





import logging
import random

import ray

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


_a						=   logging.getLogger(__name__)






class       __A					:

			'''simple docstring'''







			def __init__(					self							):



				'''simple docstring'''







				lowerCamelCase__              =	False





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				self.retriever.index.init_index()





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







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






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase=None							):



				'''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 ,						)
				lowerCamelCase__              =	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 	__lowerCamelCase       (					self							):



				'''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 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







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





			@classmethod
			def 	__lowerCamelCase       (					cls ,						__lowerCAmelCase ,						__lowerCAmelCase=None ,						**__lowerCAmelCase							):



				'''simple docstring'''







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





			@classmethod
			def 	__lowerCamelCase       (					cls ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase=None ,						**__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	kwargs.pop('''config''' ,						__lowerCAmelCase							) or RagConfig.from_pretrained(__lowerCAmelCase ,						**__lowerCAmelCase							)
				lowerCamelCase__              =	RagTokenizer.from_pretrained(__lowerCAmelCase ,						config=__lowerCAmelCase							)
				lowerCamelCase__              =	rag_tokenizer.question_encoder
				lowerCamelCase__              =	rag_tokenizer.generator
				if indexed_dataset is not None:
					lowerCamelCase__              =	'''custom'''
					lowerCamelCase__              =	CustomHFIndex(config.retrieval_vector_size ,						__lowerCAmelCase							)
				else:
					lowerCamelCase__              =	cls._build_index(__lowerCAmelCase							)
				return cls(
				    __lowerCAmelCase ,						question_encoder_tokenizer=__lowerCAmelCase ,						generator_tokenizer=__lowerCAmelCase ,						retrieval_workers=__lowerCAmelCase ,						index=__lowerCAmelCase ,						)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_a						=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTNeoForCausalLM",
			    "GPTNeoForQuestionAnswering",
			    "GPTNeoForSequenceClassification",
			    "GPTNeoForTokenClassification",
			    "GPTNeoModel",
			    "GPTNeoPreTrainedModel",
			    "load_tf_weights_in_gpt_neo",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "FlaxGPTNeoForCausalLM",
			    "FlaxGPTNeoModel",
			    "FlaxGPTNeoPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neo import (
						    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoForCausalLM,
						    GPTNeoForQuestionAnswering,
						    GPTNeoForSequenceClassification,
						    GPTNeoForTokenClassification,
						    GPTNeoModel,
						    GPTNeoPreTrainedModel,
						    load_tf_weights_in_gpt_neo,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
			import sys

			_a						=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



