


'''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    _snake_case  (	A			)		->       Dict[str, torch.Tensor]:
							lowerCAmelCase__			  =      []
							lowerCAmelCase__			  =      []
							lowerCAmelCase__			  =      []

							for rt in rc.restypes:
														lowerCAmelCase__			  =      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]			)
														lowerCAmelCase__			  =      {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] * 14			)
							restype_atomaa_to_atomaa_list.append([0] * 37			)
							restype_atomaa_mask_list.append([0.0] * 14			)

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

							lowerCAmelCase__			  =      residx_atomaa_mask
							lowerCAmelCase__			  =      residx_atomaa_to_atomaa.long()

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

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

							lowerCAmelCase__			  =      restype_atomaa_mask[protein_aatype]
							lowerCAmelCase__			  =      residx_atomaa_mask

							return protein






def    _snake_case  (	A			)		->       Dict[str, np.ndarray]:
							lowerCAmelCase__			  =      tree_map(lambda A			: torch.tensor(A					,       device=batch['''aatype'''].device			)					,       A					,       np.ndarray			)
							lowerCAmelCase__			  =      tensor_tree_map(lambda A			: np.array(A			)					,       make_atomaa_masks(A			)			)
							return out


"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu





@require_cpu
class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     str:
						SCREAMING_SNAKE_CASE__										=   torch.nn.Linear(1_0     ,					1_0							)
						SCREAMING_SNAKE_CASE__										=   torch.optim.SGD(model.parameters()     ,					0.1							)
						SCREAMING_SNAKE_CASE__										=   Accelerator()
						SCREAMING_SNAKE_CASE__										=   accelerator.prepare(__UpperCAmelCase							)
						try:
									pickle.loads(pickle.dumps(__UpperCAmelCase							)							)
						except Exception as e:
									self.fail(F"""Accelerated optimizer pickling failed with {e}"""							)
						AcceleratorState._reset_state()


