





from __future__ import annotations

UpperCAmelCase_					:   Tuple 							=						"""Muhammad Umer Farooq"""
UpperCAmelCase_					:   str 							=						"""MIT"""
UpperCAmelCase_					:   Optional[Any] 							=						"""1.0.0"""
UpperCAmelCase_					:   Union[str, Any] 							=						"""Muhammad Umer Farooq"""
UpperCAmelCase_					:   Optional[int] 							=						"""contact@muhammadumerfarooq.me"""
UpperCAmelCase_					:   Any 							=						"""Alpha"""

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

import requests

class      lowercase__    (							_UpperCAmelCase  ):







  def __init__(	self    ,						_lowercase						):
       super().__init__()
       lowerCAmelCase_					:       list[str]    =		[]
       lowerCAmelCase_					:       Optional[Any]    =		domain







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase						):
       # Only parse the 'anchor' tag.
       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:
                           lowerCAmelCase_					:       Dict    =		parse.urljoin(self.domain    ,						lowercase__						)
                           self.urls.append(lowercase__						)






def 							_lowerCAmelCase (  _a   :					str			)	->			List[Any]:
     return ".".join(get_sub_domain_name(SCREAMING_SNAKE_CASE__			).split("""."""			)[-2:]			)






def 							_lowerCAmelCase (  _a   :					str			)	->			Tuple:
     return parse.urlparse(SCREAMING_SNAKE_CASE__			).netloc






def 							_lowerCAmelCase (  _a   :					str = "https://github.com"			)	->			int:
     lowerCAmelCase_					:       int    =		get_domain_name(SCREAMING_SNAKE_CASE__			)

     # Initialize the parser
     lowerCAmelCase_					:       str    =		Parser(SCREAMING_SNAKE_CASE__			)

     try:
          # Open URL
          lowerCAmelCase_					:       Any    =		requests.get(SCREAMING_SNAKE_CASE__			)

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

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

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


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







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  None





class      lowercase__    (							__A     ,					__A  ):
  __UpperCamelCase	     =  2







  @register_to_config
  def __init__(	self    ,						_lowercase = 0.02    ,						_lowercase = 100    ,						_lowercase = 1.007    ,						_lowercase = 80    ,						_lowercase = 0.05    ,						_lowercase = 50    ,						):
       # standard deviation of the initial noise distribution
       lowerCAmelCase_					:       int    =		sigma_max

       # setable values
       lowerCAmelCase_					:       int    =		None
       lowerCAmelCase_					:       np.IntTensor    =		None
       lowerCAmelCase_					:       torch.FloatTensor    =		None  # sigma(t_i)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       return sample







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       lowerCAmelCase_					:       int    =		num_inference_steps
       lowerCAmelCase_					:       int    =		np.arange(0    ,						self.num_inference_steps						)[::-1].copy()
       lowerCAmelCase_					:       List[Any]    =		torch.from_numpy(_lowercase						).to(_lowercase						)
       lowerCAmelCase_					:       List[str]    =		[
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       lowerCAmelCase_					:       Optional[int]    =		torch.tensor(_lowercase    ,						dtype=torch.floataa    ,						device=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase = None						):
       if self.config.s_min <= sigma <= self.config.s_max:
            lowerCAmelCase_					:       List[Any]    =		min(self.config.s_churn / self.num_inference_steps    ,						2**0.5 - 1						)
       else:
            lowerCAmelCase_					:       List[str]    =		0

       # sample eps ~ N(0, S_noise^2 * I)
       lowerCAmelCase_					:       List[Any]    =		self.config.s_noise * randn_tensor(sample.shape    ,						generator=_lowercase						).to(sample.device						)
       lowerCAmelCase_					:       Optional[int]    =		sigma + gamma * sigma
       lowerCAmelCase_					:       int    =		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = True    ,						):
       lowerCAmelCase_					:       Union[str, Any]    =		sample_hat + sigma_hat * model_output
       lowerCAmelCase_					:       Dict    =		(sample_hat - pred_original_sample) / sigma_hat
       lowerCAmelCase_					:       List[Any]    =		sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
            return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowercase    ,						derivative=_lowercase    ,						pred_original_sample=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase = True    ,						):
       lowerCAmelCase_					:       str    =		sample_prev + sigma_prev * model_output
       lowerCAmelCase_					:       List[Any]    =		(sample_prev - pred_original_sample) / sigma_prev
       lowerCAmelCase_					:       Tuple    =		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
            return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=_lowercase    ,						derivative=_lowercase    ,						pred_original_sample=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase						):
       raise NotImplementedError()
