
'''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


UpperCamelCase__      :		Union[str, Any]       							=  get_logger(__name__)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=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						):
      _SCREAMING_SNAKE_CASE      =				model.state_dict()
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         _SCREAMING_SNAKE_CASE      =				F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
         _SCREAMING_SNAKE_CASE      =				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:
         _SCREAMING_SNAKE_CASE      =				(
             F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
             if model_index == 0
             else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
         )
         _SCREAMING_SNAKE_CASE      =				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:
         _SCREAMING_SNAKE_CASE      =				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}"						)
         _SCREAMING_SNAKE_CASE      =				{"""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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=0						)     ->     Union[str, 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
         _SCREAMING_SNAKE_CASE      =				F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
         _SCREAMING_SNAKE_CASE      =				os.path.join(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
         logger.info(F"Loading model from {input_model_file}"						)
         _SCREAMING_SNAKE_CASE      =				torch.load(SCREAMING_SNAKE_CASE_						)
         logger.info(F"Model loaded from {input_model_file}"						)
      elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
         _SCREAMING_SNAKE_CASE      =				(
             F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
             if model_index == 0
             else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
         )
         _SCREAMING_SNAKE_CASE      =				os.path.join(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
         logger.info(F"Loading model from {input_model_file}"						)
         _SCREAMING_SNAKE_CASE      =				torch.load(SCREAMING_SNAKE_CASE_						)
         logger.info(F"Model loaded from {input_model_file}"						)
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         _SCREAMING_SNAKE_CASE      =				(
             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}"						)
         _SCREAMING_SNAKE_CASE      =				{"""model""": model.state_dict()}
         dist_cp.load_state_dict(
             state_dict=SCREAMING_SNAKE_CASE_    ,      storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_						)    ,      planner=DefaultLoadPlanner()    ,      )
         _SCREAMING_SNAKE_CASE      =				state_dict["""model"""]
         logger.info(F"Model loaded from {ckpt_dir}"						)
      model.load_state_dict(SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=0						)     ->     str:




   """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						):
      _SCREAMING_SNAKE_CASE      =				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:
            _SCREAMING_SNAKE_CASE      =				(
                F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
            )
            _SCREAMING_SNAKE_CASE      =				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:
         _SCREAMING_SNAKE_CASE      =				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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=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:
         _SCREAMING_SNAKE_CASE      =				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:
         _SCREAMING_SNAKE_CASE      =				(
             F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
         )
         _SCREAMING_SNAKE_CASE      =				os.path.join(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
         logger.info(F"Loading Optimizer state from {input_optimizer_file}"						)
         _SCREAMING_SNAKE_CASE      =				torch.load(SCREAMING_SNAKE_CASE_						)
         logger.info(F"Optimizer state loaded from {input_optimizer_file}"						)
      else:
         _SCREAMING_SNAKE_CASE      =				(
             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}"						)
         _SCREAMING_SNAKE_CASE      =				load_sharded_optimizer_state_dict(
             model_state_dict=model.state_dict()    ,      optimizer_key="""optimizer"""    ,      storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_						)    ,      )
         _SCREAMING_SNAKE_CASE      =				optim_state["""optimizer"""]
         logger.info(F"Optimizer loaded from {ckpt_dir}"						)
      _SCREAMING_SNAKE_CASE      =				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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""

   if not isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      raise ValueError("""multiplicative_persistence() only accepts integral values"""						)
   if num < 0:
      raise ValueError("""multiplicative_persistence() does not accept negative values"""						)

   _SCREAMING_SNAKE_CASE      =				0
   _SCREAMING_SNAKE_CASE      =				str(SCREAMING_SNAKE_CASE_						)

   while len(SCREAMING_SNAKE_CASE_						) != 1:
      _SCREAMING_SNAKE_CASE      =				[int(SCREAMING_SNAKE_CASE_						) for i in num_string]

      _SCREAMING_SNAKE_CASE      =				1
      for i in range(0    ,      len(SCREAMING_SNAKE_CASE_						)						):
         total *= numbers[i]

      _SCREAMING_SNAKE_CASE      =				str(SCREAMING_SNAKE_CASE_						)

      steps += 1
   return steps
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""

   if not isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      raise ValueError("""additive_persistence() only accepts integral values"""						)
   if num < 0:
      raise ValueError("""additive_persistence() does not accept negative values"""						)

   _SCREAMING_SNAKE_CASE      =				0
   _SCREAMING_SNAKE_CASE      =				str(SCREAMING_SNAKE_CASE_						)

   while len(SCREAMING_SNAKE_CASE_						) != 1:
      _SCREAMING_SNAKE_CASE      =				[int(SCREAMING_SNAKE_CASE_						) for i in num_string]

      _SCREAMING_SNAKE_CASE      =				0
      for i in range(0    ,      len(SCREAMING_SNAKE_CASE_						)						):
         total += numbers[i]

      _SCREAMING_SNAKE_CASE      =				str(SCREAMING_SNAKE_CASE_						)

      steps += 1
   return steps


if __name__ == "__main__":
     import doctest

     doctest.testmod()






