





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__          =logging.get_logger(__name__)

UpperCamelCase__          ={
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       __snake_case       	=			'ernie'




       def __init__(					self			,  __lowerCamelCase=3_0_5_2_2			,  __lowerCamelCase=7_6_8			,  __lowerCamelCase=1_2			,  __lowerCamelCase=1_2			,  __lowerCamelCase=3_0_7_2			,  __lowerCamelCase="gelu"			,  __lowerCamelCase=0.1			,  __lowerCamelCase=0.1			,  __lowerCamelCase=5_1_2			,  __lowerCamelCase=2			,  __lowerCamelCase=3			,  __lowerCamelCase=False			,  __lowerCamelCase=0.02			,  __lowerCamelCase=1E-12			,  __lowerCamelCase=0			,  __lowerCamelCase="absolute"			,  __lowerCamelCase=True			,  __lowerCamelCase=None			,  **__lowerCamelCase			,  )	->   Any:
            super().__init__(pad_token_id=__lowerCamelCase			,  **__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Dict  			=							vocab_size
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							hidden_size
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							num_hidden_layers
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							num_attention_heads
            _SCREAMING_SNAKE_CASE				:		int  			=							hidden_act
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							intermediate_size
            _SCREAMING_SNAKE_CASE				:		Any  			=							hidden_dropout_prob
            _SCREAMING_SNAKE_CASE				:		int  			=							attention_probs_dropout_prob
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							max_position_embeddings
            _SCREAMING_SNAKE_CASE				:		Dict  			=							type_vocab_size
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							task_type_vocab_size
            _SCREAMING_SNAKE_CASE				:		Any  			=							use_task_id
            _SCREAMING_SNAKE_CASE				:		Any  			=							initializer_range
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							layer_norm_eps
            _SCREAMING_SNAKE_CASE				:		Any  			=							position_embedding_type
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							use_cache
            _SCREAMING_SNAKE_CASE				:		Any  			=							classifier_dropout





class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       @property
       def 			UpperCamelCase_	(					self							)	->   Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                 _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							{0: "batch", 1: "choice", 2: "sequence"}
            else:
                 _SCREAMING_SNAKE_CASE				:		Any  			=							{0: "batch", 1: "sequence"}
            return OrderedDict(
                [
                    ("input_ids", dynamic_axis),
                    ("attention_mask", dynamic_axis),
                    ("token_type_ids", dynamic_axis),
                    ("task_type_ids", dynamic_axis),
                ]							)







from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__snake_case			:			Union[str, Any]			   =   """\
@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},
}
"""

__snake_case			:			Dict			   =   """\
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
"""


__snake_case			:			int			   =   """
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      (			UpperCamelCase_				:    Optional[Any]			,				UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    int						)     ->      Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  [rgram for rgrams in rgramslist for rgram in rgrams]
						lowerCAmelCase__			  =  Counter(UpperCamelCase_						)

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

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

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

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

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

						# ADDITION
						lowerCAmelCase__			  =  set(UpperCamelCase_						) - set(UpperCamelCase_						)
						lowerCAmelCase__			  =  set(UpperCamelCase_						) & set(UpperCamelCase_						)
						lowerCAmelCase__			  =  set(UpperCamelCase_						) - set(UpperCamelCase_						)

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

						return (keepscore, delscore_precision, addscore)







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Optional[int]						)     ->      List[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  len(UpperCamelCase_						)

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

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

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

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

						((lowerCAmelCase__)  ,				(lowerCAmelCase__)  ,				(lowerCAmelCase__))			  =  SARIngram(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
						((lowerCAmelCase__)  ,				(lowerCAmelCase__)  ,				(lowerCAmelCase__))			  =  SARIngram(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
						((lowerCAmelCase__)  ,				(lowerCAmelCase__)  ,				(lowerCAmelCase__))			  =  SARIngram(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
						((lowerCAmelCase__)  ,				(lowerCAmelCase__)  ,				(lowerCAmelCase__))			  =  SARIngram(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  sum([keepascore, keepascore, keepascore, keepascore]						) / 4
						lowerCAmelCase__			  =  sum([delascore, delascore, delascore, delascore]						) / 4
						lowerCAmelCase__			  =  sum([addascore, addascore, addascore, addascore]						) / 4
						lowerCAmelCase__			  =  (avgkeepscore + avgdelscore + avgaddscore) / 3
						return finalscore







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    bool = True			,				UpperCamelCase_				:    str = "13a"			,				UpperCamelCase_				:    bool = True						)     ->      List[str]:

						"""simple docstring"""



						if lowercase:
												lowerCAmelCase__			  =  sentence.lower()

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

						if not return_str:
												lowerCAmelCase__			  =  normalized_sent.split()

						return normalized_sent


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






						def 					UpperCamelCase__					(		self      ):






												"""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 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												if not (len(_UpperCamelCase      ) == len(_UpperCamelCase      ) == len(_UpperCamelCase      )):
																		raise ValueError('Sources length must match predictions and references lengths.'      )
												lowerCAmelCase__			  =  0
												for src, pred, refs in zip(_UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      ):
																		sari_score += SARIsent(normalize(_UpperCamelCase      )   ,  normalize(_UpperCamelCase      )   ,  [normalize(_UpperCamelCase      ) for sent in refs]      )
												lowerCAmelCase__			  =  sari_score / len(_UpperCamelCase      )
												return {"sari": 1_00 * sari_score}





