






"""simple docstring"""




import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english





@require_tokenizers
class  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      RoCBertTokenizer
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      False
							UpperCAmelCase__						     =      True
							UpperCAmelCase__						     =      filter_non_english

							def _lowercase					(self    ):

									"""simple docstring"""







									super().setUp()

									SCREAMING_SNAKE_CASE_						 = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''你''', '''好''', '''是''', '''谁''', '''a''', '''b''', '''c''', '''d''']
									SCREAMING_SNAKE_CASE_						 = {}
									SCREAMING_SNAKE_CASE_						 = {}
									for i, value in enumerate(SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = i
											SCREAMING_SNAKE_CASE_						 = i
									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''vocab_file''']    )
									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''word_shape_file''']    )
									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''word_pronunciation_file''']    )
									with open(self.vocab_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as vocab_writer:
											vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]    )    )
									with open(self.word_shape_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as word_shape_writer:
											json.dump(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       ensure_ascii=SCREAMING_SNAKE_CASE_    )
									with open(self.word_pronunciation_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as word_pronunciation_writer:
											json.dump(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       ensure_ascii=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.tokenizer_class(self.vocab_file       ,       self.word_shape_file       ,       self.word_pronunciation_file    )

									SCREAMING_SNAKE_CASE_						 = tokenizer.tokenize('''你好[SEP]你是谁'''    )
									self.assertListEqual(SCREAMING_SNAKE_CASE_       ,       ['''你''', '''好''', '''[SEP]''', '''你''', '''是''', '''谁''']    )
									self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )       ,       [5, 6, 2, 5, 7, 8]    )
									self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE_    )       ,       [5, 6, 2, 5, 7, 8]    )
									self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE_    )       ,       [5, 6, 2, 5, 7, 8]    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = RoCBertBasicTokenizer()

									self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz'''    )       ,       ['''ah''', '''\u535A''', '''\u63A8''', '''zz''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_    )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''    )       ,       ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']    )
									self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''    )       ,       ['''hello''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_       ,       strip_accents=SCREAMING_SNAKE_CASE_    )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''    )       ,       ['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?''']    )
									self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''    )       ,       ['''h\u00E9llo''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_       ,       strip_accents=SCREAMING_SNAKE_CASE_    )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''    )       ,       ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']    )
									self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''    )       ,       ['''hello''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_    )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''    )       ,       ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']    )
									self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''    )       ,       ['''hello''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_    )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''    )       ,       ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_       ,       strip_accents=SCREAMING_SNAKE_CASE_    )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''    )       ,       ['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_       ,       strip_accents=SCREAMING_SNAKE_CASE_    )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''    )       ,       ['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_       ,       never_split=['''[UNK]''']    )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]'''    )       ,       ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

									SCREAMING_SNAKE_CASE_						 = {}
									for i, token in enumerate(SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = i
									SCREAMING_SNAKE_CASE_						 = RoCBertWordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE_       ,       unk_token='''[UNK]'''    )

									self.assertListEqual(tokenizer.tokenize(''''''    )       ,       []    )

									self.assertListEqual(tokenizer.tokenize('''unwanted running'''    )       ,       ['''un''', '''##want''', '''##ed''', '''runn''', '''##ing''']    )

									self.assertListEqual(tokenizer.tokenize('''unwantedX running'''    )       ,       ['''[UNK]''', '''runn''', '''##ing''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									self.assertTrue(_is_whitespace(''' '''    )    )
									self.assertTrue(_is_whitespace('''\t'''    )    )
									self.assertTrue(_is_whitespace('''\r'''    )    )
									self.assertTrue(_is_whitespace('''\n'''    )    )
									self.assertTrue(_is_whitespace('''\u00A0'''    )    )

									self.assertFalse(_is_whitespace('''A'''    )    )
									self.assertFalse(_is_whitespace('''-'''    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									self.assertTrue(_is_control('''\u0005'''    )    )

									self.assertFalse(_is_control('''A'''    )    )
									self.assertFalse(_is_control(''' '''    )    )
									self.assertFalse(_is_control('''\t'''    )    )
									self.assertFalse(_is_control('''\r'''    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									self.assertTrue(_is_punctuation('''-'''    )    )
									self.assertTrue(_is_punctuation('''$'''    )    )
									self.assertTrue(_is_punctuation('''`'''    )    )
									self.assertTrue(_is_punctuation('''.'''    )    )

									self.assertFalse(_is_punctuation('''A'''    )    )
									self.assertFalse(_is_punctuation(''' '''    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_tokenizer()

									# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
									self.assertListEqual([tokenizer.tokenize(SCREAMING_SNAKE_CASE_    ) for t in ['''Test''', '''\xad''', '''test''']]       ,       [['''[UNK]'''], [], ['''[UNK]''']]    )

									if self.test_rust_tokenizer:
											SCREAMING_SNAKE_CASE_						 = self.get_rust_tokenizer()
											self.assertListEqual(
											    [rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_    ) for t in ['''Test''', '''\xad''', '''test''']]       ,       [['''[UNK]'''], [], ['''[UNK]''']]    )

							def _lowercase					(self    ):

									"""simple docstring"""







									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'    ):
													SCREAMING_SNAKE_CASE_						 = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )

													SCREAMING_SNAKE_CASE_						 = f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
													SCREAMING_SNAKE_CASE_						 = tokenizer_r.encode_plus(
													    SCREAMING_SNAKE_CASE_       ,       return_attention_mask=SCREAMING_SNAKE_CASE_       ,       return_token_type_ids=SCREAMING_SNAKE_CASE_       ,       return_offsets_mapping=SCREAMING_SNAKE_CASE_       ,       add_special_tokens=SCREAMING_SNAKE_CASE_       ,       )

													SCREAMING_SNAKE_CASE_						 = tokenizer_r.do_lower_case if hasattr(SCREAMING_SNAKE_CASE_       ,       '''do_lower_case'''    ) else False
													SCREAMING_SNAKE_CASE_						 = (
													    [
													        ((0, 0), tokenizer_r.cls_token),
													        ((0, 1), '''A'''),
													        ((1, 2), ''','''),
													        ((3, 5), '''na'''),
													        ((5, 6), '''##ï'''),
													        ((6, 8), '''##ve'''),
													        ((9, 15), tokenizer_r.mask_token),
													        ((16, 21), '''Allen'''),
													        ((21, 23), '''##NL'''),
													        ((23, 24), '''##P'''),
													        ((25, 33), '''sentence'''),
													        ((33, 34), '''.'''),
													        ((0, 0), tokenizer_r.sep_token),
													    ]
													    if not do_lower_case
													    else [
													        ((0, 0), tokenizer_r.cls_token),
													        ((0, 1), '''a'''),
													        ((1, 2), ''','''),
													        ((3, 8), '''naive'''),
													        ((9, 15), tokenizer_r.mask_token),
													        ((16, 21), '''allen'''),
													        ((21, 23), '''##nl'''),
													        ((23, 24), '''##p'''),
													        ((25, 33), '''sentence'''),
													        ((33, 34), '''.'''),
													        ((0, 0), tokenizer_r.sep_token),
													    ]
													)

													self.assertEqual(
													    [e[1] for e in expected_results]       ,       tokenizer_r.convert_ids_to_tokens(tokens['''input_ids''']    )    )
													self.assertEqual([e[0] for e in expected_results]       ,       tokens['''offset_mapping''']    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = ['''的''', '''人''', '''有''']
									SCREAMING_SNAKE_CASE_						 = ''''''.join(SCREAMING_SNAKE_CASE_    )
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'    ):
													SCREAMING_SNAKE_CASE_						 = True
													SCREAMING_SNAKE_CASE_						 = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )

													SCREAMING_SNAKE_CASE_						 = tokenizer_p.encode(SCREAMING_SNAKE_CASE_       ,       add_special_tokens=SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = tokenizer_r.encode(SCREAMING_SNAKE_CASE_       ,       add_special_tokens=SCREAMING_SNAKE_CASE_    )

													SCREAMING_SNAKE_CASE_						 = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_    )

													# it is expected that each Chinese character is not preceded by "##"
													self.assertListEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
													self.assertListEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

													SCREAMING_SNAKE_CASE_						 = False
													SCREAMING_SNAKE_CASE_						 = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )

													SCREAMING_SNAKE_CASE_						 = tokenizer_r.encode(SCREAMING_SNAKE_CASE_       ,       add_special_tokens=SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = tokenizer_p.encode(SCREAMING_SNAKE_CASE_       ,       add_special_tokens=SCREAMING_SNAKE_CASE_    )

													SCREAMING_SNAKE_CASE_						 = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_    )

													# it is expected that only the first Chinese character is not preceded by "##".
													SCREAMING_SNAKE_CASE_						 = [
													    f'##{token}' if idx != 0 else token for idx, token in enumerate(SCREAMING_SNAKE_CASE_    )
													]
													self.assertListEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
													self.assertListEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.tokenizer_class(self.vocab_file       ,       self.word_shape_file       ,       self.word_pronunciation_file    )

									SCREAMING_SNAKE_CASE_						 = tokenizer.encode('''你好'''       ,       add_special_tokens=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = tokenizer.encode('''你是谁'''       ,       add_special_tokens=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

									assert encoded_sentence == [1] + text + [2]
									assert encoded_pair == [1] + text + [2] + text_a + [2]







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE_    )
									for tokenizer in tokenizers:
											with self.subTest(f'{tokenizer.__class__.__name__}'    ):
													SCREAMING_SNAKE_CASE_						 = '''你好，你是谁'''
													SCREAMING_SNAKE_CASE_						 = tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = tokenizer.prepare_for_model(
													    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       add_special_tokens=SCREAMING_SNAKE_CASE_    )

													SCREAMING_SNAKE_CASE_						 = tokenizer.encode_plus(SCREAMING_SNAKE_CASE_       ,       add_special_tokens=SCREAMING_SNAKE_CASE_    )

													self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

"""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  snake_case     (							__lowercase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = tempfile.mkdtemp()
									SCREAMING_SNAKE_CASE_						 = 8

									# DPR tok
									SCREAMING_SNAKE_CASE_						 = [
									    '''[UNK]''',
									    '''[CLS]''',
									    '''[SEP]''',
									    '''[PAD]''',
									    '''[MASK]''',
									    '''want''',
									    '''##want''',
									    '''##ed''',
									    '''wa''',
									    '''un''',
									    '''runn''',
									    '''##ing''',
									    ''',''',
									    '''low''',
									    '''lowest''',
									]
									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       '''dpr_tokenizer'''    )
									os.makedirs(SCREAMING_SNAKE_CASE_       ,       exist_ok=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = 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
									SCREAMING_SNAKE_CASE_						 = [
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''\u0120''',
									    '''\u0120l''',
									    '''\u0120n''',
									    '''\u0120lo''',
									    '''\u0120low''',
									    '''er''',
									    '''\u0120lowest''',
									    '''\u0120newer''',
									    '''\u0120wider''',
									    '''<unk>''',
									]
									SCREAMING_SNAKE_CASE_						 = dict(zip(SCREAMING_SNAKE_CASE_       ,       range(len(SCREAMING_SNAKE_CASE_    )    )    )    )
									SCREAMING_SNAKE_CASE_						 = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
									SCREAMING_SNAKE_CASE_						 = {'''unk_token''': '''<unk>'''}

									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       '''bart_tokenizer'''    )
									os.makedirs(SCREAMING_SNAKE_CASE_       ,       exist_ok=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = os.path.join(SCREAMING_SNAKE_CASE_       ,       BART_VOCAB_FILES_NAMES['''vocab_file''']    )
									SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self    ):

									"""simple docstring"""







									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,       '''dpr_tokenizer'''    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,       '''dpr_tokenizer'''    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,       '''bart_tokenizer'''    )    )

							def _lowercase					(self    ):

									"""simple docstring"""







									shutil.rmtree(self.tmpdirname    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_dataset()
									SCREAMING_SNAKE_CASE_						 = 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:
											SCREAMING_SNAKE_CASE_						 = dataset
											SCREAMING_SNAKE_CASE_						 = RagRetriever(
											    SCREAMING_SNAKE_CASE_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer()       ,       )
									return retriever

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_dataset()
									SCREAMING_SNAKE_CASE_						 = RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size       ,       question_encoder=DPRConfig().to_dict()       ,       generator=BartConfig().to_dict()       ,       index_name='''custom'''       ,       )
									if from_disk:
											SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       '''dataset'''    )
											SCREAMING_SNAKE_CASE_						 = 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
											SCREAMING_SNAKE_CASE_						 = RagRetriever(
											    SCREAMING_SNAKE_CASE_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer()       ,       )
									else:
											SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 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    )

									SCREAMING_SNAKE_CASE_						 = 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'''    )    )

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

									SCREAMING_SNAKE_CASE_						 = RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size       ,       question_encoder=DPRConfig().to_dict()       ,       generator=BartConfig().to_dict()       ,       index_name='''legacy'''       ,       index_path=self.tmpdirname       ,       )
									SCREAMING_SNAKE_CASE_						 = RagRetriever(
									    SCREAMING_SNAKE_CASE_       ,       question_encoder_tokenizer=self.get_dpr_tokenizer()       ,       generator_tokenizer=self.get_bart_tokenizer()    )
									return retriever

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_canonical_hf_index_retriever()
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 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:
													SCREAMING_SNAKE_CASE_						 = self.get_dummy_dataset()
													retriever.save_pretrained(SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_    )
													self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
													SCREAMING_SNAKE_CASE_						 = np.array(
													    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
													SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=1    )
													self.assertTrue(out is not None    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_    )
											self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = np.array(
											    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
											SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=1    )
											self.assertTrue(out is not None    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_    )
											self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = np.array(
											    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
											SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=1    )
											self.assertTrue(out is not None    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_legacy_index_retriever()
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_legacy_index_retriever()
									with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_    )
											self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = np.array(
											    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
											SCREAMING_SNAKE_CASE_						 = retriever.retrieve(SCREAMING_SNAKE_CASE_       ,       n_docs=1    )
											self.assertTrue(out is not None    )

							@require_torch
							@require_tokenizers
							@require_sentencepiece
							def _lowercase					(self    ):

									"""simple docstring"""







									import torch

									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_canonical_hf_index_retriever()
									SCREAMING_SNAKE_CASE_						 = [[5, 7], [10, 11]]
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_						 = retriever(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       prefix=retriever.config.generator.prefix       ,       n_docs=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = (
									    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    )

									SCREAMING_SNAKE_CASE_						 = retriever(
									    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       prefix=retriever.config.generator.prefix       ,       n_docs=SCREAMING_SNAKE_CASE_       ,       return_tensors='''pt'''       ,       )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = (  # 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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dpr_ctx_encoder_tokenizer()
									SCREAMING_SNAKE_CASE_						 = 1
									SCREAMING_SNAKE_CASE_						 = self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE_    )
									retriever.set_ctx_encoder_tokenizer(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = [[5, 7], [10, 11]]
									SCREAMING_SNAKE_CASE_						 = np.array(
									    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]       ,       dtype=np.floataa    )
									SCREAMING_SNAKE_CASE_						 = 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.