






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   :						Tuple     						= get_logger(__name__)




def 				a (     UpperCamelCase_							:				Any     ,	UpperCamelCase_							:				Union[str, Any]     ,	UpperCamelCase_							:				Optional[int]     ,	UpperCamelCase_							:				Union[str, Any]     ,	UpperCamelCase_							:				Any     ,	UpperCamelCase_							:				List[Any] = None     ,	)  ->		Tuple:




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




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

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

 # Scheduler states
 for i, scheduler in enumerate(lowerCamelCase__		):
  __A	:       Any       =	F"{SCHEDULER_NAME}.bin" if i == 0 else F"{SCHEDULER_NAME}_{i}.bin"
  __A	:       Union[str, Any]       =	os.path.join(lowerCamelCase__     ,	lowerCamelCase__		)
  scheduler.load_state_dict(torch.load(lowerCamelCase__		)		)
 logger.info('All scheduler states loaded successfully'		)

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

 # Random states
 try:
  __A	:       int       =	torch.load(os.path.join(lowerCamelCase__     ,	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_							:				int     ,	UpperCamelCase_							:				int     ,	UpperCamelCase_							:				Optional[Any] = 0		)  ->		Optional[int]:
 __A	:       str       =	Path(lowerCamelCase__		) / F"custom_checkpoint_{index}.pkl"
 logger.info(F"Saving the state of {get_pretty_name(lowerCamelCase__		)} to {save_location}"		)
 torch.save(obj.state_dict()     ,	lowerCamelCase__		)




def 				a (     UpperCamelCase_							:				List[str]     ,	UpperCamelCase_							:				Any     ,	UpperCamelCase_							:				Tuple = 0		)  ->		List[str]:
 __A	:       int       =	F"{path}/custom_checkpoint_{index}.pkl"
 logger.info(F"Loading the state of {get_pretty_name(lowerCamelCase__		)} from {load_location}"		)
 obj.load_state_dict(torch.load(lowerCamelCase__     ,	map_location='cpu'		)		)













"""simple docstring"""


import math




def 				a (     UpperCamelCase_							:				int		)  ->		list[int]:
 if not isinstance(UpperCamelCase_     ,	UpperCamelCase_		):
  __A	:       Any       =	F"Input value of [number={number}] must be an integer"
  raise TypeError(UpperCamelCase_		)
 if number < 1:
  __A	:       int       =	F"Input value of [number={number}] must be a positive integer"
  raise ValueError(UpperCamelCase_		)
 __A	:       Optional[Any]       =	[number]
 while number != 1:
  if number % 2 == 0:
   __A	:       Optional[int]       =	math.floor(math.sqrt(UpperCamelCase_		)		)
  else:
   __A	:       List[str]       =	math.floor(
       math.sqrt(UpperCamelCase_		) * math.sqrt(UpperCamelCase_		) * math.sqrt(UpperCamelCase_		)		)
  sequence.append(UpperCamelCase_		)
 return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()





