

"""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					(						__magic_name__       ):





       """simple docstring"""


       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				tempfile.mkdtemp()
        lowerCAmelCase__   :				List[str] 			=				8

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

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        shutil.rmtree(self.tmpdirname   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				self.get_dummy_dataset()
        lowerCAmelCase__   :				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:
         lowerCAmelCase__   :				Any 			=				dataset
         lowerCAmelCase__   :				List[str] 			=				RagRetriever(
             snake_case			,					question_encoder_tokenizer=self.get_dpr_tokenizer()			,					generator_tokenizer=self.get_bart_tokenizer()			,					)
        return retriever






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				self.get_dummy_dataset()
        lowerCAmelCase__   :				Optional[Any] 			=				RagConfig(
            retrieval_vector_size=self.retrieval_vector_size			,					question_encoder=DPRConfig().to_dict()			,					generator=BartConfig().to_dict()			,					index_name="custom"			,					)
        if from_disk:
         lowerCAmelCase__   :				Union[str, Any] 			=				os.path.join(self.tmpdirname			,					"dataset"   )
         lowerCAmelCase__   :				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
         lowerCAmelCase__   :				Union[str, Any] 			=				RagRetriever(
             snake_case			,					question_encoder_tokenizer=self.get_dpr_tokenizer()			,					generator_tokenizer=self.get_bart_tokenizer()			,					)
        else:
         lowerCAmelCase__   :				str 			=				RagRetriever(
             snake_case			,					question_encoder_tokenizer=self.get_dpr_tokenizer()			,					generator_tokenizer=self.get_bart_tokenizer()			,					index=CustomHFIndex(config.retrieval_vector_size			,					snake_case   )			,					)
        return retriever






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				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__   :				Dict 			=				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__   :				Any 			=				os.path.join(self.tmpdirname			,					"psgs_w100.tsv.pkl"   )
        lowerCAmelCase__   :				Optional[Any] 			=				{sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
        pickle.dump(snake_case			,					open(snake_case			,					"wb"   )   )

        lowerCAmelCase__   :				Dict 			=				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__   :				List[Any] 			=				RagRetriever(
            snake_case			,					question_encoder_tokenizer=self.get_dpr_tokenizer()			,					generator_tokenizer=self.get_bart_tokenizer()   )
        return retriever






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				1
        lowerCAmelCase__   :				int 			=				self.get_dummy_canonical_hf_index_retriever()
        lowerCAmelCase__   :				str 			=				np.array(
            [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,					dtype=np.floataa   )
        lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				int 			=				retriever.retrieve(snake_case			,					n_docs=snake_case   )
        self.assertEqual(retrieved_doc_embeds.shape			,					(2, n_docs, self.retrieval_vector_size)   )
        self.assertEqual(len(snake_case   )			,					2   )
        self.assertEqual(sorted(doc_dicts[0]   )			,					["embeddings", "id", "text", "title"]   )
        self.assertEqual(len(doc_dicts[0]["id"]   )			,					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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				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__   :				Any 			=				self.get_dummy_dataset()
          retriever.save_pretrained(snake_case   )
          lowerCAmelCase__   :				List[str] 			=				RagRetriever.from_pretrained(snake_case   )
          self.assertIsInstance(snake_case			,					snake_case   )
          lowerCAmelCase__   :				Optional[Any] 			=				np.array(
              [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,					dtype=np.floataa   )
          lowerCAmelCase__   :				Tuple 			=				retriever.retrieve(snake_case			,					n_docs=1   )
          self.assertTrue(out is not None   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				1
        lowerCAmelCase__   :				int 			=				self.get_dummy_custom_hf_index_retriever(from_disk=snake_case   )
        lowerCAmelCase__   :				Optional[Any] 			=				np.array(
            [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,					dtype=np.floataa   )
        lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				Any 			=				retriever.retrieve(snake_case			,					n_docs=snake_case   )
        self.assertEqual(retrieved_doc_embeds.shape			,					(2, n_docs, self.retrieval_vector_size)   )
        self.assertEqual(len(snake_case   )			,					2   )
        self.assertEqual(sorted(doc_dicts[0]   )			,					["embeddings", "id", "text", "title"]   )
        self.assertEqual(len(doc_dicts[0]["id"]   )			,					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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				self.get_dummy_custom_hf_index_retriever(from_disk=snake_case   )
        with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(snake_case   )
         lowerCAmelCase__   :				List[str] 			=				RagRetriever.from_pretrained(snake_case   )
         self.assertIsInstance(snake_case			,					snake_case   )
         lowerCAmelCase__   :				List[Any] 			=				np.array(
             [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,					dtype=np.floataa   )
         lowerCAmelCase__   :				Dict 			=				retriever.retrieve(snake_case			,					n_docs=1   )
         self.assertTrue(out is not None   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				1
        lowerCAmelCase__   :				int 			=				self.get_dummy_custom_hf_index_retriever(from_disk=snake_case   )
        lowerCAmelCase__   :				Optional[int] 			=				np.array(
            [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,					dtype=np.floataa   )
        lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				Tuple 			=				retriever.retrieve(snake_case			,					n_docs=snake_case   )
        self.assertEqual(retrieved_doc_embeds.shape			,					(2, n_docs, self.retrieval_vector_size)   )
        self.assertEqual(len(snake_case   )			,					2   )
        self.assertEqual(sorted(doc_dicts[0]   )			,					["embeddings", "id", "text", "title"]   )
        self.assertEqual(len(doc_dicts[0]["id"]   )			,					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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				self.get_dummy_custom_hf_index_retriever(from_disk=snake_case   )
        with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(snake_case   )
         lowerCAmelCase__   :				List[str] 			=				RagRetriever.from_pretrained(snake_case   )
         self.assertIsInstance(snake_case			,					snake_case   )
         lowerCAmelCase__   :				Optional[Any] 			=				np.array(
             [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,					dtype=np.floataa   )
         lowerCAmelCase__   :				List[Any] 			=				retriever.retrieve(snake_case			,					n_docs=1   )
         self.assertTrue(out is not None   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				1
        lowerCAmelCase__   :				Dict 			=				self.get_dummy_legacy_index_retriever()
        lowerCAmelCase__   :				str 			=				np.array(
            [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,					dtype=np.floataa   )
        lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				List[Any] 			=				retriever.retrieve(snake_case			,					n_docs=snake_case   )
        self.assertEqual(retrieved_doc_embeds.shape			,					(2, n_docs, self.retrieval_vector_size)   )
        self.assertEqual(len(snake_case   )			,					2   )
        self.assertEqual(sorted(doc_dicts[0]   )			,					["text", "title"]   )
        self.assertEqual(len(doc_dicts[0]["text"]   )			,					snake_case   )
        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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				self.get_dummy_legacy_index_retriever()
        with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(snake_case   )
         lowerCAmelCase__   :				List[Any] 			=				RagRetriever.from_pretrained(snake_case   )
         self.assertIsInstance(snake_case			,					snake_case   )
         lowerCAmelCase__   :				Optional[Any] 			=				np.array(
             [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,					dtype=np.floataa   )
         lowerCAmelCase__   :				List[str] 			=				retriever.retrieve(snake_case			,					n_docs=1   )
         self.assertTrue(out is not None   )






       @require_torch
       @require_tokenizers
       @require_sentencepiece
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        import torch

        lowerCAmelCase__   :				List[Any] 			=				1
        lowerCAmelCase__   :				Tuple 			=				self.get_dummy_canonical_hf_index_retriever()
        lowerCAmelCase__   :				Any 			=				[[5, 7], [10, 11]]
        lowerCAmelCase__   :				List[Any] 			=				np.array(
            [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,					dtype=np.floataa   )
        lowerCAmelCase__   :				Any 			=				retriever(snake_case			,					snake_case			,					prefix=retriever.config.generator.prefix			,					n_docs=snake_case   )
        lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				Dict 			=				(
            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(snake_case			,					snake_case   )
        self.assertIsInstance(snake_case			,					snake_case   )
        self.assertIsInstance(snake_case			,					np.ndarray   )

        lowerCAmelCase__   :				Optional[int] 			=				retriever(
            snake_case			,					snake_case			,					prefix=retriever.config.generator.prefix			,					n_docs=snake_case			,					return_tensors="pt"			,					)
        lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				Tuple 			=				(  # 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(snake_case			,					torch.Tensor   )
        self.assertIsInstance(snake_case			,					torch.Tensor   )
        self.assertIsInstance(snake_case			,					torch.Tensor   )






       @require_torch
       @require_tokenizers
       @require_sentencepiece
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				self.get_dpr_ctx_encoder_tokenizer()
        lowerCAmelCase__   :				Union[str, Any] 			=				1
        lowerCAmelCase__   :				str 			=				self.get_dummy_custom_hf_index_retriever(from_disk=snake_case   )
        retriever.set_ctx_encoder_tokenizer(snake_case   )

        lowerCAmelCase__   :				int 			=				[[5, 7], [10, 11]]
        lowerCAmelCase__   :				Optional[int] 			=				np.array(
            [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]			,					dtype=np.floataa   )
        lowerCAmelCase__   :				Optional[int] 			=				retriever(snake_case			,					snake_case			,					prefix=retriever.config.generator.prefix			,					n_docs=snake_case   )

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









"""simple docstring"""

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


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    {
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      str    =						'dpr'






       def __init__(   self			,					snake_case=30_522			,					snake_case=768			,					snake_case=12			,					snake_case=12			,					snake_case=3_072			,					snake_case="gelu"			,					snake_case=0.1			,					snake_case=0.1			,					snake_case=512			,					snake_case=2			,					snake_case=0.02			,					snake_case=1e-12			,					snake_case=0			,					snake_case="absolute"			,					snake_case = 0			,					**snake_case			,					):





        """simple docstring"""

        super().__init__(pad_token_id=snake_case			,					**snake_case   )

        lowerCAmelCase__   :				Dict 			=				vocab_size
        lowerCAmelCase__   :				int 			=				hidden_size
        lowerCAmelCase__   :				List[str] 			=				num_hidden_layers
        lowerCAmelCase__   :				List[Any] 			=				num_attention_heads
        lowerCAmelCase__   :				Any 			=				hidden_act
        lowerCAmelCase__   :				Any 			=				intermediate_size
        lowerCAmelCase__   :				str 			=				hidden_dropout_prob
        lowerCAmelCase__   :				List[str] 			=				attention_probs_dropout_prob
        lowerCAmelCase__   :				List[Any] 			=				max_position_embeddings
        lowerCAmelCase__   :				Optional[Any] 			=				type_vocab_size
        lowerCAmelCase__   :				Tuple 			=				initializer_range
        lowerCAmelCase__   :				Dict 			=				layer_norm_eps
        lowerCAmelCase__   :				Dict 			=				projection_dim
        lowerCAmelCase__   :				Any 			=				position_embedding_type






