




'''simple docstring'''





import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_	):
				if isinstance(UpperCAmelCase_      ,  (list, tuple)	):
								return honor_type(UpperCAmelCase_      ,  (move_to_device(UpperCAmelCase_      ,  UpperCAmelCase_	) for t in state)	)
				elif isinstance(UpperCAmelCase_      ,  UpperCAmelCase_	):
								return type(UpperCAmelCase_	)({k: move_to_device(UpperCAmelCase_      ,  UpperCAmelCase_	) for k, v in state.items()}	)
				elif isinstance(UpperCAmelCase_      ,  torch.Tensor	):
								return state.to(UpperCAmelCase_	)
				return state





class     UpperCamelCase					(  torch.optim.Optimizer					):



		def __init__( self		,    __lowerCAmelCase		,    __lowerCAmelCase=True		,    __lowerCAmelCase=None   ):







						'''simple docstring'''


						_a							:       Any       					=			optimizer
						_a							:       Optional[Any]       					=			scaler
						_a							:       Dict       					=			AcceleratorState()
						_a							:       Tuple       					=			GradientState()
						_a							:       Dict       					=			device_placement
						_a							:       Dict       					=			False
						_a							:       Tuple       					=			None

						# Handle device placement
						if device_placement:
										_a							:       List[str]       					=			self.optimizer.state_dict()
										if self.accelerator_state.distributed_type == DistributedType.TPU:
														xm.send_cpu_data_to_device(__lowerCAmelCase		,    self.accelerator_state.device   )
										else:
														_a							:       int       					=			move_to_device(__lowerCAmelCase		,    self.accelerator_state.device   )
										self.optimizer.load_state_dict(__lowerCAmelCase   )



		@property
		def 						a_       ( self   ):







						'''simple docstring'''


						return self.optimizer.state



		@state.setter
		def 						a_       ( self		,    __lowerCAmelCase   ):







						'''simple docstring'''


						_a							:       List[str]       					=			state



		@property
		def 						a_       ( self   ):







						'''simple docstring'''


						return self.optimizer.param_groups



		@param_groups.setter
		def 						a_       ( self		,    __lowerCAmelCase   ):







						'''simple docstring'''


						_a							:       Any       					=			param_groups



		@property
		def 						a_       ( self   ):







						'''simple docstring'''


						return self.optimizer.defaults



		@defaults.setter
		def 						a_       ( self		,    __lowerCAmelCase   ):







						'''simple docstring'''


						_a							:       List[Any]       					=			defaults



		def 						a_       ( self		,    __lowerCAmelCase   ):







						'''simple docstring'''


						self.optimizer.add_param_group(__lowerCAmelCase   )



		def 						a_       ( self		,    __lowerCAmelCase   ):







						'''simple docstring'''


						if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
										xm.send_cpu_data_to_device(__lowerCAmelCase		,    self.accelerator_state.device   )
						self.optimizer.load_state_dict(__lowerCAmelCase   )



		def 						a_       ( self   ):







						'''simple docstring'''


						return self.optimizer.state_dict()



		def 						a_       ( self		,    __lowerCAmelCase=None   ):







						'''simple docstring'''


						if self.gradient_state.sync_gradients:
										_a							:       Dict       					=			"""set_to_none""" in inspect.signature(self.optimizer.zero_grad   ).parameters
										if accept_arg:
														if set_to_none is None:
																		_a							:       int       					=			False
														self.optimizer.zero_grad(set_to_none=__lowerCAmelCase   )
										else:
														if set_to_none is not None:
																		raise ValueError("""`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."""   )
														self.optimizer.zero_grad()



		def 						a_       ( self		,    __lowerCAmelCase=None   ):







						'''simple docstring'''


						if self.gradient_state.sync_gradients:
										if self.accelerator_state.distributed_type == DistributedType.TPU:
														_a							:       Any       					=			{"""closure""": closure} if closure is not None else {}
														xm.optimizer_step(self.optimizer		,    optimizer_args=__lowerCAmelCase   )
										elif self.scaler is not None:
														_a							:       Any       					=			False
														if self._last_scale is None:
																		# `get_scale` is an async operation requiring full synchronization
																		# on CPU and GPUs before finishing. As a result, we store away
																		# the prior one to reduce the call overhead
																		_a							:       Union[str, Any]       					=			self.scaler.get_scale()
																		_a							:       Optional[int]       					=			True
														self.scaler.step(self.optimizer		,    __lowerCAmelCase   )
														self.scaler.update()
														_a							:       Optional[int]       					=			self.scaler.get_scale()
														if not new_scale:
																		# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																		_a							:       List[str]       					=			scale_after < self._last_scale
														_a							:       Union[str, Any]       					=			scale_after

										else:
														self.optimizer.step(__lowerCAmelCase   )



		def 						a_       ( self		,    __lowerCAmelCase   ):







						'''simple docstring'''


						for param_group in self.optimizer.param_groups:
										_a							:       Optional[int]       					=			[parameters_map.get(__lowerCAmelCase		,    __lowerCAmelCase   ) for p in param_group["""params"""]]



		@property
		def 						a_       ( self   ):







						'''simple docstring'''


						warnings.warn(
						    """The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use """
						    """`optimizer.step_was_skipped` instead."""		,    __lowerCAmelCase		,    )
						return self._is_overflow



		@property
		def 						a_       ( self   ):







						'''simple docstring'''


						return self._is_overflow



		def __getstate__( self   ):







						'''simple docstring'''


						return self.__dict__.copy()



		def __setstate__( self		,    __lowerCAmelCase   ):







						'''simple docstring'''


						self.__dict__.update(__lowerCAmelCase   )








'''simple docstring'''





def UpperCAmelCase						(					UpperCAmelCase_	):
    _a							:       List[Any]       					=			len(UpperCAmelCase_	)
    for cycle_start in range(0      ,  array_len - 1	):
        _a							:       int       					=			array[cycle_start]

        _a							:       int       					=			cycle_start
        for i in range(cycle_start + 1      ,  UpperCAmelCase_	):
            if array[i] < item:
                pos += 1

        if pos == cycle_start:
            continue

        while item == array[pos]:
            pos += 1

        _a					,   _a							:       List[str]       					=			item, array[pos]
        while pos != cycle_start:
            _a							:       Any       					=			cycle_start
            for i in range(cycle_start + 1      ,  UpperCAmelCase_	):
                if array[i] < item:
                    pos += 1

            while item == array[pos]:
                pos += 1

            _a					,   _a							:       List[Any]       					=			item, array[pos]

    return array


if __name__ == "__main__":
  assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
  assert cycle_sort([0, 1, -1_0, 1_5, 2, -2]) == [-1_0, -2, 0, 1, 2, 1_5]


