






'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class    _snake_case   :

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	Any:
										a_									=   0
										# max greyscale value for #FFFFFF
										a_									=   int(self.get_greyscale(255   , 255   , 255     )     )

										if not self.min_threshold < threshold < self.max_threshold:
															a_									=   F'''Factor value should be from 0 to {self.max_threshold}'''
															raise ValueError(UpperCAmelCase__     )

										a_									=   input_img
										a_									=   threshold
										a_							,					a_									=   self.input_img.shape[1], self.input_img.shape[0]

										# error table size (+4 columns and +1 row) greater than input image because of
										# lack of if statements
										a_									=   [
										    [0 for _ in range(self.height + 4     )] for __ in range(self.width + 1     )
										]
										a_									=   np.ones((self.width, self.height, 3)   , np.uinta     ) * 255




					@classmethod
					def  __SCREAMING_SNAKE_CASE (     cls   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	float:
										return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	None:
										for y in range(self.height     ):
															for x in range(self.width     ):
																				a_									=   int(self.get_greyscale(*self.input_img[y][x]     )     )
																				if self.threshold > greyscale + self.error_table[y][x]:
																									a_									=   (0, 0, 0)
																									a_									=   greyscale + self.error_table[x][y]
																				else:
																									a_									=   (255, 255, 255)
																									a_									=   greyscale + self.error_table[x][y] - 255
																				self.error_table[y][x + 1] += int(8 / 32 * current_error     )
																				self.error_table[y][x + 2] += int(4 / 32 * current_error     )
																				self.error_table[y + 1][x] += int(8 / 32 * current_error     )
																				self.error_table[y + 1][x + 1] += int(4 / 32 * current_error     )
																				self.error_table[y + 1][x + 2] += int(2 / 32 * current_error     )
																				self.error_table[y + 1][x - 1] += int(4 / 32 * current_error     )
																				self.error_table[y + 1][x - 2] += int(2 / 32 * current_error     )


if __name__ == "__main__":
						# create Burke's instances with original images in greyscale
						__lowerCAmelCase							=[
						    Burkes(imread("image_data/lena.jpg", 1), threshold)
						    for threshold in (1, 126, 130, 140)
						]

						for burkes in burkes_instances:
												burkes.process()

						for burkes in burkes_instances:
												imshow(
												    f'''Original image with dithering threshold: {burkes.threshold}''',
												    burkes.output_img,
												)

						waitKey(0)
						destroyAllWindows()









'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = False )				->				List[str]:






					"""simple docstring"""







					if device_specific:
										seed += AcceleratorState().process_index
					random.seed(_UpperCAmelCase )
					np.random.seed(_UpperCAmelCase )
					torch.manual_seed(_UpperCAmelCase )
					if is_xpu_available():
										torch.xpu.manual_seed_all(_UpperCAmelCase )
					elif is_npu_available():
										torch.npu.manual_seed_all(_UpperCAmelCase )
					else:
										torch.cuda.manual_seed_all(_UpperCAmelCase )
					# ^^ safe to call this function even if cuda is not available
					if is_tpu_available():
										xm.set_rng_state(_UpperCAmelCase )

def 							a	(   _UpperCAmelCase = None					,  _UpperCAmelCase = None )				->				Tuple:






					"""simple docstring"""







					if rng_type == RNGType.TORCH:
										a_									=   torch.get_rng_state()
					elif rng_type == RNGType.CUDA:
										a_									=   torch.cuda.get_rng_state()
					elif rng_type == RNGType.XLA:
										assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
										a_									=   torch.tensor(xm.get_rng_state() )
					elif rng_type == RNGType.NPU:
										assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
										a_									=   torch.npu.get_rng_state()
					elif rng_type == RNGType.XPU:
										assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
										a_									=   torch.xpu.get_rng_state()
					elif rng_type == RNGType.GENERATOR:
										assert generator is not None, "Need a generator to synchronize its seed."
										a_									=   generator.get_state()

					# Broadcast the rng state from device 0 to other devices
					a_									=   AcceleratorState()
					if state.distributed_type == DistributedType.TPU:
										a_									=   rng_state.to(xm.xla_device() )
										xm.collective_broadcast([rng_state] )
										xm.mark_step()
										a_									=   rng_state.cpu()
					elif (
					    state.distributed_type in CUDA_DISTRIBUTED_TYPES
					    or state.distributed_type == DistributedType.MULTI_NPU
					    or state.distributed_type == DistributedType.MULTI_XPU
					):
										a_									=   rng_state.to(state.device )
										torch.distributed.broadcast(_UpperCAmelCase					,  0 )
										a_									=   rng_state.cpu()
					elif state.distributed_type == DistributedType.MULTI_CPU:
										torch.distributed.broadcast(_UpperCAmelCase					,  0 )

					# Set the broadcast rng state
					if rng_type == RNGType.TORCH:
										torch.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.CUDA:
										torch.cuda.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.NPU:
										torch.npu.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.XPU:
										torch.xpu.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.XLA:
										xm.set_rng_state(rng_state.item() )
					elif rng_type == RNGType.GENERATOR:
										generator.set_state(_UpperCAmelCase )





def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = None )				->				Any:






					"""simple docstring"""







					for rng_type in rng_types:
										synchronize_rng_state(RNGType(_UpperCAmelCase )					,  generator=_UpperCAmelCase )

