






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
     import torch


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


UpperCAmelCase__				   =		logging.get_logger(__name__)
UpperCAmelCase__				   =		{"vocab_file": "spiece.model"}

UpperCAmelCase__				   =		{
    "vocab_file": {
        "AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
    }
}

UpperCAmelCase__				   =		{
    "AI-Sweden/gpt-sw3-126m": 2048,
    "AI-Sweden/gpt-sw3-350m": 2048,
    "AI-Sweden/gpt-sw3-1.6b": 2048,
    "AI-Sweden/gpt-sw3-6.7b": 2048,
    "AI-Sweden/gpt-sw3-20b": 2048,
}






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  VOCAB_FILES_NAMES
      __snake_case       =  PRETRAINED_VOCAB_FILES_MAP
      __snake_case       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __snake_case       =  ['''input_ids''', '''attention_mask''']







      def __init__(      self      :						List[Any] ,					__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						Optional[int]=False ,					__UpperCAmelCase      :						List[str]=False ,					__UpperCAmelCase      :						Dict=False ,					__UpperCAmelCase      :						Dict=None ,					__UpperCAmelCase      :						int=None ,					__UpperCAmelCase      :						str=None ,					__UpperCAmelCase      :						Union[str, Any]=None ,					__UpperCAmelCase      :						Optional[Dict[str, Any]] = None ,					**__UpperCAmelCase      :						List[Any] ,					)    ->None:




       """simple docstring"""


       a		  =      {} if sp_model_kwargs is None else sp_model_kwargs

       a		  =      kwargs.get('''name_or_path'''  )
       if name_or_path is None:
        logger.warning(
            '''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
            ''' you are testing the model, this can safely be ignored'''  )
        a		  =      '''None'''

       # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
       a		  =      '''<|endoftext|>''' if eos_token is None else eos_token
       a		  =      '''<unk>''' if unk_token is None else unk_token
       if "gpt-sw3-7b" in name_or_path:
        a		  =      unk_token if pad_token is None else pad_token
        a		  =      eos_token if bos_token is None else bos_token
       else:
        a		  =      '''<pad>''' if pad_token is None else pad_token
        a		  =      '''<s>''' if bos_token is None else bos_token

       super().__init__(
           do_lower_case=__UpperCAmelCase ,					remove_space=__UpperCAmelCase ,					keep_accents=__UpperCAmelCase ,					bos_token=__UpperCAmelCase ,					eos_token=__UpperCAmelCase ,					unk_token=__UpperCAmelCase ,					pad_token=__UpperCAmelCase ,					sp_model_kwargs=self.sp_model_kwargs ,					**__UpperCAmelCase ,					)

       a		  =      do_lower_case
       a		  =      remove_space
       a		  =      keep_accents
       a		  =      vocab_file

       a		  =      spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(__UpperCAmelCase  )

       # Used for whitespace normalization in input texts
       # fmt : off
       a		  =      {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
       # fmt : on

       # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
       a		  =      re.compile(
           F"""[{''.join(map(__UpperCAmelCase ,					list(range(0 ,					9  )  ) + list(range(11 ,					32  )  ) + list(range(127 ,					160  )  ) + [160, 173, 8_203]  )  )}]"""  )







      def __getstate__(      self      :						int  )    ->Any:




       """simple docstring"""


       a		  =      self.__dict__.copy()
       a		  =      None
       return state







      def __setstate__(      self      :						int ,					__UpperCAmelCase      :						List[Any]  )    ->Optional[int]:




       """simple docstring"""


       a		  =      d

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

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







      @property
      # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->int:




       """simple docstring"""


       return len(self.sp_model  )







      def       __lowerCAmelCase					(      self      :						List[Any] ,					__UpperCAmelCase      :						str  )    ->str:




       """simple docstring"""


       a		  =      self.non_printing_characters_re.sub('''''' ,					__UpperCAmelCase  )

       # Normalize whitespaces
       a		  =      ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]  )

       # NFC Unicode normalization
       a		  =      unicodedata.normalize('''NFC''' ,					__UpperCAmelCase  )
       return text







      def       __lowerCAmelCase					(      self      :						List[Any] ,					__UpperCAmelCase      :						str ,					**__UpperCAmelCase      :						Tuple  )    ->List[str]:




       """simple docstring"""


       a		  =      self.preprocess_text(__UpperCAmelCase  )
       return self.sp_model.encode(__UpperCAmelCase ,					out_type=__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						List[Any] ,					__UpperCAmelCase      :						str  )    ->int:




       """simple docstring"""


       return self.sp_model.PieceToId(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Any ,					__UpperCAmelCase      :						int  )    ->str:




       """simple docstring"""


       return self.sp_model.IdToPiece(__UpperCAmelCase  )







      @staticmethod
      def       __lowerCAmelCase					(      __UpperCAmelCase      :						str  )    ->str:




       """simple docstring"""


       return out_string







      def       __lowerCAmelCase					(      self      :						str ,					__UpperCAmelCase      :						List[str]  )    ->str:




       """simple docstring"""


       a		  =      []
       a		  =      ''''''
       a		  =      False
       for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
         # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
         if not prev_is_special:
          out_string += " "

         out_string += self.sp_model.decode(__UpperCAmelCase  ) + token
         a		  =      True
         a		  =      []
        else:
         current_sub_tokens.append(__UpperCAmelCase  )
         a		  =      False
       out_string += self.sp_model.decode(__UpperCAmelCase  )

       return out_string







      def       __lowerCAmelCase					(      self      :						List[str]  )    ->Dict[str, int]:




       """simple docstring"""


       a		  =      {self.convert_ids_to_tokens(__UpperCAmelCase  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab







      def       __lowerCAmelCase					(      self      :						Dict ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						Optional[str] = None  )    ->Tuple[str]:




       """simple docstring"""


       if not os.path.isdir(__UpperCAmelCase  ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
        return
       a		  =      os.path.join(
           __UpperCAmelCase ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

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

       return (out_vocab_file,)







      def       __lowerCAmelCase					(      self      :						Optional[int] ,					__UpperCAmelCase      :						Union[str, List[str]] ,					__UpperCAmelCase      :						Union[str, bool] = False  )    ->Union[List[int], List[List[int]], "torch.Tensor"]:




       """simple docstring"""


       if isinstance(__UpperCAmelCase ,					__UpperCAmelCase  ):
        a		  =      self.preprocess_text(__UpperCAmelCase  )
        a		  =      self.sp_model.encode(__UpperCAmelCase  )
       else:
        a		  =      [self.preprocess_text(__UpperCAmelCase  ) for t in text]
        a		  =      self.sp_model.encode(__UpperCAmelCase  )

       if return_tensors is True or return_tensors == "pt":
        a		  =      torch.tensor(__UpperCAmelCase  )

       return token_ids







      def       __lowerCAmelCase					(      self      :						Dict ,					__UpperCAmelCase      :						Union[int, List[int]]  )    ->str:




       """simple docstring"""


       return self.sp_model.decode(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						"Conversation"  )    ->List[int]:




       """simple docstring"""


       a		  =      [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
       a		  =      (
           F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(__UpperCAmelCase  ) + F"""{self.bos_token}Bot:"""
       )
       return self.encode(text=__UpperCAmelCase  )










from __future__ import annotations

UpperCAmelCase__				   =		"Muhammad Umer Farooq"
UpperCAmelCase__				   =		"MIT"
UpperCAmelCase__				   =		"1.0.0"
UpperCAmelCase__				   =		"Muhammad Umer Farooq"
UpperCAmelCase__				   =		"contact@muhammadumerfarooq.me"
UpperCAmelCase__				   =		"Alpha"

import re
from html.parser import HTMLParser
from urllib import parse

import requests






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def __init__(      self      :						Union[str, Any] ,					__UpperCAmelCase      :						str  )    ->None:




       """simple docstring"""


       super().__init__()
       a		  =      []
       a		  =      domain







      def       __lowerCAmelCase					(      self      :						List[str] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						list[tuple[str, str | None]]  )    ->None:




       """simple docstring"""


       if tag == "a":
        # Check the list of defined attributes.
        for name, value in attrs:
         # If href is defined, and not empty nor # print it.
         if name == "href" and value != "#" and value != "":
          # If not already in urls.
          if value not in self.urls:
           a		  =      parse.urljoin(self.domain ,					__UpperCAmelCase  )
           self.urls.append(__UpperCAmelCase  )
def 						_a    (      a :str )							->			str:
 return ".".join(get_sub_domain_name(a ).split('''.''' )[-2:] )
def 						_a    (      a :str )							->			str:
 return parse.urlparse(a ).netloc
def 						_a    (      a :str = "https://github.com" )							->			list[str]:
 a		  =      get_domain_name(a )

 # Initialize the parser
 a		  =      Parser(a )

 try:
  # Open URL
  a		  =      requests.get(a )

  # pass the raw HTML to the parser to get links
  parser.feed(r.text )

  # Get links and loop through
  a		  =      set()
  for link in parser.urls:
   # open URL.
   # read = requests.get(link)
   try:
    a		  =      requests.get(a )
    # Get the valid email.
    a		  =      re.findall('''[a-zA-Z0-9]+@''' + domain	,							read.text )
    # If not in list then append it.
    for email in emails:
     valid_emails.add(a )
   except ValueError:
    pass
 except ValueError:
  raise SystemExit(1 )

 # Finally return a sorted list of email addresses with no duplicates.
 return sorted(a )


if __name__ == "__main__":
     UpperCAmelCase__				   =		emails_from_url("https://github.com")
     print(f"""{len(emails)} emails found:""")
     print("\n".join(sorted(emails)))


