





'''simple docstring'''

def 							UpperCAmelCase_    (	lowerCAmelCase_							):

    """simple docstring"""






    lowercase         = ""
    for ch in key:
        if ch == " " or ch not in key_no_dups and ch.isalpha():
            key_no_dups += ch
    return key_no_dups







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

    """simple docstring"""






    lowercase         = [chr(i + 65							) for i in range(26							)]
    # Remove duplicate characters from key
    lowercase         = remove_duplicates(key.upper()							)
    lowercase         = len(_UpperCamelCase							)
    # First fill cipher with key characters
    lowercase         = {alphabet[i]: char for i, char in enumerate(_UpperCamelCase							)}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(_UpperCamelCase							)		,							26							):
        lowercase         = alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            lowercase         = alphabet[i - offset]
        lowercase         = char
    return cipher_alphabet







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

    """simple docstring"""






    return "".join(cipher_map.get(_UpperCamelCase		,							_UpperCamelCase							) for ch in message.upper()							)







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

    """simple docstring"""






    lowercase         = {v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(_UpperCamelCase		,							_UpperCamelCase							) for ch in message.upper()							)







def 							UpperCAmelCase_    (	):

    """simple docstring"""






    lowercase         = input("Enter message to encode or decode: "							).strip()
    lowercase         = input("Enter keyword: "							).strip()
    lowercase         = input("Encipher or decipher? E/D:"							).strip()[0].lower()
    try:
        lowercase         = {"e": encipher, "d": decipher}[option]
    except KeyError:
        raise KeyError("invalid input option"							)
    lowercase         = create_cipher_map(_UpperCamelCase							)
    print(func(_UpperCamelCase		,							_UpperCamelCase							)							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()











'''simple docstring'''

import datasets

from .evaluate import evaluate


__lowerCamelCase : Optional[Any]											=			"\\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"

__lowerCamelCase : Any											=			"\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"

__lowerCamelCase : Union[str, Any]											=			"\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       UpperCAmelCase (						datasets.Metric  ):




		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Union[str, Any]:
						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 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	List[Any]							,			A__      :	List[str]				)					-> str:
						lowercase         = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
						lowercase         = [
						    {
						        "paragraphs": [
						            {
						                "qas": [
						                    {
						                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
						                        "id": ref["id"],
						                    }
						                    for ref in references
						                ]
						            }
						        ]
						    }
						]
						lowercase         = evaluate(dataset=A__							,			predictions=A__				)
						return score




