from typing import TYPE_CHECKING

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


lowerCamelCase       :	Dict									=     {
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Dict									=     ["DebertaTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	List[Any]									=     [
     "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DebertaForMaskedLM",
     "DebertaForQuestionAnswering",
     "DebertaForSequenceClassification",
     "DebertaForTokenClassification",
     "DebertaModel",
     "DebertaPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	int									=     [
     "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDebertaForMaskedLM",
     "TFDebertaForQuestionAnswering",
     "TFDebertaForSequenceClassification",
     "TFDebertaForTokenClassification",
     "TFDebertaModel",
     "TFDebertaPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
 from .tokenization_deberta import DebertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_fast import DebertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta import (
      DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
      DebertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta import (
      TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
      TFDebertaPreTrainedModel,
  )


else:
 import sys

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









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        a__				(       self      :						Optional[int]		,							A_      :						Tuple		,							A_      :						str		,							A_      :						int						)     ->			Any:

       """simple docstring"""






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

 def        a__				(       self      :						int						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					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        a__				(       self      :						List[Any]						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					None
       ops.enable_eager_execution_internal()
       lowerCamelCase_	  =					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()]						)
       lowerCamelCase_	  =					tf.config.list_logical_devices(device_type='CPU'						)
       lowerCamelCase_	  =					tf.distribute.MirroredStrategy(devices=devices[:2]						)

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

       def accumulate_on_replica(A_      :						Any						):
             accumulator([gradient]						)

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

       @tf.function
       def accumulate(A_      :						List[Any]		,							A_      :						Tuple						):
             with strategy.scope():
                   lowerCamelCase_	  =					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_      :						List[Any]		,							A_      :						str						):
             lowerCamelCase_	  =					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]						)



