


"""simple docstring"""



import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
  from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image







class _lowerCAmelCase       :







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__=13		,			UpperCamelCase__=64		,			UpperCamelCase__=2		,			UpperCamelCase__=3		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=32		,			UpperCamelCase__=5		,			UpperCamelCase__=4		,			UpperCamelCase__=37		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=10		,			UpperCamelCase__=0.02		,			UpperCamelCase__=[1, 16, 4, 4]		,			UpperCamelCase__=None		,			)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Tuple         =					parent
       snake_case					:   Dict         =					batch_size
       snake_case					:   Union[str, Any]         =					image_size
       snake_case					:   List[Any]         =					patch_size
       snake_case					:   Dict         =					num_channels
       snake_case					:   Optional[Any]         =					is_training
       snake_case					:   Tuple         =					use_labels
       snake_case					:   str         =					hidden_size
       snake_case					:   Union[str, Any]         =					num_hidden_layers
       snake_case					:   Optional[int]         =					num_attention_heads
       snake_case					:   Any         =					intermediate_size
       snake_case					:   Dict         =					hidden_act
       snake_case					:   Union[str, Any]         =					hidden_dropout_prob
       snake_case					:   str         =					attention_probs_dropout_prob
       snake_case					:   Dict         =					type_sequence_label_size
       snake_case					:   List[str]         =					initializer_range
       snake_case					:   List[Any]         =					scope
       snake_case					:   Dict         =					backbone_featmap_shape

       # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       # the number of patches is based on the feature map of the backbone, which by default uses an output stride
       # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
       snake_case					:   Tuple         =					(self.image_size // 32) ** 2
       snake_case					:   Optional[int]         =					num_patches + 1







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Tuple         =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

       snake_case					:   Dict         =					None
       if self.use_labels:
           snake_case					:   List[Any]         =					ids_tensor([self.batch_size]		,			self.type_sequence_label_size		)

       snake_case					:   Any         =					self.get_config()

       return config, pixel_values, labels







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Tuple         =					{
           "global_padding": "same",
           "layer_type": "bottleneck",
           "depths": [3, 4, 9],
           "out_features": ["stage1", "stage2", "stage3"],
           "embedding_dynamic_padding": True,
           "hidden_sizes": [4, 8, 16, 32],
           "num_groups": 2,
       }

       return ViTHybridConfig(
           image_size=self.image_size		,			patch_size=self.patch_size		,			num_channels=self.num_channels		,			hidden_size=self.hidden_size		,			num_hidden_layers=self.num_hidden_layers		,			num_attention_heads=self.num_attention_heads		,			intermediate_size=self.intermediate_size		,			hidden_act=self.hidden_act		,			hidden_dropout_prob=self.hidden_dropout_prob		,			attention_probs_dropout_prob=self.attention_probs_dropout_prob		,			is_decoder=UpperCamelCase__		,			initializer_range=self.initializer_range		,			backbone_featmap_shape=self.backbone_featmap_shape		,			backbone_config=UpperCamelCase__		,			)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					ViTHybridModel(config=UpperCamelCase__		)
       model.to(UpperCamelCase__		)
       model.eval()
       snake_case					:   int         =					model(UpperCamelCase__		)
       self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)		)







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)			->	List[Any]:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					self.type_sequence_label_size
       snake_case					:   Optional[Any]         =					ViTHybridForImageClassification(UpperCamelCase__		)
       model.to(UpperCamelCase__		)
       model.eval()
       snake_case					:   Dict         =					model(UpperCamelCase__		,			labels=UpperCamelCase__		)
       self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.type_sequence_label_size)		)







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Any         =					self.prepare_config_and_inputs()
       snake_case      ,snake_case      ,snake_case					:   Optional[Any]         =					config_and_inputs
       snake_case					:   Optional[Any]         =					{"pixel_values": pixel_values}
       return config, inputs_dict







@require_torch
class _lowerCAmelCase       (   snake_case_					,							snake_case_					,							unittest.TestCase			):
   __UpperCAmelCase				: Optional[int]													=						(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
   __UpperCAmelCase				: Any													=						(
       {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
       if is_torch_available()
       else {}
   )
   __UpperCAmelCase				: Any													=						False
   __UpperCAmelCase				: List[Any]													=						False
   __UpperCAmelCase				: str													=						False







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					ViTHybridModelTester(self		)
       snake_case					:   Dict         =					ConfigTester(self		,			config_class=UpperCamelCase__		,			has_text_modality=UpperCamelCase__		,			hidden_size=37		)







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

       '''simple docstring'''




       self.config_tester.run_common_tests()







   @unittest.skip(reason="ViT does not use inputs_embeds"		)
   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       pass







   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




       snake_case      ,snake_case					:   Union[str, Any]         =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           snake_case					:   str         =					model_class(UpperCamelCase__		)
           self.assertIsInstance(model.get_input_embeddings()		,			(nn.Module)		)
           snake_case					:   Optional[Any]         =					model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(UpperCamelCase__		,			nn.Linear		)		)







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       snake_case      ,snake_case					:   int         =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           snake_case					:   Dict         =					model_class(UpperCamelCase__		)
           snake_case					:   Dict         =					inspect.signature(model.forward		)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           snake_case					:   List[Any]         =					[*signature.parameters.keys()]

           snake_case					:   Union[str, Any]         =					["pixel_values"]
           self.assertListEqual(arg_names[:1]		,			UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case      ,snake_case					:   List[str]         =					self.model_tester.prepare_config_and_inputs_for_common()

       snake_case					:   Any         =					_config_zero_init(UpperCamelCase__		)
       for model_class in self.all_model_classes:
           snake_case					:   Tuple         =					model_class(config=UpperCamelCase__		)
           # Skip the check for the backbone
           for name, module in model.named_modules():
               if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                   snake_case					:   List[str]         =					[F'{name}.{key}' for key in module.state_dict().keys()]
                   break

           for name, param in model.named_parameters():
               if param.requires_grad:
                   if name in backbone_params:
                       continue
                   self.assertIn(
                       ((param.data.mean() * 1e9).round() / 1e9).item()		,			[0.0, 1.0]		,			msg=F'Parameter {name} of model {model_class} seems not properly initialized'		,			)







   @slow
   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




       for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           snake_case					:   str         =					ViTHybridModel.from_pretrained(UpperCamelCase__		)
           self.assertIsNotNone(UpperCamelCase__		)







def 						__lowerCAmelCase		(		)			->		List[Any]:


    """simple docstring"""





    snake_case					:   List[str]         =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
    return image







@require_torch
@require_vision
class _lowerCAmelCase       (   unittest.TestCase			):







   @cached_property
   def 				lowerCamelCase	(			self		)			->	List[Any]:

       '''simple docstring'''




       return (
           ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
           if is_vision_available()
           else None
       )







   @slow
   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
           UpperCamelCase__		)

       snake_case					:   Optional[Any]         =					self.default_image_processor
       snake_case					:   int         =					prepare_img()
       snake_case					:   Tuple         =					image_processor(images=UpperCamelCase__		,			return_tensors="pt"		).to(UpperCamelCase__		)

       # forward pass
       with torch.no_grad():
           snake_case					:   Any         =					model(**UpperCamelCase__		)

       # verify the logits
       snake_case					:   Any         =					torch.Size((1, 1000)		)
       self.assertEqual(outputs.logits.shape		,			UpperCamelCase__		)

       snake_case					:   str         =					torch.tensor([-1.9090, -0.4993, -0.2389]		).to(UpperCamelCase__		)

       self.assertTrue(torch.allclose(outputs.logits[0, :3]		,			UpperCamelCase__		,			atol=1e-4		)		)







   @slow
   @require_accelerate
   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       snake_case					:   List[Any]         =					ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"		)
       snake_case					:   Any         =					ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"		,			device_map="auto"		)

       snake_case					:   Tuple         =					prepare_img()

       snake_case					:   Union[str, Any]         =					image_processor(images=UpperCamelCase__		,			return_tensors="pt"		)
       snake_case					:   Dict         =					model(**UpperCamelCase__		)
       snake_case					:   int         =					outputs.logits
       # model predicts one of the 1000 ImageNet classes
       snake_case					:   Optional[Any]         =					logits.argmax(-1		).item()

       self.assertTrue(model.config.idalabel[predicted_class_idx]		,			"tabby, tabby cat"		)









"""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 _lowerCAmelCase       (   snake_case_			):







   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   List[Any]         =					tempfile.mkdtemp()
       snake_case					:   Union[str, Any]         =					8

       # DPR tok
       snake_case					:   str         =					[
           "[UNK]",
           "[CLS]",
           "[SEP]",
           "[PAD]",
           "[MASK]",
           "want",
           "##want",
           "##ed",
           "wa",
           "un",
           "runn",
           "##ing",
           ",",
           "low",
           "lowest",
       ]
       snake_case					:   Union[str, Any]         =					os.path.join(self.tmpdirname		,			"dpr_tokenizer"		)
       os.makedirs(UpperCamelCase__		,			exist_ok=UpperCamelCase__		)
       snake_case					:   Union[str, Any]         =					os.path.join(UpperCamelCase__		,			DPR_VOCAB_FILES_NAMES["vocab_file"]		)
       with open(self.vocab_file		,			"w"		,			encoding="utf-8"		) as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)

       # BART tok
       snake_case					:   Union[str, Any]         =					[
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
       ]
       snake_case					:   Tuple         =					dict(zip(UpperCamelCase__		,			range(len(UpperCamelCase__		)		)		)		)
       snake_case					:   List[Any]         =					["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       snake_case					:   Optional[Any]         =					{"unk_token": "<unk>"}

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







   def 				lowerCamelCase	(			self		)			->	DPRQuestionEncoderTokenizer:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	DPRContextEncoderTokenizer:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	BartTokenizer:

       '''simple docstring'''




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







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

       '''simple docstring'''




       shutil.rmtree(self.tmpdirname		)







   def 				lowerCamelCase	(			self		)			->	Dict:

       '''simple docstring'''




       snake_case					:   Dict         =					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		)			->	Union[str, Any]:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.get_dummy_dataset()
       snake_case					:   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:
           snake_case					:   int         =					dataset
           snake_case					:   int         =					RagRetriever(
               UpperCamelCase__		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		,			)
       return retriever







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   List[str]         =					self.get_dummy_dataset()
       snake_case					:   Any         =					RagConfig(
           retrieval_vector_size=self.retrieval_vector_size		,			question_encoder=DPRConfig().to_dict()		,			generator=BartConfig().to_dict()		,			index_name="custom"		,			)
       if from_disk:
           snake_case					:   str         =					os.path.join(self.tmpdirname		,			"dataset"		)
           snake_case					:   Any         =					os.path.join(self.tmpdirname		,			"index.faiss"		)
           dataset.get_index("embeddings"		).save(os.path.join(self.tmpdirname		,			"index.faiss"		)		)
           dataset.drop_index("embeddings"		)
           dataset.save_to_disk(os.path.join(self.tmpdirname		,			"dataset"		)		)
           del dataset
           snake_case					:   Any         =					RagRetriever(
               UpperCamelCase__		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		,			)
       else:
           snake_case					:   str         =					RagRetriever(
               UpperCamelCase__		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		,			index=CustomHFIndex(config.retrieval_vector_size		,			UpperCamelCase__		)		,			)
       return retriever







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   Optional[int]         =					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		)

       snake_case					:   Dict         =					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"		)		)

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

       snake_case					:   List[Any]         =					RagConfig(
           retrieval_vector_size=self.retrieval_vector_size		,			question_encoder=DPRConfig().to_dict()		,			generator=BartConfig().to_dict()		,			index_name="legacy"		,			index_path=self.tmpdirname		,			)
       snake_case					:   Dict         =					RagRetriever(
           UpperCamelCase__		,			question_encoder_tokenizer=self.get_dpr_tokenizer()		,			generator_tokenizer=self.get_bart_tokenizer()		)
       return retriever







   def 				lowerCamelCase	(			self		)			->	str:

       '''simple docstring'''




       snake_case					:   str         =					1
       snake_case					:   Any         =					self.get_dummy_canonical_hf_index_retriever()
       snake_case					:   Dict         =					np.array(
           [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
       snake_case      ,snake_case      ,snake_case					:   Any         =					retriever.retrieve(UpperCamelCase__		,			n_docs=UpperCamelCase__		)
       self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
       self.assertEqual(len(UpperCamelCase__		)		,			2		)
       self.assertEqual(sorted(doc_dicts[0]		)		,			["embeddings", "id", "text", "title"]		)
       self.assertEqual(len(doc_dicts[0]["id"]		)		,			UpperCamelCase__		)
       self.assertEqual(doc_dicts[0]["id"][0]		,			"1"		)  # max inner product is reached with second doc
       self.assertEqual(doc_dicts[1]["id"][0]		,			"0"		)  # max inner product is reached with first doc
       self.assertListEqual(doc_ids.tolist()		,			[[1], [0]]		)







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




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







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

       '''simple docstring'''




       snake_case					:   Dict         =					1
       snake_case					:   Union[str, Any]         =					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__		)
       snake_case					:   int         =					np.array(
           [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
       snake_case      ,snake_case      ,snake_case					:   Any         =					retriever.retrieve(UpperCamelCase__		,			n_docs=UpperCamelCase__		)
       self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
       self.assertEqual(len(UpperCamelCase__		)		,			2		)
       self.assertEqual(sorted(doc_dicts[0]		)		,			["embeddings", "id", "text", "title"]		)
       self.assertEqual(len(doc_dicts[0]["id"]		)		,			UpperCamelCase__		)
       self.assertEqual(doc_dicts[0]["id"][0]		,			"1"		)  # max inner product is reached with second doc
       self.assertEqual(doc_dicts[1]["id"][0]		,			"0"		)  # max inner product is reached with first doc
       self.assertListEqual(doc_ids.tolist()		,			[[1], [0]]		)







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

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       snake_case					:   List[str]         =					1
       snake_case					:   int         =					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__		)
       snake_case					:   int         =					np.array(
           [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
       snake_case      ,snake_case      ,snake_case					:   List[str]         =					retriever.retrieve(UpperCamelCase__		,			n_docs=UpperCamelCase__		)
       self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
       self.assertEqual(len(UpperCamelCase__		)		,			2		)
       self.assertEqual(sorted(doc_dicts[0]		)		,			["embeddings", "id", "text", "title"]		)
       self.assertEqual(len(doc_dicts[0]["id"]		)		,			UpperCamelCase__		)
       self.assertEqual(doc_dicts[0]["id"][0]		,			"1"		)  # max inner product is reached with second doc
       self.assertEqual(doc_dicts[1]["id"][0]		,			"0"		)  # max inner product is reached with first doc
       self.assertListEqual(doc_ids.tolist()		,			[[1], [0]]		)







   def 				lowerCamelCase	(			self		)			->	Tuple:

       '''simple docstring'''




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







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

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					1
       snake_case					:   Tuple         =					self.get_dummy_legacy_index_retriever()
       snake_case					:   str         =					np.array(
           [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
       snake_case      ,snake_case      ,snake_case					:   Any         =					retriever.retrieve(UpperCamelCase__		,			n_docs=UpperCamelCase__		)
       self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
       self.assertEqual(len(UpperCamelCase__		)		,			2		)
       self.assertEqual(sorted(doc_dicts[0]		)		,			["text", "title"]		)
       self.assertEqual(len(doc_dicts[0]["text"]		)		,			UpperCamelCase__		)
       self.assertEqual(doc_dicts[0]["text"][0]		,			"bar"		)  # max inner product is reached with second doc
       self.assertEqual(doc_dicts[1]["text"][0]		,			"foo"		)  # max inner product is reached with first doc
       self.assertListEqual(doc_ids.tolist()		,			[[1], [0]]		)







   def 				lowerCamelCase	(			self		)			->	List[str]:

       '''simple docstring'''




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







   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def 				lowerCamelCase	(			self		)			->	Optional[Any]:

       '''simple docstring'''




       import torch

       snake_case					:   str         =					1
       snake_case					:   Dict         =					self.get_dummy_canonical_hf_index_retriever()
       snake_case					:   str         =					[[5, 7], [10, 11]]
       snake_case					:   Any         =					np.array(
           [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
       snake_case					:   int         =					retriever(UpperCamelCase__		,			UpperCamelCase__		,			prefix=retriever.config.generator.prefix		,			n_docs=UpperCamelCase__		)
       snake_case      ,snake_case      ,snake_case					:   Dict         =					(
           out["context_input_ids"],
           out["context_attention_mask"],
           out["retrieved_doc_embeds"],
       )
       self.assertEqual(retrieved_doc_embeds.shape		,			(2, n_docs, self.retrieval_vector_size)		)
       self.assertIsInstance(UpperCamelCase__		,			UpperCamelCase__		)
       self.assertIsInstance(UpperCamelCase__		,			UpperCamelCase__		)
       self.assertIsInstance(UpperCamelCase__		,			np.ndarray		)

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







   @require_torch
   @require_tokenizers
   @require_sentencepiece
   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       snake_case					:   Tuple         =					self.get_dpr_ctx_encoder_tokenizer()
       snake_case					:   Union[str, Any]         =					1
       snake_case					:   List[str]         =					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__		)
       retriever.set_ctx_encoder_tokenizer(UpperCamelCase__		)

       snake_case					:   str         =					[[5, 7], [10, 11]]
       snake_case					:   str         =					np.array(
           [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]		,			dtype=np.floataa		)
       snake_case					:   Dict         =					retriever(UpperCamelCase__		,			UpperCamelCase__		,			prefix=retriever.config.generator.prefix		,			n_docs=UpperCamelCase__		)

       self.assertEqual(
           len(UpperCamelCase__		)		,			6		)  # check whether the retriever output consist of 6 attributes including tokenized docs
       self.assertEqual(
           all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask")		)		,			UpperCamelCase__		)  # check for doc token related keys in dictionary.





