"""HuggingFace Transformers-compatible wrapper for JSONTokenizer.

Provides JSONPreTrainedTokenizer, a PreTrainedTokenizer subclass that
wraps JSONTokenizer for use with the HuggingFace ecosystem:
  - save_pretrained / from_pretrained
  - AutoTokenizer.from_pretrained (with trust_remote_code=True)
  - tokenizer(json_string) -> BatchEncoding
  - Padding, truncation, batch processing, return_tensors

Requires: pip install json-tokenizer[huggingface]
"""

from __future__ import annotations

import json
import os
from typing import Any, Dict, List, Optional, Tuple, Union

try:
    from transformers import PreTrainedTokenizer
except ImportError:
    raise ImportError(
        "The HuggingFace transformers library is required for this module. "
        "Install it with: pip install json-tokenizer[huggingface]"
    )

from json_tokenizer.tokenizer import JSONTokenizer, StructuralTokens
from json_tokenizer.bpe import BPETrainer


VOCAB_FILES_NAMES = {"vocab_file": "json_tokenizer_vocab.json"}

# Structural token ID -> HF-compatible string name.
# Uses <name> format which cannot collide with BPE tokens because
# the BPE pre-tokenizer splits <, >, : into separate tokens.
_STRUCTURAL_TOKEN_NAMES = {
    StructuralTokens.PAD: "<pad>",
    StructuralTokens.START: "<s>",
    StructuralTokens.END: "</s>",
    StructuralTokens.OBJ_START: "<obj_start>",
    StructuralTokens.OBJ_END: "<obj_end>",
    StructuralTokens.ARR_START: "<arr_start>",
    StructuralTokens.ARR_END: "<arr_end>",
    StructuralTokens.COLON: "<colon>",
    StructuralTokens.COMMA: "<comma>",
    StructuralTokens.NULL: "<null>",
    StructuralTokens.TRUE: "<true>",
    StructuralTokens.FALSE: "<false>",
    StructuralTokens.STR_DELIM: "<str_delim>",
    StructuralTokens.NUM_PREFIX: "<num_prefix>",
    StructuralTokens.KEY_PREFIX: "<key_prefix>",
    StructuralTokens.UNK: "<unk>",
}

_STRUCTURAL_NAME_TO_ID = {v: k for k, v in _STRUCTURAL_TOKEN_NAMES.items()}


class JSONPreTrainedTokenizer(PreTrainedTokenizer):
    """HuggingFace-compatible wrapper around JSONTokenizer.

    Usage:
        # From a trained JSONTokenizer:
        tok = JSONTokenizer(bpe_vocab_size=4096)
        tok.train(data)
        hf_tok = JSONPreTrainedTokenizer.from_json_tokenizer(tok)

        # Encode/decode via HF API:
        output = hf_tok('{"name": "Alice", "age": 30}')
        print(output["input_ids"])
        print(hf_tok.decode(output["input_ids"]))

        # Save and reload:
        hf_tok.save_pretrained("./my_tokenizer")
        loaded = JSONPreTrainedTokenizer.from_pretrained("./my_tokenizer")
    """

    vocab_files_names = VOCAB_FILES_NAMES
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        vocab_file: Optional[str] = None,
        unk_token: str = "<unk>",
        bos_token: str = "<s>",
        eos_token: str = "</s>",
        pad_token: str = "<pad>",
        **kwargs,
    ):
        # Internal state — populated from vocab_file or from_json_tokenizer
        if not hasattr(self, "_json_tokenizer"):
            self._json_tokenizer: Optional[JSONTokenizer] = None
        if not hasattr(self, "_hf_vocab"):
            self._hf_vocab: Dict[str, int] = {}
        if not hasattr(self, "_hf_id_to_token"):
            self._hf_id_to_token: Dict[int, str] = {}

        if vocab_file is not None and os.path.isfile(vocab_file):
            self._load_vocab_file(vocab_file)

        super().__init__(
            unk_token=unk_token,
            bos_token=bos_token,
            eos_token=eos_token,
            pad_token=pad_token,
            **kwargs,
        )

    # ── Factory ────────────────────────────────────────────────────────

    @classmethod
    def from_json_tokenizer(
        cls, tokenizer: JSONTokenizer, **kwargs
    ) -> "JSONPreTrainedTokenizer":
        """Create from a trained JSONTokenizer instance.

        Args:
            tokenizer: A trained JSONTokenizer.
            **kwargs: Additional arguments passed to __init__.

        Returns:
            A new JSONPreTrainedTokenizer wrapping the provided tokenizer.
        """
        if not tokenizer._trained:
            raise ValueError("JSONTokenizer must be trained before wrapping.")

        instance = cls.__new__(cls)
        instance._json_tokenizer = tokenizer
        instance._hf_vocab = {}
        instance._hf_id_to_token = {}
        instance._build_hf_vocab()
        instance.__init__(vocab_file=None, **kwargs)
        return instance

    # ── Vocab building ─────────────────────────────────────────────────

    def _load_vocab_file(self, vocab_file: str) -> None:
        """Reconstruct a JSONTokenizer from our saved vocab file."""
        with open(vocab_file, "r", encoding="utf-8") as f:
            data = json.load(f)

        config = data["config"]
        tok = JSONTokenizer(
            bpe_vocab_size=config["bpe_vocab_size"],
            max_key_vocab=config["max_key_vocab"],
            min_key_freq=config["min_key_freq"],
            bpe_min_freq=config["bpe_min_freq"],
        )
        tok._key_to_id = {k: int(v) for k, v in data["key_vocab"].items()}
        tok._id_to_key = {int(v): k for k, v in data["key_vocab"].items()}
        tok._key_offset = config["key_offset"]
        tok._bpe_offset = config["bpe_offset"]

        bpe_data = data["bpe_model"]
        bpe = BPETrainer(
            vocab_size=bpe_data["vocab_size"],
            min_frequency=bpe_data["min_frequency"],
        )
        bpe.merges = [tuple(m) for m in bpe_data["merges"]]
        bpe.vocab = bpe_data["vocab"]
        bpe._id_to_tok = None
        tok._bpe = bpe

        tok._build_vocab_lookup()
        tok._trained = True

        self._json_tokenizer = tok
        self._build_hf_vocab()

    def _build_hf_vocab(self) -> None:
        """Build the unified {token_string: id} mapping across all tiers."""
        tok = self._json_tokenizer
        self._hf_vocab = {}
        self._hf_id_to_token = {}

        # Structural tokens (0-15)
        for tid, name in _STRUCTURAL_TOKEN_NAMES.items():
            self._hf_vocab[name] = tid
            self._hf_id_to_token[tid] = name

        # Reserved tokens (16-31)
        for tid in range(16, StructuralTokens.RESERVED_END):
            name = f"<reserved_{tid}>"
            self._hf_vocab[name] = tid
            self._hf_id_to_token[tid] = name

        # Key vocabulary tokens
        for key_str, tid in tok._key_to_id.items():
            name = f"<key:{key_str}>"
            self._hf_vocab[name] = tid
            self._hf_id_to_token[tid] = name

        # BPE tokens
        for bpe_token, bpe_local_id in tok._bpe.vocab.items():
            full_id = tok._bpe_offset + bpe_local_id
            # Collision guard (only <UNK> from BPE could theoretically collide)
            if bpe_token in self._hf_vocab:
                bpe_token_name = f"bpe:{bpe_token}"
            else:
                bpe_token_name = bpe_token
            self._hf_vocab[bpe_token_name] = full_id
            self._hf_id_to_token[full_id] = bpe_token_name

    # ── Required PreTrainedTokenizer overrides ─────────────────────────

    @property
    def vocab_size(self) -> int:
        if self._json_tokenizer is None:
            return len(_STRUCTURAL_TOKEN_NAMES)
        return self._json_tokenizer.vocab_size

    def get_vocab(self) -> Dict[str, int]:
        vocab = dict(self._hf_vocab)
        vocab.update(self.added_tokens_encoder)
        return vocab

    def _tokenize(self, text: str, **kwargs) -> List[str]:
        """Tokenize a JSON string into HF token strings.

        The HF pipeline calls: tokenize(text) -> _tokenize -> list[str]
        then convert_tokens_to_ids maps those to IDs.

        We parse the JSON, encode via JSONTokenizer (skipping START/END
        since HF adds special tokens via build_inputs_with_special_tokens),
        then convert IDs to our HF token string names.
        """
        if self._json_tokenizer is None:
            return [self.unk_token]

        try:
            ids = self._json_tokenizer.encode(text)
        except (ValueError, json.JSONDecodeError):
            # Not valid JSON — encode as raw string via BPE
            ids = [StructuralTokens.START]
            self._json_tokenizer._encode_string(text, ids)
            ids.append(StructuralTokens.END)

        # Strip START/END — HF adds them via build_inputs_with_special_tokens
        if ids and ids[0] == StructuralTokens.START:
            ids = ids[1:]
        if ids and ids[-1] == StructuralTokens.END:
            ids = ids[:-1]

        return [self._hf_id_to_token.get(tid, self.unk_token) for tid in ids]

    def _convert_token_to_id(self, token: str) -> int:
        return self._hf_vocab.get(
            token, self._hf_vocab.get(self.unk_token, StructuralTokens.UNK)
        )

    def _convert_id_to_token(self, index: int) -> str:
        return self._hf_id_to_token.get(index, self.unk_token)

    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        """Reconstruct a JSON string from token strings.

        Converts token strings -> IDs, wraps with START/END,
        and delegates to JSONTokenizer.decode().
        """
        if self._json_tokenizer is None:
            return ""

        ids = [StructuralTokens.START]
        for token in tokens:
            tid = self._convert_token_to_id(token)
            ids.append(tid)
        ids.append(StructuralTokens.END)

        try:
            return self._json_tokenizer.decode(ids)
        except Exception:
            return " ".join(tokens)

    # ── Special tokens ─────────────────────────────────────────────────

    def build_inputs_with_special_tokens(
        self,
        token_ids_0: List[int],
        token_ids_1: Optional[List[int]] = None,
    ) -> List[int]:
        """Wrap with START (bos) and END (eos) tokens."""
        bos = [self.bos_token_id]
        eos = [self.eos_token_id]
        if token_ids_1 is None:
            return bos + token_ids_0 + eos
        return bos + token_ids_0 + eos + bos + token_ids_1 + eos

    def get_special_tokens_mask(
        self,
        token_ids_0: List[int],
        token_ids_1: Optional[List[int]] = None,
        already_has_special_tokens: bool = False,
    ) -> List[int]:
        """1 for special tokens (START/END), 0 for content tokens."""
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_0=token_ids_0,
                token_ids_1=token_ids_1,
                already_has_special_tokens=True,
            )
        if token_ids_1 is None:
            return [1] + [0] * len(token_ids_0) + [1]
        return (
            [1] + [0] * len(token_ids_0) + [1]
            + [1] + [0] * len(token_ids_1) + [1]
        )

    def create_token_type_ids_from_sequences(
        self,
        token_ids_0: List[int],
        token_ids_1: Optional[List[int]] = None,
    ) -> List[int]:
        """Segment IDs: 0 for first sequence, 1 for second."""
        bos_eos = 2  # one bos + one eos
        if token_ids_1 is None:
            return [0] * (len(token_ids_0) + bos_eos)
        return [0] * (len(token_ids_0) + bos_eos) + [1] * (len(token_ids_1) + bos_eos)

    # ── Persistence ────────────────────────────────────────────────────

    def save_vocabulary(
        self,
        save_directory: str,
        filename_prefix: Optional[str] = None,
    ) -> Tuple[str]:
        """Save the vocabulary to a single JSON file.

        This file contains everything needed to reconstruct the
        JSONTokenizer: config, key vocab, and BPE model.
        """
        if not os.path.isdir(save_directory):
            raise ValueError(f"Not a directory: {save_directory}")

        vocab_file = os.path.join(
            save_directory,
            (filename_prefix + "-" if filename_prefix else "")
            + VOCAB_FILES_NAMES["vocab_file"],
        )

        tok = self._json_tokenizer
        data = {
            "version": "json-tokenizer-hf-v1",
            "config": {
                "bpe_vocab_size": tok.bpe_vocab_size,
                "max_key_vocab": tok.max_key_vocab,
                "min_key_freq": tok.min_key_freq,
                "bpe_min_freq": tok.bpe_min_freq,
                "key_offset": tok._key_offset,
                "bpe_offset": tok._bpe_offset,
            },
            "key_vocab": tok._key_to_id,
            "bpe_model": {
                "vocab_size": tok._bpe.vocab_size,
                "min_frequency": tok._bpe.min_frequency,
                "merges": [list(m) for m in tok._bpe.merges],
                "vocab": tok._bpe.vocab,
            },
        }

        with open(vocab_file, "w", encoding="utf-8") as f:
            json.dump(data, f, indent=2, ensure_ascii=False)

        return (vocab_file,)