






'''simple docstring'''







import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
		import jax

		from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel





class 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def __init__( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Any=13  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=30  ,						__SCREAMING_SNAKE_CASE      :			str=2  ,						__SCREAMING_SNAKE_CASE      :			Any=3  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=True  ,						__SCREAMING_SNAKE_CASE      :			int=True  ,						__SCREAMING_SNAKE_CASE      :			List[str]=32  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=5  ,						__SCREAMING_SNAKE_CASE      :			Any=4  ,						__SCREAMING_SNAKE_CASE      :			Tuple=37  ,						__SCREAMING_SNAKE_CASE      :			str="gelu"  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]=0.1  ,						__SCREAMING_SNAKE_CASE      :			List[str]=0.1  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]=10  ,						__SCREAMING_SNAKE_CASE      :			str=0.02  ,						)			->			Optional[Any]:
							a_		:  Optional[Any]											=      parent
							a_		:  Optional[int]											=      batch_size
							a_		:  int											=      image_size
							a_		:  int											=      patch_size
							a_		:  Optional[Any]											=      num_channels
							a_		:  Any											=      is_training
							a_		:  Union[str, Any]											=      use_labels
							a_		:  List[Any]											=      hidden_size
							a_		:  Optional[int]											=      num_hidden_layers
							a_		:  Union[str, Any]											=      num_attention_heads
							a_		:  List[str]											=      intermediate_size
							a_		:  Dict											=      hidden_act
							a_		:  Optional[Any]											=      hidden_dropout_prob
							a_		:  Optional[int]											=      attention_probs_dropout_prob
							a_		:  List[str]											=      type_sequence_label_size
							a_		:  Union[str, Any]											=      initializer_range

							# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							a_		:  Tuple											=      (image_size // patch_size) ** 2
							a_		:  Optional[Any]											=      num_patches + 1






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Union[str, Any]:
							a_		:  List[str]											=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

							a_		:  Any											=      ViTConfig(
							    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=__SCREAMING_SNAKE_CASE  ,						initializer_range=self.initializer_range  ,						)

							return config, pixel_values






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			Dict					)			->			Optional[Any]:
							a_		:  Tuple											=      FlaxViTModel(config=__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[Any]											=      model(__SCREAMING_SNAKE_CASE					)
							# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
							a_		:  str											=      (self.image_size, self.image_size)
							a_		:  Union[str, Any]											=      (self.patch_size, self.patch_size)
							a_		:  int											=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
							self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, num_patches + 1, self.hidden_size)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int  ,						__SCREAMING_SNAKE_CASE      :			Any  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			Union[str, Any]:
							a_		:  Dict											=      self.type_sequence_label_size
							a_		:  Tuple											=      FlaxViTForImageClassification(config=__SCREAMING_SNAKE_CASE					)
							a_		:  List[str]											=      model(__SCREAMING_SNAKE_CASE					)
							self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)					)

							# test greyscale images
							a_		:  int											=      1
							a_		:  Optional[Any]											=      FlaxViTForImageClassification(__SCREAMING_SNAKE_CASE					)

							a_		:  List[Any]											=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
							a_		:  Optional[int]											=      model(__SCREAMING_SNAKE_CASE					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			int:
							a_		:  str											=      self.prepare_config_and_inputs()
							(
							    (
							    a_
							)				,			(
							    a_
							)				,			
							)		:  Tuple											=      config_and_inputs
							a_		:  Dict											=      {'''pixel_values''': pixel_values}
							return config, inputs_dict





@require_flax
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			None:
							a_		:  List[Any]											=      FlaxViTModelTester(self					)
							a_		:  List[str]											=      ConfigTester(self  ,						config_class=__SCREAMING_SNAKE_CASE  ,						has_text_modality=__SCREAMING_SNAKE_CASE  ,						hidden_size=37					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			List[Any]:
							self.config_tester.run_common_tests()






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Any:
							a_		:  int											=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			List[Any]:
							a_		:  Any											=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Union[str, Any]:
							a_				,			a_		:  List[Any]											=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											a_		:  Dict											=      model_class(__SCREAMING_SNAKE_CASE					)
											a_		:  Any											=      inspect.signature(model.__call__					)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											a_		:  Optional[int]											=      [*signature.parameters.keys()]

											a_		:  Union[str, Any]											=      ['''pixel_values''']
											self.assertListEqual(arg_names[:1]  ,						__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Tuple:
							a_				,			a_		:  Tuple											=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__					):
															a_		:  str											=      self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
															a_		:  int											=      model_class(__SCREAMING_SNAKE_CASE					)

															@jax.jit
															def model_jitted(__SCREAMING_SNAKE_CASE      :			Tuple  ,						**__SCREAMING_SNAKE_CASE      :			int					):
																			return model(pixel_values=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)

															with self.subTest('''JIT Enabled'''					):
																			a_		:  int											=      model_jitted(**__SCREAMING_SNAKE_CASE					).to_tuple()

															with self.subTest('''JIT Disabled'''					):
																			with jax.disable_jit():
																							a_		:  Optional[int]											=      model_jitted(**__SCREAMING_SNAKE_CASE					).to_tuple()

															self.assertEqual(len(__SCREAMING_SNAKE_CASE					)  ,						len(__SCREAMING_SNAKE_CASE					)					)
															for jitted_output, output in zip(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					):
																			self.assertEqual(jitted_output.shape  ,						output.shape					)







			@slow
			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			List[str]:
							for model_class_name in self.all_model_classes:
											a_		:  Union[str, Any]											=      model_class_name.from_pretrained('''google/vit-base-patch16-224'''					)
											a_		:  Optional[int]											=      model(np.ones((1, 3, 224, 224)					)					)
											self.assertIsNotNone(__SCREAMING_SNAKE_CASE					)












'''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 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Dict:
							a_		:  Union[str, Any]											=      tempfile.mkdtemp()
							a_		:  Union[str, Any]											=      8

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

							# BART tok
							a_		:  Union[str, Any]											=      [
							    '''l''',
							    '''o''',
							    '''w''',
							    '''e''',
							    '''r''',
							    '''s''',
							    '''t''',
							    '''i''',
							    '''d''',
							    '''n''',
							    '''\u0120''',
							    '''\u0120l''',
							    '''\u0120n''',
							    '''\u0120lo''',
							    '''\u0120low''',
							    '''er''',
							    '''\u0120lowest''',
							    '''\u0120newer''',
							    '''\u0120wider''',
							    '''<unk>''',
							]
							a_		:  int											=      dict(zip(__SCREAMING_SNAKE_CASE  ,						range(len(__SCREAMING_SNAKE_CASE					)					)					)					)
							a_		:  int											=      ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
							a_		:  Optional[int]											=      {'''unk_token''': '''<unk>'''}

							a_		:  List[str]											=      os.path.join(self.tmpdirname  ,						'''bart_tokenizer'''					)
							os.makedirs(__SCREAMING_SNAKE_CASE  ,						exist_ok=__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      os.path.join(__SCREAMING_SNAKE_CASE  ,						BART_VOCAB_FILES_NAMES['''vocab_file''']					)
							a_		:  int											=      os.path.join(__SCREAMING_SNAKE_CASE  ,						BART_VOCAB_FILES_NAMES['''merges_file''']					)
							with open(self.vocab_file  ,						'''w'''  ,						encoding='''utf-8'''					) as fp:
											fp.write(json.dumps(__SCREAMING_SNAKE_CASE					) + '''\n'''					)
							with open(self.merges_file  ,						'''w'''  ,						encoding='''utf-8'''					) as fp:
											fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE					)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			DPRQuestionEncoderTokenizer:
							return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,						'''dpr_tokenizer'''					)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			DPRContextEncoderTokenizer:
							return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,						'''dpr_tokenizer'''					)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			BartTokenizer:
							return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,						'''bart_tokenizer'''					)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			Union[str, Any]:
							shutil.rmtree(self.tmpdirname					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Union[str, Any]:
							a_		:  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 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Tuple:
							a_		:  List[str]											=      self.get_dummy_dataset()
							a_		:  Tuple											=      RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size  ,						question_encoder=DPRConfig().to_dict()  ,						generator=BartConfig().to_dict()  ,						)
							with patch('''transformers.models.rag.retrieval_rag.load_dataset'''					) as mock_load_dataset:
											a_		:  Tuple											=      dataset
											a_		:  Any											=      RagRetriever(
											    __SCREAMING_SNAKE_CASE  ,						question_encoder_tokenizer=self.get_dpr_tokenizer()  ,						generator_tokenizer=self.get_bart_tokenizer()  ,						)
							return retriever






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			bool					)			->			Dict:
							a_		:  Dict											=      self.get_dummy_dataset()
							a_		:  Dict											=      RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size  ,						question_encoder=DPRConfig().to_dict()  ,						generator=BartConfig().to_dict()  ,						index_name='''custom'''  ,						)
							if from_disk:
											a_		:  Optional[int]											=      os.path.join(self.tmpdirname  ,						'''dataset'''					)
											a_		:  str											=      os.path.join(self.tmpdirname  ,						'''index.faiss'''					)
											dataset.get_index('''embeddings'''					).save(os.path.join(self.tmpdirname  ,						'''index.faiss'''					)					)
											dataset.drop_index('''embeddings'''					)
											dataset.save_to_disk(os.path.join(self.tmpdirname  ,						'''dataset'''					)					)
											del dataset
											a_		:  int											=      RagRetriever(
											    __SCREAMING_SNAKE_CASE  ,						question_encoder_tokenizer=self.get_dpr_tokenizer()  ,						generator_tokenizer=self.get_bart_tokenizer()  ,						)
							else:
											a_		:  Optional[Any]											=      RagRetriever(
											    __SCREAMING_SNAKE_CASE  ,						question_encoder_tokenizer=self.get_dpr_tokenizer()  ,						generator_tokenizer=self.get_bart_tokenizer()  ,						index=CustomHFIndex(config.retrieval_vector_size  ,						__SCREAMING_SNAKE_CASE					)  ,						)
							return retriever






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Optional[int]:
							a_		:  str											=      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					)

							a_		:  Optional[int]											=      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'''					)					)

							a_		:  Union[str, Any]											=      os.path.join(self.tmpdirname  ,						'''psgs_w100.tsv.pkl'''					)
							a_		:  Dict											=      {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
							pickle.dump(__SCREAMING_SNAKE_CASE  ,						open(__SCREAMING_SNAKE_CASE  ,						'''wb'''					)					)

							a_		:  Optional[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  ,						)
							a_		:  int											=      RagRetriever(
							    __SCREAMING_SNAKE_CASE  ,						question_encoder_tokenizer=self.get_dpr_tokenizer()  ,						generator_tokenizer=self.get_bart_tokenizer()					)
							return retriever






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Dict:
							a_		:  Optional[Any]											=      1
							a_		:  Dict											=      self.get_dummy_canonical_hf_index_retriever()
							a_		:  Tuple											=      np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
							a_				,			a_				,			a_		:  str											=      retriever.retrieve(__SCREAMING_SNAKE_CASE  ,						n_docs=__SCREAMING_SNAKE_CASE					)
							self.assertEqual(retrieved_doc_embeds.shape  ,						(2, n_docs, self.retrieval_vector_size)					)
							self.assertEqual(len(__SCREAMING_SNAKE_CASE					)  ,						2					)
							self.assertEqual(sorted(doc_dicts[0]					)  ,						['''embeddings''', '''id''', '''text''', '''title''']					)
							self.assertEqual(len(doc_dicts[0]['''id''']					)  ,						__SCREAMING_SNAKE_CASE					)
							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 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Dict:
							a_		:  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:
															a_		:  List[str]											=      self.get_dummy_dataset()
															retriever.save_pretrained(__SCREAMING_SNAKE_CASE					)
															a_		:  Optional[Any]											=      RagRetriever.from_pretrained(__SCREAMING_SNAKE_CASE					)
															self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
															a_		:  Union[str, Any]											=      np.array(
															    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
															a_		:  List[str]											=      retriever.retrieve(__SCREAMING_SNAKE_CASE  ,						n_docs=1					)
															self.assertTrue(out is not None					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			int:
							a_		:  Union[str, Any]											=      1
							a_		:  Optional[Any]											=      self.get_dummy_custom_hf_index_retriever(from_disk=__SCREAMING_SNAKE_CASE					)
							a_		:  List[Any]											=      np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
							a_				,			a_				,			a_		:  Any											=      retriever.retrieve(__SCREAMING_SNAKE_CASE  ,						n_docs=__SCREAMING_SNAKE_CASE					)
							self.assertEqual(retrieved_doc_embeds.shape  ,						(2, n_docs, self.retrieval_vector_size)					)
							self.assertEqual(len(__SCREAMING_SNAKE_CASE					)  ,						2					)
							self.assertEqual(sorted(doc_dicts[0]					)  ,						['''embeddings''', '''id''', '''text''', '''title''']					)
							self.assertEqual(len(doc_dicts[0]['''id''']					)  ,						__SCREAMING_SNAKE_CASE					)
							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 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			List[str]:
							a_		:  Dict											=      self.get_dummy_custom_hf_index_retriever(from_disk=__SCREAMING_SNAKE_CASE					)
							with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(__SCREAMING_SNAKE_CASE					)
											a_		:  List[str]											=      RagRetriever.from_pretrained(__SCREAMING_SNAKE_CASE					)
											self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
											a_		:  Tuple											=      np.array(
											    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
											a_		:  Dict											=      retriever.retrieve(__SCREAMING_SNAKE_CASE  ,						n_docs=1					)
											self.assertTrue(out is not None					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Optional[int]:
							a_		:  Union[str, Any]											=      1
							a_		:  str											=      self.get_dummy_custom_hf_index_retriever(from_disk=__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
							a_				,			a_				,			a_		:  Tuple											=      retriever.retrieve(__SCREAMING_SNAKE_CASE  ,						n_docs=__SCREAMING_SNAKE_CASE					)
							self.assertEqual(retrieved_doc_embeds.shape  ,						(2, n_docs, self.retrieval_vector_size)					)
							self.assertEqual(len(__SCREAMING_SNAKE_CASE					)  ,						2					)
							self.assertEqual(sorted(doc_dicts[0]					)  ,						['''embeddings''', '''id''', '''text''', '''title''']					)
							self.assertEqual(len(doc_dicts[0]['''id''']					)  ,						__SCREAMING_SNAKE_CASE					)
							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 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			Dict:
							a_		:  List[str]											=      self.get_dummy_custom_hf_index_retriever(from_disk=__SCREAMING_SNAKE_CASE					)
							with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(__SCREAMING_SNAKE_CASE					)
											a_		:  Any											=      RagRetriever.from_pretrained(__SCREAMING_SNAKE_CASE					)
											self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
											a_		:  Tuple											=      np.array(
											    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
											a_		:  Dict											=      retriever.retrieve(__SCREAMING_SNAKE_CASE  ,						n_docs=1					)
											self.assertTrue(out is not None					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			Any:
							a_		:  str											=      1
							a_		:  Tuple											=      self.get_dummy_legacy_index_retriever()
							a_		:  Union[str, Any]											=      np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
							a_				,			a_				,			a_		:  Any											=      retriever.retrieve(__SCREAMING_SNAKE_CASE  ,						n_docs=__SCREAMING_SNAKE_CASE					)
							self.assertEqual(retrieved_doc_embeds.shape  ,						(2, n_docs, self.retrieval_vector_size)					)
							self.assertEqual(len(__SCREAMING_SNAKE_CASE					)  ,						2					)
							self.assertEqual(sorted(doc_dicts[0]					)  ,						['''text''', '''title''']					)
							self.assertEqual(len(doc_dicts[0]['''text''']					)  ,						__SCREAMING_SNAKE_CASE					)
							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 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			Optional[int]:
							a_		:  List[str]											=      self.get_dummy_legacy_index_retriever()
							with tempfile.TemporaryDirectory() as tmp_dirname:
											retriever.save_pretrained(__SCREAMING_SNAKE_CASE					)
											a_		:  Any											=      RagRetriever.from_pretrained(__SCREAMING_SNAKE_CASE					)
											self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
											a_		:  List[str]											=      np.array(
											    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
											a_		:  Optional[Any]											=      retriever.retrieve(__SCREAMING_SNAKE_CASE  ,						n_docs=1					)
											self.assertTrue(out is not None					)






			@require_torch
			@require_tokenizers
			@require_sentencepiece
			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			List[str]:
							import torch

							a_		:  Any											=      1
							a_		:  List[Any]											=      self.get_dummy_canonical_hf_index_retriever()
							a_		:  Union[str, Any]											=      [[5, 7], [10, 11]]
							a_		:  Optional[int]											=      np.array(
							    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]  ,						dtype=np.floataa					)
							a_		:  str											=      retriever(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						prefix=retriever.config.generator.prefix  ,						n_docs=__SCREAMING_SNAKE_CASE					)
							a_				,			a_				,			a_		:  List[str]											=      (
							    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(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
							self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
							self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						np.ndarray					)

							a_		:  Any											=      retriever(
							    __SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						prefix=retriever.config.generator.prefix  ,						n_docs=__SCREAMING_SNAKE_CASE  ,						return_tensors='''pt'''  ,						)
							a_				,			a_				,			a_				,			a_		:  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(__SCREAMING_SNAKE_CASE  ,						torch.Tensor					)
							self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						torch.Tensor					)
							self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,						torch.Tensor					)







			@require_torch
			@require_tokenizers
			@require_sentencepiece
			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Any:
							a_		:  str											=      self.get_dpr_ctx_encoder_tokenizer()
							a_		:  Tuple											=      1
							a_		:  Any											=      self.get_dummy_custom_hf_index_retriever(from_disk=__SCREAMING_SNAKE_CASE					)
							retriever.set_ctx_encoder_tokenizer(__SCREAMING_SNAKE_CASE					)

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

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




