

'''simple docstring'''




from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
      import tensorflow as tf


_lowerCAmelCase :    List[str]         =						logging.get_logger(__name__)






@dataclass
class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Dict      	=					[
     """no_inference""",
     """no_cuda""",
     """no_tpu""",
     """no_speed""",
     """no_memory""",
     """no_env_print""",
     """no_multi_process""",
 ]



 def __init__(   self		:   int			,				**a__		:   List[Any]      ):
      for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
                __magic_name__						       =    deprecated_arg[3:]
                __magic_name__						       =    not kwargs.pop(a__      )
                logger.warning(
                    F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
                    F''' {positive_arg}={kwargs[positive_arg]}'''      )
      __magic_name__						       =    kwargs.pop('''tpu_name'''			,				self.tpu_name      )
      __magic_name__						       =    kwargs.pop('''device_idx'''			,				self.device_idx      )
      __magic_name__						       =    kwargs.pop('''eager_mode'''			,				self.eager_mode      )
      __magic_name__						       =    kwargs.pop('''use_xla'''			,				self.use_xla      )
      super().__init__(**a__      )

 __SCREAMING_SNAKE_CASE					:str      	=					field(
     default=__a		,metadata={"""help""": """Name of TPU"""}		,)
 __SCREAMING_SNAKE_CASE					:int      	=					field(
     default=0		,metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}		,)
 __SCREAMING_SNAKE_CASE					:bool      	=					field(default=__a		,metadata={"""help""": """Benchmark models in eager model."""}     )
 __SCREAMING_SNAKE_CASE					:bool      	=					field(
     default=__a		,metadata={
         """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
     }		,)



 @cached_property
 def 			snake_case__  (   self		:   int      ):
      requires_backends(self			,				['''tf''']      )
      __magic_name__						       =    None
      if self.tpu:
           try:
                if self.tpu_name:
                     __magic_name__						       =    tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name      )
                else:
                     __magic_name__						       =    tf.distribute.cluster_resolver.TPUClusterResolver()
           except ValueError:
                __magic_name__						       =    None
      return tpu



 @cached_property
 def 			snake_case__  (   self		:   Optional[Any]      ):
      requires_backends(self			,				['''tf''']      )
      if self.is_tpu:
           tf.config.experimental_connect_to_cluster(self._setup_tpu      )
           tf.tpu.experimental.initialize_tpu_system(self._setup_tpu      )

           __magic_name__						       =    tf.distribute.TPUStrategy(self._setup_tpu      )
      else:
           # currently no multi gpu is allowed
           if self.is_gpu:
                # TODO: Currently only single GPU is supported
                tf.config.set_visible_devices(self.gpu_list[self.device_idx]			,				'''GPU'''      )
                __magic_name__						       =    tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''      )
           else:
                tf.config.set_visible_devices([]			,				'''GPU'''      )  # disable GPU
                __magic_name__						       =    tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''      )

      return strategy



 @property
 def 			snake_case__  (   self		:   List[str]      ):
      requires_backends(self			,				['''tf''']      )
      return self._setup_tpu is not None



 @property
 def 			snake_case__  (   self		:   int      ):
      requires_backends(self			,				['''tf''']      )
      return self._setup_strategy



 @property
 def 			snake_case__  (   self		:   str      ):
      requires_backends(self			,				['''tf''']      )
      return tf.config.list_physical_devices('''GPU'''      )



 @property
 def 			snake_case__  (   self		:   Dict      ):
      requires_backends(self			,				['''tf''']      )
      if self.cuda:
           return len(self.gpu_list      )
      return 0



 @property
 def 			snake_case__  (   self		:   Optional[Any]      ):
      return self.n_gpu > 0









'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase         =						{
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase         =						[
          "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
          "ClapModel",
          "ClapPreTrainedModel",
          "ClapTextModel",
          "ClapTextModelWithProjection",
          "ClapAudioModel",
          "ClapAudioModelWithProjection",
      ]
      _lowerCAmelCase         =						["ClapFeatureExtractor"]

if TYPE_CHECKING:
      from .configuration_clap import (
          CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
          ClapAudioConfig,
          ClapConfig,
          ClapTextConfig,
      )
      from .processing_clap import ClapProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_clap import ClapFeatureExtractor
            from .modeling_clap import (
                CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
                ClapAudioModel,
                ClapAudioModelWithProjection,
                ClapModel,
                ClapPreTrainedModel,
                ClapTextModel,
                ClapTextModelWithProjection,
            )


else:
      import sys

      _lowerCAmelCase         =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






