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


__magic_name__    							=							logging.get_logger(__name__)
@dataclass
class 					__SCREAMING_SNAKE_CASE       (       UpperCAmelCase_):






       """simple docstring"""

       __UpperCAmelCase       =					[
           "no_inference",
           "no_cuda",
           "no_tpu",
           "no_speed",
           "no_memory",
           "no_env_print",
           "no_multi_process",
       ]







       def __init__( self			,		**_UpperCAmelCase				):



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

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







       @cached_property
       def      lowercase_   ( self				):
          requires_backends(self			,		['tf']				)
          __snake_case   :			Optional[int]										=						None
          if self.tpu:
             try:
                if self.tpu_name:
                   __snake_case   :			Union[str, Any]										=						tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name				)
                else:
                   __snake_case   :			Union[str, Any]										=						tf.distribute.cluster_resolver.TPUClusterResolver()
             except ValueError:
                __snake_case   :			Dict										=						None
          return tpu







       @cached_property
       def      lowercase_   ( 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   :			Optional[int]										=						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   :			Any										=						tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""				)
             else:
                tf.config.set_visible_devices([]			,		'GPU'				)  # disable GPU
                __snake_case   :			List[Any]										=						tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""				)

          return strategy







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







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







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







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







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








from __future__ import annotations

from math import pow, sqrt

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if (resistance, reactance, impedance).count(0			) != 1:
            raise ValueError("One and only one argument must be 0"			)
      if resistance == 0:
            return {"resistance": sqrt(pow(UpperCAmelCase__ , 2			) - pow(UpperCAmelCase__ , 2			)			)}
      elif reactance == 0:
            return {"reactance": sqrt(pow(UpperCAmelCase__ , 2			) - pow(UpperCAmelCase__ , 2			)			)}
      elif impedance == 0:
            return {"impedance": sqrt(pow(UpperCAmelCase__ , 2			) + pow(UpperCAmelCase__ , 2			)			)}
      else:
            raise ValueError("Exactly one argument must be 0"			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()