


"""simple docstring"""






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


_lowerCAmelCase							:    Union[str, Any]					   =	logging.get_logger(__name__)




@dataclass
class 							A_      (       _a ):
  lowerCAmelCase__      			=	[
      'no_inference',
      'no_cuda',
      'no_tpu',
      'no_speed',
      'no_memory',
      'no_env_print',
      'no_multi_process',
  ]


  def __init__(			self:						Dict							,**__lowerCAmelCase:						str    ):







     '''simple docstring'''







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

     _lowerCamelCase   :				Optional[Any]							=     kwargs.pop("torchscript"							,self.torchscript    )
     _lowerCamelCase   :				List[str]							=     kwargs.pop("torch_xla_tpu_print_metrics"							,self.torch_xla_tpu_print_metrics    )
     _lowerCamelCase   :				Optional[int]							=     kwargs.pop("fp16_opt_level"							,self.fpaa_opt_level    )
     super().__init__(**__lowerCAmelCase    )

  lowerCAmelCase__      			=	field(default=_a     ,	metadata={'help': 'Trace the models using torchscript'} )
  lowerCAmelCase__      			=	field(default=_a     ,	metadata={'help': 'Print Xla/PyTorch tpu metrics'} )
  lowerCAmelCase__      			=	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:						Tuple    ):







     '''simple docstring'''







     requires_backends(self							,["torch"]    )
     logger.info("PyTorch: setting up devices"    )
     if not self.cuda:
        _lowerCamelCase   :				Optional[int]							=     torch.device("cpu"    )
        _lowerCamelCase   :				Optional[int]							=     0
     elif is_torch_tpu_available():
        _lowerCamelCase   :				Union[str, Any]							=     xm.xla_device()
        _lowerCamelCase   :				Tuple							=     0
     else:
        _lowerCamelCase   :				Tuple							=     torch.device("cuda" if torch.cuda.is_available() else "cpu"    )
        _lowerCamelCase   :				Optional[Any]							=     torch.cuda.device_count()
     return device, n_gpu


  @property
  def 							_lowercase					(			self:						Optional[Any]    ):







     '''simple docstring'''







     return is_torch_tpu_available() and self.tpu


  @property
  def 							_lowercase					(			self:						Any    ):







     '''simple docstring'''







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


  @property
  def 							_lowercase					(			self:						Dict    ):







     '''simple docstring'''







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


  @property
  def 							_lowercase					(			self:						Optional[Any]    ):







     '''simple docstring'''







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






  @property
  def 							_lowercase					(			self:						Any    ):







     '''simple docstring'''







     return self.n_gpu > 0




'''simple docstring'''

import re

from filelock import FileLock


try:
							import nltk

							__lowerCAmelCase		     =  True
except (ImportError, ModuleNotFoundError):
							__lowerCAmelCase		     =  False

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



def 	__lowerCamelCase	( lowerCAmelCase_    )     -> str:
		re.sub('<n>'		,     ''		,     lowerCAmelCase_    )  # 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(lowerCAmelCase_    )    )




