



'''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  :				Optional[int]							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				Dict		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[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(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  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	None
					ops.enable_eager_execution_internal()
					__SCREAMING_SNAKE_CASE :	Any					 =	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()]		)
					__SCREAMING_SNAKE_CASE :	str					 =	tf.config.list_logical_devices(device_type="""CPU"""		)
					__SCREAMING_SNAKE_CASE :	int					 =	tf.distribute.MirroredStrategy(devices=devices[:2]		)

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

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

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

					@tf.function
					def accumulate(lowerCAmelCase__  :				Optional[int]							,  lowerCAmelCase__  :				Tuple		):
							with strategy.scope():
									__SCREAMING_SNAKE_CASE :	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__  :				Dict							,  lowerCAmelCase__  :				Any		):
							__SCREAMING_SNAKE_CASE :	int					 =	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 ..utils import DummyObject, requires_backends






class 		_UpperCamelCase						(							metaclass=lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Optional[int]    					=	['''onnx''']

			def __init__(							self  :				List[str]							,  *lowerCAmelCase__  :				Optional[int]							,  **lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					requires_backends(self							,  ["""onnx"""]		)

			@classmethod
			def UpperCamelCase__      (							cls  :				Optional[int]							,  *lowerCAmelCase__  :				int							,  **lowerCAmelCase__  :				int		):





					"""simple docstring"""






					requires_backends(cls							,  ["""onnx"""]		)



			@classmethod
			def UpperCamelCase__      (							cls  :				Tuple							,  *lowerCAmelCase__  :				List[str]							,  **lowerCAmelCase__  :				Optional[Any]		):





					"""simple docstring"""






					requires_backends(cls							,  ["""onnx"""]		)