





'''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


lowercase__    :      Union[str, Any]						=    get_logger(__name__)






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






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




"""simple docstring"""







def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      str:
		if not isinstance(__lowerCamelCase						,     __lowerCamelCase	):
				raise ValueError('''iterations must be defined as integers'''	)
		if not isinstance(__lowerCamelCase						,     __lowerCamelCase	) or not number >= 1:
				raise ValueError(
				    '''starting number must be
                         and integer and be more than 0'''	)
		if not iterations >= 1:
				raise ValueError('''Iterations must be done more than 0 times to play FizzBuzz'''	)

		lowercase__   :    Tuple				=			''''''
		while number <= iterations:
				if number % 3 == 0:
						out += "Fizz"
				if number % 5 == 0:
						out += "Buzz"
				if 0 not in (number % 3, number % 5):
						out += str(__lowerCamelCase	)

				# print(out)
				number += 1
				out += " "
		return out


if __name__ == "__main__":
							import doctest

							doctest.testmod()
