

'''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


snake_case__          =						get_logger(__name__)




def 						snake_case__    ( lowerCamelCase__				:   Dict			,		lowerCamelCase__				:   Tuple			,		lowerCamelCase__				:   Tuple			,		lowerCamelCase__				:   Optional[Any]			,		lowerCamelCase__				:   Optional[Any]			,		lowerCamelCase__				:   List[str] = None			,		)   ->   Tuple:
					# Model states
					for i, state in enumerate(lowercase__			):
										A_ :     Union[str, Any] =							f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
										A_ :     Union[str, Any] =							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__			):
										A_ :     str =							opt.state_dict()
										A_ :     Dict =							f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
										A_ :     Dict =							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__			):
										A_ :     int =							scheduler.state_dict()
										A_ :     Optional[int] =							f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
										A_ :     Dict =							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:
										A_ :     Dict =							scaler.state_dict()
										A_ :     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
					A_ :     str =							{}
					A_ :     int =							f'{RNG_STATE_NAME}_{process_index}.pkl'
					A_ :     Tuple =							random.getstate()
					A_ :     Dict =							np.random.get_state()
					A_ :     Optional[int] =							torch.get_rng_state()
					if is_xpu_available():
										A_ :     List[Any] =							torch.xpu.get_rng_state_all()
					else:
										A_ :     List[str] =							torch.cuda.get_rng_state_all()
					if is_tpu_available():
										A_ :     str =							xm.get_rng_state()
					A_ :     Tuple =							os.path.join(lowercase__			,		lowercase__			)
					torch.save(lowercase__			,		lowercase__			)
					logger.info(f'Random states saved in {output_states_file}'			)
					return output_dir




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

					# Random states
					try:
										A_ :     List[Any] =							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 						snake_case__    ( lowerCamelCase__				:   List[str]			,		lowerCamelCase__				:   Any			,		lowerCamelCase__				:   List[str] = 0			)   ->   int:
					A_ :     str =							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 						snake_case__    ( lowerCamelCase__				:   Optional[Any]			,		lowerCamelCase__				:   Dict			,		lowerCamelCase__				:   Dict = 0			)   ->   int:
					A_ :     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'''




def 						snake_case__    ( lowerCamelCase__				:   float			)   ->   float:
					return -num if num < 0 else num




def 						snake_case__    ( lowerCamelCase__				:   list[int]			)   ->   int:
					if len(lowerCamelCase__			) == 0:
										raise ValueError('''abs_min() arg is an empty sequence'''			)
					A_ :     Any =							x[0]
					for i in x:
										if abs_val(lowerCamelCase__			) < abs_val(lowerCamelCase__			):
															A_ :     Tuple =							i
					return j




def 						snake_case__    ( lowerCamelCase__				:   list[int]			)   ->   int:
					if len(lowerCamelCase__			) == 0:
										raise ValueError('''abs_max() arg is an empty sequence'''			)
					A_ :     List[str] =							x[0]
					for i in x:
										if abs(lowerCamelCase__			) > abs(lowerCamelCase__			):
															A_ :     Any =							i
					return j




def 						snake_case__    ( lowerCamelCase__				:   list[int]			)   ->   int:
					if len(lowerCamelCase__			) == 0:
										raise ValueError('''abs_max_sort() arg is an empty sequence'''			)
					return sorted(lowerCamelCase__			,		key=lowerCamelCase__			)[-1]




def 						snake_case__    ( )   ->   Optional[Any]:
					assert abs_val(0			) == 0
					assert abs_val(3_4			) == 3_4
					assert abs_val(-1_0_0_0_0_0_0_0_0_0_0_0			) == 1_0_0_0_0_0_0_0_0_0_0_0

					A_ :     Dict =							[-3, -1, 2, -1_1]
					assert abs_max(lowerCamelCase__			) == -1_1
					assert abs_max_sort(lowerCamelCase__			) == -1_1
					assert abs_min(lowerCamelCase__			) == -1


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							test_abs_val()
							print(abs_val(-34))  # --> 34



