"""simple docstring"""






import logging
import random

import ray

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


lowerCAmelCase__						:						int		  =			logging.getLogger(__name__)
class        snake_case :




					"""simple docstring"""

					def __init__(       self     :			str    ):
									UpperCAmelCase__    =							False







					def 	__lowerCAmelCase		(       self     :			List[Any]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			List[str]    ):
									if not self.initialized:
													UpperCAmelCase__    =							RagRetriever(
													    UpperCamelCase_					,question_encoder_tokenizer=UpperCamelCase_					,generator_tokenizer=UpperCamelCase_					,index=UpperCamelCase_					,init_retrieval=UpperCamelCase_					,)
													UpperCAmelCase__    =							True







					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									self.retriever.index.init_index()







					def 	__lowerCAmelCase		(       self     :			List[Any]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Optional[Any]    ):
									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.retriever._main_retrieve(UpperCamelCase_					,UpperCamelCase_    )
									return doc_ids, retrieved_doc_embeds






class        snake_case (				__lowerCamelCase       ):




					"""simple docstring"""

					def __init__(       self     :			Union[str, Any]					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Any=None    ):
									if index is not None and index.is_initialized() and len(UpperCamelCase_    ) > 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__(
									    UpperCamelCase_					,question_encoder_tokenizer=UpperCamelCase_					,generator_tokenizer=UpperCamelCase_					,index=UpperCamelCase_					,init_retrieval=UpperCamelCase_					,)
									UpperCAmelCase__    =							retrieval_workers
									if len(self.retrieval_workers    ) > 0:
													ray.get(
													    [
													        worker.create_rag_retriever.remote(UpperCamelCase_					,UpperCamelCase_					,UpperCamelCase_					,UpperCamelCase_    )
													        for worker in self.retrieval_workers
													    ]    )







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									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     :			Dict					,lowerCamelCase__     :			str					,lowerCamelCase__     :			Optional[Any]    ):
									if len(self.retrieval_workers    ) > 0:
													# Select a random retrieval actor.
													UpperCAmelCase__    =							self.retrieval_workers[random.randint(0					,len(self.retrieval_workers    ) - 1    )]
													UpperCAmelCase__   ,			UpperCAmelCase__    =							ray.get(random_worker.retrieve.remote(UpperCamelCase_					,UpperCamelCase_    )    )
									else:
													UpperCAmelCase__   ,			UpperCAmelCase__    =							self._main_retrieve(UpperCamelCase_					,UpperCamelCase_    )
									return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase_    )







					@classmethod
					def 	__lowerCAmelCase		(       cls     :			Any					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Tuple=None					,**lowerCamelCase__     :			int    ):
									return super(UpperCamelCase_					,cls    ).get_tokenizers(UpperCamelCase_					,UpperCamelCase_					,**UpperCamelCase_    )







					@classmethod
					def 	__lowerCAmelCase		(       cls     :			Optional[Any]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			int=None					,**lowerCamelCase__     :			int    ):
									UpperCAmelCase__    =							kwargs.pop('config'					,UpperCamelCase_    ) or RagConfig.from_pretrained(UpperCamelCase_					,**UpperCamelCase_    )
									UpperCAmelCase__    =							RagTokenizer.from_pretrained(UpperCamelCase_					,config=UpperCamelCase_    )
									UpperCAmelCase__    =							rag_tokenizer.question_encoder
									UpperCAmelCase__    =							rag_tokenizer.generator
									if indexed_dataset is not None:
													UpperCAmelCase__    =							'custom'
													UpperCAmelCase__    =							CustomHFIndex(config.retrieval_vector_size					,UpperCamelCase_    )
									else:
													UpperCAmelCase__    =							cls._build_index(UpperCamelCase_    )
									return cls(
									    UpperCamelCase_					,question_encoder_tokenizer=UpperCamelCase_					,generator_tokenizer=UpperCamelCase_					,retrieval_workers=UpperCamelCase_					,index=UpperCamelCase_					,)


"""simple docstring"""






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


lowerCAmelCase__						:						Any		  =			logging.get_logger(__name__)

lowerCAmelCase__						:						str		  =			{'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				"ctrl"
					snake_case__       					=				["past_key_values"]
					snake_case__       					=				{
					    "max_position_embeddings": "n_positions",
					    "hidden_size": "n_embd",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}







					def __init__(       self     :			Any					,lowerCamelCase__     :			str=246_534					,lowerCamelCase__     :			List[str]=256					,lowerCamelCase__     :			Optional[int]=1_280					,lowerCamelCase__     :			Any=8_192					,lowerCamelCase__     :			int=48					,lowerCamelCase__     :			Optional[Any]=16					,lowerCamelCase__     :			Union[str, Any]=0.1					,lowerCamelCase__     :			Dict=0.1					,lowerCamelCase__     :			List[str]=1e-6					,lowerCamelCase__     :			List[str]=0.0_2					,lowerCamelCase__     :			Tuple=True					,**lowerCamelCase__     :			Optional[Any]					,):
									UpperCAmelCase__    =							vocab_size
									UpperCAmelCase__    =							n_positions
									UpperCAmelCase__    =							n_embd
									UpperCAmelCase__    =							n_layer
									UpperCAmelCase__    =							n_head
									UpperCAmelCase__    =							dff
									UpperCAmelCase__    =							resid_pdrop
									UpperCAmelCase__    =							embd_pdrop
									UpperCAmelCase__    =							layer_norm_epsilon
									UpperCAmelCase__    =							initializer_range

									UpperCAmelCase__    =							use_cache

									super().__init__(**lowerCamelCase__    )



