






"""simple docstring"""
import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCamelCase_       =		version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
  from nltk import word_tokenize


lowerCamelCase_       =		"\\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_       =		"\\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_       =		"\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  _SCREAMING_SNAKE_CASE(       datasets.Metric  ):




       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """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    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Union[str, Any]:





             """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    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=0.9		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=0.5							)      ->				int:





             """simple docstring"""






             if NLTK_VERSION >= version.Version('''3.6.5'''							):
                   __SCREAMING_SNAKE_CASE       :Dict		    =  [
                       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:
                   __SCREAMING_SNAKE_CASE       :Any		    =  [
                       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"""
import colorsys

from PIL import Image  # type: ignore



def 					__lowerCamelCase       (							a_					:		float			,       a_					:		float			,       a_					:		int							)      ->				float:
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  x
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  y
      for step in range(a_							):  # noqa: B007
            __SCREAMING_SNAKE_CASE       :Dict		    =  a * a - b * b + x
            __SCREAMING_SNAKE_CASE       :Tuple		    =  2 * a * b + y
            __SCREAMING_SNAKE_CASE       :Dict		    =  a_new

            # divergence happens for all complex number with an absolute value
            # greater than 4
            if a * a + b * b > 4:
                  break
      return step / (max_step - 1)



def 					__lowerCamelCase       (							a_					:		float							)      ->				tuple:
      if distance == 1:
            return (0, 0, 0)
      else:
            return (2_55, 2_55, 2_55)



def 					__lowerCamelCase       (							a_					:		float							)      ->				tuple:
      if distance == 1:
            return (0, 0, 0)
      else:
            return tuple(round(i * 2_55							) for i in colorsys.hsv_to_rgb(a_			,       1			,       1							)							)



def 					__lowerCamelCase       (							a_					:		int = 8_00			,       a_					:		int = 6_00			,       a_					:		float = -0.6			,       a_					:		float = 0			,       a_					:		float = 3.2			,       a_					:		int = 50			,       a_					:		bool = True			,       )      ->				Image.Image:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  Image.new('''RGB'''			,       (image_width, image_height)							)
      __SCREAMING_SNAKE_CASE       :Tuple		    =  img.load()

      # loop through the image-coordinates
      for image_x in range(a_							):
            for image_y in range(a_							):
                  # determine the figure-coordinates based on the image-coordinates
                  __SCREAMING_SNAKE_CASE       :Dict		    =  figure_width / image_width * image_height
                  __SCREAMING_SNAKE_CASE       :str		    =  figure_center_x + (image_x / image_width - 0.5) * figure_width
                  __SCREAMING_SNAKE_CASE       :Tuple		    =  figure_center_y + (image_y / image_height - 0.5) * figure_height

                  __SCREAMING_SNAKE_CASE       :List[Any]		    =  get_distance(a_			,       a_			,       a_							)

                  # color the corresponding pixel based on the selected coloring-function
                  if use_distance_color_coding:
                        __SCREAMING_SNAKE_CASE       :Optional[int]		    =  get_color_coded_rgb(a_							)
                  else:
                        __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  get_black_and_white_rgb(a_							)

      return img


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # colored version, full figure
  lowerCamelCase_       =		get_image()

  # uncomment for colored version, different section, zoomed in
  # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
  # figure_width = 0.8)

  # uncomment for black and white version, full figure
  # img = get_image(use_distance_color_coding = False)

  # uncomment to save the image
  # img.save("mandelbrot.png")

  img.show()