





'''simple docstring'''

from __future__ import annotations

__lowerCamelCase : Optional[Any]											=			[]







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

    """simple docstring"""






    for i in range(len(_UpperCAmelCase							)							):
        if board[row][i] == 1:
            return False
    for i in range(len(_UpperCAmelCase							)							):
        if board[i][column] == 1:
            return False
    for i, j in zip(range(_UpperCAmelCase		,							-1		,							-1							)		,							range(_UpperCAmelCase		,							-1		,							-1							)							):
        if board[i][j] == 1:
            return False
    for i, j in zip(range(_UpperCAmelCase		,							-1		,							-1							)		,							range(_UpperCAmelCase		,							len(_UpperCAmelCase							)							)							):
        if board[i][j] == 1:
            return False
    return True







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

    """simple docstring"""






    if row >= len(_UpperCAmelCase							):
        solution.append(_UpperCAmelCase							)
        printboard(_UpperCAmelCase							)
        print()
        return True
    for i in range(len(_UpperCAmelCase							)							):
        if is_safe(_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase							):
            lowercase         = 1
            solve(_UpperCAmelCase		,							row + 1							)
            lowercase         = 0
    return False







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

    """simple docstring"""






    for i in range(len(_UpperCAmelCase							)							):
        for j in range(len(_UpperCAmelCase							)							):
            if board[i][j] == 1:
                print("Q"		,							end=" "							)
            else:
                print("."		,							end=" "							)
        print()


# n=int(input("The no. of queens"))
__lowerCamelCase : Optional[Any]											=			8
__lowerCamelCase : Tuple											=			[[0 for i in range(n)] for j in range(n)]
solve(board, 0)


print("The total no. of solutions are :", len(solution))











'''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




