





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     __SCREAMING_SNAKE_CASE   (       unittest.TestCase    ):






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														self.assertEqual(len(_a					)					,	len(_a					)					)
														for a, b in zip(_a					,	_a					):
																					self.assertAlmostEqual(_a					,	_a					,	delta=_a					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     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(_a					):
																					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					):
														UpperCamelCase__			       =     None
														ops.enable_eager_execution_internal()
														UpperCamelCase__			       =     tf.config.list_physical_devices("""CPU"""					)
														if len(_a					) == 1:
																					tf.config.set_logical_device_configuration(
																					    physical_devices[0]					,	[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]					)
														UpperCamelCase__			       =     tf.config.list_logical_devices(device_type="""CPU"""					)
														UpperCamelCase__			       =     tf.distribute.MirroredStrategy(devices=devices[:2]					)

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

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

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

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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


UpperCamelCase__     =	{}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__     =	["GPTSw3Tokenizer"]


if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_gpt_swa import GPTSwaTokenizer

else:
					import sys

					UpperCamelCase__     =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




