






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
       import torch

if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm


a						:			Dict     	=					logging.get_logger(__name__)
@dataclass
class        a       (					lowercase__  ):



      """simple docstring"""


      a    :					Dict							=      [
          'no_inference',
          'no_cuda',
          'no_tpu',
          'no_speed',
          'no_memory',
          'no_env_print',
          'no_multi_process',
      ]
      def __init__( self   :     List[Any]						, **__lowercase   :     Dict   )				-> Tuple:
        for deprecated_arg in self.deprecated_args:
          if deprecated_arg in kwargs:
            __UpperCAmelCase				:      List[Any]	 =    deprecated_arg[3:]
            setattr(self						, __lowercase						, not kwargs.pop(__lowercase   )   )
            logger.warning(
                f"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
                f""" {positive_arg}={kwargs[positive_arg]}"""   )

        __UpperCAmelCase				:      str	 =    kwargs.pop("""torchscript"""						, self.torchscript   )
        __UpperCAmelCase				:      Union[str, Any]	 =    kwargs.pop("""torch_xla_tpu_print_metrics"""						, self.torch_xla_tpu_print_metrics   )
        __UpperCAmelCase				:      Optional[Any]	 =    kwargs.pop("""fp16_opt_level"""						, self.fpaa_opt_level   )
        super().__init__(**__lowercase   )

      a    :					bool							=      field(default=lowercase__				,			metadata={'help': 'Trace the models using torchscript'}  )
      a    :					bool							=      field(default=lowercase__				,			metadata={'help': 'Print Xla/PyTorch tpu metrics'}  )
      a    :					str							=      field(
          default='O1'				,			metadata={
              'help': (
                  'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '
                  'See details at https://nvidia.github.io/apex/amp.html'
              )
          }				,			)
      @cached_property
      def       UpperCAmelCase ( self   :     Any   )				-> Tuple["torch.device", int]:
        requires_backends(self						, ["""torch"""]   )
        logger.info("""PyTorch: setting up devices"""   )
        if not self.cuda:
          __UpperCAmelCase				:      str	 =    torch.device("""cpu"""   )
          __UpperCAmelCase				:      int	 =    0
        elif is_torch_tpu_available():
          __UpperCAmelCase				:      Tuple	 =    xm.xla_device()
          __UpperCAmelCase				:      int	 =    0
        else:
          __UpperCAmelCase				:      Dict	 =    torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""   )
          __UpperCAmelCase				:      Optional[int]	 =    torch.cuda.device_count()
        return device, n_gpu
      @property
      def       UpperCAmelCase ( self   :     Optional[Any]   )				-> str:
        return is_torch_tpu_available() and self.tpu
      @property
      def       UpperCAmelCase ( self   :     List[str]   )				-> int:
        requires_backends(self						, ["""torch"""]   )
        # TODO(PVP): currently only single GPU is supported
        return torch.cuda.current_device()
      @property
      def       UpperCAmelCase ( self   :     int   )				-> "torch.device":
        requires_backends(self						, ["""torch"""]   )
        return self._setup_devices[0]
      @property
      def       UpperCAmelCase ( self   :     int   )				-> List[Any]:
        requires_backends(self						, ["""torch"""]   )
        return self._setup_devices[1]




      @property
      def       UpperCAmelCase ( self   :     Tuple   )				-> List[str]:
        return self.n_gpu > 0






from collections import deque

from .hash_table import HashTable


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





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




     """simple docstring"""
     super().__init__(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							Optional[int]     ,					_lowerCAmelCase							:							List[Any]	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       deque([]	) if self.values[key] is None else self.values[key]
     self.values[key].appendleft(_lowerCAmelCase	)
     snake_case_            =       self.values[key]



  def        lowerCAmelCase__	(  self							:							List[str]	) ->			List[Any]:




     """simple docstring"""
     return (
         sum(self.charge_factor - len(_lowerCAmelCase	) for slot in self.values	)
         / self.size_table
         * self.charge_factor
     )



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							List[str]=None	) ->			int:




     """simple docstring"""
     if not (
         len(self.values[key]	) == self.charge_factor and self.values.count(_lowerCAmelCase	) == 0
     ):
        return key
     return super()._collision_resolution(_lowerCAmelCase     ,					_lowerCAmelCase	)




