


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE      :			Tuple      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			str      				=   {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'xlm-roberta-xl'
    def __init__(	self      , __snake_case=2_5_0_8_8_0      , __snake_case=2_5_6_0      , __snake_case=3_6      , __snake_case=3_2      , __snake_case=1_0_2_4_0      , __snake_case="gelu"      , __snake_case=0.1      , __snake_case=0.1      , __snake_case=5_1_4      , __snake_case=1      , __snake_case=0.02      , __snake_case=1E-05      , __snake_case=1      , __snake_case=0      , __snake_case=2      , __snake_case="absolute"      , __snake_case=True      , __snake_case=None      , **__snake_case      , ):
         super().__init__(pad_token_id=__snake_case      , bos_token_id=__snake_case      , eos_token_id=__snake_case      , **__snake_case   )
         snake_case					=    vocab_size
         snake_case					=    hidden_size
         snake_case					=    num_hidden_layers
         snake_case					=    num_attention_heads
         snake_case					=    hidden_act
         snake_case					=    intermediate_size
         snake_case					=    hidden_dropout_prob
         snake_case					=    attention_probs_dropout_prob
         snake_case					=    max_position_embeddings
         snake_case					=    type_vocab_size
         snake_case					=    initializer_range
         snake_case					=    layer_norm_eps
         snake_case					=    position_embedding_type
         snake_case					=    use_cache
         snake_case					=    classifier_dropout






class   A__ (   snake_case__     ):







    """simple docstring"""

    @property
    def 		a_      (	self   ):
         if self.task == "multiple-choice":
              snake_case					=    {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
              snake_case					=    {0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ]   )








from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

_SCREAMING_SNAKE_CASE      :			str      				=   "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def       UpperCAmelCase__						(UpperCamelCase_ = "mumbai"    ):



     """simple docstring"""






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




