




from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
 from transformers import (
     TFAutoModel,
     TFAutoModelForCausalLM,
     TFAutoModelForMaskedLM,
     TFAutoModelForPreTraining,
     TFAutoModelForQuestionAnswering,
     TFAutoModelForSeqaSeqLM,
     TFAutoModelForSequenceClassification,
     TFAutoModelForTableQuestionAnswering,
     TFAutoModelForTokenClassification,
     TFAutoModelWithLMHead,
     TFBertForMaskedLM,
     TFBertForPreTraining,
     TFBertForQuestionAnswering,
     TFBertForSequenceClassification,
     TFBertModel,
     TFFunnelBaseModel,
     TFFunnelModel,
     TFGPTaLMHeadModel,
     TFRobertaForMaskedLM,
     TFTaForConditionalGeneration,
     TFTapasForQuestionAnswering,
 )
 from transformers.models.auto.modeling_tf_auto import (
     TF_MODEL_FOR_CAUSAL_LM_MAPPING,
     TF_MODEL_FOR_MASKED_LM_MAPPING,
     TF_MODEL_FOR_PRETRAINING_MAPPING,
     TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
     TF_MODEL_MAPPING,
 )
 from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
 from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
 from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
 from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      List[str]			=      '''new-model'''


if is_tf_available():
 class 						_A (  _lowerCamelCase				):
   _UpperCamelCase       :      Optional[Any]			=      NewModelConfig
@require_tf
class 						_A (  unittest.TestCase				):






  @slow
  def  __a      (  self		:     Tuple				)		->   str:






       """simple docstring"""







       lowercase :       List[Any]       =			'''bert-base-cased'''
       lowercase :       Union[str, Any]       =			AutoConfig.from_pretrained(_A				)
       self.assertIsNotNone(_A				)
       self.assertIsInstance(_A				,						_A				)

       lowercase :       Optional[int]       =			TFAutoModel.from_pretrained(_A				)
       self.assertIsNotNone(_A				)
       self.assertIsInstance(_A				,						_A				)






  @slow
  def  __a      (  self		:     Union[str, Any]				)		->   int:






       """simple docstring"""







       lowercase :       str       =			'''bert-base-cased'''
       lowercase :       Union[str, Any]       =			AutoConfig.from_pretrained(_A				)
       self.assertIsNotNone(_A				)
       self.assertIsInstance(_A				,						_A				)

       lowercase :       Dict       =			TFAutoModelForPreTraining.from_pretrained(_A				)
       self.assertIsNotNone(_A				)
       self.assertIsInstance(_A				,						_A				)






  @slow
  def  __a      (  self		:     Optional[int]				)		->   Optional[Any]:






       """simple docstring"""







       for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            lowercase :       Union[str, Any]       =			AutoConfig.from_pretrained(_A				)
            self.assertIsNotNone(_A				)
            self.assertIsInstance(_A				,						_A				)

            lowercase :       Union[str, Any]       =			TFAutoModelForCausalLM.from_pretrained(_A				)
            lowercase      ,	lowercase :       Dict       =			TFAutoModelForCausalLM.from_pretrained(_A				,						output_loading_info=_A				)
            self.assertIsNotNone(_A				)
            self.assertIsInstance(_A				,						_A				)






  @slow
  def  __a      (  self		:     Any				)		->   List[str]:






       """simple docstring"""







       for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            lowercase :       Tuple       =			AutoConfig.from_pretrained(_A				)
            self.assertIsNotNone(_A				)
            self.assertIsInstance(_A				,						_A				)

            lowercase :       str       =			TFAutoModelWithLMHead.from_pretrained(_A				)
            self.assertIsNotNone(_A				)
            self.assertIsInstance(_A				,						_A				)






  @slow
  def  __a      (  self		:     Union[str, Any]				)		->   Any:






       """simple docstring"""







       for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            lowercase :       Dict       =			AutoConfig.from_pretrained(_A				)
            self.assertIsNotNone(_A				)
            self.assertIsInstance(_A				,						_A				)

            lowercase :       Optional[int]       =			TFAutoModelForMaskedLM.from_pretrained(_A				)
            lowercase      ,	lowercase :       List[Any]       =			TFAutoModelForMaskedLM.from_pretrained(_A				,						output_loading_info=_A				)
            self.assertIsNotNone(_A				)
            self.assertIsInstance(_A				,						_A				)






  @slow
  def  __a      (  self		:     Tuple				)		->   Tuple:






       """simple docstring"""







       for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            lowercase :       int       =			AutoConfig.from_pretrained(_A				)
            self.assertIsNotNone(_A				)
            self.assertIsInstance(_A				,						_A				)

            lowercase :       List[str]       =			TFAutoModelForSeqaSeqLM.from_pretrained(_A				)
            lowercase      ,	lowercase :       Optional[int]       =			TFAutoModelForSeqaSeqLM.from_pretrained(_A				,						output_loading_info=_A				)
            self.assertIsNotNone(_A				)
            self.assertIsInstance(_A				,						_A				)






  @slow
  def  __a      (  self		:     Tuple				)		->   List[Any]:






       """simple docstring"""







       for model_name in ["bert-base-uncased"]:
            lowercase :       Optional[int]       =			AutoConfig.from_pretrained(_A				)
            self.assertIsNotNone(_A				)
            self.assertIsInstance(_A				,						_A				)

            lowercase :       Optional[int]       =			TFAutoModelForSequenceClassification.from_pretrained(_A				)
            self.assertIsNotNone(_A				)
            self.assertIsInstance(_A				,						_A				)






  @slow
  def  __a      (  self		:     Union[str, Any]				)		->   List[Any]:






       """simple docstring"""







       for model_name in ["bert-base-uncased"]:
            lowercase :       Optional[int]       =			AutoConfig.from_pretrained(_A				)
            self.assertIsNotNone(_A				)
            self.assertIsInstance(_A				,						_A				)

            lowercase :       List[str]       =			TFAutoModelForQuestionAnswering.from_pretrained(_A				)
            self.assertIsNotNone(_A				)
            self.assertIsInstance(_A				,						_A				)






  @slow
  @require_tensorflow_probability
  def  __a      (  self		:     Optional[int]				)		->   Any:






       """simple docstring"""







       for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
            lowercase :       Optional[Any]       =			AutoConfig.from_pretrained(_A				)
            self.assertIsNotNone(_A				)
            self.assertIsInstance(_A				,						_A				)

            lowercase :       List[str]       =			TFAutoModelForTableQuestionAnswering.from_pretrained(_A				)
            lowercase      ,	lowercase :       Any       =			TFAutoModelForTableQuestionAnswering.from_pretrained(
                _A				,						output_loading_info=_A				)
            self.assertIsNotNone(_A				)
            self.assertIsInstance(_A				,						_A				)






  def  __a      (  self		:     Tuple				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       Dict       =			TFAutoModelWithLMHead.from_pretrained(_A				)
       self.assertIsInstance(_A				,						_A				)
       self.assertEqual(model.num_parameters()				,						14_410				)
       self.assertEqual(model.num_parameters(only_trainable=_A				)				,						14_410				)






  def  __a      (  self		:     Dict				)		->   Any:






       """simple docstring"""







       lowercase :       Any       =			TFAutoModelWithLMHead.from_pretrained(_A				)
       self.assertIsInstance(_A				,						_A				)
       self.assertEqual(model.num_parameters()				,						14_410				)
       self.assertEqual(model.num_parameters(only_trainable=_A				)				,						14_410				)






  def  __a      (  self		:     Tuple				)		->   Tuple:






       """simple docstring"""







       lowercase :       Optional[Any]       =			TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny'''				)
       self.assertIsInstance(_A				,						_A				)

       lowercase :       Optional[Any]       =			copy.deepcopy(model.config				)
       lowercase :       List[Any]       =			['''FunnelBaseModel''']
       lowercase :       List[Any]       =			TFAutoModel.from_config(_A				)
       self.assertIsInstance(_A				,						_A				)

       with tempfile.TemporaryDirectory() as tmp_dir:
            model.save_pretrained(_A				)
            lowercase :       Tuple       =			TFAutoModel.from_pretrained(_A				)
            self.assertIsInstance(_A				,						_A				)






  def  __a      (  self		:     str				)		->   List[str]:






       """simple docstring"""







       try:
            AutoConfig.register('''new-model'''				,						_A				)

            lowercase :       Tuple       =			[
                TFAutoModel,
                TFAutoModelForCausalLM,
                TFAutoModelForMaskedLM,
                TFAutoModelForPreTraining,
                TFAutoModelForQuestionAnswering,
                TFAutoModelForSequenceClassification,
                TFAutoModelForTokenClassification,
            ]

            for auto_class in auto_classes:
                 with self.subTest(auto_class.__name__				):
                      # Wrong config class will raise an error
                      with self.assertRaises(_A				):
                           auto_class.register(_A				,						_A				)
                      auto_class.register(_A				,						_A				)
                      # Trying to register something existing in the Transformers library will raise an error
                      with self.assertRaises(_A				):
                           auto_class.register(_A				,						_A				)

                      # Now that the config is registered, it can be used as any other config with the auto-API
                      lowercase :       Dict       =			BertModelTester(self				).get_config()
                      lowercase :       Dict       =			NewModelConfig(**tiny_config.to_dict()				)
                      lowercase :       Optional[int]       =			auto_class.from_config(_A				)
                      self.assertIsInstance(_A				,						_A				)

                      with tempfile.TemporaryDirectory() as tmp_dir:
                           model.save_pretrained(_A				)
                           lowercase :       Tuple       =			auto_class.from_pretrained(_A				)
                           self.assertIsInstance(_A				,						_A				)

       finally:
            if "new-model" in CONFIG_MAPPING._extra_content:
                 del CONFIG_MAPPING._extra_content["new-model"]
            for mapping in (
                TF_MODEL_MAPPING,
                TF_MODEL_FOR_PRETRAINING_MAPPING,
                TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                TF_MODEL_FOR_CAUSAL_LM_MAPPING,
                TF_MODEL_FOR_MASKED_LM_MAPPING,
            ):
                 if NewModelConfig in mapping._extra_content:
                      del mapping._extra_content[NewModelConfig]






  def  __a      (  self		:     int				)		->   Union[str, Any]:






       """simple docstring"""







       with self.assertRaisesRegex(
           _A				,						'''bert-base is not a local folder and is not a valid model identifier'''				):
            lowercase :       Dict       =			TFAutoModel.from_pretrained('''bert-base'''				)






  def  __a      (  self		:     List[Any]				)		->   List[str]:






       """simple docstring"""







       with self.assertRaisesRegex(
           _A				,						r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''				):
            lowercase :       List[str]       =			TFAutoModel.from_pretrained(_A				,						revision='''aaaaaa'''				)






  def  __a      (  self		:     Tuple				)		->   Tuple:






       """simple docstring"""







       with self.assertRaisesRegex(
           _A				,						'''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'''				,						):
            lowercase :       Dict       =			TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''				)






  def  __a      (  self		:     Optional[int]				)		->   Union[str, Any]:






       """simple docstring"""







       with self.assertRaisesRegex(_A				,						'''Use `from_pt=True` to load this model'''				):
            lowercase :       List[Any]       =			TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''				)






  def  __a      (  self		:     int				)		->   Tuple:






       """simple docstring"""







       lowercase :       Optional[int]       =			TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''				)
       with RequestCounter() as counter:
            lowercase :       Any       =			TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''				)
            self.assertEqual(counter.get_request_count				,						0				)
            self.assertEqual(counter.head_request_count				,						1				)
            self.assertEqual(counter.other_request_count				,						0				)

       # With a sharded checkpoint
       lowercase :       List[str]       =			TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''				)
       with RequestCounter() as counter:
            lowercase :       List[Any]       =			TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''				)
            self.assertEqual(counter.get_request_count				,						0				)
            self.assertEqual(counter.head_request_count				,						1				)
            self.assertEqual(counter.other_request_count				,						0				)





import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
 from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
 from tensorflow.keras.optimizers import Adam
class 						_A (  tf.keras.optimizers.schedules.LearningRateSchedule				):






  def __init__(  self		:     str				,						_A		:     float				,						_A		:     Callable				,						_A		:     int				,						_A		:     float = 1.0				,						_A		:     str = None				,						)		->   List[str]:






       """simple docstring"""







       super().__init__()
       lowercase :       List[str]       =			initial_learning_rate
       lowercase :       List[str]       =			warmup_steps
       lowercase :       Tuple       =			power
       lowercase :       Any       =			decay_schedule_fn
       lowercase :       Union[str, Any]       =			name






  def __call__(  self		:     str				,						_A		:     Any				)		->   Optional[int]:






       """simple docstring"""







       with tf.name_scope(self.name or '''WarmUp'''				) as name:
            # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
            # learning rate will be `global_step/num_warmup_steps * init_lr`.
            lowercase :       List[Any]       =			tf.cast(_A				,						tf.floataa				)
            lowercase :       Union[str, Any]       =			tf.cast(self.warmup_steps				,						tf.floataa				)
            lowercase :       List[str]       =			global_step_float / warmup_steps_float
            lowercase :       int       =			self.initial_learning_rate * tf.math.pow(_A				,						self.power				)
            return tf.cond(
                global_step_float < warmup_steps_float				,						lambda: warmup_learning_rate				,						lambda: self.decay_schedule_fn(step - self.warmup_steps				)				,						name=_A				,						)






  def  __a      (  self		:     Dict				)		->   List[str]:






       """simple docstring"""







       return {
           "initial_learning_rate": self.initial_learning_rate,
           "decay_schedule_fn": self.decay_schedule_fn,
           "warmup_steps": self.warmup_steps,
           "power": self.power,
           "name": self.name,
       }






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__ = 0.0			,		__magic_name__ = 0.9			,		__magic_name__ = 0.9_9_9			,		__magic_name__ = 1e-8			,		__magic_name__ = None			,		__magic_name__ = None			,		__magic_name__ = 0.0			,		__magic_name__ = 1.0			,		__magic_name__ = None			,		)			->							int:





     '''simple docstring'''


     lowercase :       List[Any]       =			tf.keras.optimizers.schedules.PolynomialDecay(
         initial_learning_rate=__magic_name__			,		decay_steps=num_train_steps - num_warmup_steps			,		end_learning_rate=init_lr * min_lr_ratio			,		power=__magic_name__			,		)
     if num_warmup_steps:
          lowercase :       Optional[int]       =			WarmUp(
              initial_learning_rate=__magic_name__			,		decay_schedule_fn=__magic_name__			,		warmup_steps=__magic_name__			,		)
     if weight_decay_rate > 0.0:
          lowercase :       Optional[Any]       =			AdamWeightDecay(
              learning_rate=__magic_name__			,		weight_decay_rate=__magic_name__			,		beta_a=__magic_name__			,		beta_a=__magic_name__			,		epsilon=__magic_name__			,		clipnorm=__magic_name__			,		global_clipnorm=__magic_name__			,		exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']			,		include_in_weight_decay=__magic_name__			,		)
     else:
          lowercase :       str       =			tf.keras.optimizers.Adam(
              learning_rate=__magic_name__			,		beta_a=__magic_name__			,		beta_a=__magic_name__			,		epsilon=__magic_name__			,		clipnorm=__magic_name__			,		global_clipnorm=__magic_name__			,		)
     # We return the optimizer and the LR scheduler in order to better track the
     # evolution of the LR independently of the optimizer.
     return optimizer, lr_schedule
class 						_A (  _lowerCamelCase				):






  def __init__(  self		:     Optional[Any]				,						_A		:     Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001				,						_A		:     float = 0.9				,						_A		:     float = 0.999				,						_A		:     float = 1E-7				,						_A		:     bool = False				,						_A		:     float = 0.0				,						_A		:     Optional[List[str]] = None				,						_A		:     Optional[List[str]] = None				,						_A		:     str = "AdamWeightDecay"				,						**_A		:     Any				,						)		->   Optional[Any]:






       """simple docstring"""







       super().__init__(_A				,						_A				,						_A				,						_A				,						_A				,						_A				,						**_A				)
       lowercase :       Tuple       =			weight_decay_rate
       lowercase :       List[str]       =			include_in_weight_decay
       lowercase :       Optional[Any]       =			exclude_from_weight_decay






  @classmethod
  def  __a      (  cls		:     Tuple				,						_A		:     Tuple				)		->   List[str]:






       """simple docstring"""







       lowercase :       Optional[int]       =			{'''WarmUp''': WarmUp}
       return super(_A				,						cls				).from_config(_A				,						custom_objects=_A				)






  def  __a      (  self		:     Dict				,						_A		:     Tuple				,						_A		:     Dict				,						_A		:     Tuple				)		->   Tuple:






       """simple docstring"""







       super(_A				,						self				)._prepare_local(_A				,						_A				,						_A				)
       lowercase :       List[Any]       =			tf.constant(
           self.weight_decay_rate				,						name='''adam_weight_decay_rate'''				)






  def  __a      (  self		:     Tuple				,						_A		:     Optional[int]				,						_A		:     Union[str, Any]				,						_A		:     List[Any]				)		->   Any:






       """simple docstring"""







       lowercase :       str       =			self._do_use_weight_decay(var.name				)
       if do_decay:
            return var.assign_sub(
                learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate''']				,						use_locking=self._use_locking				,						)
       return tf.no_op()






  def  __a      (  self		:     Union[str, Any]				,						_A		:     Any				,						_A		:     Optional[Any]=None				,						**_A		:     Tuple				)		->   Any:






       """simple docstring"""







       lowercase      ,	lowercase :       Tuple       =			list(zip(*_A				)				)
       return super(_A				,						self				).apply_gradients(zip(_A				,						_A				)				,						name=_A				,						**_A				)






  def  __a      (  self		:     List[Any]				,						_A		:     Optional[Any]				,						_A		:     str				,						_A		:     Union[str, Any]				)		->   Union[str, Any]:






       """simple docstring"""







       if apply_state is None:
            return self._decayed_lr_t[var_dtype], {}

       lowercase :       Any       =			apply_state or {}
       lowercase :       str       =			apply_state.get((var_device, var_dtype)				)
       if coefficients is None:
            lowercase :       List[Any]       =			self._fallback_apply_state(_A				,						_A				)
            lowercase :       List[Any]       =			coefficients

       return coefficients["lr_t"], {"apply_state": apply_state}






  def  __a      (  self		:     Tuple				,						_A		:     Union[str, Any]				,						_A		:     Tuple				,						_A		:     str=None				)		->   Optional[int]:






       """simple docstring"""







       lowercase      ,	lowercase :       List[str]       =			self._get_lr(var.device				,						var.dtype.base_dtype				,						_A				)
       lowercase :       Optional[Any]       =			self._decay_weights_op(_A				,						_A				,						_A				)
       with tf.control_dependencies([decay]				):
            return super(_A				,						self				)._resource_apply_dense(_A				,						_A				,						**_A				)






  def  __a      (  self		:     Optional[int]				,						_A		:     List[Any]				,						_A		:     Dict				,						_A		:     Union[str, Any]				,						_A		:     str=None				)		->   Optional[int]:






       """simple docstring"""







       lowercase      ,	lowercase :       Optional[int]       =			self._get_lr(var.device				,						var.dtype.base_dtype				,						_A				)
       lowercase :       str       =			self._decay_weights_op(_A				,						_A				,						_A				)
       with tf.control_dependencies([decay]				):
            return super(_A				,						self				)._resource_apply_sparse(_A				,						_A				,						_A				,						**_A				)






  def  __a      (  self		:     List[Any]				)		->   str:






       """simple docstring"""







       lowercase :       Optional[Any]       =			super().get_config()
       config.update({'''weight_decay_rate''': self.weight_decay_rate}				)
       return config






  def  __a      (  self		:     str				,						_A		:     Optional[int]				)		->   Tuple:






       """simple docstring"""







       if self.weight_decay_rate == 0:
            return False

       if self._include_in_weight_decay:
            for r in self._include_in_weight_decay:
                 if re.search(_A				,						_A				) is not None:
                      return True

       if self._exclude_from_weight_decay:
            for r in self._exclude_from_weight_decay:
                 if re.search(_A				,						_A				) is not None:
                      return False
       return True


class 						_A (  _lowerCamelCase				):






  def __init__(  self		:     List[Any]				)		->   str:






       """simple docstring"""







       lowercase :       Optional[Any]       =			[]
       lowercase :       str       =			None






  @property
  def  __a      (  self		:     Any				)		->   int:






       """simple docstring"""







       if self._accum_steps is None:
            lowercase :       Optional[Any]       =			tf.Variable(
                tf.constant(0				,						dtype=tf.intaa				)				,						trainable=_A				,						synchronization=tf.VariableSynchronization.ON_READ				,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA				,						)

       return self._accum_steps.value()






  @property
  def  __a      (  self		:     int				)		->   List[Any]:






       """simple docstring"""







       if not self._gradients:
            raise ValueError('''The accumulator should be called first to initialize the gradients'''				)
       return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]






  def __call__(  self		:     str				,						_A		:     int				)		->   str:






       """simple docstring"""







       if not self._gradients:
            lowercase :       Optional[Any]       =			self.step  # Create the step variable.
            self._gradients.extend(
                [
                    tf.Variable(
                        tf.zeros_like(_A				)				,						trainable=_A				,						synchronization=tf.VariableSynchronization.ON_READ				,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA				,						)
                    if gradient is not None
                    else gradient
                    for gradient in gradients
                ]				)
       if len(_A				) != len(self._gradients				):
            raise ValueError(f"""Expected {len(self._gradients				)} gradients, but got {len(_A				)}"""				)

       for accum_gradient, gradient in zip(self._gradients				,						_A				):
            if accum_gradient is not None and gradient is not None:
                 accum_gradient.assign_add(_A				)

       self._accum_steps.assign_add(1				)






  def  __a      (  self		:     Optional[Any]				)		->   Tuple:






       """simple docstring"""







       if not self._gradients:
            return
       self._accum_steps.assign(0				)
       for gradient in self._gradients:
            if gradient is not None:
                 gradient.assign(tf.zeros_like(_A				)				)