"""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        UpperCamelCase		(			lowercase_  ):
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


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

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

        lowercase_	:				List[Any]													=	os.path.join(self.tmpdirname     ,'bart_tokenizer'			)
        os.makedirs(UpperCAmelCase__     ,exist_ok=UpperCAmelCase__			)
        lowercase_	:				Optional[Any]													=	os.path.join(UpperCAmelCase__     ,BART_VOCAB_FILES_NAMES['vocab_file']			)
        lowercase_	:				Tuple													=	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 			_UpperCAmelCase   (			self			)	->      DPRQuestionEncoderTokenizer:


        '''simple docstring'''


        return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,'dpr_tokenizer'			)			)
   def 			_UpperCAmelCase   (			self			)	->      DPRContextEncoderTokenizer:


        '''simple docstring'''


        return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,'dpr_tokenizer'			)			)
   def 			_UpperCAmelCase   (			self			)	->      BartTokenizer:


        '''simple docstring'''


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


        '''simple docstring'''


        shutil.rmtree(self.tmpdirname			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				Dict													=	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 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	self.get_dummy_dataset()
        lowercase_	:				List[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:
             lowercase_	:				List[str]													=	dataset
             lowercase_	:				Dict													=	RagRetriever(
                 UpperCAmelCase__     ,question_encoder_tokenizer=self.get_dpr_tokenizer()     ,generator_tokenizer=self.get_bart_tokenizer()     ,)
        return retriever
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				int													=	self.get_dummy_dataset()
        lowercase_	:				Any													=	RagConfig(
            retrieval_vector_size=self.retrieval_vector_size     ,question_encoder=DPRConfig().to_dict()     ,generator=BartConfig().to_dict()     ,index_name='custom'     ,)
        if from_disk:
             lowercase_	:				Any													=	os.path.join(self.tmpdirname     ,'dataset'			)
             lowercase_	:				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
             lowercase_	:				str													=	RagRetriever(
                 UpperCAmelCase__     ,question_encoder_tokenizer=self.get_dpr_tokenizer()     ,generator_tokenizer=self.get_bart_tokenizer()     ,)
        else:
             lowercase_	:				List[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 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				List[str]													=	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			)

        lowercase_	:				int													=	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'			)			)

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

        lowercase_	:				List[str]													=	RagConfig(
            retrieval_vector_size=self.retrieval_vector_size     ,question_encoder=DPRConfig().to_dict()     ,generator=BartConfig().to_dict()     ,index_name='legacy'     ,index_path=self.tmpdirname     ,)
        lowercase_	:				Tuple													=	RagRetriever(
            UpperCAmelCase__     ,question_encoder_tokenizer=self.get_dpr_tokenizer()     ,generator_tokenizer=self.get_bart_tokenizer()			)
        return retriever
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				str													=	1
        lowercase_	:				List[Any]													=	self.get_dummy_canonical_hf_index_retriever()
        lowercase_	:				Optional[int]													=	np.array(
            [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,dtype=np.floataa			)
        lowercase_							,  lowercase_							,  lowercase_	:				Dict													=	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 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				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:
                  lowercase_	:				Dict													=	self.get_dummy_dataset()
                  retriever.save_pretrained(UpperCAmelCase__			)
                  lowercase_	:				Dict													=	RagRetriever.from_pretrained(UpperCAmelCase__			)
                  self.assertIsInstance(UpperCAmelCase__     ,UpperCAmelCase__			)
                  lowercase_	:				Tuple													=	np.array(
                      [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,dtype=np.floataa			)
                  lowercase_	:				int													=	retriever.retrieve(UpperCAmelCase__     ,n_docs=1			)
                  self.assertTrue(out is not None			)
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	1
        lowercase_	:				Any													=	self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__			)
        lowercase_	:				Tuple													=	np.array(
            [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,dtype=np.floataa			)
        lowercase_							,  lowercase_							,  lowercase_	:				str													=	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 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


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


        '''simple docstring'''


        lowercase_	:				int													=	1
        lowercase_	:				str													=	self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__			)
        lowercase_	:				Any													=	np.array(
            [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,dtype=np.floataa			)
        lowercase_							,  lowercase_							,  lowercase_	:				List[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 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


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


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	1
        lowercase_	:				Optional[int]													=	self.get_dummy_legacy_index_retriever()
        lowercase_	:				int													=	np.array(
            [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,dtype=np.floataa			)
        lowercase_							,  lowercase_							,  lowercase_	:				Union[str, 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]			)     ,['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 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Dict													=	self.get_dummy_legacy_index_retriever()
        with tempfile.TemporaryDirectory() as tmp_dirname:
             retriever.save_pretrained(UpperCAmelCase__			)
             lowercase_	:				Optional[Any]													=	RagRetriever.from_pretrained(UpperCAmelCase__			)
             self.assertIsInstance(UpperCAmelCase__     ,UpperCAmelCase__			)
             lowercase_	:				Dict													=	np.array(
                 [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,dtype=np.floataa			)
             lowercase_	:				List[Any]													=	retriever.retrieve(UpperCAmelCase__     ,n_docs=1			)
             self.assertTrue(out is not None			)
   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        import torch

        lowercase_	:				List[Any]													=	1
        lowercase_	:				List[Any]													=	self.get_dummy_canonical_hf_index_retriever()
        lowercase_	:				List[Any]													=	[[5, 7], [10, 11]]
        lowercase_	:				Optional[int]													=	np.array(
            [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,dtype=np.floataa			)
        lowercase_	:				Tuple													=	retriever(UpperCAmelCase__     ,UpperCAmelCase__     ,prefix=retriever.config.generator.prefix     ,n_docs=UpperCAmelCase__			)
        lowercase_							,  lowercase_							,  lowercase_	:				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(UpperCAmelCase__     ,UpperCAmelCase__			)
        self.assertIsInstance(UpperCAmelCase__     ,UpperCAmelCase__			)
        self.assertIsInstance(UpperCAmelCase__     ,np.ndarray			)

        lowercase_	:				str													=	retriever(
            UpperCAmelCase__     ,UpperCAmelCase__     ,prefix=retriever.config.generator.prefix     ,n_docs=UpperCAmelCase__     ,return_tensors='pt'     ,)
        lowercase_							,  lowercase_							,  lowercase_							,  lowercase_	:				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 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	self.get_dpr_ctx_encoder_tokenizer()
        lowercase_	:				Tuple													=	1
        lowercase_	:				List[Any]													=	self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__			)
        retriever.set_ctx_encoder_tokenizer(UpperCAmelCase__			)

        lowercase_	:				int													=	[[5, 7], [10, 11]]
        lowercase_	:				Dict													=	np.array(
            [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,dtype=np.floataa			)
        lowercase_	:				str													=	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"""







def    UpperCAmelCase__       (lowerCAmelCase_ = 100_0000   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				set(range(3  ,						lowerCAmelCase_  ,						2   )   )
					primes.add(2   )
					for p in range(3  ,						lowerCAmelCase_  ,						2   ):
										if p not in primes:
															continue
										primes.difference_update(set(range(p * p  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )   )   )

					__SCREAMING_SNAKE_CASE						   =				[float(lowerCAmelCase_   ) for n in range(limit + 1   )]

					for p in primes:
										for n in range(lowerCAmelCase_  ,						limit + 1  ,						lowerCAmelCase_   ):
															phi[n] *= 1 - 1 / p

					return int(sum(phi[2:]   )   )


if __name__ == "__main__":
		print(F"{solution() = }")
