





'''simple docstring'''

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 lowerCamelCase						(unittest.TestCase					):







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Dict:






        """simple docstring"""





        self.assertEqual(len(lowercase__		)     ,     len(lowercase__		)		)
        for a, b in zip(lowercase__     ,     lowercase__		):
              self.assertAlmostEqual(lowercase__     ,     lowercase__     ,     delta=lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        _snake_case		:	int					     =     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(lowercase__		):
              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 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     None
        ops.enable_eager_execution_internal()
        _snake_case		:	Union[str, Any]					     =     tf.config.list_physical_devices('''CPU'''		)
        if len(lowercase__		) == 1:
              tf.config.set_logical_device_configuration(
                  physical_devices[0]     ,     [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]		)
        _snake_case		:	List[str]					     =     tf.config.list_logical_devices(device_type='''CPU'''		)
        _snake_case		:	Optional[int]					     =     tf.distribute.MirroredStrategy(devices=devices[:2]		)

        with strategy.scope():
              _snake_case		:	Union[str, Any]					     =     GradientAccumulator()
              _snake_case		:	Union[str, Any]					     =     tf.Variable([4.0, 3.0]		)
              _snake_case     ,							_snake_case		:	List[Any]					     =     create_optimizer(5E-5     ,     10     ,     5		)
              _snake_case		:	Tuple					     =     tf.Variable([0.0, 0.0]     ,     trainable=lowercase__		)

        def accumulate_on_replica(lowercase__		):
              accumulator([gradient]		)

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

        @tf.function
        def accumulate(lowercase__     ,     lowercase__		):
              with strategy.scope():
                    _snake_case		:	Optional[int]					     =     strategy.experimental_local_results(lowercase__		)
                    local_variables[0].assign(lowercase__		)
                    local_variables[1].assign(lowercase__		)
                    strategy.run(lowercase__     ,     args=(gradient_placeholder,)		)

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

        def _check_local_values(lowercase__     ,     lowercase__		):
              _snake_case		:	Union[str, Any]					     =     strategy.experimental_local_results(accumulator._gradients[0]		)
              self.assertListAlmostEqual(values[0].value()     ,     lowercase__     ,     tol=1E-2		)
              self.assertListAlmostEqual(values[1].value()     ,     lowercase__     ,     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]		)









'''simple docstring'''

UpperCAmelCase					:		Union[str, Any]		=							tuple[float, float, float]
UpperCAmelCase					:		int		=							tuple[float, float, float]




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	str					     =     end_pointa[0] - end_pointa[0]
      _snake_case		:	Tuple					     =     end_pointa[1] - end_pointa[1]
      _snake_case		:	Any					     =     end_pointa[2] - end_pointa[2]
      return (x, y, z)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Dict					     =     ab[1] * ac[2] - ab[2] * ac[1]  # *i
      _snake_case		:	List[str]					     =     (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
      _snake_case		:	Optional[int]					     =     ab[0] * ac[1] - ab[1] * ac[0]  # *k
      return (x, y, z)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      return tuple(round(lowerCAmelCase_			,      lowerCAmelCase_	) for x in vector	) == (0, 0, 0)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_ = 10	):





      """simple docstring"""



      _snake_case		:	str					     =     create_vector(lowerCAmelCase_			,      lowerCAmelCase_	)
      _snake_case		:	Tuple					     =     create_vector(lowerCAmelCase_			,      lowerCAmelCase_	)
      return is_zero_vector(get_ad_vectors_cross(lowerCAmelCase_			,      lowerCAmelCase_	)			,      lowerCAmelCase_	)


