
"""simple docstring"""




import logging
import random

import ray

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


__lowerCAmelCase							:  Tuple 				=		logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE       :






	'''simple docstring'''







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

			'''simple docstring'''






			a__   		=     False


	def    _UpperCamelCase							(  self :Any       ,    __magic_name__ :List[str]       ,    __magic_name__ :str       ,    __magic_name__ :str       ,    __magic_name__ :Tuple			)			->   str:

			'''simple docstring'''






			if not self.initialized:
					a__   		=     RagRetriever(
					    lowercase__       ,    question_encoder_tokenizer=lowercase__       ,    generator_tokenizer=lowercase__       ,    index=lowercase__       ,    init_retrieval=lowercase__       ,    )
					a__   		=     True


	def    _UpperCamelCase							(  self :Any			)			->   Tuple:

			'''simple docstring'''






			self.retriever.index.init_index()


	def    _UpperCamelCase							(  self :List[Any]			)			->   Tuple:

			'''simple docstring'''






			del self.retriever
			a__   		=     False






	def    _UpperCamelCase							(  self :Tuple       ,    __magic_name__ :str       ,    __magic_name__ :Optional[int]			)			->   Tuple:

			'''simple docstring'''






			a__		,	a__   		=     self.retriever._main_retrieve(lowercase__       ,    lowercase__			)
			a__   		=     self.retriever.index.get_doc_dicts(lowercase__			)
			return doc_ids, retrieved_doc_embeds, doc_dicts

class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







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

			'''simple docstring'''






			if index is not None and index.is_initialized() and len(lowercase__			) > 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__(
			    lowercase__       ,    question_encoder_tokenizer=lowercase__       ,    generator_tokenizer=lowercase__       ,    index=lowercase__       ,    init_retrieval=lowercase__       ,    )

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


	def    _UpperCamelCase							(  self :int			)			->   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    _UpperCamelCase							(  self :Optional[int]       ,    __magic_name__ :str       ,    __magic_name__ :List[Any]			)			->   Union[str, Any]:

			'''simple docstring'''






			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(lowercase__       ,    lowercase__			)			)
			else:
					a__		,	a__   		=     self._main_retrieve(lowercase__       ,    lowercase__			)
					a__   		=     self.index.get_doc_dicts(lowercase__			)
			return retrieved_doc_embeds, doc_ids, doc_dicts


	@classmethod
	def    _UpperCamelCase							(  cls :Dict       ,    __magic_name__ :List[str]       ,    __magic_name__ :Optional[int]=None       ,    **__magic_name__ :Union[str, Any]			)			->   List[str]:

			'''simple docstring'''






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


	@classmethod
	def    _UpperCamelCase							(  cls :int       ,    __magic_name__ :int       ,    __magic_name__ :Dict       ,    __magic_name__ :List[Any]=None       ,    **__magic_name__ :Any			)			->   List[Any]:

			'''simple docstring'''






			a__   		=     kwargs.pop('''config'''       ,    lowercase__			) or RagConfig.from_pretrained(lowercase__       ,    **lowercase__			)
			a__   		=     RagTokenizer.from_pretrained(lowercase__       ,    config=lowercase__			)
			a__   		=     rag_tokenizer.question_encoder
			a__   		=     rag_tokenizer.generator

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

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






	def    _UpperCamelCase							(  self :List[str]			)			->   Union[str, Any]:

			'''simple docstring'''






			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       ,    lowercase__			)
			        for worker in self.retrieval_workers
			    ]			)






"""simple docstring"""




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


snake_case					  =						logging.get_logger(__name__)

snake_case					  =						{
    # 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 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				UpperCAmelCase_							: Dict		    =							"graphormer"
				UpperCAmelCase_							: Optional[int]		    =							["past_key_values"]


				def __init__(							self						,      lowercase__ = 1						,      lowercase__ = 512 * 9						,      lowercase__ = 512 * 3						,      lowercase__ = 512						,      lowercase__ = 512						,      lowercase__ = 512						,      lowercase__ = 128						,      lowercase__ = 5						,      lowercase__ = 1024						,      lowercase__ = "multi_hop"						,      lowercase__ = 512						,      lowercase__ = False						,      lowercase__ = 12						,      lowercase__ = 768						,      lowercase__ = 768						,      lowercase__ = 32						,      lowercase__ = 0.1						,      lowercase__ = 0.1						,      lowercase__ = 0.0						,      lowercase__ = False						,      lowercase__ = False						,      lowercase__ = False						,      lowercase__ = "gelu"						,      lowercase__ = None						,      lowercase__ = False						,      lowercase__ = 0						,      lowercase__ = False						,      lowercase__ = 0.0						,      lowercase__ = 8						,      lowercase__ = None						,      lowercase__ = None						,      lowercase__ = True						,      lowercase__ = True						,      lowercase__=0						,      lowercase__=1						,      lowercase__=2						,      **lowercase__						,      )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      num_classes
									SCREAMING_SNAKE_CASE           =      num_atoms
									SCREAMING_SNAKE_CASE           =      num_in_degree
									SCREAMING_SNAKE_CASE           =      num_out_degree
									SCREAMING_SNAKE_CASE           =      num_edges
									SCREAMING_SNAKE_CASE           =      num_spatial
									SCREAMING_SNAKE_CASE           =      num_edge_dis
									SCREAMING_SNAKE_CASE           =      edge_type
									SCREAMING_SNAKE_CASE           =      multi_hop_max_dist
									SCREAMING_SNAKE_CASE           =      spatial_pos_max
									SCREAMING_SNAKE_CASE           =      max_nodes
									SCREAMING_SNAKE_CASE           =      num_hidden_layers
									SCREAMING_SNAKE_CASE           =      embedding_dim
									SCREAMING_SNAKE_CASE           =      embedding_dim
									SCREAMING_SNAKE_CASE           =      ffn_embedding_dim
									SCREAMING_SNAKE_CASE           =      num_attention_heads
									SCREAMING_SNAKE_CASE           =      dropout
									SCREAMING_SNAKE_CASE           =      attention_dropout
									SCREAMING_SNAKE_CASE           =      layerdrop
									SCREAMING_SNAKE_CASE           =      encoder_normalize_before
									SCREAMING_SNAKE_CASE           =      pre_layernorm
									SCREAMING_SNAKE_CASE           =      apply_graphormer_init
									SCREAMING_SNAKE_CASE           =      activation_fn
									SCREAMING_SNAKE_CASE           =      embed_scale
									SCREAMING_SNAKE_CASE           =      freeze_embeddings
									SCREAMING_SNAKE_CASE           =      num_trans_layers_to_freeze
									SCREAMING_SNAKE_CASE           =      share_input_output_embed
									SCREAMING_SNAKE_CASE           =      traceable
									SCREAMING_SNAKE_CASE           =      q_noise
									SCREAMING_SNAKE_CASE           =      qn_block_size

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

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