






__magic_name__:  Tuple  	=			[
    ["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 					UpperCamelCase		( _A				):






      """simple docstring"""







      __magic_name__    :     str     		=      set()
      for row in edge_array:
            for item in row:
                  distinct_edge.add(item[0]				)
      return list(_A				)

def 					UpperCamelCase		( _A,					_A				):






      """simple docstring"""







      __magic_name__    :     Union[str, Any]     		=      ["""0"""] * len(_A				)
      for i, row in enumerate(_A				):
            for item in row:
                  if distinct_edge in item[0]:
                        __magic_name__    :     Optional[Any]     		=      """1"""
                        break
      return "".join(_A				)

def 					UpperCamelCase		( _A				):






      """simple docstring"""







      __magic_name__    :     List[str]     		=      get_distinct_edge(_A				)
      __magic_name__    :     List[str]     		=      {}

      for item in distinct_edge:
            __magic_name__    :     Optional[Any]     		=      get_bitcode(_A,					_A				)
            # print('bit',bit)
            # bt=''.join(bit)
            __magic_name__    :     Union[str, Any]     		=      bit.count("""1"""				)
            __magic_name__    :     Dict     		=      [s, bit]
      # Store [Distinct edge, WT(Bitcode), Bitcode] in descending order
      __magic_name__    :     List[Any]     		=      [
          [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 					UpperCamelCase		( _A				):






      """simple docstring"""







      __magic_name__    :     str     		=      {}
      for _, item in enumerate(_A				):
            nodes.setdefault(item[2],					[]				).append(item[0]				)
      return nodes

def 					UpperCamelCase		( _A				):






      """simple docstring"""







      __magic_name__    :     Optional[int]     		=      {}
      for key, value in nodes.items():
            __magic_name__    :     Tuple     		=      value
      return cluster

def 					UpperCamelCase		( _A				):






      """simple docstring"""







      return [i * 100 / len(_A				) for i in cluster]

def 					UpperCamelCase		( ):






      """simple docstring"""







      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 					UpperCamelCase		( _A,					_A,					_A,					_A				):






      """simple docstring"""







      for i in cluster[ca]:
            __magic_name__    :     Any     		=      0
            __magic_name__    :     Any     		=      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:
                                    __magic_name__    :     int     		=      [nodes[j]]
                              count += 1
                  if count == 0:
                        __magic_name__    :     Optional[Any]     		=      ca + 1
                  else:
                        break

def 					UpperCamelCase		( _A,					_A				):






      """simple docstring"""







      __magic_name__    :     List[str]     		=      cluster[max(cluster.keys()				)]
      __magic_name__    :     List[Any]     		=      """Header"""
      __magic_name__    :     Tuple     		=      {}
      for i in x:
            if (["Header"],) in graph:
                  graph[(["Header"],)].append(x[i]				)
            else:
                  __magic_name__    :     List[str]     		=      [x[i]]
      for i in x:
            __magic_name__    :     Tuple     		=      [["""Header"""]]
      __magic_name__    :     Union[str, Any]     		=      1
      while i < max(_A				) - 1:
            create_edge(_A,					_A,					_A,					_A				)
            __magic_name__    :     Optional[Any]     		=      i + 1
      return graph

def 					UpperCamelCase		( _A,					_A,					_A,					_A=None				):






      """simple docstring"""







      __magic_name__    :     str     		=      (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 					UpperCamelCase		( _A,					_A,					_A				):






      """simple docstring"""







      __magic_name__    :     Union[str, Any]     		=      int(s / 100 * (len(_A				) - 1)				)
      for i in cluster[k]:
            my_dfs(_A,					tuple(cluster[k][i]				),					(["""Header"""],)				)

def 					UpperCamelCase		( _A				):






      """simple docstring"""







      __magic_name__    :     str     		=      []
      for edges in paths:
            __magic_name__    :     List[Any]     		=      []
            for j in range(len(_A				) - 1				):
                  __magic_name__    :     Any     		=      list(edges[j]				)
                  for e in temp:
                        __magic_name__    :     Optional[Any]     		=      (e[0], e[1])
                        el.append(_A				)
            freq_sub_el.append(_A				)
      return freq_sub_el

def 					UpperCamelCase		( _A				):






      """simple docstring"""







      for i in range(len(_A				)				):
            for j in range(len(edge_array[i]				)				):
                  __magic_name__    :     Any     		=      edge_array[i][j].split("""-"""				)
                  __magic_name__    :     Optional[int]     		=      t


if __name__ == "__main__":
  preprocess(edge_array)
  __magic_name__:  Optional[int]  	=			get_frequency_table(edge_array)
  __magic_name__:  str  	=			get_nodes(frequency_table)
  __magic_name__:  List[str]  	=			get_cluster(nodes)
  __magic_name__:  Optional[Any]  	=			get_support(cluster)
  __magic_name__:  Dict  	=			construct_graph(cluster, nodes)
  find_freq_subgraph_given_support(60, cluster, graph)
  __magic_name__:  list  	=			[]
  __magic_name__:  Dict  	=			freq_subgraphs_edge_list(paths)
  print_all()








import sacrebleu as scb
from packaging import version

import datasets


__magic_name__:  Optional[int]  	=			"\\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"

__magic_name__:  int  	=			"\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n"

__magic_name__:  List[Any]  	=			"\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:\n        - `'none'`: no smoothing\n        - `'floor'`: increment zero counts\n        - `'add-k'`: increment num/denom by k for n>1\n        - `'exp'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:\n        - `'none'`: No tokenization.\n        - `'zh'`: Chinese tokenization.\n        - `'13a'`: mimics the `mteval-v13a` script from Moses.\n        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `'char'`: Language-agnostic character-level tokenization.\n        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    'score': BLEU score,\n    'counts': Counts,\n    'totals': Totals,\n    'precisions': Precisions,\n    'bp': Brevity penalty,\n    'sys_len': predictions length,\n    'ref_len': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = [\"hello there general kenobi\",\n        ...                 \"on our way to ankh morpork\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        39.8\n"





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,						_KWARGS_DESCRIPTION   )
class  snake_case__      (							datasets.Metric   ):
	def     __magic_name__       (       self				)							->   Optional[int]:
							if version.parse(scb.__version__				) < version.parse("""1.4.12"""				):
													raise ImportWarning(
													    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
													    """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""				)
							return datasets.MetricInfo(
							    description=_DESCRIPTION    ,     citation=_CITATION    ,     homepage="""https://github.com/mjpost/sacreBLEU"""    ,     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/mjpost/sacreBLEU"""]    ,     reference_urls=[
							        """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     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__="exp"    ,     lowerCAmelCase__=None    ,     lowerCAmelCase__=False    ,     lowerCAmelCase__=False    ,     lowerCAmelCase__=None    ,     lowerCAmelCase__=False    ,     )							->   Optional[Any]:
							__magic_name__    :     Optional[int]     		=      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"""				)
							__magic_name__    :     Dict     		=      [[refs[i] for refs in references] for i in range(lowerCAmelCase__				)]
							__magic_name__    :     List[str]     		=      scb.corpus_bleu(
							    lowerCAmelCase__    ,     lowerCAmelCase__    ,     smooth_method=lowerCAmelCase__    ,     smooth_value=lowerCAmelCase__    ,     force=lowerCAmelCase__    ,     lowercase=lowerCAmelCase__    ,     use_effective_order=lowerCAmelCase__    ,     **({"""tokenize""": tokenize} if tokenize else {})    ,     )
							__magic_name__    :     List[str]     		=      {
							    """score""": output.score,
							    """counts""": output.counts,
							    """totals""": output.totals,
							    """precisions""": output.precisions,
							    """bp""": output.bp,
							    """sys_len""": output.sys_len,
							    """ref_len""": output.ref_len,
							}
							return output_dict
