


"""simple docstring"""


import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pa.array(TypedSequence([1, 2, 3]	)	)
										self.assertEqual(arr.type    ,   pa.intaa()	)

							def 			snake_case				(     self	):
										with self.assertRaises(__a	):
													__lowerCAmelCase				    =			pa.array(TypedSequence([1, 2, 3]	)    ,   type=pa.intaa()	)

							def 			snake_case				(     self	):
										with self.assertRaises(__a	):
													__lowerCAmelCase				    =			pa.array(TypedSequence([1, 2, 3]    ,   try_type=Value("bool"	)    ,   type=Value("int64"	)	)	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pa.array(TypedSequence([1, 2, 3]    ,   type=Value("int32"	)	)	)
										self.assertEqual(arr.type    ,   pa.intaa()	)

							def 			snake_case				(     self	):
										with self.assertRaises((TypeError, pa.lib.ArrowInvalid)	):
													__lowerCAmelCase				    =			pa.array(TypedSequence(["foo", "bar"]    ,   type=Value("int64"	)	)	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pa.array(TypedSequence([1, 2, 3]    ,   try_type=Value("int32"	)	)	)
										self.assertEqual(arr.type    ,   pa.intaa()	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pa.array(TypedSequence(["foo", "bar"]    ,   try_type=Value("int64"	)	)	)
										self.assertEqual(arr.type    ,   pa.string()	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pa.array(TypedSequence([[[1, 2, 3]]]    ,   type=ArrayaD((1, 3)    ,   "int64"	)	)	)
										self.assertEqual(arr.type    ,   ArrayaDExtensionType((1, 3)    ,   "int64"	)	)

							def 			snake_case				(     self	):
										with self.assertRaises((TypeError, pa.lib.ArrowInvalid)	):
													__lowerCAmelCase				    =			pa.array(TypedSequence(["foo", "bar"]    ,   type=ArrayaD((1, 3)    ,   "int64"	)	)	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pa.array(TypedSequence([[[1, 2, 3]]]    ,   try_type=ArrayaD((1, 3)    ,   "int64"	)	)	)
										self.assertEqual(arr.type    ,   ArrayaDExtensionType((1, 3)    ,   "int64"	)	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pa.array(TypedSequence(["foo", "bar"]    ,   try_type=ArrayaD((1, 3)    ,   "int64"	)	)	)
										self.assertEqual(arr.type    ,   pa.string()	)



							@require_pil
							def 			snake_case				(     self	):
										import PIL.Image

										__lowerCAmelCase				    =			PIL.Image.fromarray(np.arange(10    ,   dtype=np.uinta	).reshape(2    ,   5	)	)
										with patch(
										    "datasets.arrow_writer.cast_to_python_objects"    ,   side_effect=__a	) as mock_cast_to_python_objects:
													__lowerCAmelCase				    =			pa.array(TypedSequence([{"path": None, "bytes": B"image_bytes"}, pil_image]    ,   type=Image()	)	)
													__lowerCAmelCase    ,	__lowerCAmelCase				    =			mock_cast_to_python_objects.call_args_list[-1]
													self.assertIn("optimize_list_casting"    ,   __a	)
													self.assertFalse(kwargs["optimize_list_casting"]	)


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			pa.BufferReader(_UpperCamelCase      ) if isinstance(_UpperCamelCase    , pa.Buffer      ) else pa.memory_map(_UpperCamelCase      )
			__lowerCAmelCase				    =			pa.ipc.open_stream(_UpperCamelCase      )
			__lowerCAmelCase				    =			f.read_all()
			assert len(pa_table.to_batches()      ) == expected_num_chunks
			assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
			del pa_table


@pytest.mark.parametrize("writer_batch_size"    , [None, 1, 10]      )
@pytest.mark.parametrize(
    "fields"    , [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]      )
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			pa.BufferOutputStream()
			__lowerCAmelCase				    =			pa.schema(_UpperCamelCase      ) if fields else None
			with ArrowWriter(stream=_UpperCamelCase    , schema=_UpperCamelCase    , writer_batch_size=_UpperCamelCase      ) as writer:
						writer.write({"col_1": "foo", "col_2": 1}      )
						writer.write({"col_1": "bar", "col_2": 2}      )
						__lowerCAmelCase    ,	__lowerCAmelCase				    =			writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						__lowerCAmelCase				    =			{"col_1": pa.string(), "col_2": pa.intaa()}
			assert writer._schema == pa.schema(_UpperCamelCase    , metadata=writer._schema.metadata      )
			_check_output(output.getvalue()    , expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			pa.BufferOutputStream()
			__lowerCAmelCase				    =			Features({"labels": ClassLabel(names=["neg", "pos"]      )}      )
			with ArrowWriter(stream=_UpperCamelCase    , features=_UpperCamelCase      ) as writer:
						writer.write({"labels": 0}      )
						writer.write({"labels": 1}      )
						__lowerCAmelCase    ,	__lowerCAmelCase				    =			writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			assert writer._schema == features.arrow_schema
			assert writer._schema.metadata == features.arrow_schema.metadata
			__lowerCAmelCase				    =			pa.BufferReader(output.getvalue()      )
			__lowerCAmelCase				    =			pa.ipc.open_stream(_UpperCamelCase      )
			__lowerCAmelCase				    =			f.read_all()
			__lowerCAmelCase				    =			pa_table.schema
			assert pa_table.num_rows == 2
			assert schema == features.arrow_schema
			assert schema.metadata == features.arrow_schema.metadata
			assert features == Features.from_arrow_schema(_UpperCamelCase      )


@pytest.mark.parametrize("writer_batch_size"    , [None, 1, 10]      )
def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			pa.BufferOutputStream()
			with ArrowWriter(
			    stream=_UpperCamelCase    , writer_batch_size=_UpperCamelCase    , hash_salt="split_name"    , check_duplicates=_UpperCamelCase    , ) as writer:
						with pytest.raises(_UpperCamelCase      ):
									writer.write({"col_1": "foo", "col_2": 1}    , key=[1, 2]      )
									__lowerCAmelCase    ,	__lowerCAmelCase				    =			writer.finalize()


@pytest.mark.parametrize("writer_batch_size"    , [None, 2, 10]      )
def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			pa.BufferOutputStream()
			with ArrowWriter(
			    stream=_UpperCamelCase    , writer_batch_size=_UpperCamelCase    , hash_salt="split_name"    , check_duplicates=_UpperCamelCase    , ) as writer:
						with pytest.raises(_UpperCamelCase      ):
									writer.write({"col_1": "foo", "col_2": 1}    , key=10      )
									writer.write({"col_1": "bar", "col_2": 2}    , key=10      )
									__lowerCAmelCase    ,	__lowerCAmelCase				    =			writer.finalize()


@pytest.mark.parametrize("writer_batch_size"    , [None, 2, 10]      )
def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			pa.BufferOutputStream()
			with ArrowWriter(
			    stream=_UpperCamelCase    , writer_batch_size=_UpperCamelCase    , hash_salt="split_name"    , check_duplicates=_UpperCamelCase    , ) as writer:
						writer.write({"col_1": "foo", "col_2": 1}    , key=1      )
						writer.write({"col_1": "bar", "col_2": 2}    , key=2      )
						__lowerCAmelCase    ,	__lowerCAmelCase				    =			writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			_check_output(output.getvalue()    , expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )


@pytest.mark.parametrize("writer_batch_size"    , [None, 1, 10]      )
@pytest.mark.parametrize(
    "fields"    , [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]      )
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			pa.BufferOutputStream()
			__lowerCAmelCase				    =			pa.schema(_UpperCamelCase      ) if fields else None
			with ArrowWriter(stream=_UpperCamelCase    , schema=_UpperCamelCase    , writer_batch_size=_UpperCamelCase      ) as writer:
						writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}      )
						writer.write_batch({"col_1": [], "col_2": []}      )
						__lowerCAmelCase    ,	__lowerCAmelCase				    =			writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						__lowerCAmelCase				    =			{"col_1": pa.string(), "col_2": pa.intaa()}
			assert writer._schema == pa.schema(_UpperCamelCase    , metadata=writer._schema.metadata      )
			_check_output(output.getvalue()    , expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )


@pytest.mark.parametrize("writer_batch_size"    , [None, 1, 10]      )
@pytest.mark.parametrize(
    "fields"    , [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]      )
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			pa.BufferOutputStream()
			__lowerCAmelCase				    =			pa.schema(_UpperCamelCase      ) if fields else None
			with ArrowWriter(stream=_UpperCamelCase    , schema=_UpperCamelCase    , writer_batch_size=_UpperCamelCase      ) as writer:
						writer.write_table(pa.Table.from_pydict({"col_1": ["foo", "bar"], "col_2": [1, 2]}      )      )
						__lowerCAmelCase    ,	__lowerCAmelCase				    =			writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						__lowerCAmelCase				    =			{"col_1": pa.string(), "col_2": pa.intaa()}
			assert writer._schema == pa.schema(_UpperCamelCase    , metadata=writer._schema.metadata      )
			_check_output(output.getvalue()    , expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )


@pytest.mark.parametrize("writer_batch_size"    , [None, 1, 10]      )
@pytest.mark.parametrize(
    "fields"    , [None, {"col_1": pa.string(), "col_2": pa.intaa()}, {"col_1": pa.string(), "col_2": pa.intaa()}]      )
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			pa.BufferOutputStream()
			__lowerCAmelCase				    =			pa.schema(_UpperCamelCase      ) if fields else None
			with ArrowWriter(stream=_UpperCamelCase    , schema=_UpperCamelCase    , writer_batch_size=_UpperCamelCase      ) as writer:
						writer.write_row(pa.Table.from_pydict({"col_1": ["foo"], "col_2": [1]}      )      )
						writer.write_row(pa.Table.from_pydict({"col_1": ["bar"], "col_2": [2]}      )      )
						__lowerCAmelCase    ,	__lowerCAmelCase				    =			writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			if not fields:
						__lowerCAmelCase				    =			{"col_1": pa.string(), "col_2": pa.intaa()}
			assert writer._schema == pa.schema(_UpperCamelCase    , metadata=writer._schema.metadata      )
			_check_output(output.getvalue()    , expected_num_chunks=num_examples if writer_batch_size == 1 else 1      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			with tempfile.TemporaryDirectory() as tmp_dir:
						__lowerCAmelCase				    =			{"col_1": pa.string(), "col_2": pa.intaa()}
						__lowerCAmelCase				    =			os.path.join(_UpperCamelCase    , "test.arrow"      )
						with ArrowWriter(path=_UpperCamelCase    , schema=pa.schema(_UpperCamelCase      )      ) as writer:
									writer.write_batch({"col_1": ["foo", "bar"], "col_2": [1, 2]}      )
									__lowerCAmelCase    ,	__lowerCAmelCase				    =			writer.finalize()
						assert num_examples == 2
						assert num_bytes > 0
						assert writer._schema == pa.schema(_UpperCamelCase    , metadata=writer._schema.metadata      )
						_check_output(_UpperCamelCase    , 1      )


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			if pa.types.is_list(_UpperCamelCase      ):
						return get_base_dtype(arr_type.value_type      )
			else:
						return arr_type


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			if isinstance(lst[0]    , _UpperCamelCase      ):
						change_first_primitive_element_in_list(lst[0]    , _UpperCamelCase      )
			else:
						__lowerCAmelCase				    =			value


@pytest.mark.parametrize("optimized_int_type, expected_dtype"    , [(None, pa.intaa()), (Value("int32"      ), pa.intaa())]      )
@pytest.mark.parametrize("sequence"    , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]      )
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			pa.array(TypedSequence(_UpperCamelCase    , optimized_int_type=_UpperCamelCase      )      )
			assert get_base_dtype(arr.type      ) == expected_dtype


@pytest.mark.parametrize(
    "col, expected_dtype"    , [
        ("attention_mask", pa.inta()),
        ("special_tokens_mask", pa.inta()),
        ("token_type_ids", pa.inta()),
        ("input_ids", pa.intaa()),
        ("other", pa.intaa()),
    ]    , )
@pytest.mark.parametrize("sequence"    , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]      )
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			pa.array(OptimizedTypedSequence(_UpperCamelCase    , col=_UpperCamelCase      )      )
			assert get_base_dtype(arr.type      ) == expected_dtype

			# not in range
			if col != "other":
						# avoids errors due to in-place modifications
						__lowerCAmelCase				    =			copy.deepcopy(_UpperCamelCase      )
						__lowerCAmelCase				    =			np.iinfo(expected_dtype.to_pandas_dtype()      ).max + 1
						change_first_primitive_element_in_list(_UpperCamelCase    , _UpperCamelCase      )
						__lowerCAmelCase				    =			pa.array(OptimizedTypedSequence(_UpperCamelCase    , col=_UpperCamelCase      )      )
						assert get_base_dtype(arr.type      ) == pa.intaa()


@pytest.mark.parametrize("raise_exception"    , [False, True]      )
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			str(tmp_path / "dataset-train.arrow"      )
			try:
						with ArrowWriter(path=_UpperCamelCase      ) as writer:
									if raise_exception:
												raise pa.lib.ArrowInvalid()
									else:
												writer.stream.close()
			except pa.lib.ArrowInvalid:
						pass
			finally:
						assert writer.stream.closed


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			"mock://dataset-train.arrow"
			with ArrowWriter(path=_UpperCamelCase    , storage_options=mockfs.storage_options      ) as writer:
						assert isinstance(writer._fs    , type(_UpperCamelCase      )      )
						assert writer._fs.storage_options == mockfs.storage_options
						writer.write({"col_1": "foo", "col_2": 1}      )
						writer.write({"col_1": "bar", "col_2": 2}      )
						__lowerCAmelCase    ,	__lowerCAmelCase				    =			writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			assert mockfs.exists(_UpperCamelCase      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			pa.BufferOutputStream()
			with ParquetWriter(stream=_UpperCamelCase      ) as writer:
						writer.write({"col_1": "foo", "col_2": 1}      )
						writer.write({"col_1": "bar", "col_2": 2}      )
						__lowerCAmelCase    ,	__lowerCAmelCase				    =			writer.finalize()
			assert num_examples == 2
			assert num_bytes > 0
			__lowerCAmelCase				    =			pa.BufferReader(output.getvalue()      )
			__lowerCAmelCase				    =			pq.read_table(_UpperCamelCase      )
			assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}


@require_pil
@pytest.mark.parametrize("embed_local_files"    , [False, True]      )
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			import PIL.Image

			__lowerCAmelCase				    =			str(tmp_path / "test_image_rgb.jpg"      )
			PIL.Image.fromarray(np.zeros((5, 5)    , dtype=np.uinta      )      ).save(_UpperCamelCase    , format="png"      )
			__lowerCAmelCase				    =			pa.BufferOutputStream()
			with ParquetWriter(
			    stream=_UpperCamelCase    , features=Features({"image": Image()}      )    , embed_local_files=_UpperCamelCase      ) as writer:
						writer.write({"image": image_path}      )
						writer.finalize()
			__lowerCAmelCase				    =			pa.BufferReader(output.getvalue()      )
			__lowerCAmelCase				    =			pq.read_table(_UpperCamelCase      )
			__lowerCAmelCase				    =			pa_table.to_pydict()
			if embed_local_files:
						assert isinstance(out["image"][0]["path"]    , _UpperCamelCase      )
						with open(_UpperCamelCase    , "rb"      ) as f:
									assert out["image"][0]["bytes"] == f.read()
			else:
						assert out["image"][0]["path"] == image_path
						assert out["image"][0]["bytes"] is None


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			pa.schema([pa.field("col_1"    , pa.string()    , nullable=_UpperCamelCase      )]      )
			__lowerCAmelCase				    =			pa.BufferOutputStream()
			with ArrowWriter(stream=_UpperCamelCase      ) as writer:
						writer._build_writer(inferred_schema=_UpperCamelCase      )
						assert writer._schema == pa.schema([pa.field("col_1"    , pa.string()      )]      )





"""simple docstring"""
import logging
import random

import ray

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


lowercase__					       =	logging.getLogger(__name__)




class    __snake_case       :



      def __init__(						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     Optional[Any]										=	False



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			str:





        '''simple docstring'''





        if not self.initialized:
          a__:     Optional[int]										=	RagRetriever(
              lowercase    ,			question_encoder_tokenizer=lowercase    ,			generator_tokenizer=lowercase    ,			index=lowercase    ,			init_retrieval=lowercase    ,			)
          a__:     Optional[int]										=	True



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        self.retriever.index.init_index()



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        a__							,    a__:     str										=	self.retriever._main_retrieve(lowercase    ,			lowercase)
        return doc_ids, retrieved_doc_embeds







class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			int:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			Any:





        '''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   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			Union[str, Any]:





        '''simple docstring'''





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



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase    ,			lowercase=None    ,			**lowercase)	->			Tuple:





        '''simple docstring'''





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



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase    ,			lowercase    ,			lowercase=None    ,			**lowercase)	->			Union[str, Any]:





        '''simple docstring'''





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


