



import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__snake_case       :	Optional[Any]            =logging.getLogger(__name__)



class  lowerCamelCase__    :




		'''simple docstring'''



		def __init__(self				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  False



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						if not self.initialized:
										lowerCAmelCase__  :       Tuple				=  RagRetriever(
										    _lowerCAmelCase	,question_encoder_tokenizer=_lowerCAmelCase	,generator_tokenizer=_lowerCAmelCase	,index=_lowerCAmelCase	,init_retrieval=_lowerCAmelCase	,)
										lowerCAmelCase__  :       Tuple				=  True



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						self.retriever.index.init_index()



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__  :       List[str]				=  self.retriever._main_retrieve(_lowerCAmelCase	,_lowerCAmelCase				)
						return doc_ids, retrieved_doc_embeds



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase=None				)  ->    Union[str, Any]:

						"""simple docstring"""






						if index is not None and index.is_initialized() and len(_lowerCAmelCase				) > 0:
										raise ValueError(
										    '''When using Ray for distributed fine-tuning, '''
										    '''you\'ll need to provide the paths instead, '''
										    '''as the dataset and the index are loaded '''
										    '''separately. More info in examples/rag/use_own_knowledge_dataset.py '''				)
						super().__init__(
						    _lowerCAmelCase	,question_encoder_tokenizer=_lowerCAmelCase	,generator_tokenizer=_lowerCAmelCase	,index=_lowerCAmelCase	,init_retrieval=_lowerCAmelCase	,)
						lowerCAmelCase__  :       Tuple				=  retrieval_workers
						if len(self.retrieval_workers				) > 0:
										ray.get(
										    [
										        worker.create_rag_retriever.remote(_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase				)
										        for worker in self.retrieval_workers
										    ]				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						logger.info('''initializing retrieval'''				)

						if len(self.retrieval_workers				) > 0:
										ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]				)
						else:
										# Non-distributed training. Load index into this same process.
										self.index.init_index()



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						if len(self.retrieval_workers				) > 0:
										# Select a random retrieval actor.
										lowerCAmelCase__  :       Union[str, Any]				=  self.retrieval_workers[random.randint(0	,len(self.retrieval_workers				) - 1				)]
										lowerCAmelCase__		,	lowerCAmelCase__  :       int				=  ray.get(random_worker.retrieve.remote(_lowerCAmelCase	,_lowerCAmelCase				)				)
						else:
										lowerCAmelCase__		,	lowerCAmelCase__  :       Dict				=  self._main_retrieve(_lowerCAmelCase	,_lowerCAmelCase				)
						return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_lowerCAmelCase				)



		@classmethod
		def 	lowerCAmelCase__   (cls	,__lowerCamelCase	,__lowerCamelCase=None	,**__lowerCamelCase				)  ->    List[Any]:

						"""simple docstring"""






						return super(_lowerCAmelCase	,cls				).get_tokenizers(_lowerCAmelCase	,_lowerCAmelCase	,**_lowerCAmelCase				)



		@classmethod
		def 	lowerCAmelCase__   (cls	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase=None	,**__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  kwargs.pop('''config'''	,_lowerCAmelCase				) or RagConfig.from_pretrained(_lowerCAmelCase	,**_lowerCAmelCase				)
						lowerCAmelCase__  :       Optional[int]				=  RagTokenizer.from_pretrained(_lowerCAmelCase	,config=_lowerCAmelCase				)
						lowerCAmelCase__  :       Tuple				=  rag_tokenizer.question_encoder
						lowerCAmelCase__  :       List[str]				=  rag_tokenizer.generator
						if indexed_dataset is not None:
										lowerCAmelCase__  :       int				=  '''custom'''
										lowerCAmelCase__  :       Tuple				=  CustomHFIndex(config.retrieval_vector_size	,_lowerCAmelCase				)
						else:
										lowerCAmelCase__  :       str				=  cls._build_index(_lowerCAmelCase				)
						return cls(
						    _lowerCAmelCase	,question_encoder_tokenizer=_lowerCAmelCase	,generator_tokenizer=_lowerCAmelCase	,retrieval_workers=_lowerCAmelCase	,index=_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 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict[str, torch.Tensor]):





				'''simple docstring'''

				lowerCAmelCase__  :       List[Any]				=  []
				lowerCAmelCase__  :       int				=  []
				lowerCAmelCase__  :       Optional[int]				=  []

				for rt in rc.restypes:
								lowerCAmelCase__  :       int				=  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])
								lowerCAmelCase__  :       Any				=  {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)

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

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

				# create the gather indices for mapping back
				lowerCAmelCase__  :       Union[str, Any]				=  restype_atomaa_to_atomaa[protein_aatype]
				lowerCAmelCase__  :       int				=  residx_atomaa_to_atomaa.long()

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

				lowerCAmelCase__  :       int				=  restype_atomaa_mask[protein_aatype]
				lowerCAmelCase__  :       int				=  residx_atomaa_mask

				return protein






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict[str, torch.Tensor]):





				'''simple docstring'''

				lowerCAmelCase__  :       Optional[Any]				=  tree_map(lambda lowerCamelCase_: torch.tensor(lowerCamelCase_ ,device=batch['''aatype'''].device) ,lowerCamelCase_ ,np.ndarray)
				lowerCAmelCase__  :       Union[str, Any]				=  tensor_tree_map(lambda lowerCamelCase_: np.array(lowerCamelCase_) ,make_atomaa_masks(lowerCamelCase_))
				return out

