



"""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


lowercase__		:    int				   =     logging.get_logger(__name__)







@dataclass
class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




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



      def __init__(				self    :				Optional[Any]   ,      **SCREAMING_SNAKE_CASE_    :				List[str]	):
        for deprecated_arg in self.deprecated_args:
          if deprecated_arg in kwargs:
            lowerCAmelCase_       :    Dict								=			deprecated_arg[3:]
            lowerCAmelCase_       :    Optional[Any]								=			not kwargs.pop(SCREAMING_SNAKE_CASE_	)
            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_       :    Any								=			kwargs.pop('device_idx'   ,      self.device_idx	)
        lowerCAmelCase_       :    List[str]								=			kwargs.pop('eager_mode'   ,      self.eager_mode	)
        lowerCAmelCase_       :    List[str]								=			kwargs.pop('use_xla'   ,      self.use_xla	)
        super().__init__(**SCREAMING_SNAKE_CASE_	)

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



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



      @cached_property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        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_       :    Tuple								=			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_       :    Dict								=			tf.distribute.OneDeviceStrategy(device=F"/cpu:{self.device_idx}"	)

        return strategy



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



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



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



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





      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        return self.n_gpu > 0










"""simple docstring"""







from collections.abc import Iterable
from typing import Generic, TypeVar

lowercase__		:    Any				   =     TypeVar("""_T""")







class 				UpperCamelCase__       (     Generic[_T]  ):




      """simple docstring"""




      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Iterable[_T] | None = None	):
        lowerCAmelCase_       :    list[_T]								=			list(iterable or []	)
        lowerCAmelCase_       :    list[_T]								=			[]



      def __len__(				self    :				List[str]	):
        return len(self._stacka	) + len(self._stacka	)



      def __repr__(				self    :				int	):
        return F"Queue({tuple(self._stacka[::-1] + self._stacka	)})"



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				_T	):
        self._stacka.append(SCREAMING_SNAKE_CASE_	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    List[str]								=			self._stacka.pop
        lowerCAmelCase_       :    int								=			self._stacka.append

        if not self._stacka:
          while self._stacka:
            stacka_append(stacka_pop()	)

        if not self._stacka:
          raise IndexError('Queue is empty'	)
        return self._stacka.pop()


if __name__ == "__main__":
     from doctest import testmod

     testmod()





