'''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       :      Dict,  _UpperCamelCase       :      Dict  )      -> List[Any]:
    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	):





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=True       ,	_SCREAMING_SNAKE_CASE=None					)						->	Dict:
           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(_SCREAMING_SNAKE_CASE       ,	self.accelerator_state.device					)
               else:
                   A_      =  move_to_device(_SCREAMING_SNAKE_CASE       ,	self.accelerator_state.device					)
               self.optimizer.load_state_dict(_SCREAMING_SNAKE_CASE					)

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

       @state.setter
       def    __A			( self       ,	_SCREAMING_SNAKE_CASE					)						->	Tuple:
           A_      =  state

       @property
       def    __A			( self					)						->	Tuple:
           return self.optimizer.param_groups

       @param_groups.setter
       def    __A			( self       ,	_SCREAMING_SNAKE_CASE					)						->	Dict:
           A_      =  param_groups

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

       @defaults.setter
       def    __A			( self       ,	_SCREAMING_SNAKE_CASE					)						->	Dict:
           A_      =  defaults

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE					)						->	Optional[Any]:
           self.optimizer.add_param_group(_SCREAMING_SNAKE_CASE					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE					)						->	List[str]:
           if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
               xm.send_cpu_data_to_device(_SCREAMING_SNAKE_CASE       ,	self.accelerator_state.device					)
           self.optimizer.load_state_dict(_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Dict:
           return self.optimizer.state_dict()

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE=None					)						->	Union[str, Any]:
           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=_SCREAMING_SNAKE_CASE					)
               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       ,	_SCREAMING_SNAKE_CASE=None					)						->	str:
           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=_SCREAMING_SNAKE_CASE					)
               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       ,	_SCREAMING_SNAKE_CASE					)
                   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(_SCREAMING_SNAKE_CASE					)

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

       @property
       def    __A			( self					)						->	Union[str, Any]:
           warnings.warn(
               '''The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '''
               '''`optimizer.step_was_skipped` instead.'''       ,	_SCREAMING_SNAKE_CASE       ,	)
           return self._is_overflow

       @property
       def    __A			( self					)						->	List[Any]:
           return self._is_overflow

       def __getstate__( self					)						->	str:
           return self.__dict__.copy()

       def __setstate__( self       ,	_SCREAMING_SNAKE_CASE					)						->	List[str]:
           self.__dict__.update(_SCREAMING_SNAKE_CASE					)







'''simple docstring'''




def 	_UpperCAmelCase       (				_UpperCamelCase       :      list[list[int]],  _UpperCamelCase       :      list[list[int]]  )      -> list[list[int]]:
    A_      =  []
    A_      =  len(_UpperCamelCase  )
    for i in range(_UpperCamelCase  ):
        A_      =  []
        for j in range(_UpperCamelCase  ):
            A_      =  0
            for k in range(_UpperCamelCase  ):
                A_      =  val + matrix_a[i][k] * matrix_b[k][j]
            list_a.append(_UpperCamelCase  )
        matrix_c.append(_UpperCamelCase  )
    return matrix_c





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> list[list[int]]:
    return [[int(row == column  ) for column in range(_UpperCamelCase  )] for row in range(_UpperCamelCase  )]





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> int:
    if n <= 1:
        return n
    A_      =  identity(2  )
    A_      =  [[1, 1], [1, 0]]
    A_      =  n - 1
    while n > 0:
        if n % 2 == 1:
            A_      =  multiply(_UpperCamelCase,  _UpperCamelCase  )
        A_      =  multiply(_UpperCamelCase,  _UpperCamelCase  )
        A_      =  int(n / 2  )
    return res_matrix[0][0]





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int  )      -> int:
    if n <= 1:
        return n
    A_      =  0
    A_      =  1
    for _ in range(2,  n + 1  ):
        A_  ,A_      =  fiba, fiba + fiba
    return fiba





def 	_UpperCAmelCase       (				)      -> None:
    for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
        A_      =  int(''''''.join(c for c in ordinal if c in '''0123456789'''  )  )  # 1000th --> 1000
        print(
            F'''{ordinal} fibonacci number using matrix exponentiation is '''
            F'''{nth_fibonacci_matrix(_UpperCamelCase  )} and using bruteforce is '''
            F'''{nth_fibonacci_bruteforce(_UpperCamelCase  )}\n'''  )
    # from timeit import timeit
    # print(timeit("nth_fibonacci_matrix(1000000)",
    #              "from main import nth_fibonacci_matrix", number=5))
    # print(timeit("nth_fibonacci_bruteforce(1000000)",
    #              "from main import nth_fibonacci_bruteforce", number=5))
    # 2.3342058970001744
    # 57.256506615000035


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()






