




'''simple docstring'''

import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


a  :				str									=  'base_with_context'


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Optional[Any]:

					'''simple docstring'''

					snake_case_									=				nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']	)	)
					snake_case_									=				nn.Parameter(
					    torch.FloatTensor(weights['''Embed_0''']['''embedding''']	),      requires_grad=__UpperCAmelCase	)
					for lyr_num, lyr in enumerate(model.encoders	):
										snake_case_									=				weights[F"layers_{lyr_num}"]
										snake_case_									=				nn.Parameter(
										    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']	)	)

										snake_case_									=				ly_weight['''attention''']
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T	)	)

										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']	)	)

										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T	)	)

					snake_case_									=				nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']	)	)
					return model


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T	)	)

					snake_case_									=				nn.Parameter(
					    torch.FloatTensor(weights['''Embed_0''']['''embedding''']	),      requires_grad=__UpperCAmelCase	)

					for lyr_num, lyr in enumerate(model.encoders	):
										snake_case_									=				weights[F"layers_{lyr_num}"]
										snake_case_									=				ly_weight['''attention''']

										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(
										    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']	)	)

										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']	)	)

					snake_case_									=				nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']	)	)

					return model


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Tuple:

					'''simple docstring'''

					snake_case_									=				nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T	)	)
					snake_case_									=				nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T	)	)

					snake_case_									=				nn.Parameter(
					    torch.FloatTensor(weights['''Embed_0''']['''embedding''']	),      requires_grad=__UpperCAmelCase	)

					snake_case_									=				nn.Parameter(
					    torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T	)	)

					for lyr_num, lyr in enumerate(model.decoders	):
										snake_case_									=				weights[F"layers_{lyr_num}"]
										snake_case_									=				nn.Parameter(
										    torch.FloatTensor(ly_weight['''pre_self_attention_layer_norm''']['''scale''']	)	)

										snake_case_									=				nn.Parameter(
										    torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T	)	)

										snake_case_									=				ly_weight['''self_attention''']
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T	)	)

										snake_case_									=				ly_weight['''MultiHeadDotProductAttention_0''']
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(
										    torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']	)	)

										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']	)	)
										snake_case_									=				nn.Parameter(
										    torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T	)	)
										snake_case_									=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T	)	)

					snake_case_									=				nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']	)	)

					snake_case_									=				nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T	)	)

					return model


def 		__magic_name__    (	__UpperCAmelCase	)		->					Union[str, Any]:

					'''simple docstring'''

					snake_case_									=				checkpoints.load_tax_checkpoint(args.checkpoint_path	)
					snake_case_									=				jnp.tree_util.tree_map(onp.array,      __UpperCAmelCase	)

					snake_case_									=				[
					    '''from __gin__ import dynamic_registration''',
					    '''from music_spectrogram_diffusion.models.diffusion import diffusion_utils''',
					    '''diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0''',
					    '''diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()''',
					]

					snake_case_									=				os.path.join(args.checkpoint_path,      '''..''',      '''config.gin'''	)
					snake_case_									=				inference.parse_training_gin_file(__UpperCAmelCase,      __UpperCAmelCase	)
					snake_case_									=				inference.InferenceModel(args.checkpoint_path,      __UpperCAmelCase	)

					snake_case_									=				DDPMScheduler(beta_schedule='''squaredcos_cap_v2''',      variance_type='''fixed_large'''	)

					snake_case_									=				SpectrogramNotesEncoder(
					    max_length=synth_model.sequence_length['''inputs'''],      vocab_size=synth_model.model.module.config.vocab_size,      d_model=synth_model.model.module.config.emb_dim,      dropout_rate=synth_model.model.module.config.dropout_rate,      num_layers=synth_model.model.module.config.num_encoder_layers,      num_heads=synth_model.model.module.config.num_heads,      d_kv=synth_model.model.module.config.head_dim,      d_ff=synth_model.model.module.config.mlp_dim,      feed_forward_proj='''gated-gelu''',      )

					snake_case_									=				SpectrogramContEncoder(
					    input_dims=synth_model.audio_codec.n_dims,      targets_context_length=synth_model.sequence_length['''targets_context'''],      d_model=synth_model.model.module.config.emb_dim,      dropout_rate=synth_model.model.module.config.dropout_rate,      num_layers=synth_model.model.module.config.num_encoder_layers,      num_heads=synth_model.model.module.config.num_heads,      d_kv=synth_model.model.module.config.head_dim,      d_ff=synth_model.model.module.config.mlp_dim,      feed_forward_proj='''gated-gelu''',      )

					snake_case_									=				TaFilmDecoder(
					    input_dims=synth_model.audio_codec.n_dims,      targets_length=synth_model.sequence_length['''targets_context'''],      max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time,      d_model=synth_model.model.module.config.emb_dim,      num_layers=synth_model.model.module.config.num_decoder_layers,      num_heads=synth_model.model.module.config.num_heads,      d_kv=synth_model.model.module.config.head_dim,      d_ff=synth_model.model.module.config.mlp_dim,      dropout_rate=synth_model.model.module.config.dropout_rate,      )

					snake_case_									=				load_notes_encoder(ta_checkpoint['''target''']['''token_encoder'''],      __UpperCAmelCase	)
					snake_case_									=				load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder'''],      __UpperCAmelCase	)
					snake_case_									=				load_decoder(ta_checkpoint['''target''']['''decoder'''],      __UpperCAmelCase	)

					snake_case_									=				OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''	)

					snake_case_									=				SpectrogramDiffusionPipeline(
					    notes_encoder=__UpperCAmelCase,      continuous_encoder=__UpperCAmelCase,      decoder=__UpperCAmelCase,      scheduler=__UpperCAmelCase,      melgan=__UpperCAmelCase,      )
					if args.save:
										pipe.save_pretrained(args.output_path	)


if __name__ == "__main__":
							a  :				Optional[Any]									=  argparse.ArgumentParser()

							parser.add_argument('--output_path', default=None, type=str, required=True, help='Path to the converted model.')
							parser.add_argument(
							    '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
							)
							parser.add_argument(
							    '--checkpoint_path',
							    default=f'''{MODEL}/checkpoint_500000''',
							    type=str,
							    required=False,
							    help='Path to the original jax model checkpoint.',
							)
							a  :				Optional[Any]									=  parser.parse_args()

							main(args)







'''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.

