





import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      @property
      def        a			(self	:				Optional[int]				):





         """simple docstring"""



         torch.manual_seed(0				)
         __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


      @property
      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         torch.manual_seed(0				)
         __snake_case								=       VQModel(
             block_out_channels=[32, 64]    ,      in_channels=3    ,      out_channels=3    ,      down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,      up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,      latent_channels=3    ,      )
         return model


      @property
      def        a			(self	:				List[str]				):





         """simple docstring"""



         torch.manual_seed(0				)
         __snake_case								=       CLIPTextConfig(
             bos_token_id=0    ,      eos_token_id=2    ,      hidden_size=32    ,      intermediate_size=37    ,      layer_norm_eps=1E-05    ,      num_attention_heads=4    ,      num_hidden_layers=5    ,      pad_token_id=1    ,      vocab_size=1000    ,      )
         return CLIPTextModel(_SCREAMING_SNAKE_CASE				)


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       self.dummy_uncond_unet
         __snake_case								=       DDIMScheduler()
         __snake_case								=       self.dummy_vq_model

         __snake_case								=       LDMPipeline(unet=_SCREAMING_SNAKE_CASE    ,      vqvae=_SCREAMING_SNAKE_CASE    ,      scheduler=_SCREAMING_SNAKE_CASE				)
         ldm.to(_SCREAMING_SNAKE_CASE				)
         ldm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

         __snake_case								=       torch.manual_seed(0				)
         __snake_case								=       ldm(generator=_SCREAMING_SNAKE_CASE    ,      num_inference_steps=2    ,      output_type='''numpy'''				).images

         __snake_case								=       torch.manual_seed(0				)
         __snake_case								=       ldm(generator=_SCREAMING_SNAKE_CASE    ,      num_inference_steps=2    ,      output_type='''numpy'''    ,      return_dict=_SCREAMING_SNAKE_CASE				)[0]

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

         assert image.shape == (1, 64, 64, 3)
         __snake_case								=       np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]				)
         __snake_case								=       1E-2 if torch_device != '''mps''' else 3E-2

         assert np.abs(image_slice.flatten() - expected_slice				).max() < tolerance
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < tolerance






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


      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256'''				)
         ldm.to(_SCREAMING_SNAKE_CASE				)
         ldm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

         __snake_case								=       torch.manual_seed(0				)
         __snake_case								=       ldm(generator=_SCREAMING_SNAKE_CASE    ,      num_inference_steps=5    ,      output_type='''numpy'''				).images

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

         assert image.shape == (1, 256, 256, 3)
         __snake_case								=       np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]				)
         __snake_case								=       1E-2 if torch_device != '''mps''' else 3E-2

         assert np.abs(image_slice.flatten() - expected_slice				).max() < tolerance








from __future__ import annotations

snake_case_           =      'Muhammad Umer Farooq'
snake_case_           =      'MIT'
snake_case_           =      '1.0.0'
snake_case_           =      'Muhammad Umer Farooq'
snake_case_           =      'contact@muhammadumerfarooq.me'
snake_case_           =      'Alpha'

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

import requests

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				Dict    ,      a__	:				str				):





         """simple docstring"""



         super().__init__()
         __snake_case								=       []
         __snake_case								=       domain


      def        a			(self	:				Tuple    ,      a__	:				str    ,      a__	:				list[tuple[str, str | None]]				):





         """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:
                     __snake_case								=       parse.urljoin(self.domain    ,      a__				)
                     self.urls.append(a__				)






def lowerCamelCase__   (							snake_case_     :  str	)				->    str:
   return ".".join(get_sub_domain_name(snake_case_	).split('''.'''	)[-2:]	)






def lowerCamelCase__   (							snake_case_     :  str	)				->    str:
   return parse.urlparse(snake_case_	).netloc






def lowerCamelCase__   (							snake_case_     :  str = "https://github.com"	)				->    list[str]:
   __snake_case								=       get_domain_name(snake_case_	)

   # Initialize the parser
   __snake_case								=       Parser(snake_case_	)

   try:
      # Open URL
      __snake_case								=       requests.get(snake_case_	)

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

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

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


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

