




import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
 from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm






def  snake_case(    __magic_name__   )			->							Tuple:
     if is_torch_version('''<'''			,		'''2.0.0'''   ) or not hasattr(snake_case_			,		'''_dynamo'''   ):
          return False
     return isinstance(snake_case_			,		torch._dynamo.eval_frame.OptimizedModule   )






def  snake_case(    __magic_name__			,		__magic_name__ = True   )			->							Union[str, Any]:
     lowercase :       Any       =			(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

     lowercase :       Union[str, Any]       =			is_compiled_module(snake_case_   )
     if is_compiled:
          lowercase :       str       =			model
          lowercase :       List[Any]       =			model._orig_mod

     if is_deepspeed_available():
          options += (DeepSpeedEngine,)

     while isinstance(snake_case_			,		snake_case_   ):
          lowercase :       List[str]       =			model.module

     if not keep_fpaa_wrapper:
          lowercase :       Any       =			getattr(snake_case_			,		'''forward'''   )
          lowercase :       Union[str, Any]       =			model.__dict__.pop('''_original_forward'''			,		snake_case_   )
          if original_forward is not None:
               while hasattr(snake_case_			,		'''__wrapped__'''   ):
                    lowercase :       Union[str, Any]       =			forward.__wrapped__
                    if forward == original_forward:
                         break
               lowercase :       Any       =			forward
          if getattr(snake_case_			,		'''_converted_to_transformer_engine'''			,		snake_case_   ):
               convert_model(snake_case_			,		to_transformer_engine=snake_case_   )

     if is_compiled:
          lowercase :       Union[str, Any]       =			model
          lowercase :       Dict       =			compiled_model

     return model






def  snake_case(    )			->							int:
     PartialState().wait_for_everyone()






def  snake_case(    __magic_name__			,		__magic_name__   )			->							Any:
     if PartialState().distributed_type == DistributedType.TPU:
          xm.save(snake_case_			,		snake_case_   )
     elif PartialState().local_process_index == 0:
          torch.save(snake_case_			,		snake_case_   )






@contextmanager
def  snake_case(    **__magic_name__   )			->							Optional[Any]:
     for key, value in kwargs.items():
          lowercase :       Tuple       =			str(snake_case_   )

     yield

     for key in kwargs:
          if key.upper() in os.environ:
               del os.environ[key.upper()]






def  snake_case(    __magic_name__   )			->							str:
     if not hasattr(snake_case_			,		'''__qualname__'''   ) and not hasattr(snake_case_			,		'''__name__'''   ):
          lowercase :       Tuple       =			getattr(snake_case_			,		'''__class__'''			,		snake_case_   )
     if hasattr(snake_case_			,		'''__qualname__'''   ):
          return obj.__qualname__
     if hasattr(snake_case_			,		'''__name__'''   ):
          return obj.__name__
     return str(snake_case_   )






def  snake_case(    __magic_name__			,		__magic_name__   )			->							Tuple:
     for key, value in source.items():
          if isinstance(snake_case_			,		snake_case_   ):
               lowercase :       List[str]       =			destination.setdefault(snake_case_			,		{}   )
               merge_dicts(snake_case_			,		snake_case_   )
          else:
               lowercase :       List[str]       =			value

     return destination






def  snake_case(    __magic_name__ = None   )			->							List[Any]:
     if port is None:
          lowercase :       Optional[int]       =			2_95_00
     with socket.socket(socket.AF_INET			,		socket.SOCK_STREAM   ) as s:
          return s.connect_ex(('''localhost''', port)   ) == 0





from typing import Union

import fire
import torch
from tqdm import tqdm






def  snake_case(    __magic_name__			,		__magic_name__ = "cpu"			,		__magic_name__ = None   )			->							None:





     '''simple docstring'''


     lowercase :       int       =			torch.load(__magic_name__			,		map_location=__magic_name__   )
     for k, v in tqdm(state_dict.items()   ):
          if not isinstance(__magic_name__			,		torch.Tensor   ):
               raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'''   )
          lowercase :       Any       =			v.half()
     if save_path is None:  # overwrite src_path
          lowercase :       str       =			src_path
     torch.save(__magic_name__			,		__magic_name__   )


if __name__ == "__main__":
 fire.Fire(convert)