'''simple docstring'''






import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


SCREAMING_SNAKE_CASE_     :   Dict	   =			'\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n    author = "Lin, Chin-Yew  and\n      Och, Franz Josef",\n    booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n    month = "aug 23{--}aug 27",\n    year = "2004",\n    address = "Geneva, Switzerland",\n    publisher = "COLING",\n    url = "https://www.aclweb.org/anthology/C04-1072",\n    pages = "501--507",\n}\n'

SCREAMING_SNAKE_CASE_     :   Tuple	   =			'\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n'

SCREAMING_SNAKE_CASE_     :   Optional[Any]	   =			'\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    \'bleu\': bleu score,\n    \'precisions\': geometric mean of n-gram precisions,\n    \'brevity_penalty\': brevity penalty,\n    \'length_ratio\': ratio of lengths,\n    \'translation_length\': translation_length,\n    \'reference_length\': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample\n    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)\n    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric("bleu")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results["bleu"])\n    1.0\n'




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



  """simple docstring"""







  def    lowercase_  (							self					):

    '''simple docstring'''







    return datasets.MetricInfo(
        description=_DESCRIPTION   ,    citation=_CITATION   ,    inputs_description=_KWARGS_DESCRIPTION   ,    features=datasets.Features(
            {
                """predictions""": datasets.Sequence(datasets.Value("""string"""   ,    id="""token"""					)   ,    id="""sequence"""					),
                """references""": datasets.Sequence(
                    datasets.Sequence(datasets.Value("""string"""   ,    id="""token"""					)   ,    id="""sequence"""					)   ,    id="""references"""					),
            }					)   ,    codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""]   ,    reference_urls=[
            """https://en.wikipedia.org/wiki/BLEU""",
            """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
        ]   ,    )







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_=4   ,    snake_case_=False					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= compute_bleu(
        reference_corpus=snake_case_   ,    translation_corpus=snake_case_   ,    max_order=snake_case_   ,    smooth=snake_case_					)
    (__UpperCAmelCase):					Optional[Any]		= score
    return {
        "bleu": bleu,
        "precisions": precisions,
        "brevity_penalty": bp,
        "length_ratio": ratio,
        "translation_length": translation_length,
        "reference_length": reference_length,
    }
'''simple docstring'''






def  UpperCamelCase__					(					_lowercase    :				List[str]     )							->	Union[str, Any]:
  __UpperCAmelCase:					int		= []
  __UpperCAmelCase:					List[Any]		= []
  __UpperCAmelCase:					List[Any]		= {
      """^""": 3,
      """*""": 2,
      """/""": 2,
      """%""": 2,
      """+""": 1,
      """-""": 1,
  }  # Priority of each operator
  __UpperCAmelCase:					int		= len(_lowercase     ) if (len(_lowercase     ) > 7) else 7

  # Print table header for output
  print(
      """Symbol""".center(8     )   ,    """Stack""".center(_lowercase     )   ,    """Postfix""".center(_lowercase     )   ,    sep=""" | """   ,    )
  print("""-""" * (print_width * 3 + 7)     )

  for x in infix:
    if x.isalpha() or x.isdigit():
      post_fix.append(_lowercase     )  # if x is Alphabet / Digit, add it to Postfix
    elif x == "(":
      stack.append(_lowercase     )  # if x is "(" push to Stack
    elif x == ")":  # if x is ")" pop stack until "(" is encountered
      while stack[-1] != "(":
        post_fix.append(stack.pop()     )  # Pop stack & add the content to Postfix
      stack.pop()
    else:
      if len(_lowercase     ) == 0:
        stack.append(_lowercase     )  # If stack is empty, push x to stack
      else:  # while priority of x is not > priority of element in the stack
        while len(_lowercase     ) > 0 and priority[x] <= priority[stack[-1]]:
          post_fix.append(stack.pop()     )  # pop stack & add to Postfix
        stack.append(_lowercase     )  # push x to stack

    print(
        x.center(8     )   ,    ("""""".join(_lowercase     )).ljust(_lowercase     )   ,    ("""""".join(_lowercase     )).ljust(_lowercase     )   ,    sep=""" | """   ,    )  # Output in tabular format

  while len(_lowercase     ) > 0:  # while stack is not empty
    post_fix.append(stack.pop()     )  # pop stack & add to Postfix
    print(
        """ """.center(8     )   ,    ("""""".join(_lowercase     )).ljust(_lowercase     )   ,    ("""""".join(_lowercase     )).ljust(_lowercase     )   ,    sep=""" | """   ,    )  # Output in tabular format

  return "".join(_lowercase     )  # return Postfix as str






def  UpperCamelCase__					(					_lowercase    :				str     )							->	Tuple:
  __UpperCAmelCase:					Union[str, Any]		= list(infix[::-1]     )  # reverse the infix equation

  for i in range(len(_lowercase     )     ):
    if infix[i] == "(":
      __UpperCAmelCase:					Tuple		= """)"""  # change "(" to ")"
    elif infix[i] == ")":
      __UpperCAmelCase:					Union[str, Any]		= """("""  # change ")" to "("

  return (infix_2_postfix("""""".join(_lowercase     )     ))[
      ::-1
  ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			input('\nEnter an Infix Equation = ')  # Input an Infix equation
    SCREAMING_SNAKE_CASE_	   =			''.join(Infix.split())  # Remove spaces from the input
    print('\n\t', Infix, '(Infix) -> ', infix_2_prefix(Infix), '(Prefix)')