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#                                                           #
#   This file was created by: Alberto Palomo Alonso         #
# Universidad de Alcalá - Escuela Politécnica Superior      #
#                                                           #
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# Import statements:
import tokenizers
import sys
import subprocess
import logging
import spacy
import numpy as np
from tokenizers.models import BPE
from tokenizers.trainers import BpeTrainer
from tokenizers.pre_tokenizers import Whitespace
from tokenizers.normalizers import NFKC
from transformers import PreTrainedTokenizerFast

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class SegmentationTokenizer:
    """
    Wrapper class for training and using a BPE-based tokenizer for text segmentation.

    This class supports:
    - Training a Byte Pair Encoding (BPE) tokenizer from an iterator
    - Saving and loading the tokenizer
    - Tokenizing text with padding and truncation
    - Computing the unknown-token (UNK) rate over a corpus
    """

    def __init__(self, vocab_size=32_768, min_frequency=2, max_length=1024):
        """
        Initialize the segmentation tokenizer.

        Args:
            vocab_size (int): Maximum vocabulary size for the BPE tokenizer.
            min_frequency (int): Minimum token frequency to be included in the vocabulary.
            max_length (int): Maximum sequence length for tokenization.
        """
        self.max_length = max_length

        # Raw tokenizer used only during training
        self.raw_tokenizer = tokenizers.Tokenizer(
            BPE(unk_token="[UNK]")
        )
        self.raw_tokenizer.normalizer = NFKC()
        self.raw_tokenizer.pre_tokenizer = Whitespace()

        # Trainer configuration for BPE
        self.trainer = BpeTrainer(
            vocab_size=vocab_size,
            min_frequency=min_frequency,
            special_tokens=["[PAD]", "[UNK]", "[CLS]", "[SEP]", "[MASK]"]
        )

        # Hugging Face fast tokenizer (created after loading)
        self._hf_tokenizer = None

    # ------------------------------------------------------------------
    # Training utilities
    # ------------------------------------------------------------------
    @staticmethod
    def build_iterator(dataset, batch_size=1024):
        """
        Build a batched text iterator from a dataset.

        Each dataset item is expected to contain a "text" field,
        which is a list of strings.

        Args:
            dataset (Iterable[dict]): Dataset containing text entries.
            batch_size (int): Number of samples per batch.

        Yields:
            List[str]: A batch of concatenated text samples.
        """
        batch = []
        for item in dataset:
            batch.append("\n".join(item["text"]).replace("\n\n", "\n"))
            if len(batch) == batch_size:
                yield batch
                batch = []
        if batch:
            yield batch

    def train_from_iterator(self, iterator):
        """
        Train the raw tokenizer from an iterator of text batches.

        Args:
            iterator (Iterable[List[str]]): Iterator yielding batches of text.
        """
        self.raw_tokenizer.train_from_iterator(
            iterator,
            trainer=self.trainer
        )

    # ------------------------------------------------------------------
    # I/O
    # ------------------------------------------------------------------
    def save(self, path):
        """
        Save the trained raw tokenizer to disk.

        Args:
            path (str): Path where the tokenizer file will be saved.
        """
        self.raw_tokenizer.save(path)

    def load(self, tokenizer_path):
        """
        Load a tokenizer from disk as a Hugging Face fast tokenizer.

        Args:
            tokenizer_path (str): Path to the saved tokenizer file.

        Returns:
            SegmentationTokenizer: Self, for chaining.
        """
        self._hf_tokenizer = PreTrainedTokenizerFast(
            tokenizer_file=tokenizer_path,
            unk_token="[UNK]",
            pad_token="[PAD]",
            cls_token="[CLS]",
            sep_token="[SEP]",
            mask_token="[MASK]"
        )
        return self

    # ------------------------------------------------------------------
    # Tokenization utilities
    # ------------------------------------------------------------------
    def compute_unk_rate(self, corpus):
        """
        Compute the proportion of unknown tokens ([UNK]) in a corpus.

        Args:
            corpus (Iterable[str]): Collection of input texts.

        Returns:
            float: UNK token rate in the corpus.
        """
        unk_id = self._hf_tokenizer.convert_tokens_to_ids("[UNK]")

        total_tokens = 0
        unk_tokens = 0

        for text in corpus:
            enc = self._hf_tokenizer(
                text,
                add_special_tokens=False
            )["input_ids"]

            total_tokens += len(enc)
            unk_tokens += sum(1 for t in enc if t == unk_id)

        return unk_tokens / total_tokens if total_tokens > 0 else 0.0

    def __call__(
        self,
        text,
        return_tensors="pt",
        padding=True,
        truncation=True
    ):
        """
        Tokenize input text.

        Args:
            text (str or List[str]): Input text or batch of texts.
            return_tensors (str): Tensor type to return (e.g., "pt").
            padding (bool): Whether to pad sequences to max_length.
            truncation (bool): Whether to truncate sequences to max_length.

        Returns:
            dict: Dictionary containing:
                - input_ids (torch.LongTensor)
                - attention_mask (torch.LongTensor)
        """
        if self._hf_tokenizer is None:
            raise RuntimeError("Tokenizer not loaded. Call .load() first.")

        enc = self._hf_tokenizer(
            text,
            padding="max_length" if padding else False,
            truncation=truncation,
            max_length=self.max_length,
            return_tensors=return_tensors
        )

        return {
            "input_ids": enc["input_ids"],
            "attention_mask": enc["attention_mask"]
        }

    # ------------------------------------------------------------------
    # Properties and representations
    # ------------------------------------------------------------------
    @property
    def vocab_size(self):
        """
        Get the vocabulary size of the loaded tokenizer.

        Returns:
            int: Vocabulary size.
        """
        if self._hf_tokenizer is None:
            raise RuntimeError("Tokenizer not loaded.")
        return self._hf_tokenizer.vocab_size

    def __repr__(self):
        """
        String representation of the tokenizer.
        """
        return f"<SegmentationTokenizer vocab_size={self.trainer.vocab_size}>"


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#                        SENTENCE SEG                       #
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class SentenceSegmenter:
    def __init__(
        self,
        max_sentences: int,
        spacy_model: str = "es_core_news_sm",
        logger: logging.Logger | None = None
    ):
        self.max_sentences = max_sentences
        self.logger = self._get_logger(logger)
        self.nlp = self.__build_model__(spacy_model, logger=self.logger)

    @staticmethod
    def __build_model__(sentence_tokenizer_model: str, logger: logging.Logger) -> spacy.language.Language:
        """
        Download the pre-trained sentence tokenizer model.
        :param sentence_tokenizer_model: The sentence tokenizer model to download.
        :return: The spacy language model.
        """
        try:
            spacy_model = spacy.load(sentence_tokenizer_model)
        except OSError:
            result = subprocess.run(
                [sys.executable, "-m", "spacy", "download", sentence_tokenizer_model],
                capture_output=True,
                text=True
            )

            if result.returncode != 0:
                logger.error(f'[BEAST-Tokenizer]: Loading {sentence_tokenizer_model} failed.')
                raise RuntimeError(f"[BEAST-Tokenizer]: Error while downloading '{sentence_tokenizer_model}'")

            spacy_model = spacy.load(sentence_tokenizer_model)
        logger.info('[BEAST-Tokenizer]: Successfully downloaded the pre-trained sentence tokenizer model.')

        if 'parser' not in spacy_model.pipe_names:
            logger.error(f'[BEAST-Tokenizer]: The SpaCy model needs a parser installed.')
            raise RuntimeError(f'[BEAST-Tokenizer]: The SpaCy model needs a parser installed.')
        else:
            spacy_model.add_pipe("newline_segmenter_keep_exact", before="parser")

        return spacy_model

    @staticmethod
    def _get_logger(logger):
        if logger is None:
            logger = logging.getLogger(__name__)
            logger.addHandler(logging.NullHandler())
        return logger

    def __call__(self, texts: list[str]) -> dict:
        sentences = list()
        sentence_candidates = list()
        sentence_boundaries = list()
        sentence_masking = list()

        for article in texts:
            doc = self.nlp(article)
            last_was_jump = False

            for idx, sent in enumerate(doc.sents):
                if idx == 0:
                    # Article opener
                    sentence_candidates.append(1)
                    sentence_boundaries.append(1)
                elif last_was_jump:
                    # Paragraph break candidate
                    sentence_candidates.append(1)
                    sentence_boundaries.append(0)
                else:
                    sentence_candidates.append(0)
                    sentence_boundaries.append(0)

                last_was_jump = sent.text.endswith("\n")
                sentences.append(sent.text.replace('\n', '').strip())
                sentence_masking.append(1)

                if len(sentences) >= self.max_sentences:
                    self.logger.warning(f"Maximum number of sentences reached: {self.max_sentences}")
                    break

            if len(sentences) >= self.max_sentences:
                break

        # Pad with zeros:
        while len(sentences) < self.max_sentences:
            sentences.append("")
            sentence_candidates.append(0)
            sentence_boundaries.append(0)
            sentence_masking.append(0)

        return {
            "sentences": sentences,
            "sentence_candidates": np.array(sentence_candidates, dtype=np.int8),
            "sentence_boundaries": np.array(sentence_boundaries, dtype=np.int8),
            "sentence_mask": np.array(sentence_masking, dtype=np.int8)
        }


@spacy.Language.component("newline_segmenter_keep_exact")
def newline_segmenter_keep_exact(doc):
    for token in doc[:-1]:
        if token.text == "\n":
            doc[token.i + 1].is_sent_start = True
    return doc
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