



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


UpperCamelCase_	   =			logging.get_logger(__name__)



@dataclass
class        _SCREAMING_SNAKE_CASE		(       _lowerCAmelCase ):
   a_							:      Dict						= [
       "no_inference",
       "no_cuda",
       "no_tpu",
       "no_speed",
       "no_memory",
       "no_env_print",
       "no_multi_process",
   ]





   def __init__(self    ,  **UpperCAmelCase):
       '''simple docstring'''







       for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
               __UpperCAmelCase			 =deprecated_arg[3:]
               setattr(self    ,  snake_case__    ,  not kwargs.pop(snake_case__))
               logger.warning(
                   f"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
                   f""" {positive_arg}={kwargs[positive_arg]}""")

       __UpperCAmelCase			 =kwargs.pop('''torchscript'''    ,  self.torchscript)
       __UpperCAmelCase			 =kwargs.pop('''torch_xla_tpu_print_metrics'''    ,  self.torch_xla_tpu_print_metrics)
       __UpperCAmelCase			 =kwargs.pop('''fp16_opt_level'''    ,  self.fpaa_opt_level)
       super().__init__(**snake_case__)

   a_							:      bool						= field(default=_lowerCAmelCase			,			metadata={'''help''': '''Trace the models using torchscript'''} )
   a_							:      bool						= field(default=_lowerCAmelCase			,			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 	A__      (self):
       '''simple docstring'''







       requires_backends(self    ,  ['''torch'''])
       logger.info('''PyTorch: setting up devices''')
       if not self.cuda:
           __UpperCAmelCase			 =torch.device('''cpu''')
           __UpperCAmelCase			 =0
       elif is_torch_tpu_available():
           __UpperCAmelCase			 =xm.xla_device()
           __UpperCAmelCase			 =0
       else:
           __UpperCAmelCase			 =torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''')
           __UpperCAmelCase			 =torch.cuda.device_count()
       return device, n_gpu





   @property
   def 	A__      (self):
       '''simple docstring'''







       return is_torch_tpu_available() and self.tpu





   @property
   def 	A__      (self):
       '''simple docstring'''







       requires_backends(self    ,  ['''torch'''])
       # TODO(PVP): currently only single GPU is supported
       return torch.cuda.current_device()





   @property
   def 	A__      (self):
       '''simple docstring'''







       requires_backends(self    ,  ['''torch'''])
       return self._setup_devices[0]





   @property
   def 	A__      (self):
       '''simple docstring'''







       requires_backends(self    ,  ['''torch'''])
       return self._setup_devices[1]






   @property
   def 	A__      (self):
       '''simple docstring'''







       return self.n_gpu > 0





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


SCREAMING_SNAKE_CASE     =       {}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE     =       ["""GPTSw3Tokenizer"""]


if TYPE_CHECKING:
      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpt_swa import GPTSwaTokenizer

else:
      import sys

      SCREAMING_SNAKE_CASE     =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



