code: 
  
  
  """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				,					)
style_context: 
  
  
  """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
           ]		)