

'''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 :      Optional[Any]					 =     logging.get_logger(__name__)







@dataclass
class 							A__    (       A__  ):
  A__		 =				[
      'no_inference',
      'no_cuda',
      'no_tpu',
      'no_speed',
      'no_memory',
      'no_env_print',
      'no_multi_process',
  ]





  def __init__(		self      :	List[Any]							,					**_a      :	Any )	->    Tuple:





       '''simple docstring'''






       for deprecated_arg in self.deprecated_args:
            if deprecated_arg in kwargs:
                 _SCREAMING_SNAKE_CASE							    =deprecated_arg[3:]
                 _SCREAMING_SNAKE_CASE							    =not kwargs.pop(_a )
                 logger.warning(
                     f"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"
                     f" {positive_arg}={kwargs[positive_arg]}" )
       _SCREAMING_SNAKE_CASE							    =kwargs.pop('tpu_name'							,					self.tpu_name )
       _SCREAMING_SNAKE_CASE							    =kwargs.pop('device_idx'							,					self.device_idx )
       _SCREAMING_SNAKE_CASE							    =kwargs.pop('eager_mode'							,					self.eager_mode )
       _SCREAMING_SNAKE_CASE							    =kwargs.pop('use_xla'							,					self.use_xla )
       super().__init__(**_a )

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





  @cached_property
  def 						A  (		self      :	int )	->    Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:





       '''simple docstring'''






       requires_backends(self							,					['tf'] )
       _SCREAMING_SNAKE_CASE							    =None
       if self.tpu:
            try:
                 if self.tpu_name:
                      _SCREAMING_SNAKE_CASE							    =tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
                 else:
                      _SCREAMING_SNAKE_CASE							    =tf.distribute.cluster_resolver.TPUClusterResolver()
            except ValueError:
                 _SCREAMING_SNAKE_CASE							    =None
       return tpu





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





       '''simple docstring'''






       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 )

            _SCREAMING_SNAKE_CASE							    =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' )
                 _SCREAMING_SNAKE_CASE							    =tf.distribute.OneDeviceStrategy(device=f"/gpu:{self.device_idx}" )
            else:
                 tf.config.set_visible_devices([]							,					'GPU' )  # disable GPU
                 _SCREAMING_SNAKE_CASE							    =tf.distribute.OneDeviceStrategy(device=f"/cpu:{self.device_idx}" )

       return strategy





  @property
  def 						A  (		self      :	List[str] )	->    bool:





       '''simple docstring'''






       requires_backends(self							,					['tf'] )
       return self._setup_tpu is not None





  @property
  def 						A  (		self      :	str )	->    "tf.distribute.Strategy":





       '''simple docstring'''






       requires_backends(self							,					['tf'] )
       return self._setup_strategy





  @property
  def 						A  (		self      :	Optional[int] )	->    Tuple:





       '''simple docstring'''






       requires_backends(self							,					['tf'] )
       return tf.config.list_physical_devices('GPU' )





  @property
  def 						A  (		self      :	str )	->    int:





       '''simple docstring'''






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





  @property
  def 						A  (		self      :	Union[str, Any] )	->    bool:





       '''simple docstring'''






       return self.n_gpu > 0

from math import factorial, radians




def       _snake_case	(   lowerCAmelCase							:   float   , lowerCAmelCase							:   int = 1_8   , lowerCAmelCase							:   int = 1_0	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

		# Converting from degrees to radians
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    radians(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    angle_in_radians
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    3
		SCREAMING_SNAKE_CASE_  :		str				      =    -1

		for _ in range(lowerCAmelCase	):
				result += (b * (angle_in_radians**a)) / factorial(lowerCAmelCase	)

				SCREAMING_SNAKE_CASE_  :		Dict				      =    -b  # One positive term and the next will be negative and so on...
				a += 2  # Increased by 2 for every term.

		return round(lowerCAmelCase   , lowerCAmelCase	)


if __name__ == "__main__":
		__import__('''doctest''').testmod()
