
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


lowercase__		:Any			  =   logging.get_logger(__name__)







@dataclass
class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	str	 =[
							    '''no_inference''',
							    '''no_cuda''',
							    '''no_tpu''',
							    '''no_speed''',
							    '''no_memory''',
							    '''no_env_print''',
							    '''no_multi_process''',
							]




							def __init__(    self       ,**A__):
												for deprecated_arg in self.deprecated_args:
																	if deprecated_arg in kwargs:
																						lowercase						     =						deprecated_arg[3:]
																						setattr(self       ,A__       ,not kwargs.pop(A__))
																						logger.warning(
																						    f'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
																						    f' {positive_arg}={kwargs[positive_arg]}')

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

							lowercase_	:	bool	 =field(default=SCREAMING_SNAKE_CASE__	,  metadata={'''help''': '''Trace the models using torchscript'''}		)
							lowercase_	:	bool	 =field(default=SCREAMING_SNAKE_CASE__	,  metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}		)
							lowercase_	:	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):
												requires_backends(self       ,['''torch'''])
												logger.info('''PyTorch: setting up devices''')
												if not self.cuda:
																	lowercase						     =						torch.device('''cpu''')
																	lowercase						     =						0
												elif is_torch_tpu_available():
																	lowercase						     =						xm.xla_device()
																	lowercase						     =						0
												else:
																	lowercase						     =						torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''')
																	lowercase						     =						torch.cuda.device_count()
												return device, n_gpu




							@property
							def 			A__				(    self):
												return is_torch_tpu_available() and self.tpu




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




							@property
							def 			A__				(    self):
												requires_backends(self       ,['''torch'''])
												return self._setup_devices[0]




							@property
							def 			A__				(    self):
												requires_backends(self       ,['''torch'''])
												return self._setup_devices[1]





							@property
							def 			A__				(    self):
												return self.n_gpu > 0






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__		:Dict			  =   {
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:List[str]			  =   [
							    "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "LukeForEntityClassification",
							    "LukeForEntityPairClassification",
							    "LukeForEntitySpanClassification",
							    "LukeForMultipleChoice",
							    "LukeForQuestionAnswering",
							    "LukeForSequenceClassification",
							    "LukeForTokenClassification",
							    "LukeForMaskedLM",
							    "LukeModel",
							    "LukePreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
							from .tokenization_luke import LukeTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_luke import (
														    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LukeForEntityClassification,
														    LukeForEntityPairClassification,
														    LukeForEntitySpanClassification,
														    LukeForMaskedLM,
														    LukeForMultipleChoice,
														    LukeForQuestionAnswering,
														    LukeForSequenceClassification,
														    LukeForTokenClassification,
														    LukeModel,
														    LukePreTrainedModel,
														)

else:
							import sys

							lowercase__		:Tuple			  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




