



"""simple docstring"""
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    :int     =					get_logger(__name__)





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Optional[Any]=0							):
    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							):
        _UpperCAmelCase							:  Optional[Any]											=    model.state_dict()
        if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
            _UpperCAmelCase							:  Union[str, Any]											=    F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
            _UpperCAmelCase							:  Dict											=    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:
            _UpperCAmelCase							:  Dict											=    (
                F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                if model_index == 0
                else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
            )
            _UpperCAmelCase							:  Optional[int]											=    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:
            _UpperCAmelCase							:  str											=    os.path.join(UpperCamelCase__     ,       F'{MODEL_NAME}_{model_index}'							)
            os.makedirs(UpperCamelCase__     ,       exist_ok=UpperCamelCase__							)
            logger.info(F'Saving model to {ckpt_dir}'							)
            _UpperCAmelCase							:  List[str]											=    {'''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  lowerCamelCase_						(UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Union[str, Any]=0							):
    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
            _UpperCAmelCase							:  Union[str, Any]											=    F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
            _UpperCAmelCase							:  List[str]											=    os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)
            logger.info(F'Loading model from {input_model_file}'							)
            _UpperCAmelCase							:  List[str]											=    torch.load(UpperCamelCase__							)
            logger.info(F'Model loaded from {input_model_file}'							)
        elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
            _UpperCAmelCase							:  Optional[Any]											=    (
                F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                if model_index == 0
                else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
            )
            _UpperCAmelCase							:  Union[str, Any]											=    os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)
            logger.info(F'Loading model from {input_model_file}'							)
            _UpperCAmelCase							:  Tuple											=    torch.load(UpperCamelCase__							)
            logger.info(F'Model loaded from {input_model_file}'							)
        elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
            _UpperCAmelCase							:  Union[str, Any]											=    (
                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}'							)
            _UpperCAmelCase							:  Dict											=    {'''model''': model.state_dict()}
            dist_cp.load_state_dict(
                state_dict=UpperCamelCase__     ,       storage_reader=dist_cp.FileSystemReader(UpperCamelCase__							)     ,       planner=DefaultLoadPlanner()     ,       )
            _UpperCAmelCase							:  Any											=    state_dict['''model''']
            logger.info(F'Model loaded from {ckpt_dir}'							)
        model.load_state_dict(UpperCamelCase__							)





def  lowerCamelCase_						(UpperCamelCase__		:		Tuple     ,       UpperCamelCase__		:		Tuple     ,       UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Tuple     ,       UpperCamelCase__		:		Optional[Any]=0							):
    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							):
        _UpperCAmelCase							:  Optional[int]											=    FSDP.optim_state_dict(UpperCamelCase__     ,       UpperCamelCase__							)
        if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
            if accelerator.process_index == 0:
                _UpperCAmelCase							:  Tuple											=    (
                    F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
                )
                _UpperCAmelCase							:  Optional[int]											=    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:
            _UpperCAmelCase							:  Tuple											=    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  lowerCamelCase_						(UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		Any     ,       UpperCamelCase__		:		Optional[int]     ,       UpperCamelCase__		:		Optional[Any]=0							):
    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:
            _UpperCAmelCase							:  List[str]											=    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:
            _UpperCAmelCase							:  Any											=    (
                F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
            )
            _UpperCAmelCase							:  Dict											=    os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)
            logger.info(F'Loading Optimizer state from {input_optimizer_file}'							)
            _UpperCAmelCase							:  Tuple											=    torch.load(UpperCamelCase__							)
            logger.info(F'Optimizer state loaded from {input_optimizer_file}'							)
        else:
            _UpperCAmelCase							:  Union[str, Any]											=    (
                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}'							)
            _UpperCAmelCase							:  Tuple											=    load_sharded_optimizer_state_dict(
                model_state_dict=model.state_dict()     ,       optimizer_key='''optimizer'''     ,       storage_reader=dist_cp.FileSystemReader(UpperCamelCase__							)     ,       )
            _UpperCAmelCase							:  List[Any]											=    optim_state['''optimizer''']
            logger.info(F'Optimizer loaded from {ckpt_dir}'							)
        _UpperCAmelCase							:  Union[str, Any]											=    FSDP.optim_state_dict_to_load(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
        optimizer.load_state_dict(UpperCamelCase__							)







"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowerCAmelCase    :Optional[int]     =					logging.get_logger(__name__)
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     def __init__(					self       ,	*A       ,	**A		)							->					None:
         warnings.warn(
             '''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
             ''' use DonutImageProcessor instead.'''       ,	A       ,	)
         super().__init__(*A       ,	**A		)


