




'''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       ( lowerCamelCase__				):






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=tempfile.mkdtemp()
              lowerCamelCase_							=8

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

              lowerCamelCase_							=os.path.join(self.tmpdirname,		'''bart_tokenizer'''  )
              os.makedirs(lowerCAmelCase,		exist_ok=lowerCAmelCase  )
              lowerCamelCase_							=os.path.join(lowerCAmelCase,		BART_VOCAB_FILES_NAMES['''vocab_file''']  )
              lowerCamelCase_							=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      lowercase__     (							self  ):



              """simple docstring"""







              return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname,		'''dpr_tokenizer'''  )  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname,		'''dpr_tokenizer'''  )  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname,		'''bart_tokenizer'''  )  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              shutil.rmtree(self.tmpdirname  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=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      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.get_dummy_dataset()
              lowerCamelCase_							=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_							=dataset
                     lowerCamelCase_							=RagRetriever(
                         lowerCAmelCase,		question_encoder_tokenizer=self.get_dpr_tokenizer(),		generator_tokenizer=self.get_bart_tokenizer(),		)
              return retriever






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=self.get_dummy_dataset()
              lowerCamelCase_							=RagConfig(
                  retrieval_vector_size=self.retrieval_vector_size,		question_encoder=DPRConfig().to_dict(),		generator=BartConfig().to_dict(),		index_name='''custom''',		)
              if from_disk:
                     lowerCamelCase_							=os.path.join(self.tmpdirname,		'''dataset'''  )
                     lowerCamelCase_							=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_							=RagRetriever(
                         lowerCAmelCase,		question_encoder_tokenizer=self.get_dpr_tokenizer(),		generator_tokenizer=self.get_bart_tokenizer(),		)
              else:
                     lowerCamelCase_							=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      lowercase__     (							self  ):



              """simple docstring"""







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

              lowerCamelCase_							=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_							=RagRetriever(
                  lowerCAmelCase,		question_encoder_tokenizer=self.get_dpr_tokenizer(),		generator_tokenizer=self.get_bart_tokenizer()  )
              return retriever






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=1
              lowerCamelCase_							=self.get_dummy_canonical_hf_index_retriever()
              lowerCamelCase_							=np.array(
                  [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )],		dtype=np.floataa  )
              lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_							=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      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=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_							=self.get_dummy_dataset()
                            retriever.save_pretrained(lowerCAmelCase  )
                            lowerCamelCase_							=RagRetriever.from_pretrained(lowerCAmelCase  )
                            self.assertIsInstance(lowerCAmelCase,		lowerCAmelCase  )
                            lowerCamelCase_							=np.array(
                                [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )],		dtype=np.floataa  )
                            lowerCamelCase_							=retriever.retrieve(lowerCAmelCase,		n_docs=1  )
                            self.assertTrue(out is not None  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=1
              lowerCamelCase_							=self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase  )
              lowerCamelCase_							=np.array(
                  [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )],		dtype=np.floataa  )
              lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_							=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      lowercase__     (							self  ):



              """simple docstring"""







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






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=1
              lowerCamelCase_							=self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase  )
              lowerCamelCase_							=np.array(
                  [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )],		dtype=np.floataa  )
              lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_							=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      lowercase__     (							self  ):



              """simple docstring"""







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






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=1
              lowerCamelCase_							=self.get_dummy_legacy_index_retriever()
              lowerCamelCase_							=np.array(
                  [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )],		dtype=np.floataa  )
              lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_							=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      lowercase__     (							self  ):



              """simple docstring"""







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






       @require_torch
       @require_tokenizers
       @require_sentencepiece
       def      lowercase__     (							self  ):



              """simple docstring"""







              import torch

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

              lowerCamelCase_							=retriever(
                  lowerCAmelCase,		lowerCAmelCase,		prefix=retriever.config.generator.prefix,		n_docs=lowerCAmelCase,		return_tensors='''pt''',		)
              lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_,      lowerCamelCase_							=(  # 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      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.get_dpr_ctx_encoder_tokenizer()
              lowerCamelCase_							=1
              lowerCamelCase_							=self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase  )
              retriever.set_ctx_encoder_tokenizer(lowerCAmelCase  )

              lowerCamelCase_							=[[5, 7], [10, 11]]
              lowerCamelCase_							=np.array(
                  [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )],		dtype=np.floataa  )
              lowerCamelCase_							=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.












def      lowercase__      (     __snake_case    :							int     ,       __snake_case    :							int  ):


					'''simple docstring'''


					if a < 0 or b < 0:
										raise ValueError('the value of both inputs must be positive'  )

					UpperCAmelCase_    :      Tuple            =						str(bin(__snake_case  )  )[2:]  # remove the leading "0b"
					UpperCAmelCase_    :      Union[str, Any]            =						str(bin(__snake_case  )  )[2:]  # remove the leading "0b"

					UpperCAmelCase_    :      List[Any]            =						max(len(__snake_case  )     ,       len(__snake_case  )  )

					return "0b" + "".join(
					    str(int(char_a == '1' and char_b == '1'  )  )
					    for char_a, char_b in zip(a_binary.zfill(__snake_case  )     ,       b_binary.zfill(__snake_case  )  )  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






