



'''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						):






      """simple docstring"""




      def lowerCAmelCase_					(       self							:     List[str]			,a__							:     Optional[Any]			,a__							:     Tuple			,a__							:     List[str]   ):
             self.assertEqual(len(a__   )			,len(a__   )   )
             for a, b in zip(a__			,a__   ):
                    self.assertAlmostEqual(a__			,a__			,delta=a__   )




      def lowerCAmelCase_					(       self							:     Any   ):
             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(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 lowerCAmelCase_					(       self							:     Union[str, Any]   ):
             a__								= None
             ops.enable_eager_execution_internal()
             a__								= 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()]   )
             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=a__   )

             def accumulate_on_replica(a__							:     Tuple   ):
                    accumulator([gradient]   )

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

             @tf.function
             def accumulate(a__							:     Optional[int]			,a__							:     int   ):
                    with strategy.scope():
                           a__								= 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__							:     Dict			,a__							:     Optional[Any]   ):
                    a__								= 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]   )








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE   =  {
    "configuration_xmod": [
        "XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XmodConfig",
        "XmodOnnxConfig",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE   =  [
        "XMOD_PRETRAINED_MODEL_ARCHIVE_LIST",
        "XmodForCausalLM",
        "XmodForMaskedLM",
        "XmodForMultipleChoice",
        "XmodForQuestionAnswering",
        "XmodForSequenceClassification",
        "XmodForTokenClassification",
        "XmodModel",
        "XmodPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xmod import (
            XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
            XmodForCausalLM,
            XmodForMaskedLM,
            XmodForMultipleChoice,
            XmodForQuestionAnswering,
            XmodForSequenceClassification,
            XmodForTokenClassification,
            XmodModel,
            XmodPreTrainedModel,
        )

else:
    import sys

    _SCREAMING_SNAKE_CASE   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


