


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			:					Dict         =							get_logger(__name__)
def 				UpperCamelCase_	(					__snake_case      :    str						,      __snake_case      :    List[dict]						,      __snake_case      :    list						,      __snake_case      :    list						,      __snake_case      :    int						,      __snake_case      :    GradScaler = None						,      )->   str:




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




						'''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:
												__magic_name__					=      'cpu'
						elif map_location == "on_device":
												__magic_name__					=      PartialState().device
						# Model states
						for i, model in enumerate(UpperCAmelCase_  ):
												__magic_name__					=      F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
												__magic_name__					=      os.path.join(UpperCAmelCase_						,      UpperCAmelCase_  )
												models[i].load_state_dict(torch.load(UpperCAmelCase_						,      map_location=UpperCAmelCase_  )						,      **UpperCAmelCase_  )
						logger.info('All model weights loaded successfully'  )

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

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

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

						# Random states
						try:
												__magic_name__					=      torch.load(os.path.join(UpperCAmelCase_						,      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_	(					__snake_case      :    Optional[int]						,      __snake_case      :    List[Any]						,      __snake_case      :    int = 0  )->   Any:




						'''simple docstring'''
						__magic_name__					=      Path(UpperCAmelCase_  ) / F'''custom_checkpoint_{index}.pkl'''
						logger.info(F'''Saving the state of {get_pretty_name(UpperCAmelCase_  )} to {save_location}'''  )
						torch.save(obj.state_dict()						,      UpperCAmelCase_  )




def 				UpperCamelCase_	(					__snake_case      :    Union[str, Any]						,      __snake_case      :    int						,      __snake_case      :    int = 0  )->   int:




						'''simple docstring'''
						__magic_name__					=      F'''{path}/custom_checkpoint_{index}.pkl'''
						logger.info(F'''Loading the state of {get_pretty_name(UpperCAmelCase_  )} from {load_location}'''  )
						obj.load_state_dict(torch.load(UpperCAmelCase_						,      map_location='cpu'  )  )




"""simple docstring"""



from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like





def 			_snake_case  (     UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       List[Any]=3      ):
     A__				      =	int(mask / 2      )
     # copy image size
     A__				      =	zeros_like(UpperCAmelCase_      )
     for i in range(UpperCAmelCase_       , gray_img.shape[0] - bd      ):
          for j in range(UpperCAmelCase_       , gray_img.shape[1] - bd      ):
               # get mask according with mask
               A__				      =	ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]      )
               # calculate mask median
               A__				      =	sort(UpperCAmelCase_      )[inta(divide((multiply(UpperCAmelCase_       , UpperCAmelCase_      ))       , 2      ) + 1      )]
               A__				      =	median
     return median_img


if __name__ == "__main__":
 # read original image
 SCREAMING_SNAKE_CASE_       :						int					=    imread('../image_data/lena.jpg')
 # turn image in gray scale value
 SCREAMING_SNAKE_CASE_       :						str					=    cvtColor(img, COLOR_BGR2GRAY)

 # get values with two different mask size
 SCREAMING_SNAKE_CASE_       :						List[Any]					=    median_filter(gray, 3)
 SCREAMING_SNAKE_CASE_       :						str					=    median_filter(gray, 5)

 # show result images
 imshow('median filter with 3x3 mask', medianaxa)
 imshow('median filter with 5x5 mask', medianaxa)
 waitKey(0)






