


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      a_				(   SCREAMING_SNAKE_CASE__ :				Dict		,      SCREAMING_SNAKE_CASE__ :				List[str]					):
       '''simple docstring'''





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




class A  (       torch.optim.Optimizer					):






      def __init__( self						:					Union[str, Any]      ,       lowercase_						:					str      ,       lowercase_						:					Dict=True      ,       lowercase_						:					Dict=None			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =optimizer
             _lowerCamelCase	:		Optional[Any]          =scaler
             _lowerCamelCase	:		Optional[int]          =AcceleratorState()
             _lowerCamelCase	:		List[Any]          =GradientState()
             _lowerCamelCase	:		Dict          =device_placement
             _lowerCamelCase	:		List[Any]          =False
             _lowerCamelCase	:		List[str]          =None

             # Handle device placement
             if device_placement:
                    _lowerCamelCase	:		List[Any]          =self.optimizer.state_dict()
                    if self.accelerator_state.distributed_type == DistributedType.TPU:
                           xm.send_cpu_data_to_device(lowercase_      ,       self.accelerator_state.device			)
                    else:
                           _lowerCamelCase	:		Tuple          =move_to_device(lowercase_      ,       self.accelerator_state.device			)
                    self.optimizer.load_state_dict(lowercase_			)






      @property
      def 	lowerCamelCase	( self						:					Any			) ->						Optional[int]:
             """simple docstring"""




             return self.optimizer.state






      @state.setter
      def 	lowerCamelCase	( self						:					Optional[Any]      ,       lowercase_						:					int			) ->						Any:
             """simple docstring"""




             _lowerCamelCase	:		Any          =state






      @property
      def 	lowerCamelCase	( self						:					List[str]			) ->						List[str]:
             """simple docstring"""




             return self.optimizer.param_groups






      @param_groups.setter
      def 	lowerCamelCase	( self						:					Optional[Any]      ,       lowercase_						:					List[Any]			) ->						List[str]:
             """simple docstring"""




             _lowerCamelCase	:		str          =param_groups






      @property
      def 	lowerCamelCase	( self						:					Tuple			) ->						Union[str, Any]:
             """simple docstring"""




             return self.optimizer.defaults






      @defaults.setter
      def 	lowerCamelCase	( self						:					Tuple      ,       lowercase_						:					Optional[Any]			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		str          =defaults






      def 	lowerCamelCase	( self						:					Any      ,       lowercase_						:					Optional[int]			) ->						Dict:
             """simple docstring"""




             self.optimizer.add_param_group(lowercase_			)






      def 	lowerCamelCase	( self						:					Dict      ,       lowercase_						:					List[str]			) ->						List[str]:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					Tuple			) ->						int:
             """simple docstring"""




             return self.optimizer.state_dict()






      def 	lowerCamelCase	( self						:					Optional[int]      ,       lowercase_						:					List[Any]=None			) ->						Union[str, Any]:
             """simple docstring"""




             if self.gradient_state.sync_gradients:
                    _lowerCamelCase	:		Optional[int]          ='set_to_none' in inspect.signature(self.optimizer.zero_grad			).parameters
                    if accept_arg:
                           if set_to_none is None:
                                  _lowerCamelCase	:		Dict          =False
                           self.optimizer.zero_grad(set_to_none=lowercase_			)
                    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						:					Union[str, Any]      ,       lowercase_						:					Any=None			) ->						int:
             """simple docstring"""




             if self.gradient_state.sync_gradients:
                    if self.accelerator_state.distributed_type == DistributedType.TPU:
                           _lowerCamelCase	:		int          ={'closure': closure} if closure is not None else {}
                           xm.optimizer_step(self.optimizer      ,       optimizer_args=lowercase_			)
                    elif self.scaler is not None:
                           _lowerCamelCase	:		Tuple          =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
                                  _lowerCamelCase	:		List[str]          =self.scaler.get_scale()
                                  _lowerCamelCase	:		Optional[int]          =True
                           self.scaler.step(self.optimizer      ,       lowercase_			)
                           self.scaler.update()
                           _lowerCamelCase	:		List[Any]          =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.
                                  _lowerCamelCase	:		Dict          =scale_after < self._last_scale
                           _lowerCamelCase	:		List[Any]          =scale_after

                    else:
                           self.optimizer.step(lowercase_			)






      def 	lowerCamelCase	( self						:					Any      ,       lowercase_						:					Dict			) ->						List[str]:
             """simple docstring"""




             for param_group in self.optimizer.param_groups:
                    _lowerCamelCase	:		Any          =[parameters_map.get(lowercase_      ,       lowercase_			) for p in param_group['params']]






      @property
      def 	lowerCamelCase	( self						:					str			) ->						Dict:
             """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.'      ,       lowercase_      ,       )
             return self._is_overflow






      @property
      def 	lowerCamelCase	( self						:					Dict			) ->						List[Any]:
             """simple docstring"""




             return self._is_overflow






      def __getstate__( self						:					Optional[int]			) ->						List[Any]:
             """simple docstring"""




             return self.__dict__.copy()






      def __setstate__( self						:					Optional[int]      ,       lowercase_						:					str			) ->						Optional[Any]:
             """simple docstring"""




             self.__dict__.update(lowercase_			)






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve




def      a_				(   SCREAMING_SNAKE_CASE__ :				Optional[Any]					):
       '''simple docstring'''





       _lowerCamelCase	:		List[str]          =np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]					)
       _lowerCamelCase	:		Dict          =np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]					)

       _lowerCamelCase	:		Optional[Any]          =np.abs(img_convolve(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)					)
       _lowerCamelCase	:		Optional[int]          =np.abs(img_convolve(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)					)
       # modify the pix within [0, 255]
       _lowerCamelCase	:		Optional[int]          =dst_x * 255 / np.max(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		Any          =dst_y * 255 / np.max(SCREAMING_SNAKE_CASE__					)

       _lowerCamelCase	:		Tuple          =np.sqrt((np.square(SCREAMING_SNAKE_CASE__					)) + (np.square(SCREAMING_SNAKE_CASE__					))					)
       _lowerCamelCase	:		int          =dst_xy * 255 / np.max(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		int          =dst_xy.astype(np.uinta					)

       _lowerCamelCase	:		Optional[int]          =np.arctana(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       return dst, theta


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

 lowerCamelCase ,    lowerCamelCase						 =	sobel_filter(gray)

 # show result images
 imshow('sobel filter', sobel_grad)
 imshow('sobel theta', sobel_theta)
 waitKey(0)


