




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




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

    # Random states
    try:
        UpperCAmelCase_							 =     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 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						List[str]       ,     __UpperCamelCase			:						Union[str, Any]       ,     __UpperCamelCase			:						int = 0      )  ->							List[str]:
    UpperCAmelCase_							 =     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 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Union[str, Any]       ,     __UpperCamelCase			:						Tuple       ,     __UpperCamelCase			:						int = 0      )  ->							List[Any]:
    UpperCAmelCase_							 =     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'''      )      )











def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						str      )  ->							int:
    UpperCAmelCase_							 =     5381
    for x in s:
        UpperCAmelCase_							 =     ((hash_value << 5) + hash_value) + ord(__UpperCamelCase      )
    return hash_value & 0xffff_ffff





