





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    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Dict:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        a__	:					Optional[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(lowerCAmelCase__							):
          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    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




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

        with strategy.scope():
          a__	:					int										=GradientAccumulator()
          a__	:					Optional[Any]										=tf.Variable([4.0, 3.0]							)
          a__					,							a__	:					Dict										=create_optimizer(5E-5		,      1_0		,      5							)
          a__	:					Optional[int]										=tf.Variable([0.0, 0.0]		,      trainable=lowerCAmelCase__							)

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

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

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

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

        def _check_local_values(lowerCAmelCase__		,      lowerCAmelCase__							):
          a__	:					Tuple										=strategy.experimental_local_results(accumulator._gradients[0]							)
          self.assertListAlmostEqual(values[0].value()		,      lowerCAmelCase__		,      tol=1E-2							)
          self.assertListAlmostEqual(values[1].value()		,      lowerCAmelCase__		,      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]							)













def      _A       (		SCREAMING_SNAKE_CASE     :   list						):





  """simple docstring"""



  if not isinstance(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						):
    raise ValueError("Input series is not valid, valid series - [2, 4, 6]"						)
  if len(SCREAMING_SNAKE_CASE						) == 0:
    raise ValueError("Input list must be a non empty list"						)
  if len(SCREAMING_SNAKE_CASE						) == 1:
    return True
  a__	:					Union[str, Any]										=series[1] - series[0]
  for index in range(len(SCREAMING_SNAKE_CASE						) - 1						):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True
def      _A       (		SCREAMING_SNAKE_CASE     :   list						):





  """simple docstring"""



  if not isinstance(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						):
    raise ValueError("Input series is not valid, valid series - [2, 4, 6]"						)
  if len(SCREAMING_SNAKE_CASE						) == 0:
    raise ValueError("Input list must be a non empty list"						)
  a__	:					Any										=0
  for val in series:
    answer += val
  return answer / len(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






