





import requests
from bsa import BeautifulSoup







def       UpperCAmelCase							(     _lowerCamelCase      :  List[Any] = "AAPL"     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				List[str]   =					f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					BeautifulSoup(requests.get(_lowerCamelCase     ).text			,   "html.parser"     )
						SCREAMING_SNAKE_CASE__     :				str   =					"""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}")






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase       :str       =  {
    "configuration_upernet": ["UperNetConfig"],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase       :Union[str, Any]       =  [
				    "UperNetForSemanticSegmentation",
				    "UperNetPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_upernet import UperNetConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
				import sys

				__lowercase       :str       =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)