




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCAmelCase_							=			'\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

lowerCAmelCase_							=			'\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n'


lowerCAmelCase_							=			'\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=["About 95 species are currently accepted ."]\n    >>> predictions=["About 95 you now get in ."]\n    >>> references=[["About 95 species are currently known ."]]\n    >>> wiki_split = datasets.load_metric("wiki_split")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}\n'






def  snake_case(    __magic_name__   )			->							Any:





     '''simple docstring'''


     def remove_articles(__magic_name__   ):
          lowercase :       Union[str, Any]       =			re.compile(r'''\b(a|an|the)\b'''			,		re.UNICODE   )
          return re.sub(lowerCAmelCase_			,		''' '''			,		lowerCAmelCase_   )

     def white_space_fix(__magic_name__   ):
          return " ".join(text.split()   )

     def remove_punc(__magic_name__   ):
          lowercase :       Dict       =			set(string.punctuation   )
          return "".join(ch for ch in text if ch not in exclude   )

     def lower(__magic_name__   ):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase_   )   )   )   )






def  snake_case(    __magic_name__			,		__magic_name__   )			->							List[Any]:





     '''simple docstring'''


     return int(normalize_answer(lowerCAmelCase_   ) == normalize_answer(lowerCAmelCase_   )   )






def  snake_case(    __magic_name__			,		__magic_name__   )			->							Dict:





     '''simple docstring'''


     lowercase :       List[Any]       =			[any(compute_exact(lowerCAmelCase_			,		lowerCAmelCase_   ) for ref in refs   ) for pred, refs in zip(lowerCAmelCase_			,		lowerCAmelCase_   )]
     return (sum(lowerCAmelCase_   ) / len(lowerCAmelCase_   )) * 1_00






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )			->							List[Any]:





     '''simple docstring'''


     lowercase :       Optional[Any]       =			[rgram for rgrams in rgramslist for rgram in rgrams]
     lowercase :       Union[str, Any]       =			Counter(lowerCAmelCase_   )

     lowercase :       int       =			Counter(lowerCAmelCase_   )
     lowercase :       Dict       =			Counter()
     for sgram, scount in sgramcounter.items():
          lowercase :       str       =			scount * numref

     lowercase :       List[Any]       =			Counter(lowerCAmelCase_   )
     lowercase :       Dict       =			Counter()
     for cgram, ccount in cgramcounter.items():
          lowercase :       Dict       =			ccount * numref

     # KEEP
     lowercase :       List[Any]       =			sgramcounter_rep & cgramcounter_rep
     lowercase :       Union[str, Any]       =			keepgramcounter_rep & rgramcounter
     lowercase :       List[str]       =			sgramcounter_rep & rgramcounter

     lowercase :       List[str]       =			0
     lowercase :       Optional[int]       =			0
     for keepgram in keepgramcountergood_rep:
          keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
          # Fix an alleged bug [2] in the keep score computation.
          # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
          keeptmpscorea += keepgramcountergood_rep[keepgram]
     # Define 0/0=1 instead of 0 to give higher scores for predictions that match
     #      a target exactly.
     lowercase :       Optional[Any]       =			1
     lowercase :       List[Any]       =			1
     if len(lowerCAmelCase_   ) > 0:
          lowercase :       Optional[Any]       =			keeptmpscorea / len(lowerCAmelCase_   )
     if len(lowerCAmelCase_   ) > 0:
          # Fix an alleged bug [2] in the keep score computation.
          # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
          lowercase :       Any       =			keeptmpscorea / sum(keepgramcounterall_rep.values()   )
     lowercase :       List[Any]       =			0
     if keepscore_precision > 0 or keepscore_recall > 0:
          lowercase :       List[Any]       =			2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

     # DELETION
     lowercase :       Any       =			sgramcounter_rep - cgramcounter_rep
     lowercase :       Optional[int]       =			delgramcounter_rep - rgramcounter
     lowercase :       int       =			sgramcounter_rep - rgramcounter
     lowercase :       Union[str, Any]       =			0
     lowercase :       Tuple       =			0
     for delgram in delgramcountergood_rep:
          deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
          deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
     # Define 0/0=1 instead of 0 to give higher scores for predictions that match
     # a target exactly.
     lowercase :       Optional[Any]       =			1
     if len(lowerCAmelCase_   ) > 0:
          lowercase :       Tuple       =			deltmpscorea / len(lowerCAmelCase_   )

     # ADDITION
     lowercase :       List[Any]       =			set(lowerCAmelCase_   ) - set(lowerCAmelCase_   )
     lowercase :       List[str]       =			set(lowerCAmelCase_   ) & set(lowerCAmelCase_   )
     lowercase :       Optional[int]       =			set(lowerCAmelCase_   ) - set(lowerCAmelCase_   )

     lowercase :       Optional[int]       =			0
     for addgram in addgramcountergood:
          addtmpscore += 1

     # Define 0/0=1 instead of 0 to give higher scores for predictions that match
     # a target exactly.
     lowercase :       Optional[int]       =			1
     lowercase :       List[str]       =			1
     if len(lowerCAmelCase_   ) > 0:
          lowercase :       Dict       =			addtmpscore / len(lowerCAmelCase_   )
     if len(lowerCAmelCase_   ) > 0:
          lowercase :       int       =			addtmpscore / len(lowerCAmelCase_   )
     lowercase :       Optional[int]       =			0
     if addscore_precision > 0 or addscore_recall > 0:
          lowercase :       int       =			2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

     return (keepscore, delscore_precision, addscore)






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							Any:





     '''simple docstring'''


     lowercase :       int       =			len(lowerCAmelCase_   )

     lowercase :       List[str]       =			ssent.split(''' '''   )
     lowercase :       Any       =			csent.split(''' '''   )
     lowercase :       Optional[int]       =			[]
     lowercase :       Dict       =			[]
     lowercase :       List[str]       =			[]
     lowercase :       Dict       =			[]
     lowercase :       Optional[int]       =			[]
     lowercase :       int       =			[]

     lowercase :       Tuple       =			[]
     lowercase :       List[str]       =			[]
     lowercase :       Dict       =			[]
     lowercase :       List[Any]       =			[]
     for rsent in rsents:
          lowercase :       Tuple       =			rsent.split(''' '''   )
          lowercase :       str       =			[]
          lowercase :       Union[str, Any]       =			[]
          lowercase :       List[str]       =			[]
          ragramslist.append(lowerCAmelCase_   )
          for i in range(0			,		len(lowerCAmelCase_   ) - 1   ):
               if i < len(lowerCAmelCase_   ) - 1:
                    lowercase :       Union[str, Any]       =			ragrams[i] + ' ' + ragrams[i + 1]
                    ragrams.append(lowerCAmelCase_   )
               if i < len(lowerCAmelCase_   ) - 2:
                    lowercase :       Dict       =			ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
                    ragrams.append(lowerCAmelCase_   )
               if i < len(lowerCAmelCase_   ) - 3:
                    lowercase :       str       =			ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
                    ragrams.append(lowerCAmelCase_   )
          ragramslist.append(lowerCAmelCase_   )
          ragramslist.append(lowerCAmelCase_   )
          ragramslist.append(lowerCAmelCase_   )

     for i in range(0			,		len(lowerCAmelCase_   ) - 1   ):
          if i < len(lowerCAmelCase_   ) - 1:
               lowercase :       List[str]       =			sagrams[i] + ' ' + sagrams[i + 1]
               sagrams.append(lowerCAmelCase_   )
          if i < len(lowerCAmelCase_   ) - 2:
               lowercase :       str       =			sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
               sagrams.append(lowerCAmelCase_   )
          if i < len(lowerCAmelCase_   ) - 3:
               lowercase :       Union[str, Any]       =			sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
               sagrams.append(lowerCAmelCase_   )

     for i in range(0			,		len(lowerCAmelCase_   ) - 1   ):
          if i < len(lowerCAmelCase_   ) - 1:
               lowercase :       List[str]       =			cagrams[i] + ' ' + cagrams[i + 1]
               cagrams.append(lowerCAmelCase_   )
          if i < len(lowerCAmelCase_   ) - 2:
               lowercase :       Dict       =			cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
               cagrams.append(lowerCAmelCase_   )
          if i < len(lowerCAmelCase_   ) - 3:
               lowercase :       Tuple       =			cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
               cagrams.append(lowerCAmelCase_   )

     (lowercase) :       Optional[int]       =			SARIngram(lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_   )
     (lowercase) :       int       =			SARIngram(lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_   )
     (lowercase) :       Optional[Any]       =			SARIngram(lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_   )
     (lowercase) :       str       =			SARIngram(lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_   )
     lowercase :       Optional[Any]       =			sum([keepascore, keepascore, keepascore, keepascore]   ) / 4
     lowercase :       Optional[Any]       =			sum([delascore, delascore, delascore, delascore]   ) / 4
     lowercase :       Optional[int]       =			sum([addascore, addascore, addascore, addascore]   ) / 4
     lowercase :       str       =			(avgkeepscore + avgdelscore + avgaddscore) / 3
     return finalscore






def  snake_case(    __magic_name__			,		__magic_name__ = True			,		__magic_name__ = "13a"			,		__magic_name__ = True   )			->							int:





     '''simple docstring'''


     if lowercase:
          lowercase :       Dict       =			sentence.lower()

     if tokenizer in ["13a", "intl"]:
          if version.parse(sacrebleu.__version__   ).major >= 2:
               lowercase :       Optional[int]       =			sacrebleu.metrics.bleu._get_tokenizer(lowerCAmelCase_   )()(lowerCAmelCase_   )
          else:
               lowercase :       Any       =			sacrebleu.TOKENIZERS[tokenizer]()(lowerCAmelCase_   )
     elif tokenizer == "moses":
          lowercase :       Tuple       =			sacremoses.MosesTokenizer().tokenize(lowerCAmelCase_			,		return_str=lowerCAmelCase_			,		escape=lowerCAmelCase_   )
     elif tokenizer == "penn":
          lowercase :       Optional[Any]       =			sacremoses.MosesTokenizer().penn_tokenize(lowerCAmelCase_			,		return_str=lowerCAmelCase_   )
     else:
          lowercase :       Union[str, Any]       =			sentence

     if not return_str:
          lowercase :       str       =			normalized_sent.split()

     return normalized_sent






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							Tuple:





     '''simple docstring'''


     if not (len(lowerCAmelCase_   ) == len(lowerCAmelCase_   ) == len(lowerCAmelCase_   )):
          raise ValueError('''Sources length must match predictions and references lengths.'''   )
     lowercase :       Any       =			0
     for src, pred, refs in zip(lowerCAmelCase_			,		lowerCAmelCase_			,		lowerCAmelCase_   ):
          sari_score += SARIsent(normalize(lowerCAmelCase_   )			,		normalize(lowerCAmelCase_   )			,		[normalize(lowerCAmelCase_   ) for sent in refs]   )
     lowercase :       int       =			sari_score / len(lowerCAmelCase_   )
     return 1_00 * sari_score






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__="exp"			,		__magic_name__=None			,		__magic_name__=False			,		__magic_name__=False			,		__magic_name__=False			,		)			->							List[Any]:





     '''simple docstring'''


     lowercase :       List[str]       =			len(references[0]   )
     if any(len(lowerCAmelCase_   ) != references_per_prediction for refs in references   ):
          raise ValueError('''Sacrebleu requires the same number of references for each prediction'''   )
     lowercase :       List[Any]       =			[[refs[i] for refs in references] for i in range(lowerCAmelCase_   )]
     lowercase :       Optional[Any]       =			sacrebleu.corpus_bleu(
         lowerCAmelCase_			,		lowerCAmelCase_			,		smooth_method=lowerCAmelCase_			,		smooth_value=lowerCAmelCase_			,		force=lowerCAmelCase_			,		lowercase=lowerCAmelCase_			,		use_effective_order=lowerCAmelCase_			,		)
     return output.score



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,       _KWARGS_DESCRIPTION				)
class 						_A (  datasets.Metric				):






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






       """simple docstring"""







       return datasets.MetricInfo(
           description=_DESCRIPTION				,						citation=_CITATION				,						inputs_description=_KWARGS_DESCRIPTION				,						features=datasets.Features(
               {
                   '''predictions''': datasets.Value('''string'''				,						id='''sequence'''				),
                   '''references''': datasets.Sequence(datasets.Value('''string'''				,						id='''sequence'''				)				,						id='''references'''				),
               }				)				,						codebase_urls=[
               '''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
               '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
               '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
               '''https://github.com/mjpost/sacreBLEU''',
           ]				,						reference_urls=[
               '''https://www.aclweb.org/anthology/Q16-1029.pdf''',
               '''https://github.com/mjpost/sacreBLEU''',
               '''https://en.wikipedia.org/wiki/BLEU''',
               '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
           ]				,						)






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






       """simple docstring"""







       lowercase :       Optional[Any]       =			{}
       result.update({'''sari''': compute_sari(sources=_UpperCAmelCase				,						predictions=_UpperCAmelCase				,						references=_UpperCAmelCase				)}				)
       result.update({'''sacrebleu''': compute_sacrebleu(predictions=_UpperCAmelCase				,						references=_UpperCAmelCase				)}				)
       result.update({'''exact''': compute_em(predictions=_UpperCAmelCase				,						references=_UpperCAmelCase				)}				)
       return result





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				)				)