
"""simple docstring"""



import logging
import random

import ray

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


UpperCAmelCase__          = logging.getLogger(__name__)

class       lowerCAmelCase__				:







     def __init__(							self			:			str			):
       _snake_case					     =       False







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			Optional[int]    ,      _lowerCamelCase			:			Optional[Any]			):
       if not self.initialized:
         _snake_case					     =       RagRetriever(
             _a    ,      question_encoder_tokenizer=_a    ,      generator_tokenizer=_a    ,      index=_a    ,      init_retrieval=_a    ,      )
         _snake_case					     =       True







     def      lowercase	(							self			:			Union[str, Any]			):
       self.retriever.index.init_index()







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			Tuple    ,      _lowerCamelCase			:			str			):
       _snake_case     ,					_snake_case					     =       self.retriever._main_retrieve(_a    ,      _a			)
       return doc_ids, retrieved_doc_embeds






class       lowerCAmelCase__				(						__SCREAMING_SNAKE_CASE	):







     def __init__(							self			:			Any    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			Tuple    ,      _lowerCamelCase			:			List[str]=None			):
       if index is not None and index.is_initialized() and len(_a			) > 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__(
           _a    ,      question_encoder_tokenizer=_a    ,      generator_tokenizer=_a    ,      index=_a    ,      init_retrieval=_a    ,      )
       _snake_case					     =       retrieval_workers
       if len(self.retrieval_workers			) > 0:
         ray.get(
             [
                 worker.create_rag_retriever.remote(_a    ,      _a    ,      _a    ,      _a			)
                 for worker in self.retrieval_workers
             ]			)







     def      lowercase	(							self			:			List[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      lowercase	(							self			:			int    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			Union[str, Any]			):
       if len(self.retrieval_workers			) > 0:
         # Select a random retrieval actor.
         _snake_case					     =       self.retrieval_workers[random.randint(0    ,      len(self.retrieval_workers			) - 1			)]
         _snake_case     ,					_snake_case					     =       ray.get(random_worker.retrieve.remote(_a    ,      _a			)			)
       else:
         _snake_case     ,					_snake_case					     =       self._main_retrieve(_a    ,      _a			)
       return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_a			)







     @classmethod
     def      lowercase	(							cls			:			Union[str, Any]    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			Optional[Any]=None    ,      **_lowerCamelCase			:			Optional[Any]			):
       return super(_a    ,      cls			).get_tokenizers(_a    ,      _a    ,      **_a			)







     @classmethod
     def      lowercase	(							cls			:			Union[str, Any]    ,      _lowerCamelCase			:			List[Any]    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			Any=None    ,      **_lowerCamelCase			:			Optional[Any]			):
       _snake_case					     =       kwargs.pop('''config'''    ,      _a			) or RagConfig.from_pretrained(_a    ,      **_a			)
       _snake_case					     =       RagTokenizer.from_pretrained(_a    ,      config=_a			)
       _snake_case					     =       rag_tokenizer.question_encoder
       _snake_case					     =       rag_tokenizer.generator
       if indexed_dataset is not None:
         _snake_case					     =       '''custom'''
         _snake_case					     =       CustomHFIndex(config.retrieval_vector_size    ,      _a			)
       else:
         _snake_case					     =       cls._build_index(_a			)
       return cls(
           _a    ,      question_encoder_tokenizer=_a    ,      generator_tokenizer=_a    ,      retrieval_workers=_a    ,      index=_a    ,      )







"""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					:


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]
		__UpperCAmelCase		:			Optional[str]										=				None
		# Automatically constructed
		__UpperCAmelCase		:			ClassVar[str]										=				"dict"
		__UpperCAmelCase		:			ClassVar[Any]										=				None
		__UpperCAmelCase		:			str										=				field(default='Translation'     ,			init=__SCREAMING_SNAKE_CASE     ,			repr=__SCREAMING_SNAKE_CASE       )

		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					:


		'''simple docstring'''



		__UpperCAmelCase		:			Optional[List]										=				None
		__UpperCAmelCase		:			Optional[int]										=				None
		__UpperCAmelCase		:			Optional[str]										=				None
		# Automatically constructed
		__UpperCAmelCase		:			ClassVar[str]										=				"dict"
		__UpperCAmelCase		:			ClassVar[Any]										=				None
		__UpperCAmelCase		:			str										=				field(default='TranslationVariableLanguages'     ,			init=__SCREAMING_SNAKE_CASE     ,			repr=__SCREAMING_SNAKE_CASE       )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			sorted(set(self.languages      )      ) if self.languages else None
				__a           =			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							, _a      ):
				__a           =			set(self.languages      )
				if self.languages and set(_a      ) - lang_set:
						raise ValueError(
						    f'''Some languages in example ({', '.join(sorted(set(_a      ) - lang_set      )      )}) are not in valid set ({', '.join(_a      )}).'''      )

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

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

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





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

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






