

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      :					List[str]					    =      logging.get_logger(__name__)





@dataclass
class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







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

       def __init__(							self ,   **lowercase__     ):







             '''simple docstring'''



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

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

       @cached_property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

       @cached_property
       def     __UpperCamelCase     (							self     ):







             '''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     )

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

             return strategy

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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


       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self.n_gpu > 0





from collections import deque





def      A__					(			__A  :						Optional[Any]			)					->Tuple:
      __A    =len(__A			)
      __A    =deque()
      __A    =[False for _ in range(__A			)]
      __A    =[-1 for _ in range(__A			)]
      __A    =index_of[:]

      def strong_connect(__A  :						Union[str, Any]     ,				__A  :						int     ,				__A  :						Optional[int]			):
            __A    =index  # the number when this node is seen
            __A    =index  # lowest rank node reachable from here
            index += 1
            stack.append(__A			)
            __A    =True

            for w in g[v]:
                  if index_of[w] == -1:
                        __A    =strong_connect(__A     ,				__A     ,				__A			)
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )
                  elif on_stack[w]:
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )

            if lowlink_of[v] == index_of[v]:
                  __A    =[]
                  __A    =stack.pop()
                  __A    =False
                  component.append(__A			)
                  while w != v:
                        __A    =stack.pop()
                        __A    =False
                        component.append(__A			)
                  components.append(__A			)
            return index

      __A    =[]
      for v in range(__A			):
            if index_of[v] == -1:
                  strong_connect(__A     ,				0     ,				__A			)

      return components





def      A__					(			__A  :						List[Any]     ,				__A  :						Optional[int]			)					->Tuple:
      __A    =[[] for _ in range(__A			)]
      for u, v in edges:
            g[u].append(__A			)
      return g


if __name__ == "__main__":
   # Test
   _lowerCamelCase      :					int					    =      7
   _lowerCamelCase      :					Tuple					    =      [0, 0, 1, 2, 3, 3, 4, 4, 6]
   _lowerCamelCase      :					Dict					    =      [1, 3, 2, 0, 1, 4, 5, 6, 5]
   _lowerCamelCase      :					Optional[Any]					    =      [(u, v) for u, v in zip(source, target)]
   _lowerCamelCase      :					Dict					    =      create_graph(n_vertices, edges)

   assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


