






'''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      ):




		'''simple docstring'''





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






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	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       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	None
								ops.enable_eager_execution_internal()
								lowerCamelCase__								=	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()]							)
								lowerCamelCase__								=	tf.config.list_logical_devices(device_type="""CPU"""							)
								lowerCamelCase__								=	tf.distribute.MirroredStrategy(devices=devices[:2]							)

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








'''simple docstring'''






from __future__ import annotations







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase = None    ,	_lowerCamelCase = None ) ->				None:



			"""simple docstring"""


			if start is None:
						__snake_case					:      Optional[Any]							      =     0

			if end is None:
						__snake_case					:      Optional[Any]							      =     len(_lowerCamelCase ) - 1

			if start >= end:
						return

			__snake_case					:      Tuple							      =     (start + end) // 2

			slowsort(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )
			slowsort(_lowerCamelCase    ,	mid + 1    ,	_lowerCamelCase )

			if sequence[end] < sequence[mid]:
						__snake_case      ,     __snake_case					:      str							      =     sequence[mid], sequence[end]

			slowsort(_lowerCamelCase    ,	_lowerCamelCase    ,	end - 1 )


if __name__ == "__main__":
	from doctest import testmod

	testmod()


