"""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 			A__						(  lowercase_    :  Any=2			,   lowercase_    :  Optional[int]=True			,   lowercase_    :  int=False	)     ->       Any:
   __lowerCamelCase								=				Accelerator(step_scheduler_with_optimizer=lowercase_			,   split_batches=lowercase_	)
   __lowerCamelCase								=				torch.nn.Linear(2			,   4	)
   __lowerCamelCase								=				torch.optim.AdamW(model.parameters()			,   lr=1.0	)
   __lowerCamelCase								=				torch.optim.lr_scheduler.OneCycleLR(lowercase_			,   max_lr=0.0_1			,   steps_per_epoch=2			,   epochs=1	)
   __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase								=				accelerator.prepare(lowercase_			,   lowercase_			,   lowercase_	)

   # 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__						(  lowercase_    :  List[Any]=2			,   lowercase_    :  List[Any]=True			,   lowercase_    :  Dict=False	)     ->       int:
   __lowerCamelCase								=				Accelerator(step_scheduler_with_optimizer=lowercase_			,   split_batches=lowercase_	)
   __lowerCamelCase								=				torch.nn.Linear(2			,   4	)
   __lowerCamelCase								=				torch.optim.AdamW(model.parameters()			,   lr=1.0	)
   __lowerCamelCase								=				torch.optim.lr_scheduler.LambdaLR(lowercase_			,   lr_lambda=lambda lowercase_	: 1 - n / 10	)
   __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase								=				accelerator.prepare(lowercase_			,   lowercase_			,   lowercase_	)

   # 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 			A__						(  lowercase_    :  int = 2	)     ->       List[str]:
   from transformers import get_linear_schedule_with_warmup

   __lowerCamelCase								=				[1, 2, 4]
   for num_steps in steps:
      __lowerCamelCase								=				GradientAccumulationPlugin(num_steps=lowercase_			,   adjust_scheduler=num_steps > 1	)
      __lowerCamelCase								=				Accelerator(gradient_accumulation_plugin=lowercase_	)
      __lowerCamelCase								=				torch.nn.Linear(2			,   4	)
      __lowerCamelCase								=				torch.optim.AdamW(model.parameters()			,   lr=1_0.0	)
      __lowerCamelCase								=				get_linear_schedule_with_warmup(optimizer=lowercase_			,   num_warmup_steps=0			,   num_training_steps=20	)

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase								=				accelerator.prepare(lowercase_			,   lowercase_			,   lowercase_	)

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



   '''simple docstring'''



   def       _A							(  self  ):


      '''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       _A							(  self  ):


      '''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       _A							(  self  ):


      '''simple docstring'''

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







   def       _A							(  self  ):


      '''simple docstring'''

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







   def       _A							(  self  ):


      '''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       _A							(  self  ):


      '''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       _A							(  self  ):


      '''simple docstring'''

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







   def       _A							(  self  ):


      '''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       _A							(  self  ):


      '''simple docstring'''

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







"""simple docstring"""







_SCREAMING_SNAKE_CASE  :		Optional[Any]        =      {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

_SCREAMING_SNAKE_CASE  :		List[str]        =      [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_SCREAMING_SNAKE_CASE  :		Optional[Any]        =      [0 for x in range(len(graph))]
_SCREAMING_SNAKE_CASE  :		List[Any]        =      []



def 			A__						(  lowercase_    :  Optional[int]			,   lowercase_    :  List[str]	)     ->       List[str]:
   __lowerCamelCase								=				1
   while stack:
      __lowerCamelCase								=				stack.pop()
      print(lowercase_			,   clothes[current_clothing]	)
      order += 1



def 			A__						(  lowercase_    :  List[Any]			,   lowercase_    :  List[str]			,   lowercase_    :  Union[str, Any]	)     ->       List[Any]:
   __lowerCamelCase								=				1
   for v in graph[u]:
      if not visited[v]:
         depth_first_search(lowercase_			,   lowercase_			,   lowercase_	)

   stack.append(lowercase_	)



def 			A__						(  lowercase_    :  Dict			,   lowercase_    :  Optional[Any]	)     ->       Union[str, Any]:
   for v in range(len(lowercase_	)	):
      if not visited[v]:
         depth_first_search(lowercase_			,   lowercase_			,   lowercase_	)


if __name__ == "__main__":
     topological_sort(graph, visited)
     print(stack)
     print_stack(stack, clothes)






