





import logging
import random

import ray

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


A   : List[Any]			=				logging.getLogger(__name__)


class   A      :
			'''simple docstring'''




			def __init__(      self       :   Any       )     ->     int:







					"""simple docstring"""






					A__  				=       False






			def        a_	(      self       :   List[str]							,     __lowerCAmelCase       :   Optional[int]							,     __lowerCAmelCase       :   Dict							,     __lowerCAmelCase       :   Union[str, Any]							,     __lowerCAmelCase       :   Optional[int]       )     ->     List[str]:







					"""simple docstring"""






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






			def        a_	(      self       :   str       )     ->     Optional[int]:







					"""simple docstring"""






					self.retriever.index.init_index()







			def        a_	(      self       :   Union[str, Any]							,     __lowerCAmelCase       :   int							,     __lowerCAmelCase       :   Tuple       )     ->     Tuple:







					"""simple docstring"""






					A__				,      A__  				=       self.retriever._main_retrieve(__lowerCAmelCase							,     __lowerCAmelCase       )
					return doc_ids, retrieved_doc_embeds


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			def __init__(      self       :   int							,     __lowerCAmelCase       :   str							,     __lowerCAmelCase       :   Optional[int]							,     __lowerCAmelCase       :   int							,     __lowerCAmelCase       :   Dict							,     __lowerCAmelCase       :   List[Any]=None       )     ->     Tuple:







					"""simple docstring"""






					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
					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       :   Tuple       )     ->     Optional[Any]:







					"""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        a_	(      self       :   Tuple							,     __lowerCAmelCase       :   List[Any]							,     __lowerCAmelCase       :   List[str]       )     ->     Tuple:







					"""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__  				=       ray.get(random_worker.retrieve.remote(__lowerCAmelCase							,     __lowerCAmelCase       )       )
					else:
							A__				,      A__  				=       self._main_retrieve(__lowerCAmelCase							,     __lowerCAmelCase       )
					return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCAmelCase       )






			@classmethod
			def        a_	(      cls       :   Union[str, Any]							,     __lowerCAmelCase       :   List[Any]							,     __lowerCAmelCase       :   Any=None							,     **__lowerCAmelCase       :   Any       )     ->     Tuple:







					"""simple docstring"""






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







			@classmethod
			def        a_	(      cls       :   Tuple							,     __lowerCAmelCase       :   Optional[int]							,     __lowerCAmelCase       :   int							,     __lowerCAmelCase       :   Optional[Any]=None							,     **__lowerCAmelCase       :   List[Any]       )     ->     List[str]:







					"""simple docstring"""






					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							,     )










from collections import OrderedDict
from typing import Mapping

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


A   : Tuple			=				logging.get_logger(__name__)

A   : Optional[int]			=				{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Union[str, Any]          =   '''roberta'''






			def __init__(      self       :   Any							,     __lowerCAmelCase       :   Tuple=5_02_65							,     __lowerCAmelCase       :   Optional[int]=7_68							,     __lowerCAmelCase       :   Union[str, Any]=12							,     __lowerCAmelCase       :   Dict=12							,     __lowerCAmelCase       :   Optional[Any]=30_72							,     __lowerCAmelCase       :   Any="gelu"							,     __lowerCAmelCase       :   Optional[Any]=0.1							,     __lowerCAmelCase       :   Optional[int]=0.1							,     __lowerCAmelCase       :   Optional[Any]=5_12							,     __lowerCAmelCase       :   int=2							,     __lowerCAmelCase       :   List[str]=0.0_2							,     __lowerCAmelCase       :   Dict=1e-12							,     __lowerCAmelCase       :   Optional[int]=1							,     __lowerCAmelCase       :   Tuple=0							,     __lowerCAmelCase       :   int=2							,     __lowerCAmelCase       :   Dict="absolute"							,     __lowerCAmelCase       :   Optional[Any]=True							,     __lowerCAmelCase       :   Union[str, Any]=None							,     **__lowerCAmelCase       :   str							,     )     ->     str:







					"""simple docstring"""






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

					A__  				=       vocab_size
					A__  				=       hidden_size
					A__  				=       num_hidden_layers
					A__  				=       num_attention_heads
					A__  				=       hidden_act
					A__  				=       intermediate_size
					A__  				=       hidden_dropout_prob
					A__  				=       attention_probs_dropout_prob
					A__  				=       max_position_embeddings
					A__  				=       type_vocab_size
					A__  				=       initializer_range
					A__  				=       layer_norm_eps
					A__  				=       position_embedding_type
					A__  				=       use_cache
					A__  				=       classifier_dropout


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			@property
			def        a_	(      self       :   int       )     ->     Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					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),
					    ]       )



