

from __future__ import annotations

from typing import Any


class 			A						:







    """simple docstring"""





    def __init__(				self			:	int,lowercase_			:	int,lowercase_			:	int,lowercase_			:	float = 0		)->   None:





        '''simple docstring'''







        A__				,						A__   				=       row, column
        A__   				=       [[default_value for c in range(lowercase_		)] for r in range(lowercase_		)]


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





        '''simple docstring'''







        A__   				=       F'Matrix consist of {self.row} rows and {self.column} columns\n'

        # Make string identifier
        A__   				=       0
        for row_vector in self.array:
            for obj in row_vector:
                A__   				=       max(lowercase_,len(str(lowercase_		)		)		)
        A__   				=       F'%{max_element_length}s'

        # Make string and return
        def single_line(lowercase_			:	list[float]		) -> str:
            nonlocal string_format_identifier
            A__   				=       '['
            line += ", ".join(string_format_identifier % (obj,) for obj in row_vector		)
            line += "]"
            return line

        s += "\n".join(single_line(lowercase_		) for row_vector in self.array		)
        return s


    def __repr__(				self			:	Any		)->   str:





        '''simple docstring'''







        return str(self		)


    def 						snake_case__					(				self			:	Optional[int],lowercase_			:	tuple[int, int]		)->   bool:





        '''simple docstring'''







        if not (isinstance(lowercase_,(list, tuple)		) and len(lowercase_		) == 2):
            return False
        elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
            return False
        else:
            return True


    def __getitem__(				self			:	int,lowercase_			:	tuple[int, int]		)->   Any:





        '''simple docstring'''







        assert self.validate_indicies(lowercase_		)
        return self.array[loc[0]][loc[1]]


    def __setitem__(				self			:	int,lowercase_			:	tuple[int, int],lowercase_			:	float		)->   None:





        '''simple docstring'''







        assert self.validate_indicies(lowercase_		)
        A__   				=       value


    def __add__(				self			:	Optional[Any],lowercase_			:	Matrix		)->   Matrix:





        '''simple docstring'''







        assert isinstance(lowercase_,lowercase_		)
        assert self.row == another.row and self.column == another.column

        # Add
        A__   				=       Matrix(self.row,self.column		)
        for r in range(self.row		):
            for c in range(self.column		):
                A__   				=       self[r, c] + another[r, c]
        return result


    def __neg__(				self			:	Dict		)->   Matrix:





        '''simple docstring'''







        A__   				=       Matrix(self.row,self.column		)
        for r in range(self.row		):
            for c in range(self.column		):
                A__   				=       -self[r, c]
        return result


    def __sub__(				self			:	str,lowercase_			:	Matrix		)->   Matrix:





        '''simple docstring'''







        return self + (-another)


    def __mul__(				self			:	List[Any],lowercase_			:	int | float | Matrix		)->   Matrix:





        '''simple docstring'''







        if isinstance(lowercase_,(int, float)		):  # Scalar multiplication
            A__   				=       Matrix(self.row,self.column		)
            for r in range(self.row		):
                for c in range(self.column		):
                    A__   				=       self[r, c] * another
            return result
        elif isinstance(lowercase_,lowercase_		):  # Matrix multiplication
            assert self.column == another.row
            A__   				=       Matrix(self.row,another.column		)
            for r in range(self.row		):
                for c in range(another.column		):
                    for i in range(self.column		):
                        result[r, c] += self[r, i] * another[i, c]
            return result
        else:
            A__   				=       F'Unsupported type given for another ({type(lowercase_		)})'
            raise TypeError(lowercase_		)


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





        '''simple docstring'''







        A__   				=       Matrix(self.column,self.row		)
        for r in range(self.row		):
            for c in range(self.column		):
                A__   				=       self[r, c]
        return result


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





        '''simple docstring'''







        assert isinstance(lowercase_,lowercase_		) and isinstance(lowercase_,lowercase_		)
        assert self.row == self.column == u.row == v.row  # u, v should be column vector
        assert u.column == v.column == 1  # u, v should be column vector

        # Calculate
        A__   				=       v.transpose()
        A__   				=       (v_t * self * u)[0, 0] + 1
        if numerator_factor == 0:
            return None  # It's not invertable
        return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":
     def 						_snake_case(   )							->				None:
         A__   				=       Matrix(3   ,      3   ,      0				)
         for i in range(3				):
             A__   				=       1
         print(f'a^(-1) is {ainv}'				)
         # u, v
         A__   				=       Matrix(3   ,      1   ,      0				)
         A__				,						A__				,						A__   				=       1, 2, -3
         A__   				=       Matrix(3   ,      1   ,      0				)
         A__				,						A__				,						A__   				=       4, -2, 5
         print(f'u is {u}'				)
         print(f'v is {v}'				)
         print(f'uv^T is {u * v.transpose()}'				)
         # Sherman Morrison
         print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)}'				)
     def 						_snake_case(   )							->				None:
         import doctest

         doctest.testmod()

     testa()




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

