





'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase					:						List[Any]					     =							logging.get_logger(__name__)

_lowerCamelCase					:						Any					     =							{
    'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/config.json',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}



class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     '''rembert'''







		def __init__(self    :   List[str]   ,   _lowerCAmelCase    :   List[str]=25_0300   ,   _lowerCAmelCase    :   Any=1152   ,   _lowerCAmelCase    :   Optional[Any]=32   ,   _lowerCAmelCase    :   Any=18   ,   _lowerCAmelCase    :   Tuple=256   ,   _lowerCAmelCase    :   Tuple=1664   ,   _lowerCAmelCase    :   List[str]=4608   ,   _lowerCAmelCase    :   List[str]="gelu"   ,   _lowerCAmelCase    :   int=0.0   ,   _lowerCAmelCase    :   Optional[Any]=0.0   ,   _lowerCAmelCase    :   List[str]=0.1   ,   _lowerCAmelCase    :   List[Any]=512   ,   _lowerCAmelCase    :   Any=2   ,   _lowerCAmelCase    :   List[str]=0.02   ,   _lowerCAmelCase    :   Union[str, Any]=1e-12   ,   _lowerCAmelCase    :   List[str]=True   ,   _lowerCAmelCase    :   Dict=0   ,   _lowerCAmelCase    :   Union[str, Any]=312   ,   _lowerCAmelCase    :   Optional[Any]=313   ,   **_lowerCAmelCase    :   Union[str, Any]   ,   ):
			super().__init__(pad_token_id=_lowerCAmelCase   ,   bos_token_id=_lowerCAmelCase   ,   eos_token_id=_lowerCAmelCase   ,   **_lowerCAmelCase   )

			A					=   vocab_size
			A					=   input_embedding_size
			A					=   output_embedding_size
			A					=   max_position_embeddings
			A					=   hidden_size
			A					=   num_hidden_layers
			A					=   num_attention_heads
			A					=   intermediate_size
			A					=   hidden_act
			A					=   hidden_dropout_prob
			A					=   attention_probs_dropout_prob
			A					=   classifier_dropout_prob
			A					=   initializer_range
			A					=   type_vocab_size
			A					=   layer_norm_eps
			A					=   use_cache
			A					=   False






class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		@property
		def 							A  (self    :   Dict   ):
			if self.task == "multiple-choice":
				A					=   {0: """batch""", 1: """choice""", 2: """sequence"""}
			else:
				A					=   {0: """batch""", 1: """sequence"""}
			return OrderedDict(
			    [
			        ("""input_ids""", dynamic_axis),
			        ("""attention_mask""", dynamic_axis),
			        ("""token_type_ids""", dynamic_axis),
			    ]   )







		@property
		def 							A  (self    :   int   ):
			return 1e-4








'''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 				__UpperCAmelCase	:







		'''simple docstring'''







		def __init__(self    :   Any   ):
			A					=   False







		def 							A  (self    :   Tuple   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Any   ,   _lowerCAmelCase    :   Tuple   ,   _lowerCAmelCase    :   Dict   ):
			if not self.initialized:
				A					=   RagRetriever(
				    _lowerCAmelCase   ,   question_encoder_tokenizer=_lowerCAmelCase   ,   generator_tokenizer=_lowerCAmelCase   ,   index=_lowerCAmelCase   ,   init_retrieval=_lowerCAmelCase   ,   )
				A					=   True







		def 							A  (self    :   List[str]   ):
			self.retriever.index.init_index()







		def 							A  (self    :   List[str]   ):
			# delete the old self.retriever object before assigning the new index
			del self.retriever
			A					=   False







		def 							A  (self    :   Any   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   Optional[int]   ):
			A	,     A					=   self.retriever._main_retrieve(_lowerCAmelCase   ,   _lowerCAmelCase   )
			A					=   self.retriever.index.get_doc_dicts(_lowerCAmelCase   )
			return doc_ids, retrieved_doc_embeds, doc_dicts






class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		def __init__(self    :   Any   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Union[str, Any]   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   List[Any]=None   ):
			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					=   retrieval_workers
			A					=   question_encoder_tokenizer
			A					=   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 							A  (self    :   Optional[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 							A  (self    :   int   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   List[Any]   ):
			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(_lowerCAmelCase   ,   _lowerCAmelCase   )   )
			else:
				A	,     A					=   self._main_retrieve(_lowerCAmelCase   ,   _lowerCAmelCase   )
				A					=   self.index.get_doc_dicts(_lowerCAmelCase   )
			return retrieved_doc_embeds, doc_ids, doc_dicts







		@classmethod
		def 							A  (cls    :   Dict   ,   _lowerCAmelCase    :   Tuple   ,   _lowerCAmelCase    :   str=None   ,   **_lowerCAmelCase    :   List[Any]   ):
			return super(_lowerCAmelCase   ,   cls   ).get_tokenizers(_lowerCAmelCase   ,   _lowerCAmelCase   ,   **_lowerCAmelCase   )







		@classmethod
		def 							A  (cls    :   List[str]   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   List[Any]   ,   _lowerCAmelCase    :   Optional[int]=None   ,   **_lowerCAmelCase    :   Optional[int]   ):
			A					=   kwargs.pop("""config"""   ,   _lowerCAmelCase   ) or RagConfig.from_pretrained(_lowerCAmelCase   ,   **_lowerCAmelCase   )
			A					=   RagTokenizer.from_pretrained(_lowerCAmelCase   ,   config=_lowerCAmelCase   )
			A					=   rag_tokenizer.question_encoder
			A					=   rag_tokenizer.generator

			if indexed_dataset is not None:
				A					=   """custom"""
				A					=   CustomHFIndex(config.retrieval_vector_size   ,   _lowerCAmelCase   )
			else:
				A					=   cls._build_index(_lowerCAmelCase   )

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







		def 							A  (self    :   str   ):
			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   ,   _lowerCAmelCase   )
			        for worker in self.retrieval_workers
			    ]   )

