



import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				__snake_case     :Optional[int]			       ='platform'
				import jax
				import jax.numpy as jnp
				import numpy as np

				from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel



@require_flax
class        lowerCAmelCase__					:
				A_   :     List[Any]								=     PegasusConfig
				A_   :     Optional[int]								=     {}
				A_   :     Dict								=     'gelu'


				def __init__(							self      :  List[str]						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  Optional[Any]=13						, __UpperCamelCase      :  Optional[int]=7						, __UpperCamelCase      :  List[Any]=True						, __UpperCamelCase      :  Tuple=False						, __UpperCamelCase      :  Optional[Any]=99						, __UpperCamelCase      :  List[Any]=32						, __UpperCamelCase      :  Dict=5						, __UpperCamelCase      :  Optional[Any]=4						, __UpperCamelCase      :  Optional[Any]=37						, __UpperCamelCase      :  List[Any]=0.1						, __UpperCamelCase      :  Dict=0.1						, __UpperCamelCase      :  Dict=20						, __UpperCamelCase      :  Tuple=2						, __UpperCamelCase      :  str=1						, __UpperCamelCase      :  Union[str, Any]=0						, )		->     Optional[int]:
											A      						=    parent
											A      						=    batch_size
											A      						=    seq_length
											A      						=    is_training
											A      						=    use_labels
											A      						=    vocab_size
											A      						=    hidden_size
											A      						=    num_hidden_layers
											A      						=    num_attention_heads
											A      						=    intermediate_size

											A      						=    hidden_dropout_prob
											A      						=    attention_probs_dropout_prob
											A      						=    max_position_embeddings
											A      						=    eos_token_id
											A      						=    pad_token_id
											A      						=    bos_token_id


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     int:
											A      						=    ids_tensor([self.batch_size, self.seq_length - 1]						, self.vocab_size       ).clip(3						, self.vocab_size       )
											A      						=    np.expand_dims(np.array([self.eos_token_id] * self.batch_size       )						, 1       )
											A      						=    np.concatenate([input_ids, eos_tensor]						, axis=1       )

											A      						=    ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size       )

											A      						=    self.config_cls(
											    vocab_size=self.vocab_size						, d_model=self.hidden_size						, encoder_layers=self.num_hidden_layers						, decoder_layers=self.num_hidden_layers						, encoder_attention_heads=self.num_attention_heads						, decoder_attention_heads=self.num_attention_heads						, encoder_ffn_dim=self.intermediate_size						, decoder_ffn_dim=self.intermediate_size						, dropout=self.hidden_dropout_prob						, attention_dropout=self.attention_probs_dropout_prob						, max_position_embeddings=self.max_position_embeddings						, eos_token_ids=[2]						, bos_token_id=self.bos_token_id						, pad_token_id=self.pad_token_id						, decoder_start_token_id=self.pad_token_id						, **self.config_updates						, )
											A      						=    prepare_pegasus_inputs_dict(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase       )
											return config, inputs_dict


				def __UpperCamelCase						(							self      :  Tuple						, __UpperCamelCase      :  Dict						, __UpperCamelCase      :  Union[str, Any]						, __UpperCamelCase      :  Optional[int]       )		->     List[Any]:
											A      						=    20
											A      						=    model_class_name(__UpperCamelCase       )

											A      						=    model.encode(inputs_dict['input_ids']       )

											A						,   A      						=    (
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

											A      						=    model.init_cache(decoder_input_ids.shape[0]						, __UpperCamelCase						, __UpperCamelCase       )
											A      						=    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						, dtype='i4'       )

											A      						=    jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]						, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						, )
											A      						=    model.decode(
											    decoder_input_ids[:, :-1]						, __UpperCamelCase						, decoder_attention_mask=__UpperCamelCase						, past_key_values=__UpperCamelCase						, decoder_position_ids=__UpperCamelCase						, )

											A      						=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						, dtype='i4'       )
											A      						=    model.decode(
											    decoder_input_ids[:, -1:]						, __UpperCamelCase						, decoder_attention_mask=__UpperCamelCase						, past_key_values=outputs_cache.past_key_values						, decoder_position_ids=__UpperCamelCase						, )

											A      						=    model.decode(__UpperCamelCase						, __UpperCamelCase       )

											A      						=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
											self.parent.assertTrue(diff < 1e-3						, msg=f'''Max diff is {diff}'''       )


				def __UpperCamelCase						(							self      :  Union[str, Any]						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  Tuple						, __UpperCamelCase      :  Dict       )		->     Union[str, Any]:
											A      						=    20
											A      						=    model_class_name(__UpperCamelCase       )

											A      						=    model.encode(inputs_dict['input_ids']       )

											A						,   A      						=    (
											    inputs_dict['decoder_input_ids'],
											    inputs_dict['decoder_attention_mask'],
											)

											A      						=    jnp.concatenate(
											    [
											        decoder_attention_mask,
											        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])       ),
											    ]						, axis=-1						, )

											A      						=    model.init_cache(decoder_input_ids.shape[0]						, __UpperCamelCase						, __UpperCamelCase       )
											A      						=    jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]						, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						, )

											A      						=    model.decode(
											    decoder_input_ids[:, :-1]						, __UpperCamelCase						, decoder_attention_mask=__UpperCamelCase						, past_key_values=__UpperCamelCase						, decoder_position_ids=__UpperCamelCase						, )
											A      						=    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						, dtype='i4'       )
											A      						=    model.decode(
											    decoder_input_ids[:, -1:]						, __UpperCamelCase						, past_key_values=outputs_cache.past_key_values						, decoder_attention_mask=__UpperCamelCase						, decoder_position_ids=__UpperCamelCase						, )

											A      						=    model.decode(__UpperCamelCase						, __UpperCamelCase						, decoder_attention_mask=__UpperCamelCase       )

											A      						=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
											self.parent.assertTrue(diff < 1e-3						, msg=f'''Max diff is {diff}'''       )






def  lowerCamelCase_	(      lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	Optional[Any]			,	lowerCAmelCase__	:	List[str]=None			,	lowerCAmelCase__	:	str=None			,	)       ->							Tuple:

							'''simple docstring'''






							if attention_mask is None:
														A      						=    np.not_equal(lowerCAmelCase__			,	config.pad_token_id				).astype(np.inta				)
							if decoder_attention_mask is None:
														A      						=    np.concatenate(
														    [
														        np.ones(decoder_input_ids[:, :1].shape			,	dtype=np.inta				),
														        np.not_equal(decoder_input_ids[:, 1:]			,	config.pad_token_id				).astype(np.inta				),
														    ]			,	axis=-1			,	)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							}



@require_flax
class        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     int								=     (
				    (
				        FlaxPegasusForConditionalGeneration,
				        FlaxPegasusModel,
				    )
				    if is_flax_available()
				    else ()
				)
				A_   :     Tuple								=     (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
				A_   :     Optional[int]								=     True
				A_   :     List[Any]								=     False
				A_   :     List[str]								=     False
				A_   :     Optional[int]								=     False


				def __UpperCamelCase						(							self      :  Dict       )		->     Union[str, Any]:
											A      						=    FlaxPegasusModelTester(self       )
											A      						=    ConfigTester(self						, config_class=__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     List[str]:
											self.config_tester.run_common_tests()


				def __UpperCamelCase						(							self      :  int       )		->     List[Any]:
											A						,   A      						=    self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Dict       )		->     List[Any]:
											A						,   A      						=    self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     List[Any]:
											A						,   A      						=    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__       ):
																									A      						=    self._prepare_for_class(__UpperCamelCase						, __UpperCamelCase       )
																									A      						=    model_class(__UpperCamelCase       )

																									@jax.jit
																									def encode_jitted(__UpperCamelCase      :  Union[str, Any]						, __UpperCamelCase      :  List[str]=None						, **__UpperCamelCase      :  List[Any]       ):
																																return model.encode(input_ids=__UpperCamelCase						, attention_mask=__UpperCamelCase       )

																									with self.subTest('JIT Enabled'       ):
																																A      						=    encode_jitted(**__UpperCamelCase       ).to_tuple()

																									with self.subTest('JIT Disabled'       ):
																																with jax.disable_jit():
																																							A      						=    encode_jitted(**__UpperCamelCase       ).to_tuple()

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


				def __UpperCamelCase						(							self      :  Any       )		->     Tuple:
											A						,   A      						=    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__       ):
																									A      						=    model_class(__UpperCamelCase       )
																									A      						=    model.encode(inputs_dict['input_ids']						, inputs_dict['attention_mask']       )

																									A      						=    {
																									    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																									    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																									    'encoder_outputs': encoder_outputs,
																									}

																									@jax.jit
																									def decode_jitted(__UpperCamelCase      :  Union[str, Any]						, __UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  Dict       ):
																																return model.decode(
																																    decoder_input_ids=__UpperCamelCase						, decoder_attention_mask=__UpperCamelCase						, encoder_outputs=__UpperCamelCase						, )

																									with self.subTest('JIT Enabled'       ):
																																A      						=    decode_jitted(**__UpperCamelCase       ).to_tuple()

																									with self.subTest('JIT Disabled'       ):
																																with jax.disable_jit():
																																							A      						=    decode_jitted(**__UpperCamelCase       ).to_tuple()

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


				@slow
				def __UpperCamelCase						(							self      :  List[Any]       )		->     List[str]:
											for model_class_name in self.all_model_classes:
																		A      						=    model_class_name.from_pretrained('google/pegasus-large'						, from_pt=__UpperCamelCase       )
																		A      						=    np.ones((1, 1)       )
																		A      						=    model(__UpperCamelCase       )
																		self.assertIsNotNone(__UpperCamelCase       )


				@slow
				def __UpperCamelCase						(							self      :  Tuple       )		->     Optional[int]:
											A      						=    FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum'       )
											A      						=    PegasusTokenizer.from_pretrained('google/pegasus-xsum'       )

											A      						=    [
											    ' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
											    ' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
											]

											A      						=    [
											    'California\'s largest electricity provider has turned off power to hundreds of thousands of customers.',
											    'Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.',
											]

											A      						=    tokenizer(__UpperCamelCase						, return_tensors='np'						, truncation=__UpperCamelCase						, max_length=512						, padding=__UpperCamelCase       )
											A      						=    model.generate(**__UpperCamelCase						, num_beams=2       ).sequences
											A      						=    tokenizer.batch_decode(__UpperCamelCase						, skip_special_tokens=__UpperCamelCase       )
											assert tgt_text == decoded


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 					lowercase							(			lowercase_     ):




				def        a				(   self			):
											snake_case_     							=       tempfile.mkdtemp()
											snake_case_     							=       8

											# DPR tok
											snake_case_     							=       [
											    '[UNK]',
											    '[CLS]',
											    '[SEP]',
											    '[PAD]',
											    '[MASK]',
											    'want',
											    '##want',
											    '##ed',
											    'wa',
											    'un',
											    'runn',
											    '##ing',
											    ',',
											    'low',
											    'lowest',
											]
											snake_case_     							=       os.path.join(self.tmpdirname ,  'dpr_tokenizer'			)
											os.makedirs(snake_case ,  exist_ok=snake_case			)
											snake_case_     							=       os.path.join(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
											snake_case_     							=       [
											    'l',
											    'o',
											    'w',
											    'e',
											    'r',
											    's',
											    't',
											    'i',
											    'd',
											    'n',
											    '\u0120',
											    '\u0120l',
											    '\u0120n',
											    '\u0120lo',
											    '\u0120low',
											    'er',
											    '\u0120lowest',
											    '\u0120newer',
											    '\u0120wider',
											    '<unk>',
											]
											snake_case_     							=       dict(zip(snake_case ,  range(len(snake_case			)			)			)			)
											snake_case_     							=       ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
											snake_case_     							=       {'unk_token': '<unk>'}

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




				def        a				(   self			):
											return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname ,  'dpr_tokenizer'			)			)




				def        a				(   self			):
											return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname ,  'dpr_tokenizer'			)			)




				def        a				(   self			):
											return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname ,  'bart_tokenizer'			)			)




				def        a				(   self			):
											shutil.rmtree(self.tmpdirname			)




				def        a				(   self			):
											snake_case_     							=       Dataset.from_dict(
											    {
											        'id': ['0', '1'],
											        'text': ['foo', 'bar'],
											        'title': ['Foo', 'Bar'],
											        'embeddings': [np.ones(self.retrieval_vector_size			), 2 * np.ones(self.retrieval_vector_size			)],
											    }			)
											dataset.add_faiss_index('embeddings' ,  string_factory='Flat' ,  metric_type=faiss.METRIC_INNER_PRODUCT			)
											return dataset




				def        a				(   self			):
											snake_case_     							=       self.get_dummy_dataset()
											snake_case_     							=       RagConfig(
											    retrieval_vector_size=self.retrieval_vector_size ,  question_encoder=DPRConfig().to_dict() ,  generator=BartConfig().to_dict() ,  )
											with patch('transformers.models.rag.retrieval_rag.load_dataset'			) as mock_load_dataset:
																		snake_case_     							=       dataset
																		snake_case_     							=       RagRetriever(
																		    snake_case ,  question_encoder_tokenizer=self.get_dpr_tokenizer() ,  generator_tokenizer=self.get_bart_tokenizer() ,  )
											return retriever




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self.get_dummy_dataset()
											snake_case_     							=       RagConfig(
											    retrieval_vector_size=self.retrieval_vector_size ,  question_encoder=DPRConfig().to_dict() ,  generator=BartConfig().to_dict() ,  index_name='custom' ,  )
											if from_disk:
																		snake_case_     							=       os.path.join(self.tmpdirname ,  'dataset'			)
																		snake_case_     							=       os.path.join(self.tmpdirname ,  'index.faiss'			)
																		dataset.get_index('embeddings'			).save(os.path.join(self.tmpdirname ,  'index.faiss'			)			)
																		dataset.drop_index('embeddings'			)
																		dataset.save_to_disk(os.path.join(self.tmpdirname ,  'dataset'			)			)
																		del dataset
																		snake_case_     							=       RagRetriever(
																		    snake_case ,  question_encoder_tokenizer=self.get_dpr_tokenizer() ,  generator_tokenizer=self.get_bart_tokenizer() ,  )
											else:
																		snake_case_     							=       RagRetriever(
																		    snake_case ,  question_encoder_tokenizer=self.get_dpr_tokenizer() ,  generator_tokenizer=self.get_bart_tokenizer() ,  index=CustomHFIndex(config.retrieval_vector_size ,  snake_case			) ,  )
											return retriever




				def        a				(   self			):
											snake_case_     							=       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_     							=       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_     							=       os.path.join(self.tmpdirname ,  'psgs_w100.tsv.pkl'			)
											snake_case_     							=       {sample['id']: [sample['text'], sample['title']] for sample in dataset}
											pickle.dump(snake_case ,  open(snake_case ,  'wb'			)			)

											snake_case_     							=       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_     							=       RagRetriever(
											    snake_case ,  question_encoder_tokenizer=self.get_dpr_tokenizer() ,  generator_tokenizer=self.get_bart_tokenizer()			)
											return retriever




				def        a				(   self			):
											snake_case_     							=       1
											snake_case_     							=       self.get_dummy_canonical_hf_index_retriever()
											snake_case_     							=       np.array(
											    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
											snake_case_							,       snake_case_							,       snake_case_     							=       retriever.retrieve(snake_case ,  n_docs=snake_case			)
											self.assertEqual(retrieved_doc_embeds.shape ,  (2, n_docs, self.retrieval_vector_size)			)
											self.assertEqual(len(snake_case			) ,  2			)
											self.assertEqual(sorted(doc_dicts[0]			) ,  ['embeddings', 'id', 'text', 'title']			)
											self.assertEqual(len(doc_dicts[0]['id']			) ,  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        a				(   self			):
											snake_case_     							=       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_     							=       self.get_dummy_dataset()
																									retriever.save_pretrained(snake_case			)
																									snake_case_     							=       RagRetriever.from_pretrained(snake_case			)
																									self.assertIsInstance(snake_case ,  snake_case			)
																									snake_case_     							=       np.array(
																									    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
																									snake_case_     							=       retriever.retrieve(snake_case ,  n_docs=1			)
																									self.assertTrue(out is not None			)




				def        a				(   self			):
											snake_case_     							=       1
											snake_case_     							=       self.get_dummy_custom_hf_index_retriever(from_disk=snake_case			)
											snake_case_     							=       np.array(
											    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
											snake_case_							,       snake_case_							,       snake_case_     							=       retriever.retrieve(snake_case ,  n_docs=snake_case			)
											self.assertEqual(retrieved_doc_embeds.shape ,  (2, n_docs, self.retrieval_vector_size)			)
											self.assertEqual(len(snake_case			) ,  2			)
											self.assertEqual(sorted(doc_dicts[0]			) ,  ['embeddings', 'id', 'text', 'title']			)
											self.assertEqual(len(doc_dicts[0]['id']			) ,  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        a				(   self			):
											snake_case_     							=       self.get_dummy_custom_hf_index_retriever(from_disk=snake_case			)
											with tempfile.TemporaryDirectory() as tmp_dirname:
																		retriever.save_pretrained(snake_case			)
																		snake_case_     							=       RagRetriever.from_pretrained(snake_case			)
																		self.assertIsInstance(snake_case ,  snake_case			)
																		snake_case_     							=       np.array(
																		    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
																		snake_case_     							=       retriever.retrieve(snake_case ,  n_docs=1			)
																		self.assertTrue(out is not None			)




				def        a				(   self			):
											snake_case_     							=       1
											snake_case_     							=       self.get_dummy_custom_hf_index_retriever(from_disk=snake_case			)
											snake_case_     							=       np.array(
											    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
											snake_case_							,       snake_case_							,       snake_case_     							=       retriever.retrieve(snake_case ,  n_docs=snake_case			)
											self.assertEqual(retrieved_doc_embeds.shape ,  (2, n_docs, self.retrieval_vector_size)			)
											self.assertEqual(len(snake_case			) ,  2			)
											self.assertEqual(sorted(doc_dicts[0]			) ,  ['embeddings', 'id', 'text', 'title']			)
											self.assertEqual(len(doc_dicts[0]['id']			) ,  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        a				(   self			):
											snake_case_     							=       self.get_dummy_custom_hf_index_retriever(from_disk=snake_case			)
											with tempfile.TemporaryDirectory() as tmp_dirname:
																		retriever.save_pretrained(snake_case			)
																		snake_case_     							=       RagRetriever.from_pretrained(snake_case			)
																		self.assertIsInstance(snake_case ,  snake_case			)
																		snake_case_     							=       np.array(
																		    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
																		snake_case_     							=       retriever.retrieve(snake_case ,  n_docs=1			)
																		self.assertTrue(out is not None			)




				def        a				(   self			):
											snake_case_     							=       1
											snake_case_     							=       self.get_dummy_legacy_index_retriever()
											snake_case_     							=       np.array(
											    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
											snake_case_							,       snake_case_							,       snake_case_     							=       retriever.retrieve(snake_case ,  n_docs=snake_case			)
											self.assertEqual(retrieved_doc_embeds.shape ,  (2, n_docs, self.retrieval_vector_size)			)
											self.assertEqual(len(snake_case			) ,  2			)
											self.assertEqual(sorted(doc_dicts[0]			) ,  ['text', 'title']			)
											self.assertEqual(len(doc_dicts[0]['text']			) ,  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        a				(   self			):
											snake_case_     							=       self.get_dummy_legacy_index_retriever()
											with tempfile.TemporaryDirectory() as tmp_dirname:
																		retriever.save_pretrained(snake_case			)
																		snake_case_     							=       RagRetriever.from_pretrained(snake_case			)
																		self.assertIsInstance(snake_case ,  snake_case			)
																		snake_case_     							=       np.array(
																		    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
																		snake_case_     							=       retriever.retrieve(snake_case ,  n_docs=1			)
																		self.assertTrue(out is not None			)




				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def        a				(   self			):
											import torch

											snake_case_     							=       1
											snake_case_     							=       self.get_dummy_canonical_hf_index_retriever()
											snake_case_     							=       [[5, 7], [10, 11]]
											snake_case_     							=       np.array(
											    [np.ones(self.retrieval_vector_size			), -np.ones(self.retrieval_vector_size			)] ,  dtype=np.floataa			)
											snake_case_     							=       retriever(snake_case ,  snake_case ,  prefix=retriever.config.generator.prefix ,  n_docs=snake_case			)
											snake_case_							,       snake_case_							,       snake_case_     							=       (
											    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(snake_case ,  snake_case			)
											self.assertIsInstance(snake_case ,  snake_case			)
											self.assertIsInstance(snake_case ,  np.ndarray			)

											snake_case_     							=       retriever(
											    snake_case ,  snake_case ,  prefix=retriever.config.generator.prefix ,  n_docs=snake_case ,  return_tensors='pt' ,  )
											snake_case_							,       snake_case_							,       snake_case_							,       snake_case_     							=       (  # 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(snake_case ,  torch.Tensor			)
											self.assertIsInstance(snake_case ,  torch.Tensor			)
											self.assertIsInstance(snake_case ,  torch.Tensor			)




				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def        a				(   self			):
											snake_case_     							=       self.get_dpr_ctx_encoder_tokenizer()
											snake_case_     							=       1
											snake_case_     							=       self.get_dummy_custom_hf_index_retriever(from_disk=snake_case			)
											retriever.set_ctx_encoder_tokenizer(snake_case			)

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

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




