


import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


__UpperCamelCase       :       Optional[int]						       =				version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
 from nltk import word_tokenize


__UpperCamelCase       :       List[str]						       =				"\\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"

__UpperCamelCase       :       List[Any]						       =				"\\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"

__UpperCamelCase       :       Union[str, 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       __magic_name__	(     datasets.Metric):


     def      UpperCAmelCase__	(      self							:     int			)  ->      str:





      '''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							:     Dict							,	lowerCamelCase__							:     List[Any]			)  ->      Dict:





      '''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							:     Union[str, Any]							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     Dict							,	lowerCamelCase__							:     Any=0.9							,	lowerCamelCase__							:     Tuple=3							,	lowerCamelCase__							:     Optional[int]=0.5			)  ->      Any:





      '''simple docstring'''






      if NLTK_VERSION >= version.Version('''3.6.5'''			):
       UpperCamelCase__					:    List[str]          =   [
           meteor_score.single_meteor_score(
               word_tokenize(lowerCAmelCase__			)							,	word_tokenize(lowerCAmelCase__			)							,	alpha=lowerCAmelCase__							,	beta=lowerCAmelCase__							,	gamma=lowerCAmelCase__			)
           for ref, pred in zip(lowerCAmelCase__							,	lowerCAmelCase__			)
       ]
      else:
       UpperCamelCase__					:    Union[str, Any]          =   [
           meteor_score.single_meteor_score(lowerCAmelCase__							,	lowerCAmelCase__							,	alpha=lowerCAmelCase__							,	beta=lowerCAmelCase__							,	gamma=lowerCAmelCase__			)
           for ref, pred in zip(lowerCAmelCase__							,	lowerCAmelCase__			)
       ]

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






from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

__UpperCamelCase       :       int						       =				"https://www.indeed.co.in/jobs?q=mobile+app+development&l="

def 			_a  (				SCREAMING_SNAKE_CASE	:				str = "mumbai"     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[int]          =   BeautifulSoup(requests.get(url + location     ).content    , '''html.parser'''     )
 # This attribute finds out all the specifics listed in a job
 for job in soup.find_all('''div'''    , attrs={'''data-tn-component''': '''organicJob'''}     ):
  UpperCamelCase__					:    Optional[Any]          =   job.find('''a'''    , attrs={'''data-tn-element''': '''jobTitle'''}     ).text.strip()
  UpperCamelCase__					:    Union[str, Any]          =   job.find('''span'''    , {'''class''': '''company'''}     ).text.strip()
  yield job_title, company_name


if __name__ == "__main__":
       for i, job in enumerate(fetch_jobs("Bangalore"), 1):
              print(f"Job {i:>2} is {job[0]} at {job[1]}")


