


"""simple docstring"""







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


lowerCamelCase_     :     Optional[Any]    		=						logging.get_logger(__name__)

lowerCamelCase_     :     int    		=						{
    # 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    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 __lowerCAmelCase				      =						"graphormer"
 __lowerCAmelCase				      =						["past_key_values"]


 def __init__(	self				,					__A = 1				,					__A = 512 * 9				,					__A = 512 * 3				,					__A = 512				,					__A = 512				,					__A = 512				,					__A = 128				,					__A = 5				,					__A = 1024				,					__A = "multi_hop"				,					__A = 512				,					__A = False				,					__A = 12				,					__A = 768				,					__A = 768				,					__A = 32				,					__A = 0.1				,					__A = 0.1				,					__A = 0.0				,					__A = False				,					__A = False				,					__A = False				,					__A = "gelu"				,					__A = None				,					__A = False				,					__A = 0				,					__A = False				,					__A = 0.0				,					__A = 8				,					__A = None				,					__A = None				,					__A = True				,					__A = True				,					__A=0				,					__A=1				,					__A=2				,					**__A				,					)      ->  int:
     a					=num_classes
     a					=num_atoms
     a					=num_in_degree
     a					=num_out_degree
     a					=num_edges
     a					=num_spatial
     a					=num_edge_dis
     a					=edge_type
     a					=multi_hop_max_dist
     a					=spatial_pos_max
     a					=max_nodes
     a					=num_hidden_layers
     a					=embedding_dim
     a					=embedding_dim
     a					=ffn_embedding_dim
     a					=num_attention_heads
     a					=dropout
     a					=attention_dropout
     a					=layerdrop
     a					=encoder_normalize_before
     a					=pre_layernorm
     a					=apply_graphormer_init
     a					=activation_fn
     a					=embed_scale
     a					=freeze_embeddings
     a					=num_trans_layers_to_freeze
     a					=share_input_output_embed
     a					=traceable
     a					=q_noise
     a					=qn_block_size

     # These parameters are here for future extensions
     # atm, the model only supports self attention
     a					=kdim
     a					=vdim
     a					=self_attention
     a					=bias

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



"""simple docstring"""







import logging
import random

import ray

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


lowerCamelCase_     :     List[str]    		=						logging.getLogger(__name__)






class    __A       :






 """simple docstring"""




 def __init__(	self		)      ->  Dict:
     a					=False


 def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A				,					__A		)      ->  Tuple:
     if not self.initialized:
         a					=RagRetriever(
             __A				,					question_encoder_tokenizer=__A				,					generator_tokenizer=__A				,					index=__A				,					init_retrieval=__A				,					)
         a					=True


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
     self.retriever.index.init_index()


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
     # delete the old self.retriever object before assigning the new index
     del self.retriever
     a					=False





 def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A		)      ->  str:
     a	,   a					=self.retriever._main_retrieve(__A				,					__A		)
     a					=self.retriever.index.get_doc_dicts(__A		)
     return doc_ids, retrieved_doc_embeds, doc_dicts






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 def __init__(	self				,					__A				,					__A				,					__A				,					__A				,					__A=None		)      ->  int:
     if index is not None and index.is_initialized() and len(__A		) > 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__(
         __A				,					question_encoder_tokenizer=__A				,					generator_tokenizer=__A				,					index=__A				,					init_retrieval=__A				,					)

     a					=retrieval_workers
     a					=question_encoder_tokenizer
     a					=generator_tokenizer
     if len(self.retrieval_workers		) > 0:
         ray.get(
             [
                 worker.create_rag_retriever.remote(__A				,					__A				,					__A				,					__A		)
                 for worker in self.retrieval_workers
             ]		)


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


 @classmethod
 def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A=None				,					**__A		)      ->  Tuple:
     return super(__A				,					cls		).get_tokenizers(__A				,					__A				,					**__A		)


 @classmethod
 def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A				,					__A=None				,					**__A		)      ->  Optional[Any]:
     a					=kwargs.pop('''config'''				,					__A		) or RagConfig.from_pretrained(__A				,					**__A		)
     a					=RagTokenizer.from_pretrained(__A				,					config=__A		)
     a					=rag_tokenizer.question_encoder
     a					=rag_tokenizer.generator

     if indexed_dataset is not None:
         a					='''custom'''
         a					=CustomHFIndex(config.retrieval_vector_size				,					__A		)
     else:
         a					=cls._build_index(__A		)

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





 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
     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					=self._build_index(self.config		)

     ray.get(
         [
             worker.create_rag_retriever.remote(
                 self.config				,					self.question_encoder_tokenizer				,					self.generator_tokenizer				,					__A		)
             for worker in self.retrieval_workers
         ]		)