




'''simple docstring'''



import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:







 class 	lowercase_					:


    """simple docstring"""

    @staticmethod
    def 							lowerCAmelCase_    (					*__lowerCamelCase      :			Union[str, Any]						,   **__lowerCamelCase      :			str					):

       """simple docstring"""
       pass







@is_pipeline_test
@require_torch_or_tf
@require_vision
class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   lowerCamelCase_       						=		MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
   lowerCamelCase_       						=		TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   ImageClassificationPipeline(model=__lowerCamelCase						,   image_processor=__lowerCamelCase						,   top_k=2					)
      _SCREAMING_SNAKE_CASE =   [
          Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
      ]
      return image_classifier, examples



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"					)

      self.assertEqual(
          __lowerCamelCase						,   [
              {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
          ]						,   )

      import datasets

      _SCREAMING_SNAKE_CASE =   datasets.load_dataset("hf-internal-testing/fixtures_image_utils"						,   "image"						,   split="test"					)

      # Accepts URL + PIL.Image + lists
      _SCREAMING_SNAKE_CASE =   image_classifier(
          [
              Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              # RGBA
              dataset[0]["file"],
              # LA
              dataset[1]["file"],
              # L
              dataset[2]["file"],
          ]					)
      self.assertEqual(
          __lowerCamelCase						,   [
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
              [
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
                  {"score": ANY(__lowerCamelCase					), "label": ANY(__lowerCamelCase					)},
              ],
          ]						,   )



   @require_torch
   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "hf-internal-testing/tiny-random-vit"
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]						,   )

      _SCREAMING_SNAKE_CASE =   image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]						,   top_k=2						,   )
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]						,   )



   @require_tf
   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "hf-internal-testing/tiny-random-vit"
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model=__lowerCamelCase						,   framework="tf"					)

      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]						,   )

      _SCREAMING_SNAKE_CASE =   image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]						,   top_k=2						,   )
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]						,   )



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   PreTrainedTokenizer()

      # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
      _SCREAMING_SNAKE_CASE =   pipeline(
          "image-classification"						,   model="hf-internal-testing/tiny-random-vit"						,   tokenizer=__lowerCamelCase					)

      self.assertIs(image_classifier.tokenizer						,   __lowerCamelCase					)



   @slow
   @require_torch
   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      # Perceiver is not tested by `run_pipeline_test` properly.
      # That is because the type of feature_extractor and model preprocessor need to be kept
      # in sync, which is not the case in the current design
      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-conv"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              {"score": 0.4_3_8_5, "label": "tabby, tabby cat"},
              {"score": 0.3_2_1, "label": "tiger cat"},
              {"score": 0.0_5_0_2, "label": "Egyptian cat"},
              {"score": 0.0_1_3_7, "label": "crib, cot"},
              {"score": 0.0_0_7, "label": "radiator"},
          ]						,   )

      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-fourier"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              {"score": 0.5_6_5_8, "label": "tabby, tabby cat"},
              {"score": 0.1_3_0_9, "label": "tiger cat"},
              {"score": 0.0_7_2_2, "label": "Egyptian cat"},
              {"score": 0.0_7_0_7, "label": "remote control, remote"},
              {"score": 0.0_0_8_2, "label": "computer keyboard, keypad"},
          ]						,   )

      _SCREAMING_SNAKE_CASE =   pipeline("image-classification"						,   model="deepmind/vision-perceiver-learned"					)
      _SCREAMING_SNAKE_CASE =   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"					)
      self.assertEqual(
          nested_simplify(__lowerCamelCase						,   decimals=4					)						,   [
              {"score": 0.3_0_2_2, "label": "tabby, tabby cat"},
              {"score": 0.2_3_6_2, "label": "Egyptian cat"},
              {"score": 0.1_8_5_6, "label": "tiger cat"},
              {"score": 0.0_3_2_4, "label": "remote control, remote"},
              {"score": 0.0_0_9_6, "label": "quilt, comforter, comfort, puff"},
          ]						,   )







'''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()

