from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
      import torch

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


SCREAMING_SNAKE_CASE__		:					Tuple						=					logging.get_logger(__name__)

@dataclass
class       snake_case					(      _a						):
  lowercase_			=     [
      """no_inference""",
      """no_cuda""",
      """no_tpu""",
      """no_speed""",
      """no_memory""",
      """no_env_print""",
      """no_multi_process""",
  ]




  def __init__(      self     :	Optional[Any]     , **a_     :	str				)->       str:




       """simple docstring"""
       for deprecated_arg in self.deprecated_args:
            if deprecated_arg in kwargs:
                 SCREAMING_SNAKE_CASE__				:				str			=		deprecated_arg[3:]
                 setattr(self     , snake_case_     , not kwargs.pop(snake_case_				)				)
                 logger.warning(
                     F'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
                     F''' {positive_arg}={kwargs[positive_arg]}'''				)

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		kwargs.pop('torchscript'     , self.torchscript				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		kwargs.pop('torch_xla_tpu_print_metrics'     , self.torch_xla_tpu_print_metrics				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		kwargs.pop('fp16_opt_level'     , self.fpaa_opt_level				)
       super().__init__(**snake_case_				)

  lowercase_			=     field(default=_a   ,						metadata={'help': 'Trace the models using torchscript'}						)
  lowercase_			=     field(default=_a   ,						metadata={'help': 'Print Xla/PyTorch tpu metrics'}						)
  lowercase_			=     field(
      default='O1'   ,						metadata={
          'help': (
              'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '
              'See details at https://nvidia.github.io/apex/amp.html'
          )
      }   ,						)




  @cached_property
  def 	__lowercase(      self     :	List[Any]				)->       Tuple["torch.device", int]:




       """simple docstring"""
       requires_backends(self     , ['torch']				)
       logger.info('PyTorch: setting up devices'				)
       if not self.cuda:
            SCREAMING_SNAKE_CASE__				:				str			=		torch.device('cpu'				)
            SCREAMING_SNAKE_CASE__				:				Dict			=		0
       elif is_torch_tpu_available():
            SCREAMING_SNAKE_CASE__				:				Any			=		xm.xla_device()
            SCREAMING_SNAKE_CASE__				:				Any			=		0
       else:
            SCREAMING_SNAKE_CASE__				:				Dict			=		torch.device('cuda' if torch.cuda.is_available() else 'cpu'				)
            SCREAMING_SNAKE_CASE__				:				Dict			=		torch.cuda.device_count()
       return device, n_gpu




  @property
  def 	__lowercase(      self     :	List[str]				)->       Tuple:




       """simple docstring"""
       return is_torch_tpu_available() and self.tpu




  @property
  def 	__lowercase(      self     :	Union[str, Any]				)->       int:




       """simple docstring"""
       requires_backends(self     , ['torch']				)
       # TODO(PVP): currently only single GPU is supported
       return torch.cuda.current_device()




  @property
  def 	__lowercase(      self     :	List[str]				)->       "torch.device":




       """simple docstring"""
       requires_backends(self     , ['torch']				)
       return self._setup_devices[0]




  @property
  def 	__lowercase(      self     :	List[str]				)->       int:




       """simple docstring"""
       requires_backends(self     , ['torch']				)
       return self._setup_devices[1]




  @property
  def 	__lowercase(      self     :	List[str]				)->       Dict:




       """simple docstring"""
       return self.n_gpu > 0




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				(      lowercase__  :			int ):







     '''simple docstring'''


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







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			bool = True ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

     SCREAMING_SNAKE_CASE__				:				Dict			=		is_compiled_module(lowercase__ )
     if is_compiled:
          SCREAMING_SNAKE_CASE__				:				Tuple			=		model
          SCREAMING_SNAKE_CASE__				:				int			=		model._orig_mod

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

     while isinstance(lowercase__     ,  lowercase__ ):
          SCREAMING_SNAKE_CASE__				:				Any			=		model.module

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

     if is_compiled:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		model
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		compiled_model

     return model







def     _a				(      ):







     '''simple docstring'''


     PartialState().wait_for_everyone()







def     _a				(      lowercase__  :			str     ,  lowercase__  :			Optional[Any] ):







     '''simple docstring'''


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







@contextmanager
def     _a				(      **lowercase__  :			str ):







     '''simple docstring'''


     for key, value in kwargs.items():
          SCREAMING_SNAKE_CASE__				:				int			=		str(lowercase__ )

     yield

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







def     _a				(      lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if not hasattr(lowercase__     ,  '__qualname__' ) and not hasattr(lowercase__     ,  '__name__' ):
          SCREAMING_SNAKE_CASE__				:				Any			=		getattr(lowercase__     ,  '__class__'     ,  lowercase__ )
     if hasattr(lowercase__     ,  '__qualname__' ):
          return obj.__qualname__
     if hasattr(lowercase__     ,  '__name__' ):
          return obj.__name__
     return str(lowercase__ )







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			List[Any] ):







     '''simple docstring'''


     for key, value in source.items():
          if isinstance(lowercase__     ,  lowercase__ ):
               SCREAMING_SNAKE_CASE__				:				List[str]			=		destination.setdefault(lowercase__     ,  {} )
               merge_dicts(lowercase__     ,  lowercase__ )
          else:
               SCREAMING_SNAKE_CASE__				:				List[Any]			=		value

     return destination







def     _a				(      lowercase__  :			int = None ):







     '''simple docstring'''


     if port is None:
          SCREAMING_SNAKE_CASE__				:				int			=		2_95_00
     with socket.socket(socket.AF_INET     ,  socket.SOCK_STREAM ) as s:
          return s.connect_ex(('localhost', port) ) == 0



