
"""simple docstring"""

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 	_lowercase       :




	def __init__(			self )		->							Tuple:
			lowerCamelCase					:      Dict          =   False




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Any:
			if not self.initialized:
					lowerCamelCase					:      Optional[int]          =   RagRetriever(
					    UpperCAmelCase_		,			question_encoder_tokenizer=UpperCAmelCase_		,			generator_tokenizer=UpperCAmelCase_		,			index=UpperCAmelCase_		,			init_retrieval=UpperCAmelCase_		,			)
					lowerCamelCase					:      Union[str, Any]          =   True




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			self.retriever.index.init_index()




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							str:
			lowerCamelCase     ,   lowerCamelCase					:      int          =   self.retriever._main_retrieve(UpperCAmelCase_		,			UpperCAmelCase_ )
			return doc_ids, retrieved_doc_embeds






class 	_lowercase       (    __UpperCAmelCase		):




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							Dict:
			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_		,			)
			lowerCamelCase					:      List[str]          =   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       _UpperCamelCase   (			self )		->							Dict:
			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		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							List[Any]:
			if len(self.retrieval_workers ) > 0:
					# Select a random retrieval actor.
					lowerCamelCase					:      Tuple          =   self.retrieval_workers[random.randint(0		,			len(self.retrieval_workers ) - 1 )]
					lowerCamelCase     ,   lowerCamelCase					:      Tuple          =   ray.get(random_worker.retrieve.remote(UpperCAmelCase_		,			UpperCAmelCase_ ) )
			else:
					lowerCamelCase     ,   lowerCamelCase					:      int          =   self._main_retrieve(UpperCAmelCase_		,			UpperCAmelCase_ )
			return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCAmelCase_ )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							Dict:
			return super(UpperCAmelCase_		,			cls ).get_tokenizers(UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							Dict:
			lowerCamelCase					:      str          =   kwargs.pop('config'		,			UpperCAmelCase_ ) or RagConfig.from_pretrained(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   RagTokenizer.from_pretrained(UpperCAmelCase_		,			config=UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   rag_tokenizer.question_encoder
			lowerCamelCase					:      Union[str, Any]          =   rag_tokenizer.generator
			if indexed_dataset is not None:
					lowerCamelCase					:      int          =   'custom'
					lowerCamelCase					:      int          =   CustomHFIndex(config.retrieval_vector_size		,			UpperCAmelCase_ )
			else:
					lowerCamelCase					:      Optional[Any]          =   cls._build_index(UpperCAmelCase_ )
			return cls(
			    UpperCAmelCase_		,			question_encoder_tokenizer=UpperCAmelCase_		,			generator_tokenizer=UpperCAmelCase_		,			retrieval_workers=UpperCAmelCase_		,			index=UpperCAmelCase_		,			)








"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     ['image_processor', 'tokenizer']
	lowercase_				=     'ChineseCLIPImageProcessor'
	lowercase_				=     ('BertTokenizer', 'BertTokenizerFast')




	def __init__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							List[str]:
			lowerCamelCase					:      Tuple          =   None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
					    ' instead.'		,			UpperCAmelCase_		,			)
					lowerCamelCase					:      int          =   kwargs.pop('feature_extractor' )

			lowerCamelCase					:      Any          =   image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
					raise ValueError('You need to specify an `image_processor`.' )
			if tokenizer is None:
					raise ValueError('You need to specify a `tokenizer`.' )

			super().__init__(UpperCAmelCase_		,			UpperCAmelCase_ )
			lowerCamelCase					:      Tuple          =   self.image_processor




	def __call__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							str:

			if text is None and images is None:
					raise ValueError('You have to specify either text or images. Both cannot be none.' )

			if text is not None:
					lowerCamelCase					:      Any          =   self.tokenizer(UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			**UpperCAmelCase_ )

			if images is not None:
					lowerCamelCase					:      Dict          =   self.image_processor(UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			**UpperCAmelCase_ )

			if text is not None and images is not None:
					lowerCamelCase					:      Tuple          =   image_features.pixel_values
					return encoding
			elif text is not None:
					return encoding
			else:
					return BatchEncoding(data=dict(**UpperCAmelCase_ )		,			tensor_type=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							List[str]:
			return self.tokenizer.batch_decode(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							Optional[int]:
			return self.tokenizer.decode(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	@property
	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      List[Any]          =   self.tokenizer.model_input_names
			lowerCamelCase					:      Union[str, Any]          =   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )




	@property
	def       _UpperCamelCase   (			self )		->							List[str]:
			warnings.warn(
			    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,			UpperCAmelCase_		,			)
			return self.image_processor_class






