

from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput






@dataclass
class 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						None
 UpperCAmelCase							    =						None






class 						__magic_name__    (       _snake_case    ):


 def __init__(						self		: int			,						lowerCAmelCase__		: Dict=1			,						lowerCAmelCase__		: Optional[int]=0			,						lowerCAmelCase__		: str=2			,						lowerCAmelCase__		: List[Any]=5_1_2			,						lowerCAmelCase__		: Dict="cls"			,						lowerCAmelCase__		: Tuple=False			,						lowerCAmelCase__		: List[Any]=True			,						**lowerCAmelCase__		: int			,						)    ->      Optional[Any]:
   super().__init__(pad_token_id=lowerCAmelCase__			,						bos_token_id=lowerCAmelCase__			,						eos_token_id=lowerCAmelCase__			,						**lowerCAmelCase__    )
   UpperCAmelCase					      =  project_dim
   UpperCAmelCase					      =  pooler_fn
   UpperCAmelCase					      =  learn_encoder
   UpperCAmelCase					      =  use_attention_mask






class 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						[r"""pooler""", r"""logit_scale"""]
 UpperCAmelCase							    =						[r"""position_ids""", r"""predictions.decoder.bias"""]
 UpperCAmelCase							    =						"""roberta"""
 UpperCAmelCase							    =						RobertaSeriesConfig


 def __init__(						self		: Tuple			,						lowerCAmelCase__		: Tuple    )    ->      Union[str, Any]:
   super().__init__(lowerCAmelCase__    )
   UpperCAmelCase					      =  XLMRobertaModel(lowerCAmelCase__    )
   UpperCAmelCase					      =  nn.Linear(config.hidden_size			,						config.project_dim    )
   UpperCAmelCase					      =  getattr(lowerCAmelCase__			,						"has_pre_transformation"			,						lowerCAmelCase__    )
   if self.has_pre_transformation:
     UpperCAmelCase					      =  nn.Linear(config.hidden_size			,						config.project_dim    )
     UpperCAmelCase					      =  nn.LayerNorm(config.hidden_size			,						eps=config.layer_norm_eps    )
   self.post_init()


 def _UpperCamelCase	(						self		: str			,						lowerCAmelCase__		: Optional[torch.Tensor] = None			,						lowerCAmelCase__		: Optional[torch.Tensor] = None			,						lowerCAmelCase__		: Optional[torch.Tensor] = None			,						lowerCAmelCase__		: Optional[torch.Tensor] = None			,						lowerCAmelCase__		: Optional[torch.Tensor] = None			,						lowerCAmelCase__		: Optional[torch.Tensor] = None			,						lowerCAmelCase__		: Optional[torch.Tensor] = None			,						lowerCAmelCase__		: Optional[torch.Tensor] = None			,						lowerCAmelCase__		: Optional[bool] = None			,						lowerCAmelCase__		: Optional[bool] = None			,						lowerCAmelCase__		: Optional[bool] = None			,						)    ->      Union[str, Any]:
   UpperCAmelCase					      =  return_dict if return_dict is not None else self.config.use_return_dict

   UpperCAmelCase					      =  self.base_model(
       input_ids=lowerCAmelCase__			,						attention_mask=lowerCAmelCase__			,						token_type_ids=lowerCAmelCase__			,						position_ids=lowerCAmelCase__			,						head_mask=lowerCAmelCase__			,						inputs_embeds=lowerCAmelCase__			,						encoder_hidden_states=lowerCAmelCase__			,						encoder_attention_mask=lowerCAmelCase__			,						output_attentions=lowerCAmelCase__			,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states			,						return_dict=lowerCAmelCase__			,						)

   if self.has_pre_transformation:
     UpperCAmelCase					      =  outputs["hidden_states"][-2]
     UpperCAmelCase					      =  self.pre_LN(lowerCAmelCase__    )
     UpperCAmelCase					      =  self.transformation_pre(lowerCAmelCase__    )

     return TransformationModelOutput(
         projection_state=lowerCAmelCase__			,						last_hidden_state=outputs.last_hidden_state			,						hidden_states=outputs.hidden_states			,						attentions=outputs.attentions			,						)
   else:
     UpperCAmelCase					      =  self.transformation(outputs.last_hidden_state    )
     return TransformationModelOutput(
         projection_state=lowerCAmelCase__			,						last_hidden_state=outputs.last_hidden_state			,						hidden_states=outputs.hidden_states			,						attentions=outputs.attentions			,						)






from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowerCAmelCase__       = {"UserAgent": UserAgent().random}
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  script.contents[0]
  UpperCAmelCase					      =  json.loads(data[data.find("{\"config\""			) : -1]			)
  return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]






class 						__magic_name__    :


 def __init__(						self		: Optional[Any]			,						lowerCAmelCase__		: Optional[int]    )    ->      Any:
   UpperCAmelCase					      =  f"https://www.instagram.com/{username}/"
   UpperCAmelCase					      =  self.get_json()


 def _UpperCamelCase	(						self		: List[str]    )    ->      dict:
   UpperCAmelCase					      =  requests.get(self.url			,						headers=lowerCAmelCase__    ).text
   UpperCAmelCase					      =  BeautifulSoup(lowerCAmelCase__			,						"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		: Tuple    )    ->      str:
   return f"{self.__class__.__name__}('{self.username}')"


 def __str__(						self		: Optional[int]    )    ->      str:
   return f"{self.fullname} ({self.username}) is {self.biography}"


 @property
 def _UpperCamelCase	(						self		: Any    )    ->      str:
   return self.user_data["username"]


 @property
 def _UpperCamelCase	(						self		: List[Any]    )    ->      str:
   return self.user_data["full_name"]


 @property
 def _UpperCamelCase	(						self		: List[str]    )    ->      str:
   return self.user_data["biography"]


 @property
 def _UpperCamelCase	(						self		: Optional[int]    )    ->      str:
   return self.user_data["business_email"]


 @property
 def _UpperCamelCase	(						self		: str    )    ->      str:
   return self.user_data["external_url"]


 @property
 def _UpperCamelCase	(						self		: int    )    ->      int:
   return self.user_data["edge_followed_by"]["count"]


 @property
 def _UpperCamelCase	(						self		: List[Any]    )    ->      int:
   return self.user_data["edge_follow"]["count"]


 @property
 def _UpperCamelCase	(						self		: List[str]    )    ->      int:
   return self.user_data["edge_owner_to_timeline_media"]["count"]


 @property
 def _UpperCamelCase	(						self		: Tuple    )    ->      str:
   return self.user_data["profile_pic_url_hd"]


 @property
 def _UpperCamelCase	(						self		: Optional[int]    )    ->      bool:
   return self.user_data["is_verified"]


 @property
 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      bool:
   return self.user_data["is_private"]

def      _lowerCAmelCase(	__A = "github"			):
  import os

  if os.environ.get("CI"			):
    return  # test failing on GitHub Actions
  UpperCAmelCase					      =  InstagramUser(__A			)
  assert instagram_user.user_data
  assert isinstance(instagram_user.user_data   ,       __A			)
  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 = }")



