






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__	:			str			   = logging.get_logger(__name__)

A__	:			Any			   = {
    'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       """roc_bert"""




				def __init__(     self						:    str,  lowerCamelCase						:    Tuple=30_522,  lowerCamelCase						:    int=768,  lowerCamelCase						:    int=12,  lowerCamelCase						:    str=12,  lowerCamelCase						:    str=3_072,  lowerCamelCase						:    List[Any]="gelu",  lowerCamelCase						:    Union[str, Any]=0.1,  lowerCamelCase						:    Tuple=0.1,  lowerCamelCase						:    Dict=512,  lowerCamelCase						:    Union[str, Any]=2,  lowerCamelCase						:    Dict=0.02,  lowerCamelCase						:    Optional[Any]=1E-12,  lowerCamelCase						:    str=True,  lowerCamelCase						:    List[str]=0,  lowerCamelCase						:    Optional[Any]="absolute",  lowerCamelCase						:    List[Any]=None,  lowerCamelCase						:    Tuple=True,  lowerCamelCase						:    str=True,  lowerCamelCase						:    Union[str, Any]=768,  lowerCamelCase						:    str=910,  lowerCamelCase						:    Optional[int]=512,  lowerCamelCase						:    Dict=24_858,  lowerCamelCase						:    str=True,  **lowerCamelCase						:    Dict,  ):

								'''simple docstring'''
								lowercase__    =			vocab_size
								lowercase__    =			max_position_embeddings
								lowercase__    =			hidden_size
								lowercase__    =			num_hidden_layers
								lowercase__    =			num_attention_heads
								lowercase__    =			intermediate_size
								lowercase__    =			hidden_act
								lowercase__    =			hidden_dropout_prob
								lowercase__    =			attention_probs_dropout_prob
								lowercase__    =			initializer_range
								lowercase__    =			type_vocab_size
								lowercase__    =			layer_norm_eps
								lowercase__    =			use_cache
								lowercase__    =			enable_pronunciation
								lowercase__    =			enable_shape
								lowercase__    =			pronunciation_embed_dim
								lowercase__    =			pronunciation_vocab_size
								lowercase__    =			shape_embed_dim
								lowercase__    =			shape_vocab_size
								lowercase__    =			concat_input
								lowercase__    =			position_embedding_type
								lowercase__    =			classifier_dropout
								super().__init__(pad_token_id=lowerCamelCase,  **lowerCamelCase  )












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   a			(						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   a			(						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




