





'''simple docstring'''






from __future__ import annotations

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

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

import requests







class 			lowerCamelCase					(						__UpperCAmelCase  ):


					def __init__(					self	:  Optional[int]       ,     __snake_case	:  str       ):





											'''simple docstring'''





											super().__init__()
											_snake_case:   list[str]  					=		[]
											_snake_case:   Union[str, Any]  					=		domain




					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  str       ,     __snake_case	:  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:   int  					=		parse.urljoin(self.domain       ,     __snake_case       )
																																			self.urls.append(__snake_case       )





def       lowercase_      (		lowercase__						)   ->str:
						return ".".join(get_sub_domain_name(SCREAMING_SNAKE_CASE_						).split('.'						)[-2:]						)





def       lowercase_      (		lowercase__						)   ->str:
						return parse.urlparse(SCREAMING_SNAKE_CASE_						).netloc





def       lowercase_      (		lowercase__ = "https://github.com"						)   ->list[str]:
						_snake_case:   Optional[Any]  					=		get_domain_name(SCREAMING_SNAKE_CASE_						)

						# Initialize the parser
						_snake_case:   Optional[int]  					=		Parser(SCREAMING_SNAKE_CASE_						)

						try:
												# Open URL
												_snake_case:   Union[str, Any]  					=		requests.get(SCREAMING_SNAKE_CASE_						)

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

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

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


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












'''simple docstring'''






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    "Speech2TextFeatureExtractor"
					_SCREAMING_SNAKE_CASE							   =    "Speech2TextTokenizer"


					def __init__(					self	:  List[str]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  List[str]       ):





											'''simple docstring'''





											super().__init__(__snake_case       ,     __snake_case       )
											_snake_case:   Optional[int]  					=		self.feature_extractor
											_snake_case:   Dict  					=		False


					def __call__(					self	:  Optional[int]       ,     *__snake_case	:  Optional[Any]       ,     **__snake_case	:  Union[str, Any]       ):





											'''simple docstring'''





											if self._in_target_context_manager:
																	return self.current_processor(*__snake_case       ,     **__snake_case       )

											if "raw_speech" in kwargs:
																	warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'       )
																	_snake_case:   int  					=		kwargs.pop('raw_speech'       )
											else:
																	_snake_case:   Tuple  					=		kwargs.pop('audio'       ,     __snake_case       )
											_snake_case:   Optional[Any]  					=		kwargs.pop('sampling_rate'       ,     __snake_case       )
											_snake_case:   Optional[Any]  					=		kwargs.pop('text'       ,     __snake_case       )
											if len(__snake_case       ) > 0:
																	_snake_case:   Optional[Any]  					=		args[0]
																	_snake_case:   Union[str, Any]  					=		args[1:]

											if audio is None and text is None:
																	raise ValueError('You need to specify either an `audio` or `text` input to process.'       )

											if audio is not None:
																	_snake_case:   List[Any]  					=		self.feature_extractor(__snake_case       ,     *__snake_case       ,     sampling_rate=__snake_case       ,     **__snake_case       )
											if text is not None:
																	_snake_case:   Tuple  					=		self.tokenizer(__snake_case       ,     **__snake_case       )

											if text is None:
																	return inputs
											elif audio is None:
																	return encodings
											else:
																	_snake_case:   List[Any]  					=		encodings['input_ids']
																	return inputs


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     *__snake_case	:  Optional[int]       ,     **__snake_case	:  str       ):





											'''simple docstring'''





											return self.tokenizer.batch_decode(*__snake_case       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     *__snake_case	:  List[str]       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





											return self.tokenizer.decode(*__snake_case       ,     **__snake_case       )




					@contextmanager
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											warnings.warn(
											    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
											    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
											    'your audio inputs, or in a separate call.'       )
											_snake_case:   Any  					=		True
											_snake_case:   str  					=		self.tokenizer
											yield
											_snake_case:   Union[str, Any]  					=		self.feature_extractor
											_snake_case:   Tuple  					=		False





