





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    (						snake_case							,			snake_case      )    ->		Dict:


   '''simple docstring'''







   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 		_lowerCAmelCase(		torch.optim.Optimizer):

  """simple docstring"""







  def __init__(   self    ,							UpperCAmelCase    ,							UpperCAmelCase=True    ,							UpperCAmelCase=None      )->   Union[str, Any]:
     __A    =  optimizer
     __A    =  scaler
     __A    =  AcceleratorState()
     __A    =  GradientState()
     __A    =  device_placement
     __A    =  False
     __A    =  None

     # Handle device placement
     if device_placement:
        __A    =  self.optimizer.state_dict()
        if self.accelerator_state.distributed_type == DistributedType.TPU:
           xm.send_cpu_data_to_device(A_    ,							self.accelerator_state.device      )
        else:
           __A    =  move_to_device(A_    ,							self.accelerator_state.device      )
        self.optimizer.load_state_dict(A_      )

  @property
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Optional[Any]:
     return self.optimizer.state

  @state.setter
  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   Tuple:
     __A    =  state

  @property
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Optional[int]:
     return self.optimizer.param_groups

  @param_groups.setter
  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   Dict:
     __A    =  param_groups

  @property
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Dict:
     return self.optimizer.defaults

  @defaults.setter
  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   int:
     __A    =  defaults

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   Optional[Any]:
     self.optimizer.add_param_group(A_      )

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   Optional[Any]:
     if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
        xm.send_cpu_data_to_device(A_    ,							self.accelerator_state.device      )
     self.optimizer.load_state_dict(A_      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   str:
     return self.optimizer.state_dict()

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase=None      )->   Tuple:
     if self.gradient_state.sync_gradients:
        __A    =  '''set_to_none''' in inspect.signature(self.optimizer.zero_grad      ).parameters
        if accept_arg:
           if set_to_none is None:
              __A    =  False
           self.optimizer.zero_grad(set_to_none=A_      )
        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 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase=None      )->   Optional[Any]:
     if self.gradient_state.sync_gradients:
        if self.accelerator_state.distributed_type == DistributedType.TPU:
           __A    =  {'''closure''': closure} if closure is not None else {}
           xm.optimizer_step(self.optimizer    ,							optimizer_args=A_      )
        elif self.scaler is not None:
           __A    =  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    =  self.scaler.get_scale()
              __A    =  True
           self.scaler.step(self.optimizer    ,							A_      )
           self.scaler.update()
           __A    =  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    =  scale_after < self._last_scale
           __A    =  scale_after

        else:
           self.optimizer.step(A_      )

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   List[Any]:
     for param_group in self.optimizer.param_groups:
        __A    =  [parameters_map.get(A_    ,							A_      ) for p in param_group['''params''']]

  @property
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   int:
     warnings.warn(
         '''The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '''
         '''`optimizer.step_was_skipped` instead.'''    ,							A_    ,							)
     return self._is_overflow

  @property
  def 						SCREAMING_SNAKE_CASE__			(   self      )->   int:
     return self._is_overflow

  def __getstate__(   self      )->   List[Any]:
     return self.__dict__.copy()





  def __setstate__(   self    ,							UpperCAmelCase      )->   Tuple:
     self.__dict__.update(A_      )


from hashlib import mda, shaaaa

snake_case__						      =			(shaaaa, mda)



class 			UpperCamelCase					:

     '''simple docstring'''

     def __init__(       self					,					A_ = 8    )			->					None:






           """simple docstring"""
           _lowerCamelCase           =					0b0
           _lowerCamelCase           =					size



     def 		UpperCamelCase_    (       self					,					A_    )			->					None:






           """simple docstring"""
           _lowerCamelCase           =					self.hash_(A_    )
           self.bitarray |= h



     def 		UpperCamelCase_    (       self					,					A_    )			->					bool:






           """simple docstring"""
           _lowerCamelCase           =					self.hash_(A_    )
           return (h & self.bitarray) == h



     def __contains__(       self					,					A_    )			->					bool:






           """simple docstring"""
           return self.exists(A_    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					str:






           """simple docstring"""
           _lowerCamelCase           =					bin(A_    )[2:]
           return res.zfill(self.size    )



     @property
     def 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           return self.format_bin(self.bitarray    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					0b0
           for func in HASH_FUNCTIONS:
                 _lowerCamelCase           =					(
                     int.from_bytes(func(value.encode()    ).digest()					,					'''little'''    ) % self.size
                 )
                 res |= 2**position
           return res



     def 		UpperCamelCase_    (       self					,					A_    )			->					str:






           """simple docstring"""
           return self.format_bin(self.hash_(A_    )    )





     @property
     def 		UpperCamelCase_    (       self    )			->					float:






           """simple docstring"""
           _lowerCamelCase           =					bin(self.bitarray    ).count('''1'''    )
           return (n_ones / self.size) ** len(A_    )