# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import os
from collections import defaultdict

import numpy as np
import torch
from VisualBERT.mmf.utils.configuration import get_mmf_cache_dir
from VisualBERT.mmf.utils.distributed import is_master, synchronize
from VisualBERT.mmf.utils.file_io import PathManager
from VisualBERT.mmf.utils.general import get_absolute_path
from torchtext import vocab


EMBEDDING_NAME_CLASS_MAPPING = {"glove": "GloVe", "fasttext": "FastText"}

logger = logging.getLogger(__name__)


class Vocab:
    def __init__(self, *args, **params):
        vocab_type = params.get("type", "pretrained")
        # Stores final parameters extracted from vocab_params

        if vocab_type == "random":
            if params["vocab_file"] is None:
                raise ValueError("No vocab path passed for vocab")

            self.vocab = BaseVocab(*args, **params)

        elif vocab_type == "custom":
            if params["vocab_file"] is None or params["embedding_file"] is None:
                raise ValueError("No vocab path or embedding_file passed for vocab")
            self.vocab = CustomVocab(*args, **params)

        elif vocab_type == "pretrained":
            self.vocab = PretrainedVocab(*args, **params)

        elif vocab_type == "intersected":
            if params["vocab_file"] is None or params["embedding_name"] is None:
                raise ValueError("No vocab path or embedding_name passed for vocab")

            self.vocab = IntersectedVocab(*args, **params)

        elif vocab_type == "extracted":
            if params["base_path"] is None or params["embedding_dim"] is None:
                raise ValueError("No base_path or embedding_dim passed for vocab")
            self.vocab = ExtractedVocab(*args, **params)

        elif vocab_type == "model":
            if params["name"] is None or params["model_file"] is None:
                raise ValueError("No name or model_file passed for vocab")
            if params["name"] == "fasttext":
                self.vocab = ModelVocab(*args, **params)
        else:
            raise ValueError("Unknown vocab type: %s" % vocab_type)

        self._dir_representation = dir(self)

    def __call__(self, *args, **kwargs):
        return self.vocab(*args, **kwargs)

    def __getattr__(self, name):
        if "_dir_representation" in self.__dict__ and name in self._dir_representation:
            return getattr(self, name)
        elif "vocab" in self.__dict__ and hasattr(self.vocab, name):
            return getattr(self.vocab, name)
        else:
            type_vocab = "Vocab"
            if "vocab" in self.__dict__:
                type_vocab = type(self.vocab)

            raise AttributeError(f"{type_vocab} vocab type has no attribute {name}.")


class BaseVocab:
    PAD_TOKEN = "<pad>"
    SOS_TOKEN = "<s>"
    EOS_TOKEN = "</s>"
    UNK_TOKEN = "<unk>"

    PAD_INDEX = 0
    SOS_INDEX = 1
    EOS_INDEX = 2
    UNK_INDEX = 3

    def __init__(
        self, vocab_file=None, embedding_dim=300, data_dir=None, *args, **kwargs
    ):
        """Vocab class to be used when you want to train word embeddings from
        scratch based on a custom vocab. This will initialize the random
        vectors for the vocabulary you pass. Get the vectors using
        `get_vectors` function. This will also create random embeddings for
        some predefined words like PAD - <pad>, SOS - <s>, EOS - </s>,
        UNK - <unk>.

        Parameters
        ----------
        vocab_file : str
            Path of the vocabulary file containing one word per line
        embedding_dim : int
            Size of the embedding

        """
        self.type = "base"
        self.word_dict = {}
        self.itos = {}

        self.itos[self.PAD_INDEX] = self.PAD_TOKEN
        self.itos[self.SOS_INDEX] = self.SOS_TOKEN
        self.itos[self.EOS_INDEX] = self.EOS_TOKEN
        self.itos[self.UNK_INDEX] = self.UNK_TOKEN

        self.word_dict[self.SOS_TOKEN] = self.SOS_INDEX
        self.word_dict[self.EOS_TOKEN] = self.EOS_INDEX
        self.word_dict[self.PAD_TOKEN] = self.PAD_INDEX
        self.word_dict[self.UNK_TOKEN] = self.UNK_INDEX

        index = len(self.itos.keys())

        self.total_predefined = len(self.itos.keys())

        if vocab_file is not None:
            if not os.path.isabs(vocab_file) and data_dir is not None:
                vocab_file = os.path.join(data_dir, vocab_file)
                vocab_file = get_absolute_path(vocab_file)

            if not PathManager.exists(vocab_file):
                raise RuntimeError("Vocab not found at " + vocab_file)

            with PathManager.open(vocab_file, "r") as f:
                for line in f:
                    self.itos[index] = line.strip()
                    self.word_dict[line.strip()] = index
                    index += 1

        self.word_dict[self.SOS_TOKEN] = self.SOS_INDEX
        self.word_dict[self.EOS_TOKEN] = self.EOS_INDEX
        self.word_dict[self.PAD_TOKEN] = self.PAD_INDEX
        self.word_dict[self.UNK_TOKEN] = self.UNK_INDEX
        # Return unk index by default
        self.stoi = defaultdict(self.get_unk_index)
        self.stoi.update(self.word_dict)

        self.vectors = torch.FloatTensor(self.get_size(), embedding_dim)

    def get_itos(self):
        return self.itos

    def get_stoi(self):
        return self.stoi

    def get_size(self):
        return len(self.itos)

    def get_pad_index(self):
        return self.PAD_INDEX

    def get_pad_token(self):
        return self.PAD_TOKEN

    def get_start_index(self):
        return self.SOS_INDEX

    def get_start_token(self):
        return self.SOS_TOKEN

    def get_end_index(self):
        return self.EOS_INDEX

    def get_end_token(self):
        return self.EOS_TOKEN

    def get_unk_index(self):
        return self.UNK_INDEX

    def get_unk_token(self):
        return self.UNK_TOKEN

    def get_vectors(self):
        return getattr(self, "vectors", None)

    def get_embedding(self, cls, **embedding_kwargs):
        vector_dim = len(self.vectors[0])
        embedding_kwargs["vocab_size"] = self.get_size()

        embedding_dim = embedding_kwargs["embedding_dim"]
        embedding_kwargs["embedding_dim"] = vector_dim

        embedding = None

        if cls == torch.nn.Embedding:
            embedding = torch.nn.Embedding(self.get_size(), vector_dim)
        else:
            embedding = cls(**embedding_kwargs)

        if hasattr(embedding, "embedding"):
            embedding.embedding = torch.nn.Embedding.from_pretrained(
                self.vectors, freeze=False
            )
        else:
            embedding = torch.nn.Embedding.from_pretrained(self.vectors, freeze=False)

        if vector_dim == embedding_dim:
            return embedding
        else:
            return torch.nn.Sequential(
                [embedding, torch.nn.Linear(vector_dim, embedding_dim)]
            )


class CustomVocab(BaseVocab):
    def __init__(self, vocab_file, embedding_file, data_dir=None, *args, **kwargs):
        """Use this vocab class when you have a custom vocab as well as a
        custom embeddings file.

        This will inherit vocab class, so you will get predefined tokens with
        this one.

        IMPORTANT: To init your embedding, get your vectors from this class's
        object by calling `get_vectors` function

        Parameters
        ----------
        vocab_file : str
            Path of custom vocabulary
        embedding_file : str
            Path to custom embedding inititalization file
        data_dir : str
            Path to data directory if embedding file is not an absolute path.
            Default: None
        """
        super().__init__(vocab_file)
        self.type = "custom"

        if not os.path.isabs(embedding_file) and data_dir is not None:
            embedding_file = os.path.join(data_dir, embedding_file)
            embedding_file = get_absolute_path(embedding_file)

        if not PathManager.exists(embedding_file):
            raise RuntimeError(f"Embedding file path {embedding_file} doesn't exist")

        embedding_vectors = torch.from_numpy(np.load(embedding_file))

        self.vectors = torch.FloatTensor(self.get_size(), len(embedding_vectors[0]))

        for i in range(0, 4):
            self.vectors[i] = torch.ones_like(self.vectors[i]) * 0.1 * i

        for i in range(4, self.get_size()):
            self.vectors[i] = embedding_vectors[i - 4]


class IntersectedVocab(BaseVocab):
    def __init__(self, vocab_file, embedding_name, *args, **kwargs):
        """Use this vocab class when you have a custom vocabulary class but you
        want to use pretrained embedding vectos for it. This will only load
        the vectors which intersect with your vocabulary. Use the
        embedding_name specified in torchtext's pretrained aliases:
        ['charngram.100d', 'fasttext.en.300d', 'fasttext.simple.300d',
         'glove.42B.300d', 'glove.840B.300d', 'glove.twitter.27B.25d',
         'glove.twitter.27B.50d', 'glove.twitter.27B.100d',
         'glove.twitter.27B.200d', 'glove.6B.50d', 'glove.6B.100d',
         'glove.6B.200d', 'glove.6B.300d']

        Parameters
        ----------
        vocab_file : str
            Vocabulary file containing list of words with one word per line
            which will be used to collect vectors
        embedding_name : str
            Embedding name picked up from the list of the pretrained aliases
            mentioned above
        """
        super().__init__(vocab_file, *args, **kwargs)

        self.type = "intersected"

        name = embedding_name.split(".")[0]
        dim = embedding_name.split(".")[2][:-1]
        middle = embedding_name.split(".")[1]

        class_name = EMBEDDING_NAME_CLASS_MAPPING[name]

        if not hasattr(vocab, class_name):
            raise RuntimeError(f"Unknown embedding type: {name}")

        params = [middle]

        if name == "glove":
            params.append(int(dim))

        vector_cache = get_mmf_cache_dir()

        # First test loading the vectors in master so that everybody doesn't
        # download it in case it doesn't exist
        if is_master():
            vocab.pretrained_aliases[embedding_name](cache=vector_cache)
        synchronize()

        embedding = getattr(vocab, class_name)(*params, cache=vector_cache)

        self.vectors = torch.empty(
            (self.get_size(), len(embedding.vectors[0])), dtype=torch.float
        )

        self.embedding_dim = len(embedding.vectors[0])

        for i in range(0, 4):
            self.vectors[i] = torch.ones_like(self.vectors[i]) * 0.1 * i

        for i in range(4, self.get_size()):
            word = self.itos[i]
            embedding_index = embedding.stoi.get(word, None)

            if embedding_index is None:
                self.vectors[i] = self.vectors[self.UNK_INDEX]
            else:
                self.vectors[i] = embedding.vectors[embedding_index]

    def get_embedding_dim(self):
        return self.embedding_dim


class PretrainedVocab(BaseVocab):
    def __init__(self, embedding_name, *args, **kwargs):
        """Use this if you want to use pretrained embedding. See description
        of IntersectedVocab to get a list of the embedding available from
        torchtext

        Parameters
        ----------
        embedding_name : str
            Name of the pretrained alias for the embedding to used
        """
        self.type = "pretrained"

        if embedding_name not in vocab.pretrained_aliases:
            raise RuntimeError(f"Unknown embedding type: {embedding_name}")

        vector_cache = get_mmf_cache_dir()

        # First test loading the vectors in master so that everybody doesn't
        # download it in case it doesn't exist
        if is_master():
            vocab.pretrained_aliases[embedding_name](cache=vector_cache)
        synchronize()

        embedding = vocab.pretrained_aliases[embedding_name](cache=vector_cache)

        self.UNK_INDEX = 3
        self.stoi = defaultdict(lambda: self.UNK_INDEX)
        self.itos = {}

        self.itos[self.PAD_INDEX] = self.PAD_TOKEN
        self.itos[self.SOS_INDEX] = self.SOS_TOKEN
        self.itos[self.EOS_INDEX] = self.EOS_TOKEN
        self.itos[self.UNK_INDEX] = self.UNK_TOKEN

        self.stoi[self.SOS_TOKEN] = self.SOS_INDEX
        self.stoi[self.EOS_TOKEN] = self.EOS_INDEX
        self.stoi[self.PAD_TOKEN] = self.PAD_INDEX
        self.stoi[self.UNK_TOKEN] = self.UNK_INDEX

        self.vectors = torch.FloatTensor(
            len(self.itos.keys()) + len(embedding.itos), len(embedding.vectors[0])
        )

        for i in range(4):
            self.vectors[i] = torch.ones_like(self.vectors[i]) * 0.1 * i

        index = 4
        for word in embedding.stoi:
            self.itos[index] = word
            self.stoi[word] = index
            actual_index = embedding.stoi[word]
            self.vectors[index] = embedding.vectors[actual_index]
            index += 1


class WordToVectorDict:
    def __init__(self, model):
        self.model = model

    def __getitem__(self, word):
        # Check if mean for word split needs to be done here
        return np.mean([self.model.get_word_vector(w) for w in word.split(" ")], axis=0)


class ModelVocab(BaseVocab):
    def __init__(self, name, model_file, *args, **kwargs):
        """Special vocab which is not really vocabulary but instead a model
        which returns embedding directly instead of vocabulary. This is just
        an abstraction over a model which generates embeddings directly.
        For e.g. for fasttext model we encapsulate it inside this and provide
        it as a vocab so that the API of the vocab remains same.

        NOTE: stoi's functionality will remain same but it is actually calling
        a function to get word vectors. Currently, only fasttext is supported.

        Parameters
        ----------
        name : str
            Name of the embedding model which this vocab currently is loading
        model_file : str
            File from which model will be loaded. This API might need to be
            changed in future.
        """
        super().__init__(*args, **kwargs)
        self.type = "model"
        if name != "fasttext":
            raise ValueError("Model vocab only supports fasttext as of now")
        else:
            self._load_fasttext_model(model_file)

    def _load_fasttext_model(self, model_file):
        from fastText import load_model

        model_file = os.path.join(get_mmf_cache_dir(), model_file)

        logger.info(f"Loading fasttext model now from {model_file}")

        self.model = load_model(model_file)
        self.stoi = WordToVectorDict(self.model)

    def get_embedding_dim(self):
        return self.model.get_dimension()


class ExtractedVocab(BaseVocab):
    def __init__(self, base_path, emb_dim, *args, **kwargs):
        """Special vocab which is not really vocabulary but instead a class
        which returns embedding pre-extracted from files. Can be used load
        word embeddings from popular models like ELMo and BERT


        Parameters
        ----------
        base_path: str
            path containing saved files with embeddings one file per txt item
        """
        super().__init__(*args, **kwargs)
        self.type = "extracted"
        self.emb_dim = emb_dim
        self.base_path = base_path

    def get_dim(self):
        return self.emb_dim
