
'''simple docstring'''




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowercase     :							Dict       						=     '\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'

lowercase     :							Dict       						=     '\\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'


lowercase     :							List[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 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    def remove_articles(snake_case__		):
        A			:  Any   =		re.compile(R'''\b(a|an|the)\b'''						,							re.UNICODE		)
        return re.sub(snake_case__						,							''' '''						,							snake_case__		)

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

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

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

    return white_space_fix(remove_articles(remove_punc(lower(snake_case__		)		)		)		)







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    return int(normalize_answer(snake_case__		) == normalize_answer(snake_case__		)		)







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  int   =		[any(compute_exact(snake_case__						,							snake_case__		) for ref in refs		) for pred, refs in zip(snake_case__						,							snake_case__		)]
    return (sum(snake_case__		) / len(snake_case__		)) * 100







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  List[Any]   =		[rgram for rgrams in rgramslist for rgram in rgrams]
    A			:  List[str]   =		Counter(snake_case__		)

    A			:  Dict   =		Counter(snake_case__		)
    A			:  str   =		Counter()
    for sgram, scount in sgramcounter.items():
        A			:  List[str]   =		scount * numref

    A			:  List[Any]   =		Counter(snake_case__		)
    A			:  Optional[Any]   =		Counter()
    for cgram, ccount in cgramcounter.items():
        A			:  List[Any]   =		ccount * numref

    # KEEP
    A			:  Optional[Any]   =		sgramcounter_rep & cgramcounter_rep
    A			:  Optional[Any]   =		keepgramcounter_rep & rgramcounter
    A			:  Optional[int]   =		sgramcounter_rep & rgramcounter

    A			:  List[str]   =		0
    A			:  Union[str, Any]   =		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			:  List[Any]   =		1
    A			:  Dict   =		1
    if len(snake_case__		) > 0:
        A			:  Any   =		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			:  Dict   =		keeptmpscorea / sum(keepgramcounterall_rep.values()		)
    A			:  str   =		0
    if keepscore_precision > 0 or keepscore_recall > 0:
        A			:  Any   =		2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    A			:  str   =		sgramcounter_rep - cgramcounter_rep
    A			:  Dict   =		delgramcounter_rep - rgramcounter
    A			:  Optional[int]   =		sgramcounter_rep - rgramcounter
    A			:  int   =		0
    A			:  int   =		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			:  Any   =		1
    if len(snake_case__		) > 0:
        A			:  Optional[Any]   =		deltmpscorea / len(snake_case__		)

    # ADDITION
    A			:  List[Any]   =		set(snake_case__		) - set(snake_case__		)
    A			:  Dict   =		set(snake_case__		) & set(snake_case__		)
    A			:  str   =		set(snake_case__		) - set(snake_case__		)

    A			:  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.
    A			:  Dict   =		1
    A			:  str   =		1
    if len(snake_case__		) > 0:
        A			:  Dict   =		addtmpscore / len(snake_case__		)
    if len(snake_case__		) > 0:
        A			:  List[str]   =		addtmpscore / len(snake_case__		)
    A			:  str   =		0
    if addscore_precision > 0 or addscore_recall > 0:
        A			:  Union[str, Any]   =		2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  List[Any]   =		len(snake_case__		)

    A			:  Union[str, Any]   =		ssent.split(''' '''		)
    A			:  Dict   =		csent.split(''' '''		)
    A			:  List[Any]   =		[]
    A			:  Optional[Any]   =		[]
    A			:  str   =		[]
    A			:  Tuple   =		[]
    A			:  Tuple   =		[]
    A			:  int   =		[]

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

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







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__ = True						,							snake_case__ = "13a"						,							snake_case__ = True		):




    '''simple docstring'''



    if lowercase:
        A			:  Union[str, Any]   =		sentence.lower()

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

    if not return_str:
        A			:  List[Any]   =		normalized_sent.split()

    return normalized_sent







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    if not (len(snake_case__		) == len(snake_case__		) == len(snake_case__		)):
        raise ValueError('''Sources length must match predictions and references lengths.'''		)
    A			:  Any   =		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]		)
    A			:  List[Any]   =		sari_score / len(snake_case__		)
    return 100 * sari_score







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__="exp"						,							snake_case__=None						,							snake_case__=False						,							snake_case__=False						,							snake_case__=False						,							):




    '''simple docstring'''



    A			:  List[Any]   =		len(references[0]		)
    if any(len(snake_case__		) != references_per_prediction for refs in references		):
        raise ValueError('''Sacrebleu requires the same number of references for each prediction'''		)
    A			:  Any   =		[[refs[i] for refs in references] for i in range(snake_case__		)]
    A			:  Optional[int]   =		sacrebleu.corpus_bleu(
        snake_case__						,							snake_case__						,							smooth_method=snake_case__						,							smooth_value=snake_case__						,							force=snake_case__						,							lowercase=snake_case__						,							use_effective_order=snake_case__						,							)
    return output.score







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





     def 							__lowerCAmelCase				(				self      )					->							List[Any]:






         """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 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							int:






         """simple docstring"""






         A			:  Optional[Any]   =		{}
         result.update({'''sari''': compute_sari(sources=SCREAMING_SNAKE_CASE		,     predictions=SCREAMING_SNAKE_CASE		,     references=SCREAMING_SNAKE_CASE      )}      )
         result.update({'''sacrebleu''': compute_sacrebleu(predictions=SCREAMING_SNAKE_CASE		,     references=SCREAMING_SNAKE_CASE      )}      )
         result.update({'''exact''': compute_em(predictions=SCREAMING_SNAKE_CASE		,     references=SCREAMING_SNAKE_CASE      )}      )
         return result






'''simple docstring'''




import numpy

# List of input, output pairs
lowercase     :							Any       						=     (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowercase     :							str       						=     (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
lowercase     :							Union[str, Any]       						=     [2, 4, 1, 5]
lowercase     :							Any       						=     len(train_data)
lowercase     :							Optional[int]       						=     0.0_09







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__="train"		):




    '''simple docstring'''



    return calculate_hypothesis_value(snake_case__						,							snake_case__		) - output(
        snake_case__						,							snake_case__		)







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  Dict   =		0
    for i in range(len(snake_case__		) - 1		):
        hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
    hyp_val += parameter_vector[0]
    return hyp_val







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    if data_set == "train":
        return train_data[example_no][1]
    elif data_set == "test":
        return test_data[example_no][1]
    return None







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    if data_set == "train":
        return _hypothesis_value(train_data[example_no][0]		)
    elif data_set == "test":
        return _hypothesis_value(test_data[example_no][0]		)
    return None







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__=m		):




    '''simple docstring'''



    A			:  List[Any]   =		0
    for i in range(snake_case__		):
        if index == -1:
            summation_value += _error(snake_case__		)
        else:
            summation_value += _error(snake_case__		) * train_data[i][0][index]
    return summation_value







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  List[Any]   =		summation_of_cost_derivative(snake_case__						,							snake_case__		) / m
    return cost_derivative_value







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    global parameter_vector
    # Tune these values to set a tolerance value for predicted output
    A			:  Dict   =		0.00_00_02
    A			:  Optional[Any]   =		0
    A			:  int   =		0
    while True:
        j += 1
        A			:  List[str]   =		[0, 0, 0, 0]
        for i in range(0						,							len(snake_case__		)		):
            A			:  Union[str, Any]   =		get_cost_derivative(i - 1		)
            A			:  Tuple   =		(
                parameter_vector[i] - LEARNING_RATE * cost_derivative
            )
        if numpy.allclose(
            snake_case__						,							snake_case__						,							atol=snake_case__						,							rtol=snake_case__						,							):
            break
        A			:  List[Any]   =		temp_parameter_vector
    print(('''Number of iterations:''', j)		)







def 						lowerCAmelCase_							(  ):




    '''simple docstring'''



    for i in range(len(snake_case__		)		):
        print(('''Actual output value:''', output(snake_case__						,							'''test'''		))		)
        print(('''Hypothesis output:''', calculate_hypothesis_value(snake_case__						,							'''test'''		))		)


if __name__ == "__main__":
      run_gradient_descent()
      print('\nTesting gradient descent for a linear hypothesis function.\n')
      test_gradient_descent()




