



"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


A:     Dict										=	"\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n"

A:     int										=	"\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"

A:     str										=	"\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for 'record': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'prediction_text': the predicted answer text\n        - for 'multirc': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question-answer pair as specified by the dataset\n            - 'prediction': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for 'record': list of question-answers dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'answers': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for 'record':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1': F1 score\n    - for 'multirc':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1_m': Per-question macro-F1 score\n        - 'f1_a': Average F1 score over all answers\n    - for 'axb':\n        'matthews_correlation': Matthew Correlation\n    - for 'cb':\n        - 'accuracy': Accuracy\n        - 'f1': F1 score\n    - for all others:\n        - 'accuracy': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"

def    _snake_case		(		UpperCamelCase		:	List[Any]    ,	UpperCamelCase		:	List[str]		):
 return float((preds == labels).mean()		)

def    _snake_case		(		UpperCamelCase		:	Dict    ,	UpperCamelCase		:	str    ,	UpperCamelCase		:	str="binary"		):
 UpperCAmelCase			:      Union[str, Any]			=			simple_accuracy(UpperCamelCase    ,	UpperCamelCase		)
 UpperCAmelCase			:      Dict			=			float(fa_score(y_true=UpperCamelCase    ,	y_pred=UpperCamelCase    ,	average=UpperCamelCase		)		)
 return {
     "accuracy": acc,
     "f1": fa,
 }

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	List[Any]		):
 UpperCAmelCase			:      int			=			{}
 for id_pred, label in zip(UpperCamelCase    ,	UpperCamelCase		):
  UpperCAmelCase			:      List[str]			=			F"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"
  UpperCAmelCase			:      Optional[int]			=			id_pred["""prediction"""]
  if question_id in question_map:
   question_map[question_id].append((pred, label)		)
  else:
   UpperCAmelCase			:      List[str]			=			[(pred, label)]
 UpperCAmelCase			,	UpperCAmelCase			:      List[Any]			=			[], []
 for question, preds_labels in question_map.items():
  UpperCAmelCase			,	UpperCAmelCase			:      Union[str, Any]			=			zip(*UpperCamelCase		)
  UpperCAmelCase			:      Tuple			=			fa_score(y_true=UpperCamelCase    ,	y_pred=UpperCamelCase    ,	average="""macro"""		)
  fas.append(UpperCamelCase		)
  UpperCAmelCase			:      str			=			int(sum(pred == label for pred, label in preds_labels		) == len(UpperCamelCase		)		)
  ems.append(UpperCamelCase		)
 UpperCAmelCase			:      Any			=			float(sum(UpperCamelCase		) / len(UpperCamelCase		)		)
 UpperCAmelCase			:      Optional[Any]			=			sum(UpperCamelCase		) / len(UpperCamelCase		)
 UpperCAmelCase			:      str			=			float(fa_score(y_true=UpperCamelCase    ,	y_pred=[id_pred["""prediction"""] for id_pred in ids_preds]		)		)
 return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,    _KWARGS_DESCRIPTION				)
class    SCREAMING_SNAKE_CASE__						(      datasets.Metric				):

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





    '''simple docstring'''




    if self.config_name not in [
        "boolq",
        "cb",
        "copa",
        "multirc",
        "record",
        "rte",
        "wic",
        "wsc",
        "wsc.fixed",
        "axb",
        "axg",
    ]:
     raise KeyError(
         """You should supply a configuration name selected in """
         """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""		)
    return datasets.MetricInfo(
        description=_DESCRIPTION   ,  citation=_CITATION   ,  inputs_description=_KWARGS_DESCRIPTION   ,  features=datasets.Features(self._get_feature_types()		)   ,  codebase_urls=[]   ,  reference_urls=[]   ,  format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None   ,  )

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





    '''simple docstring'''




    if self.config_name == "record":
     return {
         "predictions": {
             "idx": {
                 "passage": datasets.Value("""int64"""		),
                 "query": datasets.Value("""int64"""		),
             },
             "prediction_text": datasets.Value("""string"""		),
         },
         "references": {
             "idx": {
                 "passage": datasets.Value("""int64"""		),
                 "query": datasets.Value("""int64"""		),
             },
             "answers": datasets.Sequence(datasets.Value("""string"""		)		),
         },
     }
    elif self.config_name == "multirc":
     return {
         "predictions": {
             "idx": {
                 "answer": datasets.Value("""int64"""		),
                 "paragraph": datasets.Value("""int64"""		),
                 "question": datasets.Value("""int64"""		),
             },
             "prediction": datasets.Value("""int64"""		),
         },
         "references": datasets.Value("""int64"""		),
     }
    else:
     return {
         "predictions": datasets.Value("""int64"""		),
         "references": datasets.Value("""int64"""		),
     }




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





    '''simple docstring'''




    if self.config_name == "axb":
     return {"matthews_correlation": matthews_corrcoef(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)}
    elif self.config_name == "cb":
     return acc_and_fa(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  fa_avg="""macro"""		)
    elif self.config_name == "record":
     UpperCAmelCase			:      str			=			[
         {
             """qas""": [
                 {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
                 for ref in references
             ]
         }
     ]
     UpperCAmelCase			:      Tuple			=			{pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
     return evaluate_record(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)[0]
    elif self.config_name == "multirc":
     return evaluate_multirc(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
     return {"accuracy": simple_accuracy(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)}
    else:
     raise KeyError(
         """You should supply a configuration name selected in """
         """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""		)





"""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.
