'''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    a (       unittest.TestCase				):



		"""simple docstring"""







		def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

				'''simple docstring'''







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







		def    lowercase_  (							self					):

				'''simple docstring'''







				__UpperCAmelCase:					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(__lowerCamelCase					):
						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					):

				'''simple docstring'''







				__UpperCAmelCase:					int		= None
				ops.enable_eager_execution_internal()
				__UpperCAmelCase:					Optional[Any]		= tf.config.list_physical_devices("""CPU"""					)
				if len(__lowerCamelCase					) == 1:
						tf.config.set_logical_device_configuration(
						    physical_devices[0]   ,    [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]					)
				__UpperCAmelCase:					Optional[Any]		= tf.config.list_logical_devices(device_type="""CPU"""					)
				__UpperCAmelCase:					str		= tf.distribute.MirroredStrategy(devices=devices[:2]					)

				with strategy.scope():
						__UpperCAmelCase:					Tuple		= GradientAccumulator()
						__UpperCAmelCase:					Union[str, Any]		= tf.Variable([4.0, 3.0]					)
						__UpperCAmelCase:					Union[str, Any]		= create_optimizer(5e-5   ,    10   ,    5					)
						__UpperCAmelCase:					str		= tf.Variable([0.0, 0.0]   ,    trainable=__lowerCamelCase					)

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

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

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

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

				def _check_local_values(snake_case_   ,    snake_case_					):
						__UpperCAmelCase:					Optional[Any]		= strategy.experimental_local_results(accumulator._gradients[0]					)
						self.assertListAlmostEqual(values[0].value()   ,    __lowerCamelCase   ,    tol=1e-2					)
						self.assertListAlmostEqual(values[1].value()   ,    __lowerCamelCase   ,    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,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_	   =			{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			['RemBertTokenizer']

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			['RemBertTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'RemBertForCausalLM',
        'RemBertForMaskedLM',
        'RemBertForMultipleChoice',
        'RemBertForQuestionAnswering',
        'RemBertForSequenceClassification',
        'RemBertForTokenClassification',
        'RemBertLayer',
        'RemBertModel',
        'RemBertPreTrainedModel',
        'load_tf_weights_in_rembert',
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFRemBertForCausalLM',
        'TFRemBertForMaskedLM',
        'TFRemBertForMultipleChoice',
        'TFRemBertForQuestionAnswering',
        'TFRemBertForSequenceClassification',
        'TFRemBertForTokenClassification',
        'TFRemBertLayer',
        'TFRemBertModel',
        'TFRemBertPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_rembert import RemBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_rembert_fast import RemBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_rembert import (
            REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            RemBertForCausalLM,
            RemBertForMaskedLM,
            RemBertForMultipleChoice,
            RemBertForQuestionAnswering,
            RemBertForSequenceClassification,
            RemBertForTokenClassification,
            RemBertLayer,
            RemBertModel,
            RemBertPreTrainedModel,
            load_tf_weights_in_rembert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_rembert import (
            TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRemBertForCausalLM,
            TFRemBertForMaskedLM,
            TFRemBertForMultipleChoice,
            TFRemBertForQuestionAnswering,
            TFRemBertForSequenceClassification,
            TFRemBertForTokenClassification,
            TFRemBertLayer,
            TFRemBertModel,
            TFRemBertPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_	   =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)