# coding=utf-8
# Copyright 2020, The RAG Authors and The HuggingFace Inc. team.
#
# 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.
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"""RAG Retriever model implementation."""

import os
import pickle
import time
from typing import Iterable, List, Optional, Tuple

import numpy as np

from .configuration_rag import RagConfig
from .file_utils import (
    cached_path,
    is_datasets_available,
    is_faiss_available,
    is_remote_url,
    requires_datasets,
    requires_faiss,
)
from .tokenization_rag import RagTokenizer
from .tokenization_utils_base import BatchEncoding
from .utils import logging


if is_datasets_available():
    from datasets import load_dataset

if is_faiss_available():
    import faiss


logger = logging.get_logger(__name__)


LEGACY_INDEX_PATH = "https://storage.googleapis.com/huggingface-nlp/datasets/wiki_dpr/"


class Index:
    """
    A base class for the Indices encapsulated by the :class:`~transformers.RagRetriever`.
    """

    def get_doc_dicts(self, doc_ids: np.ndarray) -> List[dict]:
        """
        Returns a list of dictionaries, containing titles and text of the retrieved documents.

        Args:
            doc_ids (:obj:`np.ndarray` of shape :obj:`(batch_size, n_docs)`):
                A tensor of document indices.
        """
        raise NotImplementedError

    def get_top_docs(self, question_hidden_states: np.ndarray, n_docs=5) -> Tuple[np.ndarray, np.ndarray]:
        """
        For each query in the batch, retrieves ``n_docs`` documents.

        Args:
            question_hidden_states (:obj:`np.ndarray` of shape :obj:`(batch_size, vector_size):
                An array of query vectors.
            n_docs (:obj:`int`):
                The number of docs retrieved per query.

        Returns:
            :obj:`np.ndarray` of shape :obj:`(batch_size, n_docs)`: A tensor of indices of retrieved documents.
            :obj:`np.ndarray` of shape :obj:`(batch_size, vector_size)`: A tensor of vector representations of retrieved documents.
        """
        raise NotImplementedError

    def is_initialized(self):
        """
        Returns :obj:`True` if index is already initialized.
        """
        raise NotImplementedError

    def init_index(self):
        """
        A function responsible for loading the index into memory. Should be called only once per training run of a RAG model.
        E.g. if the model is trained on multiple GPUs in a distributed setup, only one of the workers will load the index.
        """
        raise NotImplementedError


class LegacyIndex:
    """
    An index which can be deserialized from the files built using https://github.com/facebookresearch/DPR.
    We use default faiss index parameters as specified in that repository.

    Args:
        vector_size (:obj:`int`):
            The dimension of indexed vectors.
        index_path (:obj:`str`):
            A path to a `directory` containing index files compatible with
            :class:`~transformers.retrieval_rag.LegacyIndex`
    """

    INDEX_FILENAME = "hf_bert_base.hnswSQ8_correct_phi_128.c_index"
    PASSAGE_FILENAME = "psgs_w100.tsv.pkl"

    def __init__(self, vector_size, index_path):
        self.index_id_to_db_id = []
        self.index_path = index_path
        self.passages = self._load_passages()
        self.vector_size = vector_size
        self.index = None
        self._index_initialize = False

    def _resolve_path(self, index_path, filename):
        assert os.path.isdir(index_path) or is_remote_url(index_path), "Please specify a valid ``index_path``."
        archive_file = os.path.join(index_path, filename)
        try:
            # Load from URL or cache if already cached
            resolved_archive_file = cached_path(archive_file)
            if resolved_archive_file is None:
                raise EnvironmentError
        except EnvironmentError:
            msg = (
                f"Can't load '{archive_file}'. Make sure that:\n\n"
                f"- '{index_path}' is a correct remote path to a directory containing a file named {filename}"
                f"- or '{index_path}' is the correct path to a directory containing a file named {filename}.\n\n"
            )
            raise EnvironmentError(msg)
        if resolved_archive_file == archive_file:
            logger.info("loading file {}".format(archive_file))
        else:
            logger.info("loading file {} from cache at {}".format(archive_file, resolved_archive_file))
        return resolved_archive_file

    def _load_passages(self):
        logger.info("Loading passages from {}".format(self.index_path))
        passages_path = self._resolve_path(self.index_path, self.PASSAGE_FILENAME)
        with open(passages_path, "rb") as passages_file:
            passages = pickle.load(passages_file)
        return passages

    def _deserialize_index(self):
        logger.info("Loading index from {}".format(self.index_path))
        resolved_index_path = self._resolve_path(self.index_path, self.INDEX_FILENAME + ".index.dpr")
        self.index = faiss.read_index(resolved_index_path)
        resolved_meta_path = self._resolve_path(self.index_path, self.INDEX_FILENAME + ".index_meta.dpr")
        with open(resolved_meta_path, "rb") as metadata_file:
            self.index_id_to_db_id = pickle.load(metadata_file)
        assert (
            len(self.index_id_to_db_id) == self.index.ntotal
        ), "Deserialized index_id_to_db_id should match faiss index size"

    def is_initialized(self):
        return self._index_initialize

    def init_index(self):
        index = faiss.IndexHNSWFlat(self.vector_size + 1, 512)
        index.hnsw.efSearch = 128
        index.hnsw.efConstruction = 200
        self.index = index
        self._deserialize_index()
        self._index_initialize = True

    def get_doc_dicts(self, doc_ids: np.array):
        doc_list = []
        for doc_ids_i in doc_ids:
            ids = [str(int(doc_id)) for doc_id in doc_ids_i]
            docs = [self.passages[doc_id] for doc_id in ids]
            doc_list.append(docs)
        doc_dicts = []
        for docs in doc_list:
            doc_dict = {}
            doc_dict["title"] = [doc[1] for doc in docs]
            doc_dict["text"] = [doc[0] for doc in docs]
            doc_dicts.append(doc_dict)
        return doc_dicts

    def get_top_docs(self, question_hidden_states: np.ndarray, n_docs=5) -> Tuple[np.ndarray, np.ndarray]:
        aux_dim = np.zeros(len(question_hidden_states), dtype="float32").reshape(-1, 1)
        query_nhsw_vectors = np.hstack((question_hidden_states, aux_dim))
        _, docs_ids = self.index.search(query_nhsw_vectors, n_docs)
        vectors = [[self.index.reconstruct(int(doc_id))[:-1] for doc_id in doc_ids] for doc_ids in docs_ids]
        ids = [[int(self.index_id_to_db_id[doc_id]) for doc_id in doc_ids] for doc_ids in docs_ids]
        return np.array(ids), np.array(vectors)


class HFIndex:
    """
    A wrapper around an instance of :class:`~datasets.Datasets`. If ``index_path`` is set to ``None``,
    we load the pre-computed index available with the :class:`~datasets.arrow_dataset.Dataset`, otherwise, we load the index from the indicated path on disk.

    Args:
        dataset (:obj:`str`, optional, defaults to ``wiki_dpr``):
            A datatset identifier of the indexed dataset on HuggingFace AWS bucket (list all available datasets and ids with ``datasets.list_datasets()``).
        dataset_split (:obj:`str`, optional, defaults to ``train``)
            Which split of the ``dataset`` to load.
        index_name (:obj:`str`, optional, defaults to ``train``)
            The index_name of the index associated with the ``dataset``. The index loaded from ``index_path`` will be saved under this name.
        index_path (:obj:`str`, optional, defaults to ``None``)
            The path to the serialized faiss index on disk.
    """

    def __init__(
        self,
        dataset_name: str,
        dataset_split: str,
        index_name: str,
        vector_size: int,
        index_path: Optional[str] = None,
        use_dummy_dataset=False,
    ):
        super().__init__()
        self.dataset_name = dataset_name
        self.dataset_split = dataset_split
        self.index_name = index_name
        self.vector_size = vector_size
        self.index_path = index_path
        self.use_dummy_dataset = use_dummy_dataset
        self._index_initialize = False

        logger.info("Loading passages from {}".format(self.dataset_name))
        self.dataset = load_dataset(
            self.dataset_name, with_index=False, split=self.dataset_split, dummy=self.use_dummy_dataset
        )
        self.dataset.set_format("numpy", columns=["embeddings"], output_all_columns=True)

    def is_initialized(self):
        return self._index_initialize

    def init_index(self):
        if self.index_path is not None:
            logger.info("Loading index from {}".format(self.index_path))
            self.index.load_faiss_index(index_name=self.index_name, file=self.index_path)
        else:
            logger.info("Loading index from {}".format(self.dataset_name + " with index name " + self.index_name))
            self.dataset = load_dataset(
                self.dataset_name,
                with_embeddings=True,
                with_index=True,
                split=self.dataset_split,
                index_name=self.index_name,
                dummy=self.use_dummy_dataset,
            )
            self.dataset.set_format("numpy", columns=["embeddings"], output_all_columns=True)
        self._index_initialize = True

    def get_doc_dicts(self, doc_ids: np.ndarray) -> List[dict]:
        return [self.dataset[doc_ids[i].tolist()] for i in range(doc_ids.shape[0])]

    def get_top_docs(self, question_hidden_states: np.ndarray, n_docs=5) -> Tuple[np.ndarray, np.ndarray]:
        _, ids = self.dataset.search_batch("embeddings", question_hidden_states, n_docs)
        docs = [self.dataset[[i for i in indices if i >= 0]] for indices in ids]
        vectors = [doc["embeddings"] for doc in docs]
        for i in range(len(vectors)):
            if len(vectors[i]) < n_docs:
                vectors[i] = np.vstack([vectors[i], np.zeros((n_docs - len(vectors[i]), self.vector_size))])
        return np.array(ids), np.array(vectors)  # shapes (batch_size, n_docs) and (batch_size, n_docs, d)


class RagRetriever:
    """
    Retriever used to get documents from vector queries.
    It retrieves the documents embeddings as well as the documents contents, and it formats them to be used with a RagModel.

    Args:
        config (:class:`~transformers.RagConfig`):
            The configuration of the RAG model this Retriever is used with. Contains parameters indicating which ``Index`` to build.
        question_encoder_tokenizer (:class:`~transformers.PreTrainedTokenizer`):
            The tokenizer that was used to tokenize the question.
            It is used to decode the question and then use the generator_tokenizer.
        generator_tokenizer (:class:`~transformers.PreTrainedTokenizer`):
            The tokenizer used for the generator part of the RagModel.
    """

    _init_retrieval = True

    def __init__(self, config, question_encoder_tokenizer, generator_tokenizer):
        requires_datasets(self)
        requires_faiss(self)
        super().__init__()
        self.index = (
            LegacyIndex(
                config.retrieval_vector_size,
                config.index_path or LEGACY_INDEX_PATH,
            )
            if config.index_name == "legacy"
            else HFIndex(
                config.dataset,
                config.dataset_split,
                config.index_name,
                config.retrieval_vector_size,
                config.index_path,
                config.use_dummy_dataset,
            )
        )
        self.generator_tokenizer = generator_tokenizer
        self.question_encoder_tokenizer = question_encoder_tokenizer

        self.n_docs = config.n_docs
        self.batch_size = config.retrieval_batch_size

        self.config = config
        if self._init_retrieval:
            self.init_retrieval()

    @classmethod
    def from_pretrained(cls, retriever_name_or_path, **kwargs):
        requires_datasets(cls)
        requires_faiss(cls)
        config = RagConfig.from_pretrained(retriever_name_or_path, **kwargs)
        rag_tokenizer = RagTokenizer.from_pretrained(retriever_name_or_path, config=config)
        question_encoder_tokenizer = rag_tokenizer.question_encoder
        generator_tokenizer = rag_tokenizer.generator
        return cls(
            config, question_encoder_tokenizer=question_encoder_tokenizer, generator_tokenizer=generator_tokenizer
        )

    def save_pretrained(self, save_directory):
        self.config.save_pretrained(save_directory)
        rag_tokenizer = RagTokenizer(
            question_encoder=self.question_encoder_tokenizer,
            generator=self.generator_tokenizer,
        )
        rag_tokenizer.save_pretrained(save_directory)

    def init_retrieval(self):
        """
        Retriever initalization function. It loads the index into memory.
        """

        logger.info("initializing retrieval")
        self.index.init_index()

    def postprocess_docs(self, docs, input_strings, prefix, n_docs, return_tensors=None):
        r"""
        Postprocessing retrieved ``docs`` and combining them with ``input_strings``.

        Args:
            doc_scores (:obj:`np.ndarray` of shape :obj:`(batch_size, n_docs)`):
                Retrieval scores of respective docs - passed for logging.
            docs  (:obj:`dict`):
                Retrieved documents.
            input_strings (:obj:`str`):
                Input strings decoded by ``preprocess_query``.
            prefix (:obj:`str`):
                Prefix added at the beginning of each input, typically used with T5-based models.

        Return:
            :obj:`tuple(tensors)`:
                a tuple consisting of two elements: contextualized ``input_ids`` and a compatible ``attention_mask``.
        """

        def cat_input_and_doc(doc_title, doc_text, input_string, prefix):
            # TODO(Patrick): if we train more RAG models, I want to put the input first to take advantage of effortless truncation
            # TODO(piktus): better handling of truncation
            if doc_title.startswith('"'):
                doc_title = doc_title[1:]
            if doc_title.endswith('"'):
                doc_title = doc_title[:-1]
            if prefix is None:
                prefix = ""
            out = (prefix + doc_title + self.config.title_sep + doc_text + self.config.doc_sep + input_string).replace(
                "  ", " "
            )
            return out

        rag_input_strings = [
            cat_input_and_doc(
                docs[i]["title"][j],
                docs[i]["text"][j],
                input_strings[i],
                prefix,
            )
            for i in range(len(docs))
            for j in range(n_docs)
        ]

        contextualized_inputs = self.generator_tokenizer.batch_encode_plus(
            rag_input_strings,
            max_length=self.config.max_combined_length,
            return_tensors=return_tensors,
            padding="max_length",
            truncation=True,
        )

        return contextualized_inputs["input_ids"], contextualized_inputs["attention_mask"]

    def _chunk_tensor(self, t: Iterable, chunk_size: int) -> List[Iterable]:
        return [t[i : i + chunk_size] for i in range(0, len(t), chunk_size)]

    def _main_retrieve(self, question_hidden_states: np.ndarray, n_docs: int) -> Tuple[np.ndarray, np.ndarray]:
        question_hidden_states_batched = self._chunk_tensor(question_hidden_states, self.batch_size)
        ids_batched = []
        vectors_batched = []
        for question_hidden_states in question_hidden_states_batched:
            start_time = time.time()
            ids, vectors = self.index.get_top_docs(question_hidden_states, n_docs)
            logger.debug(
                "index search time: {} sec, batch size {}".format(
                    time.time() - start_time, question_hidden_states.shape
                )
            )
            ids_batched.extend(ids)
            vectors_batched.extend(vectors)
        return (
            np.array(ids_batched),
            np.array(vectors_batched),
        )  # shapes (batch_size, n_docs) and (batch_size, n_docs, d)

    def retrieve(self, question_hidden_states: np.ndarray, n_docs: int) -> Tuple[np.ndarray, List[dict]]:
        """
        Retrieves documents for specified ``question_hidden_states``.

        Args:
            question_hidden_states (:obj:`np.ndarray` of shape :obj:`(batch_size, vector_size)`):
                A batch of query vectors to retrieve with.
            n_docs (:obj:`int`):
                The number of docs retrieved per query.

        Return:
            :obj:`Tuple[np.ndarray, np.ndarray, List[dict]]`:
            A tuple with the following objects:

            - **retrieved_doc_embeds** (:obj:`np.ndarray` of shape :obj:`(batch_size, n_docs, dim)`) -- The
              retrieval embeddings of the retrieved docs per query.
            - **doc_ids** (:obj:`np.ndarray` of shape :obj:`(batch_size, n_docs)`) -- The ids of the documents in the
              index
            - **doc_dicts** (:obj:`List[dict]`): The :obj:`retrieved_doc_embeds` examples per query.
        """

        doc_ids, retrieved_doc_embeds = self._main_retrieve(question_hidden_states, n_docs)
        return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(doc_ids)

    def __call__(
        self,
        question_input_ids: List[List[int]],
        question_hidden_states: np.ndarray,
        prefix=None,
        n_docs=None,
        return_tensors=None,
    ) -> BatchEncoding:
        """
        Retrieves documents for specified :obj:`question_hidden_states`.

        Args:
            question_input_ids: (:obj:`List[List[int]]`) batch of input ids
            question_hidden_states (:obj:`np.ndarray` of shape :obj:`(batch_size, vector_size)`:
                A batch of query vectors to retrieve with.
            prefix: (:obj:`str`, `optional`):
                The prefix used by the generator's tokenizer.
            n_docs (:obj:`int`, `optional`):
                The number of docs retrieved per query.
            return_tensors (:obj:`str` or :class:`~transformers.tokenization_utils_base.TensorType`, `optional`, defaults to "pt"):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                * :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
                * :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
                * :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.

        Output:
            :class:`~transformers.BatchEncoding`: A :class:`~transformers.BatchEncoding` with the following fields:

            - **context_input_ids** -- List of token ids to be fed to a model.

              `What are input IDs? <../glossary.html#input-ids>`__
            - **context_attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
              :obj:`return_attention_mask=True` or if `"attention_mask"` is in :obj:`self.model_input_names`).

              `What are attention masks? <../glossary.html#attention-mask>`__
            - **retrieved_doc_embeds** -- List of embeddings of the retrieved documents
            - **doc_ids** -- List of ids of the retrieved documents
        """

        n_docs = n_docs if n_docs is not None else self.n_docs
        prefix = prefix if prefix is not None else self.config.generator.prefix
        retrieved_doc_embeds, doc_ids, docs = self.retrieve(question_hidden_states, n_docs)

        input_strings = self.question_encoder_tokenizer.batch_decode(question_input_ids, skip_special_tokens=True)
        context_input_ids, context_attention_mask = self.postprocess_docs(
            docs, input_strings, prefix, n_docs, return_tensors=return_tensors
        )

        return BatchEncoding(
            {
                "context_input_ids": context_input_ids,
                "context_attention_mask": context_attention_mask,
                "retrieved_doc_embeds": retrieved_doc_embeds,
                "doc_ids": doc_ids,
            },
            tensor_type=return_tensors,
        )
