
'''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


_SCREAMING_SNAKE_CASE =	get_logger(__name__)





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_ = None					,)		->		Optional[Any]:
			# Model states
			for i, state in enumerate(SCREAMING_SNAKE_CASE_    ):
						A_       		=   F'{MODEL_NAME}.bin' if i == 0 else F'{MODEL_NAME}_{i}.bin'
						A_       		=   os.path.join(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )
						save(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )
						logger.info(F'Model weights saved in {output_model_file}'    )
			# Optimizer states
			for i, opt in enumerate(SCREAMING_SNAKE_CASE_    ):
						A_       		=   opt.state_dict()
						A_       		=   F'{OPTIMIZER_NAME}.bin' if i == 0 else F'{OPTIMIZER_NAME}_{i}.bin'
						A_       		=   os.path.join(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )
						save(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )
						logger.info(F'Optimizer state saved in {output_optimizer_file}'    )
			# Scheduler states
			for i, scheduler in enumerate(SCREAMING_SNAKE_CASE_    ):
						A_       		=   scheduler.state_dict()
						A_       		=   F'{SCHEDULER_NAME}.bin' if i == 0 else F'{SCHEDULER_NAME}_{i}.bin'
						A_       		=   os.path.join(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )
						save(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )
						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(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )
						torch.save(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )
						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(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )
			torch.save(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )
			logger.info(F'Random states saved in {output_states_file}'    )
			return output_dir





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_=None					,SCREAMING_SNAKE_CASE_=None					,**SCREAMING_SNAKE_CASE_					,)		->		str:
			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(SCREAMING_SNAKE_CASE_    ):
						A_       		=   F'{MODEL_NAME}.bin' if i == 0 else F'{MODEL_NAME}_{i}.bin'
						A_       		=   os.path.join(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )
						models[i].load_state_dict(torch.load(SCREAMING_SNAKE_CASE_					,map_location=SCREAMING_SNAKE_CASE_    )					,**SCREAMING_SNAKE_CASE_    )
			logger.info('All model weights loaded successfully'    )

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

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

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

			# Random states
			try:
						A_       		=   torch.load(os.path.join(SCREAMING_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  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_ = 0    )		->		Union[str, Any]:
			A_       		=   Path(SCREAMING_SNAKE_CASE_    ) / F'custom_checkpoint_{index}.pkl'
			logger.info(F'Saving the state of {get_pretty_name(SCREAMING_SNAKE_CASE_    )} to {save_location}'    )
			torch.save(obj.state_dict()					,SCREAMING_SNAKE_CASE_    )






def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_ = 0    )		->		str:
			A_       		=   F'{path}/custom_checkpoint_{index}.pkl'
			logger.info(F'Loading the state of {get_pretty_name(SCREAMING_SNAKE_CASE_    )} from {load_location}'    )
			obj.load_state_dict(torch.load(SCREAMING_SNAKE_CASE_					,map_location='cpu'    )    )










'''simple docstring'''
from __future__ import annotations

import sys





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )		->		tuple[int, int]:
			# base cases
			if abs(SCREAMING_SNAKE_CASE_    ) == 1:
						return a, 0
			elif abs(SCREAMING_SNAKE_CASE_    ) == 1:
						return 0, b

			A_		,   A_       		=   a, b
			A_		,   A_       		=   1, 0
			A_		,   A_       		=   0, 1

			while remainder != 0:
						A_       		=   old_remainder // remainder
						A_		,   A_       		=   remainder, old_remainder - quotient * remainder
						A_		,   A_       		=   coeff_a, old_coeff_a - quotient * coeff_a
						A_		,   A_       		=   coeff_b, old_coeff_b - quotient * coeff_b

			# sign correction for negative numbers
			if a < 0:
						A_       		=   -old_coeff_a
			if b < 0:
						A_       		=   -old_coeff_b

			return old_coeff_a, old_coeff_b





def  lowerCamelCase(       )		->		Tuple:
			if len(sys.argv    ) < 3:
						print('2 integer arguments required'    )
						return 1
			A_       		=   int(sys.argv[1]    )
			A_       		=   int(sys.argv[2]    )
			print(extended_euclidean_algorithm(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )    )
			return 0


if __name__ == "__main__":
							raise SystemExit(main())




