


import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


_snake_case					:		List[Any]      =    logging.get_logger(__name__)

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							def __init__(							self	:  Any   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  int    )     ->   Union[str, Any]:
														warnings.warn(
														    "The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
														    " Please use ChineseCLIPImageProcessor instead."   ,     _snake_case   ,     )
														super().__init__(*_snake_case   ,     **_snake_case    )





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_					( __lowerCamelCase     ):
							__snake_case		:      Any										=       []
							__snake_case		:      Optional[Any]										=       []
							__snake_case		:      List[Any]										=       []

							for rt in rc.restypes:
														__snake_case		:      Any										=       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]     )
														__snake_case		:      Tuple										=       {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] * 1_4     )
							restype_atomaa_to_atomaa_list.append([0] * 3_7     )
							restype_atomaa_mask_list.append([0.0] * 1_4     )

							__snake_case		:      int										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.intaa							,     device=protein["aatype"].device							,     )
							__snake_case		:      List[str]										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.intaa							,     device=protein["aatype"].device							,     )
							__snake_case		:      Optional[Any]										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.floataa							,     device=protein["aatype"].device							,     )
							__snake_case		:      Optional[int]										=       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
							__snake_case		:      Optional[Any]										=       restype_atomaa_to_atomaa[protein_aatype]
							__snake_case		:      Tuple										=       restype_atomaa_mask[protein_aatype]

							__snake_case		:      Optional[Any]										=       residx_atomaa_mask
							__snake_case		:      Union[str, Any]										=       residx_atomaa_to_atomaa.long()

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

							# create the corresponding mask
							__snake_case		:      List[str]										=       torch.zeros([2_1, 3_7]							,     dtype=torch.floataa							,     device=protein["aatype"].device     )
							for restype, restype_letter in enumerate(rc.restypes     ):
														__snake_case		:      List[str]										=       rc.restype_atoa[restype_letter]
														__snake_case		:      List[Any]										=       rc.residue_atoms[restype_name]
														for atom_name in atom_names:
																					__snake_case		:      Union[str, Any]										=       rc.atom_order[atom_name]
																					__snake_case		:      str										=       1

							__snake_case		:      List[str]										=       restype_atomaa_mask[protein_aatype]
							__snake_case		:      List[str]										=       residx_atomaa_mask

							return protein







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

