'''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	:					Optional[Any]					=  get_logger(__name__)

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


							"""simple docstring"""







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

def      UpperCamelCase_							( _UpperCAmelCase  :				Optional[int]      ,     _UpperCAmelCase  :				Tuple      ,     _UpperCAmelCase  :				Optional[Any]      ,     _UpperCAmelCase  :				int      ,     _UpperCAmelCase  :				int      ,     _UpperCAmelCase  :				Dict=None      ,     _UpperCAmelCase  :				int=None      ,     **_UpperCAmelCase  :				Any      ,     )   ->   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:
														_UpperCAmelCase					:  int											=   "cpu"
							elif map_location == "on_device":
														_UpperCAmelCase					:  Optional[int]											=   PartialState().device
							# Model states
							for i, model in enumerate(_UpperCAmelCase   ):
														_UpperCAmelCase					:  Any											=   F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
														_UpperCAmelCase					:  Dict											=   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   ):
														_UpperCAmelCase					:  int											=   F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
														_UpperCAmelCase					:  Any											=   os.path.join(_UpperCAmelCase      ,     _UpperCAmelCase   )
														_UpperCAmelCase					:  Optional[Any]											=   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   ):
														_UpperCAmelCase					:  Optional[Any]											=   F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
														_UpperCAmelCase					:  Optional[Any]											=   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:
														_UpperCAmelCase					:  Union[str, Any]											=   os.path.join(_UpperCAmelCase      ,     _UpperCAmelCase   )
														scaler.load_state_dict(torch.load(_UpperCAmelCase   )   )
														logger.info("GradScaler state loaded successfully"   )

							# Random states
							try:
														_UpperCAmelCase					:  Dict											=   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_							( _UpperCAmelCase  :				List[str]      ,     _UpperCAmelCase  :				str      ,     _UpperCAmelCase  :				int = 0   )   ->   int:


							"""simple docstring"""







							_UpperCAmelCase					:  str											=   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_							( _UpperCAmelCase  :				List[str]      ,     _UpperCAmelCase  :				Dict      ,     _UpperCAmelCase  :				int = 0   )   ->   Tuple:


							"""simple docstring"""







							_UpperCAmelCase					:  Tuple											=   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'''




def      UpperCamelCase_							( _UpperCAmelCase  :				Optional[int]      ,     _UpperCAmelCase  :				List[Any]   )   ->   List[str]:


       """simple docstring"""







       if len(str(_UpperCAmelCase   )   ) == 1 or len(str(_UpperCAmelCase   )   ) == 1:
              return a * b

       _UpperCAmelCase					:  Any											=   max(len(str(_UpperCAmelCase   )   )      ,     len(str(_UpperCAmelCase   )   )   )
       _UpperCAmelCase					:  Any											=   ma // 2

       _UpperCAmelCase				,  _UpperCAmelCase					:  Union[str, Any]											=   divmod(_UpperCAmelCase      ,     10**ma   )
       _UpperCAmelCase				,  _UpperCAmelCase					:  int											=   divmod(_UpperCAmelCase      ,     10**ma   )

       _UpperCAmelCase					:  List[str]											=   karatsuba(_UpperCAmelCase      ,     _UpperCAmelCase   )
       _UpperCAmelCase					:  int											=   karatsuba((aa + aa)      ,     (ba + ba)   )
       _UpperCAmelCase					:  Any											=   karatsuba(_UpperCAmelCase      ,     _UpperCAmelCase   )

       return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)

def      UpperCamelCase_							( )   ->   List[str]:


       """simple docstring"""







       print(karatsuba(15_463      ,     23_489   )   )


if __name__ == "__main__":
  main()

