



import requests
from bsa import BeautifulSoup





def 							A			(  _UpperCAmelCase       :     str = "AAPL"					)       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     F"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
			_UpperCAmelCase	 =     BeautifulSoup(requests.get(_UpperCAmelCase					).text							,     'html.parser'					)
			_UpperCAmelCase	 =     '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}""")





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()





def 							A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Optional[int]					)       ->				int:







			'''simple docstring'''
			# Initialise PyTorch model
			_UpperCAmelCase	 =     TaConfig.from_json_file(_UpperCAmelCase					)
			print(F"Building PyTorch model from configuration: {config}"					)
			_UpperCAmelCase	 =     TaForConditionalGeneration(_UpperCAmelCase					)

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)

			# Save pytorch-model
			print(F"Save PyTorch model to {pytorch_dump_path}"					)
			model.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
							UpperCAmelCase__     = argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
							)
							parser.add_argument(
							    "--config_file",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
							    ),
							)
							parser.add_argument(
							    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCAmelCase__     = parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
