



"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A:     List[Any]										=	logging.get_logger(__name__)

A:     str										=	"▁"

A:     Optional[int]										=	{"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}

A:     Any										=	{
    "vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model",
    },
    "monolingual_vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt",
    },
}

A:     str										=	{"vinai/bartpho-syllable": 1_0_2_4}



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     str		=		VOCAB_FILES_NAMES
   __lowerCAmelCase						:     str		=		PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase						:     Tuple		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase						:     Optional[int]		=		['input_ids', 'attention_mask']

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE="<s>"   ,  _SCREAMING_SNAKE_CASE="</s>"   ,  _SCREAMING_SNAKE_CASE="</s>"   ,  _SCREAMING_SNAKE_CASE="<s>"   ,  _SCREAMING_SNAKE_CASE="<unk>"   ,  _SCREAMING_SNAKE_CASE="<pad>"   ,  _SCREAMING_SNAKE_CASE="<mask>"   ,  _SCREAMING_SNAKE_CASE = None   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   None:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			AddedToken(_SCREAMING_SNAKE_CASE   ,  lstrip=_SCREAMING_SNAKE_CASE   ,  rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) else mask_token

    UpperCAmelCase			:      str			=			{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=_SCREAMING_SNAKE_CASE   ,  eos_token=_SCREAMING_SNAKE_CASE   ,  unk_token=_SCREAMING_SNAKE_CASE   ,  sep_token=_SCREAMING_SNAKE_CASE   ,  cls_token=_SCREAMING_SNAKE_CASE   ,  pad_token=_SCREAMING_SNAKE_CASE   ,  mask_token=_SCREAMING_SNAKE_CASE   ,  sp_model_kwargs=self.sp_model_kwargs   ,  **_SCREAMING_SNAKE_CASE   ,  )

    UpperCAmelCase			:      Dict			=			vocab_file
    UpperCAmelCase			:      str			=			monolingual_vocab_file
    UpperCAmelCase			:      Optional[int]			=			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
    self.sp_model.Load(str(_SCREAMING_SNAKE_CASE		)		)

    # Load the reduced vocab

    # Keep order of special tokens for backward compatibility
    UpperCAmelCase			:      Optional[int]			=			{}
    UpperCAmelCase			:      str			=			0
    for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
     if str(_SCREAMING_SNAKE_CASE		) not in self.fairseq_tokens_to_ids:
      UpperCAmelCase			:      Optional[int]			=			cnt
      cnt += 1
    with open(_SCREAMING_SNAKE_CASE   ,  """r"""   ,  encoding="""utf-8"""		) as f:
     for line in f.readlines():
      UpperCAmelCase			:      Optional[Any]			=			line.strip().split()[0]
      UpperCAmelCase			:      Optional[Any]			=			len(self.fairseq_tokens_to_ids		)
    if str(_SCREAMING_SNAKE_CASE		) not in self.fairseq_tokens_to_ids:
     UpperCAmelCase			:      Any			=			len(self.fairseq_tokens_to_ids		)

    UpperCAmelCase			:      Tuple			=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}

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





    '''simple docstring'''




    UpperCAmelCase			:      str			=			self.__dict__.copy()
    UpperCAmelCase			:      str			=			None
    UpperCAmelCase			:      Union[str, Any]			=			self.sp_model.serialized_model_proto()
    return state

   def __setstate__(     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			d

    # for backward compatibility
    if not hasattr(self   ,  """sp_model_kwargs"""		):
     UpperCAmelCase			:      str			=			{}

    UpperCAmelCase			:      str			=			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None		)						->   List[int]:





    '''simple docstring'''




    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    UpperCAmelCase			:      Any			=			[self.cls_token_id]
    UpperCAmelCase			:      Any			=			[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = False		)						->   List[int]:





    '''simple docstring'''




    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=_SCREAMING_SNAKE_CASE   ,  token_ids_a=_SCREAMING_SNAKE_CASE   ,  already_has_special_tokens=_SCREAMING_SNAKE_CASE		)

    if token_ids_a is None:
     return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
    return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1, 1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None		)						->   List[int]:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			[self.sep_token_id]
    UpperCAmelCase			:      List[Any]			=			[self.cls_token_id]

    if token_ids_a is None:
     return len(cls + token_ids_a + sep		) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]

   @property
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    return len(self.fairseq_ids_to_tokens		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			{self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
    vocab.update(self.added_tokens_encoder		)
    return vocab

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    return self.sp_model.encode(_SCREAMING_SNAKE_CASE   ,  out_type=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
    else:
     return self.unk_token_id

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   int:





    '''simple docstring'''




    return self.fairseq_ids_to_tokens[index]

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			"""""".join(_SCREAMING_SNAKE_CASE		).replace(_SCREAMING_SNAKE_CASE   ,  """ """		).strip()
    return out_string




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None		)						->   Tuple[str]:





    '''simple docstring'''




    if not os.path.isdir(_SCREAMING_SNAKE_CASE		):
     logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
     return
    UpperCAmelCase			:      Tuple			=			os.path.join(
        _SCREAMING_SNAKE_CASE   ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)
    UpperCAmelCase			:      Dict			=			os.path.join(
        _SCREAMING_SNAKE_CASE   ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""]   ,  )

    if os.path.abspath(self.vocab_file		) != os.path.abspath(_SCREAMING_SNAKE_CASE		) and os.path.isfile(self.vocab_file		):
     copyfile(self.vocab_file   ,  _SCREAMING_SNAKE_CASE		)
    elif not os.path.isfile(self.vocab_file		):
     with open(_SCREAMING_SNAKE_CASE   ,  """wb"""		) as fi:
      UpperCAmelCase			:      Dict			=			self.sp_model.serialized_model_proto()
      fi.write(_SCREAMING_SNAKE_CASE		)

    if os.path.abspath(self.monolingual_vocab_file		) != os.path.abspath(
        _SCREAMING_SNAKE_CASE		) and os.path.isfile(self.monolingual_vocab_file		):
     copyfile(self.monolingual_vocab_file   ,  _SCREAMING_SNAKE_CASE		)
    elif not os.path.isfile(self.monolingual_vocab_file		):
     with open(_SCREAMING_SNAKE_CASE   ,  """w"""   ,  encoding="""utf-8"""		) as fp:
      for token in self.fairseq_tokens_to_ids:
       if token not in self.all_special_tokens:
        fp.write(F"{str(_SCREAMING_SNAKE_CASE		)} \n"		)

    return out_vocab_file, out_monolingual_vocab_file





"""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    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			tempfile.mkdtemp()
    UpperCAmelCase			:      str			=			8

    # DPR tok
    UpperCAmelCase			:      int			=			[
        """[UNK]""",
        """[CLS]""",
        """[SEP]""",
        """[PAD]""",
        """[MASK]""",
        """want""",
        """##want""",
        """##ed""",
        """wa""",
        """un""",
        """runn""",
        """##ing""",
        """,""",
        """low""",
        """lowest""",
    ]
    UpperCAmelCase			:      Tuple			=			os.path.join(self.tmpdirname   ,  """dpr_tokenizer"""		)
    os.makedirs(_SCREAMING_SNAKE_CASE   ,  exist_ok=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      str			=			os.path.join(_SCREAMING_SNAKE_CASE   ,  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			:      int			=			[
        """l""",
        """o""",
        """w""",
        """e""",
        """r""",
        """s""",
        """t""",
        """i""",
        """d""",
        """n""",
        """\u0120""",
        """\u0120l""",
        """\u0120n""",
        """\u0120lo""",
        """\u0120low""",
        """er""",
        """\u0120lowest""",
        """\u0120newer""",
        """\u0120wider""",
        """<unk>""",
    ]
    UpperCAmelCase			:      Tuple			=			dict(zip(_SCREAMING_SNAKE_CASE   ,  range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
    UpperCAmelCase			:      List[Any]			=			["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
    UpperCAmelCase			:      List[Any]			=			{"""unk_token""": """<unk>"""}

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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   DPRQuestionEncoderTokenizer:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   DPRContextEncoderTokenizer:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   BartTokenizer:





    '''simple docstring'''




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

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





    '''simple docstring'''




    shutil.rmtree(self.tmpdirname		)

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





    '''simple docstring'''




    UpperCAmelCase			:      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 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			self.get_dummy_dataset()
    UpperCAmelCase			:      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:
     UpperCAmelCase			:      Tuple			=			dataset
     UpperCAmelCase			:      List[str]			=			RagRetriever(
         _SCREAMING_SNAKE_CASE   ,  question_encoder_tokenizer=self.get_dpr_tokenizer()   ,  generator_tokenizer=self.get_bart_tokenizer()   ,  )
    return retriever

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			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			:      str			=			os.path.join(self.tmpdirname   ,  """dataset"""		)
     UpperCAmelCase			:      Union[str, 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
     UpperCAmelCase			:      List[Any]			=			RagRetriever(
         _SCREAMING_SNAKE_CASE   ,  question_encoder_tokenizer=self.get_dpr_tokenizer()   ,  generator_tokenizer=self.get_bart_tokenizer()   ,  )
    else:
     UpperCAmelCase			:      Optional[Any]			=			RagRetriever(
         _SCREAMING_SNAKE_CASE   ,  question_encoder_tokenizer=self.get_dpr_tokenizer()   ,  generator_tokenizer=self.get_bart_tokenizer()   ,  index=CustomHFIndex(config.retrieval_vector_size   ,  _SCREAMING_SNAKE_CASE		)   ,  )
    return retriever

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      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		)

    UpperCAmelCase			:      Optional[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"""		)		)

    UpperCAmelCase			:      Dict			=			os.path.join(self.tmpdirname   ,  """psgs_w100.tsv.pkl"""		)
    UpperCAmelCase			:      Any			=			{sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
    pickle.dump(_SCREAMING_SNAKE_CASE   ,  open(_SCREAMING_SNAKE_CASE   ,  """wb"""		)		)

    UpperCAmelCase			:      Dict			=			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			:      str			=			RagRetriever(
        _SCREAMING_SNAKE_CASE   ,  question_encoder_tokenizer=self.get_dpr_tokenizer()   ,  generator_tokenizer=self.get_bart_tokenizer()		)
    return retriever

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			1
    UpperCAmelCase			:      List[Any]			=			self.get_dummy_canonical_hf_index_retriever()
    UpperCAmelCase			:      List[str]			=			np.array(
        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      List[Any]			=			retriever.retrieve(_SCREAMING_SNAKE_CASE   ,  n_docs=_SCREAMING_SNAKE_CASE		)
    self.assertEqual(retrieved_doc_embeds.shape   ,  (2, n_docs, self.retrieval_vector_size)		)
    self.assertEqual(len(_SCREAMING_SNAKE_CASE		)   ,  2		)
    self.assertEqual(sorted(doc_dicts[0]		)   ,  ["""embeddings""", """id""", """text""", """title"""]		)
    self.assertEqual(len(doc_dicts[0]["""id"""]		)   ,  _SCREAMING_SNAKE_CASE		)
    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 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			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			:      Optional[int]			=			self.get_dummy_dataset()
      retriever.save_pretrained(_SCREAMING_SNAKE_CASE		)
      UpperCAmelCase			:      Tuple			=			RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE		)
      self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
      UpperCAmelCase			:      List[Any]			=			np.array(
          [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
      UpperCAmelCase			:      Optional[Any]			=			retriever.retrieve(_SCREAMING_SNAKE_CASE   ,  n_docs=1		)
      self.assertTrue(out is not None		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			1
    UpperCAmelCase			:      Dict			=			self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			np.array(
        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      Union[str, Any]			=			retriever.retrieve(_SCREAMING_SNAKE_CASE   ,  n_docs=_SCREAMING_SNAKE_CASE		)
    self.assertEqual(retrieved_doc_embeds.shape   ,  (2, n_docs, self.retrieval_vector_size)		)
    self.assertEqual(len(_SCREAMING_SNAKE_CASE		)   ,  2		)
    self.assertEqual(sorted(doc_dicts[0]		)   ,  ["""embeddings""", """id""", """text""", """title"""]		)
    self.assertEqual(len(doc_dicts[0]["""id"""]		)   ,  _SCREAMING_SNAKE_CASE		)
    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 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			1
    UpperCAmelCase			:      Optional[Any]			=			self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE		)
    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(_SCREAMING_SNAKE_CASE   ,  n_docs=_SCREAMING_SNAKE_CASE		)
    self.assertEqual(retrieved_doc_embeds.shape   ,  (2, n_docs, self.retrieval_vector_size)		)
    self.assertEqual(len(_SCREAMING_SNAKE_CASE		)   ,  2		)
    self.assertEqual(sorted(doc_dicts[0]		)   ,  ["""embeddings""", """id""", """text""", """title"""]		)
    self.assertEqual(len(doc_dicts[0]["""id"""]		)   ,  _SCREAMING_SNAKE_CASE		)
    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 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			1
    UpperCAmelCase			:      List[Any]			=			self.get_dummy_legacy_index_retriever()
    UpperCAmelCase			:      Any			=			np.array(
        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      Dict			=			retriever.retrieve(_SCREAMING_SNAKE_CASE   ,  n_docs=_SCREAMING_SNAKE_CASE		)
    self.assertEqual(retrieved_doc_embeds.shape   ,  (2, n_docs, self.retrieval_vector_size)		)
    self.assertEqual(len(_SCREAMING_SNAKE_CASE		)   ,  2		)
    self.assertEqual(sorted(doc_dicts[0]		)   ,  ["""text""", """title"""]		)
    self.assertEqual(len(doc_dicts[0]["""text"""]		)   ,  _SCREAMING_SNAKE_CASE		)
    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 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




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

   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    import torch

    UpperCAmelCase			:      Dict			=			1
    UpperCAmelCase			:      List[Any]			=			self.get_dummy_canonical_hf_index_retriever()
    UpperCAmelCase			:      List[str]			=			[[5, 7], [10, 11]]
    UpperCAmelCase			:      List[Any]			=			np.array(
        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
    UpperCAmelCase			:      Union[str, Any]			=			retriever(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  prefix=retriever.config.generator.prefix   ,  n_docs=_SCREAMING_SNAKE_CASE		)
    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(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  np.ndarray		)

    UpperCAmelCase			:      Any			=			retriever(
        _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  prefix=retriever.config.generator.prefix   ,  n_docs=_SCREAMING_SNAKE_CASE   ,  return_tensors="""pt"""   ,  )
    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      Dict			=			(  # 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(_SCREAMING_SNAKE_CASE   ,  torch.Tensor		)
    self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  torch.Tensor		)
    self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,  torch.Tensor		)




   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.get_dpr_ctx_encoder_tokenizer()
    UpperCAmelCase			:      Any			=			1
    UpperCAmelCase			:      List[str]			=			self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE		)
    retriever.set_ctx_encoder_tokenizer(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Dict			=			[[5, 7], [10, 11]]
    UpperCAmelCase			:      Optional[Any]			=			np.array(
        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]   ,  dtype=np.floataa		)
    UpperCAmelCase			:      Tuple			=			retriever(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  prefix=retriever.config.generator.prefix   ,  n_docs=_SCREAMING_SNAKE_CASE		)

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