



'''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 					UpperCAmelCase__	(		UpperCamelCase__ ):



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  tempfile.mkdtemp()
						__lowerCAmelCase												=  8

						# DPR tok
						__lowerCAmelCase												=  [
						    "[UNK]",
						    "[CLS]",
						    "[SEP]",
						    "[PAD]",
						    "[MASK]",
						    "want",
						    "##want",
						    "##ed",
						    "wa",
						    "un",
						    "runn",
						    "##ing",
						    ",",
						    "low",
						    "lowest",
						]
						__lowerCAmelCase												=  os.path.join(self.tmpdirname			,				"dpr_tokenizer"     )
						os.makedirs(__UpperCamelCase			,				exist_ok=__UpperCamelCase     )
						__lowerCAmelCase												=  os.path.join(__UpperCamelCase			,				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
						__lowerCAmelCase												=  [
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "\u0120",
						    "\u0120l",
						    "\u0120n",
						    "\u0120lo",
						    "\u0120low",
						    "er",
						    "\u0120lowest",
						    "\u0120newer",
						    "\u0120wider",
						    "<unk>",
						]
						__lowerCAmelCase												=  dict(zip(__UpperCamelCase			,				range(len(__UpperCamelCase     )     )     )     )
						__lowerCAmelCase												=  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
						__lowerCAmelCase												=  {"unk_token": "<unk>"}

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



					def        UpperCAmelCase_ (    self     )     ->			DPRQuestionEncoderTokenizer:
						return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,				"dpr_tokenizer"     )     )



					def        UpperCAmelCase_ (    self     )     ->			DPRContextEncoderTokenizer:
						return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,				"dpr_tokenizer"     )     )



					def        UpperCAmelCase_ (    self     )     ->			BartTokenizer:
						return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,				"bart_tokenizer"     )     )



					def        UpperCAmelCase_ (    self     )     ->			Tuple:
						shutil.rmtree(self.tmpdirname     )



					def        UpperCAmelCase_ (    self     )     ->			Dict:
						__lowerCAmelCase												=  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     )     ->			List[Any]:
						__lowerCAmelCase												=  self.get_dummy_dataset()
						__lowerCAmelCase												=  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:
							__lowerCAmelCase												=  dataset
							__lowerCAmelCase												=  RagRetriever(
							    __UpperCamelCase			,				question_encoder_tokenizer=self.get_dpr_tokenizer()			,				generator_tokenizer=self.get_bart_tokenizer()			,				)
						return retriever



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			int:
						__lowerCAmelCase												=  self.get_dummy_dataset()
						__lowerCAmelCase												=  RagConfig(
						    retrieval_vector_size=self.retrieval_vector_size			,				question_encoder=DPRConfig().to_dict()			,				generator=BartConfig().to_dict()			,				index_name="custom"			,				)
						if from_disk:
							__lowerCAmelCase												=  os.path.join(self.tmpdirname			,				"dataset"     )
							__lowerCAmelCase												=  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
							__lowerCAmelCase												=  RagRetriever(
							    __UpperCamelCase			,				question_encoder_tokenizer=self.get_dpr_tokenizer()			,				generator_tokenizer=self.get_bart_tokenizer()			,				)
						else:
							__lowerCAmelCase												=  RagRetriever(
							    __UpperCamelCase			,				question_encoder_tokenizer=self.get_dpr_tokenizer()			,				generator_tokenizer=self.get_bart_tokenizer()			,				index=CustomHFIndex(config.retrieval_vector_size			,				__UpperCamelCase     )			,				)
						return retriever



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  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     )

						__lowerCAmelCase												=  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"     )     )

						__lowerCAmelCase												=  os.path.join(self.tmpdirname			,				"psgs_w100.tsv.pkl"     )
						__lowerCAmelCase												=  {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
						pickle.dump(__UpperCamelCase			,				open(__UpperCamelCase			,				"wb"     )     )

						__lowerCAmelCase												=  RagConfig(
						    retrieval_vector_size=self.retrieval_vector_size			,				question_encoder=DPRConfig().to_dict()			,				generator=BartConfig().to_dict()			,				index_name="legacy"			,				index_path=self.tmpdirname			,				)
						__lowerCAmelCase												=  RagRetriever(
						    __UpperCamelCase			,				question_encoder_tokenizer=self.get_dpr_tokenizer()			,				generator_tokenizer=self.get_bart_tokenizer()     )
						return retriever



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__lowerCAmelCase												=  1
						__lowerCAmelCase												=  self.get_dummy_canonical_hf_index_retriever()
						__lowerCAmelCase												=  np.array(
						    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]			,				dtype=np.floataa     )
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  retriever.retrieve(__UpperCamelCase			,				n_docs=__UpperCamelCase     )
						self.assertEqual(retrieved_doc_embeds.shape			,				(2, n_docs, self.retrieval_vector_size)     )
						self.assertEqual(len(__UpperCamelCase     )			,				2     )
						self.assertEqual(sorted(doc_dicts[0]     )			,				["embeddings", "id", "text", "title"]     )
						self.assertEqual(len(doc_dicts[0]["id"]     )			,				__UpperCamelCase     )
						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     )     ->			Any:
						__lowerCAmelCase												=  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:
								__lowerCAmelCase												=  self.get_dummy_dataset()
								retriever.save_pretrained(__UpperCamelCase     )
								__lowerCAmelCase												=  RagRetriever.from_pretrained(__UpperCamelCase     )
								self.assertIsInstance(__UpperCamelCase			,				__UpperCamelCase     )
								__lowerCAmelCase												=  np.array(
								    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]			,				dtype=np.floataa     )
								__lowerCAmelCase												=  retriever.retrieve(__UpperCamelCase			,				n_docs=1     )
								self.assertTrue(out is not None     )



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



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



					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						__lowerCAmelCase												=  1
						__lowerCAmelCase												=  self.get_dummy_legacy_index_retriever()
						__lowerCAmelCase												=  np.array(
						    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]			,				dtype=np.floataa     )
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  retriever.retrieve(__UpperCamelCase			,				n_docs=__UpperCamelCase     )
						self.assertEqual(retrieved_doc_embeds.shape			,				(2, n_docs, self.retrieval_vector_size)     )
						self.assertEqual(len(__UpperCamelCase     )			,				2     )
						self.assertEqual(sorted(doc_dicts[0]     )			,				["text", "title"]     )
						self.assertEqual(len(doc_dicts[0]["text"]     )			,				__UpperCamelCase     )
						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        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  self.get_dummy_legacy_index_retriever()
						with tempfile.TemporaryDirectory() as tmp_dirname:
							retriever.save_pretrained(__UpperCamelCase     )
							__lowerCAmelCase												=  RagRetriever.from_pretrained(__UpperCamelCase     )
							self.assertIsInstance(__UpperCamelCase			,				__UpperCamelCase     )
							__lowerCAmelCase												=  np.array(
							    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]			,				dtype=np.floataa     )
							__lowerCAmelCase												=  retriever.retrieve(__UpperCamelCase			,				n_docs=1     )
							self.assertTrue(out is not None     )



					@require_torch
					@require_tokenizers
					@require_sentencepiece
					def        UpperCAmelCase_ (    self     )     ->			str:
						import torch

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

						__lowerCAmelCase												=  retriever(
						    __UpperCamelCase			,				__UpperCamelCase			,				prefix=retriever.config.generator.prefix			,				n_docs=__UpperCamelCase			,				return_tensors="pt"			,				)
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  (  # 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(__UpperCamelCase			,				torch.Tensor     )
						self.assertIsInstance(__UpperCamelCase			,				torch.Tensor     )
						self.assertIsInstance(__UpperCamelCase			,				torch.Tensor     )



					@require_torch
					@require_tokenizers
					@require_sentencepiece
					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  self.get_dpr_ctx_encoder_tokenizer()
						__lowerCAmelCase												=  1
						__lowerCAmelCase												=  self.get_dummy_custom_hf_index_retriever(from_disk=__UpperCamelCase     )
						retriever.set_ctx_encoder_tokenizer(__UpperCamelCase     )

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

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




'''simple docstring'''



import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


lowerCAmelCase	:		Optional[Any]				=   '''scheduler_config.json'''






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     str        =							1
					a      :     Optional[int]        =							2
					a      :     int        =							3
					a      :     Union[str, Any]        =							4
					a      :     int        =							5
					a      :     Optional[int]        =							6
					a      :     str        =							7
					a      :     List[Any]        =							8
					a      :     List[str]        =							9
					a      :     List[str]        =							1_0
					a      :     int        =							1_1
					a      :     Any        =							1_2
					a      :     Any        =							1_3
					a      :     Tuple        =							1_4






@dataclass
class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     torch.FloatTensor






class 					UpperCAmelCase__	:
					a      :     Tuple        =							SCHEDULER_CONFIG_NAME
					a      :     Union[str, Any]        =							[]
					a      :     str        =							True



					@classmethod
					def        UpperCAmelCase_ (    cls			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase=False			,				**UpperCamelCase			,				)     ->			int:
						__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  cls.load_config(
						    pretrained_model_name_or_path=UpperCamelCase			,				subfolder=UpperCamelCase			,				return_unused_kwargs=UpperCamelCase			,				return_commit_hash=UpperCamelCase			,				**UpperCamelCase			,				)
						return cls.from_config(UpperCamelCase			,				return_unused_kwargs=UpperCamelCase			,				**UpperCamelCase     )



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = False			,				**UpperCamelCase     )     ->			Dict:
						self.save_config(save_directory=UpperCamelCase			,				push_to_hub=UpperCamelCase			,				**UpperCamelCase     )



					@property
					def        UpperCAmelCase_ (    self     )     ->			str:
						return self._get_compatibles()



					@classmethod
					def        UpperCAmelCase_ (    cls     )     ->			Tuple:
						__lowerCAmelCase												=  list(set([cls.__name__] + cls._compatibles     )     )
						__lowerCAmelCase												=  importlib.import_module(__name__.split("."     )[0]     )
						__lowerCAmelCase												=  [
						    getattr(UpperCamelCase			,				UpperCamelCase     ) for c in compatible_classes_str if hasattr(UpperCamelCase			,				UpperCamelCase     )
						]
						return compatible_classes