


'''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__							(		_lowercase    ):






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Tuple							   =tempfile.mkdtemp()
          _UpperCAmelCase       :       Optional[int]							   =8

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

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




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






       def       lowerCAmelCase		(      self)						->  int:




          '''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)						->  List[Any]:




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






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   =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       :       Union[str, Any]							   =os.path.join(self.tmpdirname  ,							'dataset')
             _UpperCAmelCase       :       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
             _UpperCAmelCase       :       int							   =RagRetriever(
                 __UpperCamelCase  ,							question_encoder_tokenizer=self.get_dpr_tokenizer()  ,							generator_tokenizer=self.get_bart_tokenizer()  ,							)
          else:
             _UpperCAmelCase       :       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       lowerCAmelCase		(      self)						->  Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       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)

          _UpperCAmelCase       :       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'))

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

          _UpperCAmelCase       :       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  ,							)
          _UpperCAmelCase       :       int							   =RagRetriever(
              __UpperCamelCase  ,							question_encoder_tokenizer=self.get_dpr_tokenizer()  ,							generator_tokenizer=self.get_bart_tokenizer())
          return retriever






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   =1
          _UpperCAmelCase       :       Any							   =self.get_dummy_canonical_hf_index_retriever()
          _UpperCAmelCase       :       Tuple							   =np.array(
              [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]  ,							dtype=np.floataa)
          _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       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       lowerCAmelCase		(      self)						->  Optional[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(__UpperCamelCase)
                _UpperCAmelCase       :       int							   =RagRetriever.from_pretrained(__UpperCamelCase)
                self.assertIsInstance(__UpperCamelCase  ,							__UpperCamelCase)
                _UpperCAmelCase       :       Dict							   =np.array(
                    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]  ,							dtype=np.floataa)
                _UpperCAmelCase       :       Union[str, Any]							   =retriever.retrieve(__UpperCamelCase  ,							n_docs=1)
                self.assertTrue(out is not None)






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[Any]							   =1
          _UpperCAmelCase       :       str							   =self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCamelCase)
          _UpperCAmelCase       :       Union[str, Any]							   =np.array(
              [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]  ,							dtype=np.floataa)
          _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       List[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       lowerCAmelCase		(      self)						->  Union[str, Any]:




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






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




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =1
          _UpperCAmelCase       :       Any							   =self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCamelCase)
          _UpperCAmelCase       :       Tuple							   =np.array(
              [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]  ,							dtype=np.floataa)
          _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       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)						->  Tuple:




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






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




          '''simple docstring'''
          _UpperCAmelCase       :       List[Any]							   =1
          _UpperCAmelCase       :       List[Any]							   =self.get_dummy_legacy_index_retriever()
          _UpperCAmelCase       :       Dict							   =np.array(
              [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]  ,							dtype=np.floataa)
          _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       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])  ,							['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[str]:




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






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




          '''simple docstring'''
          import torch

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

          _UpperCAmelCase       :       Dict							   =retriever(
              __UpperCamelCase  ,							__UpperCamelCase  ,							prefix=retriever.config.generator.prefix  ,							n_docs=__UpperCamelCase  ,							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(__UpperCamelCase  ,							torch.Tensor)
          self.assertIsInstance(__UpperCamelCase  ,							torch.Tensor)
          self.assertIsInstance(__UpperCamelCase  ,							torch.Tensor)






       @require_torch
       @require_tokenizers
       @require_sentencepiece
       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =self.get_dpr_ctx_encoder_tokenizer()
          _UpperCAmelCase       :       Dict							   =1
          _UpperCAmelCase       :       str							   =self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCamelCase)
          retriever.set_ctx_encoder_tokenizer(__UpperCamelCase)

          _UpperCAmelCase       :       Optional[Any]							   =[[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       :       int							   =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'''






lowercase							=[0, 2, 4, 6, 8]
lowercase							=[1, 3, 5, 7, 9]





def lowerCamelCase__      (						__lowerCamelCase	:							int	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   if remaining_length == 0:
      if digits[0] == 0 or digits[-1] == 0:
         return 0

      for i in range(length // 2 - 1	,   -1	,   -1							):
         remainder += digits[i] + digits[length - i - 1]

         if remainder % 2 == 0:
            return 0

         remainder //= 1_0

      return 1

   if remaining_length == 1:
      if remainder % 2 == 0:
         return 0

      _UpperCAmelCase       :       Union[str, Any]							   =0
      for digit in range(1_0							):
         _UpperCAmelCase       :       str							   =digit
         result += reversible_numbers(
             0	,   (remainder + 2 * digit) // 1_0	,   __lowerCamelCase	,   __lowerCamelCase							)
      return result

   _UpperCAmelCase       :       Optional[Any]							   =0
   for digita in range(1_0							):
      _UpperCAmelCase       :       Any							   =digita

      if (remainder + digita) % 2 == 0:
         _UpperCAmelCase       :       Optional[int]							   =ODD_DIGITS
      else:
         _UpperCAmelCase       :       Union[str, Any]							   =EVEN_DIGITS

      for digita in other_parity_digits:
         _UpperCAmelCase       :       int							   =digita
         result += reversible_numbers(
             remaining_length - 2	,   (remainder + digita + digita) // 1_0	,   __lowerCamelCase	,   __lowerCamelCase	,   )
   return result





def lowerCamelCase__      (						__lowerCamelCase	:							int = 9							):







   '''simple docstring'''




   _UpperCAmelCase       :       Optional[int]							   =0
   for length in range(1	,   max_power + 1							):
      result += reversible_numbers(__lowerCamelCase	,   0	,   [0] * length	,   __lowerCamelCase							)
   return result


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

