


'''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						=					get_logger(__name__)

def 					lowerCamelCase_ ( __UpperCamelCase					:       Dict							,    __UpperCamelCase					:       str							,    __UpperCamelCase					:       List[str]							,    __UpperCamelCase					:       Optional[int]							,    __UpperCamelCase					:       Optional[int]=0		)					->	List[str]:







     """simple docstring"""
     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		):
          _A				=							model.state_dict()
          if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
               _A				=							F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
               _A				=							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:
               _A				=							(
                   F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                   if model_index == 0
                   else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
               )
               _A				=							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:
               _A				=							os.path.join(__UpperCamelCase							,    F'{MODEL_NAME}_{model_index}'		)
               os.makedirs(__UpperCamelCase							,    exist_ok=__UpperCamelCase		)
               logger.info(F'Saving model to {ckpt_dir}'		)
               _A				=							{'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					:       Union[str, Any]							,    __UpperCamelCase					:       Dict							,    __UpperCamelCase					:       Union[str, Any]							,    __UpperCamelCase					:       Dict							,    __UpperCamelCase					:       List[Any]=0		)					->	Optional[int]:







     """simple docstring"""
     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
               _A				=							F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
               _A				=							os.path.join(__UpperCamelCase							,    __UpperCamelCase		)
               logger.info(F'Loading model from {input_model_file}'		)
               _A				=							torch.load(__UpperCamelCase		)
               logger.info(F'Model loaded from {input_model_file}'		)
          elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
               _A				=							(
                   F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                   if model_index == 0
                   else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
               )
               _A				=							os.path.join(__UpperCamelCase							,    __UpperCamelCase		)
               logger.info(F'Loading model from {input_model_file}'		)
               _A				=							torch.load(__UpperCamelCase		)
               logger.info(F'Model loaded from {input_model_file}'		)
          elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
               _A				=							(
                   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}'		)
               _A				=							{'model': model.state_dict()}
               dist_cp.load_state_dict(
                   state_dict=__UpperCamelCase							,    storage_reader=dist_cp.FileSystemReader(__UpperCamelCase		)							,    planner=DefaultLoadPlanner()							,    )
               _A				=							state_dict['model']
               logger.info(F'Model loaded from {ckpt_dir}'		)
          model.load_state_dict(__UpperCamelCase		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       List[Any]							,    __UpperCamelCase					:       Optional[Any]							,    __UpperCamelCase					:       Union[str, Any]							,    __UpperCamelCase					:       Any							,    __UpperCamelCase					:       Any							,    __UpperCamelCase					:       Dict=0		)					->	Tuple:







     """simple docstring"""
     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		):
          _A				=							FSDP.optim_state_dict(__UpperCamelCase							,    __UpperCamelCase		)
          if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
               if accelerator.process_index == 0:
                    _A				=							(
                        F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
                    )
                    _A				=							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:
               _A				=							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					:       Tuple							,    __UpperCamelCase					:       str							,    __UpperCamelCase					:       str							,    __UpperCamelCase					:       Tuple							,    __UpperCamelCase					:       str							,    __UpperCamelCase					:       Any=0		)					->	str:







     """simple docstring"""
     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:
               _A				=							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				=							(
                   F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
               )
               _A				=							os.path.join(__UpperCamelCase							,    __UpperCamelCase		)
               logger.info(F'Loading Optimizer state from {input_optimizer_file}'		)
               _A				=							torch.load(__UpperCamelCase		)
               logger.info(F'Optimizer state loaded from {input_optimizer_file}'		)
          else:
               _A				=							(
                   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}'		)
               _A				=							load_sharded_optimizer_state_dict(
                   model_state_dict=model.state_dict()							,    optimizer_key='optimizer'							,    storage_reader=dist_cp.FileSystemReader(__UpperCamelCase		)							,    )
               _A				=							optim_state['optimizer']
               logger.info(F'Optimizer loaded from {ckpt_dir}'		)
          _A				=							FSDP.optim_state_dict_to_load(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase		)
          optimizer.load_state_dict(__UpperCamelCase		)









'''simple docstring'''
from __future__ import annotations

def 					lowerCamelCase_ ( __UpperCamelCase					:       dict							,    __UpperCamelCase					:       str		)					->	set[str]:







     """simple docstring"""
     _A		,				_A				=							set(__UpperCamelCase		), [start]

     while stack:
          _A				=							stack.pop()
          explored.add(__UpperCamelCase		)
          # Differences from BFS:
          # 1) pop last element instead of first one
          # 2) add adjacent elements to stack without exploring them
          for adj in reversed(graph[v]		):
               if adj not in explored:
                    stack.append(__UpperCamelCase		)
     return explored


lowerCAmelCase						=					{
    """A""": ["""B""", """C""", """D"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B""", """D"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E""", """G"""],
    """G""": ["""F"""],
}

if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(depth_first_search(G, """A"""))





