

'''simple docstring'''
import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowerCamelCase			:				Dict 			=					data_utils.TransfoXLTokenizer
lowerCamelCase			:				Dict 			=					data_utils.TransfoXLCorpus
lowerCamelCase			:				List[Any] 			=					data_utils
lowerCamelCase			:				Any 			=					data_utils


def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					Dict							,			_UpperCamelCase						:					Tuple							,			_UpperCamelCase						:					Optional[Any]    )				->      List[Any]:
     """simple docstring"""




     if transfo_xl_dataset_file:
          # Convert a pre-processed corpus (see original TensorFlow repo)
          with open(_UpperCamelCase							,			'rb'    ) as fp:
               _SCREAMING_SNAKE_CASE							   =pickle.load(_UpperCamelCase							,			encoding='latin1'    )
          # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
          _SCREAMING_SNAKE_CASE							   =pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
          print(f"Save vocabulary to {pytorch_vocab_dump_path}"    )
          _SCREAMING_SNAKE_CASE							   =corpus.vocab.__dict__
          torch.save(_UpperCamelCase							,			_UpperCamelCase    )

          _SCREAMING_SNAKE_CASE							   =corpus.__dict__
          corpus_dict_no_vocab.pop('vocab'							,			_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   =pytorch_dump_folder_path + '/' + CORPUS_NAME
          print(f"Save dataset to {pytorch_dataset_dump_path}"    )
          torch.save(_UpperCamelCase							,			_UpperCamelCase    )

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          _SCREAMING_SNAKE_CASE							   =os.path.abspath(_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   =os.path.abspath(_UpperCamelCase    )

          print(f"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."    )
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               _SCREAMING_SNAKE_CASE							   =TransfoXLConfig()
          else:
               _SCREAMING_SNAKE_CASE							   =TransfoXLConfig.from_json_file(_UpperCamelCase    )
          print(f"Building PyTorch model from configuration: {config}"    )
          _SCREAMING_SNAKE_CASE							   =TransfoXLLMHeadModel(_UpperCamelCase    )

          _SCREAMING_SNAKE_CASE							   =load_tf_weights_in_transfo_xl(_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase    )
          # Save pytorch-model
          _SCREAMING_SNAKE_CASE							   =os.path.join(_UpperCamelCase							,			_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   =os.path.join(_UpperCamelCase							,			_UpperCamelCase    )
          print(f"Save PyTorch model to {os.path.abspath(_UpperCamelCase    )}"    )
          torch.save(model.state_dict()							,			_UpperCamelCase    )
          print(f"Save configuration file to {os.path.abspath(_UpperCamelCase    )}"    )
          with open(_UpperCamelCase							,			'w'							,			encoding='utf-8'    ) as f:
               f.write(config.to_json_string()    )


if __name__ == "__main__":
     lowerCamelCase			:				Any 			=					argparse.ArgumentParser()
     parser.add_argument(
         "--pytorch_dump_folder_path",
         default=None,
         type=str,
         required=True,
         help="Path to the folder to store the PyTorch model or dataset/vocab.",
     )
     parser.add_argument(
         "--tf_checkpoint_path",
         default="",
         type=str,
         help="An optional path to a TensorFlow checkpoint path to be converted.",
     )
     parser.add_argument(
         "--transfo_xl_config_file",
         default="",
         type=str,
         help=(
             "An optional config json file corresponding to the pre-trained BERT model. \n"
             "This specifies the model architecture."
         ),
     )
     parser.add_argument(
         "--transfo_xl_dataset_file",
         default="",
         type=str,
         help="An optional dataset file to be converted in a vocabulary.",
     )
     lowerCamelCase			:				Optional[int] 			=					parser.parse_args()
     convert_transfo_xl_checkpoint_to_pytorch(
         args.tf_checkpoint_path,
         args.transfo_xl_config_file,
         args.pytorch_dump_folder_path,
         args.transfo_xl_dataset_file,
     )



'''simple docstring'''
from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowerCamelCase			:				List[str] 			=					{"UserAgent": UserAgent().random}


def 	_lowerCAmelCase (						_UpperCamelCase						:					str    )				->      dict:
     """simple docstring"""




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







class 							A__   :





  def __init__(		self				:     int							, _a				:     List[Any]			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =f"https://www.instagram.com/{username}/"
       _SCREAMING_SNAKE_CASE							   =self.get_json()





  def 						A  (		self				:     Optional[int]			)     ->   dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =requests.get(self.url							, headers=_a			).text
       _SCREAMING_SNAKE_CASE							   =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				:     str			)     ->   str:





       '''simple docstring'''






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





  def __str__(		self				:     Optional[int]			)     ->   str:





       '''simple docstring'''






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





  @property
  def 						A  (		self				:     List[Any]			)     ->   str:





       '''simple docstring'''






       return self.user_data["username"]





  @property
  def 						A  (		self				:     str			)     ->   str:





       '''simple docstring'''






       return self.user_data["full_name"]





  @property
  def 						A  (		self				:     Any			)     ->   str:





       '''simple docstring'''






       return self.user_data["biography"]





  @property
  def 						A  (		self				:     Optional[Any]			)     ->   str:





       '''simple docstring'''






       return self.user_data["business_email"]





  @property
  def 						A  (		self				:     Tuple			)     ->   str:





       '''simple docstring'''






       return self.user_data["external_url"]





  @property
  def 						A  (		self				:     Optional[int]			)     ->   int:





       '''simple docstring'''






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





  @property
  def 						A  (		self				:     Union[str, Any]			)     ->   int:





       '''simple docstring'''






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





  @property
  def 						A  (		self				:     List[str]			)     ->   int:





       '''simple docstring'''






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





  @property
  def 						A  (		self				:     Union[str, Any]			)     ->   str:





       '''simple docstring'''






       return self.user_data["profile_pic_url_hd"]





  @property
  def 						A  (		self				:     Dict			)     ->   bool:





       '''simple docstring'''






       return self.user_data["is_verified"]





  @property
  def 						A  (		self				:     Tuple			)     ->   bool:





       '''simple docstring'''






       return self.user_data["is_private"]


def 	_lowerCAmelCase (						_UpperCamelCase						:					str = "github"    )				->      None:
     """simple docstring"""




     import os

     if os.environ.get('CI'    ):
          return  # test failing on GitHub Actions
     _SCREAMING_SNAKE_CASE							   =InstagramUser(_UpperCamelCase    )
     assert instagram_user.user_data
     assert isinstance(instagram_user.user_data							,			_UpperCamelCase    )
     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 > 1_50
     assert instagram_user.number_of_followers > 12_00_00
     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			:				Optional[int] 			=					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 = }''')
