text: # coding=utf-8
  # Copyright 2018 The HuggingFace Inc. team.
  # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from dataclasses import dataclass, field
  from typing import Tuple
  
  from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, torch_required
  from .benchmark_args_utils import BenchmarkArguments
  
  
  if is_torch_available():
      import torch
  
  if is_torch_tpu_available():
      import torch_xla.core.xla_model as xm
  
  
  logger = logging.get_logger(__name__)
  
  
  @dataclass
  class PyTorchBenchmarkArguments(BenchmarkArguments):
  
      deprecated_args = [
          "no_inference",
          "no_cuda",
          "no_tpu",
          "no_speed",
          "no_memory",
          "no_env_print",
          "no_multi_process",
      ]
  
      def __init__(self, **kwargs):
          """
          This __init__ is there for legacy code. When removing deprecated args completely, the class can simply be
          deleted
          """
          for deprecated_arg in self.deprecated_args:
              if deprecated_arg in kwargs:
                  positive_arg = deprecated_arg[3:]
                  setattr(self, positive_arg, not kwargs.pop(deprecated_arg))
                  logger.warning(
                      f"{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"
                      f" {positive_arg}={kwargs[positive_arg]}"
                  )
  
          self.torchscript = kwargs.pop("torchscript", self.torchscript)
          self.torch_xla_tpu_print_metrics = kwargs.pop("torch_xla_tpu_print_metrics", self.torch_xla_tpu_print_metrics)
          self.fp16_opt_level = kwargs.pop("fp16_opt_level", self.fp16_opt_level)
          super().__init__(**kwargs)
  
      torchscript: bool = field(default=False, metadata={"help": "Trace the models using torchscript"})
      torch_xla_tpu_print_metrics: bool = field(default=False, metadata={"help": "Print Xla/PyTorch tpu metrics"})
      fp16_opt_level: str = 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
      @torch_required
      def _setup_devices(self) -> Tuple["torch.device", int]:
          logger.info("PyTorch: setting up devices")
          if not self.cuda:
              device = torch.device("cpu")
              n_gpu = 0
          elif is_torch_tpu_available():
              device = xm.xla_device()
              n_gpu = 0
          else:
              device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
              n_gpu = torch.cuda.device_count()
          return device, n_gpu
  
      @property
      def is_tpu(self):
          return is_torch_tpu_available() and self.tpu
  
      @property
      @torch_required
      def device_idx(self) -> int:
          # TODO(PVP): currently only single GPU is supported
          return torch.cuda.current_device()
  
      @property
      @torch_required
      def device(self) -> "torch.device":
          return self._setup_devices[0]
  
      @property
      @torch_required
      def n_gpu(self):
          return self._setup_devices[1]
  
      @property
      def is_gpu(self):
          return self.n_gpu > 0
  