

A__        =			[
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "be-e6", "bh-e12", "cd-e2", "ce-e4",
     "de-e1", "df-e8", "dg-e5", "dh-e10", "ef-e3", "eg-e2", "fg-e6", "gh-e6", "hi-e3"],
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "be-e6", "cd-e2", "de-e1", "df-e8",
     "ef-e3", "eg-e2", "fg-e6"],
    ["ab-e1", "ac-e3", "bc-e4", "bd-e2", "de-e1", "df-e8", "dg-e5", "ef-e3", "eg-e2",
     "eh-e12", "fg-e6", "fh-e10", "gh-e6"],
    ["ab-e1", "ac-e3", "bc-e4", "bd-e2", "bh-e12", "cd-e2", "df-e8", "dh-e10"],
    ["ab-e1", "ac-e3", "ad-e5", "bc-e4", "bd-e2", "cd-e2", "ce-e4", "de-e1", "df-e8",
     "dg-e5", "ef-e3", "eg-e2", "fg-e6"]
]





def 							__a       (					__A	):
    SCREAMING_SNAKE_CASE__					     =						set()
    for row in edge_array:
        for item in row:
            distinct_edge.add(item[0]	)
    return list(__A	)





def 							__a       (					__A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						["0"] * len(__A	)
    for i, row in enumerate(__A	):
        for item in row:
            if distinct_edge in item[0]:
                SCREAMING_SNAKE_CASE__					     =						"1"
                break
    return "".join(__A	)





def 							__a       (					__A	):
    SCREAMING_SNAKE_CASE__					     =						get_distinct_edge(__A	)
    SCREAMING_SNAKE_CASE__					     =						{}

    for item in distinct_edge:
        SCREAMING_SNAKE_CASE__					     =						get_bitcode(__A ,       __A	)
        # print('bit',bit)
        # bt=''.join(bit)
        SCREAMING_SNAKE_CASE__					     =						bit.count("1"	)
        SCREAMING_SNAKE_CASE__					     =						[s, bit]
    # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
    SCREAMING_SNAKE_CASE__					     =						[
        [k, v[0], v[1]]
        for k, v in sorted(frequency_table.items() ,       key=lambda __A	: v[1][0] ,       reverse=__A	)
    ]
    return sorted_frequency_table





def 							__a       (					__A	):
    SCREAMING_SNAKE_CASE__					     =						{}
    for _, item in enumerate(__A	):
        nodes.setdefault(item[2] ,       []	).append(item[0]	)
    return nodes





def 							__a       (					__A	):
    SCREAMING_SNAKE_CASE__					     =						{}
    for key, value in nodes.items():
        SCREAMING_SNAKE_CASE__					     =						value
    return cluster





def 							__a       (					__A	):
    return [i * 100 / len(__A	) for i in cluster]





def 							__a       (					):
    print("\nNodes\n"	)
    for key, value in nodes.items():
        print(__A ,       __A	)
    print("\nSupport\n"	)
    print(__A	)
    print("\n Cluster \n"	)
    for key, value in sorted(cluster.items() ,       reverse=__A	):
        print(__A ,       __A	)
    print("\n Graph\n"	)
    for key, value in graph.items():
        print(__A ,       __A	)
    print("\n Edge List of Frequent subgraphs \n"	)
    for edge_list in freq_subgraph_edge_list:
        print(__A	)





def 							__a       (					__A ,       __A ,       __A ,       __A	):
    for i in cluster[ca]:
        SCREAMING_SNAKE_CASE__					     =						0
        SCREAMING_SNAKE_CASE__					     =						ca + 1
        while ca < max(cluster.keys()	):
            for j in cluster[ca]:
                if int(__A ,       2	) & int(__A ,       2	) == int(__A ,       2	):
                    if tuple(nodes[i]	) in graph:
                        graph[tuple(nodes[i]	)].append(nodes[j]	)
                    else:
                        SCREAMING_SNAKE_CASE__					     =						[nodes[j]]
                    count += 1
            if count == 0:
                SCREAMING_SNAKE_CASE__					     =						ca + 1
            else:
                break





def 							__a       (					__A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						cluster[max(cluster.keys()	)]
    SCREAMING_SNAKE_CASE__					     =						"Header"
    SCREAMING_SNAKE_CASE__					     =						{}
    for i in x:
        if (["Header"],) in graph:
            graph[(["Header"],)].append(x[i]	)
        else:
            SCREAMING_SNAKE_CASE__					     =						[x[i]]
    for i in x:
        SCREAMING_SNAKE_CASE__					     =						[["Header"]]
    SCREAMING_SNAKE_CASE__					     =						1
    while i < max(__A	) - 1:
        create_edge(__A ,       __A ,       __A ,       __A	)
        SCREAMING_SNAKE_CASE__					     =						i + 1
    return graph





def 							__a       (					__A ,       __A ,       __A ,       __A=None	):
    SCREAMING_SNAKE_CASE__					     =						(path or []) + [start]
    if start == end:
        paths.append(__A	)
    for node in graph[start]:
        if tuple(__A	) not in path:
            my_dfs(__A ,       tuple(__A	) ,       __A ,       __A	)





def 							__a       (					__A ,       __A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						int(s / 100 * (len(__A	) - 1)	)
    for i in cluster[k]:
        my_dfs(__A ,       tuple(cluster[k][i]	) ,       (["Header"],)	)





def 							__a       (					__A	):
    SCREAMING_SNAKE_CASE__					     =						[]
    for edges in paths:
        SCREAMING_SNAKE_CASE__					     =						[]
        for j in range(len(__A	) - 1	):
            SCREAMING_SNAKE_CASE__					     =						list(edges[j]	)
            for e in temp:
                SCREAMING_SNAKE_CASE__					     =						(e[0], e[1])
                el.append(__A	)
        freq_sub_el.append(__A	)
    return freq_sub_el





def 							__a       (					__A	):
    for i in range(len(__A	)	):
        for j in range(len(edge_array[i]	)	):
            SCREAMING_SNAKE_CASE__					     =						edge_array[i][j].split("-"	)
            SCREAMING_SNAKE_CASE__					     =						t


if __name__ == "__main__":
  preprocess(edge_array)
  A__        =			get_frequency_table(edge_array)
  A__        =			get_nodes(frequency_table)
  A__        =			get_cluster(nodes)
  A__        =			get_support(cluster)
  A__        =			construct_graph(cluster, nodes)
  find_freq_subgraph_given_support(60, cluster, graph)
  A__        =			[]
  A__        =			freq_subgraphs_edge_list(paths)
  print_all()





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 							__a       (					__A ,       __A ,       __A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						[rgram for rgrams in rgramslist for rgram in rgrams]
    SCREAMING_SNAKE_CASE__					     =						Counter(__A	)

    SCREAMING_SNAKE_CASE__					     =						Counter(__A	)
    SCREAMING_SNAKE_CASE__					     =						Counter()
    for sgram, scount in sgramcounter.items():
        SCREAMING_SNAKE_CASE__					     =						scount * numref

    SCREAMING_SNAKE_CASE__					     =						Counter(__A	)
    SCREAMING_SNAKE_CASE__					     =						Counter()
    for cgram, ccount in cgramcounter.items():
        SCREAMING_SNAKE_CASE__					     =						ccount * numref

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

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

    # DELETION
    SCREAMING_SNAKE_CASE__					     =						sgramcounter_rep - cgramcounter_rep
    SCREAMING_SNAKE_CASE__					     =						delgramcounter_rep - rgramcounter
    SCREAMING_SNAKE_CASE__					     =						sgramcounter_rep - rgramcounter
    SCREAMING_SNAKE_CASE__					     =						0
    SCREAMING_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.
    SCREAMING_SNAKE_CASE__					     =						1
    if len(__A	) > 0:
        SCREAMING_SNAKE_CASE__					     =						deltmpscorea / len(__A	)

    # ADDITION
    SCREAMING_SNAKE_CASE__					     =						set(__A	) - set(__A	)
    SCREAMING_SNAKE_CASE__					     =						set(__A	) & set(__A	)
    SCREAMING_SNAKE_CASE__					     =						set(__A	) - set(__A	)

    SCREAMING_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.
    SCREAMING_SNAKE_CASE__					     =						1
    SCREAMING_SNAKE_CASE__					     =						1
    if len(__A	) > 0:
        SCREAMING_SNAKE_CASE__					     =						addtmpscore / len(__A	)
    if len(__A	) > 0:
        SCREAMING_SNAKE_CASE__					     =						addtmpscore / len(__A	)
    SCREAMING_SNAKE_CASE__					     =						0
    if addscore_precision > 0 or addscore_recall > 0:
        SCREAMING_SNAKE_CASE__					     =						2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)





def 							__a       (					__A ,       __A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						len(__A	)

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

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

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





def 							__a       (					__A ,       __A = True ,       __A = "13a" ,       __A = 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:
        SCREAMING_SNAKE_CASE__					     =						sentence.lower()

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

    if not return_str:
        SCREAMING_SNAKE_CASE__					     =						normalized_sent.split()

    return normalized_sent







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


  """simple docstring"""



  def      _UpperCamelCase    (	self    :				List[str]	)					-> int:
      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    :				Optional[Any]     ,  A_    :				int     ,  A_    :				Tuple     ,  A_    :				int	)					-> Dict:
      if not (len(A_	) == len(A_	) == len(A_	)):
          raise ValueError("Sources length must match predictions and references lengths."	)
      SCREAMING_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]	)
      SCREAMING_SNAKE_CASE__					     =						sari_score / len(A_	)
      return {"sari": 100 * sari_score}


