
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


_a      :			List[Any]					=							logging.get_logger(__name__)






@dataclass
class      _UpperCAmelCase				(					snake_case_):
    __lowercase							: List[str]  =					[
        """no_inference""",
        """no_cuda""",
        """no_tpu""",
        """no_speed""",
        """no_memory""",
        """no_env_print""",
        """no_multi_process""",
    ]
    def __init__(    self						,    **snake_case_     ):
      for deprecated_arg in self.deprecated_args:
        if deprecated_arg in kwargs:
          _snake_case     :						List[Any]						=   deprecated_arg[3:]
          _snake_case     :						Tuple						=   not kwargs.pop(snake_case_     )
          logger.warning(
              F'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'
              F' {positive_arg}={kwargs[positive_arg]}'     )
      _snake_case     :						Any						=   kwargs.pop("tpu_name"						,    self.tpu_name     )
      _snake_case     :						Optional[Any]						=   kwargs.pop("device_idx"						,    self.device_idx     )
      _snake_case     :						Optional[int]						=   kwargs.pop("eager_mode"						,    self.eager_mode     )
      _snake_case     :						Tuple						=   kwargs.pop("use_xla"						,    self.use_xla     )
      super().__init__(**snake_case_     )

    __lowercase							: str  =					field(
        default=snake_case_		,					metadata={"""help""": """Name of TPU"""}		,					)
    __lowercase							: int  =					field(
        default=0		,					metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}		,					)
    __lowercase							: bool  =					field(default=snake_case_		,					metadata={"""help""": """Benchmark models in eager model."""})
    __lowercase							: bool  =					field(
        default=snake_case_		,					metadata={
            """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
        }		,					)
    @cached_property
    def 					lowerCamelCase__	(    self     ):
      requires_backends(self						,    ["tf"]     )
      _snake_case     :						Tuple						=   None
      if self.tpu:
        try:
          if self.tpu_name:
            _snake_case     :						List[str]						=   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name     )
          else:
            _snake_case     :						List[str]						=   tf.distribute.cluster_resolver.TPUClusterResolver()
        except ValueError:
          _snake_case     :						int						=   None
      return tpu
    @cached_property
    def 					lowerCamelCase__	(    self     ):
      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     )

        _snake_case     :						Dict						=   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"     )
          _snake_case     :						Optional[int]						=   tf.distribute.OneDeviceStrategy(device=F'/gpu:{self.device_idx}'     )
        else:
          tf.config.set_visible_devices([]						,    "GPU"     )  # disable GPU
          _snake_case     :						str						=   tf.distribute.OneDeviceStrategy(device=F'/cpu:{self.device_idx}'     )

      return strategy
    @property
    def 					lowerCamelCase__	(    self     ):
      requires_backends(self						,    ["tf"]     )
      return self._setup_tpu is not None
    @property
    def 					lowerCamelCase__	(    self     ):
      requires_backends(self						,    ["tf"]     )
      return self._setup_strategy
    @property
    def 					lowerCamelCase__	(    self     ):
      requires_backends(self						,    ["tf"]     )
      return tf.config.list_physical_devices("GPU"     )
    @property
    def 					lowerCamelCase__	(    self     ):
      requires_backends(self						,    ["tf"]     )
      if self.cuda:
        return len(self.gpu_list     )
      return 0


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



"""simple docstring"""
from __future__ import annotations

from collections import namedtuple
def 				a__   (   a   :				float       ,     a   :				float       ,     a   :				float		):

  """simple docstring"""







  _snake_case     :						Optional[Any]						=   namedtuple("result"       ,     "name value"		)
  if (voltage, current, power).count(0		) != 1:
    raise ValueError("Only one argument must be 0"		)
  elif power < 0:
    raise ValueError(
        "Power cannot be negative in any electrical/electronics system"		)
  elif voltage == 0:
    return result("voltage"       ,     power / current		)
  elif current == 0:
    return result("current"       ,     power / voltage		)
  elif power == 0:
    return result("power"       ,     float(round(abs(voltage * current		)       ,     2		)		)		)
  else:
    raise ValueError("Exactly one argument must be 0"		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

