





"""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


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

@dataclass
class 						lowercase   (   lowerCAmelCase_   ):
      _SCREAMING_SNAKE_CASE							      = [
          'no_inference',
          'no_cuda',
          'no_tpu',
          'no_speed',
          'no_memory',
          'no_env_print',
          'no_multi_process',
      ]





      def __init__(			self ,		**lowercase					)						->	Dict:
            for deprecated_arg in self.deprecated_args:
                  if deprecated_arg in kwargs:
                        lowerCAmelCase    		=			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    		=			kwargs.pop("""torchscript""" ,		self.torchscript					)
            lowerCAmelCase    		=			kwargs.pop("""torch_xla_tpu_print_metrics""" ,		self.torch_xla_tpu_print_metrics					)
            lowerCAmelCase    		=			kwargs.pop("""fp16_opt_level""" ,		self.fpaa_opt_level					)
            super().__init__(**__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE							      = field(default=lowerCAmelCase_	,    metadata={'help': 'Trace the models using torchscript'}   )
      _SCREAMING_SNAKE_CASE							      = field(default=lowerCAmelCase_	,    metadata={'help': 'Print Xla/PyTorch tpu metrics'}   )
      _SCREAMING_SNAKE_CASE							      = 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        _snake_case     (			self					)						->	Dict:
            requires_backends(self ,		["""torch"""]					)
            logger.info("""PyTorch: setting up devices"""					)
            if not self.cuda:
                  lowerCAmelCase    		=			torch.device("""cpu"""					)
                  lowerCAmelCase    		=			0
            elif is_torch_tpu_available():
                  lowerCAmelCase    		=			xm.xla_device()
                  lowerCAmelCase    		=			0
            else:
                  lowerCAmelCase    		=			torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""					)
                  lowerCAmelCase    		=			torch.cuda.device_count()
            return device, n_gpu





      @property
      def        _snake_case     (			self					)						->	Tuple:
            return is_torch_tpu_available() and self.tpu





      @property
      def        _snake_case     (			self					)						->	Optional[int]:
            requires_backends(self ,		["""torch"""]					)
            # TODO(PVP): currently only single GPU is supported
            return torch.cuda.current_device()





      @property
      def        _snake_case     (			self					)						->	Optional[int]:
            requires_backends(self ,		["""torch"""]					)
            return self._setup_devices[0]





      @property
      def        _snake_case     (			self					)						->	Optional[int]:
            requires_backends(self ,		["""torch"""]					)
            return self._setup_devices[1]





      @property
      def        _snake_case     (			self					)						->	List[Any]:
            return self.n_gpu > 0












'''simple docstring'''






from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *
