
"""simple docstring"""



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  _UpperCamelCase	(						_A      )    ->  List[str]:





    """simple docstring"""

    if is_torch_version("""<"""					,      """2.0.0"""      ) or not hasattr(_A					,      """_dynamo"""      ):
        return False
    return isinstance(_A					,      torch._dynamo.eval_frame.OptimizedModule      )


def  _UpperCamelCase	(						_A					,      _A = True      )    ->  List[str]:





    """simple docstring"""

    _UpperCAmelCase          =   (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

    _UpperCAmelCase          =   is_compiled_module(_A      )
    if is_compiled:
        _UpperCAmelCase          =   model
        _UpperCAmelCase          =   model._orig_mod

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

    while isinstance(_A					,      _A      ):
        _UpperCAmelCase          =   model.module

    if not keep_fpaa_wrapper:
        _UpperCAmelCase          =   getattr(_A					,      """forward"""      )
        _UpperCAmelCase          =   model.__dict__.pop("""_original_forward"""					,      _A      )
        if original_forward is not None:
            while hasattr(_A					,      """__wrapped__"""      ):
                _UpperCAmelCase          =   forward.__wrapped__
                if forward == original_forward:
                    break
            _UpperCAmelCase          =   forward
        if getattr(_A					,      """_converted_to_transformer_engine"""					,      _A      ):
            convert_model(_A					,      to_transformer_engine=_A      )

    if is_compiled:
        _UpperCAmelCase          =   model
        _UpperCAmelCase          =   compiled_model

    return model


def  _UpperCamelCase	(						)    ->  str:





    """simple docstring"""

    PartialState().wait_for_everyone()


def  _UpperCamelCase	(						_A					,      _A      )    ->  str:





    """simple docstring"""

    if PartialState().distributed_type == DistributedType.TPU:
        xm.save(_A					,      _A      )
    elif PartialState().local_process_index == 0:
        torch.save(_A					,      _A      )


@contextmanager
def  _UpperCamelCase	(						**_A      )    ->  List[str]:





    """simple docstring"""

    for key, value in kwargs.items():
        _UpperCAmelCase          =   str(_A      )

    yield

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


def  _UpperCamelCase	(						_A      )    ->  Tuple:





    """simple docstring"""

    if not hasattr(_A					,      """__qualname__"""      ) and not hasattr(_A					,      """__name__"""      ):
        _UpperCAmelCase          =   getattr(_A					,      """__class__"""					,      _A      )
    if hasattr(_A					,      """__qualname__"""      ):
        return obj.__qualname__
    if hasattr(_A					,      """__name__"""      ):
        return obj.__name__
    return str(_A      )


def  _UpperCamelCase	(						_A					,      _A      )    ->  str:





    """simple docstring"""

    for key, value in source.items():
        if isinstance(_A					,      _A      ):
            _UpperCAmelCase          =   destination.setdefault(_A					,      {}      )
            merge_dicts(_A					,      _A      )
        else:
            _UpperCAmelCase          =   value

    return destination







def  _UpperCamelCase	(						_A = None      )    ->  bool:





    """simple docstring"""

    if port is None:
        _UpperCAmelCase          =   2_9_5_0_0
    with socket.socket(socket.AF_INET					,      socket.SOCK_STREAM      ) as s:
        return s.connect_ex(("""localhost""", port)      ) == 0

"""simple docstring"""



import re

from filelock import FileLock


try:
 import nltk

 a					:	str							=		True
except (ImportError, ModuleNotFoundError):
 a					:	List[str]							=		False

if NLTK_AVAILABLE:
 with FileLock('''.lock''') as lock:
  nltk.download('''punkt''', quiet=True)


def  _UpperCamelCase	(						_A      )    ->  str:





    """simple docstring"""

    re.sub("""<n>"""					,      """"""					,      _A      )  # remove pegasus newline char
    assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
    return "\n".join(nltk.sent_tokenize(_A      )      )