






"""simple docstring"""







from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

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

def _a			(		_snake_case							):







      """simple docstring"""






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







class  lowerCamelCase__ :






    def __init__(			self     ,A ):
          UpperCAmelCase          =	F'''https://www.instagram.com/{username}/'''
          UpperCAmelCase          =	self.get_json()






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	requests.get(self.url     ,headers=A ).text
          UpperCAmelCase          =	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 ):
          return F'''{self.__class__.__name__}(\'{self.username}\')'''






    def __str__(			self ):
          return F'''{self.fullname} ({self.username}) is {self.biography}'''






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["username"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["full_name"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["biography"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["business_email"]






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






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






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["edge_follow"]["count"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["edge_owner_to_timeline_media"]["count"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["profile_pic_url_hd"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["is_verified"]






    @property
    def     _UpperCamelCase							(			self ):
          return self.user_data["is_private"]







def _a			(		_snake_case = "github"							):







      """simple docstring"""






      import os

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











"""simple docstring"""







import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
_UpperCamelCase					      =  logging.get_logger(__name__)

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForSequenceClassification.from_pretrained(_snake_case				,	config=_snake_case							)
      UpperCAmelCase          =	downstream_dict["""projector.weight"""]
      UpperCAmelCase          =	downstream_dict["""projector.bias"""]
      UpperCAmelCase          =	downstream_dict["""model.post_net.linear.weight"""]
      UpperCAmelCase          =	downstream_dict["""model.post_net.linear.bias"""]
      return model

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForAudioFrameClassification.from_pretrained(_snake_case				,	config=_snake_case							)
      UpperCAmelCase          =	downstream_dict["""model.linear.weight"""]
      UpperCAmelCase          =	downstream_dict["""model.linear.bias"""]
      return model

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForXVector.from_pretrained(_snake_case				,	config=_snake_case							)
      UpperCAmelCase          =	downstream_dict["""connector.weight"""]
      UpperCAmelCase          =	downstream_dict["""connector.bias"""]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
            UpperCAmelCase          =	downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            UpperCAmelCase          =	downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      UpperCAmelCase          =	downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
      UpperCAmelCase          =	downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
      UpperCAmelCase          =	downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
      UpperCAmelCase          =	downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
      UpperCAmelCase          =	downstream_dict["""objective.W"""]
      return model

@torch.no_grad()
def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	torch.load(_snake_case				,	map_location="""cpu"""							)

      UpperCAmelCase          =	checkpoint["""Downstream"""]

      UpperCAmelCase          =	WavaVecaConfig.from_pretrained(_snake_case							)
      UpperCAmelCase          =	WavaVecaFeatureExtractor.from_pretrained(
          _snake_case				,	return_attention_mask=_snake_case				,	do_normalize=_snake_case							)

      UpperCAmelCase          =	hf_config.architectures[0]
      if arch.endswith("""ForSequenceClassification"""							):
            UpperCAmelCase          =	convert_classification(_snake_case				,	_snake_case				,	_snake_case							)
      elif arch.endswith("""ForAudioFrameClassification"""							):
            UpperCAmelCase          =	convert_diarization(_snake_case				,	_snake_case				,	_snake_case							)
      elif arch.endswith("""ForXVector"""							):
            UpperCAmelCase          =	convert_xvector(_snake_case				,	_snake_case				,	_snake_case							)
      else:
            raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''							)

      if hf_config.use_weighted_layer_sum:
            UpperCAmelCase          =	checkpoint["""Featurizer"""]["""weights"""]

      hf_feature_extractor.save_pretrained(_snake_case							)
      hf_model.save_pretrained(_snake_case							)


if __name__ == "__main__":
  _UpperCamelCase					      =  argparse.ArgumentParser()
  parser.add_argument(
      """--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
  )
  parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
  parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
  parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
  _UpperCamelCase					      =  parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



