


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  (		A_  :	Any=2							,  A_  :	Dict=True							,  A_  :	Tuple=False      )			->			int:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=Accelerator(step_scheduler_with_optimizer=A_							,  split_batches=A_      )
   UpperCamelCase__ :			Optional[Any] 						=torch.nn.Linear(2							,  4      )
   UpperCamelCase__ :			Tuple 						=torch.optim.AdamW(model.parameters()							,  lr=1.0      )
   UpperCamelCase__ :			Tuple 						=torch.optim.lr_scheduler.OneCycleLR(A_							,  max_lr=0.01							,  steps_per_epoch=2							,  epochs=1      )
   UpperCamelCase__ :			Optional[int] 						=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  (		A_  :	List[Any]=2							,  A_  :	Tuple=True							,  A_  :	Optional[Any]=False      )			->			Union[str, Any]:

   '''simple docstring'''






   UpperCamelCase__ :			Dict 						=Accelerator(step_scheduler_with_optimizer=A_							,  split_batches=A_      )
   UpperCamelCase__ :			Optional[Any] 						=torch.nn.Linear(2							,  4      )
   UpperCamelCase__ :			Tuple 						=torch.optim.AdamW(model.parameters()							,  lr=1.0      )
   UpperCamelCase__ :			List[str] 						=torch.optim.lr_scheduler.LambdaLR(A_							,  lr_lambda=lambda A_      : 1 - n / 1_0      )
   UpperCamelCase__ :			List[Any] 						=accelerator.prepare(A_							,  A_							,  A_      )

   # Optimizer has stepped
   UpperCamelCase__ :			Union[str, Any] 						=False
   scheduler.step()
   UpperCamelCase__ :			Optional[int] 						=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
   UpperCamelCase__ :			Dict 						=True
   scheduler.step()
   if not step_scheduler_with_optimizer:
      UpperCamelCase__ :			Optional[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 	_lowerCamelCase  (		A_  :	Tuple = 2      )			->			Dict:

   '''simple docstring'''






   from transformers import get_linear_schedule_with_warmup

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

      UpperCamelCase__ :			str 						=accelerator.prepare(A_							,  A_							,  A_      )

      for i in range(1_0 * num_steps      ):
         with accelerator.accumulate(A_      ):
            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 							lowercase__(						unittest.TestCase			):






      '''simple docstring'''







      def    UpperCAmelCase							(		self)					->						List[Any]:







         """simple docstring"""







         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)					->						List[Any]:







         """simple docstring"""







         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)					->						str:







         """simple docstring"""







         debug_launcher(partial(__SCREAMING_SNAKE_CASE   ,			num_processes=1   ,			step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE)   ,			num_processes=1)




      def    UpperCAmelCase							(		self)					->						str:







         """simple docstring"""







         debug_launcher(partial(__SCREAMING_SNAKE_CASE   ,			num_processes=1   ,			step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE)   ,			num_processes=1)




      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         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)					->						Dict:







         """simple docstring"""







         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)					->						List[Any]:







         """simple docstring"""







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




      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         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:







         """simple docstring"""







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





'''simple docstring'''
from __future__ import annotations







class 					_A						:



   '''simple docstring'''






   def __init__(	self	:					Any					, lowerCamelCase	:					int						)->	None:
          snake_case__					:	Optional[int]          =					value
          snake_case__					:	Node | None          =					None
          snake_case__					:	Node | None          =					None







class 					_A						:



   '''simple docstring'''






   _lowercase    						= 42




   def __init__(	self	:					str						)->	None:
          snake_case__					:	int          =					0




   def   __lowerCAmelCase						(	self	:					Tuple					, lowerCamelCase	:					Node | None					, lowerCamelCase	:					int						)->	None:
          if node is None:
                 return

          if path_sum == self.target:
                 self.paths += 1

          if node.left:
                 self.depth_first_search(node.left					, path_sum + node.left.value						)
          if node.right:
                 self.depth_first_search(node.right					, path_sum + node.right.value						)




   def   __lowerCAmelCase						(	self	:					Tuple					, lowerCamelCase	:					Node | None					, lowerCamelCase	:					int | None = None						)->	int:
          if node is None:
                 return 0
          if target is not None:
                 snake_case__					:	Dict          =					target

          self.depth_first_search(lowerCamelCase					, node.value						)
          self.path_sum(node.left						)
          self.path_sum(node.right						)

          return self.paths


if __name__ == "__main__":
     import doctest

     doctest.testmod()




