






'''simple docstring'''





import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


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

from .logging import get_logger
from .state import PartialState


UpperCAmelCase__      =							get_logger(__name__)






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   str,_SCREAMING_SNAKE_CASE				:   List[dict],_SCREAMING_SNAKE_CASE				:   list,_SCREAMING_SNAKE_CASE				:   list,_SCREAMING_SNAKE_CASE				:   int,_SCREAMING_SNAKE_CASE				:   GradScaler = None,):






			"""simple docstring"""




			for i, state in enumerate(a_   ):
						__A=  f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
						__A=  os.path.join(a_,a_   )
						save(a_,a_   )
						logger.info(f"""Model weights saved in {output_model_file}"""   )
			# Optimizer states
			for i, opt in enumerate(a_   ):
						__A=  opt.state_dict()
						__A=  f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
						__A=  os.path.join(a_,a_   )
						save(a_,a_   )
						logger.info(f"""Optimizer state saved in {output_optimizer_file}"""   )
			# Scheduler states
			for i, scheduler in enumerate(a_   ):
						__A=  scheduler.state_dict()
						__A=  f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
						__A=  os.path.join(a_,a_   )
						save(a_,a_   )
						logger.info(f"""Scheduler state saved in {output_scheduler_file}"""   )
			# GradScaler state
			if scaler is not None:
						__A=  scaler.state_dict()
						__A=  os.path.join(a_,a_   )
						torch.save(a_,a_   )
						logger.info(f"""Gradient scaler state saved in {output_scaler_file}"""   )
			# Random number generator states
			__A=  {}
			__A=  f"""{RNG_STATE_NAME}_{process_index}.pkl"""
			__A=  random.getstate()
			__A=  np.random.get_state()
			__A=  torch.get_rng_state()
			if is_xpu_available():
						__A=  torch.xpu.get_rng_state_all()
			else:
						__A=  torch.cuda.get_rng_state_all()
			if is_tpu_available():
						__A=  xm.get_rng_state()
			__A=  os.path.join(a_,a_   )
			torch.save(a_,a_   )
			logger.info(f"""Random states saved in {output_states_file}"""   )
			return output_dir






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Optional[int],_SCREAMING_SNAKE_CASE				:   int,_SCREAMING_SNAKE_CASE				:   Any,_SCREAMING_SNAKE_CASE				:   Tuple,_SCREAMING_SNAKE_CASE				:   Optional[Any],_SCREAMING_SNAKE_CASE				:   Tuple=None,_SCREAMING_SNAKE_CASE				:   Union[str, Any]=None,**_SCREAMING_SNAKE_CASE				:   Dict,):






			"""simple docstring"""




			if map_location not in [None, "cpu", "on_device"]:
						raise TypeError(
						    'Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'   )
			if map_location is None:
						__A=  'cpu'
			elif map_location == "on_device":
						__A=  PartialState().device
			# Model states
			for i, model in enumerate(a_   ):
						__A=  f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
						__A=  os.path.join(a_,a_   )
						models[i].load_state_dict(torch.load(a_,map_location=a_   ),**a_   )
			logger.info('All model weights loaded successfully'   )

			# Optimizer states
			for i, opt in enumerate(a_   ):
						__A=  f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
						__A=  os.path.join(a_,a_   )
						__A=  torch.load(a_,map_location=a_   )
						optimizers[i].load_state_dict(a_   )
			logger.info('All optimizer states loaded successfully'   )

			# Scheduler states
			for i, scheduler in enumerate(a_   ):
						__A=  f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
						__A=  os.path.join(a_,a_   )
						scheduler.load_state_dict(torch.load(a_   )   )
			logger.info('All scheduler states loaded successfully'   )

			# GradScaler state
			if scaler is not None:
						__A=  os.path.join(a_,a_   )
						scaler.load_state_dict(torch.load(a_   )   )
						logger.info('GradScaler state loaded successfully'   )

			# Random states
			try:
						__A=  torch.load(os.path.join(a_,f"""{RNG_STATE_NAME}_{process_index}.pkl"""   )   )
						random.setstate(states['random_state']   )
						np.random.set_state(states['numpy_random_seed']   )
						torch.set_rng_state(states['torch_manual_seed']   )
						if is_xpu_available():
									torch.xpu.set_rng_state_all(states['torch_xpu_manual_seed']   )
						else:
									torch.cuda.set_rng_state_all(states['torch_cuda_manual_seed']   )
						if is_tpu_available():
									xm.set_rng_state(states['xm_seed']   )
						logger.info('All random states loaded successfully'   )
			except Exception:
						logger.info('Could not load random states'   )






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   int,_SCREAMING_SNAKE_CASE				:   str,_SCREAMING_SNAKE_CASE				:   int = 0   ):






			"""simple docstring"""




			__A=  Path(a_   ) / f"""custom_checkpoint_{index}.pkl"""
			logger.info(f"""Saving the state of {get_pretty_name(a_   )} to {save_location}"""   )
			torch.save(obj.state_dict(),a_   )






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Tuple,_SCREAMING_SNAKE_CASE				:   List[Any],_SCREAMING_SNAKE_CASE				:   int = 0   ):






			"""simple docstring"""




			__A=  f"""{path}/custom_checkpoint_{index}.pkl"""
			logger.info(f"""Loading the state of {get_pretty_name(a_   )} from {load_location}"""   )
			obj.load_state_dict(torch.load(a_,map_location='cpu'   )   )







'''simple docstring'''





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class 	a__				:





			'''simple docstring'''

			def __init__(     self       : Dict ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : int  )			->		Optional[int]:
						__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(lowerCAmelCase_  )

						__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 				lowerCAmelCase    (     cls       : Tuple ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : int  )			->		float:
						return 0.1_14 * blue + 0.5_87 * green + 0.21_26 * red

			def 				lowerCAmelCase    (     self       : str  )			->		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
					UpperCAmelCase__      =							[
					    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
					    for threshold in (1, 1_2_6, 1_3_0, 1_4_0)
					]

					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()



