
"""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


_lowercase							      = logging.get_logger(__name__)







@dataclass
class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			Optional[int]					=			[
						    '''no_inference''',
						    '''no_cuda''',
						    '''no_tpu''',
						    '''no_speed''',
						    '''no_memory''',
						    '''no_env_print''',
						    '''no_multi_process''',
						]


						def __init__(       self   :					int     ,**A_   :					Any					)   ->      Any:
							for deprecated_arg in self.deprecated_args:
								if deprecated_arg in kwargs:
									A        =      deprecated_arg[3:]
									A        =      not kwargs.pop(A_					)
									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__(**A_					)

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


						@cached_property
						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]					)   ->      Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
							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   _SCREAMING_SNAKE_CASE					(       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					)

								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   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      bool:
							requires_backends(self     ,['tf']					)
							return self._setup_tpu is not None


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      "tf.distribute.Strategy":
							requires_backends(self     ,['tf']					)
							return self._setup_strategy


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      str:
							requires_backends(self     ,['tf']					)
							return tf.config.list_physical_devices('GPU'					)


						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      int:
							requires_backends(self     ,['tf']					)
							if self.cuda:
								return len(self.gpu_list					)
							return 0




						@property
						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      bool:
							return self.n_gpu > 0

from abc import ABC, abstractmethod
from argparse import ArgumentParser




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	@staticmethod
	@abstractmethod
	def     SCREAMING_SNAKE_CASE_(    lowercase )				->       int:
					raise NotImplementedError()



	@abstractmethod
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					raise NotImplementedError()


