
"""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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Tuple:



											"""simple docstring"""




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



				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     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(SCREAMING_SNAKE_CASE__					):
																		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 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     None
											ops.enable_eager_execution_internal()
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     tf.config.list_physical_devices("""CPU"""					)
											if len(SCREAMING_SNAKE_CASE__					) == 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__		: Optional[Any]     =     tf.distribute.MirroredStrategy(devices=devices[:2]					)

											with strategy.scope():
																		SCREAMING_SNAKE_CASE__		: Tuple     =     GradientAccumulator()
																		SCREAMING_SNAKE_CASE__		: Any     =     tf.Variable([4.0, 3.0]					)
																		SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Optional[int]     =     create_optimizer(5E-5     , 10     , 5					)
																		SCREAMING_SNAKE_CASE__		: int     =     tf.Variable([0.0, 0.0]     , trainable=SCREAMING_SNAKE_CASE__					)

											def accumulate_on_replica(SCREAMING_SNAKE_CASE__					):
																		accumulator([gradient]					)

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

											@tf.function
											def accumulate(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																		with strategy.scope():
																									SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     strategy.experimental_local_results(SCREAMING_SNAKE_CASE__					)
																									local_variables[0].assign(SCREAMING_SNAKE_CASE__					)
																									local_variables[1].assign(SCREAMING_SNAKE_CASE__					)
																									strategy.run(SCREAMING_SNAKE_CASE__     , args=(gradient_placeholder,)					)

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

											def _check_local_values(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     strategy.experimental_local_results(accumulator._gradients[0]					)
																		self.assertListAlmostEqual(values[0].value()     , SCREAMING_SNAKE_CASE__     , tol=1E-2					)
																		self.assertListAlmostEqual(values[1].value()     , SCREAMING_SNAKE_CASE__     , 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"""




import logging

from transformers import PretrainedConfig


UpperCAmelCase__   :					Union[str, Any]												=				logging.getLogger(__name__)


UpperCAmelCase__   :					Any												=				{
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		List[Any]       =     '''bertabs'''



				def __init__(self     , SCREAMING_SNAKE_CASE__=3_05_22     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=0.2     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=7_68     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=20_48     , SCREAMING_SNAKE_CASE__=0.2     , **SCREAMING_SNAKE_CASE__     , )      ->	Union[str, Any]:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     vocab_size
											SCREAMING_SNAKE_CASE__		: Any     =     max_pos

											SCREAMING_SNAKE_CASE__		: List[Any]     =     enc_layers
											SCREAMING_SNAKE_CASE__		: Tuple     =     enc_hidden_size
											SCREAMING_SNAKE_CASE__		: Dict     =     enc_heads
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     enc_ff_size
											SCREAMING_SNAKE_CASE__		: Dict     =     enc_dropout

											SCREAMING_SNAKE_CASE__		: Any     =     dec_layers
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     dec_hidden_size
											SCREAMING_SNAKE_CASE__		: Any     =     dec_heads
											SCREAMING_SNAKE_CASE__		: List[Any]     =     dec_ff_size
											SCREAMING_SNAKE_CASE__		: str     =     dec_dropout




