




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 		lowercase__   (		__A:	Dict[str, torch.Tensor]				):





					'''simple docstring'''






					__magic_name__							:	Dict    							=							[]
					__magic_name__							:	Union[str, Any]    							=							[]
					__magic_name__							:	Dict    							=							[]

					for rt in rc.restypes:
										__magic_name__							:	Any    							=							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]				)
										__magic_name__							:	List[Any]    							=							{name: i for i, name in enumerate(__A				)}
										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				)

					__magic_name__							:	List[str]    							=							torch.tensor(
					    __A							,dtype=torch.intaa							,device=protein['''aatype'''].device							,)
					__magic_name__							:	Optional[int]    							=							torch.tensor(
					    __A							,dtype=torch.intaa							,device=protein['''aatype'''].device							,)
					__magic_name__							:	Union[str, Any]    							=							torch.tensor(
					    __A							,dtype=torch.floataa							,device=protein['''aatype'''].device							,)
					__magic_name__							:	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
					__magic_name__							:	Optional[Any]    							=							restype_atomaa_to_atomaa[protein_aatype]
					__magic_name__							:	Any    							=							restype_atomaa_mask[protein_aatype]

					__magic_name__							:	Dict    							=							residx_atomaa_mask
					__magic_name__							:	Tuple    							=							residx_atomaa_to_atomaa.long()

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

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

					__magic_name__							:	str    							=							restype_atomaa_mask[protein_aatype]
					__magic_name__							:	Optional[int]    							=							residx_atomaa_mask

					return protein







def 		lowercase__   (		__A:	Dict[str, torch.Tensor]				):





					'''simple docstring'''






					__magic_name__							:	int    							=							tree_map(lambda __A				: torch.tensor(__A							,device=batch['''aatype'''].device				)							,__A							,np.ndarray				)
					__magic_name__							:	Optional[Any]    							=							tensor_tree_map(lambda __A				: np.array(__A				)							,make_atomaa_masks(__A				)				)
					return out









import requests







def 		lowercase__   (		__A:	str							,__A:	str				):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							{'''Content-Type''': '''application/json'''}
					__magic_name__							:	Union[str, Any]    							=							requests.post(__A							,json={'''text''': message_body}							,headers=__A				)
					if response.status_code != 2_0_0:
										__magic_name__							:	int    							=							(
										    '''Request to slack returned an error '''
										    F'''{response.status_code}, the response is:\n{response.text}'''
										)
										raise ValueError(__A				)


if __name__ == "__main__":
	# Set the slack url to the one provided by Slack when you create the webhook at
	# https://my.slack.com/services/new/incoming-webhook/
	send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')



