




'''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 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     Union[str, Any]   ):

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



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				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 :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				None
      ops.enable_eager_execution_internal()
      _SCREAMING_SNAKE_CASE	=				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()]   )
      _SCREAMING_SNAKE_CASE	=				tf.config.list_logical_devices(device_type="CPU"   )
      _SCREAMING_SNAKE_CASE	=				tf.distribute.MirroredStrategy(devices=devices[:2]   )

      with strategy.scope():
         _SCREAMING_SNAKE_CASE	=				GradientAccumulator()
         _SCREAMING_SNAKE_CASE	=				tf.Variable([4.0, 3.0]   )
         _SCREAMING_SNAKE_CASE	=				create_optimizer(5e-5  ,				1_0  ,				5   )
         _SCREAMING_SNAKE_CASE	=				tf.Variable([0.0, 0.0]  ,				trainable=_A   )

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

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

      @tf.function
      def accumulate(__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     Union[str, Any]   ):
         with strategy.scope():
            _SCREAMING_SNAKE_CASE	=				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(__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     List[Any]   ):
         _SCREAMING_SNAKE_CASE	=				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_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_			=						{
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						['LayoutLMv2TokenizerFast']

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						['LayoutLMv2FeatureExtractor']
 lowerCamelCase_			=						['LayoutLMv2ImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'LayoutLMv2ForQuestionAnswering',
     'LayoutLMv2ForSequenceClassification',
     'LayoutLMv2ForTokenClassification',
     'LayoutLMv2Layer',
     'LayoutLMv2Model',
     'LayoutLMv2PreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
 from .processing_layoutlmva import LayoutLMvaProcessor
 from .tokenization_layoutlmva import LayoutLMvaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaLayer,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )
else:
 import sys

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

