




"""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:
							__A							=     None

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



def 					UpperCamelCase__       (						lowercase__					:       Tuple				):





		if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
				snake_case						:  str        =			unittest.skip("test requires Datasets, Faiss, psutil"				)(SCREAMING_SNAKE_CASE_				)
		return test_case
@require_distributed_retrieval
class      lowerCamelCase__      (					lowerCamelCase_		):


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Dict        =			tempfile.mkdtemp()
					snake_case						:  str        =			8

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

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

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


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,	"dpr_tokenizer"							)							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,	"bart_tokenizer"							)							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					shutil.rmtree(self.tmpdirname							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			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 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=12_345							):



					"""simple docstring"""







					snake_case						:  Any        =			self.get_dummy_dataset()
					snake_case						:  List[str]        =			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						:  Any        =			dataset
							snake_case						:  List[Any]        =			RagPyTorchDistributedRetriever(
							    SCREAMING_SNAKE_CASE			,	question_encoder_tokenizer=self.get_dpr_tokenizer()			,	generator_tokenizer=self.get_bart_tokenizer()			,	)
							if init_retrieval:
									retriever.init_retrieval(SCREAMING_SNAKE_CASE							)
					return retriever


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					ray.init(local_mode=SCREAMING_SNAKE_CASE							)
					snake_case						:  int        =			RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size			,	question_encoder=DPRConfig().to_dict()			,	generator=BartConfig().to_dict()			,	)
					snake_case						:  List[str]        =			ray.remote(SCREAMING_SNAKE_CASE							)
					snake_case						:  Any        =			[remote_cls.remote() for _ in range(1							)]
					with patch("transformers.models.rag.retrieval_rag.load_dataset"							) as mock_load_dataset:
							snake_case						:  List[str]        =			self.get_dummy_dataset()
							snake_case						:  List[str]        =			RagRayDistributedRetriever(
							    SCREAMING_SNAKE_CASE			,	question_encoder_tokenizer=self.get_dpr_tokenizer()			,	generator_tokenizer=self.get_bart_tokenizer()			,	retrieval_workers=SCREAMING_SNAKE_CASE			,	)
							if init_retrieval:
									retriever.init_retrieval()
					return retriever


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=12_345							):



					"""simple docstring"""







					snake_case						:  str        =			self.get_dummy_dataset()
					snake_case						:  Any        =			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						:  Any        =			os.path.join(self.tmpdirname			,	"dataset"							)
							snake_case						:  Tuple        =			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						:  Union[str, Any]        =			RagPyTorchDistributedRetriever(
							    SCREAMING_SNAKE_CASE			,	question_encoder_tokenizer=self.get_dpr_tokenizer()			,	generator_tokenizer=self.get_bart_tokenizer()			,	)
					else:
							snake_case						:  Optional[int]        =			RagPyTorchDistributedRetriever(
							    SCREAMING_SNAKE_CASE			,	question_encoder_tokenizer=self.get_dpr_tokenizer()			,	generator_tokenizer=self.get_bart_tokenizer()			,	index=CustomHFIndex(config.retrieval_vector_size			,	SCREAMING_SNAKE_CASE							)			,	)
					if init_retrieval:
							retriever.init_retrieval(SCREAMING_SNAKE_CASE							)
					return retriever


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					ray.init(local_mode=SCREAMING_SNAKE_CASE							)
					snake_case						:  str        =			self.get_dummy_dataset()
					snake_case						:  Optional[int]        =			RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size			,	question_encoder=DPRConfig().to_dict()			,	generator=BartConfig().to_dict()			,	index_name="custom"			,	)
					snake_case						:  int        =			ray.remote(SCREAMING_SNAKE_CASE							)
					snake_case						:  List[str]        =			[remote_cls.remote() for _ in range(1							)]
					if from_disk:
							snake_case						:  List[str]        =			os.path.join(self.tmpdirname			,	"dataset"							)
							snake_case						:  List[str]        =			os.path.join(self.tmpdirname			,	"index.faiss"							)
							dataset.get_index("embeddings"							).save(os.path.join(self.tmpdirname			,	"index.faiss"							)							)
							dataset.drop_index("embeddings"							)
							dataset.save_to_disk(os.path.join(self.tmpdirname			,	"dataset"							)							)
							del dataset
							snake_case						:  Optional[int]        =			RagRayDistributedRetriever(
							    SCREAMING_SNAKE_CASE			,	question_encoder_tokenizer=self.get_dpr_tokenizer()			,	generator_tokenizer=self.get_bart_tokenizer()			,	retrieval_workers=SCREAMING_SNAKE_CASE			,	index=CustomHFIndex.load_from_disk(
							        vector_size=config.retrieval_vector_size			,	dataset_path=config.passages_path			,	index_path=config.index_path			,	)			,	)
					else:
							snake_case						:  str        =			RagRayDistributedRetriever(
							    SCREAMING_SNAKE_CASE			,	question_encoder_tokenizer=self.get_dpr_tokenizer()			,	generator_tokenizer=self.get_bart_tokenizer()			,	retrieval_workers=SCREAMING_SNAKE_CASE			,	index=CustomHFIndex(config.retrieval_vector_size			,	SCREAMING_SNAKE_CASE							)			,	)
					if init_retrieval:
							retriever.init_retrieval()
					return retriever


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case     ,			snake_case     ,			snake_case						:  List[str]        =			retriever.retrieve(SCREAMING_SNAKE_CASE			,	n_docs=SCREAMING_SNAKE_CASE							)
					self.assertEqual(retrieved_doc_embeds.shape			,	(2, n_docs, self.retrieval_vector_size)							)
					self.assertEqual(len(SCREAMING_SNAKE_CASE							)			,	2							)
					self.assertEqual(sorted(doc_dicts[0]							)			,	["embeddings", "id", "text", "title"]							)
					self.assertEqual(len(doc_dicts[0]["id"]							)			,	SCREAMING_SNAKE_CASE							)
					self.assertEqual(doc_dicts[0]["id"][0]			,	"1"							)  # max inner product is reached with second doc
					self.assertEqual(doc_dicts[1]["id"][0]			,	"0"							)  # max inner product is reached with first doc
					self.assertListEqual(doc_ids.tolist()			,	[[1], [0]]							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  str        =			1
					snake_case						:  Tuple        =			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=SCREAMING_SNAKE_CASE							)			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Any        =			1
					snake_case						:  Optional[int]        =			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=SCREAMING_SNAKE_CASE			,	from_disk=SCREAMING_SNAKE_CASE							)			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Any        =			1
					snake_case						:  Dict        =			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=SCREAMING_SNAKE_CASE			,	from_disk=SCREAMING_SNAKE_CASE							)			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	)


			@require_ray
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Union[str, Any]        =			1
					snake_case						:  Tuple        =			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=SCREAMING_SNAKE_CASE							)			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)
					ray.shutdown()


			@require_ray
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  str        =			1
					snake_case						:  Optional[Any]        =			np.array(
					    [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]			,	dtype=np.floataa							)
					with self.assertRaises(SCREAMING_SNAKE_CASE							):
							self.distributed_retriever_check(
							    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=SCREAMING_SNAKE_CASE			,	from_disk=SCREAMING_SNAKE_CASE							)			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	)
					ray.shutdown()



			@require_ray
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			1
					snake_case						:  int        =			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=SCREAMING_SNAKE_CASE			,	from_disk=SCREAMING_SNAKE_CASE							)			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)
					ray.shutdown()







"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A							=     logging.get_logger(__name__)

__A							=     {
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}
class      lowerCamelCase__      (					lowerCamelCase_     ):
			a__	: Optional[int]        =       """vilt"""


			def __init__(       self			,	SCREAMING_SNAKE_CASE=30_522			,	SCREAMING_SNAKE_CASE=2			,	SCREAMING_SNAKE_CASE=2			,	SCREAMING_SNAKE_CASE=40			,	SCREAMING_SNAKE_CASE=768			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=3_072			,	SCREAMING_SNAKE_CASE="gelu"			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.02			,	SCREAMING_SNAKE_CASE=1E-12			,	SCREAMING_SNAKE_CASE=384			,	SCREAMING_SNAKE_CASE=32			,	SCREAMING_SNAKE_CASE=3			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=-1			,	SCREAMING_SNAKE_CASE=False			,	SCREAMING_SNAKE_CASE=-1			,	**SCREAMING_SNAKE_CASE			,	):



					"""simple docstring"""







					super().__init__(tie_word_embeddings=SCREAMING_SNAKE_CASE			,	**SCREAMING_SNAKE_CASE							)

					snake_case						:  str        =			vocab_size
					snake_case						:  Optional[Any]        =			type_vocab_size
					snake_case						:  List[str]        =			modality_type_vocab_size
					snake_case						:  Optional[Any]        =			max_position_embeddings

					snake_case						:  Tuple        =			hidden_size
					snake_case						:  List[str]        =			num_hidden_layers
					snake_case						:  int        =			num_attention_heads
					snake_case						:  Optional[int]        =			intermediate_size
					snake_case						:  List[Any]        =			hidden_act
					snake_case						:  List[str]        =			hidden_dropout_prob
					snake_case						:  Union[str, Any]        =			attention_probs_dropout_prob
					snake_case						:  Any        =			initializer_range
					snake_case						:  List[Any]        =			layer_norm_eps

					snake_case						:  Any        =			image_size
					snake_case						:  str        =			patch_size
					snake_case						:  Optional[int]        =			num_channels
					snake_case						:  List[str]        =			qkv_bias
					snake_case						:  Optional[int]        =			max_image_length
					snake_case						:  Any        =			num_images

