


lowercase_	   =     8.314_4598
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 if temperature < 0:
  raise Exception('Temperature cannot be less than 0 K'						)
 if molar_mass <= 0:
  raise Exception('Molar mass cannot be less than or equal to 0 kg/mol'						)
 else:
  return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
       import doctest

       # run doctest
       doctest.testmod()

       # example
       lowercase_	   =     3_0_0
       lowercase_	   =     2_8
       lowercase_	   =     rms_speed_of_molecule(temperature, molar_mass)
       print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")





import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
       import tensorflow as tf
       from tensorflow.python.eager import context
       from tensorflow.python.framework import ops

       from transformers import GradientAccumulator, create_optimizer


@require_tf
class 	A_ (	unittest.TestCase   ):




      '''simple docstring'''


      def 					_snake_case     (    self:	Optional[int]						,				a:	int						,				a:	List[Any]						,				a:	Any    ):
       self.assertEqual(len(a    )						,				len(a    )    )
       for a, b in zip(a						,				a    ):
        self.assertAlmostEqual(a						,				a						,				delta=a    )




      def 					_snake_case     (    self:	Union[str, Any]    ):
       __lowerCamelCase		:							Union[str, Any]		 =						GradientAccumulator()
       accumulator([tf.constant([1.0, 2.0]    )]    )
       accumulator([tf.constant([-2.0, 1.0]    )]    )
       accumulator([tf.constant([-1.0, 2.0]    )]    )
       with self.assertRaises(a    ):
        accumulator([tf.constant([1.0, 1.0]    ), tf.constant([2.0, 2.0]    )]    )
       self.assertEqual(accumulator.step						,				3    )
       self.assertEqual(len(accumulator.gradients    )						,				1    )
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()						,				[-2.0, 5.0]						,				tol=1e-2    )
       accumulator.reset()
       self.assertEqual(accumulator.step						,				0    )
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()						,				[0.0, 0.0]						,				tol=1e-2    )




      def 					_snake_case     (    self:	str    ):
       __lowerCamelCase		:							Any		 =						None
       ops.enable_eager_execution_internal()
       __lowerCamelCase		:							List[str]		 =						tf.config.list_physical_devices('CPU'    )
       if len(a    ) == 1:
        tf.config.set_logical_device_configuration(
            physical_devices[0]						,				[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]    )
       __lowerCamelCase		:							Dict		 =						tf.config.list_logical_devices(device_type='CPU'    )
       __lowerCamelCase		:							List[Any]		 =						tf.distribute.MirroredStrategy(devices=devices[:2]    )

       with strategy.scope():
        __lowerCamelCase		:							Optional[Any]		 =						GradientAccumulator()
        __lowerCamelCase		:							int		 =						tf.Variable([4.0, 3.0]    )
        __lowerCamelCase ,					__lowerCamelCase		:							Any		 =						create_optimizer(5e-5						,				10						,				5    )
        __lowerCamelCase		:							Any		 =						tf.Variable([0.0, 0.0]						,				trainable=a    )

       def accumulate_on_replica(a:	str    ):
        accumulator([gradient]    )

       def apply_on_replica():
        optimizer.apply_gradients(list(zip(accumulator.gradients						,				[variable]    )    )    )

       @tf.function
       def accumulate(a:	List[Any]						,				a:	List[str]    ):
        with strategy.scope():
         __lowerCamelCase		:							Optional[int]		 =						strategy.experimental_local_results(a    )
         local_variables[0].assign(a    )
         local_variables[1].assign(a    )
         strategy.run(a						,				args=(gradient_placeholder,)    )

       @tf.function
       def apply_grad():
        with strategy.scope():
         strategy.run(a    )

       def _check_local_values(a:	str						,				a:	Union[str, Any]    ):
        __lowerCamelCase		:							Optional[Any]		 =						strategy.experimental_local_results(accumulator._gradients[0]    )
        self.assertListAlmostEqual(values[0].value()						,				a						,				tol=1e-2    )
        self.assertListAlmostEqual(values[1].value()						,				a						,				tol=1e-2    )

       accumulate([1.0, 2.0]						,				[-1.0, 1.0]    )
       accumulate([3.0, -1.0]						,				[-1.0, -1.0]    )
       accumulate([-2.0, 2.0]						,				[3.0, -2.0]    )
       self.assertEqual(accumulator.step						,				3    )
       _check_local_values([2.0, 3.0]						,				[1.0, -2.0]    )
       apply_grad()
       self.assertListAlmostEqual(variable.value()						,				[4.0, 3.0]						,				tol=1e-2    )
       accumulator.reset()
       self.assertEqual(accumulator.step						,				0    )
       _check_local_values([0.0, 0.0]						,				[0.0, 0.0]    )

