
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 						lowercase_			(   A__ ,		A__ ,		A__ ,		A__		)							->							List[Any]:
    """simple docstring"""

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

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

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

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

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

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

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

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

    return (keepscore, delscore_precision, addscore)



def 						lowercase_			(   A__ ,		A__ ,		A__		)							->							List[str]:
    """simple docstring"""

    snake_case      =					len(A__		)

    snake_case      =					ssent.split(" "		)
    snake_case      =					csent.split(" "		)
    snake_case      =					[]
    snake_case      =					[]
    snake_case      =					[]
    snake_case      =					[]
    snake_case      =					[]
    snake_case      =					[]

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

    for i in range(0 ,		len(A__		) - 1		):
        if i < len(A__		) - 1:
            snake_case      =					sagrams[i] + " " + sagrams[i + 1]
            sagrams.append(A__		)
        if i < len(A__		) - 2:
            snake_case      =					sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
            sagrams.append(A__		)
        if i < len(A__		) - 3:
            snake_case      =					sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
            sagrams.append(A__		)

    for i in range(0 ,		len(A__		) - 1		):
        if i < len(A__		) - 1:
            snake_case      =					cagrams[i] + " " + cagrams[i + 1]
            cagrams.append(A__		)
        if i < len(A__		) - 2:
            snake_case      =					cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
            cagrams.append(A__		)
        if i < len(A__		) - 3:
            snake_case      =					cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
            cagrams.append(A__		)

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



def 						lowercase_			(   A__ ,		A__ = True ,		A__ = "13a" ,		A__ = True		)							->							Optional[Any]:
    """simple docstring"""

    if lowercase:
        snake_case      =					sentence.lower()

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

    if not return_str:
        snake_case      =					normalized_sent.split()

    return normalized_sent







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





     def 		UpperCAmelCase(self      :    Union[str, Any]   )						->						Optional[Any]:
         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 		UpperCAmelCase(self      :    List[str]				,    _A      :    Union[str, Any]				,    _A      :    Dict				,    _A      :    List[Any]   )						->						Dict:
         if not (len(_A   ) == len(_A   ) == len(_A   )):
             raise ValueError("Sources length must match predictions and references lengths."   )
         snake_case      =					0
         for src, pred, refs in zip(_A				,    _A				,    _A   ):
             sari_score += SARIsent(normalize(_A   )				,    normalize(_A   )				,    [normalize(_A   ) for sent in refs]   )
         snake_case      =					sari_score / len(_A   )
         return {"sari": 1_0_0 * sari_score}



from typing import Any

import numpy as np







class      lowerCamelCase		:





     def __init__(self      :    Optional[Any]				,    _A      :    np.ndarray				,    _A      :    int   )						->						None:
         # Check if RHS is negative
         if np.any(tableau[:, -1]				,    where=tableau[:, -1] < 0   ):
             raise ValueError("RHS must be > 0"   )

         snake_case      =					tableau
         snake_case     ,							snake_case      =					tableau.shape

         # Number of decision variables x1, x2, x3...
         snake_case      =					n_vars

         # Number of artificial variables to be minimised
         snake_case      =					len(np.where(tableau[self.n_vars : -1] == -1   )[0]   )

         # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
         snake_case      =					(self.n_art_vars > 0) + 1

         # Number of slack variables added to make inequalities into equalities
         snake_case      =					self.n_rows - self.n_stages

         # Objectives for each stage
         snake_case      =					["max"]

         # In two stage simplex, first minimise then maximise
         if self.n_art_vars:
             self.objectives.append("min"   )

         snake_case      =					[""]

         # Index of current pivot row and column
         snake_case      =					None
         snake_case      =					None

         # Does objective row only contain (non)-negative values?
         snake_case      =					False





     @staticmethod
     def 		UpperCAmelCase(*_A      :    int   )						->						list[str]:
         if len(_A   ) != 3:
             raise ValueError("Must provide n_vars, n_slack, and n_art_vars"   )

         if not all(x >= 0 and isinstance(_A				,    _A   ) for x in args   ):
             raise ValueError("All arguments must be non-negative integers"   )

         # decision | slack | artificial
         snake_case      =					["x", "s", "a"]
         snake_case      =					[]
         for i in range(3   ):
             for j in range(args[i]   ):
                 titles.append(string_starts[i] + str(j + 1   )   )
         titles.append("RHS"   )
         return titles





     def 		UpperCAmelCase(self      :    int				,    _A      :    np.ndarray   )						->						tuple[Any, Any]:
         snake_case      =					self.objectives[-1]

         # Find entries of highest magnitude in objective rows
         snake_case      =					(objective == "min") - (objective == "max")
         snake_case      =					np.argmax(sign * tableau[0, : self.n_vars]   )

         # Choice is only valid if below 0 for maximise, and above for minimise
         if sign * self.tableau[0, col_idx] <= 0:
             snake_case      =					True
             return 0, 0

         # Pivot row is chosen as having the lowest quotient when elements of
         # the pivot column divide the right-hand side

         # Slice excluding the objective rows
         snake_case      =					slice(self.n_stages				,    self.n_rows   )

         # RHS
         snake_case      =					tableau[s, -1]

         # Elements of pivot column within slice
         snake_case      =					tableau[s, col_idx]

         # Array filled with nans
         snake_case      =					np.full(self.n_rows - self.n_stages				,    np.nan   )

         # If element in pivot column is greater than zeron_stages, return
         # quotient or nan otherwise
         snake_case      =					np.divide(_A				,    _A				,    out=_A				,    where=divisor > 0   )

         # Arg of minimum quotient excluding the nan values. n_stages is added
         # to compensate for earlier exclusion of objective columns
         snake_case      =					np.nanargmin(_A   ) + self.n_stages
         return row_idx, col_idx





     def 		UpperCAmelCase(self      :    int				,    _A      :    np.ndarray				,    _A      :    int				,    _A      :    int   )						->						np.ndarray:
         snake_case      =					tableau[row_idx].copy()

         snake_case      =					piv_row[col_idx]

         # Entry becomes 1
         piv_row *= 1 / piv_val

         # Variable in pivot column becomes basic, ie the only non-zero entry
         for idx, coeff in enumerate(tableau[:, col_idx]   ):
             tableau[idx] += -coeff * piv_row
         snake_case      =					piv_row
         return tableau





     def 		UpperCAmelCase(self      :    Optional[Any]				,    _A      :    np.ndarray   )						->						np.ndarray:
         self.objectives.pop()

         if not self.objectives:
             return tableau

         # Slice containing ids for artificial columns
         snake_case      =					slice(-self.n_art_vars - 1				,    -1   )

         # Delete the artificial variable columns
         snake_case      =					np.delete(_A				,    _A				,    axis=1   )

         # Delete the objective row of the first stage
         snake_case      =					np.delete(_A				,    0				,    axis=0   )

         snake_case      =					1
         self.n_rows -= 1
         snake_case      =					0
         snake_case      =					False
         return tableau





     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						dict[Any, Any]:
         # Stop simplex algorithm from cycling.
         for _ in range(1_0_0   ):
             # Completion of each stage removes an objective. If both stages
             # are complete, then no objectives are left
             if not self.objectives:
                 snake_case      =					self.generate_col_titles(
                     self.n_vars				,    self.n_slack				,    self.n_art_vars   )

                 # Find the values of each variable at optimal solution
                 return self.interpret_tableau(self.tableau				,    self.col_titles   )

             snake_case     ,							snake_case      =					self.find_pivot(self.tableau   )

             # If there are no more negative values in objective row
             if self.stop_iter:
                 # Delete artificial variable columns and rows. Update attributes
                 snake_case      =					self.change_stage(self.tableau   )
             else:
                 snake_case      =					self.pivot(self.tableau				,    _A				,    _A   )
         return {}





     def 		UpperCAmelCase(self      :    Optional[Any]				,    _A      :    np.ndarray				,    _A      :    list[str]   )						->						dict[str, float]:
         snake_case      =					{"P": abs(tableau[0, -1]   )}

         for i in range(self.n_vars   ):
             # Gives ids of nonzero entries in the ith column
             snake_case      =					np.nonzero(tableau[:, i]   )
             snake_case      =					len(nonzero[0]   )

             # First entry in the nonzero ids
             snake_case      =					nonzero[0][0]
             snake_case      =					tableau[nonzero_rowidx, i]

             # If there is only one nonzero value in column, which is one
             if n_nonzero == nonzero_val == 1:
                 snake_case      =					tableau[nonzero_rowidx, -1]
                 snake_case      =					rhs_val

        # Check for basic variables
         for title in col_titles:
             # Don't add RHS or slack variables to output dict
             if title[0] not in "R-s-a":
                 output_dict.setdefault(_A				,    0   )
         return output_dict


if __name__ == "__main__":
   import doctest

   doctest.testmod()

