






"""simple docstring"""







import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__a       :	Tuple             = '\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

__a       :	Tuple             = '\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n'


__a       :	Optional[Any]             = '\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\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\n    sacrebleu: sacrebleu score\n    exact: exact score\n\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 ."]]\n    >>> wiki_split = datasets.load_metric("wiki_split")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}\n'





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):

					def remove_articles(lowerCamelCase_):
										a__          =  re.compile(r'''\b(a|an|the)\b''' ,     re.UNICODE)
										return re.sub(lowerCamelCase_ ,     ''' ''' ,     lowerCamelCase_)

					def white_space_fix(lowerCamelCase_):
										return " ".join(text.split())

					def remove_punc(lowerCamelCase_):
										a__          =  set(string.punctuation)
										return "".join(ch for ch in text if ch not in exclude)

					def lower(lowerCamelCase_):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase_))))





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					return int(normalize_answer(lowerCamelCase_) == normalize_answer(lowerCamelCase_))





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  [any(compute_exact(lowerCamelCase_ ,     lowerCamelCase_) for ref in refs) for pred, refs in zip(lowerCamelCase_ ,     lowerCamelCase_)]
					return (sum(lowerCamelCase_) / len(lowerCamelCase_)) * 100





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  [rgram for rgrams in rgramslist for rgram in rgrams]
					a__          =  Counter(lowerCamelCase_)

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

					a__          =  Counter(lowerCamelCase_)
					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(lowerCamelCase_) > 0:
										a__          =  keeptmpscorea / len(lowerCamelCase_)
					if len(lowerCamelCase_) > 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(lowerCamelCase_) > 0:
										a__          =  deltmpscorea / len(lowerCamelCase_)

					# ADDITION
					a__          =  set(lowerCamelCase_) - set(lowerCamelCase_)
					a__          =  set(lowerCamelCase_) & set(lowerCamelCase_)
					a__          =  set(lowerCamelCase_) - set(lowerCamelCase_)

					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(lowerCamelCase_) > 0:
										a__          =  addtmpscore / len(lowerCamelCase_)
					if len(lowerCamelCase_) > 0:
										a__          =  addtmpscore / len(lowerCamelCase_)
					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  len(lowerCamelCase_)

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

					((a__)       ,(a__)       ,(a__))          =  SARIngram(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					((a__)       ,(a__)       ,(a__))          =  SARIngram(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					((a__)       ,(a__)       ,(a__))          =  SARIngram(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					((a__)       ,(a__)       ,(a__))          =  SARIngram(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ = True ,     lowerCamelCase_ = "13a" ,     lowerCamelCase_ = True):
					# Normalization is requried for the ASSET dataset (one of the primary
					# datasets in sentence simplification) to allow using space
					# to split the sentence. Even though Wiki-Auto and TURK datasets,
					# do not require normalization, we do it for consistency.
					# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
					# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

					if lowercase:
										a__          =  sentence.lower()

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

					if not return_str:
										a__          =  normalized_sent.split()

					return normalized_sent





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					if not (len(lowerCamelCase_) == len(lowerCamelCase_) == len(lowerCamelCase_)):
										raise ValueError('''Sources length must match predictions and references lengths.''')
					a__          =  0
					for src, pred, refs in zip(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
										sari_score += SARIsent(normalize(lowerCamelCase_) ,     normalize(lowerCamelCase_) ,     [normalize(lowerCamelCase_) for sent in refs])
					a__          =  sari_score / len(lowerCamelCase_)
					return 100 * sari_score





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_="exp" ,     lowerCamelCase_=None ,     lowerCamelCase_=False ,     lowerCamelCase_=False ,     lowerCamelCase_=False ,     ):
					a__          =  len(references[0])
					if any(len(lowerCamelCase_) != references_per_prediction for refs in references):
										raise ValueError('''Sacrebleu requires the same number of references for each prediction''')
					a__          =  [[refs[i] for refs in references] for i in range(lowerCamelCase_)]
					a__          =  sacrebleu.corpus_bleu(
					    lowerCamelCase_ ,     lowerCamelCase_ ,     smooth_method=lowerCamelCase_ ,     smooth_value=lowerCamelCase_ ,     force=lowerCamelCase_ ,     lowercase=lowerCamelCase_ ,     use_effective_order=lowerCamelCase_ ,     )
					return output.score







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





							"""simple docstring"""





							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												return datasets.MetricInfo(
												    description=_DESCRIPTION				,		citation=_CITATION				,		inputs_description=_KWARGS_DESCRIPTION				,		features=datasets.Features(
												        {
												            '''predictions''': datasets.Value('''string'''				,		id='''sequence'''    ),
												            '''references''': datasets.Sequence(datasets.Value('''string'''				,		id='''sequence'''    )				,		id='''references'''    ),
												        }    )				,		codebase_urls=[
												        '''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
												        '''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
												        '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
												        '''https://github.com/mjpost/sacreBLEU''',
												    ]				,		reference_urls=[
												        '''https://www.aclweb.org/anthology/Q16-1029.pdf''',
												        '''https://github.com/mjpost/sacreBLEU''',
												        '''https://en.wikipedia.org/wiki/BLEU''',
												        '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
												    ]				,		)





							def  lowercase		(   self:     List[str]				,		__A:     Dict				,		__A:     List[str]				,		__A:     List[Any]    ):

												'''simple docstring'''

												a__          =  {}
												result.update({'''sari''': compute_sari(sources=__A				,		predictions=__A				,		references=__A    )}    )
												result.update({'''sacrebleu''': compute_sacrebleu(predictions=__A				,		references=__A    )}    )
												result.update({'''exact''': compute_em(predictions=__A				,		references=__A    )}    )
												return result










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__a       :	Tuple             = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Tuple             = ['DeiTFeatureExtractor']
	__a       :	Optional[Any]             = ['DeiTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Optional[int]             = [
	    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'DeiTForImageClassification',
	    'DeiTForImageClassificationWithTeacher',
	    'DeiTForMaskedImageModeling',
	    'DeiTModel',
	    'DeiTPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Dict             = [
	    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFDeiTForImageClassification',
	    'TFDeiTForImageClassificationWithTeacher',
	    'TFDeiTForMaskedImageModeling',
	    'TFDeiTModel',
	    'TFDeiTPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deit import DeiTFeatureExtractor
		from .image_processing_deit import DeiTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deit import (
		    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeiTForImageClassification,
		    DeiTForImageClassificationWithTeacher,
		    DeiTForMaskedImageModeling,
		    DeiTModel,
		    DeiTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_deit import (
		    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDeiTForImageClassification,
		    TFDeiTForImageClassificationWithTeacher,
		    TFDeiTForMaskedImageModeling,
		    TFDeiTModel,
		    TFDeiTPreTrainedModel,
		)


else:
	import sys

	__a       :	List[Any]             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


