

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


UpperCamelCase__	:   List[str]								=     get_logger(__name__)


def        _UpperCAmelCase							(	_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(_SCREAMING_SNAKE_CASE							):
        SCREAMING_SNAKE_CASE_						=    f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
        SCREAMING_SNAKE_CASE_						=    os.path.join(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
        save(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
        logger.info(f"""Model weights saved in {output_model_file}"""							)
    # Optimizer states
    for i, opt in enumerate(_SCREAMING_SNAKE_CASE							):
        SCREAMING_SNAKE_CASE_						=    opt.state_dict()
        SCREAMING_SNAKE_CASE_						=    f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
        SCREAMING_SNAKE_CASE_						=    os.path.join(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
        save(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
        logger.info(f"""Optimizer state saved in {output_optimizer_file}"""							)
    # Scheduler states
    for i, scheduler in enumerate(_SCREAMING_SNAKE_CASE							):
        SCREAMING_SNAKE_CASE_						=    scheduler.state_dict()
        SCREAMING_SNAKE_CASE_						=    f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
        SCREAMING_SNAKE_CASE_						=    os.path.join(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
        save(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
        logger.info(f"""Scheduler state saved in {output_scheduler_file}"""							)
    # GradScaler state
    if scaler is not None:
        SCREAMING_SNAKE_CASE_						=    scaler.state_dict()
        SCREAMING_SNAKE_CASE_						=    os.path.join(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
        torch.save(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
        logger.info(f"""Gradient scaler state saved in {output_scaler_file}"""							)
    # Random number generator states
    SCREAMING_SNAKE_CASE_						=    {}
    SCREAMING_SNAKE_CASE_						=    f"""{RNG_STATE_NAME}_{process_index}.pkl"""
    SCREAMING_SNAKE_CASE_						=    random.getstate()
    SCREAMING_SNAKE_CASE_						=    np.random.get_state()
    SCREAMING_SNAKE_CASE_						=    torch.get_rng_state()
    if is_xpu_available():
        SCREAMING_SNAKE_CASE_						=    torch.xpu.get_rng_state_all()
    else:
        SCREAMING_SNAKE_CASE_						=    torch.cuda.get_rng_state_all()
    if is_tpu_available():
        SCREAMING_SNAKE_CASE_						=    xm.get_rng_state()
    SCREAMING_SNAKE_CASE_						=    os.path.join(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
    torch.save(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
    logger.info(f"""Random states saved in {output_states_file}"""							)
    return output_dir


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		int			, _SCREAMING_SNAKE_CASE       :		Optional[int]			, _SCREAMING_SNAKE_CASE       :		Tuple			, _SCREAMING_SNAKE_CASE       :		List[Any]			, _SCREAMING_SNAKE_CASE       :		Any			, _SCREAMING_SNAKE_CASE       :		int=None			, _SCREAMING_SNAKE_CASE       :		List[Any]=None			, **_SCREAMING_SNAKE_CASE       :		Optional[Any]			, ):





    """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:
        SCREAMING_SNAKE_CASE_						=    'cpu'
    elif map_location == "on_device":
        SCREAMING_SNAKE_CASE_						=    PartialState().device
    # Model states
    for i, model in enumerate(_SCREAMING_SNAKE_CASE							):
        SCREAMING_SNAKE_CASE_						=    f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
        SCREAMING_SNAKE_CASE_						=    os.path.join(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
        models[i].load_state_dict(torch.load(_SCREAMING_SNAKE_CASE			, map_location=_SCREAMING_SNAKE_CASE							)			, **_SCREAMING_SNAKE_CASE							)
    logger.info('All model weights loaded successfully'							)

    # Optimizer states
    for i, opt in enumerate(_SCREAMING_SNAKE_CASE							):
        SCREAMING_SNAKE_CASE_						=    f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
        SCREAMING_SNAKE_CASE_						=    os.path.join(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
        SCREAMING_SNAKE_CASE_						=    torch.load(_SCREAMING_SNAKE_CASE			, map_location=_SCREAMING_SNAKE_CASE							)
        optimizers[i].load_state_dict(_SCREAMING_SNAKE_CASE							)
    logger.info('All optimizer states loaded successfully'							)

    # Scheduler states
    for i, scheduler in enumerate(_SCREAMING_SNAKE_CASE							):
        SCREAMING_SNAKE_CASE_						=    f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
        SCREAMING_SNAKE_CASE_						=    os.path.join(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
        scheduler.load_state_dict(torch.load(_SCREAMING_SNAKE_CASE							)							)
    logger.info('All scheduler states loaded successfully'							)

    # GradScaler state
    if scaler is not None:
        SCREAMING_SNAKE_CASE_						=    os.path.join(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
        scaler.load_state_dict(torch.load(_SCREAMING_SNAKE_CASE							)							)
        logger.info('GradScaler state loaded successfully'							)

    # Random states
    try:
        SCREAMING_SNAKE_CASE_						=    torch.load(os.path.join(_SCREAMING_SNAKE_CASE			, 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							(	_SCREAMING_SNAKE_CASE       :		Optional[Any]			, _SCREAMING_SNAKE_CASE       :		int			, _SCREAMING_SNAKE_CASE       :		int = 0							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    Path(_SCREAMING_SNAKE_CASE							) / f"""custom_checkpoint_{index}.pkl"""
    logger.info(f"""Saving the state of {get_pretty_name(_SCREAMING_SNAKE_CASE							)} to {save_location}"""							)
    torch.save(obj.state_dict()			, _SCREAMING_SNAKE_CASE							)





def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Dict			, _SCREAMING_SNAKE_CASE       :		Tuple			, _SCREAMING_SNAKE_CASE       :		int = 0							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    f"""{path}/custom_checkpoint_{index}.pkl"""
    logger.info(f"""Loading the state of {get_pretty_name(_SCREAMING_SNAKE_CASE							)} from {load_location}"""							)
    obj.load_state_dict(torch.load(_SCREAMING_SNAKE_CASE			, map_location='cpu'							)							)





import random


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		List[Any]							):





    """simple docstring"""


    def is_sorted(_SCREAMING_SNAKE_CASE       :		List[Any]							):
        for i in range(len(_SCREAMING_SNAKE_CASE							) - 1							):
            if collection[i] > collection[i + 1]:
                return False
        return True

    while not is_sorted(_SCREAMING_SNAKE_CASE							):
        random.shuffle(_SCREAMING_SNAKE_CASE							)
    return collection


if __name__ == "__main__":
       UpperCamelCase__	:   Tuple								=     input("Enter numbers separated by a comma:\n").strip()
       UpperCamelCase__	:   List[str]								=     [int(item) for item in user_input.split(",")]
       print(bogo_sort(unsorted))


