
'''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 		__UpperCAmelCase   (      unittest.TestCase		):


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	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(UpperCAmelCase_						):
									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 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	None
								ops.enable_eager_execution_internal()
								_snake_case     		=	tf.config.list_physical_devices('CPU'						)
								if len(UpperCAmelCase_						) == 1:
									tf.config.set_logical_device_configuration(
									    physical_devices[0]					,     [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
								_snake_case     		=	tf.config.list_logical_devices(device_type='CPU'						)
								_snake_case     		=	tf.distribute.MirroredStrategy(devices=devices[:2]						)

								with strategy.scope():
									_snake_case     		=	GradientAccumulator()
									_snake_case     		=	tf.Variable([4.0, 3.0]						)
									_snake_case		,				_snake_case     		=	create_optimizer(5E-5					,     10					,     5						)
									_snake_case     		=	tf.Variable([0.0, 0.0]					,     trainable=UpperCAmelCase_						)

								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():
										_snake_case     		=	strategy.experimental_local_results(UpperCAmelCase_						)
										local_variables[0].assign(UpperCAmelCase_						)
										local_variables[1].assign(UpperCAmelCase_						)
										strategy.run(UpperCAmelCase_					,     args=(gradient_placeholder,)						)

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

								def _check_local_values(lowerCAmelCase_					,     lowerCAmelCase_						):
									_snake_case     		=	strategy.experimental_local_results(accumulator._gradients[0]						)
									self.assertListAlmostEqual(values[0].value()					,     UpperCAmelCase_					,     tol=1E-2						)
									self.assertListAlmostEqual(values[1].value()					,     UpperCAmelCase_					,     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'''







from __future__ import annotations







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			int:
	if not nums:
		return 0
	_snake_case     		=	nums[0]
	_snake_case     		=	0
	for num in nums[1:]:
		_snake_case		,				_snake_case     		=	(
		    max_excluding + num,
		    max(__A    ,       __A			),
		)
	return max(__A    ,       __A			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

