



import faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


a_						:     Dict	     =		'\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n  booktitle = {NeurIPS},\n  year      = {2021}\n}\n\n'

a_						:     Any	     =		'\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'

a_						:     int	     =		'\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n    predictions: list of generated text to score. Each predictions\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.\nOptional Args:\n    num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n    kmeans_max_iter: maximum number of k-means iterations. Default 500\n    featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].\n    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n    max_text_length: maximum number of tokens to consider. Default 1024\n    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n    mauve_scaling_factor: "c" from the paper. Default 5.\n    verbose: If True (default), print running time updates\n    seed: random seed to initialize k-means cluster assignments.\nReturns:\n    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n    q_hist: same as above, but with q_text.\nExamples:\n\n    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n    >>> import datasets\n    >>> mauve = datasets.load_metric(\'mauve\')\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n    >>> print(out.mauve) # doctest: +SKIP\n    1.0\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,    _KWARGS_DESCRIPTION)
class    lowerCamelCase__  (						datasets.Metric):
					"""simple docstring"""



					def 						_a      (self    ):






											'''simple docstring'''



											return datasets.MetricInfo(
											    description=_DESCRIPTION				,			citation=_CITATION				,			homepage="https://github.com/krishnap25/mauve"				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
											        {
											            "predictions": datasets.Value("string"				,			id="sequence"    ),
											            "references": datasets.Value("string"				,			id="sequence"    ),
											        }    )				,			codebase_urls=["https://github.com/krishnap25/mauve"]				,			reference_urls=[
											        "https://arxiv.org/abs/2102.01454",
											        "https://github.com/krishnap25/mauve",
											    ]				,			)







					def 						_a      (self				,			__a				,			__a				,			__a=None				,			__a=None				,			__a=None				,			__a=None				,			__a="auto"				,			__a=-1				,			__a=0.9				,			__a=5				,			__a=5_00				,			__a="gpt2-large"				,			__a=-1				,			__a=10_24				,			__a=25				,			__a=5				,			__a=True				,			__a=25				,			):






											'''simple docstring'''



											lowerCamelCase								=		compute_mauve(
											    p_text=__a				,			q_text=__a				,			p_features=__a				,			q_features=__a				,			p_tokens=__a				,			q_tokens=__a				,			num_buckets=__a				,			pca_max_data=__a				,			kmeans_explained_var=__a				,			kmeans_num_redo=__a				,			kmeans_max_iter=__a				,			featurize_model_name=__a				,			device_id=__a				,			max_text_length=__a				,			divergence_curve_discretization_size=__a				,			mauve_scaling_factor=__a				,			verbose=__a				,			seed=__a				,			)
											return out




from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

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

def     __lowercase(	UpperCAmelCase__ = "mumbai" ):


      """simple docstring"""



      lowerCamelCase								=		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"} ):
            lowerCamelCase								=		job.find("a"   ,   attrs={"data-tn-element": "jobTitle"} ).text.strip()
            lowerCamelCase								=		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]}""")