





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       :int       =  logging.get_logger(__name__)



@dataclass
class    _a     (				__snake_case							):






							"""simple docstring"""

							snake_case_							 =						[
							    "no_inference",
							    "no_cuda",
							    "no_tpu",
							    "no_speed",
							    "no_memory",
							    "no_env_print",
							    "no_multi_process",
							]

							def __init__(      self				:     Optional[Any]							,	**a				:     Tuple    )	->int:

													for deprecated_arg in self.deprecated_args:
																			if deprecated_arg in kwargs:
																									SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					deprecated_arg[3:]
																									SCREAMING_SNAKE_CASE__     :				Optional[int]   =					not kwargs.pop(_lowercase    )
																									logger.warning(
																									    f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																									    f""" {positive_arg}={kwargs[positive_arg]}"""    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					kwargs.pop("tpu_name"							,	self.tpu_name    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					kwargs.pop("device_idx"							,	self.device_idx    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					kwargs.pop("eager_mode"							,	self.eager_mode    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					kwargs.pop("use_xla"							,	self.use_xla    )
													super().__init__(**_lowercase    )

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

							@cached_property
							def      A_  (      self				:     Any    )	->int:
													requires_backends(self							,	["tf"]    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					None
													if self.tpu:
																			try:
																									if self.tpu_name:
																															SCREAMING_SNAKE_CASE__     :				List[str]   =					tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name    )
																									else:
																															SCREAMING_SNAKE_CASE__     :				List[Any]   =					tf.distribute.cluster_resolver.TPUClusterResolver()
																			except ValueError:
																									SCREAMING_SNAKE_CASE__     :				List[Any]   =					None
													return tpu

							@cached_property
							def      A_  (      self				:     Optional[Any]    )	->Optional[Any]:
													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    )

																			SCREAMING_SNAKE_CASE__     :				Optional[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"    )
																									SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""    )
																			else:
																									tf.config.set_visible_devices([]							,	"GPU"    )  # disable GPU
																									SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""    )

													return strategy

							@property
							def      A_  (      self				:     Optional[Any]    )	->Optional[int]:
													requires_backends(self							,	["tf"]    )
													return self._setup_tpu is not None

							@property
							def      A_  (      self				:     List[str]    )	->Dict:
													requires_backends(self							,	["tf"]    )
													return self._setup_strategy

							@property
							def      A_  (      self				:     Optional[Any]    )	->Optional[Any]:
													requires_backends(self							,	["tf"]    )
													return tf.config.list_physical_devices("GPU"    )

							@property
							def      A_  (      self				:     Dict    )	->Tuple:
													requires_backends(self							,	["tf"]    )
													if self.cuda:
																			return len(self.gpu_list    )
													return 0






							@property
							def      A_  (      self				:     Tuple    )	->Tuple:
													return self.n_gpu > 0










def       UpperCAmelCase							(     _lowerCamelCase      :  int = 1_000     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Dict   =					-1
						SCREAMING_SNAKE_CASE__     :				str   =					0
						for a in range(1			,   n // 3     ):
												# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
												SCREAMING_SNAKE_CASE__     :				Tuple   =					(n * n - 2 * a * n) // (2 * n - 2 * a)
												SCREAMING_SNAKE_CASE__     :				Dict   =					n - a - b
												if c * c == (a * a + b * b):
																		SCREAMING_SNAKE_CASE__     :				str   =					a * b * c
																		if candidate >= product:
																								SCREAMING_SNAKE_CASE__     :				List[str]   =					candidate
						return product


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