

'''simple docstring'''



import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_UpperCamelCase						:  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"

_UpperCamelCase						:  str							       =				"\\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"


_UpperCamelCase						:  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       snake_case   (	snake_case      : Union[str, Any]			) ->  Union[str, Any]:


    """simple docstring"""


    def remove_articles(snake_case      : List[str]			):
        lowerCAmelCase 						=							re.compile(r'\b(a|an|the)\b'					,		re.UNICODE			)
        return re.sub(snake_case					,		' '					,		snake_case			)

    def white_space_fix(snake_case      : Dict			):
        return " ".join(text.split()			)

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

    def lower(snake_case      : Optional[Any]			):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(snake_case			)			)			)			)


def       snake_case   (	snake_case      : Dict					,		snake_case      : int			) ->  Optional[Any]:


    """simple docstring"""


    return int(normalize_answer(snake_case			) == normalize_answer(snake_case			)			)


def       snake_case   (	snake_case      : List[str]					,		snake_case      : Tuple			) ->  int:


    """simple docstring"""


    lowerCAmelCase 						=							[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       snake_case   (	snake_case      : List[Any]					,		snake_case      : Dict					,		snake_case      : int					,		snake_case      : Any			) ->  int:


    """simple docstring"""


    lowerCAmelCase 						=							[rgram for rgrams in rgramslist for rgram in rgrams]
    lowerCAmelCase 						=							Counter(snake_case			)

    lowerCAmelCase 						=							Counter(snake_case			)
    lowerCAmelCase 						=							Counter()
    for sgram, scount in sgramcounter.items():
        lowerCAmelCase 						=							scount * numref

    lowerCAmelCase 						=							Counter(snake_case			)
    lowerCAmelCase 						=							Counter()
    for cgram, ccount in cgramcounter.items():
        lowerCAmelCase 						=							ccount * numref

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

    lowerCAmelCase 						=							0
    lowerCAmelCase 						=							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.
    lowerCAmelCase 						=							1
    lowerCAmelCase 						=							1
    if len(snake_case			) > 0:
        lowerCAmelCase 						=							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)
        lowerCAmelCase 						=							keeptmpscorea / sum(keepgramcounterall_rep.values()			)
    lowerCAmelCase 						=							0
    if keepscore_precision > 0 or keepscore_recall > 0:
        lowerCAmelCase 						=							2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

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

    # ADDITION
    lowerCAmelCase 						=							set(snake_case			) - set(snake_case			)
    lowerCAmelCase 						=							set(snake_case			) & set(snake_case			)
    lowerCAmelCase 						=							set(snake_case			) - set(snake_case			)

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

    return (keepscore, delscore_precision, addscore)


def       snake_case   (	snake_case      : Optional[int]					,		snake_case      : Union[str, Any]					,		snake_case      : Any			) ->  Union[str, Any]:


    """simple docstring"""


    lowerCAmelCase 						=							len(snake_case			)

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

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

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


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


    """simple docstring"""


    if lowercase:
        lowerCAmelCase 						=							sentence.lower()

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

    if not return_str:
        lowerCAmelCase 						=							normalized_sent.split()

    return normalized_sent


def       snake_case   (	snake_case      : List[Any]					,		snake_case      : int					,		snake_case      : str			) ->  int:


    """simple docstring"""


    if not (len(snake_case			) == len(snake_case			) == len(snake_case			)):
        raise ValueError('Sources length must match predictions and references lengths.'			)
    lowerCAmelCase 						=							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]			)
    lowerCAmelCase 						=							sari_score / len(snake_case			)
    return 100 * sari_score


def       snake_case   (	snake_case      : Optional[int]					,		snake_case      : List[str]					,		snake_case      : Dict="exp"					,		snake_case      : Optional[Any]=None					,		snake_case      : List[Any]=False					,		snake_case      : Union[str, Any]=False					,		snake_case      : Dict=False					,		) ->  Any:


    """simple docstring"""


    lowerCAmelCase 						=							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'			)
    lowerCAmelCase 						=							[[refs[i] for refs in references] for i in range(snake_case			)]
    lowerCAmelCase 						=							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 				_snake_case					(   datasets.Metric			):

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''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   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							{}
      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 colorsys

from PIL import Image  # type: ignore


def       snake_case   (	snake_case      : float					,		snake_case      : float					,		snake_case      : int			) ->  float:


    """simple docstring"""


    lowerCAmelCase 						=							x
    lowerCAmelCase 						=							y
    for step in range(snake_case			):  # noqa: B007
        lowerCAmelCase 						=							a * a - b * b + x
        lowerCAmelCase 						=							2 * a * b + y
        lowerCAmelCase 						=							a_new

        # divergence happens for all complex number with an absolute value
        # greater than 4
        if a * a + b * b > 4:
            break
    return step / (max_step - 1)


def       snake_case   (	snake_case      : float			) ->  tuple:


    """simple docstring"""


    if distance == 1:
        return (0, 0, 0)
    else:
        return (255, 255, 255)


def       snake_case   (	snake_case      : float			) ->  tuple:


    """simple docstring"""


    if distance == 1:
        return (0, 0, 0)
    else:
        return tuple(round(i * 255			) for i in colorsys.hsv_to_rgb(snake_case					,		1					,		1			)			)


def       snake_case   (	snake_case      : int = 800					,		snake_case      : int = 600					,		snake_case      : float = -0.6					,		snake_case      : float = 0					,		snake_case      : float = 3.2					,		snake_case      : int = 50					,		snake_case      : bool = True					,		) ->  Image.Image:


    """simple docstring"""


    lowerCAmelCase 						=							Image.new('RGB'					,		(image_width, image_height)			)
    lowerCAmelCase 						=							img.load()

    # loop through the image-coordinates
    for image_x in range(snake_case			):
        for image_y in range(snake_case			):
            # determine the figure-coordinates based on the image-coordinates
            lowerCAmelCase 						=							figure_width / image_width * image_height
            lowerCAmelCase 						=							figure_center_x + (image_x / image_width - 0.5) * figure_width
            lowerCAmelCase 						=							figure_center_y + (image_y / image_height - 0.5) * figure_height

            lowerCAmelCase 						=							get_distance(snake_case					,		snake_case					,		snake_case			)

            # color the corresponding pixel based on the selected coloring-function
            if use_distance_color_coding:
                lowerCAmelCase 						=							get_color_coded_rgb(snake_case			)
            else:
                lowerCAmelCase 						=							get_black_and_white_rgb(snake_case			)

    return img


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # colored version, full figure
     _UpperCamelCase						:  Dict							       =				get_image()

     # uncomment for colored version, different section, zoomed in
     # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
     # figure_width = 0.8)

     # uncomment for black and white version, full figure
     # img = get_image(use_distance_color_coding = False)

     # uncomment to save the image
     # img.save("mandelbrot.png")

     img.show()





