






"""simple docstring"""




import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


lowerCamelCase__			=   logging.get_logger(__name__)

lowerCamelCase__			=   {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

lowerCamelCase__			=   {
    "vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
    "merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
}

lowerCamelCase__			=   {
    "openai-gpt": 512,
}






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[Any]    ):
  A__											=							text.strip()
  if not text:
    return []
  A__											=							text.split()
  return tokens







class 					_UpperCamelCase		( __snake_case):






    def __init__(self						,      lowerCamelCase__=True						,      lowerCamelCase__=None						,      lowerCamelCase__=True						,      lowerCamelCase__=None						,      lowerCamelCase__=True						,      ):


      """simple docstring"""
      if never_split is None:
        A__											=							[]
      A__											=							do_lower_case
      A__											=							set(lowerCamelCase__   )
      A__											=							tokenize_chinese_chars
      A__											=							strip_accents
      A__											=							do_split_on_punc






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__=None   ):


      """simple docstring"""
      A__											=							self.never_split.union(set(lowerCamelCase__   )   ) if never_split else self.never_split
      A__											=							self._clean_text(lowerCamelCase__   )

      # This was added on November 1st, 2018 for the multilingual and Chinese
      # models. This is also applied to the English models now, but it doesn't
      # matter since the English models were not trained on any Chinese data
      # and generally don't have any Chinese data in them (there are Chinese
      # characters in the vocabulary because Wikipedia does have some Chinese
      # words in the English Wikipedia.).
      if self.tokenize_chinese_chars:
        A__											=							self._tokenize_chinese_chars(lowerCamelCase__   )
      # prevents treating the same character with different unicode codepoints as different characters
      A__											=							unicodedata.normalize("""NFC"""						,      lowerCamelCase__   )
      A__											=							whitespace_tokenize(lowerCamelCase__   )
      A__											=							[]
      for token in orig_tokens:
        if token not in never_split:
          if self.do_lower_case:
            A__											=							token.lower()
            if self.strip_accents is not False:
              A__											=							self._run_strip_accents(lowerCamelCase__   )
          elif self.strip_accents:
            A__											=							self._run_strip_accents(lowerCamelCase__   )
        split_tokens.extend(self._run_split_on_punc(lowerCamelCase__						,      lowerCamelCase__   )   )

      A__											=							whitespace_tokenize(""" """.join(lowerCamelCase__   )   )
      return output_tokens






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							unicodedata.normalize("""NFD"""						,      lowerCamelCase__   )
      A__											=							[]
      for char in text:
        A__											=							unicodedata.category(lowerCamelCase__   )
        if cat == "Mn":
          continue
        output.append(lowerCamelCase__   )
      return "".join(lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__=None   ):


      """simple docstring"""
      if not self.do_split_on_punc or (never_split is not None and text in never_split):
        return [text]
      A__											=							list(lowerCamelCase__   )
      A__											=							0
      A__											=							True
      A__											=							[]
      while i < len(lowerCamelCase__   ):
        A__											=							chars[i]
        if _is_punctuation(lowerCamelCase__   ):
          output.append([char]   )
          A__											=							True
        else:
          if start_new_word:
            output.append([]   )
          A__											=							False
          output[-1].append(lowerCamelCase__   )
        i += 1

      return ["".join(lowerCamelCase__   ) for x in output]






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							[]
      for char in text:
        A__											=							ord(lowerCamelCase__   )
        if self._is_chinese_char(lowerCamelCase__   ):
          output.append(""" """   )
          output.append(lowerCamelCase__   )
          output.append(""" """   )
        else:
          output.append(lowerCamelCase__   )
      return "".join(lowerCamelCase__   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      # This defines a "chinese character" as anything in the CJK Unicode block:
      #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
      #
      # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
      # despite its name. The modern Korean Hangul alphabet is a different block,
      # as is Japanese Hiragana and Katakana. Those alphabets are used to write
      # space-separated words, so they are not treated specially and handled
      # like the all of the other languages.
      if (
          (cp >= 0x4e_00 and cp <= 0x9f_ff)
          or (cp >= 0x34_00 and cp <= 0x4d_bf)  #
          or (cp >= 0x2_00_00 and cp <= 0x2_a6_df)  #
          or (cp >= 0x2_a7_00 and cp <= 0x2_b7_3f)  #
          or (cp >= 0x2_b7_40 and cp <= 0x2_b8_1f)  #
          or (cp >= 0x2_b8_20 and cp <= 0x2_ce_af)  #
          or (cp >= 0xf9_00 and cp <= 0xfa_ff)
          or (cp >= 0x2_f8_00 and cp <= 0x2_fa_1f)  #
      ):  #
        return True

      return False






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							[]
      for char in text:
        A__											=							ord(lowerCamelCase__   )
        if cp == 0 or cp == 0xff_fd or _is_control(lowerCamelCase__   ):
          continue
        if _is_whitespace(lowerCamelCase__   ):
          output.append(""" """   )
        else:
          output.append(lowerCamelCase__   )
      return "".join(lowerCamelCase__   )






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[str]    ):
  A__											=							set()
  A__											=							word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)    )
    A__											=							char
  return pairs






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[str]    ):
  A__											=							text.replace("""—"""		,				"""-"""    )
  A__											=							text.replace("""–"""		,				"""-"""    )
  A__											=							text.replace("""―"""		,				"""-"""    )
  A__											=							text.replace("""…"""		,				"""..."""    )
  A__											=							text.replace("""´"""		,				"""'"""    )
  A__											=							re.sub(R"""(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)"""		,				R""" \1 """		,				UpperCamelCase    )
  A__											=							re.sub(R"""\s*\n\s*"""		,				""" \n """		,				UpperCamelCase    )
  A__											=							re.sub(R"""[^\S\n]+"""		,				""" """		,				UpperCamelCase    )
  return text.strip()







class 					_UpperCamelCase		( __snake_case):
    __lowerCamelCase         =  VOCAB_FILES_NAMES
    __lowerCamelCase         =  PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase         =  ["input_ids", "attention_mask"]






    def __init__(self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__="<unk>"						,      **lowerCamelCase__   ):


      """simple docstring"""
      super().__init__(unk_token=lowerCamelCase__						,      **lowerCamelCase__   )

      try:
        import ftfy
        from spacy.lang.en import English

        A__											=							English()
        A__											=							_nlp.tokenizer
        A__											=							ftfy.fix_text
      except ImportError:
        logger.warning("""ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."""   )
        A__											=							BasicTokenizer(do_lower_case=lowerCamelCase__   )
        A__											=							None

      with open(lowerCamelCase__						,      encoding="""utf-8"""   ) as vocab_handle:
        A__											=							json.load(lowerCamelCase__   )
      A__											=							{v: k for k, v in self.encoder.items()}
      with open(lowerCamelCase__						,      encoding="""utf-8"""   ) as merges_handle:
        A__											=							merges_handle.read().split("""\n"""   )[1:-1]
      A__											=							[tuple(merge.split()   ) for merge in merges]
      A__											=							dict(zip(lowerCamelCase__						,      range(len(lowerCamelCase__   )   )   )   )
      A__											=							{}






    @property
    def  A    (self   ):


      """simple docstring"""
      return True






    @property
    def  A    (self   ):


      """simple docstring"""
      return len(self.encoder   )






    def  A    (self   ):


      """simple docstring"""
      return dict(self.encoder						,      **self.added_tokens_encoder   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							tuple(token[:-1]   ) + (token[-1] + """</w>""",)
      if token in self.cache:
        return self.cache[token]
      A__											=							get_pairs(lowerCamelCase__   )

      if not pairs:
        return token + "</w>"

      while True:
        A__											=							min(lowerCamelCase__						,      key=lambda lowerCamelCase__   : self.bpe_ranks.get(lowerCamelCase__						,      float("""inf"""   )   )   )
        if bigram not in self.bpe_ranks:
          break
        A__						,A__											=							bigram
        A__											=							[]
        A__											=							0
        while i < len(lowerCamelCase__   ):
          try:
            A__											=							word.index(lowerCamelCase__						,      lowerCamelCase__   )
          except ValueError:
            new_word.extend(word[i:]   )
            break
          else:
            new_word.extend(word[i:j]   )
            A__											=							j

          if word[i] == first and i < len(lowerCamelCase__   ) - 1 and word[i + 1] == second:
            new_word.append(first + second   )
            i += 2
          else:
            new_word.append(word[i]   )
            i += 1
        A__											=							tuple(lowerCamelCase__   )
        A__											=							new_word
        if len(lowerCamelCase__   ) == 1:
          break
        else:
          A__											=							get_pairs(lowerCamelCase__   )
      A__											=							""" """.join(lowerCamelCase__   )
      if word == "\n  </w>":
        A__											=							"""\n</w>"""
      A__											=							word
      return word






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							[]
      if self.fix_text is None:
        # Using BERT's BasicTokenizer
        A__											=							self.nlp.tokenize(lowerCamelCase__   )
        for token in text:
          split_tokens.extend(list(self.bpe(lowerCamelCase__   ).split(""" """   )   )   )
      else:
        # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
        A__											=							self.nlp(text_standardize(self.fix_text(lowerCamelCase__   )   )   )
        for token in text:
          split_tokens.extend(list(self.bpe(token.text.lower()   ).split(""" """   )   )   )
      return split_tokens






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      return self.encoder.get(lowerCamelCase__						,      self.encoder.get(self.unk_token   )   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      return self.decoder.get(lowerCamelCase__						,      self.unk_token   )






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      A__											=							"""""".join(lowerCamelCase__   ).replace("""</w>"""						,      """ """   ).strip()
      return out_string






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__ = None   ):


      """simple docstring"""
      if not os.path.isdir(lowerCamelCase__   ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
        return
      A__											=							os.path.join(
          lowerCamelCase__						,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
      A__											=							os.path.join(
          lowerCamelCase__						,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]   )

      with open(lowerCamelCase__						,      """w"""						,      encoding="""utf-8"""   ) as f:
        f.write(json.dumps(self.encoder						,      indent=2						,      sort_keys=lowerCamelCase__						,      ensure_ascii=lowerCamelCase__   ) + """\n"""   )

      A__											=							0
      with open(lowerCamelCase__						,      """w"""						,      encoding="""utf-8"""   ) as writer:
        writer.write("""#version: 0.2\n"""   )
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						,      key=lambda lowerCamelCase__   : kv[1]   ):
          if index != token_index:
            logger.warning(
                F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                """ Please check that the tokenizer is not corrupted!"""   )
            A__											=							token_index
          writer.write(""" """.join(lowerCamelCase__   ) + """\n"""   )
          index += 1

      return vocab_file, merge_file












"""simple docstring"""




import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
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_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
      from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
      lowerCamelCase__			=   None

if is_ray_available():
      import ray  # noqa: E402 # isort:skip
      from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
      lowerCamelCase__			=   None
      lowerCamelCase__			=   None
      lowerCamelCase__			=   None






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Any    ):
  if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
    A__											=							unittest.skip("""test requires Datasets, Faiss, psutil"""    )(UpperCamelCase    )
  return test_case







@require_distributed_retrieval
class 					_UpperCamelCase		( __snake_case):






    def  A    (self   ):


      """simple docstring"""
      A__											=							tempfile.mkdtemp()
      A__											=							8

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

      A__											=							os.path.join(self.tmpdirname						,      """bart_tokenizer"""   )
      os.makedirs(lowerCamelCase__						,      exist_ok=lowerCamelCase__   )
      A__											=							os.path.join(lowerCamelCase__						,      BART_VOCAB_FILES_NAMES["""vocab_file"""]   )
      A__											=							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  A    (self   ):


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






    def  A    (self   ):


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






    def  A    (self   ):


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






    def  A    (self   ):


      """simple docstring"""
      A__											=							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  A    (self						,      lowerCamelCase__						,      lowerCamelCase__=1_2_3_4_5   ):


      """simple docstring"""
      A__											=							self.get_dummy_dataset()
      A__											=							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:
        A__											=							dataset
        A__											=							RagPyTorchDistributedRetriever(
            lowerCamelCase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      )
        if init_retrieval:
          retriever.init_retrieval(lowerCamelCase__   )
      return retriever






    def  A    (self						,      lowerCamelCase__   ):


      """simple docstring"""
      # Have to run in local mode because sys.path modifications at top of
      # file are not propogated to remote workers.
      # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
      ray.init(local_mode=lowerCamelCase__   )
      A__											=							RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,      question_encoder=DPRConfig().to_dict()						,      generator=BartConfig().to_dict()						,      )
      A__											=							ray.remote(lowerCamelCase__   )
      A__											=							[remote_cls.remote() for _ in range(1   )]
      with patch("""transformers.models.rag.retrieval_rag.load_dataset"""   ) as mock_load_dataset:
        A__											=							self.get_dummy_dataset()
        A__											=							RagRayDistributedRetriever(
            lowerCamelCase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      retrieval_workers=lowerCamelCase__						,      )
        if init_retrieval:
          retriever.init_retrieval()
      return retriever






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=1_2_3_4_5   ):


      """simple docstring"""
      A__											=							self.get_dummy_dataset()
      A__											=							RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,      question_encoder=DPRConfig().to_dict()						,      generator=BartConfig().to_dict()						,      index_name="""custom"""						,      )
      if from_disk:
        A__											=							os.path.join(self.tmpdirname						,      """dataset"""   )
        A__											=							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
        A__											=							RagPyTorchDistributedRetriever(
            lowerCamelCase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      )
      else:
        A__											=							RagPyTorchDistributedRetriever(
            lowerCamelCase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      index=CustomHFIndex(config.retrieval_vector_size						,      lowerCamelCase__   )						,      )
      if init_retrieval:
        retriever.init_retrieval(lowerCamelCase__   )
      return retriever






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      # Have to run in local mode because sys.path modifications at top of
      # file are not propogated to remote workers.
      # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
      ray.init(local_mode=lowerCamelCase__   )
      A__											=							self.get_dummy_dataset()
      A__											=							RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,      question_encoder=DPRConfig().to_dict()						,      generator=BartConfig().to_dict()						,      index_name="""custom"""						,      )
      A__											=							ray.remote(lowerCamelCase__   )
      A__											=							[remote_cls.remote() for _ in range(1   )]
      if from_disk:
        A__											=							os.path.join(self.tmpdirname						,      """dataset"""   )
        A__											=							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
        A__											=							RagRayDistributedRetriever(
            lowerCamelCase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      retrieval_workers=lowerCamelCase__						,      index=CustomHFIndex.load_from_disk(
                vector_size=config.retrieval_vector_size						,      dataset_path=config.passages_path						,      index_path=config.index_path						,      )						,      )
      else:
        A__											=							RagRayDistributedRetriever(
            lowerCamelCase__						,      question_encoder_tokenizer=self.get_dpr_tokenizer()						,      generator_tokenizer=self.get_bart_tokenizer()						,      retrieval_workers=lowerCamelCase__						,      index=CustomHFIndex(config.retrieval_vector_size						,      lowerCamelCase__   )						,      )
      if init_retrieval:
        retriever.init_retrieval()
      return retriever






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      A__						,A__						,A__											=							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  A    (self   ):


      """simple docstring"""
      A__											=							1
      A__											=							np.array(
          [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]						,      dtype=np.floataa   )

      self.distributed_retriever_check(
          self.get_dummy_pytorch_distributed_retriever(init_retrieval=lowerCamelCase__   )						,      lowerCamelCase__						,      lowerCamelCase__   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							1
      A__											=							np.array(
          [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]						,      dtype=np.floataa   )

      self.distributed_retriever_check(
          self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCamelCase__						,      from_disk=lowerCamelCase__   )						,      lowerCamelCase__						,      lowerCamelCase__						,      )






    def  A    (self   ):


      """simple docstring"""
      A__											=							1
      A__											=							np.array(
          [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]						,      dtype=np.floataa   )

      self.distributed_retriever_check(
          self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCamelCase__						,      from_disk=lowerCamelCase__   )						,      lowerCamelCase__						,      lowerCamelCase__						,      )






    @require_ray
    def  A    (self   ):


      """simple docstring"""
      A__											=							1
      A__											=							np.array(
          [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]						,      dtype=np.floataa   )

      self.distributed_retriever_check(
          self.get_dummy_ray_distributed_retriever(init_retrieval=lowerCamelCase__   )						,      lowerCamelCase__						,      lowerCamelCase__   )
      ray.shutdown()






    @require_ray
    def  A    (self   ):


      """simple docstring"""
      A__											=							1
      A__											=							np.array(
          [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]						,      dtype=np.floataa   )
      with self.assertRaises(lowerCamelCase__   ):
        self.distributed_retriever_check(
            self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase__						,      from_disk=lowerCamelCase__   )						,      lowerCamelCase__						,      lowerCamelCase__						,      )
      ray.shutdown()






    @require_ray
    def  A    (self   ):


      """simple docstring"""
      A__											=							1
      A__											=							np.array(
          [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]						,      dtype=np.floataa   )

      self.distributed_retriever_check(
          self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase__						,      from_disk=lowerCamelCase__   )						,      lowerCamelCase__						,      lowerCamelCase__   )
      ray.shutdown()




