



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


UpperCAmelCase_				:     Any              =    logging.get_logger(__name__)






@dataclass
class       UpperCamelCase							(		_a      ):
	lowerCAmelCase			:       Optional[Any]								=			[
	    """no_inference""",
	    """no_cuda""",
	    """no_tpu""",
	    """no_speed""",
	    """no_memory""",
	    """no_env_print""",
	    """no_multi_process""",
	]



	def __init__(  self					,			**UpperCAmelCase__						):
							for deprecated_arg in self.deprecated_args:
													if deprecated_arg in kwargs:
																			A__					=  deprecated_arg[3:]
																			A__					=  not kwargs.pop(snake_case_						)
																			logger.warning(
																			    F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																			    F""" {positive_arg}={kwargs[positive_arg]}"""						)
							A__					=  kwargs.pop("tpu_name"					,			self.tpu_name						)
							A__					=  kwargs.pop("device_idx"					,			self.device_idx						)
							A__					=  kwargs.pop("eager_mode"					,			self.eager_mode						)
							A__					=  kwargs.pop("use_xla"					,			self.use_xla						)
							super().__init__(**snake_case_						)

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



	@cached_property
	def 					__A				(  self						):
							requires_backends(self					,			["tf"]						)
							A__					=  None
							if self.tpu:
													try:
																			if self.tpu_name:
																									A__					=  tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name						)
																			else:
																									A__					=  tf.distribute.cluster_resolver.TPUClusterResolver()
													except ValueError:
																			A__					=  None
							return tpu



	@cached_property
	def 					__A				(  self						):
							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						)

													A__					=  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"						)
																			A__					=  tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""						)
													else:
																			tf.config.set_visible_devices([]					,			"GPU"						)  # disable GPU
																			A__					=  tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""						)

							return strategy



	@property
	def 					__A				(  self						):
							requires_backends(self					,			["tf"]						)
							return self._setup_tpu is not None



	@property
	def 					__A				(  self						):
							requires_backends(self					,			["tf"]						)
							return self._setup_strategy



	@property
	def 					__A				(  self						):
							requires_backends(self					,			["tf"]						)
							return tf.config.list_physical_devices("GPU"						)



	@property
	def 					__A				(  self						):
							requires_backends(self					,			["tf"]						)
							if self.cuda:
													return len(self.gpu_list						)
							return 0



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










'''simple docstring'''





# flake8: noqa
# Lint as: python3
__a  			=						[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
