

'''simple docstring'''






import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
 from ...tokenization_utils_base import TextInput
from ...utils import logging


lowerCAmelCase__   		=      logging.get_logger(__name__)

lowerCAmelCase__   		=      {'''vocab_file''': '''spiece.model'''}

lowerCAmelCase__   		=      {
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCAmelCase__   		=      {
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

lowerCAmelCase__   		=      '''▁'''






class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Optional[int]							     =     VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE       :    Optional[int]							     =     PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE       :    List[str]							     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE       :    List[Any]							     =     ['input_ids', 'attention_mask']






  def __init__( self  :	List[str]   ,lowercase__  :	Optional[int]   ,lowercase__  :	Union[str, Any]="</s>"   ,lowercase__  :	Union[str, Any]="<unk>"   ,lowercase__  :	int="<pad>"   ,lowercase__  :	Dict=1_0_0   ,lowercase__  :	Optional[int]=None   ,lowercase__  :	Optional[Dict[str, Any]] = None   ,lowercase__  :	Optional[Any]=True   ,**lowercase__  :	Optional[int]   ,):
   # Add extra_ids to the special token list
   if extra_ids > 0 and additional_special_tokens is None:
    __lowercase				    =					[F"<extra_id_{i}>" for i in range(lowercase__     )]
   elif extra_ids > 0 and additional_special_tokens is not None:
    # Check that we have the right number of extra_id special tokens
    __lowercase				    =					len(set(filter(lambda lowercase__     : bool('''extra_id''' in str(lowercase__     )     )   ,lowercase__     )     )     )
    if extra_tokens != extra_ids:
     raise ValueError(
         F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
         ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
         ''' tokens'''     )
   if legacy:
    logger.warning_once(
        F"You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"
        ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''     )

   __lowercase				    =					legacy
   __lowercase				    =					{} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       eos_token=lowercase__   ,unk_token=lowercase__   ,pad_token=lowercase__   ,extra_ids=lowercase__   ,additional_special_tokens=lowercase__   ,sp_model_kwargs=self.sp_model_kwargs   ,legacy=lowercase__   ,**lowercase__   ,)

   __lowercase				    =					vocab_file
   __lowercase				    =					extra_ids

   __lowercase				    =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.Load(lowercase__     )






  @staticmethod
  def    SCREAMING_SNAKE_CASE ( lowercase__  :	Optional[int]   ,lowercase__  :	List[str]   ,lowercase__  :	Optional[Any]     ):
   if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
    __lowercase				    =					TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
    if init_max_model_length is not None and init_max_model_length != max_model_length:
     return init_max_model_length
    elif init_max_model_length is None:
     warnings.warn(
         '''This tokenizer was incorrectly instantiated with a model max length of'''
         F" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
         ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
         ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
         F" {pretrained_model_name_or_path} automatically truncating your input to"
         F" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
         F" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
         ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
         ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''   ,lowercase__   ,)

   return max_model_length






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   return self.sp_model.get_piece_size() + self._extra_ids






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					{self.convert_ids_to_tokens(lowercase__     ): i for i in range(self.vocab_size     )}
   vocab.update(self.added_tokens_encoder     )
   return vocab






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	List[int]   ,lowercase__  :	Optional[List[int]] = None   ,lowercase__  :	bool = False     ):
   if already_has_special_tokens:
    return super().get_special_tokens_mask(
        token_ids_a=lowercase__   ,token_ids_a=lowercase__   ,already_has_special_tokens=lowercase__     )

   # normal case: some special tokens
   if token_ids_a is None:
    return ([0] * len(lowercase__     )) + [1]
   return ([0] * len(lowercase__     )) + [1] + ([0] * len(lowercase__     )) + [1]






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   return list(
       set(filter(lambda lowercase__     : bool(re.search(r'''<extra_id_\d+>'''   ,lowercase__     )     ) is not None   ,self.additional_special_tokens     )     )     )






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   return [self._convert_token_to_id(lowercase__     ) for token in self.get_sentinel_tokens()]






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]   ,lowercase__  :	List[int]     ):
   if len(lowercase__     ) > 0 and token_ids[-1] == self.eos_token_id:
    warnings.warn(
        F"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
        ''' eos tokens being added.'''     )
    return token_ids
   else:
    return token_ids + [self.eos_token_id]






  def    SCREAMING_SNAKE_CASE ( self  :	Any   ,lowercase__  :	List[int]   ,lowercase__  :	Optional[List[int]] = None     ):
   __lowercase				    =					[self.eos_token_id]

   if token_ids_a is None:
    return len(token_ids_a + eos     ) * [0]
   return len(token_ids_a + eos + token_ids_a + eos     ) * [0]






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]   ,lowercase__  :	List[int]   ,lowercase__  :	Optional[List[int]] = None     ):
   __lowercase				    =					self._add_eos_if_not_present(lowercase__     )
   if token_ids_a is None:
    return token_ids_a
   else:
    __lowercase				    =					self._add_eos_if_not_present(lowercase__     )
    return token_ids_a + token_ids_a






  def __getstate__( self  :	List[str]     ):
   __lowercase				    =					self.__dict__.copy()
   __lowercase				    =					None
   return state






  def __setstate__( self  :	List[Any]   ,lowercase__  :	List[Any]     ):
   __lowercase				    =					d

   # for backward compatibility
   if not hasattr(self   ,'''sp_model_kwargs'''     ):
    __lowercase				    =					{}

   __lowercase				    =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.Load(self.vocab_file     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	"TextInput"   ,**lowercase__  :	Any     ):
   # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
   # the beginning of the text
   if not self.legacy:
    __lowercase				    =					SPIECE_UNDERLINE + text.replace(lowercase__   ,''' '''     )
   return super().tokenize(lowercase__   ,**lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]   ,lowercase__  :	List[str]   ,**lowercase__  :	str     ):
   if not self.legacy:
    __lowercase				    =					text.startswith(lowercase__     )
    if is_first:
     __lowercase				    =					text[1:]

   __lowercase				    =					self.sp_model.encode(lowercase__   ,out_type=lowercase__     )

   if not self.legacy and not is_first and not text.startswith(''' '''     ) and tokens[0].startswith(lowercase__     ):
    __lowercase				    =					([tokens[0][1:]] if len(tokens[0]     ) > 1 else []) + tokens[1:]
   return tokens






  def    SCREAMING_SNAKE_CASE ( self  :	str   ,lowercase__  :	Tuple     ):
   if token.startswith('''<extra_id_'''     ):
    __lowercase				    =					re.match(r'''<extra_id_(\d+)>'''   ,lowercase__     )
    __lowercase				    =					int(match.group(1     )     )
    return self.vocab_size - num - 1
   return self.sp_model.piece_to_id(lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]   ,lowercase__  :	Any     ):
   if index < self.sp_model.get_piece_size():
    __lowercase				    =					self.sp_model.IdToPiece(lowercase__     )
   else:
    __lowercase				    =					F"<extra_id_{self.vocab_size - 1 - index}>"
   return token






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,lowercase__  :	Any     ):
   __lowercase				    =					[]
   __lowercase				    =					''''''
   __lowercase				    =					False
   for token in tokens:
    # make sure that special tokens are not decoded using sentencepiece model
    if token in self.all_special_tokens:
     if not prev_is_special:
      out_string += " "
     out_string += self.sp_model.decode(lowercase__     ) + token
     __lowercase				    =					True
     __lowercase				    =					[]
    else:
     current_sub_tokens.append(lowercase__     )
     __lowercase				    =					False
   out_string += self.sp_model.decode(lowercase__     )
   return out_string.strip()






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]   ,lowercase__  :	str   ,lowercase__  :	Optional[str] = None     ):
   if not os.path.isdir(lowercase__     ):
    logger.error(F"Vocabulary path ({save_directory}) should be a directory"     )
    return
   __lowercase				    =					os.path.join(
       lowercase__   ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

   if os.path.abspath(self.vocab_file     ) != os.path.abspath(lowercase__     ) and os.path.isfile(self.vocab_file     ):
    copyfile(self.vocab_file   ,lowercase__     )
   elif not os.path.isfile(self.vocab_file     ):
    with open(lowercase__   ,'''wb'''     ) as fi:
     __lowercase				    =					self.sp_model.serialized_model_proto()
     fi.write(lowercase__     )

   return (out_vocab_file,)



'''simple docstring'''

import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
     print("""Googling.....""")
     snake_case									=    """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
     snake_case									=    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(1_00_00):
               out_file.write(data)
     snake_case									=    BeautifulSoup(res.text, """html.parser""")
     snake_case									=    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")}')





