





import enum
import shutil
import sys


lowercase						, lowercase				:	List[Any]      						=  shutil.get_terminal_size()

lowercase				:	Union[str, Any]      						=  {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}





class 			A__   (							enum.Enum     ):




   """simple docstring"""



   __A       :    List[str]	  =  0
   __A       :    str	  =  1



def A_     (		A__		,				A__=""    )  ->		int:
       sys.stdout.write(str(A__    ) + end    )
       sys.stdout.flush()



def A_     (		A__		,				A__		,				A__=""    )  ->		int:
       forceWrite(F'\u001b[{color}m{content}\u001b[0m'		,				A__    )



def A_     (		)  ->		Any:
       forceWrite('\r'    )



def A_     (		A__		,				A__    )  ->		List[str]:
       forceWrite(F'\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'    )



def A_     (		)  ->		Any:
       forceWrite(' ' * TERMINAL_WIDTH    )
       reset_cursor()



def A_     (		)  ->		Any:
       reset_cursor()
       forceWrite('-' * TERMINAL_WIDTH    )









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 A_     (		A__    )  ->		Optional[int]:
       if is_torch_version('<'		,				'2.0.0'    ) or not hasattr(A__		,				'_dynamo'    ):
              return False
       return isinstance(A__		,				torch._dynamo.eval_frame.OptimizedModule    )



def A_     (		A__		,				A__ = True    )  ->		int:
       a__						:     Optional[Any]			=    (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

       a__						:     Union[str, Any]			=    is_compiled_module(A__    )
       if is_compiled:
              a__						:     List[str]			=    model
              a__						:     Dict			=    model._orig_mod

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

       while isinstance(A__		,				A__    ):
              a__						:     str			=    model.module

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

       if is_compiled:
              a__						:     List[str]			=    model
              a__						:     Any			=    compiled_model

       return model



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



def A_     (		A__		,				A__    )  ->		Dict:
       if PartialState().distributed_type == DistributedType.TPU:
              xm.save(A__		,				A__    )
       elif PartialState().local_process_index == 0:
              torch.save(A__		,				A__    )



@contextmanager
def A_     (		**A__    )  ->		Any:
       for key, value in kwargs.items():
              a__						:     Optional[int]			=    str(A__    )

       yield

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



def A_     (		A__    )  ->		List[str]:
       if not hasattr(A__		,				'__qualname__'    ) and not hasattr(A__		,				'__name__'    ):
              a__						:     Dict			=    getattr(A__		,				'__class__'		,				A__    )
       if hasattr(A__		,				'__qualname__'    ):
              return obj.__qualname__
       if hasattr(A__		,				'__name__'    ):
              return obj.__name__
       return str(A__    )



def A_     (		A__		,				A__    )  ->		Dict:
       for key, value in source.items():
              if isinstance(A__		,				A__    ):
                     a__						:     Optional[Any]			=    destination.setdefault(A__		,				{}    )
                     merge_dicts(A__		,				A__    )
              else:
                     a__						:     Optional[int]			=    value

       return destination



def A_     (		A__ = None    )  ->		bool:
       if port is None:
              a__						:     List[Any]			=    2_9500
       with socket.socket(socket.AF_INET		,				socket.SOCK_STREAM    ) as s:
              return s.connect_ex(('localhost', port)    ) == 0


