






'''simple docstring'''







import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


__lowerCAmelCase       		=      logging.get_logger(__name__)


__lowerCAmelCase       		=      {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
__lowerCAmelCase       		=      {
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase       		=      {
    'facebook/bart-base': 1_024,
    'facebook/bart-large': 1_024,
    'facebook/bart-large-mnli': 1_024,
    'facebook/bart-large-cnn': 1_024,
    'facebook/bart-large-xsum': 1_024,
    'yjernite/bart_eli5': 1_024,
}





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				VOCAB_FILES_NAMES
			snake_case__  =				PRETRAINED_VOCAB_FILES_MAP
			snake_case__  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case__  =				["input_ids", "attention_mask"]
			snake_case__  =				BartTokenizer






			def __init__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=None  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=None  ,						__SCREAMING_SNAKE_CASE      :			Any=None  ,						__SCREAMING_SNAKE_CASE      :			Tuple="replace"  ,						__SCREAMING_SNAKE_CASE      :			Dict="<s>"  ,						__SCREAMING_SNAKE_CASE      :			int="</s>"  ,						__SCREAMING_SNAKE_CASE      :			Tuple="</s>"  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]="<s>"  ,						__SCREAMING_SNAKE_CASE      :			Any="<unk>"  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]="<pad>"  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]="<mask>"  ,						__SCREAMING_SNAKE_CASE      :			Tuple=False  ,						__SCREAMING_SNAKE_CASE      :			int=True  ,						**__SCREAMING_SNAKE_CASE      :			Tuple  ,						)			->			Tuple:
							super().__init__(
							    __SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						tokenizer_file=__SCREAMING_SNAKE_CASE  ,						errors=__SCREAMING_SNAKE_CASE  ,						bos_token=__SCREAMING_SNAKE_CASE  ,						eos_token=__SCREAMING_SNAKE_CASE  ,						sep_token=__SCREAMING_SNAKE_CASE  ,						cls_token=__SCREAMING_SNAKE_CASE  ,						unk_token=__SCREAMING_SNAKE_CASE  ,						pad_token=__SCREAMING_SNAKE_CASE  ,						mask_token=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						trim_offsets=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE  ,						)

							a_		:  List[str]											=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
							if pre_tok_state.get('''add_prefix_space'''  ,						__SCREAMING_SNAKE_CASE					) != add_prefix_space:
											a_		:  Any											=      getattr(__SCREAMING_SNAKE_CASE  ,						pre_tok_state.pop('''type'''					)					)
											a_		:  int											=      add_prefix_space
											a_		:  Union[str, Any]											=      pre_tok_class(**__SCREAMING_SNAKE_CASE					)

							a_		:  List[str]											=      add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							a_		:  int											=      '''post_processor'''
							a_		:  Optional[int]											=      getattr(self.backend_tokenizer  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
							if tokenizer_component_instance:
											a_		:  Optional[int]											=      json.loads(tokenizer_component_instance.__getstate__()					)

											# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
											if "sep" in state:
															a_		:  Optional[int]											=      tuple(state['''sep''']					)
											if "cls" in state:
															a_		:  List[str]											=      tuple(state['''cls''']					)

											a_		:  List[str]											=      False

											if state.get('''add_prefix_space'''  ,						__SCREAMING_SNAKE_CASE					) != add_prefix_space:
															a_		:  Optional[Any]											=      add_prefix_space
															a_		:  str											=      True

											if state.get('''trim_offsets'''  ,						__SCREAMING_SNAKE_CASE					) != trim_offsets:
															a_		:  Dict											=      trim_offsets
															a_		:  Any											=      True

											if changes_to_apply:
															a_		:  str											=      getattr(__SCREAMING_SNAKE_CASE  ,						state.pop('''type'''					)					)
															a_		:  List[Any]											=      component_class(**__SCREAMING_SNAKE_CASE					)
															setattr(self.backend_tokenizer  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			str:
							if self._mask_token is None:
											if self.verbose:
															logger.error('''Using mask_token, but it is not set yet.'''					)
											return None
							return str(self._mask_token					)






			@mask_token.setter
			def 			SCREAMING_SNAKE_CASE      ( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			Any					)			->			Dict:
							a_		:  int											=      AddedToken(__SCREAMING_SNAKE_CASE  ,						lstrip=__SCREAMING_SNAKE_CASE  ,						rstrip=__SCREAMING_SNAKE_CASE					) if isinstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					) else value
							a_		:  Any											=      value






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any  ,						*__SCREAMING_SNAKE_CASE      :			Tuple  ,						**__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			BatchEncoding:
							a_		:  Tuple											=      kwargs.get('''is_split_into_words'''  ,						__SCREAMING_SNAKE_CASE					)

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
											    '''to use it with pretokenized inputs.'''					)

							return super()._batch_encode_plus(*__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						*__SCREAMING_SNAKE_CASE      :			List[str]  ,						**__SCREAMING_SNAKE_CASE      :			Dict					)			->			BatchEncoding:
							a_		:  int											=      kwargs.get('''is_split_into_words'''  ,						__SCREAMING_SNAKE_CASE					)

							if is_split_into_words and not self.add_prefix_space:
											raise ValueError(
											    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
											    '''to use it with pretokenized inputs.'''					)

							return super()._encode_plus(*__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[str] = None					)			->			Tuple[str]:
							a_		:  List[Any]											=      self._tokenizer.model.save(__SCREAMING_SNAKE_CASE  ,						name=__SCREAMING_SNAKE_CASE					)
							return tuple(__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=None					)			->			Dict:
							a_		:  int											=      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
											return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None					)			->			List[int]:
							a_		:  Optional[Any]											=      [self.sep_token_id]
							a_		:  str											=      [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep					) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]












'''simple docstring'''







import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
		import faiss





@require_faiss
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Dict:
							a_		:  Union[str, Any]											=      tempfile.mkdtemp()
							a_		:  Union[str, Any]											=      8

							# DPR tok
							a_		:  Dict											=      [
							    '''[UNK]''',
							    '''[CLS]''',
							    '''[SEP]''',
							    '''[PAD]''',
							    '''[MASK]''',
							    '''want''',
							    '''##want''',
							    '''##ed''',
							    '''wa''',
							    '''un''',
							    '''runn''',
							    '''##ing''',
							    ''',''',
							    '''low''',
							    '''lowest''',
							]
							a_		:  str											=      os.path.join(self.tmpdirname  ,						'''dpr_tokenizer'''					)
							os.makedirs(__SCREAMING_SNAKE_CASE  ,						exist_ok=__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      os.path.join(__SCREAMING_SNAKE_CASE  ,						DPR_VOCAB_FILES_NAMES['''vocab_file''']					)
							with open(self.vocab_file  ,						'''w'''  ,						encoding='''utf-8'''					) as vocab_writer:
											vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]					)					)

							# BART tok
							a_		:  Union[str, Any]											=      [
							    '''l''',
							    '''o''',
							    '''w''',
							    '''e''',
							    '''r''',
							    '''s''',
							    '''t''',
							    '''i''',
							    '''d''',
							    '''n''',
							    '''\u0120''',
							    '''\u0120l''',
							    '''\u0120n''',
							    '''\u0120lo''',
							    '''\u0120low''',
							    '''er''',
							    '''\u0120lowest''',
							    '''\u0120newer''',
							    '''\u0120wider''',
							    '''<unk>''',
							]
							a_		:  int											=      dict(zip(__SCREAMING_SNAKE_CASE  ,						range(len(__SCREAMING_SNAKE_CASE					)					)					)					)
							a_		:  int											=      ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
							a_		:  Optional[int]											=      {'''unk_token''': '''<unk>'''}

							a_		:  List[str]											=      os.path.join(self.tmpdirname  ,						'''bart_tokenizer'''					)
							os.makedirs(__SCREAMING_SNAKE_CASE  ,						exist_ok=__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      os.path.join(__SCREAMING_SNAKE_CASE  ,						BART_VOCAB_FILES_NAMES['''vocab_file''']					)
							a_		:  int											=      os.path.join(__SCREAMING_SNAKE_CASE  ,						BART_VOCAB_FILES_NAMES['''merges_file''']					)
							with open(self.vocab_file  ,						'''w'''  ,						encoding='''utf-8'''					) as fp:
											fp.write(json.dumps(__SCREAMING_SNAKE_CASE					) + '''\n'''					)
							with open(self.merges_file  ,						'''w'''  ,						encoding='''utf-8'''					) as fp:
											fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE					)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			DPRQuestionEncoderTokenizer:
							return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,						'''dpr_tokenizer'''					)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			DPRContextEncoderTokenizer:
							return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,						'''dpr_tokenizer'''					)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			BartTokenizer:
							return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,						'''bart_tokenizer'''					)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			Union[str, Any]:
							shutil.rmtree(self.tmpdirname					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Union[str, Any]:
							a_		:  str											=      Dataset.from_dict(
							    {
							        '''id''': ['''0''', '''1'''],
							        '''text''': ['''foo''', '''bar'''],
							        '''title''': ['''Foo''', '''Bar'''],
							        '''embeddings''': [np.ones(self.retrieval_vector_size					), 2 * np.ones(self.retrieval_vector_size					)],
							    }					)
							dataset.add_faiss_index('''embeddings'''  ,						string_factory='''Flat'''  ,						metric_type=faiss.METRIC_INNER_PRODUCT					)
							return dataset






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Tuple:
							a_		:  List[str]											=      self.get_dummy_dataset()
							a_		:  Tuple											=      RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size  ,						question_encoder=DPRConfig().to_dict()  ,						generator=BartConfig().to_dict()  ,						)
							with patch('''transformers.models.rag.retrieval_rag.load_dataset'''					) as mock_load_dataset:
											a_		:  Tuple											=      dataset
											a_		:  Any											=      RagRetriever(
											    __SCREAMING_SNAKE_CASE  ,						question_encoder_tokenizer=self.get_dpr_tokenizer()  ,						generator_tokenizer=self.get_bart_tokenizer()  ,						)
							return retriever






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			bool					)			->			Dict:
							a_		:  Dict											=      self.get_dummy_dataset()
							a_		:  Dict											=      RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size  ,						question_encoder=DPRConfig().to_dict()  ,						generator=BartConfig().to_dict()  ,						index_name='''custom'''  ,						)
							if from_disk:
											a_		:  Optional[int]											=      os.path.join(self.tmpdirname  ,						'''dataset'''					)
											a_		:  str											=      os.path.join(self.tmpdirname  ,						'''index.faiss'''					)
											dataset.get_index('''embeddings'''					).save(os.path.join(self.tmpdirname  ,						'''index.faiss'''					)					)
											dataset.drop_index('''embeddings'''					)
											dataset.save_to_disk(os.path.join(self.tmpdirname  ,						'''dataset'''					)					)
											del dataset
											a_		:  int											=      RagRetriever(
											    __SCREAMING_SNAKE_CASE  ,						question_encoder_tokenizer=self.get_dpr_tokenizer()  ,						generator_tokenizer=self.get_bart_tokenizer()  ,						)
							else:
											a_		:  Optional[Any]											=      RagRetriever(
											    __SCREAMING_SNAKE_CASE  ,						question_encoder_tokenizer=self.get_dpr_tokenizer()  ,						generator_tokenizer=self.get_bart_tokenizer()  ,						index=CustomHFIndex(config.retrieval_vector_size  ,						__SCREAMING_SNAKE_CASE					)  ,						)
							return retriever






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Optional[int]:
							a_		:  str											=      Dataset.from_dict(
							    {
							        '''id''': ['''0''', '''1'''],
							        '''text''': ['''foo''', '''bar'''],
							        '''title''': ['''Foo''', '''Bar'''],
							        '''embeddings''': [np.ones(self.retrieval_vector_size + 1					), 2 * np.ones(self.retrieval_vector_size + 1					)],
							    }					)
							dataset.add_faiss_index('''embeddings'''  ,						string_factory='''Flat'''  ,						metric_type=faiss.METRIC_INNER_PRODUCT					)

							a_		:  Optional[int]											=      os.path.join(self.tmpdirname  ,						'''hf_bert_base.hnswSQ8_correct_phi_128.c_index'''					)
							dataset.save_faiss_index('''embeddings'''  ,						index_file_name + '''.index.dpr'''					)
							pickle.dump(dataset['''id''']  ,						open(index_file_name + '''.index_meta.dpr'''  ,						'''wb'''					)					)

							a_		:  Union[str, Any]											=      os.path.join(self.tmpdirname  ,						'''psgs_w100.tsv.pkl'''					)
							a_		:  Dict											=      {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
							pickle.dump(__SCREAMING_SNAKE_CASE  ,						open(__SCREAMING_SNAKE_CASE  ,						'''wb'''					)					)

							a_		:  Optional[Any]											=      RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size  ,						question_encoder=DPRConfig().to_dict()  ,						generator=BartConfig().to_dict()  ,						index_name='''legacy'''  ,						index_path=self.tmpdirname  ,						)
							a_		:  int											=      RagRetriever(
							    __SCREAMING_SNAKE_CASE  ,						question_encoder_tokenizer=self.get_dpr_tokenizer()  ,						generator_tokenizer=self.get_bart_tokenizer()					)
							return retriever






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Dict:
							a_		:  Optional[Any]											=      1
							a_		:  Dict											=      self.get_dummy_canonical_hf_index_retriever()
							a_		:  Tuple											=      np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
							a_				,			a_				,			a_		:  str											=      retriever.retrieve(__SCREAMING_SNAKE_CASE  ,						n_docs=__SCREAMING_SNAKE_CASE					)
							self.assertEqual(retrieved_doc_embeds.shape  ,						(2, n_docs, self.retrieval_vector_size)					)
							self.assertEqual(len(__SCREAMING_SNAKE_CASE					)  ,						2					)
							self.assertEqual(sorted(doc_dicts[0]					)  ,						['''embeddings''', '''id''', '''text''', '''title''']					)
							self.assertEqual(len(doc_dicts[0]['''id''']					)  ,						__SCREAMING_SNAKE_CASE					)
							self.assertEqual(doc_dicts[0]['''id'''][0]  ,						'''1'''					)  # max inner product is reached with second doc
							self.assertEqual(doc_dicts[1]['''id'''][0]  ,						'''0'''					)  # max inner product is reached with first doc
							self.assertListEqual(doc_ids.tolist()  ,						[[1], [0]]					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Dict:
							a_		:  str											=      self.get_dummy_canonical_hf_index_retriever()
							with tempfile.TemporaryDirectory() as tmp_dirname:
											with patch('''transformers.models.rag.retrieval_rag.load_dataset'''					) as mock_load_dataset:
															a_		:  List[str]											=      self.get_dummy_dataset()
															retriever.save_pretrained(__SCREAMING_SNAKE_CASE					)
															a_		:  Optional[Any]											=      RagRetriever.from_pretrained(__SCREAMING_SNAKE_CASE					)
															self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
															a_		:  Union[str, Any]											=      np.array(
															    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
															a_		:  List[str]											=      retriever.retrieve(__SCREAMING_SNAKE_CASE  ,						n_docs=1					)
															self.assertTrue(out is not None					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			int:
							a_		:  Union[str, Any]											=      1
							a_		:  Optional[Any]											=      self.get_dummy_custom_hf_index_retriever(from_disk=__SCREAMING_SNAKE_CASE					)
							a_		:  List[Any]											=      np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
							a_				,			a_				,			a_		:  Any											=      retriever.retrieve(__SCREAMING_SNAKE_CASE  ,						n_docs=__SCREAMING_SNAKE_CASE					)
							self.assertEqual(retrieved_doc_embeds.shape  ,						(2, n_docs, self.retrieval_vector_size)					)
							self.assertEqual(len(__SCREAMING_SNAKE_CASE					)  ,						2					)
							self.assertEqual(sorted(doc_dicts[0]					)  ,						['''embeddings''', '''id''', '''text''', '''title''']					)
							self.assertEqual(len(doc_dicts[0]['''id''']					)  ,						__SCREAMING_SNAKE_CASE					)
							self.assertEqual(doc_dicts[0]['''id'''][0]  ,						'''1'''					)  # max inner product is reached with second doc
							self.assertEqual(doc_dicts[1]['''id'''][0]  ,						'''0'''					)  # max inner product is reached with first doc
							self.assertListEqual(doc_ids.tolist()  ,						[[1], [0]]					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			List[str]:
							a_		:  Dict											=      self.get_dummy_custom_hf_index_retriever(from_disk=__SCREAMING_SNAKE_CASE					)
							with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(__SCREAMING_SNAKE_CASE					)
											a_		:  List[str]											=      RagRetriever.from_pretrained(__SCREAMING_SNAKE_CASE					)
											self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
											a_		:  Tuple											=      np.array(
											    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
											a_		:  Dict											=      retriever.retrieve(__SCREAMING_SNAKE_CASE  ,						n_docs=1					)
											self.assertTrue(out is not None					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Optional[int]:
							a_		:  Union[str, Any]											=      1
							a_		:  str											=      self.get_dummy_custom_hf_index_retriever(from_disk=__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
							a_				,			a_				,			a_		:  Tuple											=      retriever.retrieve(__SCREAMING_SNAKE_CASE  ,						n_docs=__SCREAMING_SNAKE_CASE					)
							self.assertEqual(retrieved_doc_embeds.shape  ,						(2, n_docs, self.retrieval_vector_size)					)
							self.assertEqual(len(__SCREAMING_SNAKE_CASE					)  ,						2					)
							self.assertEqual(sorted(doc_dicts[0]					)  ,						['''embeddings''', '''id''', '''text''', '''title''']					)
							self.assertEqual(len(doc_dicts[0]['''id''']					)  ,						__SCREAMING_SNAKE_CASE					)
							self.assertEqual(doc_dicts[0]['''id'''][0]  ,						'''1'''					)  # max inner product is reached with second doc
							self.assertEqual(doc_dicts[1]['''id'''][0]  ,						'''0'''					)  # max inner product is reached with first doc
							self.assertListEqual(doc_ids.tolist()  ,						[[1], [0]]					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			Dict:
							a_		:  List[str]											=      self.get_dummy_custom_hf_index_retriever(from_disk=__SCREAMING_SNAKE_CASE					)
							with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(__SCREAMING_SNAKE_CASE					)
											a_		:  Any											=      RagRetriever.from_pretrained(__SCREAMING_SNAKE_CASE					)
											self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
											a_		:  Tuple											=      np.array(
											    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
											a_		:  Dict											=      retriever.retrieve(__SCREAMING_SNAKE_CASE  ,						n_docs=1					)
											self.assertTrue(out is not None					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			Any:
							a_		:  str											=      1
							a_		:  Tuple											=      self.get_dummy_legacy_index_retriever()
							a_		:  Union[str, Any]											=      np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
							a_				,			a_				,			a_		:  Any											=      retriever.retrieve(__SCREAMING_SNAKE_CASE  ,						n_docs=__SCREAMING_SNAKE_CASE					)
							self.assertEqual(retrieved_doc_embeds.shape  ,						(2, n_docs, self.retrieval_vector_size)					)
							self.assertEqual(len(__SCREAMING_SNAKE_CASE					)  ,						2					)
							self.assertEqual(sorted(doc_dicts[0]					)  ,						['''text''', '''title''']					)
							self.assertEqual(len(doc_dicts[0]['''text''']					)  ,						__SCREAMING_SNAKE_CASE					)
							self.assertEqual(doc_dicts[0]['''text'''][0]  ,						'''bar'''					)  # max inner product is reached with second doc
							self.assertEqual(doc_dicts[1]['''text'''][0]  ,						'''foo'''					)  # max inner product is reached with first doc
							self.assertListEqual(doc_ids.tolist()  ,						[[1], [0]]					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			Optional[int]:
							a_		:  List[str]											=      self.get_dummy_legacy_index_retriever()
							with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(__SCREAMING_SNAKE_CASE					)
											a_		:  Any											=      RagRetriever.from_pretrained(__SCREAMING_SNAKE_CASE					)
											self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
											a_		:  List[str]											=      np.array(
											    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
											a_		:  Optional[Any]											=      retriever.retrieve(__SCREAMING_SNAKE_CASE  ,						n_docs=1					)
											self.assertTrue(out is not None					)






			@require_torch
			@require_tokenizers
			@require_sentencepiece
			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			List[str]:
							import torch

							a_		:  Any											=      1
							a_		:  List[Any]											=      self.get_dummy_canonical_hf_index_retriever()
							a_		:  Union[str, Any]											=      [[5, 7], [10, 11]]
							a_		:  Optional[int]											=      np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
							a_		:  str											=      retriever(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						prefix=retriever.config.generator.prefix  ,						n_docs=__SCREAMING_SNAKE_CASE					)
							a_				,			a_				,			a_		:  List[str]											=      (
							    out['''context_input_ids'''],
							    out['''context_attention_mask'''],
							    out['''retrieved_doc_embeds'''],
							)
							self.assertEqual(retrieved_doc_embeds.shape  ,						(2, n_docs, self.retrieval_vector_size)					)
							self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
							self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
							self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						np.ndarray					)

							a_		:  Any											=      retriever(
							    __SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						prefix=retriever.config.generator.prefix  ,						n_docs=__SCREAMING_SNAKE_CASE  ,						return_tensors='''pt'''  ,						)
							a_				,			a_				,			a_				,			a_		:  str											=      (  # noqa: F841
							    out['''context_input_ids'''],
							    out['''context_attention_mask'''],
							    out['''retrieved_doc_embeds'''],
							    out['''doc_ids'''],
							)
							self.assertEqual(retrieved_doc_embeds.shape  ,						(2, n_docs, self.retrieval_vector_size)					)
							self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						torch.Tensor					)
							self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						torch.Tensor					)
							self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						torch.Tensor					)







			@require_torch
			@require_tokenizers
			@require_sentencepiece
			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Any:
							a_		:  str											=      self.get_dpr_ctx_encoder_tokenizer()
							a_		:  Tuple											=      1
							a_		:  Any											=      self.get_dummy_custom_hf_index_retriever(from_disk=__SCREAMING_SNAKE_CASE					)
							retriever.set_ctx_encoder_tokenizer(__SCREAMING_SNAKE_CASE					)

							a_		:  Dict											=      [[5, 7], [10, 11]]
							a_		:  List[str]											=      np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
							a_		:  List[Any]											=      retriever(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						prefix=retriever.config.generator.prefix  ,						n_docs=__SCREAMING_SNAKE_CASE					)

							self.assertEqual(
							    len(__SCREAMING_SNAKE_CASE					)  ,						6					)  # check whether the retriever output consist of 6 attributes including tokenized docs
							self.assertEqual(
							    all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''')					)  ,						__SCREAMING_SNAKE_CASE					)  # check for doc token related keys in dictionary.




