
"""simple docstring"""




import torch
from torch import nn





class  snake_case     (							nn.Module   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=1       ,       SCREAMING_SNAKE_CASE_=False    ):

									"""simple docstring"""







									super().__init__()

									SCREAMING_SNAKE_CASE_						 = n_token
									SCREAMING_SNAKE_CASE_						 = d_embed
									SCREAMING_SNAKE_CASE_						 = d_proj

									SCREAMING_SNAKE_CASE_						 = cutoffs + [n_token]
									SCREAMING_SNAKE_CASE_						 = [0] + self.cutoffs
									SCREAMING_SNAKE_CASE_						 = div_val

									SCREAMING_SNAKE_CASE_						 = self.cutoffs[0]
									SCREAMING_SNAKE_CASE_						 = len(self.cutoffs    ) - 1
									SCREAMING_SNAKE_CASE_						 = self.shortlist_size + self.n_clusters

									if self.n_clusters > 0:
											SCREAMING_SNAKE_CASE_						 = nn.Parameter(torch.zeros(self.n_clusters       ,       self.d_embed    )    )
											SCREAMING_SNAKE_CASE_						 = nn.Parameter(torch.zeros(self.n_clusters    )    )

									SCREAMING_SNAKE_CASE_						 = nn.ModuleList()
									SCREAMING_SNAKE_CASE_						 = nn.ParameterList()

									if div_val == 1:
											for i in range(len(self.cutoffs    )    ):
													if d_proj != d_embed:
															self.out_projs.append(nn.Parameter(torch.FloatTensor(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )    )    )
													else:
															self.out_projs.append(SCREAMING_SNAKE_CASE_    )

											self.out_layers.append(nn.Linear(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )    )
									else:
											for i in range(len(self.cutoffs    )    ):
													SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self.cutoff_ends[i], self.cutoff_ends[i + 1]
													SCREAMING_SNAKE_CASE_						 = d_embed // (div_val**i)

													self.out_projs.append(nn.Parameter(torch.FloatTensor(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )    )    )

													self.out_layers.append(nn.Linear(SCREAMING_SNAKE_CASE_       ,       r_idx - l_idx    )    )

									SCREAMING_SNAKE_CASE_						 = keep_order

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if proj is None:
											SCREAMING_SNAKE_CASE_						 = nn.functional.linear(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       bias=SCREAMING_SNAKE_CASE_    )
									else:
											# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
											SCREAMING_SNAKE_CASE_						 = nn.functional.linear(SCREAMING_SNAKE_CASE_       ,       proj.t().contiguous()    )
											SCREAMING_SNAKE_CASE_						 = nn.functional.linear(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       bias=SCREAMING_SNAKE_CASE_    )
											# else:
											#     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
											#     if bias is not None:
											#         logit = logit + bias

									return logit

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=False    ):

									"""simple docstring"""







									if labels is not None:
											# Shift so that tokens < n predict n
											SCREAMING_SNAKE_CASE_						 = hidden[..., :-1, :].contiguous()
											SCREAMING_SNAKE_CASE_						 = labels[..., 1:].contiguous()
											SCREAMING_SNAKE_CASE_						 = hidden.view(-1       ,       hidden.size(-1    )    )
											SCREAMING_SNAKE_CASE_						 = labels.view(-1    )
											if hidden.size(0    ) != labels.size(0    ):
													raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''    )
									else:
											SCREAMING_SNAKE_CASE_						 = hidden.view(-1       ,       hidden.size(-1    )    )

									if self.n_clusters == 0:
											SCREAMING_SNAKE_CASE_						 = self._compute_logit(SCREAMING_SNAKE_CASE_       ,       self.out_layers[0].weight       ,       self.out_layers[0].bias       ,       self.out_projs[0]    )
											if labels is not None:
													SCREAMING_SNAKE_CASE_						 = labels != -1_00
													SCREAMING_SNAKE_CASE_						 = torch.zeros_like(SCREAMING_SNAKE_CASE_       ,       dtype=hidden.dtype       ,       device=hidden.device    )
													SCREAMING_SNAKE_CASE_						 = (
													    -nn.functional.log_softmax(SCREAMING_SNAKE_CASE_       ,       dim=-1    )[mask].gather(1       ,       labels[mask].unsqueeze(1    )    ).squeeze(1    )
													)
											else:
													SCREAMING_SNAKE_CASE_						 = nn.functional.log_softmax(SCREAMING_SNAKE_CASE_       ,       dim=-1    )
									else:
											# construct weights and biases
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = [], []
											for i in range(len(self.cutoffs    )    ):
													if self.div_val == 1:
															SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self.cutoff_ends[i], self.cutoff_ends[i + 1]
															SCREAMING_SNAKE_CASE_						 = self.out_layers[0].weight[l_idx:r_idx]
															SCREAMING_SNAKE_CASE_						 = self.out_layers[0].bias[l_idx:r_idx]
													else:
															SCREAMING_SNAKE_CASE_						 = self.out_layers[i].weight
															SCREAMING_SNAKE_CASE_						 = self.out_layers[i].bias

													if i == 0:
															SCREAMING_SNAKE_CASE_						 = torch.cat([weight_i, self.cluster_weight]       ,       dim=0    )
															SCREAMING_SNAKE_CASE_						 = torch.cat([bias_i, self.cluster_bias]       ,       dim=0    )

													weights.append(SCREAMING_SNAKE_CASE_    )
													biases.append(SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = weights[0], biases[0], self.out_projs[0]

											SCREAMING_SNAKE_CASE_						 = self._compute_logit(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = nn.functional.log_softmax(SCREAMING_SNAKE_CASE_       ,       dim=1    )

											if labels is None:
													SCREAMING_SNAKE_CASE_						 = hidden.new_empty((head_logit.size(0    ), self.n_token)    )
											else:
													SCREAMING_SNAKE_CASE_						 = torch.zeros_like(SCREAMING_SNAKE_CASE_       ,       dtype=hidden.dtype       ,       device=hidden.device    )

											SCREAMING_SNAKE_CASE_						 = 0
											SCREAMING_SNAKE_CASE_						 = [0] + self.cutoffs
											for i in range(len(SCREAMING_SNAKE_CASE_    ) - 1    ):
													SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = cutoff_values[i], cutoff_values[i + 1]

													if labels is not None:
															SCREAMING_SNAKE_CASE_						 = (labels >= l_idx) & (labels < r_idx)
															SCREAMING_SNAKE_CASE_						 = mask_i.nonzero().squeeze()

															if indices_i.numel() == 0:
																	continue

															SCREAMING_SNAKE_CASE_						 = labels.index_select(0       ,       SCREAMING_SNAKE_CASE_    ) - l_idx
															SCREAMING_SNAKE_CASE_						 = head_logprob.index_select(0       ,       SCREAMING_SNAKE_CASE_    )
															SCREAMING_SNAKE_CASE_						 = hidden.index_select(0       ,       SCREAMING_SNAKE_CASE_    )
													else:
															SCREAMING_SNAKE_CASE_						 = hidden

													if i == 0:
															if labels is not None:
																	SCREAMING_SNAKE_CASE_						 = head_logprob_i.gather(1       ,       target_i[:, None]    ).squeeze(1    )
															else:
																	SCREAMING_SNAKE_CASE_						 = head_logprob[:, : self.cutoffs[0]]
													else:
															SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = weights[i], biases[i], self.out_projs[i]

															SCREAMING_SNAKE_CASE_						 = self._compute_logit(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
															SCREAMING_SNAKE_CASE_						 = nn.functional.log_softmax(SCREAMING_SNAKE_CASE_       ,       dim=1    )
															SCREAMING_SNAKE_CASE_						 = self.cutoffs[0] + i - 1  # No probability for the head cluster
															if labels is not None:
																	SCREAMING_SNAKE_CASE_						 = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
																	    1       ,       target_i[:, None]    ).squeeze(1    )
															else:
																	SCREAMING_SNAKE_CASE_						 = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
																	SCREAMING_SNAKE_CASE_						 = logprob_i

													if labels is not None:
															if (hasattr(self       ,       '''keep_order'''    ) and self.keep_order) or keep_order:
																	out.index_copy_(0       ,       SCREAMING_SNAKE_CASE_       ,       -logprob_i    )
															else:
																	out[offset : offset + logprob_i.size(0    )].copy_(-logprob_i    )
															offset += logprob_i.size(0    )

									return out







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if self.n_clusters == 0:
											SCREAMING_SNAKE_CASE_						 = self._compute_logit(SCREAMING_SNAKE_CASE_       ,       self.out_layers[0].weight       ,       self.out_layers[0].bias       ,       self.out_projs[0]    )
											return nn.functional.log_softmax(SCREAMING_SNAKE_CASE_       ,       dim=-1    )
									else:
											# construct weights and biases
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = [], []
											for i in range(len(self.cutoffs    )    ):
													if self.div_val == 1:
															SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = self.cutoff_ends[i], self.cutoff_ends[i + 1]
															SCREAMING_SNAKE_CASE_						 = self.out_layers[0].weight[l_idx:r_idx]
															SCREAMING_SNAKE_CASE_						 = self.out_layers[0].bias[l_idx:r_idx]
													else:
															SCREAMING_SNAKE_CASE_						 = self.out_layers[i].weight
															SCREAMING_SNAKE_CASE_						 = self.out_layers[i].bias

													if i == 0:
															SCREAMING_SNAKE_CASE_						 = torch.cat([weight_i, self.cluster_weight]       ,       dim=0    )
															SCREAMING_SNAKE_CASE_						 = torch.cat([bias_i, self.cluster_bias]       ,       dim=0    )

													weights.append(SCREAMING_SNAKE_CASE_    )
													biases.append(SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = weights[0], biases[0], self.out_projs[0]
											SCREAMING_SNAKE_CASE_						 = self._compute_logit(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = hidden.new_empty((head_logit.size(0    ), self.n_token)    )
											SCREAMING_SNAKE_CASE_						 = nn.functional.log_softmax(SCREAMING_SNAKE_CASE_       ,       dim=1    )

											SCREAMING_SNAKE_CASE_						 = [0] + self.cutoffs
											for i in range(len(SCREAMING_SNAKE_CASE_    ) - 1    ):
													SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = cutoff_values[i], cutoff_values[i + 1]

													if i == 0:
															SCREAMING_SNAKE_CASE_						 = head_logprob[:, : self.cutoffs[0]]
													else:
															SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = weights[i], biases[i], self.out_projs[i]

															SCREAMING_SNAKE_CASE_						 = self._compute_logit(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
															SCREAMING_SNAKE_CASE_						 = nn.functional.log_softmax(SCREAMING_SNAKE_CASE_       ,       dim=1    )

															SCREAMING_SNAKE_CASE_						 = head_logprob[:, -i] + tail_logprob_i
															SCREAMING_SNAKE_CASE_						 = logprob_i

											return out

"""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.