

from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_lowerCAmelCase      :    List[str]						       =    "\\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"

_lowerCAmelCase      :    Tuple						       =    "\\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"


_lowerCAmelCase      :    int						       =    "\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  UpperCamelCase_(							_snake_case     :						List[str] ,  _snake_case     :						Tuple ,  _snake_case     :						Tuple ,  _snake_case     :						List[Any]			):




			"""simple docstring"""






			__a						   =[rgram for rgrams in rgramslist for rgram in rgrams]
			__a						   =Counter(snake_case_			)

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

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

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

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

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

			# ADDITION
			__a						   =set(snake_case_			) - set(snake_case_			)
			__a						   =set(snake_case_			) & set(snake_case_			)
			__a						   =set(snake_case_			) - set(snake_case_			)

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

			return (keepscore, delscore_precision, addscore)






def  UpperCamelCase_(							_snake_case     :						List[str] ,  _snake_case     :						Tuple ,  _snake_case     :						Tuple			):




			"""simple docstring"""






			__a						   =len(snake_case_			)

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

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

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

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

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






def  UpperCamelCase_(							_snake_case     :						Optional[int] ,  _snake_case     :						Any = True ,  _snake_case     :						Any = "13a" ,  _snake_case     :						List[Any] = True			):




			"""simple docstring"""






			if lowercase:
						__a						   =sentence.lower()

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

			if not return_str:
						__a						   =normalized_sent.split()

			return normalized_sent






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





			def 	__magic_name__					(	self      )     ->				List[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 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      )     ->				Optional[Any]:




						'''simple docstring'''


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








# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
						import tensorflow as tf






class 			__magic_name__					(    TensorFormatter[Mapping, 'tf.Tensor', Mapping]   ):





			def __init__(	self      ,						__snake_case=None      ,						**__snake_case      )     ->				Union[str, Any]:




						'''simple docstring'''
						super().__init__(features=__snake_case      )
						__a						   =tf_tensor_kwargs
						import tensorflow as tf  # noqa: import tf at initialization





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				int:




						'''simple docstring'''
						import tensorflow as tf

						if isinstance(__snake_case      ,						__snake_case      ) and column:
									if all(
									    isinstance(__snake_case      ,						tf.Tensor      ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column      ):
												return tf.stack(__snake_case      )
									elif all(
									    isinstance(__snake_case      ,						(tf.Tensor, tf.RaggedTensor)      ) and x.ndim == 1 and x.dtype == column[0].dtype
									    for x in column      ):
												# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
												return tf.ragged.stack(__snake_case      )

						return column





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Any:




						'''simple docstring'''
						import tensorflow as tf

						if value is None:
									return value

						__a						   ={}

						if isinstance(__snake_case      ,						(np.number, np.ndarray)      ) and np.issubdtype(value.dtype      ,						np.integer      ):
									__a						   ={'dtype': tf.intaa}
						elif isinstance(__snake_case      ,						(np.number, np.ndarray)      ) and np.issubdtype(value.dtype      ,						np.floating      ):
									__a						   ={'dtype': tf.floataa}
						elif config.PIL_AVAILABLE and "PIL" in sys.modules:
									import PIL.Image

									if isinstance(__snake_case      ,						PIL.Image.Image      ):
												__a						   =np.asarray(__snake_case      )

						return tf.convert_to_tensor(__snake_case      ,						**{**default_dtype, **self.tf_tensor_kwargs}      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Dict:




						'''simple docstring'''
						import tensorflow as tf

						# support for torch, tf, jax etc.
						if config.TORCH_AVAILABLE and "torch" in sys.modules:
									import torch

									if isinstance(__snake_case      ,						torch.Tensor      ):
												return self._tensorize(data_struct.detach().cpu().numpy()[()]      )
						if hasattr(__snake_case      ,						'__array__'      ) and not isinstance(__snake_case      ,						tf.Tensor      ):
									__a						   =data_struct.__array__()
						# support for nested types like struct of list of struct
						if isinstance(__snake_case      ,						np.ndarray      ):
									if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
												return self._consolidate([self.recursive_tensorize(__snake_case      ) for substruct in data_struct]      )
						elif isinstance(__snake_case      ,						(list, tuple)      ):
									return self._consolidate([self.recursive_tensorize(__snake_case      ) for substruct in data_struct]      )
						return self._tensorize(__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Optional[Any]:




						'''simple docstring'''
						return map_nested(self._recursive_tensorize      ,						__snake_case      ,						map_list=__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Mapping:




						'''simple docstring'''
						__a						   =self.numpy_arrow_extractor().extract_row(__snake_case      )
						__a						   =self.python_features_decoder.decode_row(__snake_case      )
						return self.recursive_tensorize(__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				"tf.Tensor":




						'''simple docstring'''
						__a						   =self.numpy_arrow_extractor().extract_column(__snake_case      )
						__a						   =self.python_features_decoder.decode_column(__snake_case      ,						pa_table.column_names[0]      )
						__a						   =self.recursive_tensorize(__snake_case      )
						__a						   =self._consolidate(__snake_case      )
						return column





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Mapping:




						'''simple docstring'''
						__a						   =self.numpy_arrow_extractor().extract_batch(__snake_case      )
						__a						   =self.python_features_decoder.decode_batch(__snake_case      )
						__a						   =self.recursive_tensorize(__snake_case      )
						for column_name in batch:
									__a						   =self._consolidate(batch[column_name]      )
						return batch






