

from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


A__							=				"\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n"

A__							=				"\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow's tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n"


A__							=				"\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\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\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 .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]\n    >>> sari = datasets.load_metric(\"sari\")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 26.953601953601954}\n"




def        _lowercase  (					a_					:       Any       ,a_					:       str       ,a_					:       Optional[Any]       ,a_					:       Any					)					->						List[str]:




					'''simple docstring'''







					__magic_name__				=     [rgram for rgrams in rgramslist for rgram in rgrams]
					__magic_name__				=     Counter(a_					)

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

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

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

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

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

					# ADDITION
					__magic_name__				=     set(a_					) - set(a_					)
					__magic_name__				=     set(a_					) & set(a_					)
					__magic_name__				=     set(a_					) - set(a_					)

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

					return (keepscore, delscore_precision, addscore)




def        _lowercase  (					a_					:       Optional[Any]       ,a_					:       List[str]       ,a_					:       Union[str, Any]					)					->						Optional[int]:




					'''simple docstring'''







					__magic_name__				=     len(a_					)

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

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

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

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

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




def        _lowercase  (					a_					:       Optional[int]       ,a_					:       bool = True       ,a_					:       str = "13a"       ,a_					:       bool = True					)					->						str:




					'''simple docstring'''







					if lowercase:
										__magic_name__				=     sentence.lower()

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

					if not return_str:
										__magic_name__				=     normalized_sent.split()

					return normalized_sent


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







	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''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:			Tuple	,			__UpperCamelCase:			Dict	,			__UpperCamelCase:			List[Any]	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





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







from collections import OrderedDict
from typing import Mapping

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


A__							=				logging.get_logger(__name__)

A__							=				{
    "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 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: Dict												=	"ernie"







	def __init__( self:			List[Any]	,			__UpperCamelCase:			Dict=3_05_22	,			__UpperCamelCase:			str=7_68	,			__UpperCamelCase:			Tuple=12	,			__UpperCamelCase:			int=12	,			__UpperCamelCase:			Optional[Any]=30_72	,			__UpperCamelCase:			str="gelu"	,			__UpperCamelCase:			Any=0.1	,			__UpperCamelCase:			int=0.1	,			__UpperCamelCase:			int=5_12	,			__UpperCamelCase:			Any=2	,			__UpperCamelCase:			Tuple=3	,			__UpperCamelCase:			Any=False	,			__UpperCamelCase:			Optional[int]=0.02	,			__UpperCamelCase:			List[Any]=1E-12	,			__UpperCamelCase:			Union[str, Any]=0	,			__UpperCamelCase:			Tuple="absolute"	,			__UpperCamelCase:			Union[str, Any]=True	,			__UpperCamelCase:			Optional[Any]=None	,			**__UpperCamelCase:			List[Any]	,			):

						'''simple docstring'''





						super().__init__(pad_token_id=__UpperCamelCase	,			**__UpperCamelCase   )

						__magic_name__				=     vocab_size
						__magic_name__				=     hidden_size
						__magic_name__				=     num_hidden_layers
						__magic_name__				=     num_attention_heads
						__magic_name__				=     hidden_act
						__magic_name__				=     intermediate_size
						__magic_name__				=     hidden_dropout_prob
						__magic_name__				=     attention_probs_dropout_prob
						__magic_name__				=     max_position_embeddings
						__magic_name__				=     type_vocab_size
						__magic_name__				=     task_type_vocab_size
						__magic_name__				=     use_task_id
						__magic_name__				=     initializer_range
						__magic_name__				=     layer_norm_eps
						__magic_name__				=     position_embedding_type
						__magic_name__				=     use_cache
						__magic_name__				=     classifier_dropout




class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						if self.task == "multiple-choice":
											__magic_name__				=     {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											__magic_name__				=     {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						        ('token_type_ids', dynamic_axis),
						        ('task_type_ids', dynamic_axis),
						    ]   )




