
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


_snake_case      :							str     	=    logging.get_logger(__name__)





@dataclass
class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Optional[Any]				   =					[
						    '''no_inference''',
						    '''no_cuda''',
						    '''no_tpu''',
						    '''no_speed''',
						    '''no_memory''',
						    '''no_env_print''',
						    '''no_multi_process''',
						]





						def __init__(			self							:Union[str, Any]		,    **lowerCAmelCase__							:Any     )  ->List[str]:
										for deprecated_arg in self.deprecated_args:
														if deprecated_arg in kwargs:
																		lowercase         =      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]}'''     )

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

						UpperCamelCase      :  bool				   =					field(default=__a		,    metadata={'''help''': '''Trace the models using torchscript'''}		)
						UpperCamelCase      :  bool				   =					field(default=__a		,    metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}		)
						UpperCamelCase      :  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
						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Tuple["torch.device", int]:
										requires_backends(self		,    ["torch"]     )
										logger.info("PyTorch: setting up devices"     )
										if not self.cuda:
														lowercase         =      torch.device("cpu"     )
														lowercase         =      0
										elif is_torch_tpu_available():
														lowercase         =      xm.xla_device()
														lowercase         =      0
										else:
														lowercase         =      torch.device("cuda" if torch.cuda.is_available() else "cpu"     )
														lowercase         =      torch.cuda.device_count()
										return device, n_gpu





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





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





						@property
						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->"torch.device":
										requires_backends(self		,    ["torch"]     )
										return self._setup_devices[0]





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





						@property
						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Dict:
										return self.n_gpu > 0


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_snake_case      :							Union[str, Any]     	=    {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case      :							Union[str, Any]     	=    [
						    "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

						_snake_case      :							str     	=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
