

'''simple docstring'''

from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

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




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




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					Tuple					,      lowercase_			:					Union[str, Any]	)						->     Dict:
      UpperCAmelCase	:     int						 =			f"""https://www.instagram.com/{username}/"""
      UpperCAmelCase	:     int						 =			self.get_json()


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     dict:
      UpperCAmelCase	:     List[Any]						 =			requests.get(self.url					,      headers=snake_case__	).text
      UpperCAmelCase	:     Optional[Any]						 =			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	)						->     str:
      return f"""{self.__class__.__name__}(\'{self.username}\')"""


     def __str__(		self			:					List[Any]	)						->     str:
      return f"""{self.fullname} ({self.username}) is {self.biography}"""


     @property
     def        UpperCAmelCase_	(		self			:					List[str]	)						->     str:
      return self.user_data["username"]


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     str:
      return self.user_data["full_name"]


     @property
     def        UpperCAmelCase_	(		self			:					Tuple	)						->     str:
      return self.user_data["biography"]


     @property
     def        UpperCAmelCase_	(		self			:					List[str]	)						->     str:
      return self.user_data["business_email"]


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     str:
      return self.user_data["external_url"]


     @property
     def        UpperCAmelCase_	(		self			:					List[str]	)						->     int:
      return self.user_data["edge_followed_by"]["count"]


     @property
     def        UpperCAmelCase_	(		self			:					int	)						->     int:
      return self.user_data["edge_follow"]["count"]


     @property
     def        UpperCAmelCase_	(		self			:					Tuple	)						->     int:
      return self.user_data["edge_owner_to_timeline_media"]["count"]


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     str:
      return self.user_data["profile_pic_url_hd"]


     @property
     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     bool:
      return self.user_data["is_verified"]




     @property
     def        UpperCAmelCase_	(		self			:					Tuple	)						->     bool:
      return self.user_data["is_private"]




def 				UpperCamelCase(					UpperCAmelCase_ = "github"     ):
 import os

 if os.environ.get('CI'     ):
  return  # test failing on GitHub Actions
 UpperCAmelCase	:     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 > 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()
 lowercase__         =      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()
lowercase__         =      logging.get_logger("transformers.models.speecht5")




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 hf_model.apply_weight_norm()

 UpperCAmelCase	:     Dict						 =			checkpoint['input_conv.weight_g']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.weight_v']
 UpperCAmelCase	:     Any						 =			checkpoint['input_conv.bias']

 for i in range(len(config.upsample_rates     )     ):
  UpperCAmelCase	:     Any						 =			checkpoint[F"""upsamples.{i}.1.weight_g"""]
  UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""upsamples.{i}.1.weight_v"""]
  UpperCAmelCase	:     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     )     ):
   UpperCAmelCase	:     Any						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   UpperCAmelCase	:     Dict						 =			checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

   UpperCAmelCase	:     Union[str, Any]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCAmelCase	:     int						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCAmelCase	:     Optional[int]						 =			checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCAmelCase	:     Dict						 =			checkpoint['output_conv.1.weight_g']
 UpperCAmelCase	:     str						 =			checkpoint['output_conv.1.weight_v']
 UpperCAmelCase	:     Union[str, Any]						 =			checkpoint['output_conv.1.bias']

 hf_model.remove_weight_norm()




@torch.no_grad()
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_=None  ,	):
 if config_path is not None:
  UpperCAmelCase	:     Any						 =			SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase_     )
 else:
  UpperCAmelCase	:     Optional[Any]						 =			SpeechTaHifiGanConfig()

 UpperCAmelCase	:     List[Any]						 =			SpeechTaHifiGan(UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			torch.load(UpperCAmelCase_     )
 load_weights(orig_checkpoint['model']['generator']  ,	UpperCAmelCase_  ,	UpperCAmelCase_     )

 UpperCAmelCase	:     int						 =			np.load(UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			stats[0].reshape(-1     )
 UpperCAmelCase	:     List[str]						 =			stats[1].reshape(-1     )
 UpperCAmelCase	:     Union[str, Any]						 =			torch.from_numpy(UpperCAmelCase_     ).float()
 UpperCAmelCase	:     List[str]						 =			torch.from_numpy(UpperCAmelCase_     ).float()

 model.save_pretrained(UpperCAmelCase_     )

 if repo_id:
  print('Pushing to the hub...'     )
  model.push_to_hub(UpperCAmelCase_     )


if __name__ == "__main__":
 lowercase__         =      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."
 )

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




