
# 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				:   str    ,   _A				:   Optional[Any]    ,   _A				:   List[str]    ,   _A				:   bool = True    ,   _A				:   bool = False       )						->						str:
         snake_case								= scheduler
         snake_case								= optimizers if isinstance(_A    ,   (list, tuple)       ) else [optimizers]
         snake_case								= split_batches
         snake_case								= step_with_optimizer
         snake_case								= GradientState()





     def 		UpperCAmelCase(self				:   List[Any]    ,   *_A				:   int    ,   **_A				:   Union[str, Any]       )						->						int:
         if not self.step_with_optimizer:
             # No link between scheduler and optimizer -> just step
             self.scheduler.step(*_A    ,   **_A       )
             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(*_A    ,   **_A       )
         else:
             # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
             # num_processes steps per training step
             snake_case								= AcceleratorState().num_processes
             for _ in range(_A       ):
                 # 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(*_A    ,   **_A       )
                 else:
                     self.scheduler.step(*_A    ,   **_A       )





     def 		UpperCAmelCase(self				:   List[str]       )						->						str:
         return self.scheduler.get_last_lr()





     def 		UpperCAmelCase(self				:   List[str]       )						->						Union[str, Any]:
         return self.scheduler.state_dict()





     def 		UpperCAmelCase(self				:   str    ,   _A				:   Optional[Any]       )						->						Any:
         self.scheduler.load_state_dict(_A       )





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Tuple:
         return self.scheduler.get_lr()





     def 		UpperCAmelCase(self				:   Tuple    ,   *_A				:   int    ,   **_A				:   Union[str, Any]       )						->						str:
         return self.scheduler.print_lr(*_A    ,   **_A       )



from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap



def 						lowercase_      (       A__		)							->							str:
    """simple docstring"""

    return getitem, k



def 						lowercase_      (       A__			,		A__		)							->							str:
    """simple docstring"""

    return setitem, k, v



def 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

    return delitem, k



def 						lowercase_      (       A__			,		A__			,		*A__		)							->							str:
    """simple docstring"""

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


_A   =			(
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
)

_A   =			[
    _set("key_a", "val_a"),
    _set("key_a", "val_b"),
]

_A   =			[
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
    _del("key_a"),
    _del("key_b"),
    _set("key_a", "val_a"),
    _del("key_a"),
]

_A   =			[
    _get("key_a"),
    _del("key_a"),
    _set("key_a", "val_a"),
    _del("key_a"),
    _del("key_a"),
    _get("key_a"),
]

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

_A   =			[
    *[_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 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

    snake_case								= HashMap(initial_block_size=4		)
    snake_case								= {}
    for _, (fun, *args) in enumerate(A__		):
        snake_case					,							snake_case								= _run_operation(A__			,		A__			,		*A__		)
        snake_case					,							snake_case								= _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 						lowercase_      (       )							->							Optional[int]:
    """simple docstring"""

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

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

    assert dict_public_names > hash_public_names

