



'''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      						=	logging.get_logger(__name__)






@dataclass
class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




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




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

  __lowercase							:      str				=      field(
      default=SCREAMING_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=SCREAMING_SNAKE_CASE_						, metadata={'''help''': '''Benchmark models in eager model.'''}     )
  __lowercase							:      bool				=      field(
      default=SCREAMING_SNAKE_CASE_						, metadata={
          '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
      }						, )




  @cached_property
  def 							UpperCAmelCase_	(    self      )    ->		Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
      requires_backends(self  ,["""tf"""]      )
      lowerCAmelCase__ :  Any							      =					None
      if self.tpu:
          try:
              if self.tpu_name:
                  lowerCAmelCase__ :  List[Any]							      =					tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name      )
              else:
                  lowerCAmelCase__ :  Any							      =					tf.distribute.cluster_resolver.TPUClusterResolver()
          except ValueError:
              lowerCAmelCase__ :  Optional[Any]							      =					None
      return tpu




  @cached_property
  def 							UpperCAmelCase_	(    self      )    ->		Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
      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      )

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

      return strategy




  @property
  def 							UpperCAmelCase_	(    self      )    ->		bool:
      requires_backends(self  ,["""tf"""]      )
      return self._setup_tpu is not None




  @property
  def 							UpperCAmelCase_	(    self      )    ->		"tf.distribute.Strategy":
      requires_backends(self  ,["""tf"""]      )
      return self._setup_strategy




  @property
  def 							UpperCAmelCase_	(    self      )    ->		str:
      requires_backends(self  ,["""tf"""]      )
      return tf.config.list_physical_devices("""GPU"""      )




  @property
  def 							UpperCAmelCase_	(    self      )    ->		int:
      requires_backends(self  ,["""tf"""]      )
      if self.cuda:
          return len(self.gpu_list      )
      return 0






  @property
  def 							UpperCAmelCase_	(    self      )    ->		bool:
      return self.n_gpu > 0



import math
def       A	(    _lowercase						):
						return math.sqrt(_lowercase						) * math.sqrt(_lowercase						) == num
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Dict							=							0
						SCREAMING_SNAKE_CASE							:    Tuple							=							n
						while left <= right:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							(left + right) // 2
												if mid**2 == n:
																		return True
												elif mid**2 > n:
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							mid - 1
												else:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							mid + 1
						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()





