





import logging
import random

import ray

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


UpperCAmelCase							   =      logging.getLogger(__name__)






class 							_SCREAMING_SNAKE_CASE:
   def __init__(    self    )	->int:
        a  :    Optional[Any]				=						False
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->Tuple:
        if not self.initialized:
             a  :    Optional[Any]				=						RagRetriever(
                 lowerCAmelCase_							,     question_encoder_tokenizer=lowerCAmelCase_							,     generator_tokenizer=lowerCAmelCase_							,     index=lowerCAmelCase_							,     init_retrieval=lowerCAmelCase_							,     )
             a  :    str				=						True
   def _lowercase(    self    )	->str:
        self.retriever.index.init_index()
   def _lowercase(    self    )	->Any:
        # delete the old self.retriever object before assigning the new index
        del self.retriever
        a  :    Dict				=						False


   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->Optional[Any]:
        a  :    Tuple				=						self.retriever._main_retrieve(lowerCAmelCase_							,     lowerCAmelCase_    )
        a  :    List[Any]				=						self.retriever.index.get_doc_dicts(lowerCAmelCase_    )
        return doc_ids, retrieved_doc_embeds, doc_dicts






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   def __init__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_=None    )	->List[Any]:
        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_							,     )

        a  :    str				=						retrieval_workers
        a  :    Optional[int]				=						question_encoder_tokenizer
        a  :    Dict				=						generator_tokenizer
        if len(self.retrieval_workers    ) > 0:
             ray.get(
                 [
                     worker.create_rag_retriever.remote(lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )
                     for worker in self.retrieval_workers
                 ]    )
   def _lowercase(    self    )	->Dict:
        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 _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->int:
        if len(self.retrieval_workers    ) > 0:
             # Select a random retrieval actor.
             a  :    Optional[Any]				=						self.retrieval_workers[random.randint(0							,     len(self.retrieval_workers    ) - 1    )]
             a  :    List[str]				=						ray.get(
                 random_worker.retrieve.remote(lowerCAmelCase_							,     lowerCAmelCase_    )    )
        else:
             a  :    List[Any]				=						self._main_retrieve(lowerCAmelCase_							,     lowerCAmelCase_    )
             a  :    Dict				=						self.index.get_doc_dicts(lowerCAmelCase_    )
        return retrieved_doc_embeds, doc_ids, doc_dicts
   @classmethod
   def _lowercase(    cls							,     lowerCAmelCase_							,     lowerCAmelCase_=None							,     **lowerCAmelCase_    )	->str:
        return super(lowerCAmelCase_							,     cls    ).get_tokenizers(lowerCAmelCase_							,     lowerCAmelCase_							,     **lowerCAmelCase_    )
   @classmethod
   def _lowercase(    cls							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_=None							,     **lowerCAmelCase_    )	->Union[str, Any]:
        a  :    Optional[int]				=						kwargs.pop('''config'''							,     lowerCAmelCase_    ) or RagConfig.from_pretrained(lowerCAmelCase_							,     **lowerCAmelCase_    )
        a  :    str				=						RagTokenizer.from_pretrained(lowerCAmelCase_							,     config=lowerCAmelCase_    )
        a  :    Optional[Any]				=						rag_tokenizer.question_encoder
        a  :    Union[str, Any]				=						rag_tokenizer.generator

        if indexed_dataset is not None:
             a  :    Any				=						'''custom'''
             a  :    Dict				=						CustomHFIndex(config.retrieval_vector_size							,     lowerCAmelCase_    )
        else:
             a  :    Optional[Any]				=						cls._build_index(lowerCAmelCase_    )

        return cls(
            lowerCAmelCase_							,     question_encoder_tokenizer=lowerCAmelCase_							,     generator_tokenizer=lowerCAmelCase_							,     retrieval_workers=lowerCAmelCase_							,     index=lowerCAmelCase_							,     )


   def _lowercase(    self    )	->Tuple:
        logger.info('''re-loading the new dataset with embeddings'''    )
        # access from the training loop

        ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]    )

        # build the index object again
        a  :    List[Any]				=						self._build_index(self.config    )

        ray.get(
            [
                worker.create_rag_retriever.remote(
                    self.config							,     self.question_encoder_tokenizer							,     self.generator_tokenizer							,     lowerCAmelCase_    )
                for worker in self.retrieval_workers
            ]    )
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase							   =      logging.get_logger(__name__)

UpperCAmelCase							   =      {
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						int 			=   'graphormer'
   UpperCamelCase_				:						Tuple 			=   ['past_key_values']
   def __init__(    self							,     lowerCAmelCase_ = 1							,     lowerCAmelCase_ = 5_12 * 9							,     lowerCAmelCase_ = 5_12 * 3							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 1_28							,     lowerCAmelCase_ = 5							,     lowerCAmelCase_ = 10_24							,     lowerCAmelCase_ = "multi_hop"							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 12							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 32							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = "gelu"							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = 8							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = True							,     lowerCAmelCase_=0							,     lowerCAmelCase_=1							,     lowerCAmelCase_=2							,     **lowerCAmelCase_							,     )	->Optional[int]:
        a  :    List[Any]				=						num_classes
        a  :    Any				=						num_atoms
        a  :    Dict				=						num_in_degree
        a  :    int				=						num_out_degree
        a  :    Dict				=						num_edges
        a  :    Tuple				=						num_spatial
        a  :    Dict				=						num_edge_dis
        a  :    Optional[int]				=						edge_type
        a  :    List[Any]				=						multi_hop_max_dist
        a  :    Optional[int]				=						spatial_pos_max
        a  :    str				=						max_nodes
        a  :    Optional[Any]				=						num_hidden_layers
        a  :    Any				=						embedding_dim
        a  :    Dict				=						embedding_dim
        a  :    Dict				=						ffn_embedding_dim
        a  :    List[Any]				=						num_attention_heads
        a  :    List[str]				=						dropout
        a  :    int				=						attention_dropout
        a  :    Optional[Any]				=						layerdrop
        a  :    str				=						encoder_normalize_before
        a  :    Optional[int]				=						pre_layernorm
        a  :    Tuple				=						apply_graphormer_init
        a  :    int				=						activation_fn
        a  :    Any				=						embed_scale
        a  :    Optional[int]				=						freeze_embeddings
        a  :    Tuple				=						num_trans_layers_to_freeze
        a  :    List[str]				=						share_input_output_embed
        a  :    List[Any]				=						traceable
        a  :    Optional[Any]				=						q_noise
        a  :    Dict				=						qn_block_size

        # These parameters are here for future extensions
        # atm, the model only supports self attention
        a  :    List[Any]				=						kdim
        a  :    List[str]				=						vdim
        a  :    int				=						self_attention
        a  :    List[Any]				=						bias

        super().__init__(
            pad_token_id=lowerCAmelCase_							,     bos_token_id=lowerCAmelCase_							,     eos_token_id=lowerCAmelCase_							,     **lowerCAmelCase_							,     )