import logging
import random

import ray

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


lowerCamelCase_    :		str   						=	logging.getLogger(__name__)


class 						a__							:



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



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Any:
								if not self.initialized:
													__a		 =       RagRetriever(
													    UpperCAmelCase				,						question_encoder_tokenizer=UpperCAmelCase				,						generator_tokenizer=UpperCAmelCase				,						index=UpperCAmelCase				,						init_retrieval=UpperCAmelCase				,						)
													__a		 =       True



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								self.retriever.index.init_index()







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   str:
								__a ,				__a		 =       self.retriever._main_retrieve(UpperCAmelCase				,						UpperCAmelCase   )
								return doc_ids, retrieved_doc_embeds






class 						a__							(       __snake_case		):



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase=None   )			->   List[str]:
								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				,						)
								__a		 =       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 __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								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 __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Optional[int]:
								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(UpperCAmelCase				,						UpperCAmelCase   )   )
								else:
													__a ,				__a		 =       self._main_retrieve(UpperCAmelCase				,						UpperCAmelCase   )
								return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCAmelCase   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						UpperCAmelCase				,						UpperCAmelCase=None				,						**UpperCAmelCase   )			->   Optional[int]:
								return super(UpperCAmelCase				,						cls   ).get_tokenizers(UpperCAmelCase				,						UpperCAmelCase				,						**UpperCAmelCase   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase=None				,						**UpperCAmelCase   )			->   str:
								__a		 =       kwargs.pop('config'				,						UpperCAmelCase   ) or RagConfig.from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )
								__a		 =       RagTokenizer.from_pretrained(UpperCAmelCase				,						config=UpperCAmelCase   )
								__a		 =       rag_tokenizer.question_encoder
								__a		 =       rag_tokenizer.generator
								if indexed_dataset is not None:
													__a		 =       'custom'
													__a		 =       CustomHFIndex(config.retrieval_vector_size				,						UpperCAmelCase   )
								else:
													__a		 =       cls._build_index(UpperCAmelCase   )
								return cls(
								    UpperCAmelCase				,						question_encoder_tokenizer=UpperCAmelCase				,						generator_tokenizer=UpperCAmelCase				,						retrieval_workers=UpperCAmelCase				,						index=UpperCAmelCase				,						)





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class 						a__							(       __snake_case		):
			A__    :       Any        =      'Wav2Vec2FeatureExtractor'
			A__    :       str        =      'AutoTokenizer'



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Union[str, Any]:
								super().__init__(UpperCAmelCase				,						UpperCAmelCase   )
								__a		 =       self.feature_extractor
								__a		 =       False



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						UpperCAmelCase				,						**UpperCAmelCase   )			->   Dict:
								try:
													return super().from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )
								except OSError:
													warnings.warn(
													    f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
													    ' include a `tokenizer_class` attribute is deprecated and will be '
													    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
													    ' attribute to either your `config.json` or `tokenizer_config.json` '
													    'file to suppress this warning: '				,						UpperCAmelCase				,						)

													__a		 =       WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )
													__a		 =       WavaVecaCTCTokenizer.from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )

													return cls(feature_extractor=UpperCAmelCase				,						tokenizer=UpperCAmelCase   )



			def __call__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[Any]:
								# For backward compatibility
								if self._in_target_context_manager:
													return self.current_processor(*UpperCAmelCase				,						**UpperCAmelCase   )

								if "raw_speech" in kwargs:
													warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'   )
													__a		 =       kwargs.pop('raw_speech'   )
								else:
													__a		 =       kwargs.pop('audio'				,						UpperCAmelCase   )
								__a		 =       kwargs.pop('sampling_rate'				,						UpperCAmelCase   )
								__a		 =       kwargs.pop('text'				,						UpperCAmelCase   )
								if len(UpperCAmelCase   ) > 0:
													__a		 =       args[0]
													__a		 =       args[1:]

								if audio is None and text is None:
													raise ValueError('You need to specify either an `audio` or `text` input to process.'   )

								if audio is not None:
													__a		 =       self.feature_extractor(UpperCAmelCase				,						*UpperCAmelCase				,						sampling_rate=UpperCAmelCase				,						**UpperCAmelCase   )
								if text is not None:
													__a		 =       self.tokenizer(UpperCAmelCase				,						**UpperCAmelCase   )

								if text is None:
													return inputs
								elif audio is None:
													return encodings
								else:
													__a		 =       encodings['input_ids']
													return inputs



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Tuple:
								# For backward compatibility
								if self._in_target_context_manager:
													return self.current_processor.pad(*UpperCAmelCase				,						**UpperCAmelCase   )

								__a		 =       kwargs.pop('input_features'				,						UpperCAmelCase   )
								__a		 =       kwargs.pop('labels'				,						UpperCAmelCase   )
								if len(UpperCAmelCase   ) > 0:
													__a		 =       args[0]
													__a		 =       args[1:]

								if input_features is not None:
													__a		 =       self.feature_extractor.pad(UpperCAmelCase				,						*UpperCAmelCase				,						**UpperCAmelCase   )
								if labels is not None:
													__a		 =       self.tokenizer.pad(UpperCAmelCase				,						**UpperCAmelCase   )

								if labels is None:
													return input_features
								elif input_features is None:
													return labels
								else:
													__a		 =       labels['input_ids']
													return input_features



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



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Union[str, Any]:
								return self.tokenizer.decode(*UpperCAmelCase				,						**UpperCAmelCase   )







			@contextmanager
			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								warnings.warn(
								    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
								    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
								    'your audio inputs, or in a separate call.'   )
								__a		 =       True
								__a		 =       self.tokenizer
								yield
								__a		 =       self.feature_extractor
								__a		 =       False




