




from __future__ import annotations

_UpperCAmelCase		:  Dict	=   'Muhammad Umer Farooq'
_UpperCAmelCase		:  Any	=   'MIT'
_UpperCAmelCase		:  Tuple	=   '1.0.0'
_UpperCAmelCase		:  Optional[Any]	=   'Muhammad Umer Farooq'
_UpperCAmelCase		:  Union[str, Any]	=   'contact@muhammadumerfarooq.me'
_UpperCAmelCase		:  Dict	=   'Alpha'

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

import requests




class       __lowerCAmelCase				(							lowerCAmelCase):






       def __init__(      self:    List[Any]				,    _lowerCAmelCase:    str ):
         super().__init__()
         lowercase			:int							=  []
         lowercase			:str							=  domain






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any]				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    list[tuple[str, str | None]] ):




         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:
                 lowercase			:Dict							=  parse.urljoin(self.domain				,    __SCREAMING_SNAKE_CASE )
                 self.urls.append(__SCREAMING_SNAKE_CASE )







def 					UpperCAmelCase__     (   lowerCamelCase      ):
  return ".".join(get_sub_domain_name(a__      ).split("."      )[-2:]      )



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  return parse.urlparse(a__      ).netloc



def 					UpperCAmelCase__     (   lowerCamelCase = "https://github.com"      ):
  lowercase			:str							=  get_domain_name(a__      )

  # Initialize the parser
  lowercase			:int							=  Parser(a__      )

  try:
    # Open URL
    lowercase			:int							=  requests.get(a__      )

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

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

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


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






'''simple docstring'''



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 @property
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Optional[int]:


      """simple docstring"""




      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      UNetaDModel(
          block_out_channels=(32, 64)					,							layers_per_block=2					,							sample_size=32					,							in_channels=3					,							out_channels=3					,							down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")					,							up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")					,							)
      return model


 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.dummy_uncond_unet
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler()

      __SCREAMING_SNAKE_CASE     =      PNDMPipeline(unet=__SCREAMING_SNAKE_CASE					,							scheduler=__SCREAMING_SNAKE_CASE			)
      pndm.to(__SCREAMING_SNAKE_CASE			)
      pndm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pndm(generator=__SCREAMING_SNAKE_CASE					,							num_inference_steps=20					,							output_type="""numpy"""			).images

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pndm(generator=__SCREAMING_SNAKE_CASE					,							num_inference_steps=20					,							output_type="""numpy"""					,							return_dict=__SCREAMING_SNAKE_CASE			)[0]

      __SCREAMING_SNAKE_CASE     =      image[0, -3:, -3:, -1]
      __SCREAMING_SNAKE_CASE     =      image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE     =      np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]			)

      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2







@slow
@require_torch
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """google/ddpm-cifar10-32"""

      __SCREAMING_SNAKE_CASE     =      UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler()

      __SCREAMING_SNAKE_CASE     =      PNDMPipeline(unet=__SCREAMING_SNAKE_CASE					,							scheduler=__SCREAMING_SNAKE_CASE			)
      pndm.to(__SCREAMING_SNAKE_CASE			)
      pndm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pndm(generator=__SCREAMING_SNAKE_CASE					,							output_type="""numpy"""			).images

      __SCREAMING_SNAKE_CASE     =      image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      __SCREAMING_SNAKE_CASE     =      np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]			)

      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2






