


'''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    __magic_name__				(						lowerCAmelCase							):






       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =tempfile.mkdtemp()
          _UpperCAmelCase  :  str				       =8

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

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






       def      lowerCAmelCase						(     self)		->    DPRQuestionEncoderTokenizer:




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






       def      lowerCAmelCase						(     self)		->    DPRContextEncoderTokenizer:




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






       def      lowerCAmelCase						(     self)		->    BartTokenizer:




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






       def      lowerCAmelCase						(     self)		->    List[str]:




          '''simple docstring'''
          shutil.rmtree(self.tmpdirname)






       def      lowerCAmelCase						(     self)		->    List[str]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[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      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[Any]				       =self.get_dummy_dataset()
          _UpperCAmelCase  :  List[str]				       =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:
             _UpperCAmelCase  :  Any				       =dataset
             _UpperCAmelCase  :  Tuple				       =RagRetriever(
                 snake_case    ,				question_encoder_tokenizer=self.get_dpr_tokenizer()    ,				generator_tokenizer=self.get_bart_tokenizer()    ,				)
          return retriever






       def      lowerCAmelCase						(     self    ,				snake_case)		->    Tuple:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =self.get_dummy_dataset()
          _UpperCAmelCase  :  Any				       =RagConfig(
              retrieval_vector_size=self.retrieval_vector_size    ,				question_encoder=DPRConfig().to_dict()    ,				generator=BartConfig().to_dict()    ,				index_name='custom'    ,				)
          if from_disk:
             _UpperCAmelCase  :  int				       =os.path.join(self.tmpdirname    ,				'dataset')
             _UpperCAmelCase  :  str				       =os.path.join(self.tmpdirname    ,				'index.faiss')
             dataset.get_index('embeddings').save(os.path.join(self.tmpdirname    ,				'index.faiss'))
             dataset.drop_index('embeddings')
             dataset.save_to_disk(os.path.join(self.tmpdirname    ,				'dataset'))
             del dataset
             _UpperCAmelCase  :  Optional[Any]				       =RagRetriever(
                 snake_case    ,				question_encoder_tokenizer=self.get_dpr_tokenizer()    ,				generator_tokenizer=self.get_bart_tokenizer()    ,				)
          else:
             _UpperCAmelCase  :  Union[str, Any]				       =RagRetriever(
                 snake_case    ,				question_encoder_tokenizer=self.get_dpr_tokenizer()    ,				generator_tokenizer=self.get_bart_tokenizer()    ,				index=CustomHFIndex(config.retrieval_vector_size    ,				snake_case)    ,				)
          return retriever






       def      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  int				       =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)

          _UpperCAmelCase  :  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'))

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

          _UpperCAmelCase  :  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    ,				)
          _UpperCAmelCase  :  Dict				       =RagRetriever(
              snake_case    ,				question_encoder_tokenizer=self.get_dpr_tokenizer()    ,				generator_tokenizer=self.get_bart_tokenizer())
          return retriever






       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =1
          _UpperCAmelCase  :  Any				       =self.get_dummy_canonical_hf_index_retriever()
          _UpperCAmelCase  :  List[str]				       =np.array(
              [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,				dtype=np.floataa)
          _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase  :  Union[str, Any]				       =retriever.retrieve(snake_case    ,				n_docs=snake_case)
          self.assertEqual(retrieved_doc_embeds.shape    ,				(2, n_docs, self.retrieval_vector_size))
          self.assertEqual(len(snake_case)    ,				2)
          self.assertEqual(sorted(doc_dicts[0])    ,				['embeddings', 'id', 'text', 'title'])
          self.assertEqual(len(doc_dicts[0]['id'])    ,				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      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Dict				       =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:
                _UpperCAmelCase  :  str				       =self.get_dummy_dataset()
                retriever.save_pretrained(snake_case)
                _UpperCAmelCase  :  Union[str, Any]				       =RagRetriever.from_pretrained(snake_case)
                self.assertIsInstance(snake_case    ,				snake_case)
                _UpperCAmelCase  :  List[Any]				       =np.array(
                    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,				dtype=np.floataa)
                _UpperCAmelCase  :  List[Any]				       =retriever.retrieve(snake_case    ,				n_docs=1)
                self.assertTrue(out is not None)






       def      lowerCAmelCase						(     self)		->    List[str]:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[int]				       =1
          _UpperCAmelCase  :  int				       =self.get_dummy_custom_hf_index_retriever(from_disk=snake_case)
          _UpperCAmelCase  :  Tuple				       =np.array(
              [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,				dtype=np.floataa)
          _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase  :  Any				       =retriever.retrieve(snake_case    ,				n_docs=snake_case)
          self.assertEqual(retrieved_doc_embeds.shape    ,				(2, n_docs, self.retrieval_vector_size))
          self.assertEqual(len(snake_case)    ,				2)
          self.assertEqual(sorted(doc_dicts[0])    ,				['embeddings', 'id', 'text', 'title'])
          self.assertEqual(len(doc_dicts[0]['id'])    ,				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      lowerCAmelCase						(     self)		->    Optional[int]:




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






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




          '''simple docstring'''
          _UpperCAmelCase  :  Tuple				       =1
          _UpperCAmelCase  :  int				       =self.get_dummy_custom_hf_index_retriever(from_disk=snake_case)
          _UpperCAmelCase  :  Dict				       =np.array(
              [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,				dtype=np.floataa)
          _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase  :  Optional[int]				       =retriever.retrieve(snake_case    ,				n_docs=snake_case)
          self.assertEqual(retrieved_doc_embeds.shape    ,				(2, n_docs, self.retrieval_vector_size))
          self.assertEqual(len(snake_case)    ,				2)
          self.assertEqual(sorted(doc_dicts[0])    ,				['embeddings', 'id', 'text', 'title'])
          self.assertEqual(len(doc_dicts[0]['id'])    ,				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      lowerCAmelCase						(     self)		->    Any:




          '''simple docstring'''
          _UpperCAmelCase  :  Optional[Any]				       =self.get_dummy_custom_hf_index_retriever(from_disk=snake_case)
          with tempfile.TemporaryDirectory() as tmp_dirname:
             retriever.save_pretrained(snake_case)
             _UpperCAmelCase  :  Union[str, Any]				       =RagRetriever.from_pretrained(snake_case)
             self.assertIsInstance(snake_case    ,				snake_case)
             _UpperCAmelCase  :  Dict				       =np.array(
                 [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,				dtype=np.floataa)
             _UpperCAmelCase  :  Any				       =retriever.retrieve(snake_case    ,				n_docs=1)
             self.assertTrue(out is not None)






       def      lowerCAmelCase						(     self)		->    Dict:




          '''simple docstring'''
          _UpperCAmelCase  :  Union[str, Any]				       =1
          _UpperCAmelCase  :  int				       =self.get_dummy_legacy_index_retriever()
          _UpperCAmelCase  :  int				       =np.array(
              [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,				dtype=np.floataa)
          _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase  :  int				       =retriever.retrieve(snake_case    ,				n_docs=snake_case)
          self.assertEqual(retrieved_doc_embeds.shape    ,				(2, n_docs, self.retrieval_vector_size))
          self.assertEqual(len(snake_case)    ,				2)
          self.assertEqual(sorted(doc_dicts[0])    ,				['text', 'title'])
          self.assertEqual(len(doc_dicts[0]['text'])    ,				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      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Dict				       =self.get_dummy_legacy_index_retriever()
          with tempfile.TemporaryDirectory() as tmp_dirname:
             retriever.save_pretrained(snake_case)
             _UpperCAmelCase  :  Optional[int]				       =RagRetriever.from_pretrained(snake_case)
             self.assertIsInstance(snake_case    ,				snake_case)
             _UpperCAmelCase  :  List[str]				       =np.array(
                 [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,				dtype=np.floataa)
             _UpperCAmelCase  :  int				       =retriever.retrieve(snake_case    ,				n_docs=1)
             self.assertTrue(out is not None)






       @require_torch
       @require_tokenizers
       @require_sentencepiece
       def      lowerCAmelCase						(     self)		->    int:




          '''simple docstring'''
          import torch

          _UpperCAmelCase  :  List[str]				       =1
          _UpperCAmelCase  :  List[str]				       =self.get_dummy_canonical_hf_index_retriever()
          _UpperCAmelCase  :  str				       =[[5, 7], [1_0, 1_1]]
          _UpperCAmelCase  :  Tuple				       =np.array(
              [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,				dtype=np.floataa)
          _UpperCAmelCase  :  List[Any]				       =retriever(snake_case    ,				snake_case    ,				prefix=retriever.config.generator.prefix    ,				n_docs=snake_case)
          _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase  :  Tuple				       =(
              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(snake_case    ,				snake_case)
          self.assertIsInstance(snake_case    ,				snake_case)
          self.assertIsInstance(snake_case    ,				np.ndarray)

          _UpperCAmelCase  :  Any				       =retriever(
              snake_case    ,				snake_case    ,				prefix=retriever.config.generator.prefix    ,				n_docs=snake_case    ,				return_tensors='pt'    ,				)
          _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase  :  Tuple				       =(  # 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(snake_case    ,				torch.Tensor)
          self.assertIsInstance(snake_case    ,				torch.Tensor)
          self.assertIsInstance(snake_case    ,				torch.Tensor)






       @require_torch
       @require_tokenizers
       @require_sentencepiece
       def      lowerCAmelCase						(     self)		->    List[str]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       =self.get_dpr_ctx_encoder_tokenizer()
          _UpperCAmelCase  :  str				       =1
          _UpperCAmelCase  :  Union[str, Any]				       =self.get_dummy_custom_hf_index_retriever(from_disk=snake_case)
          retriever.set_ctx_encoder_tokenizer(snake_case)

          _UpperCAmelCase  :  Dict				       =[[5, 7], [1_0, 1_1]]
          _UpperCAmelCase  :  Tuple				       =np.array(
              [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,				dtype=np.floataa)
          _UpperCAmelCase  :  List[str]				       =retriever(snake_case    ,				snake_case    ,				prefix=retriever.config.generator.prefix    ,				n_docs=snake_case)

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





'''simple docstring'''






import warnings
from functools import wraps
from typing import Callable





def lowerCamelCase__     (		__lowerCamelCase			:				Callable				):







   '''simple docstring'''




   @wraps(__lowerCamelCase				)
   def _inner_fn(*__lowerCamelCase			:				int			,       **__lowerCamelCase			:				Optional[Any]				):
      warnings.warn(
          (f"'{fn.__name__}' is experimental and might be subject to breaking changes in the future.")			,       __lowerCamelCase			,       )
      return fn(*__lowerCamelCase			,       **__lowerCamelCase				)

   return _inner_fn

