

import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


UpperCAmelCase__ :     Any				    =    logging.getLogger(__name__)







class        a__       (   a__       ):







  """simple docstring"""




  def __init__(			self    : List[Any]				,				UpperCAmelCase__    : str=-1		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   label_idx


  def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : Tuple		)					->int:
        """simple docstring"""







        if isinstance(_A				,				_A		):
              SCREAMING_SNAKE_CASE :      Any       =   mode.value
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   os.path.join(_A				,				f"{mode}.txt"		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   1
        SCREAMING_SNAKE_CASE :      Tuple       =   []
        with open(_A				,				encoding="""utf-8"""		) as f:
              SCREAMING_SNAKE_CASE :      Any       =   []
              SCREAMING_SNAKE_CASE :      Any       =   []
              for line in f:
                    if line.startswith("""-DOCSTART-"""		) or line == "" or line == "\n":
                          if words:
                                examples.append(InputExample(guid=f"{mode}-{guid_index}"				,				words=_A				,				labels=_A		)		)
                                guid_index += 1
                                SCREAMING_SNAKE_CASE :      str       =   []
                                SCREAMING_SNAKE_CASE :      int       =   []
                    else:
                          SCREAMING_SNAKE_CASE :      int       =   line.split(""" """		)
                          words.append(splits[0]		)
                          if len(_A		) > 1:
                                labels.append(splits[self.label_idx].replace("""\n"""				,				""""""		)		)
                          else:
                                # Examples could have no label for mode = "test"
                                labels.append("""O"""		)
              if words:
                    examples.append(InputExample(guid=f"{mode}-{guid_index}"				,				words=_A				,				labels=_A		)		)
        return examples


  def  _lowercase      (			self    : int				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : Any		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   0
        for line in test_input_reader:
              if line.startswith("""-DOCSTART-"""		) or line == "" or line == "\n":
                    writer.write(_A		)
                    if not preds_list[example_id]:
                          example_id += 1
              elif preds_list[example_id]:
                    SCREAMING_SNAKE_CASE :      int       =   line.split()[0] + ' ' + preds_list[example_id].pop(0		) + '\n'
                    writer.write(_A		)
              else:
                    logger.warning("""Maximum sequence length exceeded: No prediction for \'%s\'."""				,				line.split()[0]		)




  def  _lowercase      (			self    : int				,				UpperCAmelCase__    : List[Any]		)					->Optional[int]:
        """simple docstring"""







        if path:
              with open(_A				,				"""r"""		) as f:
                    SCREAMING_SNAKE_CASE :      Any       =   f.read().splitlines()
              if "O" not in labels:
                    SCREAMING_SNAKE_CASE :      Optional[int]       =   ['O'] + labels
              return labels
        else:
              return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class        a__       (   a__       ):







  """simple docstring"""




  def __init__(			self    : Any		)					->List[str]:
        """simple docstring"""







        super().__init__(label_idx=-2		)




  def  _lowercase      (			self    : Union[str, Any]				,				UpperCAmelCase__    : Any		)					->Dict:
        """simple docstring"""







        if path:
              with open(_A				,				"""r"""		) as f:
                    SCREAMING_SNAKE_CASE :      Union[str, Any]       =   f.read().splitlines()
              if "O" not in labels:
                    SCREAMING_SNAKE_CASE :      Any       =   ['O'] + labels
              return labels
        else:
              return [
                  "O",
                  "B-ADVP",
                  "B-INTJ",
                  "B-LST",
                  "B-PRT",
                  "B-NP",
                  "B-SBAR",
                  "B-VP",
                  "B-ADJP",
                  "B-CONJP",
                  "B-PP",
                  "I-ADVP",
                  "I-INTJ",
                  "I-LST",
                  "I-PRT",
                  "I-NP",
                  "I-SBAR",
                  "I-VP",
                  "I-ADJP",
                  "I-CONJP",
                  "I-PP",
              ]







class        a__       (   a__       ):







  """simple docstring"""




  def  _lowercase      (			self    : int				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : Dict		)					->List[Any]:
        """simple docstring"""







        if isinstance(_A				,				_A		):
              SCREAMING_SNAKE_CASE :      Dict       =   mode.value
        SCREAMING_SNAKE_CASE :      Dict       =   os.path.join(_A				,				f"{mode}.txt"		)
        SCREAMING_SNAKE_CASE :      List[str]       =   1
        SCREAMING_SNAKE_CASE :      str       =   []

        with open(_A				,				encoding="""utf-8"""		) as f:
              for sentence in parse_incr(_A		):
                    SCREAMING_SNAKE_CASE :      List[Any]       =   []
                    SCREAMING_SNAKE_CASE :      Any       =   []
                    for token in sentence:
                          words.append(token["""form"""]		)
                          labels.append(token["""upos"""]		)
                    assert len(_A		) == len(_A		)
                    if words:
                          examples.append(InputExample(guid=f"{mode}-{guid_index}"				,				words=_A				,				labels=_A		)		)
                          guid_index += 1
        return examples


  def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : Tuple				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Tuple		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   0
        for sentence in parse_incr(_A		):
              SCREAMING_SNAKE_CASE :      str       =   preds_list[example_id]
              SCREAMING_SNAKE_CASE :      Tuple       =   ''
              for token in sentence:
                    out += f"{token['form']} ({token['upos']}|{s_p.pop(0		)}) "
              out += "\n"
              writer.write(_A		)
              example_id += 1




  def  _lowercase      (			self    : Optional[Any]				,				UpperCAmelCase__    : Optional[int]		)					->int:
        """simple docstring"""







        if path:
              with open(_A				,				"""r"""		) as f:
                    return f.read().splitlines()
        else:
              return [
                  "ADJ",
                  "ADP",
                  "ADV",
                  "AUX",
                  "CCONJ",
                  "DET",
                  "INTJ",
                  "NOUN",
                  "NUM",
                  "PART",
                  "PRON",
                  "PROPN",
                  "PUNCT",
                  "SCONJ",
                  "SYM",
                  "VERB",
                  "X",
              ]







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        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  def  _lowercase      (			self    : Optional[int]		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   tempfile.mkdtemp()
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   8

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

        SCREAMING_SNAKE_CASE :      int       =   os.path.join(self.tmpdirname				,				"""bart_tokenizer"""		)
        os.makedirs(UpperCAmelCase__				,				exist_ok=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   os.path.join(UpperCAmelCase__				,				BART_VOCAB_FILES_NAMES["""vocab_file"""]		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   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  _lowercase      (			self    : Optional[Any]		)					->DPRQuestionEncoderTokenizer:
        """simple docstring"""







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


  def  _lowercase      (			self    : Optional[Any]		)					->DPRContextEncoderTokenizer:
        """simple docstring"""







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


  def  _lowercase      (			self    : Optional[int]		)					->BartTokenizer:
        """simple docstring"""







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


  def  _lowercase      (			self    : Any		)					->List[str]:
        """simple docstring"""







        shutil.rmtree(self.tmpdirname		)


  def  _lowercase      (			self    : Tuple		)					->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   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    : Union[str, Any]		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   self.get_dummy_dataset()
        SCREAMING_SNAKE_CASE :      str       =   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:
              SCREAMING_SNAKE_CASE :      Tuple       =   dataset
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   RagRetriever(
                  UpperCAmelCase__				,				question_encoder_tokenizer=self.get_dpr_tokenizer()				,				generator_tokenizer=self.get_bart_tokenizer()				,				)
        return retriever


  def  _lowercase      (			self    : List[Any]				,				UpperCAmelCase__    : bool		)					->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   self.get_dummy_dataset()
        SCREAMING_SNAKE_CASE :      Dict       =   RagConfig(
            retrieval_vector_size=self.retrieval_vector_size				,				question_encoder=DPRConfig().to_dict()				,				generator=BartConfig().to_dict()				,				index_name="""custom"""				,				)
        if from_disk:
              SCREAMING_SNAKE_CASE :      List[str]       =   os.path.join(self.tmpdirname				,				"""dataset"""		)
              SCREAMING_SNAKE_CASE :      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
              SCREAMING_SNAKE_CASE :      Any       =   RagRetriever(
                  UpperCAmelCase__				,				question_encoder_tokenizer=self.get_dpr_tokenizer()				,				generator_tokenizer=self.get_bart_tokenizer()				,				)
        else:
              SCREAMING_SNAKE_CASE :      Union[str, 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  _lowercase      (			self    : int		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[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		)

        SCREAMING_SNAKE_CASE :      Any       =   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"""		)		)

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

        SCREAMING_SNAKE_CASE :      str       =   RagConfig(
            retrieval_vector_size=self.retrieval_vector_size				,				question_encoder=DPRConfig().to_dict()				,				generator=BartConfig().to_dict()				,				index_name="""legacy"""				,				index_path=self.tmpdirname				,				)
        SCREAMING_SNAKE_CASE :      Any       =   RagRetriever(
            UpperCAmelCase__				,				question_encoder_tokenizer=self.get_dpr_tokenizer()				,				generator_tokenizer=self.get_bart_tokenizer()		)
        return retriever


  def  _lowercase      (			self    : Dict		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   1
        SCREAMING_SNAKE_CASE :      str       =   self.get_dummy_canonical_hf_index_retriever()
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.array(
            [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]				,				dtype=np.floataa		)
        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Union[str, Any]       =   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  _lowercase      (			self    : List[str]		)					->int:
        """simple docstring"""







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


  def  _lowercase      (			self    : List[Any]		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   1
        SCREAMING_SNAKE_CASE :      str       =   self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      str       =   np.array(
            [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]				,				dtype=np.floataa		)
        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      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  _lowercase      (			self    : Optional[int]		)					->Optional[Any]:
        """simple docstring"""







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


  def  _lowercase      (			self    : Dict		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   1
        SCREAMING_SNAKE_CASE :      List[str]       =   self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   np.array(
            [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]				,				dtype=np.floataa		)
        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[Any]       =   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  _lowercase      (			self    : Union[str, Any]		)					->Any:
        """simple docstring"""







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


  def  _lowercase      (			self    : Tuple		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   1
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.get_dummy_legacy_index_retriever()
        SCREAMING_SNAKE_CASE :      int       =   np.array(
            [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]				,				dtype=np.floataa		)
        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[Any]       =   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  _lowercase      (			self    : Any		)					->str:
        """simple docstring"""







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


  @require_torch
  @require_tokenizers
  @require_sentencepiece
  def  _lowercase      (			self    : Tuple		)					->Tuple:
        """simple docstring"""







        import torch

        SCREAMING_SNAKE_CASE :      int       =   1
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.get_dummy_canonical_hf_index_retriever()
        SCREAMING_SNAKE_CASE :      str       =   [[5, 7], [1_0, 1_1]]
        SCREAMING_SNAKE_CASE :      Tuple       =   np.array(
            [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]				,				dtype=np.floataa		)
        SCREAMING_SNAKE_CASE :      int       =   retriever(UpperCAmelCase__				,				UpperCAmelCase__				,				prefix=retriever.config.generator.prefix				,				n_docs=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[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		)

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   retriever(
            UpperCAmelCase__				,				UpperCAmelCase__				,				prefix=retriever.config.generator.prefix				,				n_docs=UpperCAmelCase__				,				return_tensors="""pt"""				,				)
        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (  # noqa: F841
            out["""context_input_ids"""],
            out["""context_attention_mask"""],
            out["""retrieved_doc_embeds"""],
            out["""doc_ids"""],
        )
        self.assertEqual(retrieved_doc_embeds.shape				,				(2, n_docs, self.retrieval_vector_size)		)
        self.assertIsInstance(UpperCAmelCase__				,				torch.Tensor		)
        self.assertIsInstance(UpperCAmelCase__				,				torch.Tensor		)
        self.assertIsInstance(UpperCAmelCase__				,				torch.Tensor		)




  @require_torch
  @require_tokenizers
  @require_sentencepiece
  def  _lowercase      (			self    : Union[str, Any]		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   self.get_dpr_ctx_encoder_tokenizer()
        SCREAMING_SNAKE_CASE :      Optional[int]       =   1
        SCREAMING_SNAKE_CASE :      List[Any]       =   self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__		)
        retriever.set_ctx_encoder_tokenizer(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      List[str]       =   [[5, 7], [1_0, 1_1]]
        SCREAMING_SNAKE_CASE :      Optional[int]       =   np.array(
            [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]				,				dtype=np.floataa		)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   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.




