"""simple docstring"""






from typing import List

import numpy as np


def 			UpperCAmelCase__       (				lowerCAmelCase__     :dict		) ->    int:
      '''simple docstring'''


      lowercase														=	{key: len(lowerCAmelCase__		) for key, value in gen_kwargs.items() if isinstance(lowerCAmelCase__					,		lowerCAmelCase__		)}
      if len(set(lists_lengths.values()		)		) > 1:
            raise RuntimeError(
                (
                    """Sharding is ambiguous for this dataset: """
                    + """we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"""
                    + """\n""".join(f'\t- key {key} has length {length}' for key, length in lists_lengths.items()		)
                    + """\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, """
                    + """and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."""
                )		)
      lowercase														=	max(lists_lengths.values()					,		default=0		)
      return max(1					,		lowerCAmelCase__		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :int		) ->    List[range]:
      '''simple docstring'''


      lowercase														=	[]
      for group_idx in range(lowerCAmelCase__		):
            lowercase														=	num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
            if num_shards_to_add == 0:
                  break
            lowercase														=	shards_indices_per_group[-1].stop if shards_indices_per_group else 0
            lowercase														=	range(lowerCAmelCase__					,		start + num_shards_to_add		)
            shards_indices_per_group.append(lowerCAmelCase__		)
      return shards_indices_per_group


def 			UpperCAmelCase__       (				lowerCAmelCase__     :dict					,		lowerCAmelCase__     :int		) ->    List[dict]:
      '''simple docstring'''


      lowercase														=	_number_of_shards_in_gen_kwargs(lowerCAmelCase__		)
      if num_shards == 1:
            return [dict(lowerCAmelCase__		)]
      else:
            lowercase														=	_distribute_shards(num_shards=lowerCAmelCase__					,		max_num_jobs=lowerCAmelCase__		)
            return [
                {
                    key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                    if isinstance(lowerCAmelCase__					,		lowerCAmelCase__		)
                    else value
                    for key, value in gen_kwargs.items()
                }
                for group_idx in range(len(lowerCAmelCase__		)		)
            ]


def 			UpperCAmelCase__       (				lowerCAmelCase__     :List[dict]		) ->    dict:
      '''simple docstring'''


      return {
          key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
          if isinstance(gen_kwargs_list[0][key]					,		lowerCAmelCase__		)
          else gen_kwargs_list[0][key]
          for key in gen_kwargs_list[0]
      }






def 			UpperCAmelCase__       (				lowerCAmelCase__     :np.random.Generator					,		lowerCAmelCase__     :dict		) ->    dict:
      '''simple docstring'''


      lowercase														=	{len(lowerCAmelCase__		) for value in gen_kwargs.values() if isinstance(lowerCAmelCase__					,		lowerCAmelCase__		)}
      lowercase														=	{}
      for size in list_sizes:
            lowercase														=	list(range(lowerCAmelCase__		)		)
            rng.shuffle(indices_per_size[size]		)
      # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
      lowercase														=	dict(lowerCAmelCase__		)
      for key, value in shuffled_kwargs.items():
            if isinstance(lowerCAmelCase__					,		lowerCAmelCase__		):
                  lowercase														=	[value[i] for i in indices_per_size[len(lowerCAmelCase__		)]]
      return shuffled_kwargs




"""simple docstring"""






import logging
import random

import ray

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


__lowerCAmelCase    :       int					     =logging.getLogger(__name__)





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	False

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            if not self.initialized:
                  lowercase														=	RagRetriever(
                      __lowerCAmelCase		,       question_encoder_tokenizer=__lowerCAmelCase		,       generator_tokenizer=__lowerCAmelCase		,       index=__lowerCAmelCase		,       init_retrieval=__lowerCAmelCase		,       )
                  lowercase														=	True

      def 				A__       (						self						):







            """simple docstring"""



            self.retriever.index.init_index()







      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase							,							lowercase														=	self.retriever._main_retrieve(__lowerCAmelCase		,       __lowerCAmelCase						)
            return doc_ids, retrieved_doc_embeds






class  _A						(       lowerCAmelCase	):

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None						):







            """simple docstring"""



            if index is not None and index.is_initialized() and len(__lowerCAmelCase						) > 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__(
                __lowerCAmelCase		,       question_encoder_tokenizer=__lowerCAmelCase		,       generator_tokenizer=__lowerCAmelCase		,       index=__lowerCAmelCase		,       init_retrieval=__lowerCAmelCase		,       )
            lowercase														=	retrieval_workers
            if len(self.retrieval_workers						) > 0:
                  ray.get(
                      [
                          worker.create_rag_retriever.remote(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						)
                          for worker in self.retrieval_workers
                      ]						)

      def 				A__       (						self						):







            """simple docstring"""



            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 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            if len(self.retrieval_workers						) > 0:
                  # Select a random retrieval actor.
                  lowercase														=	self.retrieval_workers[random.randint(0		,       len(self.retrieval_workers						) - 1						)]
                  lowercase							,							lowercase														=	ray.get(random_worker.retrieve.remote(__lowerCAmelCase		,       __lowerCAmelCase						)						)
            else:
                  lowercase							,							lowercase														=	self._main_retrieve(__lowerCAmelCase		,       __lowerCAmelCase						)
            return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCAmelCase						)

      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       __lowerCAmelCase=None		,       **__lowerCAmelCase						):







            """simple docstring"""



            return super(__lowerCAmelCase		,       cls						).get_tokenizers(__lowerCAmelCase		,       __lowerCAmelCase		,       **__lowerCAmelCase						)







      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None		,       **__lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	kwargs.pop("""config"""		,       __lowerCAmelCase						) or RagConfig.from_pretrained(__lowerCAmelCase		,       **__lowerCAmelCase						)
            lowercase														=	RagTokenizer.from_pretrained(__lowerCAmelCase		,       config=__lowerCAmelCase						)
            lowercase														=	rag_tokenizer.question_encoder
            lowercase														=	rag_tokenizer.generator
            if indexed_dataset is not None:
                  lowercase														=	"""custom"""
                  lowercase														=	CustomHFIndex(config.retrieval_vector_size		,       __lowerCAmelCase						)
            else:
                  lowercase														=	cls._build_index(__lowerCAmelCase						)
            return cls(
                __lowerCAmelCase		,       question_encoder_tokenizer=__lowerCAmelCase		,       generator_tokenizer=__lowerCAmelCase		,       retrieval_workers=__lowerCAmelCase		,       index=__lowerCAmelCase		,       )



