





'''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 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""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(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							)

				lowercase         = torch.tensor(
				    lowerCAmelCase_		,							dtype=torch.intaa		,							device=protein["aatype"].device		,							)
				lowercase         = torch.tensor(
				    lowerCAmelCase_		,							dtype=torch.intaa		,							device=protein["aatype"].device		,							)
				lowercase         = torch.tensor(
				    lowerCAmelCase_		,							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_    (	lowerCAmelCase_							):

				"""simple docstring"""






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











'''simple docstring'''

from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return DownloadCommand(args.model		,							args.cache_dir		,							args.force		,							args.trust_remote_code							)


class       UpperCAmelCase (						_lowercase  ):




		@staticmethod
		def 				UpperCAmelCase__   (A__      :	ArgumentParser				)					-> List[Any]:
						lowercase         = parser.add_parser("download"				)
						download_parser.add_argument(
						    "--cache-dir"							,			type=A__							,			default=A__							,			help="Path to location to store the models"				)
						download_parser.add_argument(
						    "--force"							,			action="store_true"							,			help="Force the model to be download even if already in cache-dir"				)
						download_parser.add_argument(
						    "--trust-remote-code"							,			action="store_true"							,			help="Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"							,			)
						download_parser.add_argument("model"							,			type=A__							,			help="Name of the model to download"				)
						download_parser.set_defaults(func=A__				)




		def __init__(self      :	Tuple							,			A__      :	str							,			A__      :	str							,			A__      :	bool							,			A__      :	bool				)					-> str:
						lowercase         = model
						lowercase         = cache
						lowercase         = force
						lowercase         = trust_remote_code





		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Dict:
						from ..models.auto import AutoModel, AutoTokenizer

						AutoModel.from_pretrained(
						    self._model							,			cache_dir=self._cache							,			force_download=self._force							,			trust_remote_code=self._trust_remote_code				)
						AutoTokenizer.from_pretrained(
						    self._model							,			cache_dir=self._cache							,			force_download=self._force							,			trust_remote_code=self._trust_remote_code				)




