


"""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			):







						def __a						(							self   :    Any			,			_lowercase   :    Any			,			_lowercase   :    int			,			_lowercase   :    Any		):
								self.assertEqual(len(_lowercase		)			,			len(_lowercase		)		)
								for a, b in zip(_lowercase			,			_lowercase		):
										self.assertAlmostEqual(_lowercase			,			_lowercase			,			delta=_lowercase		)







						def __a						(							self   :    List[str]		):
								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(_lowercase		):
										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   :    int		):
								A					       = None
								ops.enable_eager_execution_internal()
								A					       = tf.config.list_physical_devices('CPU'		)
								if len(_lowercase		) == 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=_lowercase		)

								def accumulate_on_replica(_lowercase   :    Optional[Any]		):
										accumulator([gradient]		)

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

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

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

								def _check_local_values(_lowercase   :    List[str]			,			_lowercase   :    int		):
										A					       = strategy.experimental_local_results(accumulator._gradients[0]		)
										self.assertListAlmostEqual(values[0].value()			,			_lowercase			,			tol=1e-2		)
										self.assertListAlmostEqual(values[1].value()			,			_lowercase			,			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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase						:				Any             =      {
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				int             =      ["LxmertTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				Union[str, Any]             =      [
	    "LxmertEncoder",
	    "LxmertForPreTraining",
	    "LxmertForQuestionAnswering",
	    "LxmertModel",
	    "LxmertPreTrainedModel",
	    "LxmertVisualFeatureEncoder",
	    "LxmertXLayer",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				Union[str, Any]             =      [
	    "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFLxmertForPreTraining",
	    "TFLxmertMainLayer",
	    "TFLxmertModel",
	    "TFLxmertPreTrainedModel",
	    "TFLxmertVisualFeatureEncoder",
	]


if TYPE_CHECKING:
	from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
	from .tokenization_lxmert import LxmertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_lxmert_fast import LxmertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_lxmert import (
		    LxmertEncoder,
		    LxmertForPreTraining,
		    LxmertForQuestionAnswering,
		    LxmertModel,
		    LxmertPreTrainedModel,
		    LxmertVisualFeatureEncoder,
		    LxmertXLayer,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_lxmert import (
		    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFLxmertForPreTraining,
		    TFLxmertMainLayer,
		    TFLxmertModel,
		    TFLxmertPreTrainedModel,
		    TFLxmertVisualFeatureEncoder,
		)

else:
	import sys

	UpperCamelCase						:				Dict             =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





