




'''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 		lowercase__   (		__A:	Dict[str, torch.Tensor]				):





					'''simple docstring'''






					__magic_name__							:	Dict    							=							[]
					__magic_name__							:	Union[str, Any]    							=							[]
					__magic_name__							:	Dict    							=							[]

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

					__magic_name__							:	List[str]    							=							torch.tensor(
					    __A							,dtype=torch.intaa							,device=protein['''aatype'''].device							,)
					__magic_name__							:	Optional[int]    							=							torch.tensor(
					    __A							,dtype=torch.intaa							,device=protein['''aatype'''].device							,)
					__magic_name__							:	Union[str, Any]    							=							torch.tensor(
					    __A							,dtype=torch.floataa							,device=protein['''aatype'''].device							,)
					__magic_name__							:	Any    							=							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
					__magic_name__							:	Optional[Any]    							=							restype_atomaa_to_atomaa[protein_aatype]
					__magic_name__							:	Any    							=							restype_atomaa_mask[protein_aatype]

					__magic_name__							:	Dict    							=							residx_atomaa_mask
					__magic_name__							:	Tuple    							=							residx_atomaa_to_atomaa.long()

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

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

					__magic_name__							:	str    							=							restype_atomaa_mask[protein_aatype]
					__magic_name__							:	Optional[int]    							=							residx_atomaa_mask

					return protein







def 		lowercase__   (		__A:	Dict[str, torch.Tensor]				):





					'''simple docstring'''






					__magic_name__							:	int    							=							tree_map(lambda __A				: torch.tensor(__A							,device=batch['''aatype'''].device				)							,__A							,np.ndarray				)
					__magic_name__							:	Optional[Any]    							=							tensor_tree_map(lambda __A				: np.array(__A				)							,make_atomaa_masks(__A				)				)
					return out









from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput


@dataclass
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   List[Any]       ,						lowerCamelCase_			:   Optional[Any]=1       ,						lowerCamelCase_			:   Any=0       ,						lowerCamelCase_			:   Dict=2       ,						lowerCamelCase_			:   Dict=512       ,						lowerCamelCase_			:   int="cls"       ,						lowerCamelCase_			:   Dict=False       ,						lowerCamelCase_			:   List[Any]=True       ,						**lowerCamelCase_			:   Optional[Any]       ,						)						->   Optional[int]:
								super().__init__(pad_token_id=lowerCamelCase_       ,						bos_token_id=lowerCamelCase_       ,						eos_token_id=lowerCamelCase_       ,						**lowerCamelCase_  )
								__magic_name__							:	Dict    							=							project_dim
								__magic_name__							:	int    							=							pooler_fn
								__magic_name__							:	str    							=							learn_encoder
								__magic_name__							:	Optional[int]    							=							use_attention_mask






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=[R'''pooler''', R'''logit_scale''']
			UpperCamelCase__								=[R'''position_ids''', R'''predictions.decoder.bias''']
			UpperCamelCase__								='''roberta'''
			UpperCamelCase__								=RobertaSeriesConfig

			def __init__( self			:   str       ,						lowerCamelCase_			:   Optional[Any]  )						->   Optional[Any]:
								super().__init__(lowerCamelCase_  )
								__magic_name__							:	Tuple    							=							XLMRobertaModel(lowerCamelCase_  )
								__magic_name__							:	Any    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
								__magic_name__							:	Optional[int]    							=							getattr(lowerCamelCase_       ,						'''has_pre_transformation'''       ,						lowerCamelCase_  )
								if self.has_pre_transformation:
													__magic_name__							:	List[Any]    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
													__magic_name__							:	Union[str, Any]    							=							nn.LayerNorm(config.hidden_size       ,						eps=config.layer_norm_eps  )
								self.post_init()


			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						)						->   List[Any]:
								__magic_name__							:	Optional[int]    							=							return_dict if return_dict is not None else self.config.use_return_dict

								__magic_name__							:	Any    							=							self.base_model(
								    input_ids=lowerCamelCase_       ,						attention_mask=lowerCamelCase_       ,						token_type_ids=lowerCamelCase_       ,						position_ids=lowerCamelCase_       ,						head_mask=lowerCamelCase_       ,						inputs_embeds=lowerCamelCase_       ,						encoder_hidden_states=lowerCamelCase_       ,						encoder_attention_mask=lowerCamelCase_       ,						output_attentions=lowerCamelCase_       ,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,						return_dict=lowerCamelCase_       ,						)

								if self.has_pre_transformation:
													__magic_name__							:	Any    							=							outputs['''hidden_states'''][-2]
													__magic_name__							:	Dict    							=							self.pre_LN(lowerCamelCase_  )
													__magic_name__							:	int    							=							self.transformation_pre(lowerCamelCase_  )

													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)
								else:
													__magic_name__							:	Union[str, Any]    							=							self.transformation(outputs.last_hidden_state  )
													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)



