

"""simple docstring"""






import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
_lowerCAmelCase						=		logging.getLogger(__name__)


_lowerCAmelCase						=		"""Hello world! cécé herlolip"""


_lowerCAmelCase						=		namedtuple(
    """BertAbsConfig""",
    [
        """temp_dir""",
        """large""",
        """use_bert_emb""",
        """finetune_bert""",
        """encoder""",
        """share_emb""",
        """max_pos""",
        """enc_layers""",
        """enc_hidden_size""",
        """enc_heads""",
        """enc_ff_size""",
        """enc_dropout""",
        """dec_layers""",
        """dec_hidden_size""",
        """dec_heads""",
        """dec_ff_size""",
        """dec_dropout""",
    ],
)

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

  '''simple docstring'''



  _lowerCAmelCase       :   List[Any]  =    BertAbsConfig(
      temp_dir='.' ,   finetune_bert=_lowerCamelCase ,   large=_lowerCamelCase ,   share_emb=_lowerCamelCase ,   use_bert_emb=_lowerCamelCase ,   encoder='bert' ,   max_pos=512 ,   enc_layers=6 ,   enc_hidden_size=512 ,   enc_heads=8 ,   enc_ff_size=512 ,   enc_dropout=0.2 ,   dec_layers=6 ,   dec_hidden_size=768 ,   dec_heads=8 ,   dec_ff_size=2048 ,   dec_dropout=0.2 ,   )
  _lowerCAmelCase       :   Optional[Any]  =    torch.load(_lowerCamelCase ,   lambda _lowerCamelCase ,   _lowerCamelCase   : storage   )
  _lowerCAmelCase       :   Any  =    AbsSummarizer(_lowerCamelCase ,   torch.device('cpu'   ) ,   _lowerCamelCase   )
  original.eval()

  _lowerCAmelCase       :   Tuple  =    BertAbsSummarizer(_lowerCamelCase ,   torch.device('cpu'   )   )
  new_model.eval()

  # -------------------
  # Convert the weights
  # -------------------

  logging.info('convert the model'   )
  new_model.bert.load_state_dict(original.bert.state_dict()   )
  new_model.decoder.load_state_dict(original.decoder.state_dict()   )
  new_model.generator.load_state_dict(original.generator.state_dict()   )

  # ----------------------------------
  # Make sure the outpus are identical
  # ----------------------------------

  logging.info('Make sure that the models\' outputs are identical'   )
  _lowerCAmelCase       :   Union[str, Any]  =    BertTokenizer.from_pretrained('bert-base-uncased'   )

  # prepare the model inputs
  _lowerCAmelCase       :   Any  =    tokenizer.encode('This is sample éàalj\'-.'   )
  encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_lowerCamelCase   ))   )
  _lowerCAmelCase       :   Optional[int]  =    torch.tensor(_lowerCamelCase   ).unsqueeze(0   )
  _lowerCAmelCase       :   Any  =    tokenizer.encode('This is sample 3 éàalj\'-.'   )
  decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_lowerCamelCase   ))   )
  _lowerCAmelCase       :   Any  =    torch.tensor(_lowerCamelCase   ).unsqueeze(0   )

  # failsafe to make sure the weights reset does not affect the
  # loaded weights.
  assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight   )   ) == 0

  # forward pass
  _lowerCAmelCase       :   int  =    encoder_input_ids
  _lowerCAmelCase       :   Any  =    decoder_input_ids
  _lowerCAmelCase       :   Optional[Any]  =    None
  _lowerCAmelCase       :   List[Any]  =    None
  _lowerCAmelCase       :   Dict  =    None
  _lowerCAmelCase       :   int  =    None
  _lowerCAmelCase       :   Any  =    None

  # The original model does not apply the geneator layer immediatly but rather in
  # the beam search (where it combines softmax + linear layer). Since we already
  # apply the softmax in our generation process we only apply the linear layer here.
  # We make sure that the outputs of the full stack are identical
  _lowerCAmelCase       :   Tuple  =    original(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )[0]
  _lowerCAmelCase       :   Tuple  =    original.generator(_lowerCamelCase   )

  _lowerCAmelCase       :   List[Any]  =    new_model(
      _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )[0]
  _lowerCAmelCase       :   Any  =    new_model.generator(_lowerCamelCase   )

  _lowerCAmelCase       :   List[str]  =    torch.max(torch.abs(output_converted_model - output_original_model   )   ).item()
  print('Maximum absolute difference beween weights: {:.2f}'.format(_lowerCamelCase   )   )
  _lowerCAmelCase       :   str  =    torch.max(torch.abs(output_converted_generator - output_original_generator   )   ).item()
  print('Maximum absolute difference beween weights: {:.2f}'.format(_lowerCamelCase   )   )

  _lowerCAmelCase       :   Tuple  =    torch.allclose(_lowerCamelCase ,   _lowerCamelCase ,   atol=1e-3   )
  if are_identical:
    logging.info('all weights are equal up to 1e-3'   )
  else:
    raise ValueError('the weights are different. The new model is likely different from the original one.'   )

  # The model has been saved with torch.save(model) and this is bound to the exact
  # directory structure. We save the state_dict instead.
  logging.info('saving the model\'s state dictionary'   )
  torch.save(
      new_model.state_dict() ,   './bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin'   )


if __name__ == "__main__":
 _lowerCAmelCase						=		argparse.ArgumentParser()
 parser.add_argument(
     """--bertabs_checkpoint_path""",
     default=None,
     type=str,
     required=True,
     help="""Path the official PyTorch dump.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""",
     default=None,
     type=str,
     required=True,
     help="""Path to the output PyTorch model.""",
 )
 _lowerCAmelCase						=		parser.parse_args()

 convert_bertabs_checkpoints(
     args.bertabs_checkpoint_path,
     args.pytorch_dump_folder_path,
 )








"""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 = }''')





