





from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    {"""UserAgent""": UserAgent().random}






def    _A	(   lowerCamelCase ):
   a__						:    Optional[Any]   		=	script.contents[0]
   a__						:    str   		=	json.loads(data[data.find("{\"config\"" ) : -1] )
   return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]







class 	__lowerCAmelCase      :





   def __init__(			self       ,      snake_case   )					->	str:
      """simple docstring"""





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





   def 						_snake_case			(			self   )					->	dict:
      """simple docstring"""





      a__						:    Optional[Any]   		=	requests.get(self.url       ,      headers=snake_case   ).text
      a__						:    Optional[int]   		=	BeautifulSoup(snake_case       ,      "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   )					->	str:
      """simple docstring"""





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





   def __str__(			self   )					->	str:
      """simple docstring"""





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





   @property
   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      return self.user_data["username"]





   @property
   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      return self.user_data["full_name"]





   @property
   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      return self.user_data["biography"]





   @property
   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      return self.user_data["business_email"]





   @property
   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      return self.user_data["external_url"]





   @property
   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





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





   @property
   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





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





   @property
   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





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





   @property
   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      return self.user_data["profile_pic_url_hd"]





   @property
   def 						_snake_case			(			self   )					->	bool:
      """simple docstring"""





      return self.user_data["is_verified"]





   @property
   def 						_snake_case			(			self   )					->	bool:
      """simple docstring"""





      return self.user_data["is_private"]







def    _A	(   lowerCamelCase = "github" ):
   import os

   if os.environ.get("CI" ):
      return  # test failing on GitHub Actions
   a__						:    int   		=	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 > 12_0000
   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()
  SCREAMING_SNAKE_CASE__							:      Tuple         =    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 = }')








import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel







class 	__lowerCAmelCase      (       pl.LightningModule	):





   def __init__(			self       ,      snake_case   )					->	Dict:
      """simple docstring"""





      super().__init__()
      a__						:    int   		=	model
      a__						:    Any   		=	2
      a__						:    List[str]   		=	nn.Linear(self.model.config.hidden_size       ,      self.num_labels   )





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      pass







def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # load longformer model from model identifier
   a__						:    List[str]   		=	LongformerModel.from_pretrained(lowerCamelCase )
   a__						:    Dict   		=	LightningModel(lowerCamelCase )

   a__						:    str   		=	torch.load(lowerCamelCase				,			map_location=torch.device("cpu" ) )
   lightning_model.load_state_dict(ckpt["state_dict"] )

   # init longformer question answering model
   a__						:    Dict   		=	LongformerForQuestionAnswering.from_pretrained(lowerCamelCase )

   # transfer weights
   longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
   longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
   longformer_for_qa.eval()

   # save model
   longformer_for_qa.save_pretrained(lowerCamelCase )

   print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}""" )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Any         =    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--longformer_model""",
      default=None,
      type=str,
      required=True,
      help="""model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.""",
  )
  parser.add_argument(
      """--longformer_question_answering_ckpt_path""",
      default=None,
      type=str,
      required=True,
      help="""Path the official PyTorch Lightning Checkpoint.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

