






from __future__ import annotations

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

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

import requests






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	def __init__(							self  ,      _lowercase       )						->  Optional[Any]:
							super().__init__()
							lowercase_      :   List[str]        =			[]
							lowercase_      :   Optional[Any]        =			domain







	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase       )						->  str:

							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:
																															lowercase_      :   Dict        =			parse.urljoin(self.domain  ,      _lowercase       )
																															self.urls.append(_lowercase       )






def 	_UpperCAmelCase    (  a		:	str							)			->			str:





						"""simple docstring"""
						return ".".join(get_sub_domain_name(_UpperCamelCase							).split('.'							)[-2:]							)






def 	_UpperCAmelCase    (  a		:	str							)			->			str:





						"""simple docstring"""
						return parse.urlparse(_UpperCamelCase							).netloc






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





						"""simple docstring"""
						lowercase_      :   List[Any]        =			get_domain_name(_UpperCamelCase							)

						# Initialize the parser
						lowercase_      :   str        =			Parser(_UpperCamelCase							)

						try:
												# Open URL
												lowercase_      :   int        =			requests.get(_UpperCamelCase							)

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

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

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


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








'''simple docstring'''

import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
A:      Tuple  				=				logging.getLogger(__name__)






def 	_UpperCAmelCase    (  a		:	str							)			->			List[Any]:





						"""simple docstring"""
						lowercase_      :   List[str]        =			git.Repo(search_parent_directories=a							)
						lowercase_      :   Union[str, Any]        =			{
						    'repo_id': str(a							),
						    'repo_sha': str(repo.head.object.hexsha							),
						    'repo_branch': str(repo.active_branch							),
						}

						with open(os.path.join(a						,			'git_log.json'							)						,			'w'							) as f:
												json.dump(a						,			a						,			indent=4							)






def 	_UpperCAmelCase    (  a		:	str							)			->			Union[str, Any]:





						"""simple docstring"""
						if params.n_gpu <= 0:
												lowercase_      :   int        =			0
												lowercase_      :   Union[str, Any]        =			-1
												lowercase_      :   List[str]        =			True
												lowercase_      :   Optional[Any]        =			False
												return

						assert torch.cuda.is_available()

						logger.info('Initializing GPUs'							)
						if params.n_gpu > 1:
												assert params.local_rank != -1

												lowercase_      :   Dict        =			int(os.environ['WORLD_SIZE']							)
												lowercase_      :   Union[str, Any]        =			int(os.environ['N_GPU_NODE']							)
												lowercase_      :   Optional[int]        =			int(os.environ['RANK']							)

												# number of nodes / node ID
												lowercase_      :   int        =			params.world_size // params.n_gpu_per_node
												lowercase_      :   str        =			params.global_rank // params.n_gpu_per_node
												lowercase_      :   Dict        =			True

												assert params.n_nodes == int(os.environ['N_NODES']							)
												assert params.node_id == int(os.environ['NODE_RANK']							)

						# local job (single GPU)
						else:
												assert params.local_rank == -1

												lowercase_      :   str        =			1
												lowercase_      :   Dict        =			0
												lowercase_      :   Tuple        =			0
												lowercase_      :   List[Any]        =			0
												lowercase_      :   int        =			1
												lowercase_      :   Tuple        =			1
												lowercase_      :   str        =			False

						# sanity checks
						assert params.n_nodes >= 1
						assert 0 <= params.node_id < params.n_nodes
						assert 0 <= params.local_rank <= params.global_rank < params.world_size
						assert params.world_size == params.n_nodes * params.n_gpu_per_node

						# define whether this is the master process / if we are in multi-node distributed mode
						lowercase_      :   List[str]        =			params.node_id == 0 and params.local_rank == 0
						lowercase_      :   Optional[Any]        =			params.n_nodes > 1

						# summary
						lowercase_      :   int        =			f"--- Global rank: {params.global_rank} - "
						logger.info(PREFIX + 'Number of nodes: %i' % params.n_nodes							)
						logger.info(PREFIX + 'Node ID        : %i' % params.node_id							)
						logger.info(PREFIX + 'Local rank     : %i' % params.local_rank							)
						logger.info(PREFIX + 'World size     : %i' % params.world_size							)
						logger.info(PREFIX + 'GPUs per node  : %i' % params.n_gpu_per_node							)
						logger.info(PREFIX + 'Master         : %s' % str(params.is_master							)							)
						logger.info(PREFIX + 'Multi-node     : %s' % str(params.multi_node							)							)
						logger.info(PREFIX + 'Multi-GPU      : %s' % str(params.multi_gpu							)							)
						logger.info(PREFIX + 'Hostname       : %s' % socket.gethostname()							)

						# set GPU device
						torch.cuda.set_device(params.local_rank							)

						# initialize multi-GPU
						if params.multi_gpu:
												logger.info('Initializing PyTorch distributed'							)
												torch.distributed.init_process_group(
												    init_method='env://'						,			backend='nccl'						,			)






def 	_UpperCAmelCase    (  a		:	Dict							)			->			Optional[int]:





						"""simple docstring"""
						np.random.seed(args.seed							)
						torch.manual_seed(args.seed							)
						if args.n_gpu > 0:
												torch.cuda.manual_seed_all(args.seed							)
