


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__ (							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     ):





							"""simple docstring"""







							def __init__(			self ,	lowerCAmelCase__ ,	lowerCAmelCase__=True ,	lowerCAmelCase__=None					):





										"""simple docstring"""




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

										# Handle device placement
										if device_placement:
													SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				self.optimizer.state_dict()
													if self.accelerator_state.distributed_type == DistributedType.TPU:
																xm.send_cpu_data_to_device(lowerCAmelCase__ ,	self.accelerator_state.device					)
													else:
																SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				move_to_device(lowerCAmelCase__ ,	self.accelerator_state.device					)
													self.optimizer.load_state_dict(lowerCAmelCase__					)






							@property
							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										return self.optimizer.state






							@state.setter
							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[str] 		=				state






							@property
							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										return self.optimizer.param_groups






							@param_groups.setter
							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    int 		=				param_groups






							@property
							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										return self.optimizer.defaults






							@defaults.setter
							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    str 		=				defaults






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





										"""simple docstring"""




										self.optimizer.add_param_group(lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





										"""simple docstring"""




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






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										return self.optimizer.state_dict()






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__=None					):





										"""simple docstring"""




										if self.gradient_state.sync_gradients:
													SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				'set_to_none' in inspect.signature(self.optimizer.zero_grad					).parameters
													if accept_arg:
																if set_to_none is None:
																			SCREAMING_SNAKE_CASE_		:    Any 		=				False
																self.optimizer.zero_grad(set_to_none=lowerCAmelCase__					)
													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 				UpperCamelCase__    (			self ,	lowerCAmelCase__=None					):





										"""simple docstring"""




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

													else:
																self.optimizer.step(lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





										"""simple docstring"""




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






							@property
							def 				UpperCamelCase__    (			self					):





										"""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.' ,	lowerCAmelCase__ ,	)
										return self._is_overflow






							@property
							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										return self._is_overflow






							def __getstate__(			self					):





										"""simple docstring"""




										return self.__dict__.copy()






							def __setstate__(			self ,	lowerCAmelCase__					):





										"""simple docstring"""




										self.__dict__.update(lowerCAmelCase__					)









import cva
import numpy as np






def 	a__ (							A__,			A__,			A__,			A__							):

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






def 	a__ (							A__,			A__,			A__							):
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				image[x_coordinate][y_coordinate]
			SCREAMING_SNAKE_CASE_		:    str 		=				[1, 2, 4, 8, 1_6, 3_2, 6_4, 1_2_8]

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

			# Starting from the top right, assigning value to pixels clockwise
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				[
			    get_neighbors_pixel(A__,			x_coordinate - 1,			y_coordinate + 1,			A__							),
			    get_neighbors_pixel(A__,			A__,			y_coordinate + 1,			A__							),
			    get_neighbors_pixel(A__,			x_coordinate - 1,			A__,			A__							),
			    get_neighbors_pixel(A__,			x_coordinate + 1,			y_coordinate + 1,			A__							),
			    get_neighbors_pixel(A__,			x_coordinate + 1,			A__,			A__							),
			    get_neighbors_pixel(A__,			x_coordinate + 1,			y_coordinate - 1,			A__							),
			    get_neighbors_pixel(A__,			A__,			y_coordinate - 1,			A__							),
			    get_neighbors_pixel(A__,			x_coordinate - 1,			y_coordinate - 1,			A__							),
			]

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


if __name__ == "__main__":
				# Reading the image and converting it to grayscale.
				lowerCAmelCase__ :			List[Any]         =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
				lowerCAmelCase__ :			List[Any]         =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]):
												lowerCAmelCase__ :			Optional[Any]         =local_binary_value(image, i, j)

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





