




'''simple docstring'''

import pickle

import numpy as np
from matplotlib import pyplot as plt






class 						a				:


				def __init__(		self    :					Optional[int]       ,				lowercase_    :					str       ,				lowercase_    :					Any       ,				lowercase_    :					Dict       ,				lowercase_    :					List[str]       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					Optional[Any]=0.2       ,				lowercase_    :					Optional[Any]=0.2					):
									snake_case_									=				bp_numa
									snake_case_									=				bp_numa
									snake_case_									=				bp_numa
									snake_case_									=				conva_get[:2]
									snake_case_									=				conva_get[2]
									snake_case_									=				size_pa
									snake_case_									=				rate_w
									snake_case_									=				rate_t
									snake_case_									=				[
									    np.mat(-1 * np.random.rand(self.conva[0]       ,				self.conva[0]					) + 0.5					)
									    for i in range(self.conva[1]					)
									]
									snake_case_									=				np.mat(-1 * np.random.rand(self.num_bpa       ,				self.num_bpa					) + 0.5					)
									snake_case_									=				np.mat(-1 * np.random.rand(self.num_bpa       ,				self.num_bpa					) + 0.5					)
									snake_case_									=				-2 * np.random.rand(self.conva[1]					) + 1
									snake_case_									=				-2 * np.random.rand(self.num_bpa					) + 1
									snake_case_									=				-2 * np.random.rand(self.num_bpa					) + 1


				def   A_ (		self    :					List[str]       ,				lowercase_    :					Tuple					):
									# save model dict with pickle
									snake_case_									=				{
									    '''num_bp1''': self.num_bpa,
									    '''num_bp2''': self.num_bpa,
									    '''num_bp3''': self.num_bpa,
									    '''conv1''': self.conva,
									    '''step_conv1''': self.step_conva,
									    '''size_pooling1''': self.size_poolinga,
									    '''rate_weight''': self.rate_weight,
									    '''rate_thre''': self.rate_thre,
									    '''w_conv1''': self.w_conva,
									    '''wkj''': self.wkj,
									    '''vji''': self.vji,
									    '''thre_conv1''': self.thre_conva,
									    '''thre_bp2''': self.thre_bpa,
									    '''thre_bp3''': self.thre_bpa,
									}
									with open(lowercase_       ,				'''wb'''					) as f:
														pickle.dump(lowercase_       ,				lowercase_					)

									print(F"Model saved： {save_path}"					)


				@classmethod
				def   A_ (		cls    :					List[Any]       ,				lowercase_    :					Tuple					):
									# read saved model
									with open(lowercase_       ,				'''rb'''					) as f:
														snake_case_									=				pickle.load(lowercase_					)  # noqa: S301

									snake_case_									=				model_dic.get('''conv1'''					)
									conv_get.append(model_dic.get('''step_conv1'''					)					)
									snake_case_									=				model_dic.get('''size_pooling1'''					)
									snake_case_									=				model_dic.get('''num_bp1'''					)
									snake_case_									=				model_dic.get('''num_bp2'''					)
									snake_case_									=				model_dic.get('''num_bp3'''					)
									snake_case_									=				model_dic.get('''rate_weight'''					)
									snake_case_									=				model_dic.get('''rate_thre'''					)
									# create model instance
									snake_case_									=				CNN(lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_					)
									# modify model parameter
									snake_case_									=				model_dic.get('''w_conv1'''					)
									snake_case_									=				model_dic.get('''wkj'''					)
									snake_case_									=				model_dic.get('''vji'''					)
									snake_case_									=				model_dic.get('''thre_conv1'''					)
									snake_case_									=				model_dic.get('''thre_bp2'''					)
									snake_case_									=				model_dic.get('''thre_bp3'''					)
									return conv_ins


				def   A_ (		self    :					Any       ,				lowercase_    :					Optional[Any]					):
									return 1 / (1 + np.exp(-1 * x					))


				def   A_ (		self    :					Any       ,				lowercase_    :					Dict					):
									return round(lowercase_       ,				3					)


				def   A_ (		self    :					int       ,				lowercase_    :					Dict       ,				lowercase_    :					List[Any]       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Tuple       ,				lowercase_    :					List[str]					):
									# convolution process
									snake_case_									=				convs[0]
									snake_case_									=				convs[1]
									snake_case_									=				np.shape(lowercase_					)[0]
									# get the data slice of original image data, data_focus
									snake_case_									=				[]
									for i_focus in range(0       ,				size_data - size_conv + 1       ,				lowercase_					):
														for j_focus in range(0       ,				size_data - size_conv + 1       ,				lowercase_					):
																			snake_case_									=				data[
																			    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
																			]
																			data_focus.append(lowercase_					)
        # calculate the feature map of every single kernel, and saved as list of matrix
									snake_case_									=				[]
									snake_case_									=				int((size_data - size_conv) / conv_step + 1					)
									for i_map in range(lowercase_					):
														snake_case_									=				[]
														for i_focus in range(len(lowercase_					)					):
																			snake_case_									=				(
																			    np.sum(np.multiply(data_focus[i_focus]       ,				w_convs[i_map]					)					)
																			    - thre_convs[i_map]
																			)
																			featuremap.append(self.sig(lowercase_					)					)
														snake_case_									=				np.asmatrix(lowercase_					).reshape(
														    lowercase_       ,				lowercase_					)
														data_featuremap.append(lowercase_					)

									# expanding the data slice to One dimenssion
									snake_case_									=				[]
									for each_focus in data_focus:
														focusa_list.extend(self.Expand_Mat(lowercase_					)					)
									snake_case_									=				np.asarray(lowercase_					)
									return focus_list, data_featuremap


				def   A_ (		self    :					str       ,				lowercase_    :					Any       ,				lowercase_    :					Dict       ,				lowercase_    :					Tuple="average_pool"					):
									# pooling process
									snake_case_									=				len(featuremaps[0]					)
									snake_case_									=				int(size_map / size_pooling					)
									snake_case_									=				[]
									for i_map in range(len(lowercase_					)					):
														snake_case_									=				featuremaps[i_map]
														snake_case_									=				[]
														for i_focus in range(0       ,				lowercase_       ,				lowercase_					):
																			for j_focus in range(0       ,				lowercase_       ,				lowercase_					):
																								snake_case_									=				feature_map[
																								    i_focus : i_focus + size_pooling,
																								    j_focus : j_focus + size_pooling,
																								]
																								if pooling_type == "average_pool":
																													# average pooling
																													map_pooled.append(np.average(lowercase_					)					)
																								elif pooling_type == "max_pooling":
																													# max pooling
																													map_pooled.append(np.max(lowercase_					)					)
														snake_case_									=				np.asmatrix(lowercase_					).reshape(lowercase_       ,				lowercase_					)
														featuremap_pooled.append(lowercase_					)
									return featuremap_pooled


				def   A_ (		self    :					Dict       ,				lowercase_    :					Any					):
									# expanding three dimension data to one dimension list
									snake_case_									=				[]
									for i in range(len(lowercase_					)					):
														snake_case_									=				np.shape(data[i]					)
														snake_case_									=				data[i].reshape(1       ,				shapes[0] * shapes[1]					)
														snake_case_									=				data_listed.getA().tolist()[0]
														data_expanded.extend(lowercase_					)
									snake_case_									=				np.asarray(lowercase_					)
									return data_expanded


				def   A_ (		self    :					Any       ,				lowercase_    :					Any					):
									# expanding matrix to one dimension list
									snake_case_									=				np.asarray(lowercase_					)
									snake_case_									=				np.shape(lowercase_					)
									snake_case_									=				data_mat.reshape(1       ,				shapes[0] * shapes[1]					)
									return data_expanded


				def   A_ (		self    :					str       ,				lowercase_    :					Tuple       ,				lowercase_    :					int       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					List[Any]					):
									snake_case_									=				[]
									snake_case_									=				0
									for i_map in range(lowercase_					):
														snake_case_									=				np.ones((size_map, size_map)					)
														for i in range(0       ,				lowercase_       ,				lowercase_					):
																			for j in range(0       ,				lowercase_       ,				lowercase_					):
																								snake_case_									=				pd_pool[
																								    i_pool
																								]
																								snake_case_									=				i_pool + 1
														snake_case_									=				np.multiply(
														    lowercase_       ,				np.multiply(out_map[i_map]       ,				(1 - out_map[i_map])					)					)
														pd_all.append(lowercase_					)
									return pd_all


				def   A_ (		self    :					Tuple       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					int       ,				lowercase_    :					Dict       ,				lowercase_    :					List[Any]       ,				lowercase_    :					str       ,				lowercase_    :					Dict=bool					):
									# model traning
									print('''----------------------Start Training-------------------------'''					)
									print((''' - - Shape: Train_Data  ''', np.shape(lowercase_					))					)
									print((''' - - Shape: Teach_Data  ''', np.shape(lowercase_					))					)
									snake_case_									=				0
									snake_case_									=				[]
									snake_case_									=				1_0000
									while rp < n_repeat and mse >= error_accuracy:
														snake_case_									=				0
														print(F"-------------Learning Time {rp}--------------"					)
														for p in range(len(lowercase_					)					):
																			# print('------------Learning Image: %d--------------'%p)
																			snake_case_									=				np.asmatrix(datas_train[p]					)
																			snake_case_									=				np.asarray(datas_teach[p]					)
																			snake_case_			,snake_case_									=				self.convolute(
																			    lowercase_       ,				self.conva       ,				self.w_conva       ,				self.thre_conva       ,				conv_step=self.step_conva       ,				)
																			snake_case_									=				self.pooling(lowercase_       ,				self.size_poolinga					)
																			snake_case_									=				np.shape(lowercase_					)
																			snake_case_									=				self._expand(lowercase_					)
																			snake_case_									=				data_bp_input

																			snake_case_									=				np.dot(lowercase_       ,				self.vji.T					) - self.thre_bpa
																			snake_case_									=				self.sig(lowercase_					)
																			snake_case_									=				np.dot(lowercase_       ,				self.wkj.T					) - self.thre_bpa
																			snake_case_									=				self.sig(lowercase_					)

																			# --------------Model Leaning ------------------------
																			# calculate error and gradient---------------
																			snake_case_									=				np.multiply(
																			    (data_teach - bp_outa)       ,				np.multiply(lowercase_       ,				(1 - bp_outa)					)					)
																			snake_case_									=				np.multiply(
																			    np.dot(lowercase_       ,				self.wkj					)       ,				np.multiply(lowercase_       ,				(1 - bp_outa)					)					)
																			snake_case_									=				np.dot(lowercase_       ,				self.vji					)

																			snake_case_									=				pd_i_all / (self.size_poolinga * self.size_poolinga)
																			snake_case_									=				pd_conva_pooled.T.getA().tolist()
																			snake_case_									=				self._calculate_gradient_from_pool(
																			    lowercase_       ,				lowercase_       ,				shape_featuremapa[0]       ,				shape_featuremapa[1]       ,				self.size_poolinga       ,				)
																			# weight and threshold learning process---------
																			# convolution layer
																			for k_conv in range(self.conva[1]					):
																								snake_case_									=				self._expand_mat(pd_conva_all[k_conv]					)
																								snake_case_									=				self.rate_weight * np.dot(lowercase_       ,				lowercase_					)

																								snake_case_									=				self.w_conva[k_conv] + delta_w.reshape(
																								    (self.conva[0], self.conva[0])					)

																								snake_case_									=				(
																								    self.thre_conva[k_conv]
																								    - np.sum(pd_conva_all[k_conv]					) * self.rate_thre
																								)
																			# all connected layer
																			snake_case_									=				self.wkj + pd_k_all.T * bp_outa * self.rate_weight
																			snake_case_									=				self.vji + pd_j_all.T * bp_outa * self.rate_weight
																			snake_case_									=				self.thre_bpa - pd_k_all * self.rate_thre
																			snake_case_									=				self.thre_bpa - pd_j_all * self.rate_thre
																			# calculate the sum error of all single image
																			snake_case_									=				np.sum(abs(data_teach - bp_outa					)					)
																			error_count += errors
																			# print('   ----Teach      ',data_teach)
																			# print('   ----BP_output  ',bp_out3)
														snake_case_									=				rp + 1
														snake_case_									=				error_count / patterns
														all_mse.append(lowercase_					)

									def draw_error():
														snake_case_									=				[error_accuracy for i in range(int(n_repeat * 1.2					)					)]
														plt.plot(lowercase_       ,				'''+-'''					)
														plt.plot(lowercase_       ,				'''r--'''					)
														plt.xlabel('''Learning Times'''					)
														plt.ylabel('''All_mse'''					)
														plt.grid(lowercase_       ,				alpha=0.5					)
														plt.show()

									print('''------------------Training Complished---------------------'''					)
									print((''' - - Training epoch: ''', rp, F"     - - Mse: {mse:.6f}")					)
									if draw_e:
														draw_error()
									return mse


				def   A_ (		self    :					str       ,				lowercase_    :					Union[str, Any]					):
									# model predict
									snake_case_									=				[]
									print('''-------------------Start Testing-------------------------'''					)
									print((''' - - Shape: Test_Data  ''', np.shape(lowercase_					))					)
									for p in range(len(lowercase_					)					):
														snake_case_									=				np.asmatrix(datas_test[p]					)
														snake_case_			,snake_case_									=				self.convolute(
														    lowercase_       ,				self.conva       ,				self.w_conva       ,				self.thre_conva       ,				conv_step=self.step_conva       ,				)
														snake_case_									=				self.pooling(lowercase_       ,				self.size_poolinga					)
														snake_case_									=				self._expand(lowercase_					)

														snake_case_									=				data_bp_input
														snake_case_									=				bp_outa * self.vji.T - self.thre_bpa
														snake_case_									=				self.sig(lowercase_					)
														snake_case_									=				bp_outa * self.wkj.T - self.thre_bpa
														snake_case_									=				self.sig(lowercase_					)
														produce_out.extend(bp_outa.getA().tolist()					)
									snake_case_									=				[list(map(self.do_round       ,				lowercase_					)					) for each in produce_out]
									return np.asarray(lowercase_					)


				def   A_ (		self    :					List[str]       ,				lowercase_    :					List[str]					):
									# return the data of image after convoluting process so we can check it out
									snake_case_									=				np.asmatrix(lowercase_					)
									snake_case_			,snake_case_									=				self.convolute(
									    lowercase_       ,				self.conva       ,				self.w_conva       ,				self.thre_conva       ,				conv_step=self.step_conva       ,				)
									snake_case_									=				self.pooling(lowercase_       ,				self.size_poolinga					)

									return data_conveda, data_pooleda


if __name__ == "__main__":
							pass







'''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 						a				(   _lowerCamelCase     ):


				def   A_ (		self    :					str					):
									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(lowercase_       ,				exist_ok=lowercase_					)
									snake_case_									=				os.path.join(lowercase_       ,				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(lowercase_       ,				range(len(lowercase_					)					)					)					)
									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(lowercase_       ,				exist_ok=lowercase_					)
									snake_case_									=				os.path.join(lowercase_       ,				BART_VOCAB_FILES_NAMES['''vocab_file''']					)
									snake_case_									=				os.path.join(lowercase_       ,				BART_VOCAB_FILES_NAMES['''merges_file''']					)
									with open(self.vocab_file       ,				'''w'''       ,				encoding='''utf-8'''					) as fp:
														fp.write(json.dumps(lowercase_					) + '''\n'''					)
									with open(self.merges_file       ,				'''w'''       ,				encoding='''utf-8'''					) as fp:
														fp.write('''\n'''.join(lowercase_					)					)


				def   A_ (		self    :					Union[str, Any]					):
									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,				'''dpr_tokenizer'''					)					)


				def   A_ (		self    :					Union[str, Any]					):
									return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,				'''dpr_tokenizer'''					)					)


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


				def   A_ (		self    :					str					):
									shutil.rmtree(self.tmpdirname					)


				def   A_ (		self    :					str					):
									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    :					str					):
									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(
														    lowercase_       ,				question_encoder_tokenizer=self.get_dpr_tokenizer()       ,				generator_tokenizer=self.get_bart_tokenizer()       ,				)
									return retriever


				def   A_ (		self    :					str       ,				lowercase_    :					bool					):
									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(
														    lowercase_       ,				question_encoder_tokenizer=self.get_dpr_tokenizer()       ,				generator_tokenizer=self.get_bart_tokenizer()       ,				)
									else:
														snake_case_									=				RagRetriever(
														    lowercase_       ,				question_encoder_tokenizer=self.get_dpr_tokenizer()       ,				generator_tokenizer=self.get_bart_tokenizer()       ,				index=CustomHFIndex(config.retrieval_vector_size       ,				lowercase_					)       ,				)
									return retriever


				def   A_ (		self    :					Tuple					):
									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(lowercase_       ,				open(lowercase_       ,				'''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(
									    lowercase_       ,				question_encoder_tokenizer=self.get_dpr_tokenizer()       ,				generator_tokenizer=self.get_bart_tokenizer()					)
									return retriever


				def   A_ (		self    :					Optional[Any]					):
									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(lowercase_       ,				n_docs=lowercase_					)
									self.assertEqual(retrieved_doc_embeds.shape       ,				(2, n_docs, self.retrieval_vector_size)					)
									self.assertEqual(len(lowercase_					)       ,				2					)
									self.assertEqual(sorted(doc_dicts[0]					)       ,				['''embeddings''', '''id''', '''text''', '''title''']					)
									self.assertEqual(len(doc_dicts[0]['''id''']					)       ,				lowercase_					)
									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    :					str					):
									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(lowercase_					)
																			snake_case_									=				RagRetriever.from_pretrained(lowercase_					)
																			self.assertIsInstance(lowercase_       ,				lowercase_					)
																			snake_case_									=				np.array(
																			    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]       ,				dtype=np.floataa					)
																			snake_case_									=				retriever.retrieve(lowercase_       ,				n_docs=1					)
																			self.assertTrue(out is not None					)


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


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


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


				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def   A_ (		self    :					List[str]					):
									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(lowercase_       ,				lowercase_       ,				prefix=retriever.config.generator.prefix       ,				n_docs=lowercase_					)
									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(lowercase_       ,				lowercase_					)
									self.assertIsInstance(lowercase_       ,				lowercase_					)
									self.assertIsInstance(lowercase_       ,				np.ndarray					)

									snake_case_									=				retriever(
									    lowercase_       ,				lowercase_       ,				prefix=retriever.config.generator.prefix       ,				n_docs=lowercase_       ,				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(lowercase_       ,				torch.Tensor					)
									self.assertIsInstance(lowercase_       ,				torch.Tensor					)
									self.assertIsInstance(lowercase_       ,				torch.Tensor					)


				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def   A_ (		self    :					Tuple					):
									snake_case_									=				self.get_dpr_ctx_encoder_tokenizer()
									snake_case_									=				1
									snake_case_									=				self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_					)
									retriever.set_ctx_encoder_tokenizer(lowercase_					)

									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(lowercase_       ,				lowercase_       ,				prefix=retriever.config.generator.prefix       ,				n_docs=lowercase_					)

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

