




'''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 		lowerCAmelCase_     (    UpperCAmelCase_				):







  '''simple docstring'''

  def  _snake_case			(      self							:				List[str]     )       ->						List[Any]:







       '''simple docstring'''



       A:						Optional[int]						=						tempfile.mkdtemp()
       A:						Optional[Any]						=						8

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

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



  def  _snake_case			(      self							:				Tuple     )       ->						List[str]:







       '''simple docstring'''



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



  def  _snake_case			(      self							:				List[str]     )       ->						Union[str, Any]:







       '''simple docstring'''



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



  def  _snake_case			(      self							:				Optional[Any]     )       ->						int:







       '''simple docstring'''



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



  def  _snake_case			(      self							:				List[Any]     )       ->						Tuple:







       '''simple docstring'''



       shutil.rmtree(self.tmpdirname     )



  def  _snake_case			(      self							:				int     )       ->						List[str]:







       '''simple docstring'''



       A:						Any						=						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  _snake_case			(      self							:				Optional[int]     )       ->						Dict:







       '''simple docstring'''



       A:						str						=						self.get_dummy_dataset()
       A:						Optional[Any]						=						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:						List[Any]						=						dataset
            A:						Dict						=						RagRetriever(
                _lowerCAmelCase ,   question_encoder_tokenizer=self.get_dpr_tokenizer() ,   generator_tokenizer=self.get_bart_tokenizer() ,   )
       return retriever



  def  _snake_case			(      self							:				Any ,   SCREAMING_SNAKE_CASE_							:				bool     )       ->						Optional[Any]:







       '''simple docstring'''



       A:						Tuple						=						self.get_dummy_dataset()
       A:						str						=						RagConfig(
           retrieval_vector_size=self.retrieval_vector_size ,   question_encoder=DPRConfig().to_dict() ,   generator=BartConfig().to_dict() ,   index_name='''custom''' ,   )
       if from_disk:
            A:						Any						=						os.path.join(self.tmpdirname ,   '''dataset'''     )
            A:						Optional[int]						=						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:						List[str]						=						RagRetriever(
                _lowerCAmelCase ,   question_encoder_tokenizer=self.get_dpr_tokenizer() ,   generator_tokenizer=self.get_bart_tokenizer() ,   )
       else:
            A:						List[Any]						=						RagRetriever(
                _lowerCAmelCase ,   question_encoder_tokenizer=self.get_dpr_tokenizer() ,   generator_tokenizer=self.get_bart_tokenizer() ,   index=CustomHFIndex(config.retrieval_vector_size ,   _lowerCAmelCase     ) ,   )
       return retriever



  def  _snake_case			(      self							:				Optional[int]     )       ->						Optional[Any]:







       '''simple docstring'''



       A:						Union[str, Any]						=						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:						List[Any]						=						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:						Optional[Any]						=						os.path.join(self.tmpdirname ,   '''psgs_w100.tsv.pkl'''     )
       A:						Tuple						=						{sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
       pickle.dump(_lowerCAmelCase ,   open(_lowerCAmelCase ,   '''wb'''     )     )

       A:						List[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:						Any						=						RagRetriever(
           _lowerCAmelCase ,   question_encoder_tokenizer=self.get_dpr_tokenizer() ,   generator_tokenizer=self.get_bart_tokenizer()     )
       return retriever



  def  _snake_case			(      self							:				Tuple     )       ->						Optional[Any]:







       '''simple docstring'''



       A:						Optional[Any]						=						1
       A:						str						=						self.get_dummy_canonical_hf_index_retriever()
       A:						List[str]						=						np.array(
           [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )] ,   dtype=np.floataa     )
       A:						List[Any]						=						retriever.retrieve(_lowerCAmelCase ,   n_docs=_lowerCAmelCase     )
       self.assertEqual(retrieved_doc_embeds.shape ,   (2, n_docs, self.retrieval_vector_size)     )
       self.assertEqual(len(_lowerCAmelCase     ) ,   2     )
       self.assertEqual(sorted(doc_dicts[0]     ) ,   ['''embeddings''', '''id''', '''text''', '''title''']     )
       self.assertEqual(len(doc_dicts[0]['''id''']     ) ,   _lowerCAmelCase     )
       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  _snake_case			(      self							:				Dict     )       ->						Any:







       '''simple docstring'''



       A:						Union[str, Any]						=						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:						Optional[int]						=						self.get_dummy_dataset()
                 retriever.save_pretrained(_lowerCAmelCase     )
                 A:						str						=						RagRetriever.from_pretrained(_lowerCAmelCase     )
                 self.assertIsInstance(_lowerCAmelCase ,   _lowerCAmelCase     )
                 A:						int						=						np.array(
                     [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )] ,   dtype=np.floataa     )
                 A:						Tuple						=						retriever.retrieve(_lowerCAmelCase ,   n_docs=1     )
                 self.assertTrue(out is not None     )



  def  _snake_case			(      self							:				Union[str, Any]     )       ->						int:







       '''simple docstring'''



       A:						Any						=						1
       A:						Union[str, Any]						=						self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCAmelCase     )
       A:						str						=						np.array(
           [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )] ,   dtype=np.floataa     )
       A:						Union[str, Any]						=						retriever.retrieve(_lowerCAmelCase ,   n_docs=_lowerCAmelCase     )
       self.assertEqual(retrieved_doc_embeds.shape ,   (2, n_docs, self.retrieval_vector_size)     )
       self.assertEqual(len(_lowerCAmelCase     ) ,   2     )
       self.assertEqual(sorted(doc_dicts[0]     ) ,   ['''embeddings''', '''id''', '''text''', '''title''']     )
       self.assertEqual(len(doc_dicts[0]['''id''']     ) ,   _lowerCAmelCase     )
       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  _snake_case			(      self							:				Tuple     )       ->						int:







       '''simple docstring'''



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



  def  _snake_case			(      self							:				Any     )       ->						Optional[int]:







       '''simple docstring'''



       A:						str						=						1
       A:						int						=						self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCAmelCase     )
       A:						str						=						np.array(
           [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )] ,   dtype=np.floataa     )
       A:						Any						=						retriever.retrieve(_lowerCAmelCase ,   n_docs=_lowerCAmelCase     )
       self.assertEqual(retrieved_doc_embeds.shape ,   (2, n_docs, self.retrieval_vector_size)     )
       self.assertEqual(len(_lowerCAmelCase     ) ,   2     )
       self.assertEqual(sorted(doc_dicts[0]     ) ,   ['''embeddings''', '''id''', '''text''', '''title''']     )
       self.assertEqual(len(doc_dicts[0]['''id''']     ) ,   _lowerCAmelCase     )
       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  _snake_case			(      self							:				Tuple     )       ->						List[Any]:







       '''simple docstring'''



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



  def  _snake_case			(      self							:				Tuple     )       ->						Union[str, Any]:







       '''simple docstring'''



       A:						Tuple						=						1
       A:						List[str]						=						self.get_dummy_legacy_index_retriever()
       A:						Optional[int]						=						np.array(
           [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )] ,   dtype=np.floataa     )
       A:						Optional[int]						=						retriever.retrieve(_lowerCAmelCase ,   n_docs=_lowerCAmelCase     )
       self.assertEqual(retrieved_doc_embeds.shape ,   (2, n_docs, self.retrieval_vector_size)     )
       self.assertEqual(len(_lowerCAmelCase     ) ,   2     )
       self.assertEqual(sorted(doc_dicts[0]     ) ,   ['''text''', '''title''']     )
       self.assertEqual(len(doc_dicts[0]['''text''']     ) ,   _lowerCAmelCase     )
       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  _snake_case			(      self							:				Union[str, Any]     )       ->						int:







       '''simple docstring'''



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



  @require_torch
  @require_tokenizers
  @require_sentencepiece
  def  _snake_case			(      self							:				Optional[int]     )       ->						Union[str, Any]:







       '''simple docstring'''



       import torch

       A:						Dict						=						1
       A:						List[Any]						=						self.get_dummy_canonical_hf_index_retriever()
       A:						List[Any]						=						[[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(_lowerCAmelCase ,   _lowerCAmelCase ,   prefix=retriever.config.generator.prefix ,   n_docs=_lowerCAmelCase     )
       A:						Dict						=						(
           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(_lowerCAmelCase ,   _lowerCAmelCase     )
       self.assertIsInstance(_lowerCAmelCase ,   _lowerCAmelCase     )
       self.assertIsInstance(_lowerCAmelCase ,   np.ndarray     )

       A:						Optional[Any]						=						retriever(
           _lowerCAmelCase ,   _lowerCAmelCase ,   prefix=retriever.config.generator.prefix ,   n_docs=_lowerCAmelCase ,   return_tensors='''pt''' ,   )
       A:						List[Any]						=						(  # 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(_lowerCAmelCase ,   torch.Tensor     )
       self.assertIsInstance(_lowerCAmelCase ,   torch.Tensor     )
       self.assertIsInstance(_lowerCAmelCase ,   torch.Tensor     )




  @require_torch
  @require_tokenizers
  @require_sentencepiece
  def  _snake_case			(      self							:				List[str]     )       ->						Optional[Any]:







       '''simple docstring'''



       A:						List[Any]						=						self.get_dpr_ctx_encoder_tokenizer()
       A:						Optional[int]						=						1
       A:						Optional[Any]						=						self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCAmelCase     )
       retriever.set_ctx_encoder_tokenizer(_lowerCAmelCase     )

       A:						List[Any]						=						[[5, 7], [10, 11]]
       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(_lowerCAmelCase ,   _lowerCAmelCase ,   prefix=retriever.config.generator.prefix ,   n_docs=_lowerCAmelCase     )

       self.assertEqual(
           len(_lowerCAmelCase     ) ,   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''')     ) ,   _lowerCAmelCase     )  # check for doc token related keys in dictionary.












from typing import TYPE_CHECKING

from ....utils import _LazyModule


_UpperCAmelCase		:  Dict	=   {"tokenization_tapex": ["TapexTokenizer"]}


if TYPE_CHECKING:
     from .tokenization_tapex import TapexTokenizer


else:
     import sys

     _UpperCAmelCase		:  Optional[Any]	=   _LazyModule(__name__, globals()["__file__"], _import_structure)



