



import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer


class 	lowerCamelCase__	(       tf.keras.layers.Layer):



 """simple docstring"""

 def __init__(    self					:					List[str]			,						__lowerCAmelCase					:					Dict[str, int]			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					int = None			,						__lowerCAmelCase					:					int = None      )   ->	str:
    super().__init__()
    _A      =       pad_token_id
    _A      =       max_length
    _A      =       vocab
    _A      =       merges
    _A      =       BytePairTokenizer(__lowerCAmelCase			,						__lowerCAmelCase			,						sequence_length=__lowerCAmelCase      )

 @classmethod
 def     snake_case_      (    cls					:					str			,						__lowerCAmelCase					:					GPTaTokenizer			,						*__lowerCAmelCase					:					Union[str, Any]			,						**__lowerCAmelCase					:					Dict      )   ->	str:
    _A      =       [''' '''.join(__lowerCAmelCase      ) for m in tokenizer.bpe_ranks.keys()]
    _A      =       tokenizer.get_vocab()
    return cls(__lowerCAmelCase			,						__lowerCAmelCase			,						*__lowerCAmelCase			,						**__lowerCAmelCase      )

 @classmethod
 def     snake_case_      (    cls					:					Tuple			,						__lowerCAmelCase					:					Union[str, os.PathLike]			,						*__lowerCAmelCase					:					str			,						**__lowerCAmelCase					:					Any      )   ->	Any:
    _A      =       GPTaTokenizer.from_pretrained(__lowerCAmelCase			,						*__lowerCAmelCase			,						**__lowerCAmelCase      )
    return cls.from_tokenizer(__lowerCAmelCase			,						*__lowerCAmelCase			,						**__lowerCAmelCase      )

 @classmethod
 def     snake_case_      (    cls					:					Union[str, Any]			,						__lowerCAmelCase					:					Tuple      )   ->	Dict:
    return cls(**__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple      )   ->	Tuple:
    return {
        "vocab": self.vocab,
        "merges": self.merges,
        "max_length": self.max_length,
        "pad_token_id": self.pad_token_id,
    }

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					Dict			,						__lowerCAmelCase					:					int = None      )   ->	Any:
    _A      =       self.tf_tokenizer(__lowerCAmelCase      )
    _A      =       tf.ones_like(__lowerCAmelCase      )

    if self.pad_token_id is not None:
       # pad the tokens up to max length
       _A      =       max_length if max_length is not None else self.max_length

       if max_length is not None:
          _A  ,    _A      =       pad_model_inputs(
              __lowerCAmelCase			,						max_seq_length=__lowerCAmelCase			,						pad_value=self.pad_token_id      )

    return {"attention_mask": attention_mask, "input_ids": input_ids}








import requests
from bsa import BeautifulSoup

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str = "AAPL"				)  ->     str:
   _A      =       F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
   _A      =       BeautifulSoup(requests.get(_snake_case				).text	,   '''html.parser'''				)
   _A      =       '''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}')



