


'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
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_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
							from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
							__SCREAMING_SNAKE_CASE				:							Dict		       =		None

if is_ray_available():
							import ray  # noqa: E402 # isort:skip
							from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
							__SCREAMING_SNAKE_CASE				:							str		       =		None
							__SCREAMING_SNAKE_CASE				:							int		       =		None
							__SCREAMING_SNAKE_CASE				:							Any		       =		None







def 			a_							(  UpperCamelCase_   ):
							if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
														A_ 						=		unittest.skip("test requires Datasets, Faiss, psutil"   )(UpperCamelCase_   )
							return test_case







@require_distributed_retrieval
class 				__lowerCAmelCase      (      lowercase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		tempfile.mkdtemp()
													A_ 						=		8

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

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

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






						def 							_UpperCAmelCase					(     self       :			str  ):
													return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,     "dpr_tokenizer"  )  )






						def 							_UpperCAmelCase					(     self       :			Optional[int]  ):
													return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,     "bart_tokenizer"  )  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):
													shutil.rmtree(self.tmpdirname  )






						def 							_UpperCAmelCase					(     self       :			Tuple  ):
													A_ 						=		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 							_UpperCAmelCase					(     self       :			Union[str, Any]   ,     lowerCAmelCase       :			bool   ,     lowerCAmelCase       :			int=1_23_45  ):
													A_ 						=		self.get_dummy_dataset()
													A_ 						=		RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size   ,     question_encoder=DPRConfig().to_dict()   ,     generator=BartConfig().to_dict()   ,     )
													with patch("transformers.models.rag.retrieval_rag.load_dataset"  ) as mock_load_dataset:
																				A_ 						=		dataset
																				A_ 						=		RagPyTorchDistributedRetriever(
																				    lowerCAmelCase   ,     question_encoder_tokenizer=self.get_dpr_tokenizer()   ,     generator_tokenizer=self.get_bart_tokenizer()   ,     )
																				if init_retrieval:
																											retriever.init_retrieval(lowerCAmelCase  )
													return retriever






						def 							_UpperCAmelCase					(     self       :			Optional[int]   ,     lowerCAmelCase       :			bool  ):
													# Have to run in local mode because sys.path modifications at top of
													# file are not propogated to remote workers.
													# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
													ray.init(local_mode=lowerCAmelCase  )
													A_ 						=		RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size   ,     question_encoder=DPRConfig().to_dict()   ,     generator=BartConfig().to_dict()   ,     )
													A_ 						=		ray.remote(lowerCAmelCase  )
													A_ 						=		[remote_cls.remote() for _ in range(1  )]
													with patch("transformers.models.rag.retrieval_rag.load_dataset"  ) as mock_load_dataset:
																				A_ 						=		self.get_dummy_dataset()
																				A_ 						=		RagRayDistributedRetriever(
																				    lowerCAmelCase   ,     question_encoder_tokenizer=self.get_dpr_tokenizer()   ,     generator_tokenizer=self.get_bart_tokenizer()   ,     retrieval_workers=lowerCAmelCase   ,     )
																				if init_retrieval:
																											retriever.init_retrieval()
													return retriever






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]   ,     lowerCAmelCase       :			bool   ,     lowerCAmelCase       :			bool   ,     lowerCAmelCase       :			List[str]=1_23_45  ):
													A_ 						=		self.get_dummy_dataset()
													A_ 						=		RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size   ,     question_encoder=DPRConfig().to_dict()   ,     generator=BartConfig().to_dict()   ,     index_name="custom"   ,     )
													if from_disk:
																				A_ 						=		os.path.join(self.tmpdirname   ,     "dataset"  )
																				A_ 						=		os.path.join(self.tmpdirname   ,     "index.faiss"  )
																				dataset.get_index("embeddings"  ).save(os.path.join(self.tmpdirname   ,     "index.faiss"  )  )
																				dataset.drop_index("embeddings"  )
																				dataset.save_to_disk(os.path.join(self.tmpdirname   ,     "dataset"  )  )
																				del dataset
																				A_ 						=		RagPyTorchDistributedRetriever(
																				    lowerCAmelCase   ,     question_encoder_tokenizer=self.get_dpr_tokenizer()   ,     generator_tokenizer=self.get_bart_tokenizer()   ,     )
													else:
																				A_ 						=		RagPyTorchDistributedRetriever(
																				    lowerCAmelCase   ,     question_encoder_tokenizer=self.get_dpr_tokenizer()   ,     generator_tokenizer=self.get_bart_tokenizer()   ,     index=CustomHFIndex(config.retrieval_vector_size   ,     lowerCAmelCase  )   ,     )
													if init_retrieval:
																				retriever.init_retrieval(lowerCAmelCase  )
													return retriever






						def 							_UpperCAmelCase					(     self       :			int   ,     lowerCAmelCase       :			bool   ,     lowerCAmelCase       :			bool  ):
													# Have to run in local mode because sys.path modifications at top of
													# file are not propogated to remote workers.
													# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
													ray.init(local_mode=lowerCAmelCase  )
													A_ 						=		self.get_dummy_dataset()
													A_ 						=		RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size   ,     question_encoder=DPRConfig().to_dict()   ,     generator=BartConfig().to_dict()   ,     index_name="custom"   ,     )
													A_ 						=		ray.remote(lowerCAmelCase  )
													A_ 						=		[remote_cls.remote() for _ in range(1  )]
													if from_disk:
																				A_ 						=		os.path.join(self.tmpdirname   ,     "dataset"  )
																				A_ 						=		os.path.join(self.tmpdirname   ,     "index.faiss"  )
																				dataset.get_index("embeddings"  ).save(os.path.join(self.tmpdirname   ,     "index.faiss"  )  )
																				dataset.drop_index("embeddings"  )
																				dataset.save_to_disk(os.path.join(self.tmpdirname   ,     "dataset"  )  )
																				del dataset
																				A_ 						=		RagRayDistributedRetriever(
																				    lowerCAmelCase   ,     question_encoder_tokenizer=self.get_dpr_tokenizer()   ,     generator_tokenizer=self.get_bart_tokenizer()   ,     retrieval_workers=lowerCAmelCase   ,     index=CustomHFIndex.load_from_disk(
																				        vector_size=config.retrieval_vector_size   ,     dataset_path=config.passages_path   ,     index_path=config.index_path   ,     )   ,     )
													else:
																				A_ 						=		RagRayDistributedRetriever(
																				    lowerCAmelCase   ,     question_encoder_tokenizer=self.get_dpr_tokenizer()   ,     generator_tokenizer=self.get_bart_tokenizer()   ,     retrieval_workers=lowerCAmelCase   ,     index=CustomHFIndex(config.retrieval_vector_size   ,     lowerCAmelCase  )   ,     )
													if init_retrieval:
																				retriever.init_retrieval()
													return retriever






						def 							_UpperCAmelCase					(     self       :			List[Any]   ,     lowerCAmelCase       :			RagRetriever   ,     lowerCAmelCase       :			np.array   ,     lowerCAmelCase       :			int  ):
													A_						,				A_						,				A_ 						=		retriever.retrieve(lowerCAmelCase   ,     n_docs=lowerCAmelCase  )
													self.assertEqual(retrieved_doc_embeds.shape   ,     (2, n_docs, self.retrieval_vector_size)  )
													self.assertEqual(len(lowerCAmelCase  )   ,     2  )
													self.assertEqual(sorted(doc_dicts[0]  )   ,     ["embeddings", "id", "text", "title"]  )
													self.assertEqual(len(doc_dicts[0]["id"]  )   ,     lowerCAmelCase  )
													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 							_UpperCAmelCase					(     self       :			Tuple  ):
													A_ 						=		1
													A_ 						=		np.array(
													    [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]   ,     dtype=np.floataa  )

													self.distributed_retriever_check(
													    self.get_dummy_pytorch_distributed_retriever(init_retrieval=lowerCAmelCase  )   ,     lowerCAmelCase   ,     lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Tuple  ):
													A_ 						=		1
													A_ 						=		np.array(
													    [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]   ,     dtype=np.floataa  )

													self.distributed_retriever_check(
													    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCAmelCase   ,     from_disk=lowerCAmelCase  )   ,     lowerCAmelCase   ,     lowerCAmelCase   ,     )






						def 							_UpperCAmelCase					(     self       :			Tuple  ):
													A_ 						=		1
													A_ 						=		np.array(
													    [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]   ,     dtype=np.floataa  )

													self.distributed_retriever_check(
													    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCAmelCase   ,     from_disk=lowerCAmelCase  )   ,     lowerCAmelCase   ,     lowerCAmelCase   ,     )






						@require_ray
						def 							_UpperCAmelCase					(     self       :			int  ):
													A_ 						=		1
													A_ 						=		np.array(
													    [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]   ,     dtype=np.floataa  )

													self.distributed_retriever_check(
													    self.get_dummy_ray_distributed_retriever(init_retrieval=lowerCAmelCase  )   ,     lowerCAmelCase   ,     lowerCAmelCase  )
													ray.shutdown()






						@require_ray
						def 							_UpperCAmelCase					(     self       :			Optional[int]  ):
													A_ 						=		1
													A_ 						=		np.array(
													    [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]   ,     dtype=np.floataa  )
													with self.assertRaises(lowerCAmelCase  ):
																				self.distributed_retriever_check(
																				    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCAmelCase   ,     from_disk=lowerCAmelCase  )   ,     lowerCAmelCase   ,     lowerCAmelCase   ,     )
													ray.shutdown()






						@require_ray
						def 							_UpperCAmelCase					(     self       :			Any  ):
													A_ 						=		1
													A_ 						=		np.array(
													    [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]   ,     dtype=np.floataa  )

													self.distributed_retriever_check(
													    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCAmelCase   ,     from_disk=lowerCAmelCase  )   ,     lowerCAmelCase   ,     lowerCAmelCase  )
													ray.shutdown()









'''simple docstring'''
import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
							from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


__SCREAMING_SNAKE_CASE				:							int		       =		get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_torch
@require_torchaudio
@require_sentencepiece
class 				__lowerCAmelCase      (      unittest.TestCase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[str]  ):
													A_ 						=		tempfile.mkdtemp()

													A_ 						=		["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
													A_ 						=		dict(zip(lowerCAmelCase   ,     range(len(lowerCAmelCase  )  )  )  )
													A_ 						=		Path(self.tmpdirname  )
													save_json(lowerCAmelCase   ,     save_dir / VOCAB_FILES_NAMES["vocab_file"]  )
													if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																				copyfile(lowerCAmelCase   ,     save_dir / VOCAB_FILES_NAMES["spm_file"]  )

													A_ 						=		SpeechaTextTokenizer.from_pretrained(self.tmpdirname  )
													tokenizer.save_pretrained(self.tmpdirname  )

													A_ 						=		{
													    "feature_size": 24,
													    "num_mel_bins": 24,
													    "padding_value": 0.0,
													    "sampling_rate": 1_60_00,
													    "return_attention_mask": False,
													    "do_normalize": True,
													}
													save_json(lowerCAmelCase   ,     save_dir / FEATURE_EXTRACTOR_NAME  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]   ,     **lowerCAmelCase       :			str  ):
													return SpeechaTextTokenizer.from_pretrained(self.tmpdirname   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			List[Any]   ,     **lowerCAmelCase       :			Optional[Any]  ):
													return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname   ,     **lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Any  ):
													shutil.rmtree(self.tmpdirname  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):
													A_ 						=		self.get_tokenizer()
													A_ 						=		self.get_feature_extractor()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													processor.save_pretrained(self.tmpdirname  )
													A_ 						=		SpeechaTextProcessor.from_pretrained(self.tmpdirname  )

													self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer.get_vocab()  )
													self.assertIsInstance(processor.tokenizer   ,     lowerCAmelCase  )

													self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor.to_json_string()  )
													self.assertIsInstance(processor.feature_extractor   ,     lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Dict  ):
													A_ 						=		SpeechaTextProcessor(
													    tokenizer=self.get_tokenizer()   ,     feature_extractor=self.get_feature_extractor()  )
													processor.save_pretrained(self.tmpdirname  )

													A_ 						=		self.get_tokenizer(bos_token="(BOS)"   ,     eos_token="(EOS)"  )
													A_ 						=		self.get_feature_extractor(do_normalize=lowerCAmelCase   ,     padding_value=1.0  )

													A_ 						=		SpeechaTextProcessor.from_pretrained(
													    self.tmpdirname   ,     bos_token="(BOS)"   ,     eos_token="(EOS)"   ,     do_normalize=lowerCAmelCase   ,     padding_value=1.0  )

													self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer_add_kwargs.get_vocab()  )
													self.assertIsInstance(processor.tokenizer   ,     lowerCAmelCase  )

													self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor_add_kwargs.to_json_string()  )
													self.assertIsInstance(processor.feature_extractor   ,     lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			str  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													A_ 						=		floats_list((3, 10_00)  )

													A_ 						=		feature_extractor(lowerCAmelCase   ,     return_tensors="np"  )
													A_ 						=		processor(lowerCAmelCase   ,     return_tensors="np"  )

													for key in input_feat_extract.keys():
																				self.assertAlmostEqual(input_feat_extract[key].sum()   ,     input_processor[key].sum()   ,     delta=1e-2  )






						def 							_UpperCAmelCase					(     self       :			Optional[int]  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													A_ 						=		"This is a test string"

													A_ 						=		processor(text=lowerCAmelCase  )

													A_ 						=		tokenizer(lowerCAmelCase  )

													for key in encoded_tok.keys():
																				self.assertListEqual(encoded_tok[key]   ,     encoded_processor[key]  )






						def 							_UpperCAmelCase					(     self       :			int  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													A_ 						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													A_ 						=		processor.batch_decode(lowerCAmelCase  )
													A_ 						=		tokenizer.batch_decode(lowerCAmelCase  )

													self.assertListEqual(lowerCAmelCase   ,     lowerCAmelCase  )






						def 							_UpperCAmelCase					(     self       :			Tuple  ):
													A_ 						=		self.get_feature_extractor()
													A_ 						=		self.get_tokenizer()

													A_ 						=		SpeechaTextProcessor(tokenizer=lowerCAmelCase   ,     feature_extractor=lowerCAmelCase  )

													self.assertListEqual(
													    processor.model_input_names   ,     feature_extractor.model_input_names   ,     msg="`processor` and `feature_extractor` model input names do not match"   ,     )





