
"""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


__lowerCamelCase      :Any	    =		logging.get_logger(__name__)


@dataclass
class 			A__	(						UpperCamelCase__):


     """simple docstring"""
     snake_case__	:					int													=[
         """no_inference""",
         """no_cuda""",
         """no_tpu""",
         """no_speed""",
         """no_memory""",
         """no_env_print""",
         """no_multi_process""",
     ]

     def __init__(	self:						Union[str, Any]     ,		**__a:						Optional[int]	)->    Union[str, Any]:
           for deprecated_arg in self.deprecated_args:
                 if deprecated_arg in kwargs:
                       lowerCamelCase					:					Any        =	deprecated_arg[3:]
                       lowerCamelCase					:					Tuple        =	not kwargs.pop(__a	)
                       logger.warning(
                           f'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'
                           f' {positive_arg}={kwargs[positive_arg]}'	)
           lowerCamelCase					:					Tuple        =	kwargs.pop("""tpu_name"""     ,		self.tpu_name	)
           lowerCamelCase					:					Dict        =	kwargs.pop("""device_idx"""     ,		self.device_idx	)
           lowerCamelCase					:					List[str]        =	kwargs.pop("""eager_mode"""     ,		self.eager_mode	)
           lowerCamelCase					:					Optional[int]        =	kwargs.pop("""use_xla"""     ,		self.use_xla	)
           super().__init__(**__a	)

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

     @cached_property
     def 		a__						(	self:						Optional[Any]	)->    Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
           requires_backends(self     ,		["""tf"""]	)
           lowerCamelCase					:					Tuple        =	None
           if self.tpu:
                 try:
                       if self.tpu_name:
                             lowerCamelCase					:					Any        =	tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name	)
                       else:
                             lowerCamelCase					:					List[str]        =	tf.distribute.cluster_resolver.TPUClusterResolver()
                 except ValueError:
                       lowerCamelCase					:					List[str]        =	None
           return tpu

     @cached_property
     def 		a__						(	self:						Tuple	)->    Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
           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					:					List[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"""	)
                       lowerCamelCase					:					str        =	tf.distribute.OneDeviceStrategy(device=f'/gpu:{self.device_idx}'	)
                 else:
                       tf.config.set_visible_devices([]     ,		"""GPU"""	)  # disable GPU
                       lowerCamelCase					:					Optional[int]        =	tf.distribute.OneDeviceStrategy(device=f'/cpu:{self.device_idx}'	)

           return strategy

     @property
     def 		a__						(	self:						List[Any]	)->    bool:
           requires_backends(self     ,		["""tf"""]	)
           return self._setup_tpu is not None

     @property
     def 		a__						(	self:						Tuple	)->    "tf.distribute.Strategy":
           requires_backends(self     ,		["""tf"""]	)
           return self._setup_strategy

     @property
     def 		a__						(	self:						int	)->    Union[str, Any]:
           requires_backends(self     ,		["""tf"""]	)
           return tf.config.list_physical_devices("""GPU"""	)

     @property
     def 		a__						(	self:						Union[str, Any]	)->    int:
           requires_backends(self     ,		["""tf"""]	)
           if self.cuda:
                 return len(self.gpu_list	)
           return 0







     @property
     def 		a__						(	self:						Tuple	)->    bool:
           return self.n_gpu > 0








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__lowerCamelCase      :str	    =		{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Optional[Any]	    =		['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

    __lowerCamelCase      :Tuple	    =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




