


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    (		SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




			UpperCamelCase         =					[]
			UpperCamelCase         =					[]
			UpperCamelCase         =					[]

			for rt in rc.restypes:
						UpperCamelCase         =					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         =					{name: i for i, name in enumerate(_lowerCamelCase	)}
						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] * 14	)
			restype_atomaa_to_atomaa_list.append([0] * 37	)
			restype_atomaa_mask_list.append([0.0] * 14	)

			UpperCamelCase         =					torch.tensor(
			    _lowerCamelCase       ,  dtype=torch.intaa       ,  device=protein["""aatype"""].device       ,  )
			UpperCamelCase         =					torch.tensor(
			    _lowerCamelCase       ,  dtype=torch.intaa       ,  device=protein["""aatype"""].device       ,  )
			UpperCamelCase         =					torch.tensor(
			    _lowerCamelCase       ,  dtype=torch.floataa       ,  device=protein["""aatype"""].device       ,  )
			UpperCamelCase         =					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         =					restype_atomaa_to_atomaa[protein_aatype]
			UpperCamelCase         =					restype_atomaa_mask[protein_aatype]

			UpperCamelCase         =					residx_atomaa_mask
			UpperCamelCase         =					residx_atomaa_to_atomaa.long()

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

			# create the corresponding mask
			UpperCamelCase         =					torch.zeros([21, 37]       ,  dtype=torch.floataa       ,  device=protein["""aatype"""].device	)
			for restype, restype_letter in enumerate(rc.restypes	):
						UpperCamelCase         =					rc.restype_atoa[restype_letter]
						UpperCamelCase         =					rc.residue_atoms[restype_name]
						for atom_name in atom_names:
									UpperCamelCase         =					rc.atom_order[atom_name]
									UpperCamelCase         =					1

			UpperCamelCase         =					restype_atomaa_mask[protein_aatype]
			UpperCamelCase         =					residx_atomaa_mask

			return protein






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			UpperCamelCase         =					tree_map(lambda SCREAMING_SNAKE_CASE_	: torch.tensor(_lowerCamelCase       ,  device=batch["""aatype"""].device	)       ,  _lowerCamelCase       ,  np.ndarray	)
			UpperCamelCase         =					tensor_tree_map(lambda SCREAMING_SNAKE_CASE_	: np.array(_lowerCamelCase	)       ,  make_atomaa_masks(_lowerCamelCase	)	)
			return out








from ..utils import DummyObject, requires_backends




class    UpperCAmelCase					(       metaclass=__snake_case    ):
			lowercase						= ["""keras_nlp"""]




			def __init__(						self							:		List[str]       ,       *__magic_name__							:		str       ,       **__magic_name__							:		int						):







						"""simple docstring"""







						requires_backends(self       ,       ["""keras_nlp"""]						)




