


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__						(snake_case_				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= tempfile.mkdtemp()
       snake_case__						= 8

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

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,		'''dpr_tokenizer'''   )   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,		'''dpr_tokenizer'''   )   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,		'''bart_tokenizer'''   )   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       shutil.rmtree(self.tmpdirname   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= self.get_dummy_dataset()
       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:
         snake_case__						= dataset
         snake_case__						= RagRetriever(
             _a     ,		question_encoder_tokenizer=self.get_dpr_tokenizer()     ,		generator_tokenizer=self.get_bart_tokenizer()     ,		)
       return retriever

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:bool   ):
       snake_case__						= self.get_dummy_dataset()
       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:
         snake_case__						= os.path.join(self.tmpdirname     ,		'''dataset'''   )
         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
         snake_case__						= RagRetriever(
             _a     ,		question_encoder_tokenizer=self.get_dpr_tokenizer()     ,		generator_tokenizer=self.get_bart_tokenizer()     ,		)
       else:
         snake_case__						= RagRetriever(
             _a     ,		question_encoder_tokenizer=self.get_dpr_tokenizer()     ,		generator_tokenizer=self.get_bart_tokenizer()     ,		index=CustomHFIndex(config.retrieval_vector_size     ,		_a   )     ,		)
       return retriever

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       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   )

       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'''   )   )

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

       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     ,		)
       snake_case__						= RagRetriever(
           _a     ,		question_encoder_tokenizer=self.get_dpr_tokenizer()     ,		generator_tokenizer=self.get_bart_tokenizer()   )
       return retriever

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

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

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

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       snake_case__						= 1
       snake_case__						= self.get_dummy_custom_hf_index_retriever(from_disk=_a   )
       snake_case__						= np.array(
           [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]     ,		dtype=np.floataa   )
       snake_case__				,				snake_case__				,				snake_case__						= retriever.retrieve(_a     ,		n_docs=_a   )
       self.assertEqual(retrieved_doc_embeds.shape     ,		(2, n_docs, self.retrieval_vector_size)   )
       self.assertEqual(len(_a   )     ,		2   )
       self.assertEqual(sorted(doc_dicts[0]   )     ,		['''embeddings''', '''id''', '''text''', '''title''']   )
       self.assertEqual(len(doc_dicts[0]['''id''']   )     ,		_a   )
       self.assertEqual(doc_dicts[0]['''id'''][0]     ,		'''1'''   )  # max inner product is reached with second doc
       self.assertEqual(doc_dicts[1]['''id'''][0]     ,		'''0'''   )  # max inner product is reached with first doc
       self.assertListEqual(doc_ids.tolist()     ,		[[1], [0]]   )

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

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

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

     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       import torch

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

       snake_case__						= retriever(
           _a     ,		_a     ,		prefix=retriever.config.generator.prefix     ,		n_docs=_a     ,		return_tensors='''pt'''     ,		)
       snake_case__				,				snake_case__				,				snake_case__				,				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(_a     ,		torch.Tensor   )
       self.assertIsInstance(_a     ,		torch.Tensor   )
       self.assertIsInstance(_a     ,		torch.Tensor   )







     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.get_dpr_ctx_encoder_tokenizer()
       snake_case__						= 1
       snake_case__						= self.get_dummy_custom_hf_index_retriever(from_disk=_a   )
       retriever.set_ctx_encoder_tokenizer(_a   )

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

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





import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


lowerCamelCase__							:						int							     =   logging.get_logger(__name__)
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     def __init__(					self:List[Any]     ,		*_a:Dict     ,		**_a:Tuple   ):
       warnings.warn(
           '''The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
           ''' Please use PerceiverImageProcessor instead.'''     ,		_a     ,		)
       super().__init__(*_a     ,		**_a   )

