

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


_lowerCAmelCase						=		get_logger(__name__)

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=0   ):

    '''simple docstring'''



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

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

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=0   ):

    '''simple docstring'''



    accelerator.wait_for_everyone()
    with FSDP.state_dict_type(
        _lowerCamelCase ,   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(_lowerCamelCase   ) != 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
            _lowerCAmelCase       :   Optional[Any]  =    f"""{MODEL_NAME}.bin""" if model_index == 0 else f"""{MODEL_NAME}_{model_index}.bin"""
            _lowerCAmelCase       :   Dict  =    os.path.join(_lowerCamelCase ,   _lowerCamelCase   )
            logger.info(f"""Loading model from {input_model_file}"""   )
            _lowerCAmelCase       :   List[Any]  =    torch.load(_lowerCamelCase   )
            logger.info(f"""Model loaded from {input_model_file}"""   )
        elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
            _lowerCAmelCase       :   Any  =    (
                f"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
                if model_index == 0
                else f"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
            )
            _lowerCAmelCase       :   int  =    os.path.join(_lowerCamelCase ,   _lowerCamelCase   )
            logger.info(f"""Loading model from {input_model_file}"""   )
            _lowerCAmelCase       :   Tuple  =    torch.load(_lowerCamelCase   )
            logger.info(f"""Model loaded from {input_model_file}"""   )
        elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
            _lowerCAmelCase       :   Optional[Any]  =    (
                os.path.join(_lowerCamelCase ,   f"""{MODEL_NAME}_{model_index}"""   )
                if f"""{MODEL_NAME}""" not in input_dir
                else input_dir
            )
            logger.info(f"""Loading model from {ckpt_dir}"""   )
            _lowerCAmelCase       :   Any  =    {'model': model.state_dict()}
            dist_cp.load_state_dict(
                state_dict=_lowerCamelCase ,   storage_reader=dist_cp.FileSystemReader(_lowerCamelCase   ) ,   planner=DefaultLoadPlanner() ,   )
            _lowerCAmelCase       :   List[str]  =    state_dict['model']
            logger.info(f"""Model loaded from {ckpt_dir}"""   )
        model.load_state_dict(_lowerCamelCase   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=0   ):

    '''simple docstring'''



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

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=0   ):

    '''simple docstring'''



    accelerator.wait_for_everyone()
    with FSDP.state_dict_type(
        _lowerCamelCase ,   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:
            _lowerCAmelCase       :   Optional[int]  =    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:
            _lowerCAmelCase       :   int  =    (
                f"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else f"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
            )
            _lowerCAmelCase       :   int  =    os.path.join(_lowerCamelCase ,   _lowerCamelCase   )
            logger.info(f"""Loading Optimizer state from {input_optimizer_file}"""   )
            _lowerCAmelCase       :   Tuple  =    torch.load(_lowerCamelCase   )
            logger.info(f"""Optimizer state loaded from {input_optimizer_file}"""   )
        else:
            _lowerCAmelCase       :   str  =    (
                os.path.join(_lowerCamelCase ,   f"""{OPTIMIZER_NAME}_{optimizer_index}"""   )
                if f"""{OPTIMIZER_NAME}""" not in input_dir
                else input_dir
            )
            logger.info(f"""Loading Optimizer from {ckpt_dir}"""   )
            _lowerCAmelCase       :   List[Any]  =    load_sharded_optimizer_state_dict(
                model_state_dict=model.state_dict() ,   optimizer_key='optimizer' ,   storage_reader=dist_cp.FileSystemReader(_lowerCamelCase   ) ,   )
            _lowerCAmelCase       :   int  =    optim_state['optimizer']
            logger.info(f"""Optimizer loaded from {ckpt_dir}"""   )
        _lowerCAmelCase       :   str  =    FSDP.optim_state_dict_to_load(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )
        optimizer.load_state_dict(_lowerCamelCase   )








"""simple docstring"""






from __future__ import annotations

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    create_state_space_tree(_lowerCamelCase ,   [] ,   0 ,   [0 for i in range(len(_lowerCamelCase   )   )]   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   ):

    '''simple docstring'''



    if index == len(_lowerCamelCase   ):
        print(_lowerCamelCase   )
        return

    for i in range(len(_lowerCamelCase   )   ):
        if not index_used[i]:
            current_sequence.append(sequence[i]   )
            _lowerCAmelCase       :   List[str]  =    True
            create_state_space_tree(_lowerCamelCase ,   _lowerCamelCase ,   index + 1 ,   _lowerCamelCase   )
            current_sequence.pop()
            _lowerCAmelCase       :   int  =    False
_lowerCAmelCase						=		[3, 1, 2, 4]
generate_all_permutations(sequence)

_lowerCAmelCase						=		["A", "B", "C"]

generate_all_permutations(sequence_a)





