






import logging
import random

import ray

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


_UpperCAmelCase      :		Union[str, Any] 					=     logging.getLogger(__name__)
class 			lowerCAmelCase_							:




							def __init__( self  : Dict						):
											lowerCAmelCase__         =    False




							def     __snake_case       ( self  : Optional[int]			,				SCREAMING_SNAKE_CASE_  : Union[str, Any]			,				SCREAMING_SNAKE_CASE_  : List[Any]			,				SCREAMING_SNAKE_CASE_  : Union[str, Any]			,				SCREAMING_SNAKE_CASE_  : Dict						):
											if not self.initialized:
															lowerCAmelCase__         =    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__         =    True




							def     __snake_case       ( self  : Optional[Any]						):
											self.retriever.index.init_index()




							def     __snake_case       ( self  : Tuple			,				SCREAMING_SNAKE_CASE_  : List[str]			,				SCREAMING_SNAKE_CASE_  : Optional[int]						):
											lowerCAmelCase__      ,		lowerCAmelCase__         =    self.retriever._main_retrieve(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_						)
											return doc_ids, retrieved_doc_embeds



class 			lowerCAmelCase_							(					snake_case__       ):




							def __init__( self  : Any			,				SCREAMING_SNAKE_CASE_  : Optional[Any]			,				SCREAMING_SNAKE_CASE_  : Any			,				SCREAMING_SNAKE_CASE_  : Optional[Any]			,				SCREAMING_SNAKE_CASE_  : List[str]			,				SCREAMING_SNAKE_CASE_  : List[Any]=None						):
											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__         =    retrieval_workers
											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     __snake_case       ( self  : Tuple						):
											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     __snake_case       ( self  : Optional[Any]			,				SCREAMING_SNAKE_CASE_  : List[Any]			,				SCREAMING_SNAKE_CASE_  : int						):
											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(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_						)						)
											else:
															lowerCAmelCase__      ,		lowerCAmelCase__         =    self._main_retrieve(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_						)
											return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(SCREAMING_SNAKE_CASE_						)




							@classmethod
							def     __snake_case       ( cls  : Tuple			,				SCREAMING_SNAKE_CASE_  : List[str]			,				SCREAMING_SNAKE_CASE_  : Any=None			,				**SCREAMING_SNAKE_CASE_  : List[Any]						):
											return super(SCREAMING_SNAKE_CASE_			,				cls						).get_tokenizers(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_						)




							@classmethod
							def     __snake_case       ( cls  : int			,				SCREAMING_SNAKE_CASE_  : Union[str, Any]			,				SCREAMING_SNAKE_CASE_  : List[Any]			,				SCREAMING_SNAKE_CASE_  : Any=None			,				**SCREAMING_SNAKE_CASE_  : List[Any]						):
											lowerCAmelCase__         =    kwargs.pop('''config'''			,				SCREAMING_SNAKE_CASE_						) or RagConfig.from_pretrained(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_						)
											lowerCAmelCase__         =    RagTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_			,				config=SCREAMING_SNAKE_CASE_						)
											lowerCAmelCase__         =    rag_tokenizer.question_encoder
											lowerCAmelCase__         =    rag_tokenizer.generator
											if indexed_dataset is not None:
															lowerCAmelCase__         =    '''custom'''
															lowerCAmelCase__         =    CustomHFIndex(config.retrieval_vector_size			,				SCREAMING_SNAKE_CASE_						)
											else:
															lowerCAmelCase__         =    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_			,				)







"""simple docstring"""


from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
						from .features import FeatureType




@dataclass
class  __lowerCamelCase  :
				a__:      List[str]
				a__:      Optional[str]          =    None
				# Automatically constructed
				a__:      ClassVar[str]          =    "dict"
				a__:      ClassVar[Any]          =    None
				a__:      str          =    field(default='Translation'						,	init=lowerCAmelCase						,	repr=lowerCAmelCase			)




				def __call__(				self   ):
							return pa.struct({lang: pa.string() for lang in sorted(self.languages   )}   )





				def        UpperCAmelCase__						(				self   ):
							from .features import Value

							return {k: Value('''string'''   ) for k in sorted(self.languages   )}




@dataclass
class  __lowerCamelCase  :
				a__:      Optional[List]          =    None
				a__:      Optional[int]          =    None
				a__:      Optional[str]          =    None
				# Automatically constructed
				a__:      ClassVar[str]          =    "dict"
				a__:      ClassVar[Any]          =    None
				a__:      str          =    field(default='TranslationVariableLanguages'						,	init=lowerCAmelCase						,	repr=lowerCAmelCase			)




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  sorted(set(self.languages   )   ) if self.languages else None
							lowerCamelCase_					=  len(self.languages   ) if self.languages else None




				def __call__(				self   ):
							return pa.struct({'''language''': pa.list_(pa.string()   ), '''translation''': pa.list_(pa.string()   )}   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  set(self.languages   )
							if self.languages and set(UpperCAmelCase   ) - lang_set:
										raise ValueError(
										    f"Some languages in example ({', '.join(sorted(set(UpperCAmelCase   ) - lang_set   )   )}) are not in valid set ({', '.join(UpperCAmelCase   )})."   )

							# Convert dictionary into tuples, splitting out cases where there are
							# multiple translations for a single language.
							lowerCamelCase_					=  []
							for lang, text in translation_dict.items():
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													translation_tuples.append((lang, text)   )
										else:
													translation_tuples.extend([(lang, el) for el in text]   )

        # Ensure translations are in ascending order by language code.
							lowerCamelCase_		, lowerCamelCase_					=  zip(*sorted(UpperCAmelCase   )   )

							return {"language": languages, "translation": translations}





				def        UpperCAmelCase__						(				self   ):
							from .features import Sequence, Value

							return {
							    "language": Sequence(Value('''string'''   )   ),
							    "translation": Sequence(Value('''string'''   )   ),
							}



