




import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin




def        __SCREAMING_SNAKE_CASE (	lowercase_=2       ,  lowercase_=True       ,  lowercase_=False   )							->						Tuple:

    """simple docstring"""


    __UpperCamelCase			=    Accelerator(step_scheduler_with_optimizer=lowercase_       ,  split_batches=lowercase_   )
    __UpperCamelCase			=    torch.nn.Linear(2       ,  4   )
    __UpperCamelCase			=    torch.optim.AdamW(model.parameters()       ,  lr=1.0   )
    __UpperCamelCase			=    torch.optim.lr_scheduler.OneCycleLR(lowercase_       ,  max_lr=0.01       ,  steps_per_epoch=2       ,  epochs=1   )
    __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    accelerator.prepare(lowercase_       ,  lowercase_       ,  lowercase_   )

    # Optimizer has stepped
    scheduler.step()
    if step_scheduler_with_optimizer or (num_processes == 1):
        assert (
            scheduler.scheduler.last_epoch == num_processes
        ), F"Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"
    else:
        assert (
            scheduler.scheduler.last_epoch != num_processes
        ), F"Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"




def        __SCREAMING_SNAKE_CASE (	lowercase_=2       ,  lowercase_=True       ,  lowercase_=False   )							->						Optional[int]:

    """simple docstring"""


    __UpperCamelCase			=    Accelerator(step_scheduler_with_optimizer=lowercase_       ,  split_batches=lowercase_   )
    __UpperCamelCase			=    torch.nn.Linear(2       ,  4   )
    __UpperCamelCase			=    torch.optim.AdamW(model.parameters()       ,  lr=1.0   )
    __UpperCamelCase			=    torch.optim.lr_scheduler.LambdaLR(lowercase_       ,  lr_lambda=lambda lowercase_   : 1 - n / 10   )
    __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    accelerator.prepare(lowercase_       ,  lowercase_       ,  lowercase_   )

    # Optimizer has stepped
    __UpperCamelCase			=    False
    scheduler.step()
    __UpperCamelCase			=    1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F"Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"

    # Optimizer has not stepped
    __UpperCamelCase			=    True
    scheduler.step()
    if not step_scheduler_with_optimizer:
        __UpperCamelCase			=    1 - 2 / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F"Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"




def        __SCREAMING_SNAKE_CASE (	lowercase_ = 2   )							->						Union[str, Any]:

    """simple docstring"""


    from transformers import get_linear_schedule_with_warmup

    __UpperCamelCase			=    [1, 2, 4]
    for num_steps in steps:
        __UpperCamelCase			=    GradientAccumulationPlugin(num_steps=lowercase_       ,  adjust_scheduler=num_steps > 1   )
        __UpperCamelCase			=    Accelerator(gradient_accumulation_plugin=lowercase_   )
        __UpperCamelCase			=    torch.nn.Linear(2       ,  4   )
        __UpperCamelCase			=    torch.optim.AdamW(model.parameters()       ,  lr=10.0   )
        __UpperCamelCase			=    get_linear_schedule_with_warmup(optimizer=lowercase_       ,  num_warmup_steps=0       ,  num_training_steps=20   )

        __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    accelerator.prepare(lowercase_       ,  lowercase_       ,  lowercase_   )

        for i in range(10 * num_steps   ):
            with accelerator.accumulate(lowercase_   ):
                optimizer.step()
                scheduler.step()

            if i == (10 * num_steps - 2):
                assert (
                    scheduler.get_last_lr()[0] != 0
                ), F"Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"
        assert (
            scheduler.get_last_lr()[0] == 0
        ), F"Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"
        GradientState._reset_state()


@require_cpu
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def    snake_case						(  self       :			List[str]					):
          debug_launcher(partial(snake_case			,							num_processes=1					)			,							num_processes=1					)
          debug_launcher(partial(snake_case			,							num_processes=1			,							split_batches=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			Optional[int]					):
          debug_launcher(partial(snake_case			,							num_processes=1					)			,							num_processes=1					)
          debug_launcher(partial(snake_case			,							num_processes=1			,							split_batches=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			str					):
          debug_launcher(partial(snake_case			,							num_processes=1			,							step_scheduler_with_optimizer=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			int					):
          debug_launcher(partial(snake_case			,							num_processes=1			,							step_scheduler_with_optimizer=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			Optional[int]					):
          AcceleratorState._reset_state(snake_case					)
          debug_launcher(snake_case					)
          debug_launcher(partial(snake_case			,							num_processes=1			,							split_batches=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			Dict					):
          AcceleratorState._reset_state(snake_case					)
          debug_launcher(snake_case					)
          debug_launcher(partial(snake_case			,							num_processes=1			,							split_batches=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			int					):
          AcceleratorState._reset_state(snake_case					)
          debug_launcher(partial(snake_case			,							step_scheduler_with_optimizer=snake_case					)					)







      def    snake_case						(  self       :			List[Any]					):
          AcceleratorState._reset_state(snake_case					)
          debug_launcher(partial(snake_case			,							step_scheduler_with_optimizer=snake_case					)					)







      @require_huggingface_suite
      def    snake_case						(  self       :			Dict					):
          AcceleratorState._reset_state(snake_case					)
          debug_launcher(partial(snake_case			,							num_processes=1					)					)
          debug_launcher(snake_case					)









"""simple docstring"""





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

from digital_image_processing.filters.convolve import img_convolve




def     _A    (    UpperCamelCase_	:			int)      ->		str:
   '''simple docstring'''


   __lowercase								=		np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
   __lowercase								=		np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])

   __lowercase								=		np.abs(img_convolve(UpperCamelCase_,     UpperCamelCase_))
   __lowercase								=		np.abs(img_convolve(UpperCamelCase_,     UpperCamelCase_))
   # modify the pix within [0, 255]
   __lowercase								=		dst_x * 255 / np.max(UpperCamelCase_)
   __lowercase								=		dst_y * 255 / np.max(UpperCamelCase_)

   __lowercase								=		np.sqrt((np.square(UpperCamelCase_)) + (np.square(UpperCamelCase_)))
   __lowercase								=		dst_xy * 255 / np.max(UpperCamelCase_)
   __lowercase								=		dst_xy.astype(np.uinta)

   __lowercase								=		np.arctana(UpperCamelCase_,     UpperCamelCase_)
   return dst, theta


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

   _a ,		_a					 =				sobel_filter(gray)

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




