'''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


__a: List[str]          =   get_logger(__name__)
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=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     ):
  lowercase__				:    int  =					model.state_dict()
  if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
   lowercase__				:    int  =					F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
   lowercase__				:    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:
   lowercase__				:    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"""
   )
   lowercase__				:    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:
   lowercase__				:    int  =					os.path.join(UpperCAmelCase		,     F"""{MODEL_NAME}_{model_index}"""     )
   os.makedirs(UpperCAmelCase		,     exist_ok=UpperCAmelCase     )
   logger.info(F"""Saving model to {ckpt_dir}"""     )
   lowercase__				:    Dict  =					{'''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 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=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
   lowercase__				:    Optional[int]  =					F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
   lowercase__				:    str  =					os.path.join(UpperCAmelCase		,     UpperCAmelCase     )
   logger.info(F"""Loading model from {input_model_file}"""     )
   lowercase__				:    List[str]  =					torch.load(UpperCAmelCase     )
   logger.info(F"""Model loaded from {input_model_file}"""     )
  elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
   lowercase__				:    Any  =					(
       F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
       if model_index == 0
       else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
   )
   lowercase__				:    Union[str, Any]  =					os.path.join(UpperCAmelCase		,     UpperCAmelCase     )
   logger.info(F"""Loading model from {input_model_file}"""     )
   lowercase__				:    Dict  =					torch.load(UpperCAmelCase     )
   logger.info(F"""Model loaded from {input_model_file}"""     )
  elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
   lowercase__				:    List[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}"""     )
   lowercase__				:    Any  =					{'''model''': model.state_dict()}
   dist_cp.load_state_dict(
       state_dict=UpperCAmelCase		,     storage_reader=dist_cp.FileSystemReader(UpperCAmelCase     )		,     planner=DefaultLoadPlanner()		,     )
   lowercase__				:    Union[str, Any]  =					state_dict['''model''']
   logger.info(F"""Model loaded from {ckpt_dir}"""     )
  model.load_state_dict(UpperCAmelCase     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=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     ):
  lowercase__				:    str  =					FSDP.optim_state_dict(UpperCAmelCase		,     UpperCAmelCase     )
  if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
   if accelerator.process_index == 0:
    lowercase__				:    int  =					(
        F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
    )
    lowercase__				:    Tuple  =					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:
   lowercase__				:    Dict  =					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 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=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:
   lowercase__				:    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:
   lowercase__				:    Tuple  =					(
       F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
   )
   lowercase__				:    Union[str, Any]  =					os.path.join(UpperCAmelCase		,     UpperCAmelCase     )
   logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""     )
   lowercase__				:    List[str]  =					torch.load(UpperCAmelCase     )
   logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""     )
  else:
   lowercase__				:    int  =					(
       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}"""     )
   lowercase__				:    List[str]  =					load_sharded_optimizer_state_dict(
       model_state_dict=model.state_dict()		,     optimizer_key='''optimizer'''		,     storage_reader=dist_cp.FileSystemReader(UpperCAmelCase     )		,     )
   lowercase__				:    List[str]  =					optim_state['''optimizer''']
   logger.info(F"""Optimizer loaded from {ckpt_dir}"""     )
  lowercase__				:    Union[str, Any]  =					FSDP.optim_state_dict_to_load(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
  optimizer.load_state_dict(UpperCAmelCase     )


'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a: List[str]          =   logging.get_logger(__name__)



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    "encoder-decoder"
       SCREAMING_SNAKE_CASE							=    True

       def __init__(						self	,   **__lowerCAmelCase )  ->  int:
        super().__init__(**__lowerCAmelCase )
        assert (
            "encoder" in kwargs and "decoder" in kwargs
        ), "Config has to be initialized with encoder and decoder config"
        lowercase__				:    Optional[int]  =					kwargs.pop('''encoder''' )
        lowercase__				:    Union[str, Any]  =					encoder_config.pop('''model_type''' )
        lowercase__				:    Any  =					kwargs.pop('''decoder''' )
        lowercase__				:    Any  =					decoder_config.pop('''model_type''' )

        from ..auto.configuration_auto import AutoConfig

        lowercase__				:    Union[str, Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Optional[Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Tuple  =					True

       @classmethod
       def _lowerCAmelCase(						cls	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase )  ->  PretrainedConfig:
        logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
        lowercase__				:    Union[str, Any]  =					True
        lowercase__				:    Any  =					True

        return cls(encoder=encoder_config.to_dict()	,   decoder=decoder_config.to_dict()	,   **__lowerCAmelCase )


       def _lowerCAmelCase(						self )  ->  Any:
        lowercase__				:    Any  =					copy.deepcopy(self.__dict__ )
        lowercase__				:    Optional[Any]  =					self.encoder.to_dict()
        lowercase__				:    Tuple  =					self.decoder.to_dict()
        lowercase__				:    Dict  =					self.__class__.model_type
        return output

