
'''simple docstring'''


import datasets

from .evaluate import evaluate


__lowercase							:      Optional[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'''

__lowercase							:      Optional[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'''

__lowercase							:      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 		__lowercase (     datasets.Metric			):

			def 				UpperCAmelCase__       (self					):
								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	,      A	,      A					):
								lowerCamelCase_    :					List[Any]   =						{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
								lowerCamelCase_    :					Any   =						[
								    {
								        '''paragraphs''': [
								            {
								                '''qas''': [
								                    {
								                        '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
								                        '''id''': ref['''id'''],
								                    }
								                    for ref in references
								                ]
								            }
								        ]
								    }
								]
								lowerCamelCase_    :					Dict   =						evaluate(dataset=_lowerCamelCase	,      predictions=_lowerCamelCase					)
								return score










"""simple docstring"""


from collections import defaultdict
from math import ceil, sqrt



def 					lowerCAmelCase				(    __UpperCamelCase = 1_0_0_0_0_0_0		,       __UpperCamelCase = 1_0 ):




  """simple docstring"""







  __A									= defaultdict(__UpperCamelCase )

  for outer_width in range(3		,       (t_limit // 4) + 2 ):
    if outer_width * outer_width > t_limit:
      __A									= max(
          ceil(sqrt(outer_width * outer_width - t_limit ) )		,       1 )
    else:
      __A									= 1

    hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

    for hole_width in range(__UpperCamelCase		,       outer_width - 1		,       2 ):
      count[outer_width * outer_width - hole_width * hole_width] += 1

  return sum(1 for n in count.values() if 1 <= n <= 1_0 )


if __name__ == "__main__":
       print(F'''{solution() = }''')




