


'''simple docstring'''






from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403





def lowerCamelCase__      (						__lowerCamelCase	:							str	,   __lowerCamelCase	:							complex	,   __lowerCamelCase	:							str = "x"	,   __lowerCamelCase	:							float = 1_0**-1_0	,   __lowerCamelCase	:							int = 1	,   ):







   '''simple docstring'''




   _UpperCAmelCase       :       Optional[int]							   =symbols(UpperCamelCase__							)
   _UpperCAmelCase       :       str							   =lambdify(UpperCamelCase__	,   UpperCamelCase__							)
   _UpperCAmelCase       :       Tuple							   =lambdify(UpperCamelCase__	,   diff(UpperCamelCase__	,   UpperCamelCase__							)							)

   _UpperCAmelCase       :       str							   =starting_point

   while True:
      if diff_function(UpperCamelCase__							) != 0:
         _UpperCAmelCase       :       Dict							   =prev_guess - multiplicity * func(UpperCamelCase__							) / diff_function(
             UpperCamelCase__							)
      else:
         raise ZeroDivisionError('Could not find root'							) from None

      # Precision is checked by comparing the difference of consecutive guesses
      if abs(next_guess - prev_guess							) < precision:
         return next_guess

      _UpperCAmelCase       :       Optional[int]							   =next_guess


# Let's Execute
if __name__ == "__main__":
  # Find root of trigonometric function
  # Find value of pi
  print(F"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")

  # Find root of polynomial
  # Find fourth Root of 5
  print(F"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}""")

  # Find value of e
  print(
      'The root of log(y) - 1 = 0 is ',
      F"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
  )

  # Exponential Roots
  print(
      'The root of exp(x) - 1 = 0 is',
      F"""{newton_raphson('exp(x) - 1', 10, precision=0.005)}""",
  )

  # Find root of cos(x)
  print(F"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")





'''simple docstring'''






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  __magic_name__							(		datasets.Metric    ):






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




          '''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       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   ={prediction['id']: prediction['prediction_text'] for prediction in predictions}
          _UpperCAmelCase       :       Optional[int]							   =[
              {
                  'paragraphs': [
                      {
                          'qas': [
                              {
                                  'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
                                  'id': ref['id'],
                              }
                              for ref in references
                          ]
                      }
                  ]
              }
          ]
          _UpperCAmelCase       :       List[Any]							   =evaluate(dataset=snake_case  ,							predictions=snake_case)
          return score

