






"""simple docstring"""
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      SCREAMING_SNAKE_CASE  (				__lowerCamelCase						):





	"""simple docstring"""




	_A       :				Dict    =				'char'
	_A       :				Union[str, Any]    =				'bpe'
	_A       :				str    =				'wp'






_lowerCAmelCase         =							(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)



class      SCREAMING_SNAKE_CASE  (				__lowerCamelCase						):





	"""simple docstring"""




	_A       :				Optional[int]    =				['image_processor', 'char_tokenizer']
	_A       :				int    =				'ViTImageProcessor'
	_A       :				Any    =				'MgpstrTokenizer'





	def __init__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):
		A_		:						Optional[Any]	    =   None
		if "feature_extractor" in kwargs:
			warnings.warn(
			    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
			    """ instead."""     ,     UpperCamelCase_     ,     )
			A_		:						Tuple	    =   kwargs.pop("""feature_extractor"""							)

		A_		:						List[Any]	    =   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_		:						Dict	    =   tokenizer
		A_		:						Tuple	    =   AutoTokenizer.from_pretrained("""gpt2"""							)
		A_		:						List[Any]	    =   AutoTokenizer.from_pretrained("""bert-base-uncased"""							)

		super().__init__(UpperCamelCase_     ,     UpperCamelCase_							)





	def __call__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):
		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_		:						List[Any]	    =   self.image_processor(UpperCamelCase_     ,     return_tensors=UpperCamelCase_     ,     **UpperCamelCase_							)
		if text is not None:
			A_		:						List[Any]	    =   self.char_tokenizer(UpperCamelCase_     ,     return_tensors=UpperCamelCase_     ,     **UpperCamelCase_							)

		if text is None:
			return inputs
		elif images is None:
			return encodings
		else:
			A_		:						Union[str, Any]	    =   encodings["""input_ids"""]
			return inputs





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_    ,     A_    ,     A_		:						Tuple	    =   sequences
		A_		:						int	    =   char_preds.size(0							)

		A_    ,     A_		:						Dict	    =   self._decode_helper(UpperCamelCase_     ,     """char"""							)
		A_    ,     A_		:						List[str]	    =   self._decode_helper(UpperCamelCase_     ,     """bpe"""							)
		A_    ,     A_		:						str	    =   self._decode_helper(UpperCamelCase_     ,     """wp"""							)

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

		A_		:						Union[str, Any]	    =   {}
		A_		:						Union[str, Any]	    =   final_strs
		A_		:						List[str]	    =   final_scores
		A_		:						List[str]	    =   char_strs
		A_		:						Optional[Any]	    =   bpe_strs
		A_		:						str	    =   wp_strs
		return out





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		if format == DecodeType.CHARACTER:
			A_		:						List[str]	    =   self.char_decode
			A_		:						Optional[int]	    =   1
			A_		:						Optional[int]	    =   """[s]"""
		elif format == DecodeType.BPE:
			A_		:						List[Any]	    =   self.bpe_decode
			A_		:						Union[str, Any]	    =   2
			A_		:						List[Any]	    =   """#"""
		elif format == DecodeType.WORDPIECE:
			A_		:						Optional[int]	    =   self.wp_decode
			A_		:						List[str]	    =   102
			A_		:						List[str]	    =   """[SEP]"""
		else:
			raise ValueError(f"""Format {format} is not supported."""							)

		A_    ,     A_		:						Optional[int]	    =   [], []
		A_		:						Dict	    =   pred_logits.size(0							)
		A_		:						int	    =   pred_logits.size(1							)
		A_    ,     A_		:						Tuple	    =   pred_logits.topk(1     ,     dim=-1     ,     largest=UpperCamelCase_     ,     sorted=UpperCamelCase_							)
		A_		:						Optional[Any]	    =   preds_index.view(-1     ,     UpperCamelCase_							)[:, 1:]
		A_		:						Optional[int]	    =   decoder(UpperCamelCase_							)
		A_    ,     A_		:						Optional[Any]	    =   torch.nn.functional.softmax(UpperCamelCase_     ,     dim=2							).max(dim=2							)
		A_		:						List[str]	    =   preds_max_prob[:, 1:]

		for index in range(UpperCamelCase_							):
			A_		:						Tuple	    =   preds_str[index].find(UpperCamelCase_							)
			A_		:						Optional[int]	    =   preds_str[index][:pred_eos]
			A_		:						List[str]	    =   preds_index[index].cpu().tolist()
			A_		:						Any	    =   pred_index.index(UpperCamelCase_							) if eos_token in pred_index else -1
			A_		:						Optional[Any]	    =   preds_max_prob[index][: pred_eos_index + 1]
			A_		:						Dict	    =   pred_max_prob.cumprod(dim=0							)[-1] if pred_max_prob.nelement() != 0 else 0.0
			dec_strs.append(UpperCamelCase_							)
			conf_scores.append(UpperCamelCase_							)

		return dec_strs, conf_scores





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						List[str]	    =   [seq.replace(""" """     ,     """"""							) for seq in self.char_tokenizer.batch_decode(UpperCamelCase_							)]
		return decode_strs





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		return self.bpe_tokenizer.batch_decode(UpperCamelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						Tuple	    =   [seq.replace(""" """     ,     """"""							) for seq in self.wp_tokenizer.batch_decode(UpperCamelCase_							)]
		return decode_strs









"""simple docstring"""
import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
							print("Googling.....")
							_lowerCAmelCase         =							"https://www.google.com/search?q=" + " ".join(sys.argv[1:])
							_lowerCAmelCase         =							requests.get(url, headers={"UserAgent": UserAgent().random})
							# res.raise_for_status()
							with open("project1a.html", "wb") as out_file:  # only for knowing the class
														for data in res.iter_content(10_000):
																					out_file.write(data)
							_lowerCAmelCase         =							BeautifulSoup(res.text, "html.parser")
							_lowerCAmelCase         =							list(soup.select(".eZt8xd"))[:5]

							print(len(links))
							for link in links:
														if link.text == "Maps":
																					webbrowser.open(link.get("href"))
														else:
																					webbrowser.open(F'https://google.com{link.get("href")}')

