


"""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					:       int							   =							get_logger(__name__)






def 						lowercase				(	_snake_case		: Any	,  _snake_case		: Optional[int]	,  _snake_case		: Optional[int]	,  _snake_case		: int	,  _snake_case		: Tuple	,  _snake_case		: Dict = None	,  )	->Dict:
	"""simple docstring"""





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






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

	# Random states
	try:
		__snake_case      :		Tuple         =							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 						lowercase				(	_snake_case		: Optional[int]	,  _snake_case		: Union[str, Any]	,  _snake_case		: Union[str, Any] = 0	)	->Optional[int]:
	"""simple docstring"""





	__snake_case      :		Any         =							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 						lowercase				(	_snake_case		: Dict	,  _snake_case		: Dict	,  _snake_case		: str = 0	)	->Union[str, Any]:
	"""simple docstring"""





	__snake_case      :		int         =							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'''	)	)










'''simple docstring'''

from __future__ import annotations

_A					:     Union[str, Any]      ='''Alexander Joslin'''







def     __UpperCamelCase					(				_lowercase       )			->      int:

	if number <= 0:
		_lowercase       :							Optional[Any]  	=		f'''n must be greater than 0. Got n = {number}'''
		raise ValueError(_lowercase       )

	_lowercase       :							List[str]  	=		[number + 1] * (number + 1)

	# starting position
	_lowercase       :							str  	=		0
	for i in range(1,					_lowercase       ):
		_lowercase       :							Union[str, Any]  	=		min(table[i + 1],					table[i] + 1       )
		# check if out of bounds
		if i * 2 <= number:
			_lowercase       :							Optional[Any]  	=		min(table[i * 2],					table[i] + 1       )
		# check if out of bounds
		if i * 3 <= number:
			_lowercase       :							Any  	=		min(table[i * 3],					table[i] + 1       )
	return table[number]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
