

def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str				)							->				bool:





    '''simple docstring'''






    return credit_card_number.startswith(('34', '35', '37', '4', '5', '6')				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str				)							->				bool:





    '''simple docstring'''






    A__   				=       credit_card_number
    A__   				=       0
    A__   				=       len(SCREAMING_SNAKE_CASE__				) - 2
    for i in range(SCREAMING_SNAKE_CASE__   ,      -1   ,      -2				):
        #  double the value of every second digit
        A__   				=       int(cc_number[i]				)
        digit *= 2
        # If doubling of a number results in a two digit number
        # i.e greater than 9(e.g., 6 × 2 = 12),
        # then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
        # to get a single digit number.
        if digit > 9:
            digit %= 10
            digit += 1
        A__   				=       cc_number[:i] + str(SCREAMING_SNAKE_CASE__				) + cc_number[i + 1 :]
        total += digit

    # Sum up the remaining digits
    for i in range(len(SCREAMING_SNAKE_CASE__				) - 1   ,      -1   ,      -2				):
        total += int(cc_number[i]				)

    return total % 10 == 0
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str				)							->				bool:





    '''simple docstring'''






    A__   				=       f'{credit_card_number} is an invalid credit card number because'
    if not credit_card_number.isdigit():
        print(f'{error_message} it has nonnumerical characters.'				)
        return False

    if not 13 <= len(SCREAMING_SNAKE_CASE__				) <= 16:
        print(f'{error_message} of its length.'				)
        return False

    if not validate_initial_digits(SCREAMING_SNAKE_CASE__				):
        print(f'{error_message} of its first two digits.'				)
        return False

    if not luhn_validation(SCREAMING_SNAKE_CASE__				):
        print(f'{error_message} it fails the Luhn check.'				)
        return False

    print(f'{credit_card_number} is a valid credit card number.'				)
    return True


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     validate_credit_card_number("4111111111111111")
     validate_credit_card_number("32323")




import datasets

from .evaluate import evaluate


lowercase_			=  "\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n"

lowercase_			=  "\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n"

lowercase_			=  "\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the SQuAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_metric = datasets.load_metric(\"squad\")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0}\n"


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







    """simple docstring"""





    def 						snake_case__					(				self			:	List[Any]		)->   Tuple:





        '''simple docstring'''







        return datasets.MetricInfo(
            description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
                {
                    'predictions': {'id': datasets.Value('string'		), 'prediction_text': datasets.Value('string'		)},
                    'references': {
                        'id': datasets.Value('string'		),
                        'answers': datasets.features.Sequence(
                            {
                                'text': datasets.Value('string'		),
                                'answer_start': datasets.Value('int32'		),
                            }		),
                    },
                }		),codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/'],reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/'],)


    def 						snake_case__					(				self			:	str,lowercase_			:	List[Any],lowercase_			:	Dict		)->   List[str]:





        '''simple docstring'''







        A__   				=       {prediction['id']: prediction['prediction_text'] for prediction in predictions}
        A__   				=       [
            {
                'paragraphs': [
                    {
                        'qas': [
                            {
                                'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
                                'id': ref['id'],
                            }
                            for ref in references
                        ]
                    }
                ]
            }
        ]
        A__   				=       evaluate(dataset=lowercase_,predictions=lowercase_		)
        return score

