



import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
   import torch


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: Optional[int] =  "char"
 a__					: str =  "bpe"
 a__					: Union[str, Any] =  "wp"
UpperCAmelCase_		 =					(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: str =  ["image_processor", "char_tokenizer"]
 a__					: Optional[int] =  "ViTImageProcessor"
 a__					: Optional[Any] =  "MgpstrTokenizer"

 def __init__(    self					:					Any			,						__lowerCAmelCase					:					str=None			,						__lowerCAmelCase					:					Optional[int]=None			,						**__lowerCAmelCase					:					Any      )   ->	Optional[int]:
    _A      =       None
    if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.'''			,						__lowerCAmelCase			,						)
       _A      =       kwargs.pop('''feature_extractor'''      )

    _A      =       image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
       raise ValueError('''You need to specify an `image_processor`.'''      )
    if tokenizer is None:
       raise ValueError('''You need to specify a `tokenizer`.'''      )

    _A      =       tokenizer
    _A      =       AutoTokenizer.from_pretrained('''gpt2'''      )
    _A      =       AutoTokenizer.from_pretrained('''bert-base-uncased'''      )

    super().__init__(__lowerCAmelCase			,						__lowerCAmelCase      )

 def __call__(    self					:					List[Any]			,						__lowerCAmelCase					:					Tuple=None			,						__lowerCAmelCase					:					str=None			,						__lowerCAmelCase					:					Union[str, Any]=None			,						**__lowerCAmelCase					:					str      )   ->	Optional[int]:
    if images is None and text is None:
       raise ValueError('''You need to specify either an `images` or `text` input to process.'''      )

    if images is not None:
       _A      =       self.image_processor(__lowerCAmelCase			,						return_tensors=__lowerCAmelCase			,						**__lowerCAmelCase      )
    if text is not None:
       _A      =       self.char_tokenizer(__lowerCAmelCase			,						return_tensors=__lowerCAmelCase			,						**__lowerCAmelCase      )

    if text is None:
       return inputs
    elif images is None:
       return encodings
    else:
       _A      =       encodings['''input_ids''']
       return inputs

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Tuple      )   ->	Union[str, Any]:
    _A  ,    _A  ,    _A      =       sequences
    _A      =       char_preds.size(0      )

    _A  ,    _A      =       self._decode_helper(__lowerCAmelCase			,						'''char'''      )
    _A  ,    _A      =       self._decode_helper(__lowerCAmelCase			,						'''bpe'''      )
    _A  ,    _A      =       self._decode_helper(__lowerCAmelCase			,						'''wp'''      )

    _A      =       []
    _A      =       []
    for i in range(__lowerCAmelCase      ):
       _A      =       [char_scores[i], bpe_scores[i], wp_scores[i]]
       _A      =       [char_strs[i], bpe_strs[i], wp_strs[i]]
       _A      =       scores.index(max(__lowerCAmelCase      )      )
       final_strs.append(strs[max_score_index]      )
       final_scores.append(scores[max_score_index]      )

    _A      =       {}
    _A      =       final_strs
    _A      =       final_scores
    _A      =       char_strs
    _A      =       bpe_strs
    _A      =       wp_strs
    return out

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					Optional[Any]      )   ->	List[str]:
    if format == DecodeType.CHARACTER:
       _A      =       self.char_decode
       _A      =       1
       _A      =       '''[s]'''
    elif format == DecodeType.BPE:
       _A      =       self.bpe_decode
       _A      =       2
       _A      =       '''#'''
    elif format == DecodeType.WORDPIECE:
       _A      =       self.wp_decode
       _A      =       1_02
       _A      =       '''[SEP]'''
    else:
       raise ValueError(f'''Format {format} is not supported.'''      )

    _A  ,    _A      =       [], []
    _A      =       pred_logits.size(0      )
    _A      =       pred_logits.size(1      )
    _A  ,    _A      =       pred_logits.topk(1			,						dim=-1			,						largest=__lowerCAmelCase			,						sorted=__lowerCAmelCase      )
    _A      =       preds_index.view(-1			,						__lowerCAmelCase      )[:, 1:]
    _A      =       decoder(__lowerCAmelCase      )
    _A  ,    _A      =       torch.nn.functional.softmax(__lowerCAmelCase			,						dim=2      ).max(dim=2      )
    _A      =       preds_max_prob[:, 1:]

    for index in range(__lowerCAmelCase      ):
       _A      =       preds_str[index].find(__lowerCAmelCase      )
       _A      =       preds_str[index][:pred_eos]
       _A      =       preds_index[index].cpu().tolist()
       _A      =       pred_index.index(__lowerCAmelCase      ) if eos_token in pred_index else -1
       _A      =       preds_max_prob[index][: pred_eos_index + 1]
       _A      =       pred_max_prob.cumprod(dim=0      )[-1] if pred_max_prob.nelement() != 0 else 0.0
       dec_strs.append(__lowerCAmelCase      )
       conf_scores.append(__lowerCAmelCase      )

    return dec_strs, conf_scores

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					Tuple      )   ->	Dict:
    _A      =       [seq.replace(''' '''			,						''''''      ) for seq in self.char_tokenizer.batch_decode(__lowerCAmelCase      )]
    return decode_strs

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					List[str]      )   ->	Union[str, Any]:
    return self.bpe_tokenizer.batch_decode(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					str      )   ->	List[Any]:
    _A      =       [seq.replace(''' '''			,						''''''      ) for seq in self.wp_tokenizer.batch_decode(__lowerCAmelCase      )]
    return decode_strs








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}')



