




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     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     List[Any]							)					->     Dict:
							self.assertEqual(len(__lowerCamelCase							)					,      len(__lowerCamelCase							)							)
							for a, b in zip(__lowerCamelCase					,      __lowerCamelCase							):
										self.assertAlmostEqual(__lowerCamelCase					,      __lowerCamelCase					,      delta=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Union[str, Any]:
							a    =			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				:     Dict							)					->     int:
							a    =			None
							ops.enable_eager_execution_internal()
							a    =			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    =			tf.config.list_logical_devices(device_type="CPU"							)
							a    =			tf.distribute.MirroredStrategy(devices=devices[:2]							)

							with strategy.scope():
										a    =			GradientAccumulator()
										a    =			tf.Variable([4.0, 3.0]							)
										a    ,		a    =			create_optimizer(5e-5					,      10					,      5							)
										a    =			tf.Variable([0.0, 0.0]					,      trainable=__lowerCamelCase							)

							def accumulate_on_replica(__lowerCamelCase				:     Dict							):
										accumulator([gradient]							)

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

							@tf.function
							def accumulate(__lowerCamelCase				:     Dict					,      __lowerCamelCase				:     Dict							):
										with strategy.scope():
													a    =			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				:     Union[str, Any]					,      __lowerCamelCase				:     Dict							):
										a    =			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 _SCREAMING_SNAKE_CASE      (A       )       ->       bool:






       """simple docstring"""







       return len(set(A       )       ) == len(A       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




