


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 					UpperCamelCase   (      __magic_name__	:				Dict[str, torch.Tensor]	)  ->    Dict[str, torch.Tensor]:
		"""simple docstring"""




		lowercase__	     =						[]
		lowercase__	     =						[]
		lowercase__	     =						[]

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

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

		lowercase__	     =						residx_atomaa_mask
		lowercase__	     =						residx_atomaa_to_atomaa.long()

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

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

		lowercase__	     =						restype_atomaa_mask[protein_aatype]
		lowercase__	     =						residx_atomaa_mask

		return protein







def 					UpperCamelCase   (      __magic_name__	:				Dict[str, torch.Tensor]	)  ->    Dict[str, np.ndarray]:
		"""simple docstring"""




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









"""simple docstring"""





from typing import Union

import fire
import torch
from tqdm import tqdm



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    str = "cpu"      ,   __UpperCAmelCase      :    Union[str, None] = None  )    ->							None:
						__magic_name__:	List[Any]							=       torch.load(__UpperCAmelCase      ,   map_location=__UpperCAmelCase  )
						for k, v in tqdm(state_dict.items()  ):
												if not isinstance(__UpperCAmelCase      ,   torch.Tensor  ):
																		raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"""  )
												__magic_name__:	Any							=       v.half()
						if save_path is None:  # overwrite src_path
												__magic_name__:	List[str]							=       src_path
						torch.save(__UpperCAmelCase      ,   __UpperCAmelCase  )


if __name__ == "__main__":
							fire.Fire(convert)



