


"""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 			__snake_case   (unittest.TestCase  ):




							def   __a   (     self:    List[Any]     ,							A_:    List[str]     ,							A_:    Tuple     ,							A_:    List[Any]   ):
										self.assertEqual(len(A_   )     ,							len(A_   )   )
										for a, b in zip(A_     ,							A_   ):
													self.assertAlmostEqual(A_     ,							A_     ,							delta=A_   )




							def   __a   (     self:    Tuple   ):
										__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(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   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      None
										ops.enable_eager_execution_internal()
										__lowerCamelCase          =      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()]   )
										__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=A_   )

										def accumulate_on_replica(A_:    Tuple   ):
													accumulator([gradient]   )

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

										@tf.function
										def accumulate(A_:    str     ,							A_:    Optional[int]   ):
													with strategy.scope():
																__lowerCamelCase          =      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(A_:    Tuple     ,							A_:    str   ):
													__lowerCamelCase          =      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]   )








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__magic_name__						: Tuple    =				{}


try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: str    =				['MLukeTokenizer']

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


else:
					import sys

					__magic_name__						: int    =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




