





from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


snake_case_           =  logging.get_logger(__name__)  # pylint: disable=invalid-name

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				List[Any]    ,      a__	:				int    ,      a__	:				Tuple				):





         """simple docstring"""



         super().__init__()
         self.register_modules(unet=a__    ,      scheduler=a__				)


      @torch.no_grad()
      def __call__(self	:				Any    ,      a__	:				int = 1    ,      a__	:				int = 100    ,      a__	:				Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,      a__	:				Optional[float] = None    ,      a__	:				bool = True    ,      ):





         """simple docstring"""



         if audio_length_in_s is None:
            __snake_case								=       self.unet.config.sample_size / self.unet.config.sample_rate

         __snake_case								=       audio_length_in_s * self.unet.config.sample_rate

         __snake_case								=       2 ** len(self.unet.up_blocks				)
         if sample_size < 3 * down_scale_factor:
            raise ValueError(
                f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
                f""" {3 * down_scale_factor / self.unet.config.sample_rate}."""				)

         __snake_case								=       int(a__				)
         if sample_size % down_scale_factor != 0:
            __snake_case								=       (
                (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
            ) * down_scale_factor
            logger.info(
                f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
                f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
                ''' process.'''				)
         __snake_case								=       int(a__				)

         __snake_case								=       next(iter(self.unet.parameters()				)				).dtype
         __snake_case								=       (batch_size, self.unet.config.in_channels, sample_size)
         if isinstance(a__    ,      a__				) and len(a__				) != batch_size:
            raise ValueError(
                f"""You have passed a list of generators of length {len(a__				)}, but requested an effective batch"""
                f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

         __snake_case								=       randn_tensor(a__    ,      generator=a__    ,      device=self.device    ,      dtype=a__				)

         # set step values
         self.scheduler.set_timesteps(a__    ,      device=audio.device				)
         __snake_case								=       self.scheduler.timesteps.to(a__				)

         for t in self.progress_bar(self.scheduler.timesteps				):
            # 1. predict noise model_output
            __snake_case								=       self.unet(a__    ,      a__				).sample

            # 2. compute previous image: x_t -> t_t-1
            __snake_case								=       self.scheduler.step(a__    ,      a__    ,      a__				).prev_sample

         __snake_case								=       audio.clamp(-1    ,      1				).float().cpu().numpy()

         __snake_case								=       audio[:, :, :original_sample_size]

         if not return_dict:
            return (audio,)

         return AudioPipelineOutput(audios=a__				)








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)))

