






'''simple docstring'''





import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
							import torch

if is_tf_available():
							import tensorflow as tf





@require_vision
@require_torchvision
class 		SCREAMING_SNAKE_CASE__ (  unittest.TestCase     ):



		def _lowerCAmelCase  (     self:     Any)	->Tuple:






								'''simple docstring'''


								a_					  = tempfile.mkdtemp()
								a_					  = SamImageProcessor()
								a_					  = SamProcessor(a)
								processor.save_pretrained(self.tmpdirname)



		def _lowerCAmelCase  (     self:     Optional[Any]					, **a:     Dict)	->Tuple:






								'''simple docstring'''


								return AutoProcessor.from_pretrained(self.tmpdirname					, **a).image_processor



		def _lowerCAmelCase  (     self:     Tuple)	->Dict:






								'''simple docstring'''


								shutil.rmtree(self.tmpdirname)



		def _lowerCAmelCase  (     self:     Any)	->Tuple:






								'''simple docstring'''


								a_					  = [np.random.randint(2_55					, size=(3, 30, 4_00)					, dtype=np.uinta)]

								a_					  = [Image.fromarray(np.moveaxis(a					, 0					, -1)) for x in image_inputs]

								return image_inputs



		def _lowerCAmelCase  (     self:     List[str])	->Optional[Any]:






								'''simple docstring'''


								a_					  = SamProcessor(image_processor=self.get_image_processor())
								processor.save_pretrained(self.tmpdirname)

								a_					  = self.get_image_processor(do_normalize=a					, padding_value=1.0)

								a_					  = SamProcessor.from_pretrained(self.tmpdirname					, do_normalize=a					, padding_value=1.0)

								self.assertEqual(processor.image_processor.to_json_string()					, image_processor_add_kwargs.to_json_string())
								self.assertIsInstance(processor.image_processor					, a)



		def _lowerCAmelCase  (     self:     List[Any])	->Tuple:






								'''simple docstring'''


								a_					  = self.get_image_processor()

								a_					  = SamProcessor(image_processor=a)

								a_					  = self.prepare_image_inputs()

								a_					  = image_processor(a					, return_tensors="np")
								a_					  = processor(images=a					, return_tensors="np")

								input_feat_extract.pop("original_sizes")  # pop original_sizes as it is popped in the processor
								input_feat_extract.pop("reshaped_input_sizes")  # pop original_sizes as it is popped in the processor

								for key in input_feat_extract.keys():
														self.assertAlmostEqual(input_feat_extract[key].sum()					, input_processor[key].sum()					, delta=1e-2)




		@require_torch
		def _lowerCAmelCase  (     self:     Optional[int])	->List[str]:






								'''simple docstring'''


								a_					  = self.get_image_processor()

								a_					  = SamProcessor(image_processor=a)
								a_					  = [torch.ones((1, 3, 5, 5))]

								a_					  = [[17_64, 26_46]]

								a_					  = [[6_83, 10_24]]
								a_					  = processor.post_process_masks(a					, a					, a)
								self.assertEqual(masks[0].shape					, (1, 3, 17_64, 26_46))

								a_					  = processor.post_process_masks(
								    a					, torch.tensor(a)					, torch.tensor(a))
								self.assertEqual(masks[0].shape					, (1, 3, 17_64, 26_46))

								# should also work with np
								a_					  = [np.ones((1, 3, 5, 5))]
								a_					  = processor.post_process_masks(a					, np.array(a)					, np.array(a))

								self.assertEqual(masks[0].shape					, (1, 3, 17_64, 26_46))

								a_					  = [[1, 0], [0, 1]]
								with self.assertRaises(a):
														a_					  = processor.post_process_masks(a					, np.array(a)					, np.array(a))





@require_vision
@require_tf
class 		SCREAMING_SNAKE_CASE__ (  unittest.TestCase     ):



		def _lowerCAmelCase  (     self:     List[str])	->int:






								'''simple docstring'''


								a_					  = tempfile.mkdtemp()
								a_					  = SamImageProcessor()
								a_					  = SamProcessor(a)
								processor.save_pretrained(self.tmpdirname)



		def _lowerCAmelCase  (     self:     Optional[Any]					, **a:     Any)	->str:






								'''simple docstring'''


								return AutoProcessor.from_pretrained(self.tmpdirname					, **a).image_processor



		def _lowerCAmelCase  (     self:     Tuple)	->Optional[Any]:






								'''simple docstring'''


								shutil.rmtree(self.tmpdirname)



		def _lowerCAmelCase  (     self:     List[str])	->Dict:






								'''simple docstring'''


								a_					  = [np.random.randint(2_55					, size=(3, 30, 4_00)					, dtype=np.uinta)]

								a_					  = [Image.fromarray(np.moveaxis(a					, 0					, -1)) for x in image_inputs]

								return image_inputs



		def _lowerCAmelCase  (     self:     Optional[int])	->int:






								'''simple docstring'''


								a_					  = SamProcessor(image_processor=self.get_image_processor())
								processor.save_pretrained(self.tmpdirname)

								a_					  = self.get_image_processor(do_normalize=a					, padding_value=1.0)

								a_					  = SamProcessor.from_pretrained(self.tmpdirname					, do_normalize=a					, padding_value=1.0)

								self.assertEqual(processor.image_processor.to_json_string()					, image_processor_add_kwargs.to_json_string())
								self.assertIsInstance(processor.image_processor					, a)



		def _lowerCAmelCase  (     self:     Dict)	->Optional[int]:






								'''simple docstring'''


								a_					  = self.get_image_processor()

								a_					  = SamProcessor(image_processor=a)

								a_					  = self.prepare_image_inputs()

								a_					  = image_processor(a					, return_tensors="np")
								a_					  = processor(images=a					, return_tensors="np")

								input_feat_extract.pop("original_sizes")  # pop original_sizes as it is popped in the processor
								input_feat_extract.pop("reshaped_input_sizes")  # pop reshaped_input_sizes as it is popped in the processor

								for key in input_feat_extract.keys():
														self.assertAlmostEqual(input_feat_extract[key].sum()					, input_processor[key].sum()					, delta=1e-2)




		@require_tf
		def _lowerCAmelCase  (     self:     Union[str, Any])	->Union[str, Any]:






								'''simple docstring'''


								a_					  = self.get_image_processor()

								a_					  = SamProcessor(image_processor=a)
								a_					  = [tf.ones((1, 3, 5, 5))]

								a_					  = [[17_64, 26_46]]

								a_					  = [[6_83, 10_24]]
								a_					  = processor.post_process_masks(a					, a					, a					, return_tensors="tf")
								self.assertEqual(masks[0].shape					, (1, 3, 17_64, 26_46))

								a_					  = processor.post_process_masks(
								    a					, tf.convert_to_tensor(a)					, tf.convert_to_tensor(a)					, return_tensors="tf"					, )
								self.assertEqual(masks[0].shape					, (1, 3, 17_64, 26_46))

								# should also work with np
								a_					  = [np.ones((1, 3, 5, 5))]
								a_					  = processor.post_process_masks(
								    a					, np.array(a)					, np.array(a)					, return_tensors="tf")

								self.assertEqual(masks[0].shape					, (1, 3, 17_64, 26_46))

								a_					  = [[1, 0], [0, 1]]
								with self.assertRaises(tf.errors.InvalidArgumentError):
														a_					  = processor.post_process_masks(
														    a					, np.array(a)					, np.array(a)					, return_tensors="tf")





@require_vision
@require_torchvision
class 		SCREAMING_SNAKE_CASE__ (  unittest.TestCase     ):



		def _lowerCAmelCase  (     self:     Optional[Any])	->Dict:






								'''simple docstring'''


								a_					  = tempfile.mkdtemp()
								a_					  = SamImageProcessor()
								a_					  = SamProcessor(a)
								processor.save_pretrained(self.tmpdirname)



		def _lowerCAmelCase  (     self:     Tuple					, **a:     Dict)	->Any:






								'''simple docstring'''


								return AutoProcessor.from_pretrained(self.tmpdirname					, **a).image_processor



		def _lowerCAmelCase  (     self:     List[str])	->Any:






								'''simple docstring'''


								shutil.rmtree(self.tmpdirname)



		def _lowerCAmelCase  (     self:     Union[str, Any])	->Dict:






								'''simple docstring'''


								a_					  = [np.random.randint(2_55					, size=(3, 30, 4_00)					, dtype=np.uinta)]

								a_					  = [Image.fromarray(np.moveaxis(a					, 0					, -1)) for x in image_inputs]

								return image_inputs



		@is_pt_tf_cross_test
		def _lowerCAmelCase  (     self:     str)	->Dict:






								'''simple docstring'''


								a_					  = self.get_image_processor()

								a_					  = SamProcessor(image_processor=a)
								a_					  = np.random.randint(0					, 2					, size=(1, 3, 5, 5)).astype(np.floataa)
								a_					  = [tf.convert_to_tensor(a)]
								a_					  = [torch.tensor(a)]

								a_					  = [[17_64, 26_46]]

								a_					  = [[6_83, 10_24]]
								a_					  = processor.post_process_masks(
								    a					, a					, a					, return_tensors="tf")
								a_					  = processor.post_process_masks(
								    a					, a					, a					, return_tensors="pt")

								self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()))




		@is_pt_tf_cross_test
		def _lowerCAmelCase  (     self:     int)	->Tuple:






								'''simple docstring'''


								a_					  = self.get_image_processor()

								a_					  = SamProcessor(image_processor=a)

								a_					  = self.prepare_image_inputs()

								a_					  = image_processor(a					, return_tensors="pt")["pixel_values"].numpy()
								a_					  = processor(images=a					, return_tensors="pt")["pixel_values"].numpy()

								a_					  = image_processor(a					, return_tensors="tf")["pixel_values"].numpy()
								a_					  = processor(images=a					, return_tensors="tf")["pixel_values"].numpy()

								self.assertTrue(np.allclose(a					, a))
								self.assertTrue(np.allclose(a					, a))
								self.assertTrue(np.allclose(a					, a))











'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
 import faiss


@require_faiss
class 							_UpperCAmelCase    (		snake_case      ):




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  tempfile.mkdtemp()
       lowercase_						:     str							     =  8

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

       lowercase_						:     Union[str, Any]							     =  os.path.join(self.tmpdirname	,  "bart_tokenizer"      )
       os.makedirs(a	,  exist_ok=a      )
       lowercase_						:     Union[str, Any]							     =  os.path.join(a	,  BART_VOCAB_FILES_NAMES["vocab_file"]      )
       lowercase_						:     Tuple							     =  os.path.join(a	,  BART_VOCAB_FILES_NAMES["merges_file"]      )
       with open(self.vocab_file	,  "w"	,  encoding="utf-8"      ) as fp:
          fp.write(json.dumps(a      ) + "\n"      )
       with open(self.merges_file	,  "w"	,  encoding="utf-8"      ) as fp:
          fp.write("\n".join(a      )      )




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       shutil.rmtree(self.tmpdirname      )




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     List[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 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  self.get_dummy_dataset()
       lowercase_						:     Optional[int]							     =  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:
          lowercase_						:     Any							     =  dataset
          lowercase_						:     List[str]							     =  RagRetriever(
              a	,  question_encoder_tokenizer=self.get_dpr_tokenizer()	,  generator_tokenizer=self.get_bart_tokenizer()	,  )
       return retriever




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]	,  a	:							bool      ):






       '''simple docstring'''






       lowercase_						:     int							     =  self.get_dummy_dataset()
       lowercase_						:     Optional[Any]							     =  RagConfig(
           retrieval_vector_size=self.retrieval_vector_size	,  question_encoder=DPRConfig().to_dict()	,  generator=BartConfig().to_dict()	,  index_name="custom"	,  )
       if from_disk:
          lowercase_						:     Optional[Any]							     =  os.path.join(self.tmpdirname	,  "dataset"      )
          lowercase_						:     Dict							     =  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
          lowercase_						:     Tuple							     =  RagRetriever(
              a	,  question_encoder_tokenizer=self.get_dpr_tokenizer()	,  generator_tokenizer=self.get_bart_tokenizer()	,  )
       else:
          lowercase_						:     Union[str, Any]							     =  RagRetriever(
              a	,  question_encoder_tokenizer=self.get_dpr_tokenizer()	,  generator_tokenizer=self.get_bart_tokenizer()	,  index=CustomHFIndex(config.retrieval_vector_size	,  a      )	,  )
       return retriever




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, 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      )

       lowercase_						:     str							     =  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"      )      )

       lowercase_						:     Dict							     =  os.path.join(self.tmpdirname	,  "psgs_w100.tsv.pkl"      )
       lowercase_						:     List[str]							     =  {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
       pickle.dump(a	,  open(a	,  "wb"      )      )

       lowercase_						:     Optional[int]							     =  RagConfig(
           retrieval_vector_size=self.retrieval_vector_size	,  question_encoder=DPRConfig().to_dict()	,  generator=BartConfig().to_dict()	,  index_name="legacy"	,  index_path=self.tmpdirname	,  )
       lowercase_						:     str							     =  RagRetriever(
           a	,  question_encoder_tokenizer=self.get_dpr_tokenizer()	,  generator_tokenizer=self.get_bart_tokenizer()      )
       return retriever




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  1
       lowercase_						:     Tuple							     =  self.get_dummy_canonical_hf_index_retriever()
       lowercase_						:     Any							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_     ,		lowercase_     ,		lowercase_						:     Tuple							     =  retriever.retrieve(a	,  n_docs=a      )
       self.assertEqual(retrieved_doc_embeds.shape	,  (2, n_docs, self.retrieval_vector_size)      )
       self.assertEqual(len(a      )	,  2      )
       self.assertEqual(sorted(doc_dicts[0]      )	,  ["embeddings", "id", "text", "title"]      )
       self.assertEqual(len(doc_dicts[0]["id"]      )	,  a      )
       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	:							int      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  1
       lowercase_						:     Optional[int]							     =  self.get_dummy_custom_hf_index_retriever(from_disk=a      )
       lowercase_						:     str							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_     ,		lowercase_     ,		lowercase_						:     List[str]							     =  retriever.retrieve(a	,  n_docs=a      )
       self.assertEqual(retrieved_doc_embeds.shape	,  (2, n_docs, self.retrieval_vector_size)      )
       self.assertEqual(len(a      )	,  2      )
       self.assertEqual(sorted(doc_dicts[0]      )	,  ["embeddings", "id", "text", "title"]      )
       self.assertEqual(len(doc_dicts[0]["id"]      )	,  a      )
       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	:							int      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  1
       lowercase_						:     Optional[int]							     =  self.get_dummy_custom_hf_index_retriever(from_disk=a      )
       lowercase_						:     Any							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_     ,		lowercase_     ,		lowercase_						:     Optional[int]							     =  retriever.retrieve(a	,  n_docs=a      )
       self.assertEqual(retrieved_doc_embeds.shape	,  (2, n_docs, self.retrieval_vector_size)      )
       self.assertEqual(len(a      )	,  2      )
       self.assertEqual(sorted(doc_dicts[0]      )	,  ["embeddings", "id", "text", "title"]      )
       self.assertEqual(len(doc_dicts[0]["id"]      )	,  a      )
       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	:							int      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  1
       lowercase_						:     int							     =  self.get_dummy_legacy_index_retriever()
       lowercase_						:     Dict							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_     ,		lowercase_     ,		lowercase_						:     List[str]							     =  retriever.retrieve(a	,  n_docs=a      )
       self.assertEqual(retrieved_doc_embeds.shape	,  (2, n_docs, self.retrieval_vector_size)      )
       self.assertEqual(len(a      )	,  2      )
       self.assertEqual(sorted(doc_dicts[0]      )	,  ["text", "title"]      )
       self.assertEqual(len(doc_dicts[0]["text"]      )	,  a      )
       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 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






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




    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       import torch

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

       lowercase_						:     Tuple							     =  retriever(
           a	,  a	,  prefix=retriever.config.generator.prefix	,  n_docs=a	,  return_tensors="pt"	,  )
       lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Tuple							     =  (  # 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(a	,  torch.Tensor      )
       self.assertIsInstance(a	,  torch.Tensor      )
       self.assertIsInstance(a	,  torch.Tensor      )





    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  self.get_dpr_ctx_encoder_tokenizer()
       lowercase_						:     Tuple							     =  1
       lowercase_						:     int							     =  self.get_dummy_custom_hf_index_retriever(from_disk=a      )
       retriever.set_ctx_encoder_tokenizer(a      )

       lowercase_						:     Tuple							     =  [[5, 7], [1_0, 1_1]]
       lowercase_						:     Optional[Any]							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_						:     Dict							     =  retriever(a	,  a	,  prefix=retriever.config.generator.prefix	,  n_docs=a      )

       self.assertEqual(
           len(a      )	,  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")      )	,  a      )  # check for doc token related keys in dictionary.




