
"""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 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       self.assertEqual(len(_UpperCAmelCase						)	,      len(_UpperCAmelCase						)						)
       for a, b in zip(_UpperCAmelCase	,      _UpperCAmelCase						):
            self.assertAlmostEqual(_UpperCAmelCase	,      _UpperCAmelCase	,      delta=_UpperCAmelCase						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    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 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    None
       ops.enable_eager_execution_internal()
       UpperCamelCase__				=    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()]						)
       UpperCamelCase__				=    tf.config.list_logical_devices(device_type="""CPU"""						)
       UpperCamelCase__				=    tf.distribute.MirroredStrategy(devices=devices[:2]						)

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

       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():
                 UpperCamelCase__				=    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(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    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]						)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase_						     =					{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_						     =					[
        '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''CanineForMultipleChoice''',
        '''CanineForQuestionAnswering''',
        '''CanineForSequenceClassification''',
        '''CanineForTokenClassification''',
        '''CanineLayer''',
        '''CanineModel''',
        '''CaninePreTrainedModel''',
        '''load_tf_weights_in_canine''',
    ]


if TYPE_CHECKING:
    from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
    from .tokenization_canine import CanineTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_canine import (
            CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
            CanineForMultipleChoice,
            CanineForQuestionAnswering,
            CanineForSequenceClassification,
            CanineForTokenClassification,
            CanineLayer,
            CanineModel,
            CaninePreTrainedModel,
            load_tf_weights_in_canine,
        )


else:
    import sys

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





