



'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase						      =       logging.get_logger(__name__)

class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Optional[Any]           = 'encoder-decoder'
							lowerCAmelCase	:   Any           = True







							def __init__(							self :	str     ,**_UpperCAmelCase :	List[Any]		):
									super().__init__(**_UpperCAmelCase		)
									assert (
									    "encoder" in kwargs and "decoder" in kwargs
									), "Config has to be initialized with encoder and decoder config"
									_a					:				int				=							kwargs.pop('encoder'		)
									_a					:				List[str]				=							encoder_config.pop('model_type'		)
									_a					:				Any				=							kwargs.pop('decoder'		)
									_a					:				Any				=							decoder_config.pop('model_type'		)

									from ..auto.configuration_auto import AutoConfig

									_a					:				Optional[int]				=							AutoConfig.for_model(_UpperCAmelCase     ,**_UpperCAmelCase		)
									_a					:				Dict				=							AutoConfig.for_model(_UpperCAmelCase     ,**_UpperCAmelCase		)
									_a					:				Optional[Any]				=							True







							@classmethod
							def 							__lowercase      (							cls :	str     ,_UpperCAmelCase :	PretrainedConfig     ,_UpperCAmelCase :	PretrainedConfig     ,**_UpperCAmelCase :	Tuple		):
									logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'		)
									_a					:				Tuple				=							True
									_a					:				Tuple				=							True

									return cls(encoder=encoder_config.to_dict()     ,decoder=decoder_config.to_dict()     ,**_UpperCAmelCase		)







							def 							__lowercase      (							self :	Tuple		):
									_a					:				str				=							copy.deepcopy(self.__dict__		)
									_a					:				Optional[int]				=							self.encoder.to_dict()
									_a					:				Dict				=							self.decoder.to_dict()
									_a					:				Dict				=							self.__class__.model_type
									return output









'''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      __magic_name__						(   unittest.TestCase       ):







							def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Optional[Any]		):
									self.assertEqual(len(_UpperCAmelCase		)     ,len(_UpperCAmelCase		)		)
									for a, b in zip(_UpperCAmelCase     ,_UpperCAmelCase		):
											self.assertAlmostEqual(_UpperCAmelCase     ,_UpperCAmelCase     ,delta=_UpperCAmelCase		)







							def 							__lowercase      (							self :	int		):
									_a					:				int				=							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(_UpperCAmelCase		):
											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 							__lowercase      (							self :	Any		):
									_a					:				int				=							None
									ops.enable_eager_execution_internal()
									_a					:				Optional[int]				=							tf.config.list_physical_devices('CPU'		)
									if len(_UpperCAmelCase		) == 1:
											tf.config.set_logical_device_configuration(
											    physical_devices[0]     ,[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]		)
									_a					:				Tuple				=							tf.config.list_logical_devices(device_type='CPU'		)
									_a					:				List[str]				=							tf.distribute.MirroredStrategy(devices=devices[:2]		)

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

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

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

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

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

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




