from typing import List, Optional, Tuple, Union

import numpy as np
from transformers import (AlbertModel, AlbertTokenizer, BartModel,
                          BartTokenizer, BertModel, BertTokenizer,
                          CamembertModel, CamembertTokenizer, CTRLModel,
                          CTRLTokenizer, DistilBertModel, DistilBertTokenizer,
                          GPT2Model, GPT2Tokenizer, LongformerModel,
                          LongformerTokenizer, OpenAIGPTModel,
                          OpenAIGPTTokenizer, PreTrainedModel,
                          PreTrainedTokenizer, RobertaModel, RobertaTokenizer,
                          TransfoXLModel, TransfoXLTokenizer, XLMModel,
                          XLMTokenizer, XLNetModel, XLNetTokenizer)

from extractive_summarizer.bert_parent import BertParent
from extractive_summarizer.cluster_features import ClusterFeatures
from extractive_summarizer.sentence_handler import SentenceHandler


class ModelProcessor(object):
    aggregate_map = {
        'mean': np.mean,
        'min': np.min,
        'median': np.median,
        'max': np.max,
    }

    def __init__(
        self,
        model: str = 'bert-large-uncased',
        custom_model: PreTrainedModel = None,
        custom_tokenizer: PreTrainedTokenizer = None,
        hidden: Union[List[int], int] = -2,
        reduce_option: str = 'mean',
        sentence_handler: SentenceHandler = SentenceHandler(),
        random_state: int = 12345,
        hidden_concat: bool = False,
        gpu_id: int = 0,
    ):
        """
        This is the parent Bert Summarizer model. New methods should implement this class.

        :param model: This parameter is associated with the inherit string parameters from the transformers library.
        :param custom_model: If you have a pre-trained model, you can add the model class here.
        :param custom_tokenizer: If you have a custom tokenizer, you can add the tokenizer here.
        :param hidden: This signifies which layer(s) of the BERT model you would like to use as embeddings.
        :param reduce_option: Given the output of the bert model, this param determines how you want to reduce results.
        :param sentence_handler: The handler to process sentences. If want to use coreference, instantiate and pass.
        CoreferenceHandler instance
        :param random_state: The random state to reproduce summarizations.
        :param hidden_concat: Whether or not to concat multiple hidden layers.
        :param gpu_id: GPU device index if CUDA is available. 
        """
        np.random.seed(random_state)
        self.model = BertParent(model, custom_model, custom_tokenizer, gpu_id)
        self.hidden = hidden
        self.reduce_option = reduce_option
        self.sentence_handler = sentence_handler
        self.random_state = random_state
        self.hidden_concat = hidden_concat

    def cluster_runner(
        self,
        content: List[str],
        ratio: float = 0.2,
        algorithm: str = 'kmeans',
        use_first: bool = True,
        num_sentences: int = None
    ) -> Tuple[List[str], np.ndarray]:
        """
        Runs the cluster algorithm based on the hidden state. Returns both the embeddings and sentences.

        :param content: Content list of sentences.
        :param ratio: The ratio to use for clustering.
        :param algorithm: Type of algorithm to use for clustering.
        :param use_first: Return the first sentence in the output (helpful for news stories, etc).
        :param num_sentences: Number of sentences to use for summarization.
        :return: A tuple of summarized sentences and embeddings
        """
        if num_sentences is not None:
            num_sentences = num_sentences if use_first else num_sentences

        hidden = self.model(
            content, self.hidden, self.reduce_option, hidden_concat=self.hidden_concat)
        hidden_args = ClusterFeatures(
            hidden, algorithm, random_state=self.random_state).cluster(ratio, num_sentences)

        if use_first:

            if not hidden_args:
                hidden_args.append(0)

            elif hidden_args[0] != 0:
                hidden_args.insert(0, 0)

        sentences = [content[j] for j in hidden_args]
        embeddings = np.asarray([hidden[j] for j in hidden_args])

        return sentences, embeddings

    def __run_clusters(
        self,
        content: List[str],
        ratio: float = 0.2,
        algorithm: str = 'kmeans',
        use_first: bool = True,
        num_sentences: int = None
    ) -> List[str]:
        """
        Runs clusters and returns sentences.

        :param content: The content of sentences.
        :param ratio: Ratio to use for for clustering.
        :param algorithm: Algorithm selection for clustering.
        :param use_first: Whether to use first sentence
        :param num_sentences: Number of sentences. Overrides ratio.
        :return: summarized sentences
        """
        sentences, _ = self.cluster_runner(
            content, ratio, algorithm, use_first, num_sentences)
        return sentences

    def __retrieve_summarized_embeddings(
        self,
        content: List[str],
        ratio: float = 0.2,
        algorithm: str = 'kmeans',
        use_first: bool = True,
        num_sentences: int = None
    ) -> np.ndarray:
        """
        Retrieves embeddings of the summarized sentences.

        :param content: The content of sentences.
        :param ratio: Ratio to use for for clustering.
        :param algorithm: Algorithm selection for clustering.
        :param use_first: Whether to use first sentence
        :return: Summarized embeddings
        """
        _, embeddings = self.cluster_runner(
            content, ratio, algorithm, use_first, num_sentences)
        return embeddings

    def calculate_elbow(
        self,
        body: str,
        algorithm: str = 'kmeans',
        min_length: int = 40,
        max_length: int = 600,
        k_max: int = None,
    ) -> List[float]:
        """
        Calculates elbow across the clusters.

        :param body: The input body to summarize.
        :param algorithm: The algorithm to use for clustering.
        :param min_length: The min length to use.
        :param max_length: The max length to use.
        :param k_max: The maximum number of clusters to search.
        :return: List of elbow inertia values.
        """
        sentences = self.sentence_handler(body, min_length, max_length)

        if k_max is None:
            k_max = len(sentences) - 1

        hidden = self.model(sentences, self.hidden,
                            self.reduce_option, hidden_concat=self.hidden_concat)
        elbow = ClusterFeatures(
            hidden, algorithm, random_state=self.random_state).calculate_elbow(k_max)

        return elbow

    def calculate_optimal_k(
        self,
        body: str,
        algorithm: str = 'kmeans',
        min_length: int = 40,
        max_length: int = 600,
        k_max: int = None,
    ):
        """
        Calculates the optimal Elbow K.

        :param body: The input body to summarize.
        :param algorithm: The algorithm to use for clustering.
        :param min_length: The min length to use.
        :param max_length: The max length to use.
        :param k_max: The maximum number of clusters to search.
        :return:
        """
        sentences = self.sentence_handler(body, min_length, max_length)

        if k_max is None:
            k_max = len(sentences) - 1

        hidden = self.model(sentences, self.hidden,
                            self.reduce_option, hidden_concat=self.hidden_concat)
        optimal_k = ClusterFeatures(
            hidden, algorithm, random_state=self.random_state).calculate_optimal_cluster(k_max)

        return optimal_k

    def run_embeddings(
        self,
        body: str,
        ratio: float = 0.2,
        min_length: int = 40,
        max_length: int = 600,
        use_first: bool = True,
        algorithm: str = 'kmeans',
        num_sentences: int = None,
        aggregate: str = None,
    ) -> Optional[np.ndarray]:
        """
        Preprocesses the sentences, runs the clusters to find the centroids, then combines the embeddings.

        :param body: The raw string body to process
        :param ratio: Ratio of sentences to use
        :param min_length: Minimum length of sentence candidates to utilize for the summary.
        :param max_length: Maximum length of sentence candidates to utilize for the summary
        :param use_first: Whether or not to use the first sentence
        :param algorithm: Which clustering algorithm to use. (kmeans, gmm)
        :param num_sentences: Number of sentences to use. Overrides ratio.
        :param aggregate: One of mean, median, max, min. Applied on zero axis
        :return: A summary embedding
        """
        sentences = self.sentence_handler(body, min_length, max_length)

        if sentences:
            embeddings = self.__retrieve_summarized_embeddings(
                sentences, ratio, algorithm, use_first, num_sentences)

            if aggregate is not None:
                assert aggregate in [
                    'mean', 'median', 'max', 'min'], "aggregate must be mean, min, max, or median"
                embeddings = self.aggregate_map[aggregate](embeddings, axis=0)

            return embeddings

        return None

    def run(
        self,
        body: str,
        ratio: float = 0.2,
        min_length: int = 40,
        max_length: int = 600,
        use_first: bool = True,
        algorithm: str = 'kmeans',
        num_sentences: int = None,
        return_as_list: bool = False
    ) -> Union[List, str]:
        """
        Preprocesses the sentences, runs the clusters to find the centroids, then combines the sentences.

        :param body: The raw string body to process
        :param ratio: Ratio of sentences to use
        :param min_length: Minimum length of sentence candidates to utilize for the summary.
        :param max_length: Maximum length of sentence candidates to utilize for the summary
        :param use_first: Whether or not to use the first sentence
        :param algorithm: Which clustering algorithm to use. (kmeans, gmm)
        :param num_sentences: Number of sentences to use (overrides ratio).
        :param return_as_list: Whether or not to return sentences as list.
        :return: A summary sentence
        """
        sentences = self.sentence_handler(body, min_length, max_length)

        if sentences:
            sentences = self.__run_clusters(
                sentences, ratio, algorithm, use_first, num_sentences)

        if return_as_list:
            return sentences
        else:
            return ' '.join(sentences)

    def __call__(
        self,
        body: str,
        ratio: float = 0.2,
        min_length: int = 40,
        max_length: int = 600,
        use_first: bool = True,
        algorithm: str = 'kmeans',
        num_sentences: int = None,
        return_as_list: bool = False,
    ) -> str:
        """
        (utility that wraps around the run function)
        Preprocesses the sentences, runs the clusters to find the centroids, then combines the sentences.

        :param body: The raw string body to process.
        :param ratio: Ratio of sentences to use.
        :param min_length: Minimum length of sentence candidates to utilize for the summary.
        :param max_length: Maximum length of sentence candidates to utilize for the summary.
        :param use_first: Whether or not to use the first sentence.
        :param algorithm: Which clustering algorithm to use. (kmeans, gmm)
        :param Number of sentences to use (overrides ratio).
        :param return_as_list: Whether or not to return sentences as list.
        :return: A summary sentence.
        """
        return self.run(
            body, ratio, min_length, max_length, algorithm=algorithm, use_first=use_first, num_sentences=num_sentences,
            return_as_list=return_as_list
        )


class Summarizer(ModelProcessor):

    def __init__(
        self,
        model: str = 'bert-large-uncased',
        custom_model: PreTrainedModel = None,
        custom_tokenizer: PreTrainedTokenizer = None,
        hidden: Union[List[int], int] = -2,
        reduce_option: str = 'mean',
        sentence_handler: SentenceHandler = SentenceHandler(),
        random_state: int = 12345,
        hidden_concat: bool = False,
        gpu_id: int = 0,
    ):
        """
        This is the main Bert Summarizer class.

        :param model: This parameter is associated with the inherit string parameters from the transformers library.
        :param custom_model: If you have a pre-trained model, you can add the model class here.
        :param custom_tokenizer: If you have a custom tokenizer, you can add the tokenizer here.
        :param hidden: This signifies which layer of the BERT model you would like to use as embeddings.
        :param reduce_option: Given the output of the bert model, this param determines how you want to reduce results.
        :param greedyness: associated with the neuralcoref library. Determines how greedy coref should be.
        :param language: Which language to use for training.
        :param random_state: The random state to reproduce summarizations.
        :param hidden_concat: Whether or not to concat multiple hidden layers.
        :param gpu_id: GPU device index if CUDA is available. 
        """

        super(Summarizer, self).__init__(
            model, custom_model, custom_tokenizer, hidden, reduce_option, sentence_handler, random_state, hidden_concat, gpu_id
        )


class TransformerSummarizer(ModelProcessor):
    """
    Another type of Summarizer class to choose keyword based model and tokenizer
    """

    MODEL_DICT = {
        'Bert': (BertModel, BertTokenizer),
        'OpenAIGPT': (OpenAIGPTModel, OpenAIGPTTokenizer),
        'GPT2': (GPT2Model, GPT2Tokenizer),
        'CTRL': (CTRLModel, CTRLTokenizer),
        'TransfoXL': (TransfoXLModel, TransfoXLTokenizer),
        'XLNet': (XLNetModel, XLNetTokenizer),
        'XLM': (XLMModel, XLMTokenizer),
        'DistilBert': (DistilBertModel, DistilBertTokenizer),
    }

    def __init__(
        self,
        transformer_type: str = 'Bert',
        transformer_model_key: str = 'bert-base-uncased',
        transformer_tokenizer_key: str = None,
        hidden: Union[List[int], int] = -2,
        reduce_option: str = 'mean',
        sentence_handler: SentenceHandler = SentenceHandler(),
        random_state: int = 12345,
        hidden_concat: bool = False,
        gpu_id: int = 0,
    ):
        """
        :param transformer_type: The Transformer type, such as Bert, GPT2, DistilBert, etc.
        :param transformer_model_key: The transformer model key. This is the directory for the model.
        :param transformer_tokenizer_key: The transformer tokenizer key. This is the tokenizer directory.
        :param hidden: The hidden output layers to use for the summarization.
        :param reduce_option: The reduce option, such as mean, max, min, median, etc.
        :param sentence_handler: The sentence handler class to process the raw text.
        :param random_state: The random state to use.
        :param hidden_concat: Deprecated hidden concat option.
        :param gpu_id: GPU device index if CUDA is available. 
        """
        try:
            self.MODEL_DICT['Roberta'] = (RobertaModel, RobertaTokenizer)
            self.MODEL_DICT['Albert'] = (AlbertModel, AlbertTokenizer)
            self.MODEL_DICT['Camembert'] = (CamembertModel, CamembertTokenizer)
            self.MODEL_DICT['Bart'] = (BartModel, BartTokenizer)
            self.MODEL_DICT['Longformer'] = (LongformerModel, LongformerTokenizer)
        except Exception:
            pass  # older transformer version

        model_clz, tokenizer_clz = self.MODEL_DICT[transformer_type]
        model = model_clz.from_pretrained(
            transformer_model_key, output_hidden_states=True)

        tokenizer = tokenizer_clz.from_pretrained(
            transformer_tokenizer_key if transformer_tokenizer_key is not None else transformer_model_key
        )

        super().__init__(
            None, model, tokenizer, hidden, reduce_option, sentence_handler, random_state, hidden_concat, gpu_id
        )