

# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')




class 					_SCREAMING_SNAKE_CASE  :
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase = True		,	lowerCamelCase = False ):
          snake_case__						=				scheduler
          snake_case__						=				optimizers if isinstance(lowerCamelCase		,	(list, tuple) ) else [optimizers]
          snake_case__						=				split_batches
          snake_case__						=				step_with_optimizer
          snake_case__						=				GradientState()
      def A_			(			self		,	*lowerCamelCase		,	**lowerCamelCase ):
          if not self.step_with_optimizer:
              # No link between scheduler and optimizer -> just step
              self.scheduler.step(*lowerCamelCase		,	**lowerCamelCase )
              return

          # Otherwise, first make sure the optimizer was stepped.
          if not self.gradient_state.sync_gradients:
              if self.gradient_state.adjust_scheduler:
                  self.scheduler._step_count += 1
              return

          for opt in self.optimizers:
              if opt.step_was_skipped:
                  return
          if self.split_batches:
              # Split batches -> the training dataloader batch size is not changed so one step per training step
              self.scheduler.step(*lowerCamelCase		,	**lowerCamelCase )
          else:
              # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
              # num_processes steps per training step
              snake_case__						=				AcceleratorState().num_processes
              for _ in range(lowerCamelCase ):
                  # Special case when using OneCycle and `drop_last` was not used
                  if hasattr(self.scheduler		,	"total_steps" ):
                      if self.scheduler._step_count <= self.scheduler.total_steps:
                          self.scheduler.step(*lowerCamelCase		,	**lowerCamelCase )
                  else:
                      self.scheduler.step(*lowerCamelCase		,	**lowerCamelCase )
      def A_			(			self ):
          return self.scheduler.get_last_lr()
      def A_			(			self ):
          return self.scheduler.state_dict()
      def A_			(			self		,	lowerCamelCase ):
          self.scheduler.load_state_dict(lowerCamelCase )
      def A_			(			self ):
          return self.scheduler.get_lr()



      def A_			(			self		,	*lowerCamelCase		,	**lowerCamelCase ):
          return self.scheduler.print_lr(*lowerCamelCase		,	**lowerCamelCase )





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):

    # prepare kernel
    # the kernel size have to be odd
    if (ksize % 2) == 0:
        snake_case__						=				ksize + 1
    snake_case__						=				np.zeros((ksize, ksize)    ,		dtype=np.floataa							)

    # each value
    for y in range(__lowerCAmelCase							):
        for x in range(__lowerCAmelCase							):
            # distance from center
            snake_case__						=				x - ksize // 2
            snake_case__						=				y - ksize // 2

            # degree to radiant
            snake_case__						=				theta / 180 * np.pi
            snake_case__						=				np.cos(_theta							)
            snake_case__						=				np.sin(_theta							)

            # get kernel x
            snake_case__						=				cos_theta * px + sin_theta * py

            # get kernel y
            snake_case__						=				-sin_theta * px + cos_theta * py

            # fill kernel
            snake_case__						=				np.exp(
                -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)							) * np.cos(2 * np.pi * _x / lambd + psi							)

    return gabor


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     # read original image
     __magic_name__            =  imread('''../image_data/lena.jpg''')
     # turn image in gray scale value
     __magic_name__            =  cvtColor(img, COLOR_BGR2GRAY)

     # Apply multiple Kernel to detect edges
     __magic_name__            =  np.zeros(gray.shape[:2])
     for theta in [0, 30, 60, 90, 120, 150]:
          __magic_name__            =  gabor_filter_kernel(10, 8, theta, 10, 0, 0)
          out += filteraD(gray, CV_8UC3, kernel_aa)
     __magic_name__            =  out / out.max() * 255
     __magic_name__            =  out.astype(np.uinta)

     imshow('''Original''', gray)
     imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

     waitKey(0)



