





"""simple docstring"""






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 lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					@slow
					@require_torch
					def 			lowerCAmelCase_					(						self			:				int		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"   , "prajjwal1/bert-tiny"		)
									SCREAMING_SNAKE_CASE__		  =				BertTokenizer.from_pretrained("bert-base-uncased"		)

									SCREAMING_SNAKE_CASE__		  =				bertabert.config.encoder.vocab_size
									SCREAMING_SNAKE_CASE__		  =				tokenizer.sep_token_id
									SCREAMING_SNAKE_CASE__		  =				tokenizer.cls_token_id
									SCREAMING_SNAKE_CASE__		  =				128

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

									SCREAMING_SNAKE_CASE__		  =				train_dataset.select(range(32		)		)
									SCREAMING_SNAKE_CASE__		  =				val_dataset.select(range(16		)		)

									SCREAMING_SNAKE_CASE__		  =				4

									def _map_to_encoder_decoder_inputs(_snake_case			:				Tuple		):
													# Tokenizer will automatically set [BOS] <text> [EOS]
													SCREAMING_SNAKE_CASE__		  =				tokenizer(batch["article"]   , padding="max_length"   , truncation=_snake_case   , max_length=512		)
													SCREAMING_SNAKE_CASE__		  =				tokenizer(batch["highlights"]   , padding="max_length"   , truncation=_snake_case   , max_length=128		)
													SCREAMING_SNAKE_CASE__		  =				inputs.input_ids
													SCREAMING_SNAKE_CASE__		  =				inputs.attention_mask

													SCREAMING_SNAKE_CASE__		  =				outputs.input_ids
													SCREAMING_SNAKE_CASE__		  =				outputs.input_ids.copy()
													SCREAMING_SNAKE_CASE__		  =				[
													    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
													]
													SCREAMING_SNAKE_CASE__		  =				outputs.attention_mask

													assert all(len(_snake_case		) == 512 for x in inputs.input_ids		)
													assert all(len(_snake_case		) == 128 for x in outputs.input_ids		)

													return batch

									def _compute_metrics(_snake_case			:				List[Any]		):
													SCREAMING_SNAKE_CASE__		  =				pred.label_ids
													SCREAMING_SNAKE_CASE__		  =				pred.predictions

													# all unnecessary tokens are removed
													SCREAMING_SNAKE_CASE__		  =				tokenizer.batch_decode(_snake_case   , skip_special_tokens=_snake_case		)
													SCREAMING_SNAKE_CASE__		  =				tokenizer.batch_decode(_snake_case   , skip_special_tokens=_snake_case		)

													SCREAMING_SNAKE_CASE__		  =				sum([int(pred_str[i] == label_str[i]		) for i in range(len(_snake_case		)		)]		) / len(_snake_case		)

													return {"accuracy": accuracy}

									# map train dataset
									SCREAMING_SNAKE_CASE__		  =				train_dataset.map(
									    _map_to_encoder_decoder_inputs   , batched=_snake_case   , batch_size=_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
									SCREAMING_SNAKE_CASE__		  =				val_dataset.map(
									    _map_to_encoder_decoder_inputs   , batched=_snake_case   , batch_size=_snake_case   , remove_columns=["article", "highlights"]   , )
									val_dataset.set_format(
									    type="torch"   , columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]   , )

									SCREAMING_SNAKE_CASE__		  =				self.get_auto_remove_tmp_dir()

									SCREAMING_SNAKE_CASE__		  =				SeqaSeqTrainingArguments(
									    output_dir=_snake_case   , per_device_train_batch_size=_snake_case   , per_device_eval_batch_size=_snake_case   , predict_with_generate=_snake_case   , evaluation_strategy="steps"   , do_train=_snake_case   , do_eval=_snake_case   , warmup_steps=0   , eval_steps=2   , logging_steps=2   , )

									# instantiate trainer
									SCREAMING_SNAKE_CASE__		  =				SeqaSeqTrainer(
									    model=_snake_case   , args=_snake_case   , compute_metrics=_compute_metrics   , train_dataset=_snake_case   , eval_dataset=_snake_case   , tokenizer=_snake_case   , )

									# start training
									trainer.train()





"""simple docstring"""






from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase = "mumbai"       )							->   Generator[tuple[str, str], None, None]:
				SCREAMING_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"}       ):
								SCREAMING_SNAKE_CASE__		  =				job.find("a"  ,					attrs={"data-tn-element": "jobTitle"}       ).text.strip()
								SCREAMING_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]}')




