






'''simple docstring'''

from __future__ import annotations

UpperCAmelCase__						:int          =			"""Muhammad Umer Farooq"""
UpperCAmelCase__						:Dict          =			"""MIT"""
UpperCAmelCase__						:Dict          =			"""1.0.0"""
UpperCAmelCase__						:str          =			"""Muhammad Umer Farooq"""
UpperCAmelCase__						:str          =			"""contact@muhammadumerfarooq.me"""
UpperCAmelCase__						:Any          =			"""Alpha"""

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

import requests







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




       def __init__(					self      : Tuple	,    A__      : str					):


            """simple docstring"""







            super().__init__()
            __lowerCamelCase						:       list[str]				   =				[]
            __lowerCamelCase						:       Optional[int]				   =				domain




       def 					a_					(					self      : Optional[int]	,    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:
                                __lowerCamelCase						:       Dict				   =				parse.urljoin(self.domain	,    A__					)
                                self.urls.append(A__					)

def     __lowercase  (_lowercase     )   ->     str:


     """simple docstring"""
     return ".".join(get_sub_domain_name(_lowercase     ).split("""."""     )[-2:]     )
def     __lowercase  (_lowercase     )   ->     str:


     """simple docstring"""
     return parse.urlparse(_lowercase     ).netloc
def     __lowercase  (_lowercase = "https://github.com"     )   ->     list[str]:


     """simple docstring"""
     __lowerCamelCase						:       List[str]				   =				get_domain_name(_lowercase     )

     # Initialize the parser
     __lowerCamelCase						:       Any				   =				Parser(_lowercase     )

     try:
          # Open URL
          __lowerCamelCase						:       Any				   =				requests.get(_lowercase     )

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

          # Get links and loop through
          __lowerCamelCase						:       Any				   =				set()
          for link in parser.urls:
               # open URL.
               # read = requests.get(link)
               try:
                    __lowerCamelCase						:       Tuple				   =				requests.get(_lowercase     )
                    # 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(_lowercase     )
               except ValueError:
                    pass
     except ValueError:
          raise SystemExit(1     )

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


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











'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

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


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







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




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


            """simple docstring"""







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




       @torch.no_grad()
       def __call__(					self      : List[str]	,    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:
                 __lowerCamelCase						:       Union[str, Any]				   =				self.unet.config.sample_size / self.unet.config.sample_rate

            __lowerCamelCase						:       int				   =				audio_length_in_s * self.unet.config.sample_rate

            __lowerCamelCase						:       List[Any]				   =				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}."					)

            __lowerCamelCase						:       Any				   =				int(A__					)
            if sample_size % down_scale_factor != 0:
                 __lowerCamelCase						:       Optional[Any]				   =				(
                     (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."""					)
            __lowerCamelCase						:       Dict				   =				int(A__					)

            __lowerCamelCase						:       Optional[int]				   =				next(iter(self.unet.parameters()					)					).dtype
            __lowerCamelCase						:       Optional[Any]				   =				(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."					)

            __lowerCamelCase						:       int				   =				randn_tensor(A__	,    generator=A__	,    device=self.device	,    dtype=A__					)

            # set step values
            self.scheduler.set_timesteps(A__	,    device=audio.device					)
            __lowerCamelCase						:       List[Any]				   =				self.scheduler.timesteps.to(A__					)

            for t in self.progress_bar(self.scheduler.timesteps					):
                 # 1. predict noise model_output
                 __lowerCamelCase						:       List[str]				   =				self.unet(A__	,    A__					).sample

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

            __lowerCamelCase						:       Optional[int]				   =				audio.clamp(-1	,    1					).float().cpu().numpy()

            __lowerCamelCase						:       int				   =				audio[:, :, :original_sample_size]

            if not return_dict:
                 return (audio,)

            return AudioPipelineOutput(audios=A__					)



