import re
import unicodedata
from collections import Counter
from collections.abc import Mapping
from dataclasses import dataclass, field
from string import ascii_letters, digits

from .reasoning import REASONING_CONTROL_TOKENS, TOKENIZER_NAME

PRETOKEN_PATTERN = re.compile(r"\w+|[^\w\s]", re.UNICODE)
BYTE_FALLBACK_PATTERN = re.compile(r"<byte:([0-9A-F]{2})>")
DEFAULT_FALLBACK_CHARACTERS = (
    ascii_letters
    + digits
    + "'-_/.:,;!?()[]{}@#$%&*+="
    + "’ʼ‘“”—–…"
)
MAX_TOKENIZER_VOCAB_SIZE = 65536
MAX_SEGMENT_CACHE_SIZE = 200_000
MAX_TRAINED_PAIR_MERGES = 384


def _is_word_character(character: str) -> bool:
    category = unicodedata.category(character)
    return character == "_" or category[0] in {"L", "N"} or category == "Mn"


def _is_variation_selector(character: str) -> bool:
    return "VARIATION SELECTOR" in unicodedata.name(character, "")


def _is_zero_width_joiner(character: str) -> bool:
    return unicodedata.name(character, "") == "ZERO WIDTH JOINER"


def _is_emoji_modifier(character: str) -> bool:
    return "EMOJI MODIFIER" in unicodedata.name(character, "")


def _is_emoji_base_character(character: str) -> bool:
    name = unicodedata.name(character, "")
    category = unicodedata.category(character)
    return (
        "EMOJI" in name
        or "REGIONAL INDICATOR SYMBOL" in name
        or (category in {"So", "Sk"} and ord(character) >= 0x2100)
    )


def _is_emoji_continuation_character(character: str) -> bool:
    category = unicodedata.category(character)
    name = unicodedata.name(character, "")
    return (
        _is_variation_selector(character)
        or _is_zero_width_joiner(character)
        or _is_emoji_modifier(character)
        or category in {"Mn", "Me"}
        or name.startswith("TAG ")
    )


def _consume_emoji_cluster(text: str, start: int) -> int:
    if start >= len(text) or not _is_emoji_base_character(text[start]):
        return start

    index = start + 1
    if "REGIONAL INDICATOR SYMBOL" in unicodedata.name(text[start], ""):
        if index < len(text) and "REGIONAL INDICATOR SYMBOL" in unicodedata.name(text[index], ""):
            return index + 1
        return index

    while index < len(text):
        if _is_emoji_continuation_character(text[index]):
            index += 1
            continue
        if _is_zero_width_joiner(text[index - 1]) and _is_emoji_base_character(text[index]):
            index += 1
            continue
        break
    return index


def _byte_token(value: int) -> str:
    return f"<byte:{value:02X}>"


def _byte_value(piece: str) -> int | None:
    match = BYTE_FALLBACK_PATTERN.fullmatch(piece)
    if match is None:
        return None
    return int(match.group(1), 16)


def _is_punctuation_piece(piece: str) -> bool:
    return bool(piece) and all(
        unicodedata.category(character).startswith("P")
        for character in piece
    )


def _is_opening_punctuation(piece: str) -> bool:
    return bool(piece) and all(
        unicodedata.category(character) in {"Ps", "Pi"}
        for character in piece
    )


def _is_call_opening_punctuation(piece: str) -> bool:
    return bool(piece) and all(
        unicodedata.category(character) == "Ps"
        and "PARENTHESIS" in unicodedata.name(character, "")
        for character in piece
    )


def _is_closing_or_terminal_punctuation(piece: str) -> bool:
    return bool(piece) and all(
        unicodedata.category(character) in {"Pe", "Pf", "Po"}
        for character in piece
    )


def _is_infix_joiner(piece: str) -> bool:
    if len(piece) != 1:
        return False
    category = unicodedata.category(piece)
    name = unicodedata.name(piece, "")
    return (
        category == "Pd"
        or "APOSTROPHE" in name
        or (category == "Pf" and "SINGLE QUOTATION MARK" in name)
        or "SOLIDUS" in name
    )


def _is_dash_joiner(piece: str) -> bool:
    if len(piece) != 1:
        return False
    category = unicodedata.category(piece)
    name = unicodedata.name(piece, "")
    return category == "Pd" or "HYPHEN" in name or "DASH" in name


def _is_quote_piece(piece: str) -> bool:
    if len(piece) != 1:
        return False
    if _is_infix_joiner(piece):
        return False
    name = unicodedata.name(piece, "")
    category = unicodedata.category(piece)
    return "QUOTATION MARK" in name or category in {"Pi", "Pf"}


def _merge_symbol(left: str, right: str, prefix: str) -> str:
    if right.startswith(prefix):
        return left + right[len(prefix):]
    return left + right


def _merge_sequence(symbols: list[str], pair: tuple[str, str], merged_symbol: str) -> list[str]:
    merged: list[str] = []
    index = 0
    while index < len(symbols):
        if index < len(symbols) - 1 and (symbols[index], symbols[index + 1]) == pair:
            merged.append(merged_symbol)
            index += 2
        else:
            merged.append(symbols[index])
            index += 1
    return merged


def _default_symbol_inventory(word_prefix: str) -> set[str]:
    symbols: set[str] = set()
    for character in DEFAULT_FALLBACK_CHARACTERS:
        symbols.add(character)
        symbols.add(f"{word_prefix}{character}")
    for value in range(256):
        token = _byte_token(value)
        symbols.add(token)
        symbols.add(f"{word_prefix}{token}")
    return symbols


def _whole_segment_token(segment: str, word_prefix: str) -> str:
    return f"{word_prefix}{segment}"


def recommend_vocab_size(
    text: str,
    *,
    minimum: int = 768,
    maximum: int = 1536,
    multiplier: int = 5,
    lowercase: bool = False,
) -> int:
    seed_tokenizer = NativeTokenizer(
        merges=[],
        vocab=[],
        base_symbols=[],
        lowercase=lowercase,
    )
    segments = seed_tokenizer.pretokenize(text)
    distinct_segments = len(set(segments))
    recommended = max(minimum, distinct_segments * multiplier)
    return min(maximum, recommended)


def clamp_vocab_size(requested: int, *, maximum: int = MAX_TOKENIZER_VOCAB_SIZE) -> int:
    return min(maximum, max(1, requested))


@dataclass(slots=True)
class NativeTokenizer:
    merges: list[tuple[str, str]]
    vocab: list[str]
    base_symbols: list[str]
    name: str = TOKENIZER_NAME
    lowercase: bool = False
    word_prefix: str = "▁"
    unk_token: str = "<unk>"
    bos_token: str = "<bos>"
    eos_token: str = "<eos>"
    pad_token: str = "<pad>"
    _merge_ranks: dict[tuple[str, str], int] = field(init=False, repr=False)
    _vocab_set: set[str] = field(init=False, repr=False)
    _base_symbol_set: set[str] = field(init=False, repr=False)
    _pretoken_pattern: re.Pattern[str] = field(init=False, repr=False)
    _segment_cache: dict[str, tuple[str, ...]] = field(init=False, repr=False)

    def __post_init__(self) -> None:
        self._merge_ranks = {pair: index for index, pair in enumerate(self.merges)}
        self._base_symbol_set = set(self.base_symbols)
        self._vocab_set = set(self.vocab) | self.special_tokens | self._base_symbol_set
        self.vocab = sorted(self._vocab_set)
        self._pretoken_pattern = self._build_pretoken_pattern()
        self._segment_cache = {}

    @property
    def special_tokens(self) -> set[str]:
        return {
            self.unk_token,
            self.bos_token,
            self.eos_token,
            self.pad_token,
            *REASONING_CONTROL_TOKENS,
        }

    @property
    def vocab_size(self) -> int:
        return len(self._vocab_set)

    def normalize(self, text: str) -> str:
        normalized = unicodedata.normalize("NFKC", text)
        return normalized.lower() if self.lowercase else normalized

    def pretokenize(self, text: str) -> list[str]:
        normalized = self.normalize(text)
        segments: list[str] = []
        reserved = sorted(self.special_tokens, key=len, reverse=True)
        index = 0
        while index < len(normalized):
            if normalized[index].isspace():
                if normalized[index] == "\r":
                    if index + 1 < len(normalized) and normalized[index + 1] == "\n":
                        segments.append("\n")
                        index += 2
                        continue
                    segments.append("\n")
                    index += 1
                    continue
                if normalized[index] == "\n":
                    segments.append("\n")
                    index += 1
                    continue
                index += 1
                continue

            matched_special = next(
                (
                    token
                    for token in reserved
                    if normalized.startswith(token, index)
                ),
                None,
            )
            if matched_special is not None:
                segments.append(matched_special)
                index += len(matched_special)
                continue

            emoji_end = _consume_emoji_cluster(normalized, index)
            if emoji_end > index:
                segments.append(normalized[index:emoji_end])
                index = emoji_end
                continue

            if _is_word_character(normalized[index]):
                start = index
                index += 1
                while index < len(normalized) and _is_word_character(normalized[index]):
                    index += 1
                segments.append(normalized[start:index])
                continue

            segments.append(normalized[index])
            index += 1
        return segments

    def encode(self, text: str, *, add_special_tokens: bool = False) -> list[str]:
        tokens: list[str] = []
        if add_special_tokens:
            tokens.append(self.bos_token)

        for segment in self.pretokenize(text):
            tokens.extend(self._encode_segment_cached(segment))

        if add_special_tokens:
            tokens.append(self.eos_token)

        if not tokens and text.strip():
            return [self.unk_token]
        return tokens

    def encode_many(
        self,
        texts: list[str] | tuple[str, ...],
        *,
        add_special_tokens: bool = False,
    ) -> list[list[str]]:
        return [
            self.encode(text, add_special_tokens=add_special_tokens)
            for text in texts
        ]

    def decode(self, tokens: list[str]) -> str:
        text = ""
        join_next = False
        byte_buffer = bytearray()
        byte_starts_segment = False

        def next_rendered_piece(start_index: int) -> str | None:
            for raw_token in tokens[start_index:]:
                if raw_token in self.special_tokens:
                    continue
                raw_starts_segment = raw_token.startswith(self.word_prefix)
                raw_piece = raw_token[len(self.word_prefix) :] if raw_starts_segment else raw_token
                if not raw_piece:
                    continue
                if _byte_value(raw_piece) is not None:
                    return None
                return raw_piece
            return None

        def append_piece(piece: str, starts_segment: bool, next_piece: str | None = None) -> None:
            nonlocal text, join_next

            if piece == "\n":
                text = text.rstrip(" ")
                text += "\n"
                join_next = True
                return

            had_text_before_piece = bool(text.strip())
            previous_before_piece = text.rstrip(" ")[-1:] if text.strip(" ") else ""
            if _is_quote_piece(piece):
                quote_count = sum(1 for character in text if _is_quote_piece(character))
                opens_quote = quote_count % 2 == 0
                if opens_quote:
                    if text and not text.endswith((" ", "\n")) and previous_before_piece not in {"(", "[", "{"}:
                        text += " "
                    text += piece
                    join_next = True
                    return
                text = text.rstrip(" ")
                text += piece
                join_next = False
                return

            attaches_left = _is_closing_or_terminal_punctuation(piece) or _is_infix_joiner(piece)
            continues_segment = (not starts_segment) and any(
                _is_word_character(character) or _is_emoji_continuation_character(character)
                for character in piece
            )
            if starts_segment:
                if text and not join_next:
                    attaches_to_previous_code_span = (
                        _is_opening_punctuation(piece)
                        and previous_before_piece.isalnum()
                        and next_piece is not None
                        and (
                            _is_infix_joiner(next_piece)
                            or _is_call_opening_punctuation(piece)
                        )
                    )
                    if not _is_punctuation_piece(piece) or (
                        _is_opening_punctuation(piece)
                        and not attaches_to_previous_code_span
                    ):
                        text += " "
                text += piece
            else:
                if text and not join_next and not attaches_left and not continues_segment:
                    text += " "
                text += piece

            join_next = (
                _is_infix_joiner(piece)
                and (
                    not starts_segment
                    or (
                        had_text_before_piece
                        and (
                            not _is_dash_joiner(piece)
                            or previous_before_piece.isalnum()
                            or _is_opening_punctuation(previous_before_piece)
                        )
                    )
                )
            ) or _is_opening_punctuation(piece)

        def flush_bytes() -> None:
            nonlocal byte_buffer, byte_starts_segment
            if not byte_buffer:
                return
            append_piece(bytes(byte_buffer).decode("utf-8", errors="replace"), byte_starts_segment)
            byte_buffer = bytearray()
            byte_starts_segment = False

        for token_index, token in enumerate(tokens):
            if token in self.special_tokens:
                continue
            starts_segment = token.startswith(self.word_prefix)
            piece = token[len(self.word_prefix) :] if starts_segment else token
            if not piece:
                continue
            byte_value = _byte_value(piece)
            if byte_value is not None:
                if not byte_buffer:
                    byte_starts_segment = starts_segment
                byte_buffer.append(byte_value)
                continue

            flush_bytes()
            append_piece(piece, starts_segment, next_rendered_piece(token_index + 1))
        flush_bytes()
        return text.strip()

    def _encode_segment_cached(self, segment: str) -> tuple[str, ...]:
        cached = self._segment_cache.get(segment)
        if cached is not None:
            return cached
        encoded = tuple(self._encode_segment(segment))
        if len(self._segment_cache) < MAX_SEGMENT_CACHE_SIZE:
            self._segment_cache[segment] = encoded
        return encoded

    def _encode_segment(self, segment: str) -> list[str]:
        if segment in self.special_tokens:
            return [segment]
        whole_segment = _whole_segment_token(segment, self.word_prefix)
        if whole_segment in self._vocab_set:
            return [whole_segment]
        symbols = self._seed_symbols(segment)
        if not symbols:
            return []

        while len(symbols) > 1:
            best_rank: int | None = None
            best_pair: tuple[str, str] | None = None
            for index in range(len(symbols) - 1):
                pair = (symbols[index], symbols[index + 1])
                rank = self._merge_ranks.get(pair)
                if rank is None:
                    continue
                if best_rank is None or rank < best_rank:
                    best_rank = rank
                    best_pair = pair
            if best_pair is None:
                break

            merged_symbol = _merge_symbol(best_pair[0], best_pair[1], self.word_prefix)
            symbols = _merge_sequence(symbols, best_pair, merged_symbol)

        if any(symbol not in self._vocab_set for symbol in symbols):
            return [self.unk_token]
        return symbols

    def _seed_symbols(self, segment: str) -> list[str]:
        symbols: list[str] = []
        for index, character in enumerate(segment):
            symbol = f"{self.word_prefix}{character}" if index == 0 else character
            if symbol in self._base_symbol_set:
                symbols.append(symbol)
                continue

            encoded = character.encode("utf-8")
            for byte_index, value in enumerate(encoded):
                token = _byte_token(value)
                if index == 0 and byte_index == 0:
                    token = f"{self.word_prefix}{token}"
                symbols.append(token)

        if any(symbol not in self._base_symbol_set for symbol in symbols):
            return [self.unk_token]
        return symbols

    def to_dict(self) -> dict[str, object]:
        return {
            "name": self.name,
            "merges": [[left, right] for left, right in self.merges],
            "vocab": self.vocab,
            "base_symbols": self.base_symbols,
            "lowercase": self.lowercase,
            "word_prefix": self.word_prefix,
            "unk_token": self.unk_token,
            "bos_token": self.bos_token,
            "eos_token": self.eos_token,
            "pad_token": self.pad_token,
        }

    @classmethod
    def from_dict(cls, payload: dict[str, object]) -> "NativeTokenizer":
        return cls(
            merges=[(str(left), str(right)) for left, right in payload["merges"]],
            vocab=[str(token) for token in payload["vocab"]],
            base_symbols=[str(token) for token in payload["base_symbols"]],
            name=str(payload.get("name", TOKENIZER_NAME)),
            lowercase=bool(payload["lowercase"]),
            word_prefix=str(payload["word_prefix"]),
            unk_token=str(payload["unk_token"]),
            bos_token=str(payload["bos_token"]),
            eos_token=str(payload["eos_token"]),
            pad_token=str(payload["pad_token"]),
        )

    def _build_pretoken_pattern(self) -> re.Pattern[str]:
        reserved = sorted(self.special_tokens, key=len, reverse=True)
        if not reserved:
            return PRETOKEN_PATTERN
        reserved_pattern = "|".join(re.escape(token) for token in reserved)
        return re.compile(f"{reserved_pattern}|\\w+|[^\\w\\s]", re.UNICODE)

    @classmethod
    def train(
        cls,
        text: str,
        *,
        vocab_size: int = 256,
        min_pair_frequency: int = 2,
        lowercase: bool = False,
        word_prefix: str = "▁",
    ) -> "NativeTokenizer":
        seed_tokenizer = cls(
            merges=[],
            vocab=[],
            base_symbols=[],
            lowercase=lowercase,
            word_prefix=word_prefix,
        )
        segments = seed_tokenizer.pretokenize(text)
        if not segments:
            raise ValueError("Cannot train the native tokenizer on empty text.")

        return cls.train_from_segment_counts(
            Counter(segments),
            vocab_size=vocab_size,
            min_pair_frequency=min_pair_frequency,
            lowercase=lowercase,
            word_prefix=word_prefix,
        )

    @classmethod
    def train_from_segment_counts(
        cls,
        segment_counts: Mapping[str, float],
        *,
        vocab_size: int = 256,
        min_pair_frequency: int = 2,
        lowercase: bool = False,
        word_prefix: str = "▁",
    ) -> "NativeTokenizer":
        if not segment_counts:
            raise ValueError("Cannot train the native tokenizer on empty segment counts.")
        seed_tokenizer = cls(
            merges=[],
            vocab=[],
            base_symbols=[],
            lowercase=lowercase,
            word_prefix=word_prefix,
        )

        word_counts = Counter(
            {
                str(segment): float(frequency)
                for segment, frequency in segment_counts.items()
                if str(segment) and float(frequency) > 0.0
            }
        )
        if not word_counts:
            raise ValueError("Cannot train the native tokenizer on empty segment counts.")
        observed_symbols = {
            f"{word_prefix}{character}" if index == 0 else character
            for segment in word_counts
            for index, character in enumerate(segment)
        }
        base_symbols = _default_symbol_inventory(word_prefix)
        base_symbols.update(observed_symbols)
        sequences = {
            segment: [
                f"{word_prefix}{character}" if index == 0 else character
                for index, character in enumerate(segment)
            ]
            for segment in word_counts
        }
        vocab = set(observed_symbols) | seed_tokenizer.special_tokens
        target_vocab_size = len(vocab) + max(1, vocab_size)
        segment_candidates = sorted(
            {
                segment
                for segment, frequency in word_counts.items()
                if len(segment) > 1 and frequency >= min_pair_frequency
            },
            key=lambda segment: (
                -(word_counts[segment] * len(segment)),
                -len(segment),
                segment,
            ),
        )
        for segment in segment_candidates:
            if len(vocab) >= target_vocab_size:
                break
            vocab.add(_whole_segment_token(segment, word_prefix))
        merges: list[tuple[str, str]] = []

        while len(vocab) < target_vocab_size and len(merges) < MAX_TRAINED_PAIR_MERGES:
            pair_counts: Counter[tuple[str, str]] = Counter()
            for segment, frequency in word_counts.items():
                symbols = sequences[segment]
                for index in range(len(symbols) - 1):
                    pair_counts[(symbols[index], symbols[index + 1])] += frequency

            if not pair_counts:
                break

            best_pair, best_count = min(
                pair_counts.items(),
                key=lambda item: (-item[1], item[0][0], item[0][1]),
            )
            if best_count < min_pair_frequency:
                break

            merged_symbol = _merge_symbol(best_pair[0], best_pair[1], word_prefix)
            merges.append(best_pair)
            vocab.add(merged_symbol)
            for segment in sequences:
                sequences[segment] = _merge_sequence(sequences[segment], best_pair, merged_symbol)

        return cls(
            merges=merges,
            vocab=sorted(vocab),
            base_symbols=sorted(base_symbols),
            lowercase=lowercase,
            word_prefix=word_prefix,
        )