


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   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]						) ->   Tuple:





	"""simple docstring"""







	A								=				[]
	A								=				[]
	A								=				[]

	for rt in rc.restypes:
		A								=				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]						)
		A								=				{name: i for i, name in enumerate(_lowercase						)}
		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						)

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

	A								=				residx_atomaa_mask
	A								=				residx_atomaa_to_atomaa.long()

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

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

	A								=				restype_atomaa_mask[protein_aatype]
	A								=				residx_atomaa_mask

	return protein





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple						) ->   Optional[Any]:





	"""simple docstring"""







	A								=				tree_map(lambda UpperCamelCase__						: torch.tensor(_lowercase							,					device=batch["""aatype"""].device						)							,					_lowercase							,					np.ndarray						)
	A								=				tensor_tree_map(lambda UpperCamelCase__						: np.array(_lowercase						)							,					make_atomaa_masks(_lowercase						)						)
	return out







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase       :      Any	=	{
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :      Tuple	=	[
				    "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Swinv2ForImageClassification",
				    "Swinv2ForMaskedImageModeling",
				    "Swinv2Model",
				    "Swinv2PreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swinva import (
								    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwinvaForImageClassification,
								    SwinvaForMaskedImageModeling,
								    SwinvaModel,
								    SwinvaPreTrainedModel,
								)


else:
				import sys

				_lowercase       :      Optional[int]	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



