import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__a:			List[Any]	       =       logging.getLogger(__name__)



@dataclass
class       UpperCAmelCase  :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   4_2
       SCREAMING_SNAKE_CASE 		=   4_2
       SCREAMING_SNAKE_CASE 		=   4_2



@dataclass
class       UpperCAmelCase  :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   4_2
       SCREAMING_SNAKE_CASE 		=   4_2
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   "train"
       SCREAMING_SNAKE_CASE 		=   "dev"
       SCREAMING_SNAKE_CASE 		=   "test"



class       UpperCAmelCase  :





       '''simple docstring'''



       @staticmethod
       def _lowerCAmelCase(						__lowerCAmelCase     ,       __lowerCAmelCase			)  ->  List[InputExample]:
        raise NotImplementedError

       @staticmethod
       def _lowerCAmelCase(						__lowerCAmelCase			)  ->  List[str]:
        raise NotImplementedError


       @staticmethod
       def _lowerCAmelCase(						__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase="[CLS]"     ,       __lowerCAmelCase=1     ,       __lowerCAmelCase="[SEP]"     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase=0     ,       __lowerCAmelCase=0     ,       __lowerCAmelCase=-100     ,       __lowerCAmelCase=0     ,       __lowerCAmelCase=True     ,       )  ->  List[InputFeatures]:
        lowercase__    :   Tuple						= {label: i for i, label in enumerate(__lowerCAmelCase			)}

        lowercase__    :   List[Any]						= []
        for ex_index, example in enumerate(__lowerCAmelCase			):
         if ex_index % 10000 == 0:
          logger.info('''Writing example %d of %d'''     ,       __lowerCAmelCase     ,       len(__lowerCAmelCase			)			)

         lowercase__    :   str						= []
         lowercase__    :   Dict						= []
         for word, label in zip(example.words     ,       example.labels			):
          lowercase__    :   Tuple						= tokenizer.tokenize(__lowerCAmelCase			)

          # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
          if len(__lowerCAmelCase			) > 0:
           tokens.extend(__lowerCAmelCase			)
           # Use the real label id for the first token of the word, and padding ids for the remaining tokens
           label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__lowerCAmelCase			) - 1)			)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
         lowercase__    :   List[str]						= tokenizer.num_special_tokens_to_add()
         if len(__lowerCAmelCase			) > max_seq_length - special_tokens_count:
          lowercase__    :   str						= tokens[: (max_seq_length - special_tokens_count)]
          lowercase__    :   Union[str, Any]						= label_ids[: (max_seq_length - special_tokens_count)]

         # The convention in BERT is:
         # (a) For sequence pairs:
         #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
         #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
         # (b) For single sequences:
         #  tokens:   [CLS] the dog is hairy . [SEP]
         #  type_ids:   0   0   0   0  0     0   0
         #
         # Where "type_ids" are used to indicate whether this is the first
         # sequence or the second sequence. The embedding vectors for `type=0` and
         # `type=1` were learned during pre-training and are added to the wordpiece
         # embedding vector (and position vector). This is not *strictly* necessary
         # since the [SEP] token unambiguously separates the sequences, but it makes
         # it easier for the model to learn the concept of sequences.
         #
         # For classification tasks, the first vector (corresponding to [CLS]) is
         # used as the "sentence vector". Note that this only makes sense because
         # the entire model is fine-tuned.
         tokens += [sep_token]
         label_ids += [pad_token_label_id]
         if sep_token_extra:
          # roberta uses an extra separator b/w pairs of sentences
          tokens += [sep_token]
          label_ids += [pad_token_label_id]
         lowercase__    :   Any						= [sequence_a_segment_id] * len(__lowerCAmelCase			)

         if cls_token_at_end:
          tokens += [cls_token]
          label_ids += [pad_token_label_id]
          segment_ids += [cls_token_segment_id]
         else:
          lowercase__    :   Tuple						= [cls_token] + tokens
          lowercase__    :   int						= [pad_token_label_id] + label_ids
          lowercase__    :   Dict						= [cls_token_segment_id] + segment_ids

         lowercase__    :   str						= tokenizer.convert_tokens_to_ids(__lowerCAmelCase			)

         # The mask has 1 for real tokens and 0 for padding tokens. Only real
         # tokens are attended to.
         lowercase__    :   List[Any]						= [1 if mask_padding_with_zero else 0] * len(__lowerCAmelCase			)

         # Zero-pad up to the sequence length.
         lowercase__    :   Optional[Any]						= max_seq_length - len(__lowerCAmelCase			)
         if pad_on_left:
          lowercase__    :   int						= ([pad_token] * padding_length) + input_ids
          lowercase__    :   List[str]						= ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
          lowercase__    :   List[str]						= ([pad_token_segment_id] * padding_length) + segment_ids
          lowercase__    :   Dict						= ([pad_token_label_id] * padding_length) + label_ids
         else:
          input_ids += [pad_token] * padding_length
          input_mask += [0 if mask_padding_with_zero else 1] * padding_length
          segment_ids += [pad_token_segment_id] * padding_length
          label_ids += [pad_token_label_id] * padding_length

         assert len(__lowerCAmelCase			) == max_seq_length
         assert len(__lowerCAmelCase			) == max_seq_length
         assert len(__lowerCAmelCase			) == max_seq_length
         assert len(__lowerCAmelCase			) == max_seq_length

         if ex_index < 5:
          logger.info('''*** Example ***'''			)
          logger.info('''guid: %s'''     ,       example.guid			)
          logger.info('''tokens: %s'''     ,       ''' '''.join([str(__lowerCAmelCase			) for x in tokens]			)			)
          logger.info('''input_ids: %s'''     ,       ''' '''.join([str(__lowerCAmelCase			) for x in input_ids]			)			)
          logger.info('''input_mask: %s'''     ,       ''' '''.join([str(__lowerCAmelCase			) for x in input_mask]			)			)
          logger.info('''segment_ids: %s'''     ,       ''' '''.join([str(__lowerCAmelCase			) for x in segment_ids]			)			)
          logger.info('''label_ids: %s'''     ,       ''' '''.join([str(__lowerCAmelCase			) for x in label_ids]			)			)

         if "token_type_ids" not in tokenizer.model_input_names:
          lowercase__    :   Tuple						= None

         features.append(
             InputFeatures(
                 input_ids=__lowerCAmelCase     ,       attention_mask=__lowerCAmelCase     ,       token_type_ids=__lowerCAmelCase     ,       label_ids=__lowerCAmelCase			)			)
        return features


if is_torch_available():
  import torch
  from torch import nn
  from torch.utils.data import Dataset



  class       UpperCAmelCase  (						a__ ):





         '''simple docstring'''



         SCREAMING_SNAKE_CASE 		=   4_2
         SCREAMING_SNAKE_CASE 		=   nn.CrossEntropyLoss().ignore_index

         def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase = Split.train     ,       )  ->  Optional[int]:
          # Load data features from cache or dataset file
          lowercase__    :   Union[str, Any]						= os.path.join(
              __lowerCAmelCase     ,       '''cached_{}_{}_{}'''.format(mode.value     ,       tokenizer.__class__.__name__     ,       str(__lowerCAmelCase			)			)     ,       )

          # Make sure only the first process in distributed training processes the dataset,
          # and the others will use the cache.
          lowercase__    :   Optional[int]						= cached_features_file + '''.lock'''
          with FileLock(__lowerCAmelCase			):
           if os.path.exists(__lowerCAmelCase			) and not overwrite_cache:
            logger.info(F"""Loading features from cached file {cached_features_file}"""			)
            lowercase__    :   Dict						= torch.load(__lowerCAmelCase			)
           else:
            logger.info(F"""Creating features from dataset file at {data_dir}"""			)
            lowercase__    :   List[Any]						= token_classification_task.read_examples_from_file(__lowerCAmelCase     ,       __lowerCAmelCase			)
            # TODO clean up all this to leverage built-in features of tokenizers
            lowercase__    :   List[Any]						= token_classification_task.convert_examples_to_features(
                __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       cls_token_at_end=bool(model_type in ['''xlnet''']			)     ,       cls_token=tokenizer.cls_token     ,       cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0     ,       sep_token=tokenizer.sep_token     ,       sep_token_extra=__lowerCAmelCase     ,       pad_on_left=bool(tokenizer.padding_side == '''left'''			)     ,       pad_token=tokenizer.pad_token_id     ,       pad_token_segment_id=tokenizer.pad_token_type_id     ,       pad_token_label_id=self.pad_token_label_id     ,       )
            logger.info(F"""Saving features into cached file {cached_features_file}"""			)
            torch.save(self.features     ,       __lowerCAmelCase			)

         def __len__(						self			)  ->  Optional[Any]:
          return len(self.features			)


         def __getitem__(						self     ,       __lowerCAmelCase			)  ->  InputFeatures:
          return self.features[i]


if is_tf_available():
  import tensorflow as tf



  class       UpperCAmelCase  :





         '''simple docstring'''



         SCREAMING_SNAKE_CASE 		=   4_2
         SCREAMING_SNAKE_CASE 		=   -1_0_0

         def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase=False     ,       __lowerCAmelCase = Split.train     ,       )  ->  Optional[int]:
          lowercase__    :   str						= token_classification_task.read_examples_from_file(__lowerCAmelCase     ,       __lowerCAmelCase			)
          # TODO clean up all this to leverage built-in features of tokenizers
          lowercase__    :   Union[str, Any]						= token_classification_task.convert_examples_to_features(
              __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       cls_token_at_end=bool(model_type in ['''xlnet''']			)     ,       cls_token=tokenizer.cls_token     ,       cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0     ,       sep_token=tokenizer.sep_token     ,       sep_token_extra=__lowerCAmelCase     ,       pad_on_left=bool(tokenizer.padding_side == '''left'''			)     ,       pad_token=tokenizer.pad_token_id     ,       pad_token_segment_id=tokenizer.pad_token_type_id     ,       pad_token_label_id=self.pad_token_label_id     ,       )

          def gen():
           for ex in self.features:
            if ex.token_type_ids is None:
             yield (
                 {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                 ex.label_ids,
             )
            else:
             yield (
                 {
                     "input_ids": ex.input_ids,
                     "attention_mask": ex.attention_mask,
                     "token_type_ids": ex.token_type_ids,
                 },
                 ex.label_ids,
             )

          if "token_type_ids" not in tokenizer.model_input_names:
           lowercase__    :   Dict						= tf.data.Dataset.from_generator(
               __lowerCAmelCase     ,       ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)     ,       (
                   {'''input_ids''': tf.TensorShape([None]			), '''attention_mask''': tf.TensorShape([None]			)},
                   tf.TensorShape([None]			),
               )     ,       )
          else:
           lowercase__    :   List[str]						= tf.data.Dataset.from_generator(
               __lowerCAmelCase     ,       ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)     ,       (
                   {
                       '''input_ids''': tf.TensorShape([None]			),
                       '''attention_mask''': tf.TensorShape([None]			),
                       '''token_type_ids''': tf.TensorShape([None]			),
                   },
                   tf.TensorShape([None]			),
               )     ,       )

         def _lowerCAmelCase(						self			)  ->  str:
          lowercase__    :   Optional[int]						= self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features			)			)			)

          return self.dataset

         def __len__(						self			)  ->  Optional[int]:
          return len(self.features			)


         def __getitem__(						self     ,       __lowerCAmelCase			)  ->  InputFeatures:
          return self.features[i]


'''simple docstring'''

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       UpperCAmelCase  (						tf.keras.optimizers.schedules.LearningRateSchedule ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = 1.0     ,       __lowerCAmelCase = None     ,       )  ->  Union[str, Any]:
        super().__init__()
        lowercase__    :   Any						= initial_learning_rate
        lowercase__    :   str						= warmup_steps
        lowercase__    :   Dict						= power
        lowercase__    :   int						= decay_schedule_fn
        lowercase__    :   Dict						= name

       def __call__(						self     ,       __lowerCAmelCase			)  ->  int:
        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__    :   Union[str, Any]						= tf.cast(__lowerCAmelCase     ,       tf.floataa			)
         lowercase__    :   Optional[int]						= tf.cast(self.warmup_steps     ,       tf.floataa			)
         lowercase__    :   Dict						= global_step_float / warmup_steps_float
         lowercase__    :   Dict						= self.initial_learning_rate * tf.math.pow(__lowerCAmelCase     ,       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=__lowerCAmelCase     ,       )


       def _lowerCAmelCase(						self			)  ->  List[str]:
        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       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase = 0.0		,					UpperCAmelCase = 0.9		,					UpperCAmelCase = 0.9_9_9		,					UpperCAmelCase = 1E-8		,					UpperCAmelCase = None		,					UpperCAmelCase = None		,					UpperCAmelCase = 0.0		,					UpperCAmelCase = 1.0		,					UpperCAmelCase = None		,					):
 lowercase__    :   List[Any]						= tf.keras.optimizers.schedules.PolynomialDecay(
     initial_learning_rate=UpperCAmelCase		,					decay_steps=num_train_steps - num_warmup_steps		,					end_learning_rate=init_lr * min_lr_ratio		,					power=UpperCAmelCase		,					)
 if num_warmup_steps:
  lowercase__    :   Optional[Any]						= WarmUp(
      initial_learning_rate=UpperCAmelCase		,					decay_schedule_fn=UpperCAmelCase		,					warmup_steps=UpperCAmelCase		,					)
 if weight_decay_rate > 0.0:
  lowercase__    :   str						= AdamWeightDecay(
      learning_rate=UpperCAmelCase		,					weight_decay_rate=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					epsilon=UpperCAmelCase		,					clipnorm=UpperCAmelCase		,					global_clipnorm=UpperCAmelCase		,					exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']		,					include_in_weight_decay=UpperCAmelCase		,					)
 else:
  lowercase__    :   Any						= tf.keras.optimizers.Adam(
      learning_rate=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					beta_a=UpperCAmelCase		,					epsilon=UpperCAmelCase		,					clipnorm=UpperCAmelCase		,					global_clipnorm=UpperCAmelCase		,					)
 # 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       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self     ,       __lowerCAmelCase = 0.0_0_1     ,       __lowerCAmelCase = 0.9     ,       __lowerCAmelCase = 0.9_9_9     ,       __lowerCAmelCase = 1E-7     ,       __lowerCAmelCase = False     ,       __lowerCAmelCase = 0.0     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = "AdamWeightDecay"     ,       **__lowerCAmelCase     ,       )  ->  str:
        super().__init__(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)
        lowercase__    :   int						= weight_decay_rate
        lowercase__    :   Dict						= include_in_weight_decay
        lowercase__    :   List[str]						= exclude_from_weight_decay

       @classmethod
       def _lowerCAmelCase(						cls     ,       __lowerCAmelCase			)  ->  str:
        lowercase__    :   int						= {'''WarmUp''': WarmUp}
        return super(__lowerCAmelCase     ,       cls			).from_config(__lowerCAmelCase     ,       custom_objects=__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  List[str]:
        super(__lowerCAmelCase     ,       self			)._prepare_local(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)
        lowercase__    :   Any						= tf.constant(
            self.weight_decay_rate     ,       name='''adam_weight_decay_rate'''			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Tuple:
        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 _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None     ,       **__lowerCAmelCase			)  ->  List[Any]:
        lowercase__			,							lowercase__    :   Any						= list(zip(*__lowerCAmelCase			)			)
        return super(__lowerCAmelCase     ,       self			).apply_gradients(zip(__lowerCAmelCase     ,       __lowerCAmelCase			)     ,       name=__lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Optional[int]:
        if apply_state is None:
         return self._decayed_lr_t[var_dtype], {}

        lowercase__    :   Union[str, Any]						= apply_state or {}
        lowercase__    :   Optional[Any]						= apply_state.get((var_device, var_dtype)			)
        if coefficients is None:
         lowercase__    :   Union[str, Any]						= self._fallback_apply_state(__lowerCAmelCase     ,       __lowerCAmelCase			)
         lowercase__    :   Optional[int]						= coefficients

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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None			)  ->  Any:
        lowercase__			,							lowercase__    :   Dict						= self._get_lr(var.device     ,       var.dtype.base_dtype     ,       __lowerCAmelCase			)
        lowercase__    :   List[str]						= self._decay_weights_op(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)
        with tf.control_dependencies([decay]			):
         return super(__lowerCAmelCase     ,       self			)._resource_apply_dense(__lowerCAmelCase     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None			)  ->  List[str]:
        lowercase__			,							lowercase__    :   List[Any]						= self._get_lr(var.device     ,       var.dtype.base_dtype     ,       __lowerCAmelCase			)
        lowercase__    :   Union[str, Any]						= self._decay_weights_op(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)
        with tf.control_dependencies([decay]			):
         return super(__lowerCAmelCase     ,       self			)._resource_apply_sparse(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   Optional[Any]						= super().get_config()
        config.update({'''weight_decay_rate''': self.weight_decay_rate}			)
        return config


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[Any]:
        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(__lowerCAmelCase     ,       __lowerCAmelCase			) is not None:
           return True

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



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self			)  ->  List[Any]:
        lowercase__    :   List[str]						= []
        lowercase__    :   Optional[int]						= None

       @property
       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        if self._accum_steps is None:
         lowercase__    :   int						= tf.Variable(
             tf.constant(0     ,       dtype=tf.intaa			)     ,       trainable=__lowerCAmelCase     ,       synchronization=tf.VariableSynchronization.ON_READ     ,       aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,       )

        return self._accum_steps.value()

       @property
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        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     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        if not self._gradients:
         lowercase__    :   str						= self.step  # Create the step variable.
         self._gradients.extend(
             [
                 tf.Variable(
                     tf.zeros_like(__lowerCAmelCase			)     ,       trainable=__lowerCAmelCase     ,       synchronization=tf.VariableSynchronization.ON_READ     ,       aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,       )
                 if gradient is not None
                 else gradient
                 for gradient in gradients
             ]			)
        if len(__lowerCAmelCase			) != len(self._gradients			):
         raise ValueError(F"""Expected {len(self._gradients			)} gradients, but got {len(__lowerCAmelCase			)}"""			)

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

        self._accum_steps.assign_add(1			)


       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        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(__lowerCAmelCase			)			)

