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


SCREAMING_SNAKE_CASE__		:					Optional[int]						=					logging.get_logger(__name__)

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




  def __init__(      self     :	List[Any]     , **a_     :	Optional[int]				)->       List[str]:




       """simple docstring"""
       for deprecated_arg in self.deprecated_args:
            if deprecated_arg in kwargs:
                 SCREAMING_SNAKE_CASE__				:				Any			=		deprecated_arg[3:]
                 SCREAMING_SNAKE_CASE__				:				str			=		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__				:				str			=		kwargs.pop('tpu_name'     , self.tpu_name				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		kwargs.pop('device_idx'     , self.device_idx				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		kwargs.pop('eager_mode'     , self.eager_mode				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		kwargs.pop('use_xla'     , self.use_xla				)
       super().__init__(**a_				)

  lowercase_			=     field(
      default=UpperCamelCase_   ,						metadata={'help': 'Name of TPU'}   ,						)
  lowercase_			=     field(
      default=0   ,						metadata={'help': 'CPU / GPU device index. Defaults to 0.'}   ,						)
  lowercase_			=     field(default=UpperCamelCase_   ,						metadata={'help': 'Benchmark models in eager model.'}						)
  lowercase_			=     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     :	int				)->       Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:




       """simple docstring"""
       requires_backends(self     , ['tf']				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		None
       if self.tpu:
            try:
                 if self.tpu_name:
                      SCREAMING_SNAKE_CASE__				:				Optional[int]			=		tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name				)
                 else:
                      SCREAMING_SNAKE_CASE__				:				Dict			=		tf.distribute.cluster_resolver.TPUClusterResolver()
            except ValueError:
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		None
       return tpu




  @cached_property
  def 	__lowercase(      self     :	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__				:				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'				)
                 SCREAMING_SNAKE_CASE__				:				Tuple			=		tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''				)
            else:
                 tf.config.set_visible_devices([]     , 'GPU'				)  # disable GPU
                 SCREAMING_SNAKE_CASE__				:				int			=		tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''				)

       return strategy




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




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




  @property
  def 	__lowercase(      self     :	List[Any]				)->       "tf.distribute.Strategy":




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




  @property
  def 	__lowercase(      self     :	Any				)->       Optional[Any]:




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




  @property
  def 	__lowercase(      self     :	Optional[Any]				)->       int:




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




  @property
  def 	__lowercase(      self     :	Optional[Any]				)->       bool:




       """simple docstring"""
       return self.n_gpu > 0




def     _a				(      lowercase__  :			Optional[int] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		set({'(', '[', '{'} )
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		set({')', ']', '}'} )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{'{': '}', '[': ']', '(': ')'}

     for i in range(len(lowercase__ ) ):
          if s[i] in open_brackets:
               stack.append(s[i] )

          elif s[i] in closed_brackets and (
              len(lowercase__ ) == 0 or (len(lowercase__ ) > 0 and open_to_closed[stack.pop()] != s[i])
          ):
               return False

     return len(lowercase__ ) == 0







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Tuple			=		input('Enter sequence of brackets: ' )
     if is_balanced(lowercase__ ):
          print(lowercase__     ,  'is balanced' )
     else:
          print(lowercase__     ,  'is not balanced' )


if __name__ == "__main__":
      main()



