

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


lowercase_       		=  get_logger(__name__)
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]=0      )							->      str:





    '''simple docstring'''






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

            dist_cp.save_state_dict(
                state_dict=UpperCamelCase__    ,			storage_writer=dist_cp.FileSystemWriter(UpperCamelCase__      )    ,			planner=DefaultSavePlanner()    ,			)
            logger.info(f'Model saved to {ckpt_dir}'      )
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]=0      )							->      List[Any]:





    '''simple docstring'''






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





    '''simple docstring'''






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

def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Any    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]=0      )							->      Tuple:





    '''simple docstring'''






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






"""simple docstring"""
def  lowerCamelCase_						(UpperCamelCase__		:		int     ,       UpperCamelCase__		:		int							):
    if a < 0 or b < 0:
        raise ValueError('''the value of both inputs must be positive'''							)
    _UpperCAmelCase							:  List[str]											=    str(bin(UpperCamelCase__							)							)[2:]  # remove the leading "0b"
    _UpperCAmelCase							:  str											=    str(bin(UpperCamelCase__							)							)[2:]
    _UpperCAmelCase							:  List[str]											=    max(len(UpperCamelCase__							)     ,       len(UpperCamelCase__							)							)
    return "0b" + "".join(
        str(int('''1''' in (char_a, char_b)							)							)
        for char_a, char_b in zip(a_binary.zfill(UpperCamelCase__							)     ,       b_binary.zfill(UpperCamelCase__							)							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()


