


import logging
import random

import ray

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


lowerCamelCase		=	logging.getLogger(__name__)





class       _a							:




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




	def 							UpperCAmelCase__(      self		:	Optional[Any]	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	List[Any]	,      _SCREAMING_SNAKE_CASE		:	Dict	,      _SCREAMING_SNAKE_CASE		:	Any	)-> Union[str, Any]:
			if not self.initialized:
					lowerCAmelCase__     :    str					  =      RagRetriever(
					    _SCREAMING_SNAKE_CASE	,      question_encoder_tokenizer=_SCREAMING_SNAKE_CASE	,      generator_tokenizer=_SCREAMING_SNAKE_CASE	,      index=_SCREAMING_SNAKE_CASE	,      init_retrieval=_SCREAMING_SNAKE_CASE	,      )
					lowerCAmelCase__     :    Union[str, Any]					  =      True




	def 							UpperCAmelCase__(      self		:	Dict	)-> str:
			self.retriever.index.init_index()




	def 							UpperCAmelCase__(      self		:	int	)-> Optional[int]:
			# delete the old self.retriever object before assigning the new index
			del self.retriever
			lowerCAmelCase__     :    int					  =      False




	def 							UpperCAmelCase__(      self		:	Optional[int]	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	Optional[int]	)-> str:
			lowerCAmelCase__  ,			lowerCAmelCase__     :    str					  =      self.retriever._main_retrieve(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    List[str]					  =      self.retriever.index.get_doc_dicts(_SCREAMING_SNAKE_CASE	)
			return doc_ids, retrieved_doc_embeds, doc_dicts





class       _a							(       _lowercase):




	def __init__(      self		:	int	,      _SCREAMING_SNAKE_CASE		:	List[str]	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	List[Any]	,      _SCREAMING_SNAKE_CASE		:	Any	,      _SCREAMING_SNAKE_CASE		:	Union[str, Any]=None	)-> Optional[int]:
			if index is not None and index.is_initialized() and len(_SCREAMING_SNAKE_CASE	) > 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__(
			    _SCREAMING_SNAKE_CASE	,      question_encoder_tokenizer=_SCREAMING_SNAKE_CASE	,      generator_tokenizer=_SCREAMING_SNAKE_CASE	,      index=_SCREAMING_SNAKE_CASE	,      init_retrieval=_SCREAMING_SNAKE_CASE	,      )

			lowerCAmelCase__     :    str					  =      retrieval_workers
			lowerCAmelCase__     :    int					  =      question_encoder_tokenizer
			lowerCAmelCase__     :    Tuple					  =      generator_tokenizer
			if len(self.retrieval_workers	) > 0:
					ray.get(
					    [
					        worker.create_rag_retriever.remote(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
					        for worker in self.retrieval_workers
					    ]	)




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




	@classmethod
	def 							UpperCAmelCase__(      cls		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]=None	,      **_SCREAMING_SNAKE_CASE		:	Tuple	)-> Any:
			return super(_SCREAMING_SNAKE_CASE	,      cls	).get_tokenizers(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      **_SCREAMING_SNAKE_CASE	)




	@classmethod
	def 							UpperCAmelCase__(      cls		:	int	,      _SCREAMING_SNAKE_CASE		:	Dict	,      _SCREAMING_SNAKE_CASE		:	List[str]	,      _SCREAMING_SNAKE_CASE		:	List[str]=None	,      **_SCREAMING_SNAKE_CASE		:	str	)-> int:
			lowerCAmelCase__     :    int					  =      kwargs.pop('''config'''	,      _SCREAMING_SNAKE_CASE	) or RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE	,      **_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Dict					  =      RagTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE	,      config=_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Optional[int]					  =      rag_tokenizer.question_encoder
			lowerCAmelCase__     :    Optional[Any]					  =      rag_tokenizer.generator

			if indexed_dataset is not None:
					lowerCAmelCase__     :    str					  =      '''custom'''
					lowerCAmelCase__     :    Tuple					  =      CustomHFIndex(config.retrieval_vector_size	,      _SCREAMING_SNAKE_CASE	)
			else:
					lowerCAmelCase__     :    Optional[int]					  =      cls._build_index(_SCREAMING_SNAKE_CASE	)

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




	def 							UpperCAmelCase__(      self		:	List[str]	)-> List[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
			lowerCAmelCase__     :    str					  =      self._build_index(self.config	)

			ray.get(
			    [
			        worker.create_rag_retriever.remote(
			            self.config	,      self.question_encoder_tokenizer	,      self.generator_tokenizer	,      _SCREAMING_SNAKE_CASE	)
			        for worker in self.retrieval_workers
			    ]	)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase		=	{
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase		=	['''SegformerFeatureExtractor''']
		lowerCamelCase		=	['''SegformerImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase		=	[
		    '''SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SegformerDecodeHead''',
		    '''SegformerForImageClassification''',
		    '''SegformerForSemanticSegmentation''',
		    '''SegformerLayer''',
		    '''SegformerModel''',
		    '''SegformerPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase		=	[
		    '''TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFSegformerDecodeHead''',
		    '''TFSegformerForImageClassification''',
		    '''TFSegformerForSemanticSegmentation''',
		    '''TFSegformerModel''',
		    '''TFSegformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_segformer import SegformerFeatureExtractor
				from .image_processing_segformer import SegformerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_segformer import (
				    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SegformerDecodeHead,
				    SegformerForImageClassification,
				    SegformerForSemanticSegmentation,
				    SegformerLayer,
				    SegformerModel,
				    SegformerPreTrainedModel,
				)
		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_segformer import (
				    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSegformerDecodeHead,
				    TFSegformerForImageClassification,
				    TFSegformerForSemanticSegmentation,
				    TFSegformerModel,
				    TFSegformerPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase		=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




