



"""simple docstring"""

from __future__ import annotations

lowercase__			   =  "Muhammad Umer Farooq"
lowercase__			   =  "MIT"
lowercase__			   =  "1.0.0"
lowercase__			   =  "Muhammad Umer Farooq"
lowercase__			   =  "contact@muhammadumerfarooq.me"
lowercase__			   =  "Alpha"

import re
from html.parser import HTMLParser
from urllib import parse

import requests




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



      def __init__(self						,     _lowercase							):





             '''simple docstring'''

             super().__init__()
             __a		:		list[str]									=       []
             __a		:		Optional[Any]									=       domain





      def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase							):





             '''simple docstring'''

             if tag == "a":
                    # Check the list of defined attributes.
                    for name, value in attrs:
                           # If href is defined, and not empty nor # print it.
                           if name == "href" and value != "#" and value != "":
                                  # If not already in urls.
                                  if value not in self.urls:
                                         __a		:		Any									=       parse.urljoin(self.domain						,     _lowercase							)
                                         self.urls.append(_lowercase							)







def       __magic_name__    (   _lowerCamelCase				: str					):
       return ".".join(get_sub_domain_name(_lowerCamelCase					).split("""."""					)[-2:]					)





def       __magic_name__    (   _lowerCamelCase				: str					):
       return parse.urlparse(_lowerCamelCase					).netloc





def       __magic_name__    (   _lowerCamelCase				: str = "https://github.com"					):
       __a		:		Dict									=       get_domain_name(_lowerCamelCase					)

       # Initialize the parser
       __a		:		Tuple									=       Parser(_lowerCamelCase					)

       try:
              # Open URL
              __a		:		List[Any]									=       requests.get(_lowerCamelCase					)

              # pass the raw HTML to the parser to get links
              parser.feed(r.text					)

              # Get links and loop through
              __a		:		Union[str, Any]									=       set()
              for link in parser.urls:
                     # open URL.
                     # read = requests.get(link)
                     try:
                            __a		:		List[str]									=       requests.get(_lowerCamelCase					)
                            # Get the valid email.
                            __a		:		Union[str, Any]									=       re.findall("""[a-zA-Z0-9]+@""" + domain  ,       read.text					)
                            # If not in list then append it.
                            for email in emails:
                                   valid_emails.add(_lowerCamelCase					)
                     except ValueError:
                            pass
       except ValueError:
              raise SystemExit(1					)

       # Finally return a sorted list of email addresses with no duplicates.
       return sorted(_lowerCamelCase					)


if __name__ == "__main__":
 lowercase__			   =  emails_from_url("https://github.com")
 print(f'{len(emails)} emails found:')
 print("\n".join(sorted(emails)))






"""simple docstring"""

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[Any]=False					):
							__a		:		Dict									=       OmegaConf.load(_lowerCamelCase					)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase					)					)					)
							return config





def       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: Union[str, Any]=None  ,       _lowerCamelCase				: int=None					):
							if conf_path is None:
														__a		:		str									=       """./model_checkpoints/vqgan_only.yaml"""
							__a		:		List[Any]									=       load_config(_lowerCamelCase  ,       display=_lowerCamelCase					)
							__a		:		Dict									=       VQModel(**config.model.params					)
							if ckpt_path is None:
														__a		:		List[Any]									=       """./model_checkpoints/vqgan_only.pt"""
							__a		:		Tuple									=       torch.load(_lowerCamelCase  ,       map_location=_lowerCamelCase					)
							if ".ckpt" in ckpt_path:
														__a		:		List[str]									=       sd["""state_dict"""]
							model.load_state_dict(_lowerCamelCase  ,       strict=_lowerCamelCase					)
							model.to(_lowerCamelCase					)
							del sd
							return model





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: List[str]					):
							__a							,     __a							,     __a		:		Tuple									=       model.encode(_lowerCamelCase					)
							print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''					)
							__a		:		Union[str, Any]									=       model.decode(_lowerCamelCase					)
							return xrec





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Union[str, Any]=False					):
							__a							,     __a		:		Optional[Any]									=       string.rsplit("""."""  ,       1					)
							if reload:
														__a		:		Optional[Any]									=       importlib.import_module(_lowerCamelCase					)
														importlib.reload(_lowerCamelCase					)
							return getattr(importlib.import_module(_lowerCamelCase  ,       package=_lowerCamelCase					)  ,       cls					)





def       __magic_name__    (   _lowerCamelCase				: Any					):
							if "target" not in config:
														raise KeyError("""Expected key `target` to instantiate."""					)
							return get_obj_from_str(config["""target"""]					)(**config.get("""params"""  ,       {}					)					)





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: int=True  ,       _lowerCamelCase				: int=True					):
							__a		:		Union[str, Any]									=       instantiate_from_config(_lowerCamelCase					)
							if sd is not None:
														model.load_state_dict(_lowerCamelCase					)
							if gpu:
														model.cuda()
							if eval_mode:
														model.eval()
							return {"model": model}







def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int					):
							# load the specified checkpoint
							if ckpt:
														__a		:		List[str]									=       torch.load(_lowerCamelCase  ,       map_location="""cpu"""					)
														__a		:		Any									=       pl_sd["""global_step"""]
														print(F'''loaded model from global step {global_step}.'''					)
							else:
														__a		:		List[Any]									=       {"""state_dict""": None}
														__a		:		Any									=       None
							__a		:		Union[str, Any]									=       load_model_from_config(config.model  ,       pl_sd["""state_dict"""]  ,       gpu=_lowerCamelCase  ,       eval_mode=_lowerCamelCase					)["""model"""]
							return model, global_step

