






from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCamelCase										=  '''\
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415},
}
'''

lowerCamelCase										=  '''\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
'''


lowerCamelCase										=  '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]
    >>> sari = datasets.load_metric("sari")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 26.953601953601954}
'''
def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase,      _lowercase      ):


    '''simple docstring'''



    _lowercase     =	[rgram for rgrams in rgramslist for rgram in rgrams]
    _lowercase     =	Counter(_lowercase      )

    _lowercase     =	Counter(_lowercase      )
    _lowercase     =	Counter()
    for sgram, scount in sgramcounter.items():
        _lowercase     =	scount * numref

    _lowercase     =	Counter(_lowercase      )
    _lowercase     =	Counter()
    for cgram, ccount in cgramcounter.items():
        _lowercase     =	ccount * numref

    # KEEP
    _lowercase     =	sgramcounter_rep & cgramcounter_rep
    _lowercase     =	keepgramcounter_rep & rgramcounter
    _lowercase     =	sgramcounter_rep & rgramcounter

    _lowercase     =	0
    _lowercase     =	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     =	1
    _lowercase     =	1
    if len(_lowercase      ) > 0:
        _lowercase     =	keeptmpscorea / len(_lowercase      )
    if len(_lowercase      ) > 0:
        # Fix an alleged bug [2] in the keep score computation.
        # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
        _lowercase     =	keeptmpscorea / sum(keepgramcounterall_rep.values()      )
    _lowercase     =	0
    if keepscore_precision > 0 or keepscore_recall > 0:
        _lowercase     =	2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    _lowercase     =	sgramcounter_rep - cgramcounter_rep
    _lowercase     =	delgramcounter_rep - rgramcounter
    _lowercase     =	sgramcounter_rep - rgramcounter
    _lowercase     =	0
    _lowercase     =	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     =	1
    if len(_lowercase      ) > 0:
        _lowercase     =	deltmpscorea / len(_lowercase      )

    # ADDITION
    _lowercase     =	set(_lowercase      ) - set(_lowercase      )
    _lowercase     =	set(_lowercase      ) & set(_lowercase      )
    _lowercase     =	set(_lowercase      ) - set(_lowercase      )

    _lowercase     =	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     =	1
    _lowercase     =	1
    if len(_lowercase      ) > 0:
        _lowercase     =	addtmpscore / len(_lowercase      )
    if len(_lowercase      ) > 0:
        _lowercase     =	addtmpscore / len(_lowercase      )
    _lowercase     =	0
    if addscore_precision > 0 or addscore_recall > 0:
        _lowercase     =	2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)
def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase      ):


    '''simple docstring'''



    _lowercase     =	len(_lowercase      )

    _lowercase     =	ssent.split(' '      )
    _lowercase     =	csent.split(' '      )
    _lowercase     =	[]
    _lowercase     =	[]
    _lowercase     =	[]
    _lowercase     =	[]
    _lowercase     =	[]
    _lowercase     =	[]

    _lowercase     =	[]
    _lowercase     =	[]
    _lowercase     =	[]
    _lowercase     =	[]
    for rsent in rsents:
        _lowercase     =	rsent.split(' '      )
        _lowercase     =	[]
        _lowercase     =	[]
        _lowercase     =	[]
        ragramslist.append(_lowercase      )
        for i in range(0,      len(_lowercase      ) - 1      ):
            if i < len(_lowercase      ) - 1:
                _lowercase     =	ragrams[i] + ' ' + ragrams[i + 1]
                ragrams.append(_lowercase      )
            if i < len(_lowercase      ) - 2:
                _lowercase     =	ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
                ragrams.append(_lowercase      )
            if i < len(_lowercase      ) - 3:
                _lowercase     =	ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
                ragrams.append(_lowercase      )
        ragramslist.append(_lowercase      )
        ragramslist.append(_lowercase      )
        ragramslist.append(_lowercase      )

    for i in range(0,      len(_lowercase      ) - 1      ):
        if i < len(_lowercase      ) - 1:
            _lowercase     =	sagrams[i] + ' ' + sagrams[i + 1]
            sagrams.append(_lowercase      )
        if i < len(_lowercase      ) - 2:
            _lowercase     =	sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
            sagrams.append(_lowercase      )
        if i < len(_lowercase      ) - 3:
            _lowercase     =	sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
            sagrams.append(_lowercase      )

    for i in range(0,      len(_lowercase      ) - 1      ):
        if i < len(_lowercase      ) - 1:
            _lowercase     =	cagrams[i] + ' ' + cagrams[i + 1]
            cagrams.append(_lowercase      )
        if i < len(_lowercase      ) - 2:
            _lowercase     =	cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
            cagrams.append(_lowercase      )
        if i < len(_lowercase      ) - 3:
            _lowercase     =	cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
            cagrams.append(_lowercase      )

    ((_lowercase) ,    (_lowercase) ,    (_lowercase))     =	SARIngram(_lowercase,      _lowercase,      _lowercase,      _lowercase      )
    ((_lowercase) ,    (_lowercase) ,    (_lowercase))     =	SARIngram(_lowercase,      _lowercase,      _lowercase,      _lowercase      )
    ((_lowercase) ,    (_lowercase) ,    (_lowercase))     =	SARIngram(_lowercase,      _lowercase,      _lowercase,      _lowercase      )
    ((_lowercase) ,    (_lowercase) ,    (_lowercase))     =	SARIngram(_lowercase,      _lowercase,      _lowercase,      _lowercase      )
    _lowercase     =	sum([keepascore, keepascore, keepascore, keepascore]      ) / 4
    _lowercase     =	sum([delascore, delascore, delascore, delascore]      ) / 4
    _lowercase     =	sum([addascore, addascore, addascore, addascore]      ) / 4
    _lowercase     =	(avgkeepscore + avgdelscore + avgaddscore) / 3
    return finalscore
def        UpperCAmelCase      (   _lowercase,      _lowercase = True,      _lowercase = "13a",      _lowercase = True      ):


    '''simple docstring'''



    if lowercase:
        _lowercase     =	sentence.lower()

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

    if not return_str:
        _lowercase     =	normalized_sent.split()

    return normalized_sent

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







   """simple docstring"""



   def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]	)  ->		Any:


       """simple docstring"""



       return datasets.MetricInfo(
           description=_DESCRIPTION   ,       citation=_CITATION   ,       inputs_description=_KWARGS_DESCRIPTION   ,       features=datasets.Features(
               {
                   'sources': datasets.Value('string'   ,       id='sequence'	),
                   'predictions': datasets.Value('string'   ,       id='sequence'	),
                   'references': datasets.Sequence(datasets.Value('string'   ,       id='sequence'	)   ,       id='references'	),
               }	)   ,       codebase_urls=[
               'https://github.com/cocoxu/simplification/blob/master/SARI.py',
               'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
           ]   ,       reference_urls=['https://www.aclweb.org/anthology/Q16-1029.pdf']   ,       )

   def 			SCREAMING_SNAKE_CASE__				(    self:	Any   ,       snake_case__:	int   ,       snake_case__:	Optional[Any]   ,       snake_case__:	Optional[int]	)  ->		Optional[Any]:


       """simple docstring"""



       if not (len(snake_case__	) == len(snake_case__	) == len(snake_case__	)):
           raise ValueError('Sources length must match predictions and references lengths.'	)
       _lowercase     =	0
       for src, pred, refs in zip(snake_case__   ,       snake_case__   ,       snake_case__	):
           sari_score += SARIsent(normalize(snake_case__	)   ,       normalize(snake_case__	)   ,       [normalize(snake_case__	) for sent in refs]	)
       _lowercase     =	sari_score / len(snake_case__	)
       return {"sari": 100 * sari_score}








import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase										=  logging.get_logger(__name__)

lowerCamelCase										=  {
    '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}

class 					snake_case_							(       lowercase_      ):







   """simple docstring"""



   __lowerCAmelCase      =						"""hubert"""

   def __init__(    self:	Any   ,       snake_case__:	str=32   ,       snake_case__:	int=768   ,       snake_case__:	Dict=12   ,       snake_case__:	Optional[int]=12   ,       snake_case__:	List[str]=3_072   ,       snake_case__:	Optional[Any]="gelu"   ,       snake_case__:	Optional[int]=0.1   ,       snake_case__:	List[str]=0.1   ,       snake_case__:	Optional[int]=0.1   ,       snake_case__:	List[str]=True   ,       snake_case__:	Union[str, Any]=0.0   ,       snake_case__:	Union[str, Any]=0.1   ,       snake_case__:	List[str]=0.1   ,       snake_case__:	List[Any]=0.02   ,       snake_case__:	Dict=1E-5   ,       snake_case__:	Any="group"   ,       snake_case__:	Union[str, Any]="gelu"   ,       snake_case__:	List[str]=(512, 512, 512, 512, 512, 512, 512)   ,       snake_case__:	Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)   ,       snake_case__:	Any=(10, 3, 3, 3, 3, 2, 2)   ,       snake_case__:	int=False   ,       snake_case__:	Any=128   ,       snake_case__:	Dict=16   ,       snake_case__:	Optional[Any]=False   ,       snake_case__:	Tuple=True   ,       snake_case__:	int=0.05   ,       snake_case__:	List[Any]=10   ,       snake_case__:	Tuple=2   ,       snake_case__:	List[Any]=0.0   ,       snake_case__:	Optional[int]=10   ,       snake_case__:	Optional[int]=0   ,       snake_case__:	Union[str, Any]="sum"   ,       snake_case__:	Union[str, Any]=False   ,       snake_case__:	int=False   ,       snake_case__:	Any=256   ,       snake_case__:	Union[str, Any]=0   ,       snake_case__:	Optional[int]=1   ,       snake_case__:	List[str]=2   ,       **snake_case__:	Optional[int]   ,       )  ->		Any:


       """simple docstring"""



       super().__init__(**snake_case__   ,       pad_token_id=snake_case__   ,       bos_token_id=snake_case__   ,       eos_token_id=snake_case__	)
       _lowercase     =	hidden_size
       _lowercase     =	feat_extract_norm
       _lowercase     =	feat_extract_activation
       _lowercase     =	list(snake_case__	)
       _lowercase     =	list(snake_case__	)
       _lowercase     =	list(snake_case__	)
       _lowercase     =	conv_bias
       _lowercase     =	num_conv_pos_embeddings
       _lowercase     =	num_conv_pos_embedding_groups
       _lowercase     =	len(self.conv_dim	)
       _lowercase     =	num_hidden_layers
       _lowercase     =	intermediate_size
       _lowercase     =	hidden_act
       _lowercase     =	num_attention_heads
       _lowercase     =	hidden_dropout
       _lowercase     =	attention_dropout
       _lowercase     =	activation_dropout
       _lowercase     =	feat_proj_layer_norm
       _lowercase     =	feat_proj_dropout
       _lowercase     =	final_dropout
       _lowercase     =	layerdrop
       _lowercase     =	layer_norm_eps
       _lowercase     =	initializer_range
       _lowercase     =	vocab_size
       _lowercase     =	do_stable_layer_norm
       _lowercase     =	use_weighted_layer_sum
       _lowercase     =	classifier_proj_size

       if (
           (len(self.conv_stride	) != self.num_feat_extract_layers)
           or (len(self.conv_kernel	) != self.num_feat_extract_layers)
           or (len(self.conv_dim	) != self.num_feat_extract_layers)
       ):
           raise ValueError(
               'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
               ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
               f''' {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,'''
               f''' `len(config.conv_kernel) = {len(self.conv_kernel	)}`.'''	)

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       _lowercase     =	apply_spec_augment
       _lowercase     =	mask_time_prob
       _lowercase     =	mask_time_length
       _lowercase     =	mask_time_min_masks
       _lowercase     =	mask_feature_prob
       _lowercase     =	mask_feature_length
       _lowercase     =	mask_feature_min_masks

       # ctc loss
       _lowercase     =	ctc_loss_reduction
       _lowercase     =	ctc_zero_infinity

   @property
   def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		Optional[int]:


       """simple docstring"""



       return functools.reduce(operator.mul   ,       self.conv_stride   ,       1	)
