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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool

class       __magic_name__	(     __lowerCAmelCase):
     A:		Any			=      "facebook/bart-large-mnli"
     A:		Dict			=      (
         "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
         "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
         "It returns the most likely label in the list of provided `labels` for the input text."
     )
     A:		int			=      "text_classifier"
     A:		int			=      AutoTokenizer
     A:		Optional[int]			=      AutoModelForSequenceClassification

     A:		Any			=      ["text", ["text"]]
     A:		Any			=      ["text"]


     def      UpperCAmelCase__	(      self							:     str			)  ->      Tuple:





      '''simple docstring'''






      super().setup()
      UpperCamelCase__					:    Union[str, Any]          =   self.model.config
      UpperCamelCase__					:    Any          =   -1
      for idx, label in config.idalabel.items():
       if label.lower().startswith('''entail'''			):
        UpperCamelCase__					:    Optional[Any]          =   int(lowerCamelCase__			)
      if self.entailment_id == -1:
       raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.'''			)


     def      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     str			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   labels
      return self.pre_processor(
          [text] * len(lowerCamelCase__			)							,	[F"This example is {label}" for label in labels]							,	return_tensors='''pt'''							,	padding='''max_length'''							,	)


     def      UpperCAmelCase__	(      self							:     List[Any]							,	lowerCamelCase__							:     int			)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   outputs.logits
      UpperCamelCase__					:    Optional[int]          =   torch.argmax(logits[:, 2]			).item()
      return self._labels[label_id]






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]}")


