
"""simple docstring"""





import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_=1024  ,				UpperCamelCase_=1024  ,				UpperCamelCase_=False  ,				**UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = AutoTokenizer.from_pretrained(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = SeqaSeqDataset(UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				type_path="""train"""  ,				**UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = tok.pad_token_id

		def get_lens(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = tqdm(
				    DataLoader(UpperCamelCase_  ,				batch_size=512  ,				num_workers=8  ,				shuffle=UpperCamelCase_  ,				collate_fn=ds.collate_fn				)  ,				desc=str(ds.len_file				)  ,				)
				__SCREAMING_SNAKE_CASE            = []
				for batch in dl:
						__SCREAMING_SNAKE_CASE            = batch["""input_ids"""].ne(UpperCamelCase_				).sum(1				).tolist()
						__SCREAMING_SNAKE_CASE            = batch["""labels"""].ne(UpperCamelCase_				).sum(1				).tolist()
						if consider_target:
								for src, tgt in zip(UpperCamelCase_  ,				UpperCamelCase_				):
										max_lens.append(max(UpperCamelCase_  ,				UpperCamelCase_				)				)
						else:
								max_lens.extend(UpperCamelCase_				)
				return max_lens

		__SCREAMING_SNAKE_CASE            = get_lens(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = SeqaSeqDataset(UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				type_path="""val"""  ,				**UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = get_lens(UpperCamelCase_				)
		pickle_save(UpperCamelCase_  ,				train_ds.len_file				)
		pickle_save(UpperCamelCase_  ,				val_ds.len_file				)


if __name__ == "__main__":
							fire.Fire(save_len_file)






"""simple docstring"""





import requests
from bsa import BeautifulSoup



def 						_lowerCAmelCase				(				UpperCamelCase_ = "AAPL"				):
		__SCREAMING_SNAKE_CASE            = f"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
		__SCREAMING_SNAKE_CASE            = BeautifulSoup(requests.get(UpperCamelCase_				).text  ,				"""html.parser"""				)
		__SCREAMING_SNAKE_CASE            = """My(6px) Pos(r) smartphone_Mt(6px)"""
		return soup.find("""div"""  ,				class_=class_				).find("""span"""				).text


if __name__ == "__main__":
							for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
														print(F"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")




