

import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(">=", FSDP_PYTORCH_VERSION):
       import torch.distributed.checkpoint as dist_cp
       from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
       from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
       from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
       from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


UpperCamelCase__  :      int			=    get_logger(__name__)


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		List[str] ,   _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		List[str]=0       ):





    """simple docstring"""


    os.makedirs(_SCREAMING_SNAKE_CASE ,   exist_ok=_SCREAMING_SNAKE_CASE       )
    with FSDP.state_dict_type(
        _SCREAMING_SNAKE_CASE ,   fsdp_plugin.state_dict_type ,   fsdp_plugin.state_dict_config ,   fsdp_plugin.optim_state_dict_config       ):
        SCREAMING_SNAKE_CASE_									=						model.state_dict()
        if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
            SCREAMING_SNAKE_CASE_									=						f"""{MODEL_NAME}.bin""" if model_index == 0 else f"""{MODEL_NAME}_{model_index}.bin"""
            SCREAMING_SNAKE_CASE_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
            if accelerator.process_index == 0:
                logger.info(f"""Saving model to {output_model_file}"""       )
                torch.save(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                logger.info(f"""Model saved to {output_model_file}"""       )
        elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
            SCREAMING_SNAKE_CASE_									=						(
                f"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
                if model_index == 0
                else f"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
            )
            SCREAMING_SNAKE_CASE_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
            logger.info(f"""Saving model to {output_model_file}"""       )
            torch.save(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
            logger.info(f"""Model saved to {output_model_file}"""       )
        elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
            SCREAMING_SNAKE_CASE_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   f"""{MODEL_NAME}_{model_index}"""       )
            os.makedirs(_SCREAMING_SNAKE_CASE ,   exist_ok=_SCREAMING_SNAKE_CASE       )
            logger.info(f"""Saving model to {ckpt_dir}"""       )
            SCREAMING_SNAKE_CASE_									=						{'model': state_dict}

            dist_cp.save_state_dict(
                state_dict=_SCREAMING_SNAKE_CASE ,   storage_writer=dist_cp.FileSystemWriter(_SCREAMING_SNAKE_CASE       ) ,   planner=DefaultSavePlanner() ,   )
            logger.info(f"""Model saved to {ckpt_dir}"""       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		Tuple ,   _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		Optional[int]=0       ):





    """simple docstring"""


    accelerator.wait_for_everyone()
    with FSDP.state_dict_type(
        _SCREAMING_SNAKE_CASE ,   fsdp_plugin.state_dict_type ,   fsdp_plugin.state_dict_config ,   fsdp_plugin.optim_state_dict_config       ):
        if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
            if type(_SCREAMING_SNAKE_CASE       ) != FSDP and accelerator.process_index != 0:
                if not fsdp_plugin.sync_module_states:
                    raise ValueError(
                        'Set the `sync_module_states` flag to `True` so that model states are synced across processes when '
                        'initializing FSDP object'       )
                return
            SCREAMING_SNAKE_CASE_									=						f"""{MODEL_NAME}.bin""" if model_index == 0 else f"""{MODEL_NAME}_{model_index}.bin"""
            SCREAMING_SNAKE_CASE_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
            logger.info(f"""Loading model from {input_model_file}"""       )
            SCREAMING_SNAKE_CASE_									=						torch.load(_SCREAMING_SNAKE_CASE       )
            logger.info(f"""Model loaded from {input_model_file}"""       )
        elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
            SCREAMING_SNAKE_CASE_									=						(
                f"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
                if model_index == 0
                else f"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
            )
            SCREAMING_SNAKE_CASE_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
            logger.info(f"""Loading model from {input_model_file}"""       )
            SCREAMING_SNAKE_CASE_									=						torch.load(_SCREAMING_SNAKE_CASE       )
            logger.info(f"""Model loaded from {input_model_file}"""       )
        elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
            SCREAMING_SNAKE_CASE_									=						(
                os.path.join(_SCREAMING_SNAKE_CASE ,   f"""{MODEL_NAME}_{model_index}"""       )
                if f"""{MODEL_NAME}""" not in input_dir
                else input_dir
            )
            logger.info(f"""Loading model from {ckpt_dir}"""       )
            SCREAMING_SNAKE_CASE_									=						{'model': model.state_dict()}
            dist_cp.load_state_dict(
                state_dict=_SCREAMING_SNAKE_CASE ,   storage_reader=dist_cp.FileSystemReader(_SCREAMING_SNAKE_CASE       ) ,   planner=DefaultLoadPlanner() ,   )
            SCREAMING_SNAKE_CASE_									=						state_dict['model']
            logger.info(f"""Model loaded from {ckpt_dir}"""       )
        model.load_state_dict(_SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		List[str]=0       ):





    """simple docstring"""


    os.makedirs(_SCREAMING_SNAKE_CASE ,   exist_ok=_SCREAMING_SNAKE_CASE       )
    with FSDP.state_dict_type(
        _SCREAMING_SNAKE_CASE ,   fsdp_plugin.state_dict_type ,   fsdp_plugin.state_dict_config ,   fsdp_plugin.optim_state_dict_config       ):
        SCREAMING_SNAKE_CASE_									=						FSDP.optim_state_dict(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
        if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
            if accelerator.process_index == 0:
                SCREAMING_SNAKE_CASE_									=						(
                    f"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else f"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
                )
                SCREAMING_SNAKE_CASE_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                logger.info(f"""Saving Optimizer state to {output_optimizer_file}"""       )
                torch.save(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                logger.info(f"""Optimizer state saved in {output_optimizer_file}"""       )
        else:
            SCREAMING_SNAKE_CASE_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   f"""{OPTIMIZER_NAME}_{optimizer_index}"""       )
            os.makedirs(_SCREAMING_SNAKE_CASE ,   exist_ok=_SCREAMING_SNAKE_CASE       )
            logger.info(f"""Saving Optimizer state to {ckpt_dir}"""       )
            dist_cp.save_state_dict(
                state_dict={'optimizer': optim_state} ,   storage_writer=dist_cp.FileSystemWriter(_SCREAMING_SNAKE_CASE       ) ,   planner=DefaultSavePlanner() ,   )
            logger.info(f"""Optimizer state saved in {ckpt_dir}"""       )





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple ,   _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		List[str] ,   _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any]=0       ):





    """simple docstring"""


    accelerator.wait_for_everyone()
    with FSDP.state_dict_type(
        _SCREAMING_SNAKE_CASE ,   fsdp_plugin.state_dict_type ,   fsdp_plugin.state_dict_config ,   fsdp_plugin.optim_state_dict_config       ):
        if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
            SCREAMING_SNAKE_CASE_									=						None
            # below check should work but currently it isn't working (mostly opytorch issue),
            # in the meantime disabling it at the cost of excess memory usage
            # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
            SCREAMING_SNAKE_CASE_									=						(
                f"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else f"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
            )
            SCREAMING_SNAKE_CASE_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
            logger.info(f"""Loading Optimizer state from {input_optimizer_file}"""       )
            SCREAMING_SNAKE_CASE_									=						torch.load(_SCREAMING_SNAKE_CASE       )
            logger.info(f"""Optimizer state loaded from {input_optimizer_file}"""       )
        else:
            SCREAMING_SNAKE_CASE_									=						(
                os.path.join(_SCREAMING_SNAKE_CASE ,   f"""{OPTIMIZER_NAME}_{optimizer_index}"""       )
                if f"""{OPTIMIZER_NAME}""" not in input_dir
                else input_dir
            )
            logger.info(f"""Loading Optimizer from {ckpt_dir}"""       )
            SCREAMING_SNAKE_CASE_									=						load_sharded_optimizer_state_dict(
                model_state_dict=model.state_dict() ,   optimizer_key='optimizer' ,   storage_reader=dist_cp.FileSystemReader(_SCREAMING_SNAKE_CASE       ) ,   )
            SCREAMING_SNAKE_CASE_									=						optim_state['optimizer']
            logger.info(f"""Optimizer loaded from {ckpt_dir}"""       )
        SCREAMING_SNAKE_CASE_									=						FSDP.optim_state_dict_to_load(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
        optimizer.load_state_dict(_SCREAMING_SNAKE_CASE       )





from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase__  :      int			=    Lock()


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Any       ):





    """simple docstring"""


    global process_lock

    # we perform n swaps since after n swaps we know we are sorted
    # we *could* stop early if we are sorted already, but it takes as long to
    # find out we are sorted as it does to sort the list with this algorithm
    for i in range(0 ,   10       ):
        if (i + position) % 2 == 0 and r_send is not None:
            # send your value to your right neighbor
            process_lock.acquire()
            r_send[1].send(_SCREAMING_SNAKE_CASE       )
            process_lock.release()

            # receive your right neighbor's value
            process_lock.acquire()
            SCREAMING_SNAKE_CASE_									=						rr_cv[0].recv()
            process_lock.release()

            # take the lower value since you are on the left
            SCREAMING_SNAKE_CASE_									=						min(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
        elif (i + position) % 2 != 0 and l_send is not None:
            # send your value to your left neighbor
            process_lock.acquire()
            l_send[1].send(_SCREAMING_SNAKE_CASE       )
            process_lock.release()

            # receive your left neighbor's value
            process_lock.acquire()
            SCREAMING_SNAKE_CASE_									=						lr_cv[0].recv()
            process_lock.release()

            # take the higher value since you are on the right
            SCREAMING_SNAKE_CASE_									=						max(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
    # after all swaps are performed, send the values back to main
    result_pipe[1].send(_SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    SCREAMING_SNAKE_CASE_									=						[]
    # initialize the list of pipes where the values will be retrieved
    for _ in arr:
        result_pipe.append(Pipe()       )
    # creates the processes
    # the first and last process only have one neighbor so they are made outside
    # of the loop
    SCREAMING_SNAKE_CASE_									=						Pipe()
    SCREAMING_SNAKE_CASE_									=						Pipe()
    process_array_.append(
        Process(
            target=_SCREAMING_SNAKE_CASE ,   args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) ,   )       )
    SCREAMING_SNAKE_CASE_									=						temp_rs
    SCREAMING_SNAKE_CASE_									=						temp_rr

    for i in range(1 ,   len(_SCREAMING_SNAKE_CASE       ) - 1       ):
        SCREAMING_SNAKE_CASE_									=						Pipe()
        SCREAMING_SNAKE_CASE_									=						Pipe()
        process_array_.append(
            Process(
                target=_SCREAMING_SNAKE_CASE ,   args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) ,   )       )
        SCREAMING_SNAKE_CASE_									=						temp_rs
        SCREAMING_SNAKE_CASE_									=						temp_rr

    process_array_.append(
        Process(
            target=_SCREAMING_SNAKE_CASE ,   args=(
                len(_SCREAMING_SNAKE_CASE       ) - 1,
                arr[len(_SCREAMING_SNAKE_CASE       ) - 1],
                temp_ls,
                None,
                temp_lr,
                None,
                result_pipe[len(_SCREAMING_SNAKE_CASE       ) - 1],
            ) ,   )       )

    # start the processes
    for p in process_array_:
        p.start()

    # wait for the processes to end and write their values to the list
    for p in range(0 ,   len(_SCREAMING_SNAKE_CASE       )       ):
        SCREAMING_SNAKE_CASE_									=						result_pipe[p][0].recv()
        process_array_[p].join()
    return arr


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						list(range(10 ,   0 ,   -1       )       )
    print('Initial List'       )
    print(*_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						odd_even_transposition(_SCREAMING_SNAKE_CASE       )
    print('Sorted List\n'       )
    print(*_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
       main()


