



"""simple docstring"""
from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

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





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: int     			=script.contents[0]
   SCREAMING_SNAKE_CASE_: List[str]     			=json.loads(data[data.find("""{\"config\"""" ) : -1] )
   return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class 					a	:



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=f'''https://www.instagram.com/{username}/'''
     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.get_json()



  def 	lowerCamelCase__       (						self :					str		)     ->					dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=requests.get(self.url       ,		headers=lowerCAmelCase		).text
     SCREAMING_SNAKE_CASE_: int     			=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 :					str		)     ->					str:


     '''simple docstring'''




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



  def __str__(						self :					Any		)     ->					str:


     '''simple docstring'''




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



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


     '''simple docstring'''




     return self.user_data["username"]



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


     '''simple docstring'''




     return self.user_data["full_name"]



  @property
  def 	lowerCamelCase__       (						self :					Dict		)     ->					str:


     '''simple docstring'''




     return self.user_data["biography"]



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


     '''simple docstring'''




     return self.user_data["business_email"]



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


     '''simple docstring'''




     return self.user_data["external_url"]



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


     '''simple docstring'''




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



  @property
  def 	lowerCamelCase__       (						self :					str		)     ->					int:


     '''simple docstring'''




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



  @property
  def 	lowerCamelCase__       (						self :					Dict		)     ->					int:


     '''simple docstring'''




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



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


     '''simple docstring'''




     return self.user_data["profile_pic_url_hd"]



  @property
  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					bool:


     '''simple docstring'''




     return self.user_data["is_verified"]





  @property
  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					bool:


     '''simple docstring'''




     return self.user_data["is_private"]







def      __magic_name__				(							lowercase = "github" ):
   import os

   if os.environ.get("""CI""" ):
      return  # test failing on GitHub Actions
   SCREAMING_SNAKE_CASE_: Tuple     			=InstagramUser(lowercase )
   assert instagram_user.user_data
   assert isinstance(instagram_user.user_data							,			lowercase )
   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 numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_UpperCAmelCase     =			logging.get_logger("""transformers.models.speecht5""")





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   hf_model.apply_weight_norm()

   SCREAMING_SNAKE_CASE_: Tuple     			=checkpoint["""input_conv.weight_g"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""input_conv.weight_v"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""input_conv.bias"""]

   for i in range(len(config.upsample_rates ) ):
      SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''upsamples.{i}.1.weight_g''']
      SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''upsamples.{i}.1.weight_v''']
      SCREAMING_SNAKE_CASE_: List[str]     			=checkpoint[f'''upsamples.{i}.1.bias''']

   for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
      for j in range(len(config.resblock_dilation_sizes ) ):
         SCREAMING_SNAKE_CASE_: Dict     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
         SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']

         SCREAMING_SNAKE_CASE_: Dict     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
         SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
         SCREAMING_SNAKE_CASE_: List[Any]     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']

   SCREAMING_SNAKE_CASE_: Tuple     			=checkpoint["""output_conv.1.weight_g"""]
   SCREAMING_SNAKE_CASE_: List[str]     			=checkpoint["""output_conv.1.weight_v"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""output_conv.1.bias"""]

   hf_model.remove_weight_norm()





@torch.no_grad()
def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase=None							,			lowercase=None							,			):
   if config_path is not None:
      SCREAMING_SNAKE_CASE_: List[Any]     			=SpeechTaHifiGanConfig.from_pretrained(lowercase )
   else:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=SpeechTaHifiGanConfig()

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=SpeechTaHifiGan(lowercase )

   SCREAMING_SNAKE_CASE_: Any     			=torch.load(lowercase )
   load_weights(orig_checkpoint["""model"""]["""generator"""]							,			lowercase							,			lowercase )

   SCREAMING_SNAKE_CASE_: List[Any]     			=np.load(lowercase )
   SCREAMING_SNAKE_CASE_: Any     			=stats[0].reshape(-1 )
   SCREAMING_SNAKE_CASE_: str     			=stats[1].reshape(-1 )
   SCREAMING_SNAKE_CASE_: Dict     			=torch.from_numpy(lowercase ).float()
   SCREAMING_SNAKE_CASE_: Dict     			=torch.from_numpy(lowercase ).float()

   model.save_pretrained(lowercase )

   if repo_id:
      print("""Pushing to the hub...""" )
      model.push_to_hub(lowercase )


if __name__ == "__main__":
   _UpperCAmelCase     =			argparse.ArgumentParser()
   parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
   parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
   parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
   parser.add_argument(
       """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
   )
   parser.add_argument(
       """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
   )

   _UpperCAmelCase     =			parser.parse_args()
   convert_hifigan_checkpoint(
       args.checkpoint_path,
       args.stats_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.push_to_hub,
   )



