




'''simple docstring'''

import datasets

from .evaluate import evaluate


a    :							int										=    '\\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'

a    :							str										=    '\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'

a    :							int										=    '\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	):


				def     A_					(						self       :   Tuple   ):
									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     A_					(						self       :   Dict			,							lowercase_       :   List[Any]			,							lowercase_       :   Union[str, Any]   ):
									snake_case_		     =							{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
									snake_case_		     =							[
									    {
									        '''paragraphs''': [
									            {
									                '''qas''': [
									                    {
									                        '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
									                        '''id''': ref['''id'''],
									                    }
									                    for ref in references
									                ]
									            }
									        ]
									    }
									]
									snake_case_		     =							evaluate(dataset=lowercase_			,							predictions=lowercase_   )
									return score







'''simple docstring'''

import operator as op


def 						__magic_name__       (      __UpperCAmelCase      )						->   Dict:

					'''simple docstring'''

					snake_case_		     =							[]
					snake_case_		     =							lambda __UpperCAmelCase, __UpperCAmelCase      : int(x / y      )  # noqa: E731 integer division operation
					snake_case_		     =							{
					    '''^''': op.pow,
					    '''*''': op.mul,
					    '''/''': div,
					    '''+''': op.add,
					    '''-''': op.sub,
					}  # operators & their respective operation

					# print table header
					print('''Symbol'''.center(8      ), '''Action'''.center(12      ), '''Stack''', sep=''' | '''      )
					print('''-''' * (30 + len(__UpperCAmelCase      ))      )

					for x in post_fix:
										if x.isdigit():  # if x in digit
															stack.append(__UpperCAmelCase      )  # append x to stack
															# output in tabular format
															print(x.rjust(8      ), ('''push(''' + x + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )
										else:
															snake_case_		     =							stack.pop()  # pop stack
															# output in tabular format
															print(''''''.rjust(8      ), ('''pop(''' + b + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )

															snake_case_		     =							stack.pop()  # pop stack
															# output in tabular format
															print(''''''.rjust(8      ), ('''pop(''' + a + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | '''      )

															stack.append(
															    str(opr[x](int(__UpperCAmelCase      ), int(__UpperCAmelCase      )      )      )      )  # evaluate the 2 values popped from stack & push result to stack
															# output in tabular format
															print(
															    x.rjust(8      ), ('''push(''' + a + x + b + ''')''').ljust(12      ), ''','''.join(__UpperCAmelCase      ), sep=''' | ''', )

					return int(stack[0]      )


if __name__ == "__main__":
							a    :							Any										=    input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
							print('\n\tResult = ', solve(Postfix))

