






'''simple docstring'''




import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


lowerCAmelCase__          =						logging.get_logger(__name__)







class       __lowercase					:

	def __init__(      self   :						Any  ,					UpperCAmelCase_   :						Optional[Any]  ,					UpperCAmelCase_   :						int):
			UpperCamelCase__				:					str					=					question_encoder
			UpperCamelCase__				:					Optional[int]					=					generator
			UpperCamelCase__				:					Optional[int]					=					self.question_encoder

	def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						Any):
			if os.path.isfile(UpperCAmelCase_):
					raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file')
			os.makedirs(UpperCAmelCase_  ,					exist_ok=UpperCAmelCase_)
			UpperCamelCase__				:					int					=					os.path.join(UpperCAmelCase_  ,					'question_encoder_tokenizer')
			UpperCamelCase__				:					Any					=					os.path.join(UpperCAmelCase_  ,					'generator_tokenizer')
			self.question_encoder.save_pretrained(UpperCAmelCase_)
			self.generator.save_pretrained(UpperCAmelCase_)

	@classmethod
	def      __UpperCamelCase							(      cls   :						str  ,					UpperCAmelCase_   :						Any  ,					**UpperCAmelCase_   :						List[Any]):
			# dynamically import AutoTokenizer
			from ..auto.tokenization_auto import AutoTokenizer

			UpperCamelCase__				:					Tuple					=					kwargs.pop('config'  ,					UpperCAmelCase_)

			if config is None:
					UpperCamelCase__				:					int					=					RagConfig.from_pretrained(UpperCAmelCase_)

			UpperCamelCase__				:					int					=					AutoTokenizer.from_pretrained(
			    UpperCAmelCase_  ,					config=config.question_encoder  ,					subfolder='question_encoder_tokenizer')
			UpperCamelCase__				:					Dict					=					AutoTokenizer.from_pretrained(
			    UpperCAmelCase_  ,					config=config.generator  ,					subfolder='generator_tokenizer')
			return cls(question_encoder=UpperCAmelCase_  ,					generator=UpperCAmelCase_)

	def __call__(      self   :						str  ,					*UpperCAmelCase_   :						Optional[Any]  ,					**UpperCAmelCase_   :						Any):
			return self.current_tokenizer(*UpperCAmelCase_  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						str  ,					*UpperCAmelCase_   :						List[Any]  ,					**UpperCAmelCase_   :						str):
			return self.generator.batch_decode(*UpperCAmelCase_  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Tuple  ,					*UpperCAmelCase_   :						Optional[Any]  ,					**UpperCAmelCase_   :						Dict):
			return self.generator.decode(*UpperCAmelCase_  ,					**UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					List[Any]					=					self.question_encoder

	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					str					=					self.generator





	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						List[str]  ,					UpperCAmelCase_   :						Optional[List[str]] = None  ,					UpperCAmelCase_   :						Optional[int] = None  ,					UpperCAmelCase_   :						Optional[int] = None  ,					UpperCAmelCase_   :						str = "longest"  ,					UpperCAmelCase_   :						str = None  ,					UpperCAmelCase_   :						bool = True  ,					**UpperCAmelCase_   :						Tuple  ,					):
			warnings.warn(
			    '`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
			    'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
			    'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
			    'details'  ,					UpperCAmelCase_  ,					)
			if max_length is None:
					UpperCamelCase__				:					int					=					self.current_tokenizer.model_max_length
			UpperCamelCase__				:					List[Any]					=					self(
			    UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_  ,					return_tensors=UpperCAmelCase_  ,					max_length=UpperCAmelCase_  ,					padding=UpperCAmelCase_  ,					truncation=UpperCAmelCase_  ,					**UpperCAmelCase_  ,					)
			if tgt_texts is None:
					return model_inputs
			# Process tgt_texts
			if max_target_length is None:
					UpperCamelCase__				:					List[Any]					=					self.current_tokenizer.model_max_length
			UpperCamelCase__				:					Tuple					=					self(
			    text_target=UpperCAmelCase_  ,					add_special_tokens=UpperCAmelCase_  ,					return_tensors=UpperCAmelCase_  ,					padding=UpperCAmelCase_  ,					max_length=UpperCAmelCase_  ,					truncation=UpperCAmelCase_  ,					**UpperCAmelCase_  ,					)
			UpperCamelCase__				:					List[str]					=					labels['input_ids']
			return model_inputs












'''simple docstring'''




import requests
from bsa import BeautifulSoup







def       __UpperCAmelCase						(  lowerCamelCase_ = "AAPL")							->     str:
		UpperCamelCase__				:					str					=					f'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
		UpperCamelCase__				:					Optional[Any]					=					BeautifulSoup(requests.get(lowerCamelCase_).text     ,      'html.parser')
		UpperCamelCase__				:					Union[str, Any]					=					'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}''')




