





"""simple docstring"""




from __future__ import annotations

a						: List[Any]     	=			'''Muhammad Umer Farooq'''
a						: Tuple     	=			'''MIT'''
a						: str     	=			'''1.0.0'''
a						: List[Any]     	=			'''Muhammad Umer Farooq'''
a						: List[Any]     	=			'''contact@muhammadumerfarooq.me'''
a						: Optional[Any]     	=			'''Alpha'''

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

import requests


class        __UpperCamelCase				(						a__						):






							def __init__(   self      ,					lowerCAmelCase__      )	-> None:
								super().__init__()
								a      :		list[str]	=				[]
								a      :		str	=				domain






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> None:
								# 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:
												a      :		List[Any]	=				parse.urljoin(self.domain      ,					lowerCAmelCase__      )
												self.urls.append(lowerCAmelCase__      )






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->str:






	'''simple docstring'''






	return ".".join(get_sub_domain_name(_lowercase					).split("."					)[-2:]					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->str:






	'''simple docstring'''






	return parse.urlparse(_lowercase					).netloc






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str = "https://github.com"					) ->list[str]:






	'''simple docstring'''






	a      :		Dict	=				get_domain_name(_lowercase					)

	# Initialize the parser
	a      :		List[Any]	=				Parser(_lowercase					)

	try:
		# Open URL
		a      :		str	=				requests.get(_lowercase					)

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

		# Get links and loop through
		a      :		List[Any]	=				set()
		for link in parser.urls:
			# open URL.
			# read = requests.get(link)
			try:
				a      :		Any	=				requests.get(_lowercase					)
				# Get the valid email.
				a      :		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__":
		a						: int     	=			emails_from_url('''https://github.com''')
		print(F'''{len(emails)} emails found:''')
		print('''\n'''.join(sorted(emails)))



'''simple docstring'''




import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE       :     Dict     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     List[Any]     =  {
    """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
    """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  str						=						"encodec"




      def __init__(       self						:				Optional[int]						,					A						:				Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]						,					A						:				List[Any]=24000						,					A						:				Union[str, Any]=1						,					A						:				List[Any]=False						,					A						:				Optional[int]=None						,					A						:				int=None						,					A						:				str=128						,					A						:				List[Any]=32						,					A						:				List[Any]=1						,					A						:				int=[8, 5, 4, 2]						,					A						:				Optional[int]="weight_norm"						,					A						:				List[Any]=7						,					A						:				Any=7						,					A						:				Dict=3						,					A						:				Optional[int]=2						,					A						:				Dict=True						,					A						:				Dict="reflect"						,					A						:				Any=2						,					A						:				Dict=2						,					A						:				str=1.0						,					A						:				Optional[int]=1024						,					A						:				Any=None						,					A						:				Any=True						,					**A						:				str						,					):
             _UpperCAmelCase     :  Optional[int]							      =    target_bandwidths
             _UpperCAmelCase     :  List[str]							      =    sampling_rate
             _UpperCAmelCase     :  Optional[int]							      =    audio_channels
             _UpperCAmelCase     :  str							      =    normalize
             _UpperCAmelCase     :  int							      =    chunk_length_s
             _UpperCAmelCase     :  str							      =    overlap
             _UpperCAmelCase     :  Optional[Any]							      =    hidden_size
             _UpperCAmelCase     :  int							      =    num_filters
             _UpperCAmelCase     :  Optional[Any]							      =    num_residual_layers
             _UpperCAmelCase     :  Optional[int]							      =    upsampling_ratios
             _UpperCAmelCase     :  int							      =    norm_type
             _UpperCAmelCase     :  List[Any]							      =    kernel_size
             _UpperCAmelCase     :  List[Any]							      =    last_kernel_size
             _UpperCAmelCase     :  List[Any]							      =    residual_kernel_size
             _UpperCAmelCase     :  List[str]							      =    dilation_growth_rate
             _UpperCAmelCase     :  Dict							      =    use_causal_conv
             _UpperCAmelCase     :  Tuple							      =    pad_mode
             _UpperCAmelCase     :  Tuple							      =    compress
             _UpperCAmelCase     :  List[str]							      =    num_lstm_layers
             _UpperCAmelCase     :  List[Any]							      =    trim_right_ratio
             _UpperCAmelCase     :  int							      =    codebook_size
             _UpperCAmelCase     :  Optional[Any]							      =    codebook_dim if codebook_dim is not None else hidden_size
             _UpperCAmelCase     :  Optional[int]							      =    use_conv_shortcut

             if self.norm_type not in ["weight_norm", "time_group_norm"]:
                    raise ValueError(
                        F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"""       )

             super().__init__(**A       )




      @property
      def      _A	(       self						:				Any       ):
             if self.chunk_length_s is None:
                    return None
             else:
                    return int(self.chunk_length_s * self.sampling_rate       )




      @property
      def      _A	(       self						:				Union[str, Any]       ):
             if self.chunk_length_s is None or self.overlap is None:
                    return None
             else:
                    return max(1						,					int((1.0 - self.overlap) * self.chunk_length       )       )




      @property
      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Dict							      =    np.prod(self.upsampling_ratios       )
             return math.ceil(self.sampling_rate / hop_length       )





      @property
      def      _A	(       self						:				str       ):
             return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)       )

