# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
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.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 RobertaTokenizer as BartTokenizer
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
    import faiss


@require_faiss
class RagRetrieverTest(TestCase):
    def setUp(self):
        self.tmpdirname = tempfile.mkdtemp()
        self.retrieval_vector_size = 8

        # DPR tokenizer vocab
        self.dpr_vocab_tokens = [
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[PAD]",
            "[MASK]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        self.dpr_vocab = {token: i for i, token in enumerate(self.dpr_vocab_tokens)}

        # BART tokenizer vocab and merges
        vocab = [
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "\u0120",
            "\u0120l",
            "\u0120n",
            "\u0120lo",
            "\u0120low",
            "er",
            "\u0120lowest",
            "\u0120newer",
            "\u0120wider",
            "<unk>",
        ]
        self.bart_vocab = dict(zip(vocab, range(len(vocab))))
        merges_raw = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        self.bart_merges = []
        for line in merges_raw:
            line = line.strip()
            if line and not line.startswith("#"):
                self.bart_merges.append(tuple(line.split()))

    def get_dpr_tokenizer(self) -> DPRQuestionEncoderTokenizer:
        return DPRQuestionEncoderTokenizer(vocab=self.dpr_vocab)

    def get_dpr_ctx_encoder_tokenizer(self) -> DPRContextEncoderTokenizer:
        return DPRContextEncoderTokenizer(vocab=self.dpr_vocab)

    def get_bart_tokenizer(self) -> BartTokenizer:
        return BartTokenizer(vocab=self.bart_vocab, merges=self.bart_merges, unk_token="<unk>")

    def tearDown(self):
        shutil.rmtree(self.tmpdirname)

    def get_dummy_dataset(self):
        dataset = 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 get_dummy_canonical_hf_index_retriever(self):
        dataset = self.get_dummy_dataset()
        config = 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:
            mock_load_dataset.return_value = dataset
            retriever = RagRetriever(
                config,
                question_encoder_tokenizer=self.get_dpr_tokenizer(),
                generator_tokenizer=self.get_bart_tokenizer(),
            )
        return retriever

    def get_dummy_custom_hf_index_retriever(self, from_disk: bool):
        dataset = self.get_dummy_dataset()
        config = RagConfig(
            retrieval_vector_size=self.retrieval_vector_size,
            question_encoder=DPRConfig().to_dict(),
            generator=BartConfig().to_dict(),
            index_name="custom",
        )
        if from_disk:
            config.passages_path = os.path.join(self.tmpdirname, "dataset")
            config.index_path = 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
            retriever = RagRetriever(
                config,
                question_encoder_tokenizer=self.get_dpr_tokenizer(),
                generator_tokenizer=self.get_bart_tokenizer(),
            )
        else:
            retriever = RagRetriever(
                config,
                question_encoder_tokenizer=self.get_dpr_tokenizer(),
                generator_tokenizer=self.get_bart_tokenizer(),
                index=CustomHFIndex(config.retrieval_vector_size, dataset),
            )
        return retriever

    def test_canonical_hf_index_retriever_retrieve(self):
        n_docs = 1
        retriever = self.get_dummy_canonical_hf_index_retriever()
        hidden_states = np.array(
            [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
        )
        retrieved_doc_embeds, doc_ids, doc_dicts = retriever.retrieve(hidden_states, n_docs=n_docs)
        self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size))
        self.assertEqual(len(doc_dicts), 2)
        self.assertEqual(sorted(doc_dicts[0]), ["embeddings", "id", "text", "title"])
        self.assertEqual(len(doc_dicts[0]["id"]), n_docs)
        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 test_canonical_hf_index_retriever_save_and_from_pretrained(self):
        retriever = 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:
                mock_load_dataset.return_value = self.get_dummy_dataset()
                retriever.save_pretrained(tmp_dirname)
                retriever = RagRetriever.from_pretrained(tmp_dirname)
                self.assertIsInstance(retriever, RagRetriever)
                hidden_states = np.array(
                    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
                )
                out = retriever.retrieve(hidden_states, n_docs=1)
                self.assertTrue(out is not None)

    def test_custom_hf_index_retriever_retrieve(self):
        n_docs = 1
        retriever = self.get_dummy_custom_hf_index_retriever(from_disk=False)
        hidden_states = np.array(
            [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
        )
        retrieved_doc_embeds, doc_ids, doc_dicts = retriever.retrieve(hidden_states, n_docs=n_docs)
        self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size))
        self.assertEqual(len(doc_dicts), 2)
        self.assertEqual(sorted(doc_dicts[0]), ["embeddings", "id", "text", "title"])
        self.assertEqual(len(doc_dicts[0]["id"]), n_docs)
        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 test_custom_hf_index_retriever_save_and_from_pretrained(self):
        retriever = self.get_dummy_custom_hf_index_retriever(from_disk=False)
        with tempfile.TemporaryDirectory() as tmp_dirname:
            retriever.save_pretrained(tmp_dirname)
            retriever = RagRetriever.from_pretrained(tmp_dirname)
            self.assertIsInstance(retriever, RagRetriever)
            hidden_states = np.array(
                [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
            )
            out = retriever.retrieve(hidden_states, n_docs=1)
            self.assertTrue(out is not None)

    def test_custom_hf_index_retriever_retrieve_from_disk(self):
        n_docs = 1
        retriever = self.get_dummy_custom_hf_index_retriever(from_disk=True)
        hidden_states = np.array(
            [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
        )
        retrieved_doc_embeds, doc_ids, doc_dicts = retriever.retrieve(hidden_states, n_docs=n_docs)
        self.assertEqual(retrieved_doc_embeds.shape, (2, n_docs, self.retrieval_vector_size))
        self.assertEqual(len(doc_dicts), 2)
        self.assertEqual(sorted(doc_dicts[0]), ["embeddings", "id", "text", "title"])
        self.assertEqual(len(doc_dicts[0]["id"]), n_docs)
        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 test_custom_hf_index_retriever_save_and_from_pretrained_from_disk(self):
        retriever = self.get_dummy_custom_hf_index_retriever(from_disk=True)
        with tempfile.TemporaryDirectory() as tmp_dirname:
            retriever.save_pretrained(tmp_dirname)
            retriever = RagRetriever.from_pretrained(tmp_dirname)
            self.assertIsInstance(retriever, RagRetriever)
            hidden_states = np.array(
                [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
            )
            out = retriever.retrieve(hidden_states, n_docs=1)
            self.assertTrue(out is not None)

    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def test_hf_index_retriever_call(self):
        import torch

        n_docs = 1
        retriever = self.get_dummy_canonical_hf_index_retriever()
        question_input_ids = [[5, 7], [10, 11]]
        hidden_states = np.array(
            [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
        )
        out = retriever(question_input_ids, hidden_states, prefix=None, n_docs=n_docs)
        context_input_ids, context_attention_mask, retrieved_doc_embeds = (
            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(context_input_ids, list)
        self.assertIsInstance(context_attention_mask, list)
        self.assertIsInstance(retrieved_doc_embeds, np.ndarray)

        out = retriever(
            question_input_ids,
            hidden_states,
            prefix=None,
            n_docs=n_docs,
            return_tensors="pt",
        )
        context_input_ids, context_attention_mask, retrieved_doc_embeds, doc_ids = (  # 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(context_input_ids, torch.Tensor)
        self.assertIsInstance(context_attention_mask, torch.Tensor)
        self.assertIsInstance(retrieved_doc_embeds, torch.Tensor)

    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def test_custom_hf_index_end2end_retriever_call(self):
        context_encoder_tokenizer = self.get_dpr_ctx_encoder_tokenizer()
        n_docs = 1
        retriever = self.get_dummy_custom_hf_index_retriever(from_disk=False)
        retriever.set_ctx_encoder_tokenizer(context_encoder_tokenizer)

        question_input_ids = [[5, 7], [10, 11]]
        hidden_states = np.array(
            [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)], dtype=np.float32
        )
        out = retriever(question_input_ids, hidden_states, prefix=None, n_docs=n_docs)

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