


"""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


lowerCamelCase_  :  Union[str, Any]     		=    get_logger(__name__)
def _A		(		lowercase			,       lowercase			,       lowercase			,       lowercase			,       lowercase			,       lowercase = None			,       ):







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







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

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

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

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

    # Random states
    try:
        a											=torch.load(os.path.join(__lowerCamelCase			,       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		(		lowercase			,       lowercase			,       lowercase = 0						):







    """simple docstring"""
    a											=Path(__lowerCamelCase						) / f'''custom_checkpoint_{index}.pkl'''
    logger.info(f'''Saving the state of {get_pretty_name(__lowerCamelCase						)} to {save_location}'''						)
    torch.save(obj.state_dict()			,       __lowerCamelCase						)





def _A		(		lowercase			,       lowercase			,       lowercase = 0						):







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




"""simple docstring"""






def 	snake_case__  (						__lowerCamelCase							:       Union[str, Any]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =len(__lowerCamelCase      )
			for i in range(length - 1      ):
						lowerCamelCase__						:						Any						      =False
						for j in range(length - 1 - i      ):
									if collection[j] > collection[j + 1]:
												lowerCamelCase__						:						List[str]						      =True
												lowerCamelCase__      ,  lowerCamelCase__						:						List[str]						      =collection[j + 1], collection[j]
						if not swapped:
									break  # Stop iteration if the collection is sorted.
			return collection


if __name__ == "__main__":
		import doctest
		import time

		doctest.testmod()

		_lowercase					:					str 		=		input("Enter numbers separated by a comma:").strip()
		_lowercase					:					Dict 		=		[int(item) for item in user_input.split(",")]
		_lowercase					:					Optional[Any] 		=		time.process_time()
		print(*bubble_sort(unsorted), sep=",")
		print(f'Processing time: {(time.process_time() - start)%1E9 + 7}')




