


"""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__							:List[str]       =  get_logger(__name__)





def 							A_					(   snake_case__					,				snake_case__					,				snake_case__					,				snake_case__					,				snake_case__					,				snake_case__ = None					,				)				->     Dict:
			# Model states
			for i, state in enumerate(snake_case__		):
						_UpperCamelCase				:str				    =  f"{MODEL_NAME}.bin" if i == 0 else f"{MODEL_NAME}_{i}.bin"
						_UpperCamelCase				:Optional[Any]				    =  os.path.join(snake_case__					,				snake_case__		)
						save(snake_case__					,				snake_case__		)
						logger.info(f"Model weights saved in {output_model_file}"		)
			# Optimizer states
			for i, opt in enumerate(snake_case__		):
						_UpperCamelCase				:Tuple				    =  opt.state_dict()
						_UpperCamelCase				:Union[str, Any]				    =  f"{OPTIMIZER_NAME}.bin" if i == 0 else f"{OPTIMIZER_NAME}_{i}.bin"
						_UpperCamelCase				:Tuple				    =  os.path.join(snake_case__					,				snake_case__		)
						save(snake_case__					,				snake_case__		)
						logger.info(f"Optimizer state saved in {output_optimizer_file}"		)
			# Scheduler states
			for i, scheduler in enumerate(snake_case__		):
						_UpperCamelCase				:Union[str, Any]				    =  scheduler.state_dict()
						_UpperCamelCase				:int				    =  f"{SCHEDULER_NAME}.bin" if i == 0 else f"{SCHEDULER_NAME}_{i}.bin"
						_UpperCamelCase				:Union[str, Any]				    =  os.path.join(snake_case__					,				snake_case__		)
						save(snake_case__					,				snake_case__		)
						logger.info(f"Scheduler state saved in {output_scheduler_file}"		)
			# GradScaler state
			if scaler is not None:
						_UpperCamelCase				:Optional[int]				    =  scaler.state_dict()
						_UpperCamelCase				:List[Any]				    =  os.path.join(snake_case__					,				snake_case__		)
						torch.save(snake_case__					,				snake_case__		)
						logger.info(f"Gradient scaler state saved in {output_scaler_file}"		)
			# Random number generator states
			_UpperCamelCase				:Optional[int]				    =  {}
			_UpperCamelCase				:int				    =  f"{RNG_STATE_NAME}_{process_index}.pkl"
			_UpperCamelCase				:int				    =  random.getstate()
			_UpperCamelCase				:Optional[Any]				    =  np.random.get_state()
			_UpperCamelCase				:List[Any]				    =  torch.get_rng_state()
			if is_xpu_available():
						_UpperCamelCase				:List[str]				    =  torch.xpu.get_rng_state_all()
			else:
						_UpperCamelCase				:Dict				    =  torch.cuda.get_rng_state_all()
			if is_tpu_available():
						_UpperCamelCase				:Any				    =  xm.get_rng_state()
			_UpperCamelCase				:Optional[Any]				    =  os.path.join(snake_case__					,				snake_case__		)
			torch.save(snake_case__					,				snake_case__		)
			logger.info(f"Random states saved in {output_states_file}"		)
			return output_dir





def 							A_					(   snake_case__					,				snake_case__					,				snake_case__					,				snake_case__					,				snake_case__					,				snake_case__=None					,				snake_case__=None					,				**snake_case__					,				)				->     Dict:
			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:
						_UpperCamelCase				:Dict				    =  '''cpu'''
			elif map_location == "on_device":
						_UpperCamelCase				:int				    =  PartialState().device
			# Model states
			for i, model in enumerate(snake_case__		):
						_UpperCamelCase				:Optional[int]				    =  f"{MODEL_NAME}.bin" if i == 0 else f"{MODEL_NAME}_{i}.bin"
						_UpperCamelCase				:Tuple				    =  os.path.join(snake_case__					,				snake_case__		)
						models[i].load_state_dict(torch.load(snake_case__					,				map_location=snake_case__		)					,				**snake_case__		)
			logger.info('''All model weights loaded successfully'''		)

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

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

			# GradScaler state
			if scaler is not None:
						_UpperCamelCase				:List[Any]				    =  os.path.join(snake_case__					,				snake_case__		)
						scaler.load_state_dict(torch.load(snake_case__		)		)
						logger.info('''GradScaler state loaded successfully'''		)

			# Random states
			try:
						_UpperCamelCase				:Any				    =  torch.load(os.path.join(snake_case__					,				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 							A_					(   snake_case__					,				snake_case__					,				snake_case__ = 0		)				->     str:
			_UpperCamelCase				:List[Any]				    =  Path(snake_case__		) / f"custom_checkpoint_{index}.pkl"
			logger.info(f"Saving the state of {get_pretty_name(snake_case__		)} to {save_location}"		)
			torch.save(obj.state_dict()					,				snake_case__		)





def 							A_					(   snake_case__					,				snake_case__					,				snake_case__ = 0		)				->     Union[str, Any]:
			_UpperCamelCase				:Any				    =  f"{path}/custom_checkpoint_{index}.pkl"
			logger.info(f"Loading the state of {get_pretty_name(snake_case__		)} from {load_location}"		)
			obj.load_state_dict(torch.load(snake_case__					,				map_location='''cpu'''		)		)





"""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_					(   snake_case__		)				->     str:
			_UpperCamelCase				:List[Any]				    =  np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]		)
			_UpperCamelCase				:Dict				    =  np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]		)

			_UpperCamelCase				:List[str]				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			_UpperCamelCase				:Dict				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			# modify the pix within [0, 255]
			_UpperCamelCase				:List[Any]				    =  dst_x * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Tuple				    =  dst_y * 2_55 / np.max(snake_case__		)

			_UpperCamelCase				:Tuple				    =  np.sqrt((np.square(snake_case__		)) + (np.square(snake_case__		))		)
			_UpperCamelCase				:Any				    =  dst_xy * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Optional[int]				    =  dst_xy.astype(np.uinta		)

			_UpperCamelCase				:str				    =  np.arctana(snake_case__					,				snake_case__		)
			return dst, theta


if __name__ == "__main__":
					# read original image
					UpperCamelCase__							:List[str]       =  imread("""../image_data/lena.jpg""")
					# turn image in gray scale value
					UpperCamelCase__							:Union[str, Any]       =  cvtColor(img, COLOR_BGR2GRAY)

					UpperCamelCase__      , UpperCamelCase__							:Optional[int]       =  sobel_filter(gray)

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

