


"""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_(       __UpperCAmelCase			):






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	int       =   tempfile.mkdtemp()
           lowerCAmelCase					:	Union[str, Any]       =   8

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

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






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname					,				'''dpr_tokenizer'''				)				)






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






     def      lowerCamelCase__     (	self  :					List[str]				):
           return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname					,				'''bart_tokenizer'''				)				)






     def      lowerCamelCase__     (	self  :					Tuple				):
           shutil.rmtree(self.tmpdirname				)






     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	Tuple       =   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  :					Dict				):
           lowerCAmelCase					:	Tuple       =   self.get_dummy_dataset()
           lowerCAmelCase					:	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:
                 lowerCAmelCase					:	Tuple       =   dataset
                 lowerCAmelCase					:	Dict       =   RagRetriever(
                     UpperCAmelCase_					,				question_encoder_tokenizer=self.get_dpr_tokenizer()					,				generator_tokenizer=self.get_bart_tokenizer()					,				)
           return retriever






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






     def      lowerCamelCase__     (	self  :					Optional[int]				):
           lowerCAmelCase					:	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				)

           lowerCAmelCase					:	str       =   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'''				)				)

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

           lowerCAmelCase					:	Union[str, 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					,				)
           lowerCAmelCase					:	Dict       =   RagRetriever(
               UpperCAmelCase_					,				question_encoder_tokenizer=self.get_dpr_tokenizer()					,				generator_tokenizer=self.get_bart_tokenizer()				)
           return retriever






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase					:	Optional[int]       =   1
           lowerCAmelCase					:	Union[str, Any]       =   self.get_dummy_canonical_hf_index_retriever()
           lowerCAmelCase					:	int       =   np.array(
               [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]					,				dtype=np.floataa				)
           lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	Tuple       =   retriever.retrieve(UpperCAmelCase_					,				n_docs=UpperCAmelCase_				)
           self.assertEqual(retrieved_doc_embeds.shape					,				(2, n_docs, self.retrieval_vector_size)				)
           self.assertEqual(len(UpperCAmelCase_				)					,				2				)
           self.assertEqual(sorted(doc_dicts[0]				)					,				['''embeddings''', '''id''', '''text''', '''title''']				)
           self.assertEqual(len(doc_dicts[0]['''id''']				)					,				UpperCAmelCase_				)
           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				):
           lowerCAmelCase					:	str       =   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:
                       lowerCAmelCase					:	Optional[Any]       =   self.get_dummy_dataset()
                       retriever.save_pretrained(UpperCAmelCase_				)
                       lowerCAmelCase					:	List[str]       =   RagRetriever.from_pretrained(UpperCAmelCase_				)
                       self.assertIsInstance(UpperCAmelCase_					,				UpperCAmelCase_				)
                       lowerCAmelCase					:	Dict       =   np.array(
                           [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]					,				dtype=np.floataa				)
                       lowerCAmelCase					:	str       =   retriever.retrieve(UpperCAmelCase_					,				n_docs=1				)
                       self.assertTrue(out is not None				)






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           lowerCAmelCase					:	str       =   1
           lowerCAmelCase					:	List[str]       =   self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   np.array(
               [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]					,				dtype=np.floataa				)
           lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   retriever.retrieve(UpperCAmelCase_					,				n_docs=UpperCAmelCase_				)
           self.assertEqual(retrieved_doc_embeds.shape					,				(2, n_docs, self.retrieval_vector_size)				)
           self.assertEqual(len(UpperCAmelCase_				)					,				2				)
           self.assertEqual(sorted(doc_dicts[0]				)					,				['''embeddings''', '''id''', '''text''', '''title''']				)
           self.assertEqual(len(doc_dicts[0]['''id''']				)					,				UpperCAmelCase_				)
           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]				):
           lowerCAmelCase					:	Union[str, Any]       =   self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_				)
           with tempfile.TemporaryDirectory() as tmp_dirname:
                 retriever.save_pretrained(UpperCAmelCase_				)
                 lowerCAmelCase					:	Tuple       =   RagRetriever.from_pretrained(UpperCAmelCase_				)
                 self.assertIsInstance(UpperCAmelCase_					,				UpperCAmelCase_				)
                 lowerCAmelCase					:	Any       =   np.array(
                     [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]					,				dtype=np.floataa				)
                 lowerCAmelCase					:	Union[str, Any]       =   retriever.retrieve(UpperCAmelCase_					,				n_docs=1				)
                 self.assertTrue(out is not None				)






     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	Tuple       =   1
           lowerCAmelCase					:	Union[str, Any]       =   self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_				)
           lowerCAmelCase					:	List[str]       =   np.array(
               [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]					,				dtype=np.floataa				)
           lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	Any       =   retriever.retrieve(UpperCAmelCase_					,				n_docs=UpperCAmelCase_				)
           self.assertEqual(retrieved_doc_embeds.shape					,				(2, n_docs, self.retrieval_vector_size)				)
           self.assertEqual(len(UpperCAmelCase_				)					,				2				)
           self.assertEqual(sorted(doc_dicts[0]				)					,				['''embeddings''', '''id''', '''text''', '''title''']				)
           self.assertEqual(len(doc_dicts[0]['''id''']				)					,				UpperCAmelCase_				)
           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]				):
           lowerCAmelCase					:	List[str]       =   self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_				)
           with tempfile.TemporaryDirectory() as tmp_dirname:
                 retriever.save_pretrained(UpperCAmelCase_				)
                 lowerCAmelCase					:	Dict       =   RagRetriever.from_pretrained(UpperCAmelCase_				)
                 self.assertIsInstance(UpperCAmelCase_					,				UpperCAmelCase_				)
                 lowerCAmelCase					:	Tuple       =   np.array(
                     [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]					,				dtype=np.floataa				)
                 lowerCAmelCase					:	Any       =   retriever.retrieve(UpperCAmelCase_					,				n_docs=1				)
                 self.assertTrue(out is not None				)






     def      lowerCamelCase__     (	self  :					Optional[int]				):
           lowerCAmelCase					:	Any       =   1
           lowerCAmelCase					:	Tuple       =   self.get_dummy_legacy_index_retriever()
           lowerCAmelCase					:	List[str]       =   np.array(
               [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]					,				dtype=np.floataa				)
           lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	Tuple       =   retriever.retrieve(UpperCAmelCase_					,				n_docs=UpperCAmelCase_				)
           self.assertEqual(retrieved_doc_embeds.shape					,				(2, n_docs, self.retrieval_vector_size)				)
           self.assertEqual(len(UpperCAmelCase_				)					,				2				)
           self.assertEqual(sorted(doc_dicts[0]				)					,				['''text''', '''title''']				)
           self.assertEqual(len(doc_dicts[0]['''text''']				)					,				UpperCAmelCase_				)
           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  :					List[Any]				):
           lowerCAmelCase					:	str       =   self.get_dummy_legacy_index_retriever()
           with tempfile.TemporaryDirectory() as tmp_dirname:
                 retriever.save_pretrained(UpperCAmelCase_				)
                 lowerCAmelCase					:	Union[str, Any]       =   RagRetriever.from_pretrained(UpperCAmelCase_				)
                 self.assertIsInstance(UpperCAmelCase_					,				UpperCAmelCase_				)
                 lowerCAmelCase					:	int       =   np.array(
                     [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]					,				dtype=np.floataa				)
                 lowerCAmelCase					:	Dict       =   retriever.retrieve(UpperCAmelCase_					,				n_docs=1				)
                 self.assertTrue(out is not None				)






     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           import torch

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

           lowerCAmelCase					:	Optional[int]       =   retriever(
               UpperCAmelCase_					,				UpperCAmelCase_					,				prefix=retriever.config.generator.prefix					,				n_docs=UpperCAmelCase_					,				return_tensors='''pt'''					,				)
           lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	int       =   (  # 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(UpperCAmelCase_					,				torch.Tensor				)
           self.assertIsInstance(UpperCAmelCase_					,				torch.Tensor				)
           self.assertIsInstance(UpperCAmelCase_					,				torch.Tensor				)






     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	Tuple       =   self.get_dpr_ctx_encoder_tokenizer()
           lowerCAmelCase					:	str       =   1
           lowerCAmelCase					:	Any       =   self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase_				)
           retriever.set_ctx_encoder_tokenizer(UpperCAmelCase_				)

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

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






"""simple docstring"""





snake_case__		:     List[Any]    				=				'''Tobias Carryer'''

from time import time



class 		snake_case_:






     def __init__(	self  :					Optional[Any]					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					Dict=int(time()				)				):  # noqa: B008
           lowerCAmelCase					:	str       =   multiplier
           lowerCAmelCase					:	Optional[int]       =   increment
           lowerCAmelCase					:	Optional[Any]       =   modulo
           lowerCAmelCase					:	Optional[Any]       =   seed






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Optional[int]       =   (self.multiplier * self.seed + self.increment) % self.modulo
           return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 snake_case__		:     int    				=				LinearCongruentialGenerator(1_664_525, 1_013_904_223, 2 << 31)
 while True:
  print(lcg.next_number())


