
"""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 					lowerCAmelCase_					(   unittest.TestCase     ):



						'''simple docstring'''


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   Optional[int]	,A_  :   Optional[Any]	,A_  :   Optional[int]   )  -> Dict:
							self.assertEqual(len(A_   )	,len(A_   )   )
							for a, b in zip(A_	,A_   ):
								self.assertAlmostEqual(A_	,A_	,delta=A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> 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(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     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> Dict:
							A     = None
							ops.enable_eager_execution_internal()
							A     = 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()]   )
							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=A_   )

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

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

							@tf.function
							def accumulate(A_  :   Dict	,A_  :   Tuple   ):
								with strategy.scope():
									A     = 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_  :   Optional[int]	,A_  :   int   ):
								A     = 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 torch import nn






def    _snake_case					(		snake_case__ :  Union[str, Any]  ):
	if act_fn in ["swish", "silu"]:
		return nn.SiLU()
	elif act_fn == "mish":
		return nn.Mish()
	elif act_fn == "gelu":
		return nn.GELU()
	else:
		raise ValueError(F'Unsupported activation function: {act_fn}'  )