

from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap
def 	a (     A__      :  Any	)       -> Any:





      """simple docstring"""



      return getitem, k
def 	a (     A__      :  Union[str, Any] ,  A__      :  Tuple	)       -> Tuple:





      """simple docstring"""



      return setitem, k, v
def 	a (     A__      :  Tuple	)       -> List[Any]:





      """simple docstring"""



      return delitem, k
def 	a (     A__      :  Optional[Any] ,  A__      :  Any ,  *A__      :  Tuple	)       -> List[Any]:





      """simple docstring"""



      try:
            return fun(A__ ,  *A__	), None
      except Exception as e:
            return None, e


lowercase_   							=  (
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
)

lowercase_   							=  [
    _set('key_a', 'val_a'),
    _set('key_a', 'val_b'),
]

lowercase_   							=  [
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
    _del('key_a'),
    _del('key_b'),
    _set('key_a', 'val_a'),
    _del('key_a'),
]

lowercase_   							=  [
    _get('key_a'),
    _del('key_a'),
    _set('key_a', 'val_a'),
    _del('key_a'),
    _del('key_a'),
    _get('key_a'),
]

lowercase_   							=  [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

lowercase_   							=  [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set('key_a', 'val_b'),
]
@pytest.mark.parametrize(
    'operations' ,  (
        pytest.param(_add_items ,  id='add items'	),
        pytest.param(_overwrite_items ,  id='overwrite items'	),
        pytest.param(_delete_items ,  id='delete items'	),
        pytest.param(_access_absent_items ,  id='access absent items'	),
        pytest.param(_add_with_resize_up ,  id='add with resize up'	),
        pytest.param(_add_with_resize_down ,  id='add with resize down'	),
    ) ,  )
def 	a (     A__      :  Dict	)       -> str:





      """simple docstring"""



      _lowercase      			=HashMap(initial_block_size=4	)
      _lowercase      			={}
      for _, (fun, *args) in enumerate(A__	):
            _lowercase			,			_lowercase      			=_run_operation(A__ ,  A__ ,  *A__	)
            _lowercase			,			_lowercase      			=_run_operation(A__ ,  A__ ,  *A__	)
            assert my_res == py_res
            assert str(A__	) == str(A__	)
            assert set(A__	) == set(A__	)
            assert len(A__	) == len(A__	)
            assert set(my.items()	) == set(py.items()	)







def 	a (     )       -> Any:





      """simple docstring"""



      def is_public(A__      :  str	) -> bool:
            return not name.startswith('_'	)

      _lowercase      			={name for name in dir({}	) if is_public(A__	)}
      _lowercase      			={name for name in dir(HashMap()	) if is_public(A__	)}

      assert dict_public_names > hash_public_names





# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')





class 					__lowerCAmelCase	:




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = True				,  lowerCAmelCase = False   )    ->					str:
        '''simple docstring'''






        _lowercase      			=scheduler
        _lowercase      			=optimizers if isinstance(lowerCAmelCase				,  (list, tuple)   ) else [optimizers]
        _lowercase      			=split_batches
        _lowercase      			=step_with_optimizer
        _lowercase      			=GradientState()




  def     A__	(							self				,  *lowerCAmelCase				,  **lowerCAmelCase   )    ->					List[Any]:
        '''simple docstring'''






        if not self.step_with_optimizer:
              # No link between scheduler and optimizer -> just step
              self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )
              return

        # Otherwise, first make sure the optimizer was stepped.
        if not self.gradient_state.sync_gradients:
              if self.gradient_state.adjust_scheduler:
                    self.scheduler._step_count += 1
              return

        for opt in self.optimizers:
              if opt.step_was_skipped:
                    return
        if self.split_batches:
              # Split batches -> the training dataloader batch size is not changed so one step per training step
              self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )
        else:
              # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
              # num_processes steps per training step
              _lowercase      			=AcceleratorState().num_processes
              for _ in range(lowerCAmelCase   ):
                    # Special case when using OneCycle and `drop_last` was not used
                    if hasattr(self.scheduler				,  'total_steps'   ):
                          if self.scheduler._step_count <= self.scheduler.total_steps:
                                self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )
                    else:
                          self.scheduler.step(*lowerCAmelCase				,  **lowerCAmelCase   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        return self.scheduler.get_last_lr()




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        return self.scheduler.state_dict()




  def     A__	(							self				,  lowerCAmelCase   )    ->					str:
        '''simple docstring'''






        self.scheduler.load_state_dict(lowerCAmelCase   )




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        return self.scheduler.get_lr()




  def     A__	(							self				,  *lowerCAmelCase				,  **lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        return self.scheduler.print_lr(*lowerCAmelCase				,  **lowerCAmelCase   )


