

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


A__						=							logging.get_logger(__name__)

@dataclass
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Dict       				=  [
	    "no_inference",
	    "no_cuda",
	    "no_tpu",
	    "no_speed",
	    "no_memory",
	    "no_env_print",
	    "no_multi_process",
	]







	def __init__(   self: Any  , **__UpperCamelCase: Dict      ):

						'''simple docstring'''





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

	_lowercase  :   str       				=  field(
	    default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Name of TPU"}  ,						)
	_lowercase  :   int       				=  field(
	    default=0  ,						metadata={"help": "CPU / GPU device index. Defaults to 0."}  ,						)
	_lowercase  :   bool       				=  field(default=SCREAMING_SNAKE_CASE  ,						metadata={"help": "Benchmark models in eager model."}			)
	_lowercase  :   bool       				=  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 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						requires_backends(self  , ['tf']      )
						__magic_name__							=    None
						if self.tpu:
											try:
																if self.tpu_name:
																					__magic_name__							=    tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name      )
																else:
																					__magic_name__							=    tf.distribute.cluster_resolver.TPUClusterResolver()
											except ValueError:
																__magic_name__							=    None
						return tpu







	@cached_property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						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      )

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

						return strategy







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						requires_backends(self  , ['tf']      )
						return self._setup_tpu is not None







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						requires_backends(self  , ['tf']      )
						return self._setup_strategy







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						requires_backends(self  , ['tf']      )
						return tf.config.list_physical_devices('GPU'      )







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





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







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						return self.n_gpu > 0







import os

A__						=							{"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}




def 					_lowercase     (				a_				:					str				)				->	int:




					'''simple docstring'''







					__magic_name__							=    0

					__magic_name__							=    0
					while index < len(a_				) - 1:
										__magic_name__							=    SYMBOLS[numerals[index]]
										__magic_name__							=    SYMBOLS[numerals[index + 1]]
										if current_value < next_value:
															total_value -= current_value
										else:
															total_value += current_value
										index += 1
					total_value += SYMBOLS[numerals[index]]

					return total_value




def 					_lowercase     (				a_				:					int				)				->	str:




					'''simple docstring'''







					__magic_name__							=    ''

					__magic_name__							=    num // 1_0_0_0
					numerals += m_count * "M"
					num %= 1_0_0_0

					__magic_name__							=    num // 1_0_0
					if c_count == 9:
										numerals += "CM"
										c_count -= 9
					elif c_count == 4:
										numerals += "CD"
										c_count -= 4
					if c_count >= 5:
										numerals += "D"
										c_count -= 5
					numerals += c_count * "C"
					num %= 1_0_0

					__magic_name__							=    num // 1_0
					if x_count == 9:
										numerals += "XC"
										x_count -= 9
					elif x_count == 4:
										numerals += "XL"
										x_count -= 4
					if x_count >= 5:
										numerals += "L"
										x_count -= 5
					numerals += x_count * "X"
					num %= 1_0

					if num == 9:
										numerals += "IX"
										num -= 9
					elif num == 4:
										numerals += "IV"
										num -= 4
					if num >= 5:
										numerals += "V"
										num -= 5
					numerals += num * "I"

					return numerals




def 					_lowercase     (				a_				:					str = "/p089_roman.txt"				)				->	int:




					'''simple docstring'''







					__magic_name__							=    0

					with open(os.path.dirname(a_				) + roman_numerals_filename				) as filea:
										__magic_name__							=    filea.readlines()

					for line in lines:
										__magic_name__							=    line.strip()
										__magic_name__							=    parse_roman_numerals(a_				)
										__magic_name__							=    generate_roman_numerals(a_				)
										savings += len(a_				) - len(a_				)

					return savings


if __name__ == "__main__":
						print(f'''{solution() = }''')




