



"""simple docstring"""




import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


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

if is_tf_available():
     import tensorflow as tf


@dataclass
class 	a__		(    SCREAMING_SNAKE_CASE__  ):
   _lowerCamelCase       				=					'tf'
   _lowerCamelCase       				=					field(
       default=SCREAMING_SNAKE_CASE__,	metadata={'help': 'Name of TPU'},	)

   _lowerCamelCase       				=					field(
       default=SCREAMING_SNAKE_CASE__,	metadata={'help': 'Zone of TPU'},	)

   _lowerCamelCase       				=					field(
       default=SCREAMING_SNAKE_CASE__,	metadata={'help': 'Name of Cloud TPU-enabled project'},	)

   _lowerCamelCase       				=					field(
       default=1.0,	metadata={'help': 'Power for the Polynomial decay LR scheduler.'},	)

   _lowerCamelCase       				=					field(default=SCREAMING_SNAKE_CASE__,	metadata={'help': 'Whether to activate the XLA compilation or not'}  )


   @cached_property
   def 							lowercase					(				self   :				Optional[Any]      )     ->							Tuple["tf.distribute.Strategy", int]:
         requires_backends(self,  ['tf']      )
         logger.info('Tensorflow: setting up strategy'      )

         lowercase	:   str											= tf.config.list_physical_devices('GPU'      )

         # Set to float16 at first
         if self.fpaa:
               tf.keras.mixed_precision.set_global_policy('mixed_float16'      )

         if self.no_cuda:
               lowercase	:   Tuple											= tf.distribute.OneDeviceStrategy(device='/cpu:0'      )
         else:
               try:
                     if self.tpu_name:
                           lowercase	:   Dict											= tf.distribute.cluster_resolver.TPUClusterResolver(
                               self.tpu_name,  zone=self.tpu_zone,  project=self.gcp_project      )
                     else:
                           lowercase	:   int											= tf.distribute.cluster_resolver.TPUClusterResolver()
               except ValueError:
                     if self.tpu_name:
                           raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''      )
                     else:
                           lowercase	:   int											= None

               if tpu:
                     # Set to bfloat16 in case of TPU
                     if self.fpaa:
                           tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'      )

                     tf.config.experimental_connect_to_cluster(lowerCAmelCase      )
                     tf.tpu.experimental.initialize_tpu_system(lowerCAmelCase      )

                     lowercase	:   List[str]											= tf.distribute.TPUStrategy(lowerCAmelCase      )

               elif len(lowerCAmelCase      ) == 0:
                     lowercase	:   int											= tf.distribute.OneDeviceStrategy(device='/cpu:0'      )
               elif len(lowerCAmelCase      ) == 1:
                     lowercase	:   Tuple											= tf.distribute.OneDeviceStrategy(device='/gpu:0'      )
               elif len(lowerCAmelCase      ) > 1:
                     # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                     lowercase	:   Union[str, Any]											= tf.distribute.MirroredStrategy()
               else:
                     raise ValueError('Cannot find the proper strategy, please check your environment properties.'      )

         return strategy


   @property
   def 							lowercase					(				self   :				Dict      )     ->							"tf.distribute.Strategy":
         requires_backends(self,  ['tf']      )
         return self._setup_strategy


   @property
   def 							lowercase					(				self   :				int      )     ->							int:
         requires_backends(self,  ['tf']      )
         return self._setup_strategy.num_replicas_in_sync


   @property
   def 							lowercase					(				self   :				Tuple      )     ->							List[Any]:
         return False  # TF Logging is handled by Keras not the Trainer


   @property
   def 							lowercase					(				self   :				Union[str, Any]      )     ->							int:
         if self.per_gpu_train_batch_size:
               logger.warning(
                   'Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '
                   'version. Using `--per_device_train_batch_size` is preferred.'      )
         lowercase	:   Any											= self.per_gpu_train_batch_size or self.per_device_train_batch_size
         return per_device_batch_size * self.n_replicas


   @property
   def 							lowercase					(				self   :				List[str]      )     ->							int:
         if self.per_gpu_eval_batch_size:
               logger.warning(
                   'Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '
                   'version. Using `--per_device_eval_batch_size` is preferred.'      )
         lowercase	:   Union[str, Any]											= self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
         return per_device_batch_size * self.n_replicas






   @property
   def 							lowercase					(				self   :				str      )     ->							int:
         requires_backends(self,  ['tf']      )
         warnings.warn(
             'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.',  lowerCAmelCase,  )
         return self._setup_strategy.num_replicas_in_sync





"""simple docstring"""




from __future__ import annotations

# Define the Gravitational Constant G and the function
_UpperCamelCase:					Tuple     				=	6.6_7_4_3e-1_1  # unit of G : m^3 * kg^-1 * s^-2


def 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase					)  ->      dict[str, float]:






      '''simple docstring'''


      lowercase	:   str											= mass_a * mass_a

      if (force, mass_a, mass_a, distance).count(0					) != 1:
            raise ValueError('One and only one argument must be 0'					)
      if force < 0:
            raise ValueError('Gravitational force can not be negative'					)
      if distance < 0:
            raise ValueError('Distance can not be negative'					)
      if mass_a < 0 or mass_a < 0:
            raise ValueError('Mass can not be negative'					)
      if force == 0:
            lowercase	:   str											= GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
            return {"force": force}
      elif mass_a == 0:
            lowercase	:   Optional[int]											= (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
            return {"mass_1": mass_a}
      elif mass_a == 0:
            lowercase	:   Optional[int]											= (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
            return {"mass_2": mass_a}
      elif distance == 0:
            lowercase	:   List[Any]											= (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
            return {"distance": distance}
      raise ValueError('One and only one argument must be 0'					)


# Run doctest
if __name__ == "__main__":
     import doctest

     doctest.testmod()
