


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     =		get_logger(__name__)






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase = None			,       )			->				Optional[Any]:


     """simple docstring"""


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






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

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

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

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

     # Random states
     try:
          __UpperCamelCase				=	torch.load(os.path.join(_lowercase			,       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						(						_lowercase			,       _lowercase			,       _lowercase = 0				)			->				Any:


     """simple docstring"""


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






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase = 0				)			->				int:


     """simple docstring"""


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






import math






def 						_A						(						_lowercase				)			->				bool:


     """simple docstring"""


     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(_lowercase				) + 1				)			,       6				):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True






def 						_A						(						_lowercase = 2_00_00_00				)			->				int:


     """simple docstring"""


     return sum(num for num in range(3			,       _lowercase			,       2				) if is_prime(_lowercase				)				) + 2 if n > 2 else 0


if __name__ == "__main__":
    print(f"""{solution() = }""")


