"""simple docstring"""


import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCAmelCase_:					Union[str, Any]     =					version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
 from nltk import word_tokenize


lowerCAmelCase_:					Any     =					"\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

lowerCAmelCase_:					Optional[int]     =					"\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

lowerCAmelCase_:					List[Any]     =					"\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,					_KWARGS_DESCRIPTION	)
class    a__    (   datasets.Metric	):


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return datasets.MetricInfo(
            description=_DESCRIPTION,				citation=_CITATION,				inputs_description=_KWARGS_DESCRIPTION,				features=datasets.Features(
                {
                    "predictions": datasets.Value("string",				id="sequence"  ),
                    "references": datasets.Value("string",				id="sequence"  ),
                }  ),				codebase_urls=["https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"],				reference_urls=[
                "https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score",
                "https://en.wikipedia.org/wiki/METEOR",
            ],				)


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        import nltk

        nltk.download("wordnet"  )
        if NLTK_VERSION >= version.Version("3.6.5"  ):
             nltk.download("punkt"  )
        if NLTK_VERSION >= version.Version("3.6.6"  ):
             nltk.download("omw-1.4"  )


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase=0.9,				_UpperCAmelCase=3,				_UpperCAmelCase=0.5  ):
        '''simple docstring'''



        if NLTK_VERSION >= version.Version("3.6.5"  ):
             lowercase__				 =							[
                 meteor_score.single_meteor_score(
                     word_tokenize(_UpperCAmelCase  ),				word_tokenize(_UpperCAmelCase  ),				alpha=_UpperCAmelCase,				beta=_UpperCAmelCase,				gamma=_UpperCAmelCase  )
                 for ref, pred in zip(_UpperCAmelCase,				_UpperCAmelCase  )
             ]
        else:
             lowercase__				 =							[
                 meteor_score.single_meteor_score(_UpperCAmelCase,				_UpperCAmelCase,				alpha=_UpperCAmelCase,				beta=_UpperCAmelCase,				gamma=_UpperCAmelCase  )
                 for ref, pred in zip(_UpperCAmelCase,				_UpperCAmelCase  )
             ]

        return {"meteor": np.mean(_UpperCAmelCase  )}





"""simple docstring"""


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







def 					__a   (		A    ,				A    ,				A = "x"    ,				A = 10**-10    ,				A = 1    ,				):






     '''simple docstring'''



     lowercase__				 =							symbols(A      )
     lowercase__				 =							lambdify(A    ,				A      )
     lowercase__				 =							lambdify(A    ,				diff(A    ,				A      )      )

     lowercase__				 =							starting_point

     while True:
          if diff_function(A      ) != 0:
               lowercase__				 =							prev_guess - multiplicity * func(A      ) / diff_function(
                   A      )
          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

          lowercase__				 =							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", 1_0, precision=0.005)}',
 )

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




