





'''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   UpperCAmelCase      (			unittest.TestCase							):
 def 	UpperCAmelCase_				(							self :Dict		,			lowercase_ :Dict		,			lowercase_ :List[str]		,			lowercase_ :List[str]				)->				str:
     self.assertEqual(len(lowercase_				)		,			len(lowercase_				)				)
     for a, b in zip(lowercase_		,			lowercase_				):
         self.assertAlmostEqual(lowercase_		,			lowercase_		,			delta=lowercase_				)
 def 	UpperCAmelCase_				(							self :int				)->				Tuple:
     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 	UpperCAmelCase_				(							self :int				)->				List[str]:
     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_ :Any				):
         accumulator([gradient]				)

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

     @tf.function
     def accumulate(lowercase_ :Union[str, Any]		,			lowercase_ :Union[str, Any]				):
         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_ :str				):
         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'''

# 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase				:      Optional[int]						      ={
    "configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig"],
}
try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      int						      =["VivitImageProcessor"]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      int						      =[
      "VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "VivitModel",
      "VivitPreTrainedModel",
      "VivitForVideoClassification",
  ]


if TYPE_CHECKING:
  from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_vivit import VivitImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vivit import (
        VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        VivitForVideoClassification,
        VivitModel,
        VivitPreTrainedModel,
    )


else:
  import sys

  __lowerCAmelCase				:      List[Any]						      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



