


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     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      [
         """no_inference""",
         """no_cuda""",
         """no_tpu""",
         """no_speed""",
         """no_memory""",
         """no_env_print""",
         """no_multi_process""",
     ]


     def __init__(	self  ,       **__A    ):
        for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
              __a					=  deprecated_arg[3:]
              setattr(self  ,       __A  ,       not kwargs.pop(__A    )    )
              logger.warning(
                  f'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
                  f''' {positive_arg}={kwargs[positive_arg]}'''    )

        __a					=  kwargs.pop("""torchscript"""  ,       self.torchscript    )
        __a					=  kwargs.pop("""torch_xla_tpu_print_metrics"""  ,       self.torch_xla_tpu_print_metrics    )
        __a					=  kwargs.pop("""fp16_opt_level"""  ,       self.fpaa_opt_level    )
        super().__init__(**__A    )

     _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 	snake_case_  (	self    ):
        requires_backends(self  ,       ["""torch"""]    )
        logger.info("""PyTorch: setting up devices"""    )
        if not self.cuda:
           __a					=  torch.device("""cpu"""    )
           __a					=  0
        elif is_torch_tpu_available():
           __a					=  xm.xla_device()
           __a					=  0
        else:
           __a					=  torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""    )
           __a					=  torch.cuda.device_count()
        return device, n_gpu


     @property
     def 	snake_case_  (	self    ):
        return is_torch_tpu_available() and self.tpu


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


     @property
     def 	snake_case_  (	self    ):
        requires_backends(self  ,       ["""torch"""]    )
        return self._setup_devices[0]


     @property
     def 	snake_case_  (	self    ):
        requires_backends(self  ,       ["""torch"""]    )
        return self._setup_devices[1]


     @property
     def 	snake_case_  (	self    ):
        return self.n_gpu > 0







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE							    =							{'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'WavLMForAudioFrameClassification',
      'WavLMForCTC',
      'WavLMForSequenceClassification',
      'WavLMForXVector',
      'WavLMModel',
      'WavLMPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE							    =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



