

"""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 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		"char"
  _UpperCAmelCase          =		"bpe"
  _UpperCAmelCase          =		"wp"





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







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		["image_processor", "char_tokenizer"]
  _UpperCAmelCase          =		"ViTImageProcessor"
  _UpperCAmelCase          =		"MgpstrTokenizer"

  def __init__(		self    ,_A=None    ,_A=None    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    None
      if "feature_extractor" in kwargs:
          warnings.warn(
              'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
              ' instead.'    ,_A    ,)
          _lowerCAmelCase       :   Union[str, Any]  =    kwargs.pop('feature_extractor'      )

      _lowerCAmelCase       :   int  =    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`.'      )

      _lowerCAmelCase       :   List[Any]  =    tokenizer
      _lowerCAmelCase       :   Tuple  =    AutoTokenizer.from_pretrained('gpt2'      )
      _lowerCAmelCase       :   Union[str, Any]  =    AutoTokenizer.from_pretrained('bert-base-uncased'      )

      super().__init__(_A    ,_A      )

  def __call__(		self    ,_A=None    ,_A=None    ,_A=None    ,**_A      ):
      '''simple docstring'''

      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:
          _lowerCAmelCase       :   Union[str, Any]  =    self.image_processor(_A    ,return_tensors=_A    ,**_A      )
      if text is not None:
          _lowerCAmelCase       :   Tuple  =    self.char_tokenizer(_A    ,return_tensors=_A    ,**_A      )

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

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Union[str, Any]  =    sequences
      _lowerCAmelCase       :   List[Any]  =    char_preds.size(0      )

      _lowerCAmelCase,					_lowerCAmelCase       :   Union[str, Any]  =    self._decode_helper(_A    ,'char'      )
      _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    self._decode_helper(_A    ,'bpe'      )
      _lowerCAmelCase,					_lowerCAmelCase       :   int  =    self._decode_helper(_A    ,'wp'      )

      _lowerCAmelCase       :   Dict  =    []
      _lowerCAmelCase       :   int  =    []
      for i in range(_A      ):
          _lowerCAmelCase       :   str  =    [char_scores[i], bpe_scores[i], wp_scores[i]]
          _lowerCAmelCase       :   Optional[int]  =    [char_strs[i], bpe_strs[i], wp_strs[i]]
          _lowerCAmelCase       :   Optional[Any]  =    scores.index(max(_A      )      )
          final_strs.append(strs[max_score_index]      )
          final_scores.append(scores[max_score_index]      )

      _lowerCAmelCase       :   List[Any]  =    {}
      _lowerCAmelCase       :   Any  =    final_strs
      _lowerCAmelCase       :   Optional[Any]  =    final_scores
      _lowerCAmelCase       :   Optional[int]  =    char_strs
      _lowerCAmelCase       :   int  =    bpe_strs
      _lowerCAmelCase       :   Any  =    wp_strs
      return out

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      if format == DecodeType.CHARACTER:
          _lowerCAmelCase       :   int  =    self.char_decode
          _lowerCAmelCase       :   Any  =    1
          _lowerCAmelCase       :   int  =    '[s]'
      elif format == DecodeType.BPE:
          _lowerCAmelCase       :   Optional[Any]  =    self.bpe_decode
          _lowerCAmelCase       :   Optional[int]  =    2
          _lowerCAmelCase       :   Optional[Any]  =    '#'
      elif format == DecodeType.WORDPIECE:
          _lowerCAmelCase       :   str  =    self.wp_decode
          _lowerCAmelCase       :   Any  =    102
          _lowerCAmelCase       :   Any  =    '[SEP]'
      else:
          raise ValueError(F"""Format {format} is not supported."""      )

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

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

      return dec_strs, conf_scores

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    [seq.replace(' '    ,''      ) for seq in self.char_tokenizer.batch_decode(_A      )]
      return decode_strs

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return self.bpe_tokenizer.batch_decode(_A      )







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    [seq.replace(' '    ,''      ) for seq in self.wp_tokenizer.batch_decode(_A      )]
      return decode_strs








"""simple docstring"""






from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

_lowerCAmelCase						=		{"""UserAgent""": UserAgent().random}

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Any  =    script.contents[0]
    _lowerCAmelCase       :   Union[str, Any]  =    json.loads(data[data.find('{"config"'   ) : -1]   )
    return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]







class 	__UpperCamelCase    :

  def __init__(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    F"""https://www.instagram.com/{username}/"""
      _lowerCAmelCase       :   str  =    self.get_json()

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    requests.get(self.url    ,headers=_A      ).text
      _lowerCAmelCase       :   Optional[Any]  =    BeautifulSoup(_A    ,'html.parser'      ).find_all('script'      )
      try:
          return extract_user_profile(scripts[4]      )
      except (json.decoder.JSONDecodeError, KeyError):
          return extract_user_profile(scripts[3]      )

  def __repr__(		self      ):
      '''simple docstring'''

      return F"""{self.__class__.__name__}('{self.username}')"""

  def __str__(		self      ):
      '''simple docstring'''

      return F"""{self.fullname} ({self.username}) is {self.biography}"""

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["username"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["full_name"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["biography"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["business_email"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["external_url"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["edge_followed_by"]["count"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["edge_follow"]["count"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["edge_owner_to_timeline_media"]["count"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["profile_pic_url_hd"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["is_verified"]







  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["is_private"]





def 		lowerCamelCase__		(					_lowerCamelCase = "github"   ):

    '''simple docstring'''



    import os

    if os.environ.get('CI'   ):
        return  # test failing on GitHub Actions
    _lowerCAmelCase       :   Tuple  =    InstagramUser(_lowerCamelCase   )
    assert instagram_user.user_data
    assert isinstance(instagram_user.user_data ,   _lowerCamelCase   )
    assert instagram_user.username == username
    if username != "github":
        return
    assert instagram_user.fullname == "GitHub"
    assert instagram_user.biography == "Built for developers."
    assert instagram_user.number_of_posts > 150
    assert instagram_user.number_of_followers > 120000
    assert instagram_user.number_of_followings > 15
    assert instagram_user.email == "support@github.com"
    assert instagram_user.website == "https://github.com/readme"
    assert instagram_user.profile_picture_url.startswith('https://instagram.'   )
    assert instagram_user.is_verified is True
    assert instagram_user.is_private is False


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 _lowerCAmelCase						=		InstagramUser("""github""")
 print(instagram_user)
 print(F'''{instagram_user.number_of_posts = }''')
 print(F'''{instagram_user.number_of_followers = }''')
 print(F'''{instagram_user.number_of_followings = }''')
 print(F'''{instagram_user.email = }''')
 print(F'''{instagram_user.website = }''')
 print(F'''{instagram_user.profile_picture_url = }''')
 print(F'''{instagram_user.is_verified = }''')
 print(F'''{instagram_user.is_private = }''')





