tuple								=		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 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				int = 2						)    ->     List[str]:







     '''simple docstring'''


     from transformers import get_linear_schedule_with_warmup

     __A					:					Union[str, Any]								=		[1, 2, 4]
     for num_steps in steps:
          __A					:					int								=		GradientAccumulationPlugin(num_steps=_SCREAMING_SNAKE_CASE  ,       adjust_scheduler=num_steps > 1						)
          __A					:					Tuple								=		Accelerator(gradient_accumulation_plugin=_SCREAMING_SNAKE_CASE						)
          __A					:					List[str]								=		torch.nn.Linear(2  ,       4						)
          __A					:					List[str]								=		torch.optim.AdamW(model.parameters()  ,       lr=10.0						)
          __A					:					Any								=		get_linear_schedule_with_warmup(optimizer=_SCREAMING_SNAKE_CASE  ,       num_warmup_steps=0  ,       num_training_steps=20						)

          __A						,		__A						,		__A					:					Tuple								=		accelerator.prepare(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						)

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


    '''simple docstring'''





    def 						_a							(  self	):




         '''simple docstring'''



         debug_launcher(partial(__lowerCamelCase					,       num_processes=1	)					,       num_processes=1	)
         debug_launcher(partial(__lowerCamelCase					,       num_processes=1					,       split_batches=__lowerCamelCase	)					,       num_processes=1	)
    def 						_a							(  self	):




         '''simple docstring'''



         debug_launcher(partial(__lowerCamelCase					,       num_processes=1	)					,       num_processes=1	)
         debug_launcher(partial(__lowerCamelCase					,       num_processes=1					,       split_batches=__lowerCamelCase	)					,       num_processes=1	)
    def 						_a							(  self	):




         '''simple docstring'''



         debug_launcher(partial(__lowerCamelCase					,       num_processes=1					,       step_scheduler_with_optimizer=__lowerCamelCase	)					,       num_processes=1	)
    def 						_a							(  self	):




         '''simple docstring'''



         debug_launcher(partial(__lowerCamelCase					,       num_processes=1					,       step_scheduler_with_optimizer=__lowerCamelCase	)					,       num_processes=1	)
    def 						_a							(  self	):




         '''simple docstring'''



         AcceleratorState._reset_state(__lowerCamelCase	)
         debug_launcher(__lowerCamelCase	)
         debug_launcher(partial(__lowerCamelCase					,       num_processes=1					,       split_batches=__lowerCamelCase	)					,       num_processes=1	)
    def 						_a							(  self	):




         '''simple docstring'''



         AcceleratorState._reset_state(__lowerCamelCase	)
         debug_launcher(__lowerCamelCase	)
         debug_launcher(partial(__lowerCamelCase					,       num_processes=1					,       split_batches=__lowerCamelCase	)					,       num_processes=1	)
    def 						_a							(  self	):




         '''simple docstring'''



         AcceleratorState._reset_state(__lowerCamelCase	)
         debug_launcher(partial(__lowerCamelCase					,       step_scheduler_with_optimizer=__lowerCamelCase	)	)
    def 						_a							(  self	):




         '''simple docstring'''



         AcceleratorState._reset_state(__lowerCamelCase	)
         debug_launcher(partial(__lowerCamelCase					,       step_scheduler_with_optimizer=__lowerCamelCase	)	)

    @require_huggingface_suite
    def 						_a							(  self	):




         '''simple docstring'''



         AcceleratorState._reset_state(__lowerCamelCase	)
         debug_launcher(partial(__lowerCamelCase					,       num_processes=1	)	)
         debug_launcher(__lowerCamelCase	)




"""simple docstring"""







from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

lowerCamelCase      : List[str]       =TypeVar('''T''', bound=bool)






class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					T								=		value
         __A					:					RandomizedHeapNode[T] | None								=		None
         __A					:					RandomizedHeapNode[T] | None								=		None
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return self._value

    @staticmethod
    def 						_a							(  __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         if not roota:
              return roota

         if not roota:
              return roota

         if roota.value > roota.value:
              __A						,		__A					:					List[str]								=		roota, roota

         if random.choice([True, False]	):
              __A						,		__A					:					Dict								=		roota.right, roota.left

         __A					:					Dict								=		RandomizedHeapNode.merge(roota.left					,       __lowerCamelCase	)

         return roota







class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase = ()	):




         '''simple docstring'''



         __A					:					RandomizedHeapNode[T] | None								=		None

         if data:
              for item in data:
                   self.insert(__lowerCamelCase	)
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					List[str]								=		RandomizedHeapNode.merge(self._root					,       RandomizedHeapNode(__lowerCamelCase	)	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					int								=		self.top()

         if self._root is None:
              return None

         __A					:					Dict								=		RandomizedHeapNode.merge(self._root.left					,       self._root.right	)

         return result
    def 						_a							(  self	):




         '''simple docstring'''



         if not self._root:
              raise IndexError('Can\'t get top element for the empty heap.'	)
         return self._root.value
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		None
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Tuple								=		[]
         while self:
              result.append(self.pop()	)

         return result

    def __bool__(  self	):




         '''simple docstring'''



         return self._root is not None


if __name__ == "__main__":
       import doctest

       doctest.testmod()



