
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


_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			logging.get_logger(__name__)







@dataclass
class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				[
	    "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__  :	Optional[int]         =							deprecated_arg[3:]
													A__  :	List[str]         =							not kwargs.pop(UpperCamelCase__		)
													logger.warning(
													    F"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"
													    F" {positive_arg}={kwargs[positive_arg]}"		)
					A__  :	str         =							kwargs.pop('''tpu_name'''				,       self.tpu_name		)
					A__  :	int         =							kwargs.pop('''device_idx'''				,       self.device_idx		)
					A__  :	Optional[int]         =							kwargs.pop('''eager_mode'''				,       self.eager_mode		)
					A__  :	str         =							kwargs.pop('''use_xla'''				,       self.use_xla		)
					super().__init__(**UpperCamelCase__		)

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





	@cached_property
	def   __snake_case				(			self		):
					requires_backends(self				,       ['''tf''']		)
					A__  :	List[str]         =							None
					if self.tpu:
									try:
													if self.tpu_name:
																	A__  :	str         =							tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name		)
													else:
																	A__  :	Optional[Any]         =							tf.distribute.cluster_resolver.TPUClusterResolver()
									except ValueError:
													A__  :	List[Any]         =							None
					return tpu





	@cached_property
	def   __snake_case				(			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__  :	Union[str, 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'''		)
													A__  :	Any         =							tf.distribute.OneDeviceStrategy(device=F"/gpu:{self.device_idx}"		)
									else:
													tf.config.set_visible_devices([]				,       '''GPU'''		)  # disable GPU
													A__  :	str         =							tf.distribute.OneDeviceStrategy(device=F"/cpu:{self.device_idx}"		)

					return strategy





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





	@property
	def   __snake_case				(			self		):
					requires_backends(self				,       ['''tf''']		)
					return self._setup_strategy





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





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





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

from abc import ABC, abstractmethod
from argparse import ArgumentParser







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	@staticmethod
	@abstractmethod
	def   __snake_case				(			UpperCamelCase__		):
					raise NotImplementedError()





	@abstractmethod
	def   __snake_case				(			self		):
					raise NotImplementedError()