


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


lowerCamelCase		      = get_logger(__name__)




def 			a_   (					SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   List[dict]	,    SCREAMING_SNAKE_CASE__      :   list	,    SCREAMING_SNAKE_CASE__      :   list	,    SCREAMING_SNAKE_CASE__      :   int	,    SCREAMING_SNAKE_CASE__      :   GradScaler = None	,    ):
							'''simple docstring'''





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




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

							# Random states
							try:
														_lowerCamelCase :	Optional[int]						=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_   (					SCREAMING_SNAKE_CASE__      :   int	,    SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   int = 0  ):
							'''simple docstring'''





							_lowerCamelCase :	Any						=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_   (					SCREAMING_SNAKE_CASE__      :   Tuple	,    SCREAMING_SNAKE_CASE__      :   List[Any]	,    SCREAMING_SNAKE_CASE__      :   int = 0  ):
							'''simple docstring'''





							_lowerCamelCase :	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'  )  )






from collections.abc import Callable

lowerCamelCase		      = Callable[[float], float]  # type alias for a real -> real function




def 			a_   (					SCREAMING_SNAKE_CASE__      :   RealFunc	,    SCREAMING_SNAKE_CASE__      :   RealFunc	,    SCREAMING_SNAKE_CASE__      :   int	,    ):
       '''simple docstring'''





       _lowerCamelCase :	Tuple						=float(SCREAMING_SNAKE_CASE__  )
       while True:
              try:
                     _lowerCamelCase :	int						=prev_guess - function(SCREAMING_SNAKE_CASE__  ) / derivative(SCREAMING_SNAKE_CASE__  )
              except ZeroDivisionError:
                     raise ZeroDivisionError('Could not find root'  ) from None
              if abs(prev_guess - next_guess  ) < 10**-5:
                     return next_guess
              _lowerCamelCase :	Union[str, Any]						=next_guess




def 			a_   (					SCREAMING_SNAKE_CASE__      :   float  ):
       '''simple docstring'''





       return (x**3) - (2 * x) - 5




def 			a_   (					SCREAMING_SNAKE_CASE__      :   float  ):
       '''simple docstring'''





       return 3 * (x**2) - 2


if __name__ == "__main__":
 print(newton(f, fa, 3))


