


'''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 lowercase_							(					self				:    Tuple						,						_A				:    Tuple						,						_A				:    Optional[int]						,						_A				:    List[str] ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[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(_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 lowercase_							(					self				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		Any													=				None
          ops.enable_eager_execution_internal()
          UpperCAmelCase__			:		Tuple													=				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()] )
          UpperCAmelCase__			:		Optional[int]													=				tf.config.list_logical_devices(device_type='''CPU''' )
          UpperCAmelCase__			:		Optional[Any]													=				tf.distribute.MirroredStrategy(devices=devices[:2] )

          with strategy.scope():
              UpperCAmelCase__			:		Optional[int]													=				GradientAccumulator()
              UpperCAmelCase__			:		List[Any]													=				tf.Variable([4.0, 3.0] )
              UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[Any]													=				create_optimizer(5e-5						,						10						,						5 )
              UpperCAmelCase__			:		str													=				tf.Variable([0.0, 0.0]						,						trainable=_A )

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

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

          @tf.function
          def accumulate(_A				:    Tuple						,						_A				:    int ):
              with strategy.scope():
                  UpperCAmelCase__			:		Tuple													=				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				:    Union[str, Any] ):
              UpperCAmelCase__			:		Optional[int]													=				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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


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

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

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__    					=      [
      '''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:
  UpperCamelCase__    					=      [
      '''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

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