
"""simple docstring"""





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       __a	(						__lowerCamelCase=2,						__lowerCamelCase=True,						__lowerCamelCase=False    ):
   UpperCAmelCase_   :   str     	=       Accelerator(step_scheduler_with_optimizer=__lowerCamelCase,						split_batches=__lowerCamelCase    )
   UpperCAmelCase_   :   int     	=       torch.nn.Linear(2,						4    )
   UpperCAmelCase_   :   Union[str, Any]     	=       torch.optim.AdamW(model.parameters(),						lr=1.0    )
   UpperCAmelCase_   :   Dict     	=       torch.optim.lr_scheduler.OneCycleLR(__lowerCamelCase,						max_lr=0.01,						steps_per_epoch=2,						epochs=1    )
   UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   Any     	=       accelerator.prepare(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )

   # 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       __a	(						__lowerCamelCase=2,						__lowerCamelCase=True,						__lowerCamelCase=False    ):
   UpperCAmelCase_   :   List[Any]     	=       Accelerator(step_scheduler_with_optimizer=__lowerCamelCase,						split_batches=__lowerCamelCase    )
   UpperCAmelCase_   :   Union[str, Any]     	=       torch.nn.Linear(2,						4    )
   UpperCAmelCase_   :   int     	=       torch.optim.AdamW(model.parameters(),						lr=1.0    )
   UpperCAmelCase_   :   Any     	=       torch.optim.lr_scheduler.LambdaLR(__lowerCamelCase,						lr_lambda=lambda __lowerCamelCase    : 1 - n / 10    )
   UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   Union[str, Any]     	=       accelerator.prepare(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )

   # Optimizer has stepped
   UpperCAmelCase_   :   int     	=       False
   scheduler.step()
   UpperCAmelCase_   :   int     	=       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_   :   Optional[Any]     	=       True
   scheduler.step()
   if not step_scheduler_with_optimizer:
      UpperCAmelCase_   :   Dict     	=       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       __a	(						__lowerCamelCase = 2    ):
   from transformers import get_linear_schedule_with_warmup

   UpperCAmelCase_   :   int     	=       [1, 2, 4]
   for num_steps in steps:
      UpperCAmelCase_   :   List[str]     	=       GradientAccumulationPlugin(num_steps=__lowerCamelCase,						adjust_scheduler=num_steps > 1    )
      UpperCAmelCase_   :   List[str]     	=       Accelerator(gradient_accumulation_plugin=__lowerCamelCase    )
      UpperCAmelCase_   :   Tuple     	=       torch.nn.Linear(2,						4    )
      UpperCAmelCase_   :   List[Any]     	=       torch.optim.AdamW(model.parameters(),						lr=10.0    )
      UpperCAmelCase_   :   Optional[Any]     	=       get_linear_schedule_with_warmup(optimizer=__lowerCamelCase,						num_warmup_steps=0,						num_training_steps=20    )

      UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[Any]     	=       accelerator.prepare(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )

      for i in range(10 * num_steps    ):
         with accelerator.accumulate(__lowerCamelCase    ):
            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 A_      (unittest.TestCase ):




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     debug_launcher(partial(lowercase_							,  num_processes=1    )							,  num_processes=1    )
     debug_launcher(partial(lowercase_							,  num_processes=1							,  split_batches=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     debug_launcher(partial(lowercase_							,  num_processes=1    )							,  num_processes=1    )
     debug_launcher(partial(lowercase_							,  num_processes=1							,  split_batches=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     debug_launcher(partial(lowercase_							,  num_processes=1							,  step_scheduler_with_optimizer=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     debug_launcher(partial(lowercase_							,  num_processes=1							,  step_scheduler_with_optimizer=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     AcceleratorState._reset_state(lowercase_    )
     debug_launcher(lowercase_    )
     debug_launcher(partial(lowercase_							,  num_processes=1							,  split_batches=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     AcceleratorState._reset_state(lowercase_    )
     debug_launcher(lowercase_    )
     debug_launcher(partial(lowercase_							,  num_processes=1							,  split_batches=lowercase_    )							,  num_processes=1    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     AcceleratorState._reset_state(lowercase_    )
     debug_launcher(partial(lowercase_							,  step_scheduler_with_optimizer=lowercase_    )    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     AcceleratorState._reset_state(lowercase_    )
     debug_launcher(partial(lowercase_							,  step_scheduler_with_optimizer=lowercase_    )    )







  @require_huggingface_suite
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     AcceleratorState._reset_state(lowercase_    )
     debug_launcher(partial(lowercase_							,  num_processes=1    )    )
     debug_launcher(lowercase_    )




"""simple docstring"""





import cva
import numpy as np







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):

   try:
      return int(image[x_coordinate][y_coordinate] >= center    )
   except (IndexError, TypeError):
      return 0







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[int]     	=       image[x_coordinate][y_coordinate]
   UpperCAmelCase_   :   int     	=       [1, 2, 4, 8, 16, 32, 64, 128]

   # skip get_neighbors_pixel if center is null
   if center is None:
      return 0

   # Starting from the top right, assigning value to pixels clockwise
   UpperCAmelCase_   :   List[Any]     	=       [
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						__lowerCamelCase,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						__lowerCamelCase,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						__lowerCamelCase,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						y_coordinate - 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						__lowerCamelCase,						y_coordinate - 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						y_coordinate - 1,						__lowerCamelCase    ),
   ]

   # Converting the binary value to decimal.
   return sum(
       binary_value * power for binary_value, power in zip(__lowerCamelCase,						__lowerCamelCase    )    )


if __name__ == "__main__":
    # Reading the image and converting it to grayscale.
    _a									=				cva.imread(
        'digital_image_processing/image_data/lena.jpg', cva.IMREAD_GRAYSCALE
    )

    # Create a numpy array as the same height and width of read image
    _a									=				np.zeros((image.shape[0], image.shape[1]))

    # Iterating through the image and calculating the
    # local binary pattern value for each pixel.
    for i in range(0, image.shape[0]):
        for j in range(0, image.shape[1]):
            _a									=				local_binary_value(image, i, j)

    cva.imshow('local binary pattern', lbp_image)
    cva.waitKey(0)
    cva.destroyAllWindows()


