'''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 lowerCamelCase__						(       A_       ,			A_       ):
 if isinstance(A_       ,			(list, tuple)       ):
  return honor_type(A_       ,			(move_to_device(A_       ,			A_       ) for t in state)       )
 elif isinstance(A_       ,			A_       ):
  return type(A_       )({k: move_to_device(A_       ,			A_       ) for k, v in state.items()}       )
 elif isinstance(A_       ,			torch.Tensor       ):
  return state.to(A_       )
 return state


class 							lowercase_    (     torch.optim.Optimizer						):







    def __init__(	self       ,     UpperCamelCase__       ,     UpperCamelCase__=True       ,     UpperCamelCase__=None				)				->       List[str]:





     """simple docstring"""

     UpperCAmelCase_				       =					optimizer
     UpperCAmelCase_				       =					scaler
     UpperCAmelCase_				       =					AcceleratorState()
     UpperCAmelCase_				       =					GradientState()
     UpperCAmelCase_				       =					device_placement
     UpperCAmelCase_				       =					False
     UpperCAmelCase_				       =					None

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







    @property
    def   lowerCamelCase_    (	self				)				->       Dict:





     """simple docstring"""

     return self.optimizer.state







    @state.setter
    def   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       Optional[Any]:





     """simple docstring"""

     UpperCAmelCase_				       =					state







    @property
    def   lowerCamelCase_    (	self				)				->       str:





     """simple docstring"""

     return self.optimizer.param_groups







    @param_groups.setter
    def   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       List[Any]:





     """simple docstring"""

     UpperCAmelCase_				       =					param_groups







    @property
    def   lowerCamelCase_    (	self				)				->       Optional[Any]:





     """simple docstring"""

     return self.optimizer.defaults







    @defaults.setter
    def   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       Optional[int]:





     """simple docstring"""

     UpperCAmelCase_				       =					defaults







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       Any:





     """simple docstring"""

     self.optimizer.add_param_group(UpperCamelCase__				)







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       List[str]:





     """simple docstring"""

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







    def   lowerCamelCase_    (	self				)				->       Optional[Any]:





     """simple docstring"""

     return self.optimizer.state_dict()







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__=None				)				->       Union[str, Any]:





     """simple docstring"""

     if self.gradient_state.sync_gradients:
      UpperCAmelCase_				       =					"set_to_none" in inspect.signature(self.optimizer.zero_grad				).parameters
      if accept_arg:
       if set_to_none is None:
        UpperCAmelCase_				       =					False
       self.optimizer.zero_grad(set_to_none=UpperCamelCase__				)
      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   lowerCamelCase_    (	self       ,     UpperCamelCase__=None				)				->       Tuple:





     """simple docstring"""

     if self.gradient_state.sync_gradients:
      if self.accelerator_state.distributed_type == DistributedType.TPU:
       UpperCAmelCase_				       =					{"closure": closure} if closure is not None else {}
       xm.optimizer_step(self.optimizer       ,     optimizer_args=UpperCamelCase__				)
      elif self.scaler is not None:
       UpperCAmelCase_				       =					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
        UpperCAmelCase_				       =					self.scaler.get_scale()
        UpperCAmelCase_				       =					True
       self.scaler.step(self.optimizer       ,     UpperCamelCase__				)
       self.scaler.update()
       UpperCAmelCase_				       =					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.
        UpperCAmelCase_				       =					scale_after < self._last_scale
       UpperCAmelCase_				       =					scale_after

      else:
       self.optimizer.step(UpperCamelCase__				)







    def   lowerCamelCase_    (	self       ,     UpperCamelCase__				)				->       Optional[int]:





     """simple docstring"""

     for param_group in self.optimizer.param_groups:
      UpperCAmelCase_				       =					[parameters_map.get(UpperCamelCase__       ,     UpperCamelCase__				) for p in param_group["params"]]







    @property
    def   lowerCamelCase_    (	self				)				->       Any:





     """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."       ,     UpperCamelCase__       ,     )
     return self._is_overflow







    @property
    def   lowerCamelCase_    (	self				)				->       Dict:





     """simple docstring"""

     return self._is_overflow







    def __getstate__(	self				)				->       Tuple:





     """simple docstring"""

     return self.__dict__.copy()







    def __setstate__(	self       ,     UpperCamelCase__				)				->       int:





     """simple docstring"""

     self.__dict__.update(UpperCamelCase__				)

'''simple docstring'''

import numpy as np
from PIL import Image



def lowerCamelCase__						(       A_       ):
 UpperCAmelCase_       ,       UpperCAmelCase_       ,       UpperCAmelCase_				       =					rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
 return 0.2989 * r + 0.5870 * g + 0.1140 * b



def lowerCamelCase__						(       A_       ):
 return (gray > 127) & (gray <= 255)



def lowerCamelCase__						(       A_       ,			A_       ):
 UpperCAmelCase_				       =					np.zeros_like(A_       )
 UpperCAmelCase_				       =					np.zeros(
     (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)       )

 # Copy image to padded image
 UpperCAmelCase_				       =					image

 # Iterate over image & apply kernel
 for x in range(image.shape[1]       ):
  for y in range(image.shape[0]       ):
   UpperCAmelCase_				       =					(
       kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
   ).sum()
   UpperCAmelCase_				       =					int(summation == 5       )
 return output


# kernel to be applied
__snake_case       :   List[str]  		=    np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      __snake_case       :   int  		=    np.array(Image.open(R'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      __snake_case       :   str  		=    erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      __snake_case       :   List[Any]  		=    Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')
