




'''simple docstring'''



import logging
from pathlib import Path
from typing import Any, List, Tuple

import yaml


# loading package files: https://stackoverflow.com/a/20885799
try:
 import importlib.resources as pkg_resources
except ImportError:
 # Try backported to PY<37 `importlib_resources`.
 import importlib_resources as pkg_resources

from . import resources


lowerCamelCase_	 =		'https://github.com/huggingface/datasets/tree/main/src/datasets/utils'
lowerCamelCase_	 =		f'''{BASE_REF_URL}/{__file__}'''
lowerCamelCase_	 =		logging.getLogger(__name__)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				str						)				->				Tuple[Any, str]:
   _SCREAMING_SNAKE_CASE =   pkg_resources.read_text(__A ,  __A						)
   return yaml.safe_load(__A						), f"""{BASE_REF_URL}/resources/{resource}"""


lowerCamelCase_ ,       lowerCamelCase_	 =		load_yaml_resource('readme_structure.yaml')

lowerCamelCase_	 =		[
    '[Needs More Information]',
    '[More Information Needed]',
    '(https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)',
]

# Dictionary representation of section/readme, error_list, warning_list
lowerCamelCase_	 =		Tuple[dict, List[str], List[str]]







class 	lowercase_					:


   """simple docstring"""

   def __init__(					self      :			List[str]						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			List[str] = None						,   __lowerCamelCase      :			bool = False					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   name
      _SCREAMING_SNAKE_CASE =   level
      _SCREAMING_SNAKE_CASE =   lines
      _SCREAMING_SNAKE_CASE =   ""
      _SCREAMING_SNAKE_CASE =   True
      _SCREAMING_SNAKE_CASE =   {}
      _SCREAMING_SNAKE_CASE =   []
      _SCREAMING_SNAKE_CASE =   []
      if self.lines is not None:
         self.parse(suppress_parsing_errors=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			bool = False					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   ""
      _SCREAMING_SNAKE_CASE =   []
      _SCREAMING_SNAKE_CASE =   False
      for line in self.lines:
         if line.strip(" \n"					) == "":
            continue
         elif line.strip(" \n"					)[:3] == "```":
            _SCREAMING_SNAKE_CASE =   not code_start
         elif line.split()[0] == self.level + "#" and not code_start:
            if current_sub_level != "":
               _SCREAMING_SNAKE_CASE =   Section(__lowerCamelCase						,   self.level + "#"						,   __lowerCamelCase					)
               _SCREAMING_SNAKE_CASE =   []
            else:
               if current_lines != []:
                  self.text += "".join(__lowerCamelCase					).strip()
                  if self.text != "" and self.text not in FILLER_TEXT:
                     _SCREAMING_SNAKE_CASE =   False
                  _SCREAMING_SNAKE_CASE =   []

            _SCREAMING_SNAKE_CASE =   " ".join(line.split()[1:]					).strip(" \n"					)
         else:
            current_lines.append(__lowerCamelCase					)
      else:
         if current_sub_level != "":
            if current_sub_level in self.content:
               self.parsing_error_list.append(
                   F"""Multiple sections with the same heading `{current_sub_level}` have been found. Please keep only one of these sections."""					)
            _SCREAMING_SNAKE_CASE =   Section(__lowerCamelCase						,   self.level + "#"						,   __lowerCamelCase					)
         else:
            if current_lines != []:
               self.text += "".join(__lowerCamelCase					).strip()
               if self.text != "" and self.text not in FILLER_TEXT:
                  _SCREAMING_SNAKE_CASE =   False

      if self.level == "" and not suppress_parsing_errors:
         if self.parsing_error_list != [] or self.parsing_warning_list != []:
            _SCREAMING_SNAKE_CASE =   _SCREAMING_SNAKE_CASE =   "\n".join("-\t" + x for x in self.parsing_error_list + self.parsing_warning_list					)
            _SCREAMING_SNAKE_CASE =   F"""The following issues were found while parsing the README at `{self.name}`:\n""" + errors
            raise ValueError(__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			str						,   __lowerCamelCase      :			dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   []
      _SCREAMING_SNAKE_CASE =   []
      if structure["allow_empty"] is False:
         # If content is expected
         if self.is_empty_text and self.content == {}:
            # If no content is found, mention it in the error_list
            error_list.append(F"""Expected some content in section `{self.name}` but it is empty."""					)

      if structure["allow_empty_text"] is False:
         # If some text is expected
         if self.is_empty_text:
            # If no text is found, mention it in the error_list
            error_list.append(
                F"""Expected some text in section `{self.name}` but it is empty (text in subsections are ignored)."""					)
        # Subsections Validation
      if structure["subsections"] is not None:
         # If subsections are expected
         if self.content == {}:
            # If no subsections are present
            _SCREAMING_SNAKE_CASE =   [subsection["name"] for subsection in structure["subsections"]]
            # Mention the expected values in the error_list
            error_list.append(
                F"""Section `{self.name}` expected the following subsections: {", ".join(["`"+x+"`" for x in values]					)}. Found 'None'."""					)
         else:
            # If some subsections are present
            _SCREAMING_SNAKE_CASE =   [subsection["name"] for subsection in structure["subsections"]]
            _SCREAMING_SNAKE_CASE =   False
            for idx, name in enumerate(__lowerCamelCase					):
               if name not in self.content:
                  # If the expected subsection is not present
                  error_list.append(F"""Section `{self.name}` is missing subsection: `{name}`."""					)
                  _SCREAMING_SNAKE_CASE =   True
               else:
                  # If the subsection is present, validate subsection, return the result
                  # and concat the errors from subsection to section error_list

                  # Skip sublevel validation if current level is `###`
                  if self.level == "###":
                     continue
                  else:
                     _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.content[name].validate(
                         structure["subsections"][idx]					)
                  error_list += subsec_error_list
                  warning_list += subsec_warning_list

            if has_missing_subsections:  # we only allow to have extra subsections if all the other ones are here
               for name in self.content:
                  if name not in structure_names:
                     # If an extra subsection is present
                     warning_list.append(
                         F"""`{self.name}` has an extra subsection: `{name}`. Skipping further validation checks for this subsection as expected structure is unknown."""					)
      if error_list:
         # If there are errors, do not return the dictionary as it is invalid
         return {}, error_list, warning_list
      else:
         return self.to_dict(), error_list, warning_list



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

      """simple docstring"""
      return {
          "name": self.name,
          "text": self.text,
          "is_empty_text": self.is_empty_text,
          "subsections": [value.to_dict() for value in self.content.values()],
      }







class 	lowercase_					(				A     ):  # Level 0


   """simple docstring"""

   def __init__(					self      :			Optional[Any]						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			dict = None						,   __lowerCamelCase      :			bool = False					):

      """simple docstring"""
      super().__init__(name=__lowerCamelCase						,   level=""					)  # Not using lines here as we need to use a child class parse
      _SCREAMING_SNAKE_CASE =   structure
      _SCREAMING_SNAKE_CASE =   -2
      _SCREAMING_SNAKE_CASE =   0
      _SCREAMING_SNAKE_CASE =   lines
      if self.lines is not None:
         self.parse(suppress_parsing_errors=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      if self.structure is None:
         _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self._validate(__lowerCamelCase					)
      else:
         _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self._validate(self.structure					)
      if error_list != [] or warning_list != []:
         _SCREAMING_SNAKE_CASE =   "\n".join(["-\t" + x for x in error_list + warning_list]					)
         _SCREAMING_SNAKE_CASE =   F"""The following issues were found for the README at `{self.name}`:\n""" + errors
         raise ValueError(__lowerCamelCase					)



   @classmethod
   def 							lowerCAmelCase_    (					cls      :			Union[str, Any]						,   __lowerCamelCase      :			Path						,   __lowerCamelCase      :			dict = None						,   __lowerCamelCase      :			bool = False					):

      """simple docstring"""
      with open(__lowerCamelCase						,   encoding="utf-8"					) as f:
         _SCREAMING_SNAKE_CASE =   f.readlines()
      return cls(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   suppress_parsing_errors=__lowerCamelCase					)



   @classmethod
   def 							lowerCAmelCase_    (					cls      :			Optional[int]						,   __lowerCamelCase      :			str						,   __lowerCamelCase      :			dict = None						,   __lowerCamelCase      :			str = "root"						,   __lowerCamelCase      :			bool = False					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   string.split("\n"					)
      return cls(__lowerCamelCase						,   __lowerCamelCase						,   __lowerCamelCase						,   suppress_parsing_errors=__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Dict						,   __lowerCamelCase      :			bool = False					):

      """simple docstring"""
      # Skip Tags
      _SCREAMING_SNAKE_CASE =   0

      for line in self.lines:
         self.yaml_tags_line_count += 1
         if line.strip(" \n"					) == "---":
            self.tag_count += 1
            if self.tag_count == 2:
               break
         line_count += 1
      if self.tag_count == 2:
         _SCREAMING_SNAKE_CASE =   self.lines[line_count + 1 :]  # Get the last + 1 th item.
      else:
         _SCREAMING_SNAKE_CASE =   self.lines[self.tag_count :]
      super().parse(suppress_parsing_errors=__lowerCamelCase					)



   def __str__(					self      :			Any					):

      """simple docstring"""
      return str(self.to_dict()					)



   def 							lowerCAmelCase_    (					self      :			Optional[Any]						,   __lowerCamelCase      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   []
      _SCREAMING_SNAKE_CASE =   []
      if self.yaml_tags_line_count == 0:
         warning_list.append("Empty YAML markers are present in the README."					)
      elif self.tag_count == 0:
         warning_list.append("No YAML markers are present in the README."					)
      elif self.tag_count == 1:
         warning_list.append("Only the start of YAML tags present in the README."					)
      # Check how many first level sections are present.
      _SCREAMING_SNAKE_CASE =   len(self.content.keys()					)
      if num_first_level_keys > 1:
         # If more than one, add to the error list, continue
         error_list.append(
             F"""The README has several first-level headings: {", ".join(["`"+x+"`" for x in list(self.content.keys()					)]					)}. Only one heading is expected. Skipping further validation for this README."""					)
      elif num_first_level_keys < 1:
         # If less than one, append error.
         error_list.append(
             "The README has no first-level headings. One heading is expected. Skipping further validation for this README."					)

      else:
         # If one exactly
         _SCREAMING_SNAKE_CASE =   list(self.content.keys()					)[0]  # Get the key
         if start_key.startswith("Dataset Card for"					):  # Check correct start
            # If the starting is correct, validate all the sections
            _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.content[start_key].validate(
                readme_structure["subsections"][0]					)
            error_list += sec_error_list
            warning_list += sec_warning_list
         else:
            # If not found, append error
            error_list.append(
                "No first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README."					)
      if error_list:
         # If there are errors, do not return the dictionary as it is invalid
         return {}, error_list, warning_list
      else:
         return self.to_dict(), error_list, warning_list


if __name__ == "__main__":
 from argparse import ArgumentParser

 lowerCamelCase_	 =		ArgumentParser(usage='Validate the content (excluding YAML tags) of a README.md file.')
 ap.add_argument('readme_filepath')
 lowerCamelCase_	 =		ap.parse_args()
 lowerCamelCase_	 =		Path(args.readme_filepath)
 lowerCamelCase_	 =		ReadMe.from_readme(readme_filepath)







'''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_				(							__A       :				Any						)				->				Union[str, Any]:
   if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
      _SCREAMING_SNAKE_CASE =   unittest.skip("test requires Datasets, Faiss, psutil"						)(__A						)
   return test_case







@require_distributed_retrieval
class 	lowercase_					(				A     ):


   """simple docstring"""

   def 							lowerCAmelCase_    (					self      :			List[str]					):

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

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

      _SCREAMING_SNAKE_CASE =   os.path.join(self.tmpdirname						,   "bart_tokenizer"					)
      os.makedirs(__lowerCamelCase						,   exist_ok=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   os.path.join(__lowerCamelCase						,   BART_VOCAB_FILES_NAMES["vocab_file"]					)
      _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			List[Any]					):

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



   def 							lowerCAmelCase_    (					self      :			Any					):

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



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

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



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			Optional[Any]=1_2_3_4_5					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.get_dummy_dataset()
      _SCREAMING_SNAKE_CASE =   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 =   dataset
         _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Union[str, Any]						,   __lowerCamelCase      :			bool					):

      """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					)
      _SCREAMING_SNAKE_CASE =   RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,   question_encoder=DPRConfig().to_dict()						,   generator=BartConfig().to_dict()						,   )
      _SCREAMING_SNAKE_CASE =   ray.remote(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   [remote_cls.remote() for _ in range(1					)]
      with patch("transformers.models.rag.retrieval_rag.load_dataset"					) as mock_load_dataset:
         _SCREAMING_SNAKE_CASE =   self.get_dummy_dataset()
         _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Dict						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			List[str]=1_2_3_4_5					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.get_dummy_dataset()
      _SCREAMING_SNAKE_CASE =   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 =   os.path.join(self.tmpdirname						,   "dataset"					)
         _SCREAMING_SNAKE_CASE =   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 =   RagPyTorchDistributedRetriever(
             __lowerCamelCase						,   question_encoder_tokenizer=self.get_dpr_tokenizer()						,   generator_tokenizer=self.get_bart_tokenizer()						,   )
      else:
         _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			bool						,   __lowerCamelCase      :			bool					):

      """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					)
      _SCREAMING_SNAKE_CASE =   self.get_dummy_dataset()
      _SCREAMING_SNAKE_CASE =   RagConfig(
          retrieval_vector_size=self.retrieval_vector_size						,   question_encoder=DPRConfig().to_dict()						,   generator=BartConfig().to_dict()						,   index_name="custom"						,   )
      _SCREAMING_SNAKE_CASE =   ray.remote(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   [remote_cls.remote() for _ in range(1					)]
      if from_disk:
         _SCREAMING_SNAKE_CASE =   os.path.join(self.tmpdirname						,   "dataset"					)
         _SCREAMING_SNAKE_CASE =   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 =   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:
         _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			RagRetriever						,   __lowerCamelCase      :			np.array						,   __lowerCamelCase      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   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 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   1
      _SCREAMING_SNAKE_CASE =   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()

