






from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
     import datasets
class      UpperCamelCase (				_UpperCamelCase ):







  """simple docstring"""



  @slow
  @require_torch
  def       A(  self		:       Any		)    ->   Union[str, Any]:


      '''simple docstring'''
      A	 =      EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'						,'prajjwal1/bert-tiny'		)
      A	 =      BertTokenizer.from_pretrained('bert-base-uncased'		)

      A	 =      bertabert.config.encoder.vocab_size
      A	 =      tokenizer.sep_token_id
      A	 =      tokenizer.cls_token_id
      A	 =      1_2_8

      A	 =      datasets.load_dataset('cnn_dailymail'						,'3.0.0'						,split='train[:1%]'		)
      A	 =      datasets.load_dataset('cnn_dailymail'						,'3.0.0'						,split='validation[:1%]'		)

      A	 =      train_dataset.select(range(3_2		)		)
      A	 =      val_dataset.select(range(1_6		)		)

      A	 =      4

      def _map_to_encoder_decoder_inputs(_SCREAMING_SNAKE_CASE		:       Optional[Any]		):
          # Tokenizer will automatically set [BOS] <text> [EOS]
          A	 =      tokenizer(batch['article']						,padding='max_length'						,truncation=_SCREAMING_SNAKE_CASE						,max_length=5_1_2		)
          A	 =      tokenizer(batch['highlights']						,padding='max_length'						,truncation=_SCREAMING_SNAKE_CASE						,max_length=1_2_8		)
          A	 =      inputs.input_ids
          A	 =      inputs.attention_mask

          A	 =      outputs.input_ids
          A	 =      outputs.input_ids.copy()
          A	 =      [
              [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
          ]
          A	 =      outputs.attention_mask

          assert all(len(_SCREAMING_SNAKE_CASE		) == 5_1_2 for x in inputs.input_ids		)
          assert all(len(_SCREAMING_SNAKE_CASE		) == 1_2_8 for x in outputs.input_ids		)

          return batch

      def _compute_metrics(_SCREAMING_SNAKE_CASE		:       List[str]		):
          A	 =      pred.label_ids
          A	 =      pred.predictions

          # all unnecessary tokens are removed
          A	 =      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE						,skip_special_tokens=_SCREAMING_SNAKE_CASE		)
          A	 =      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE						,skip_special_tokens=_SCREAMING_SNAKE_CASE		)

          A	 =      sum([int(pred_str[i] == label_str[i]		) for i in range(len(_SCREAMING_SNAKE_CASE		)		)]		) / len(_SCREAMING_SNAKE_CASE		)

          return {"accuracy": accuracy}

      # map train dataset
      A	 =      train_dataset.map(
          _map_to_encoder_decoder_inputs						,batched=_SCREAMING_SNAKE_CASE						,batch_size=_SCREAMING_SNAKE_CASE						,remove_columns=['article', 'highlights']						,)
      train_dataset.set_format(
          type='torch'						,columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']						,)

      # same for validation dataset
      A	 =      val_dataset.map(
          _map_to_encoder_decoder_inputs						,batched=_SCREAMING_SNAKE_CASE						,batch_size=_SCREAMING_SNAKE_CASE						,remove_columns=['article', 'highlights']						,)
      val_dataset.set_format(
          type='torch'						,columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']						,)

      A	 =      self.get_auto_remove_tmp_dir()

      A	 =      SeqaSeqTrainingArguments(
          output_dir=_SCREAMING_SNAKE_CASE						,per_device_train_batch_size=_SCREAMING_SNAKE_CASE						,per_device_eval_batch_size=_SCREAMING_SNAKE_CASE						,predict_with_generate=_SCREAMING_SNAKE_CASE						,evaluation_strategy='steps'						,do_train=_SCREAMING_SNAKE_CASE						,do_eval=_SCREAMING_SNAKE_CASE						,warmup_steps=0						,eval_steps=2						,logging_steps=2						,)

      # instantiate trainer
      A	 =      SeqaSeqTrainer(
          model=_SCREAMING_SNAKE_CASE						,args=_SCREAMING_SNAKE_CASE						,compute_metrics=_compute_metrics						,train_dataset=_SCREAMING_SNAKE_CASE						,eval_dataset=_SCREAMING_SNAKE_CASE						,tokenizer=_SCREAMING_SNAKE_CASE						,)

      # start training
      trainer.train()













from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

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







def      snake_case		(							UpperCAmelCase	:       str = "mumbai"   ):
				A	 =      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'}   ):
								A	 =      job.find('a',						attrs={'data-tn-element': 'jobTitle'}   ).text.strip()
								A	 =      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]}''')





