



'''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


snake_case_				:			Tuple        =						get_logger(__name__)






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_ = None						,						):





						for i, state in enumerate(__a							):
												_UpperCamelCase      :	Optional[int]				       =     f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
												_UpperCamelCase      :	Optional[int]				       =     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							):
												_UpperCamelCase      :	Optional[int]				       =     opt.state_dict()
												_UpperCamelCase      :	Tuple				       =     f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
												_UpperCamelCase      :	Union[str, Any]				       =     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							):
												_UpperCamelCase      :	Dict				       =     scheduler.state_dict()
												_UpperCamelCase      :	str				       =     f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
												_UpperCamelCase      :	List[str]				       =     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:
												_UpperCamelCase      :	Tuple				       =     scaler.state_dict()
												_UpperCamelCase      :	int				       =     os.path.join(__a						,						__a							)
												torch.save(__a						,						__a							)
												logger.info(f'Gradient scaler state saved in {output_scaler_file}'							)
						# Random number generator states
						_UpperCamelCase      :	Union[str, Any]				       =     {}
						_UpperCamelCase      :	Any				       =     f'{RNG_STATE_NAME}_{process_index}.pkl'
						_UpperCamelCase      :	Optional[int]				       =     random.getstate()
						_UpperCamelCase      :	List[Any]				       =     np.random.get_state()
						_UpperCamelCase      :	Tuple				       =     torch.get_rng_state()
						if is_xpu_available():
												_UpperCamelCase      :	Dict				       =     torch.xpu.get_rng_state_all()
						else:
												_UpperCamelCase      :	Optional[int]				       =     torch.cuda.get_rng_state_all()
						if is_tpu_available():
												_UpperCamelCase      :	List[Any]				       =     xm.get_rng_state()
						_UpperCamelCase      :	List[Any]				       =     os.path.join(__a						,						__a							)
						torch.save(__a						,						__a							)
						logger.info(f'Random states saved in {output_states_file}'							)
						return output_dir






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_=None						,						UpperCAmelCase_=None						,						**UpperCAmelCase_						,						):





						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      :	Tuple				       =     PartialState().device
						# Model states
						for i, model in enumerate(__a							):
												_UpperCamelCase      :	List[Any]				       =     f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
												_UpperCamelCase      :	str				       =     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							):
												_UpperCamelCase      :	int				       =     f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
												_UpperCamelCase      :	Tuple				       =     os.path.join(__a						,						__a							)
												_UpperCamelCase      :	List[Any]				       =     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							):
												_UpperCamelCase      :	Any				       =     f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
												_UpperCamelCase      :	Tuple				       =     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:
												_UpperCamelCase      :	List[str]				       =     os.path.join(__a						,						__a							)
												scaler.load_state_dict(torch.load(__a							)							)
												logger.info('GradScaler state loaded successfully'							)

						# Random states
						try:
												_UpperCamelCase      :	List[Any]				       =     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 A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_ = 0							):
						_UpperCamelCase      :	str				       =     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 A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_ = 0							):
						_UpperCamelCase      :	Optional[Any]				       =     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 math






def A__					(  UpperCAmelCase_							):

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5						,						int(math.sqrt(UpperCAmelCase_							) + 1							)						,						6							):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True






def A__					(  UpperCAmelCase_ = 1_0_0_0						,						UpperCAmelCase_ = 1_0_0_0							):
						_UpperCamelCase      :	str				       =     [0, 0, 0]  # length, a, b
						for a in range((a_limit * -1) + 1						,						UpperCAmelCase_							):
												for b in range(2						,						UpperCAmelCase_							):
																		if is_prime(UpperCAmelCase_							):
																								_UpperCamelCase      :	List[str]				       =     0
																								_UpperCamelCase      :	Union[str, Any]				       =     0
																								while is_prime((n**2) + (a * n) + b							):
																														count += 1
																														n += 1
																								if count > longest[0]:
																														_UpperCamelCase      :	Any				       =     [count, a, b]
						_UpperCamelCase      :	Dict				       =     longest[1] * longest[2]
						return ans


if __name__ == "__main__":
					print(solution(1000, 1000))


