'''simple docstring'''





from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def 						__A							(     UpperCAmelCase					)     ->			Dict[str, torch.Tensor]:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						[]
						_UpperCamelCase			:			Tuple   			=						[]
						_UpperCamelCase			:			Union[str, Any]   			=						[]

						for rt in rc.restypes:
												_UpperCamelCase			:			int   			=						rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
												restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]					)
												_UpperCamelCase			:			List[str]   			=						{name: i for i, name in enumerate(UpperCAmelCase					)}
												restype_atomaa_to_atomaa_list.append(
												    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]					)

												restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]					)

						# Add dummy mapping for restype 'UNK'
						restype_atomaa_to_atomaa_list.append([0] * 1_4					)
						restype_atomaa_to_atomaa_list.append([0] * 3_7					)
						restype_atomaa_mask_list.append([0.0] * 1_4					)

						_UpperCamelCase			:			Tuple   			=						torch.tensor(
						    UpperCAmelCase     ,dtype=torch.intaa     ,device=protein["aatype"].device     ,)
						_UpperCamelCase			:			List[str]   			=						torch.tensor(
						    UpperCAmelCase     ,dtype=torch.intaa     ,device=protein["aatype"].device     ,)
						_UpperCamelCase			:			int   			=						torch.tensor(
						    UpperCAmelCase     ,dtype=torch.floataa     ,device=protein["aatype"].device     ,)
						_UpperCamelCase			:			Any   			=						protein["aatype"].to(torch.long					)

						# create the mapping for (residx, atom14) --> atom37, i.e. an array
						# with shape (num_res, 14) containing the atom37 indices for this protein
						_UpperCamelCase			:			Union[str, Any]   			=						restype_atomaa_to_atomaa[protein_aatype]
						_UpperCamelCase			:			int   			=						restype_atomaa_mask[protein_aatype]

						_UpperCamelCase			:			Optional[int]   			=						residx_atomaa_mask
						_UpperCamelCase			:			Tuple   			=						residx_atomaa_to_atomaa.long()

						# create the gather indices for mapping back
						_UpperCamelCase			:			Dict   			=						restype_atomaa_to_atomaa[protein_aatype]
						_UpperCamelCase			:			Dict   			=						residx_atomaa_to_atomaa.long()

						# create the corresponding mask
						_UpperCamelCase			:			Optional[int]   			=						torch.zeros([2_1, 3_7]     ,dtype=torch.floataa     ,device=protein["aatype"].device					)
						for restype, restype_letter in enumerate(rc.restypes					):
												_UpperCamelCase			:			Optional[int]   			=						rc.restype_atoa[restype_letter]
												_UpperCamelCase			:			Optional[int]   			=						rc.residue_atoms[restype_name]
												for atom_name in atom_names:
																		_UpperCamelCase			:			str   			=						rc.atom_order[atom_name]
																		_UpperCamelCase			:			int   			=						1

						_UpperCamelCase			:			Dict   			=						restype_atomaa_mask[protein_aatype]
						_UpperCamelCase			:			Tuple   			=						residx_atomaa_mask

						return protein






def 						__A							(     UpperCAmelCase					)     ->			Dict[str, np.ndarray]:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						tree_map(lambda UpperCAmelCase					: torch.tensor(UpperCAmelCase     ,device=batch["aatype"].device					)     ,UpperCAmelCase     ,np.ndarray					)
						_UpperCamelCase			:			Union[str, Any]   			=						tensor_tree_map(lambda UpperCAmelCase					: np.array(UpperCAmelCase					)     ,make_atomaa_masks(UpperCAmelCase					)					)
						return out



'''simple docstring'''





from __future__ import annotations

lowerCAmelCase_	:		Optional[Any]			=     """Muhammad Umer Farooq"""
lowerCAmelCase_	:		str			=     """MIT"""
lowerCAmelCase_	:		Optional[Any]			=     """1.0.0"""
lowerCAmelCase_	:		Union[str, Any]			=     """Muhammad Umer Farooq"""
lowerCAmelCase_	:		Any			=     """contact@muhammadumerfarooq.me"""
lowerCAmelCase_	:		Dict			=     """Alpha"""

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

import requests




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						List[Any]       ,		lowercase__					:						str					)							->None:


										'''simple docstring'''




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







				def  snake_case__				(						self					:						str       ,		lowercase__					:						str       ,		lowercase__					:						list[tuple[str, str | None]]					)							->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:
																																		_UpperCamelCase			:			Optional[Any]   			=						parse.urljoin(self.domain       ,		lowercase__					)
																																		self.urls.append(lowercase__					)





def 						__A							(     UpperCAmelCase					)     ->			str:




						'''simple docstring'''
						return ".".join(get_sub_domain_name(UpperCAmelCase					).split("."					)[-2:]					)
def 						__A							(     UpperCAmelCase					)     ->			str:




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




						'''simple docstring'''
						_UpperCamelCase			:			int   			=						get_domain_name(UpperCAmelCase					)

						# Initialize the parser
						_UpperCamelCase			:			Any   			=						Parser(UpperCAmelCase					)

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

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

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


