


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 			_a      (    __UpperCamelCase=2	,	__UpperCamelCase=True	,	__UpperCamelCase=False			):
     a_     :  str				      =     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_     :  List[Any]				      =     torch.optim.lr_scheduler.OneCycleLR(SCREAMING_SNAKE_CASE_	,	max_lr=0.01	,	steps_per_epoch=2	,	epochs=1			)
     a_     :  Optional[int]				      =     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 			_a      (    __UpperCamelCase=2	,	__UpperCamelCase=True	,	__UpperCamelCase=False			):
     a_     :  List[Any]				      =     Accelerator(step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE_	,	split_batches=SCREAMING_SNAKE_CASE_			)
     a_     :  Optional[Any]				      =     torch.nn.Linear(2	,	4			)
     a_     :  Dict				      =     torch.optim.AdamW(model.parameters()	,	lr=1.0			)
     a_     :  Any				      =     torch.optim.lr_scheduler.LambdaLR(SCREAMING_SNAKE_CASE_	,	lr_lambda=lambda __UpperCamelCase			: 1 - n / 1_0			)
     a_     :  Optional[Any]				      =     accelerator.prepare(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_			)

     # Optimizer has stepped
     a_     :  str				      =     False
     scheduler.step()
     a_     :  Tuple				      =     1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
     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_     :  Union[str, Any]				      =     1 - 2 / 1_0
     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 			_a      (    __UpperCamelCase = 2			):
     from transformers import get_linear_schedule_with_warmup

     a_     :  Optional[int]				      =     [1, 2, 4]
     for num_steps in steps:
          a_     :  Union[str, Any]				      =     GradientAccumulationPlugin(num_steps=SCREAMING_SNAKE_CASE_	,	adjust_scheduler=num_steps > 1			)
          a_     :  Optional[Any]				      =     Accelerator(gradient_accumulation_plugin=SCREAMING_SNAKE_CASE_			)
          a_     :  Tuple				      =     torch.nn.Linear(2	,	4			)
          a_     :  Any				      =     torch.optim.AdamW(model.parameters()	,	lr=10.0			)
          a_     :  Optional[Any]				      =     get_linear_schedule_with_warmup(optimizer=SCREAMING_SNAKE_CASE_	,	num_warmup_steps=0	,	num_training_steps=2_0			)

          a_     :  Any				      =     accelerator.prepare(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_			)

          for i in range(1_0 * num_steps			):
               with accelerator.accumulate(SCREAMING_SNAKE_CASE_			):
                    optimizer.step()
                    scheduler.step()

               if i == (1_0 * 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 	a__						(    unittest.TestCase     ):

       def     UpperCAmelCase( self   :  List[Any]     ):
            debug_launcher(partial(__SCREAMING_SNAKE_CASE   ,				num_processes=1     )   ,				num_processes=1     )
            debug_launcher(partial(__SCREAMING_SNAKE_CASE   ,				num_processes=1   ,				split_batches=__SCREAMING_SNAKE_CASE     )   ,				num_processes=1     )

       def     UpperCAmelCase( self   :  Any     ):
            debug_launcher(partial(__SCREAMING_SNAKE_CASE   ,				num_processes=1     )   ,				num_processes=1     )
            debug_launcher(partial(__SCREAMING_SNAKE_CASE   ,				num_processes=1   ,				split_batches=__SCREAMING_SNAKE_CASE     )   ,				num_processes=1     )

       def     UpperCAmelCase( self   :  Any     ):
            debug_launcher(partial(__SCREAMING_SNAKE_CASE   ,				num_processes=1   ,				step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE     )   ,				num_processes=1     )

       def     UpperCAmelCase( self   :  int     ):
            debug_launcher(partial(__SCREAMING_SNAKE_CASE   ,				num_processes=1   ,				step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE     )   ,				num_processes=1     )

       def     UpperCAmelCase( self   :  Union[str, Any]     ):
            AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE     )
            debug_launcher(__SCREAMING_SNAKE_CASE     )
            debug_launcher(partial(__SCREAMING_SNAKE_CASE   ,				num_processes=1   ,				split_batches=__SCREAMING_SNAKE_CASE     )   ,				num_processes=1     )

       def     UpperCAmelCase( self   :  Optional[int]     ):
            AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE     )
            debug_launcher(__SCREAMING_SNAKE_CASE     )
            debug_launcher(partial(__SCREAMING_SNAKE_CASE   ,				num_processes=1   ,				split_batches=__SCREAMING_SNAKE_CASE     )   ,				num_processes=1     )

       def     UpperCAmelCase( self   :  Union[str, Any]     ):
            AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE     )
            debug_launcher(partial(__SCREAMING_SNAKE_CASE   ,				step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE     )     )

       def     UpperCAmelCase( self   :  Any     ):
            AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE     )
            debug_launcher(partial(__SCREAMING_SNAKE_CASE   ,				step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE     )     )

       @require_huggingface_suite
       def     UpperCAmelCase( self   :  Dict     ):
            AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE     )
            debug_launcher(partial(__SCREAMING_SNAKE_CASE   ,				num_processes=1     )     )
            debug_launcher(__SCREAMING_SNAKE_CASE     )







import sys


def   a						(				SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		list[int]							):






       """simple docstring"""





       if not numbers:
              return 0
       if target == 0 and target in numbers:
              return 0
       if not isinstance(SCREAMING_SNAKE_CASE_				,      (list, tuple)							) or not all(
           isinstance(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							) for number in numbers							):
              raise ValueError('''numbers must be an iterable of integers'''							)

       UpperCamelCase     :						Dict 						=		0
       UpperCamelCase     :						Tuple 						=		sys.maxsize

       while right < len(SCREAMING_SNAKE_CASE_							):
              curr_sum += numbers[right]
              while curr_sum >= target and left <= right:
                     UpperCamelCase     :						int 						=		min(SCREAMING_SNAKE_CASE_				,      right - left + 1							)
                     curr_sum -= numbers[left]
                     left += 1
              right += 1

       return 0 if min_len == sys.maxsize else min_len

