'''simple docstring'''

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        UpperCAmelCase__   (				_lowerCAmelCase    :			Any,      _lowerCAmelCase    :			Optional[int],      _lowerCAmelCase    :			Dict,      _lowerCAmelCase    :			List[str])    ->		Optional[int]:



						"""simple docstring"""

						snake_case :       List[str]				=    [rgram for rgrams in rgramslist for rgram in rgrams]
						snake_case :       List[str]				=    Counter(_lowerCAmelCase)

						snake_case :       List[Any]				=    Counter(_lowerCAmelCase)
						snake_case :       int				=    Counter()
						for sgram, scount in sgramcounter.items():
												snake_case :       List[Any]				=    scount * numref

						snake_case :       Optional[int]				=    Counter(_lowerCAmelCase)
						snake_case :       int				=    Counter()
						for cgram, ccount in cgramcounter.items():
												snake_case :       Dict				=    ccount * numref

						# KEEP
						snake_case :       Any				=    sgramcounter_rep & cgramcounter_rep
						snake_case :       Any				=    keepgramcounter_rep & rgramcounter
						snake_case :       List[str]				=    sgramcounter_rep & rgramcounter

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

						# DELETION
						snake_case :       List[str]				=    sgramcounter_rep - cgramcounter_rep
						snake_case :       Dict				=    delgramcounter_rep - rgramcounter
						snake_case :       Optional[int]				=    sgramcounter_rep - rgramcounter
						snake_case :       Tuple				=    0
						snake_case :       Dict				=    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.
						snake_case :       List[str]				=    1
						if len(_lowerCAmelCase) > 0:
												snake_case :       Optional[Any]				=    deltmpscorea / len(_lowerCAmelCase)

						# ADDITION
						snake_case :       str				=    set(_lowerCAmelCase) - set(_lowerCAmelCase)
						snake_case :       Dict				=    set(_lowerCAmelCase) & set(_lowerCAmelCase)
						snake_case :       Dict				=    set(_lowerCAmelCase) - set(_lowerCAmelCase)

						snake_case :       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.
						snake_case :       Any				=    1
						snake_case :       Union[str, Any]				=    1
						if len(_lowerCAmelCase) > 0:
												snake_case :       int				=    addtmpscore / len(_lowerCAmelCase)
						if len(_lowerCAmelCase) > 0:
												snake_case :       Optional[int]				=    addtmpscore / len(_lowerCAmelCase)
						snake_case :       Any				=    0
						if addscore_precision > 0 or addscore_recall > 0:
												snake_case :       int				=    2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

						return (keepscore, delscore_precision, addscore)


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Tuple,      _lowerCAmelCase    :			Dict,      _lowerCAmelCase    :			Tuple)    ->		Tuple:



						"""simple docstring"""

						snake_case :       List[str]				=    len(_lowerCAmelCase)

						snake_case :       Tuple				=    ssent.split(" ")
						snake_case :       Dict				=    csent.split(" ")
						snake_case :       Any				=    []
						snake_case :       Union[str, Any]				=    []
						snake_case :       Any				=    []
						snake_case :       Optional[int]				=    []
						snake_case :       Any				=    []
						snake_case :       str				=    []

						snake_case :       Union[str, Any]				=    []
						snake_case :       Tuple				=    []
						snake_case :       Optional[int]				=    []
						snake_case :       Tuple				=    []
						for rsent in rsents:
												snake_case :       str				=    rsent.split(" ")
												snake_case :       List[str]				=    []
												snake_case :       Optional[Any]				=    []
												snake_case :       List[str]				=    []
												ragramslist.append(_lowerCAmelCase)
												for i in range(0,      len(_lowerCAmelCase) - 1):
																		if i < len(_lowerCAmelCase) - 1:
																								snake_case :       List[str]				=    ragrams[i] + " " + ragrams[i + 1]
																								ragrams.append(_lowerCAmelCase)
																		if i < len(_lowerCAmelCase) - 2:
																								snake_case :       List[str]				=    ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
																								ragrams.append(_lowerCAmelCase)
																		if i < len(_lowerCAmelCase) - 3:
																								snake_case :       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:
																		snake_case :       Dict				=    sagrams[i] + " " + sagrams[i + 1]
																		sagrams.append(_lowerCAmelCase)
												if i < len(_lowerCAmelCase) - 2:
																		snake_case :       str				=    sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
																		sagrams.append(_lowerCAmelCase)
												if i < len(_lowerCAmelCase) - 3:
																		snake_case :       Dict				=    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:
																		snake_case :       Optional[int]				=    cagrams[i] + " " + cagrams[i + 1]
																		cagrams.append(_lowerCAmelCase)
												if i < len(_lowerCAmelCase) - 2:
																		snake_case :       Union[str, Any]				=    cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
																		cagrams.append(_lowerCAmelCase)
												if i < len(_lowerCAmelCase) - 3:
																		snake_case :       List[Any]				=    cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
																		cagrams.append(_lowerCAmelCase)

						((snake_case)				,					(snake_case)				,					(snake_case)) :       Dict				=    SARIngram(_lowerCAmelCase,      _lowerCAmelCase,      _lowerCAmelCase,      _lowerCAmelCase)
						((snake_case)				,					(snake_case)				,					(snake_case)) :       Optional[int]				=    SARIngram(_lowerCAmelCase,      _lowerCAmelCase,      _lowerCAmelCase,      _lowerCAmelCase)
						((snake_case)				,					(snake_case)				,					(snake_case)) :       int				=    SARIngram(_lowerCAmelCase,      _lowerCAmelCase,      _lowerCAmelCase,      _lowerCAmelCase)
						((snake_case)				,					(snake_case)				,					(snake_case)) :       str				=    SARIngram(_lowerCAmelCase,      _lowerCAmelCase,      _lowerCAmelCase,      _lowerCAmelCase)
						snake_case :       List[Any]				=    sum([keepascore, keepascore, keepascore, keepascore]) / 4
						snake_case :       str				=    sum([delascore, delascore, delascore, delascore]) / 4
						snake_case :       Dict				=    sum([addascore, addascore, addascore, addascore]) / 4
						snake_case :       Union[str, Any]				=    (avgkeepscore + avgdelscore + avgaddscore) / 3
						return finalscore


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Optional[Any],      _lowerCAmelCase    :			bool = True,      _lowerCAmelCase    :			str = "13a",      _lowerCAmelCase    :			bool = True)    ->		List[Any]:



						"""simple docstring"""

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

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

						if not return_str:
												snake_case :       Dict				=    normalized_sent.split()

						return normalized_sent




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



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									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   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_					):
									if not (len(snake_case_					) == len(snake_case_					) == len(snake_case_					)):
															raise ValueError("Sources length must match predictions and references lengths."					)
									snake_case :       Dict				=    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]					)
									snake_case :       Any				=    sari_score / len(snake_case_					)
									return {"sari": 100 * sari_score}






'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Tuple   							=       ["""image_processor""", """tokenizer"""]
			__magic_name__ :				List[str]   							=       """AutoImageProcessor"""
			__magic_name__ :				List[str]   							=       """AutoTokenizer"""



			def __init__(							self				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):
									snake_case :       Any				=    None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
															    " instead."				,			snake_case_				,			)
															snake_case :       str				=    kwargs.pop("feature_extractor"					)

									snake_case :       Optional[int]				=    image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("You have to specify an image_processor."					)
									if tokenizer is None:
															raise ValueError("You have to specify a tokenizer."					)

									super().__init__(snake_case_				,			snake_case_					)
									snake_case :       Optional[int]				=    self.image_processor



			def __call__(							self				,			snake_case_=None				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):

									if text is None and images is None:
															raise ValueError("You have to specify either text or images. Both cannot be none."					)

									if text is not None:
															snake_case :       Optional[int]				=    self.tokenizer(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if images is not None:
															snake_case :       List[Any]				=    self.image_processor(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if text is not None and images is not None:
															snake_case :       str				=    image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**snake_case_					)				,			tensor_type=snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.batch_decode(*snake_case_				,			**snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.decode(*snake_case_				,			**snake_case_					)



			@property
			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    self.tokenizer.model_input_names
									snake_case :       Any				=    self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)



			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			snake_case_				,			)
									return self.image_processor_class






			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			snake_case_				,			)
									return self.image_processor





