
"""simple docstring"""



from typing import Any





class 					lowerCAmelCase			:
     """simple docstring"""







     def __init__(      self ,		UpperCamelCase__							)					->  List[Any]:







            '''simple docstring'''





            lowerCamelCase_       							=		data
            lowerCamelCase_       							=		None





class 					lowerCAmelCase			:
     """simple docstring"""







     def __init__(      self							)					->  Tuple:







            '''simple docstring'''





            lowerCamelCase_       							=		None




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







            '''simple docstring'''





            lowerCamelCase_       							=		self.head
            while temp is not None:
                   print(temp.data ,		end=''' '''							)
                   lowerCamelCase_       							=		temp.next
            print()




     def    _lowerCAmelCase		(      self ,		UpperCamelCase__							)					->  Any:







            '''simple docstring'''





            lowerCamelCase_       							=		Node(__lowerCamelCase							)
            lowerCamelCase_       							=		self.head
            lowerCamelCase_       							=		new_node




     def    _lowerCAmelCase		(      self ,		UpperCamelCase__ ,		UpperCamelCase__							)					->  Tuple:







            '''simple docstring'''





            if node_data_a == node_data_a:
                   return
            else:
                   lowerCamelCase_       							=		self.head
                   while node_a is not None and node_a.data != node_data_a:
                          lowerCamelCase_       							=		node_a.next

                   lowerCamelCase_       							=		self.head
                   while node_a is not None and node_a.data != node_data_a:
                          lowerCamelCase_       							=		node_a.next

                   if node_a is None or node_a is None:
                          return

                   lowerCamelCase_       							=		node_a.data, node_a.data


if __name__ == "__main__":
 __lowercase						:     Union[str, Any]							      =		LinkedList()
 for i in range(5, 0, -1):
  ll.push(i)

 ll.print_list()

 ll.swap_nodes(1, 4)
 print("""After swapping""")
 ll.print_list()





'''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        lowercase_      (						lowerCAmelCase_							):





     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           snake_case__       :   Tuple	      =    tempfile.mkdtemp()
           snake_case__       :   Dict	      =    8

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

           snake_case__       :   List[str]	      =    os.path.join(self.tmpdirname     ,							'bart_tokenizer'			)
           os.makedirs(__lowerCamelCase     ,							exist_ok=__lowerCamelCase			)
           snake_case__       :   Dict	      =    os.path.join(__lowerCamelCase     ,							BART_VOCAB_FILES_NAMES['vocab_file']			)
           snake_case__       :   int	      =    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 			_lowerCAmelCase      (     self     :					Optional[int]			):
           return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,							'dpr_tokenizer'			)			)





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





     def 			_lowerCAmelCase      (     self     :					Any			):
           return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,							'bart_tokenizer'			)			)





     def 			_lowerCAmelCase      (     self     :					int			):
           shutil.rmtree(self.tmpdirname			)





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





     def 			_lowerCAmelCase      (     self     :					List[Any]     ,							__lowerCamelCase     :					bool			):
           snake_case__       :   Union[str, Any]	      =    self.get_dummy_dataset()
           snake_case__       :   Dict	      =    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__       :   List[Any]	      =    os.path.join(self.tmpdirname     ,							'dataset'			)
                 snake_case__       :   List[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
                 snake_case__       :   Optional[int]	      =    RagRetriever(
                     __lowerCamelCase     ,							question_encoder_tokenizer=self.get_dpr_tokenizer()     ,							generator_tokenizer=self.get_bart_tokenizer()     ,							)
           else:
                 snake_case__       :   Union[str, 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 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   Dict	      =    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__       :   List[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'			)			)

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

           snake_case__       :   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     ,							)
           snake_case__       :   List[str]	      =    RagRetriever(
               __lowerCamelCase     ,							question_encoder_tokenizer=self.get_dpr_tokenizer()     ,							generator_tokenizer=self.get_bart_tokenizer()			)
           return retriever





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





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





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





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





     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def 			_lowerCAmelCase      (     self     :					Any			):
           import torch

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

           snake_case__       :   Dict	      =    retriever(
               __lowerCamelCase     ,							__lowerCamelCase     ,							prefix=retriever.config.generator.prefix     ,							n_docs=__lowerCamelCase     ,							return_tensors='pt'     ,							)
           snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   Optional[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 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   List[Any]	      =    self.get_dpr_ctx_encoder_tokenizer()
           snake_case__       :   List[Any]	      =    1
           snake_case__       :   Any	      =    self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase			)
           retriever.set_ctx_encoder_tokenizer(__lowerCamelCase			)

           snake_case__       :   Optional[int]	      =    [[5, 7], [10, 11]]
           snake_case__       :   Tuple	      =    np.array(
               [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)]     ,							dtype=np.floataa			)
           snake_case__       :   Tuple	      =    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.
