




"""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   =       get_logger(__name__)

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase=0							)			->			int:


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

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

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase=0							)			->			List[Any]:


   """simple docstring"""
   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       __SCREAMING_SNAKE_CASE				,       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(__SCREAMING_SNAKE_CASE							) != 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	: int		=					f"{MODEL_NAME}.bin" if model_index == 0 else f"{MODEL_NAME}_{model_index}.bin"
         __UpperCAmelCase	: Any		=					os.path.join(__SCREAMING_SNAKE_CASE				,       __SCREAMING_SNAKE_CASE							)
         logger.info(f"Loading model from {input_model_file}"							)
         __UpperCAmelCase	: Optional[Any]		=					torch.load(__SCREAMING_SNAKE_CASE							)
         logger.info(f"Model loaded from {input_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(__SCREAMING_SNAKE_CASE				,       __SCREAMING_SNAKE_CASE							)
         logger.info(f"Loading model from {input_model_file}"							)
         __UpperCAmelCase	: Dict		=					torch.load(__SCREAMING_SNAKE_CASE							)
         logger.info(f"Model loaded from {input_model_file}"							)
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         __UpperCAmelCase	: Optional[Any]		=					(
             os.path.join(__SCREAMING_SNAKE_CASE				,       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=__SCREAMING_SNAKE_CASE				,       storage_reader=dist_cp.FileSystemReader(__SCREAMING_SNAKE_CASE							)				,       planner=DefaultLoadPlanner()				,       )
         __UpperCAmelCase	: int		=					state_dict["model"]
         logger.info(f"Model loaded from {ckpt_dir}"							)
      model.load_state_dict(__SCREAMING_SNAKE_CASE							)

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase=0							)			->			Optional[Any]:


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





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase=0							)			->			Optional[Any]:


   """simple docstring"""
   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       __SCREAMING_SNAKE_CASE				,       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	: 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:
         __UpperCAmelCase	: Tuple		=					(
             f"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else f"{OPTIMIZER_NAME}_{optimizer_index}.bin"
         )
         __UpperCAmelCase	: Union[str, Any]		=					os.path.join(__SCREAMING_SNAKE_CASE				,       __SCREAMING_SNAKE_CASE							)
         logger.info(f"Loading Optimizer state from {input_optimizer_file}"							)
         __UpperCAmelCase	: List[str]		=					torch.load(__SCREAMING_SNAKE_CASE							)
         logger.info(f"Optimizer state loaded from {input_optimizer_file}"							)
      else:
         __UpperCAmelCase	: Dict		=					(
             os.path.join(__SCREAMING_SNAKE_CASE				,       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	: str		=					load_sharded_optimizer_state_dict(
             model_state_dict=model.state_dict()				,       optimizer_key="optimizer"				,       storage_reader=dist_cp.FileSystemReader(__SCREAMING_SNAKE_CASE							)				,       )
         __UpperCAmelCase	: List[str]		=					optim_state["optimizer"]
         logger.info(f"Optimizer loaded from {ckpt_dir}"							)
      __UpperCAmelCase	: str		=					FSDP.optim_state_dict_to_load(__SCREAMING_SNAKE_CASE				,       __SCREAMING_SNAKE_CASE				,       __SCREAMING_SNAKE_CASE							)
      optimizer.load_state_dict(__SCREAMING_SNAKE_CASE							)








"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase							)			->			int:


			"""simple docstring"""

			if not grid or not grid[0]:
						raise TypeError("The grid does not contain the appropriate information"							)

			for cell_n in range(1				,       len(grid[0]							)							):
						grid[0][cell_n] += grid[0][cell_n - 1]
			__UpperCAmelCase	: int		=					grid[0]

			for row_n in range(1				,       len(UpperCamelCase							)							):
						__UpperCAmelCase	: int		=					grid[row_n]
						__UpperCAmelCase	: str		=					fill_row(UpperCamelCase				,       UpperCamelCase							)
						__UpperCAmelCase	: List[str]		=					grid[row_n]

			return grid[-1][-1]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			list:


			"""simple docstring"""
			current_row[0] += row_above[0]
			for cell_n in range(1				,       len(UpperCamelCase							)							):
						current_row[cell_n] += min(current_row[cell_n - 1]				,       row_above[cell_n]							)

			return current_row


if __name__ == "__main__":
					import doctest

					doctest.testmod()


