




'''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'''





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_=3	):
    _a							:       Optional[Any]       					=			int(mask / 2	)
    # copy image size
    _a							:       Optional[int]       					=			zeros_like(UpperCAmelCase_	)
    for i in range(UpperCAmelCase_      ,  gray_img.shape[0] - bd	):
        for j in range(UpperCAmelCase_      ,  gray_img.shape[1] - bd	):
            # get mask according with mask
            _a							:       List[str]       					=			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]	)
            # calculate mask median
            _a							:       Optional[Any]       					=			sort(UpperCAmelCase_	)[inta(divide((multiply(UpperCAmelCase_      ,  UpperCAmelCase_	))      ,  2	) + 1	)]
            _a							:       int       					=			median
    return median_img


if __name__ == "__main__":
  # read original image
  a		:  List[Any]   			=			imread('''../image_data/lena.jpg''')
  # turn image in gray scale value
  a		:  Optional[Any]   			=			cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  a		:  str   			=			median_filter(gray, 3)
  a		:  Optional[Any]   			=			median_filter(gray, 5)

  # show result images
  imshow('''median filter with 3x3 mask''', medianaxa)
  imshow('''median filter with 5x5 mask''', medianaxa)
  waitKey(0)


