



'''simple docstring'''





import re
import string

import numpy as np

import datasets


lowercase_	 =						"\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"

lowercase_	 =						"\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    33.3\n\n"

lowercase_	 =						"\n"






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



 '''simple docstring'''







 def       a__						(self )					->  List[str]:


      """simple docstring"""



      return datasets.MetricInfo(
          description=_DESCRIPTION				,     citation=_CITATION				,     inputs_description=_KWARGS_DESCRIPTION				,     features=datasets.Features(
              {
                  '''predictions''': datasets.Value('''string'''				,     id='''sequence''' ),
                  '''references''': datasets.Value('''string'''				,     id='''sequence''' ),
              } )				,     reference_urls=[]				,     )



 def       a__						(self				,     A				,     A				,     A=None				,     A=False				,     A=False				,     A=False				,     )					->  Tuple:


      """simple docstring"""



      if regexes_to_ignore is not None:
           for s in regexes_to_ignore:
                _a											=						np.array([re.sub(A				,     ''''''				,     A ) for x in predictions] )
                _a											=						np.array([re.sub(A				,     ''''''				,     A ) for x in references] )
      else:
           _a											=						np.asarray(A )
           _a											=						np.asarray(A )

      if ignore_case:
           _a											=						np.char.lower(A )
           _a											=						np.char.lower(A )

      if ignore_punctuation:
           _a											=						string.punctuation.maketrans(''''''				,     ''''''				,     string.punctuation )
           _a											=						np.char.translate(A				,     table=A )
           _a											=						np.char.translate(A				,     table=A )

      if ignore_numbers:
           _a											=						string.digits.maketrans(''''''				,     ''''''				,     string.digits )
           _a											=						np.char.translate(A				,     table=A )
           _a											=						np.char.translate(A				,     table=A )

      _a											=						predictions == references

      return {"exact_match": np.mean(A ) * 100}








'''simple docstring'''





def lowerCAmelCase						(__A = 1_000):



     """simple docstring"""



     _a											=						2**power
     _a											=						str(__A)
     _a											=						list(__A)
     _a											=						0

     for i in list_num:
          sum_of_num += int(__A)

     return sum_of_num


if __name__ == "__main__":
  lowercase_	 =						int(input("Enter the power of 2: ").strip())
  print("2 ^ ", power, " = ", 2**power)
  lowercase_	 =						solution(power)
  print("Sum of the digits is: ", result)



