from collections.abc import Generator, Iterable
from dataclasses import dataclass
from enum import StrEnum
from itertools import chain

import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from transformers import (
    AutoConfig,
    AutoModel,
    AutoTokenizer,
    ModernBertModel,
    PreTrainedConfig,
    PreTrainedModel,
)
from transformers.modeling_outputs import TokenClassifierOutput

BATCH_SIZE = 16


class ModelURI(StrEnum):
    BASE = "answerdotai/ModernBERT-base"
    LARGE = "answerdotai/ModernBERT-large"


@dataclass(slots=True, frozen=True)
class LexicalExample:
    concept: str
    definition: str


@dataclass(slots=True, frozen=True)
class PaddedBatch:
    input_ids: torch.Tensor
    attention_mask: torch.Tensor


class DisamBert(PreTrainedModel):
    def __init__(self, config: PreTrainedConfig):
        super().__init__(config)
        if config.init_basemodel:
            self.BaseModel = AutoModel.from_pretrained(config.name_or_path, device_map="auto")
            self.classifier_head = nn.UninitializedParameter()
            self.bias = nn.UninitializedParameter()
            self.__entities = None
        else:
            self.BaseModel = ModernBertModel(config)
            self.classifier_head = nn.Parameter(
                torch.empty((config.ontology_size, config.hidden_size))
            )
            self.bias = nn.Parameter(torch.empty((config.ontology_size, 1)))
            self.__entities = pd.Series(config.entities)
        config.init_basemodel = False
        self.tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_path)
        self.loss = nn.CrossEntropyLoss()
        self.post_init()

    @classmethod
    def from_base(cls, base_id: ModelURI):
        config = AutoConfig.from_pretrained(base_id)
        config.init_basemodel = True
        config.tokenizer_path = base_id
        return cls(config)

    def init_classifier(self, entities: Generator[LexicalExample]) -> None:
        entity_ids = []
        vectors = []
        batch = []
        n = 0
        with self.BaseModel.device:
            torch.cuda.empty_cache()
            for entity in entities:
                entity_ids.append(entity.concept)
                batch.append(entity.definition)

                n += 1
                if n == BATCH_SIZE:
                    tokens = self.tokenizer(batch, padding=True, return_tensors="pt")
                    encoding = self.BaseModel(tokens["input_ids"], tokens["attention_mask"])
                    vectors.append(encoding.last_hidden_state.detach()[:, 0])
                    n = 0
                    batch = []
            if n > 0:
                tokens = self.tokenizer(batch, padding=True, return_tensors="pt")
                encoding = self.BaseModel(tokens["input_ids"], tokens["attention_mask"])
                vectors.append(encoding.last_hidden_state.detach()[:, 0])

            self.__entities = pd.Series(entity_ids)
            self.config.entities = entity_ids
            self.config.ontology_size = len(entity_ids)
            self.classifier_head = nn.Parameter(torch.cat(vectors, dim=0))
            self.bias = nn.Parameter(
                torch.nn.init.normal_(
                    torch.empty((self.config.ontology_size, 1)), 
                    std=self.classifier_head.std().item() * np.sqrt(self.config.hidden_size)
                )
            )

    @property
    def entities(self) -> pd.Series:
        if self.__entities is None and hasattr(self.config, "entities"):
            self.__entities = pd.Series(self.config.entities)
        return self.__entities

    def forward(
        self,
        input_ids: torch.Tensor,
        attention_mask: torch.Tensor,
        lengths: list[list[int]],
        candidates: list[list[list[int]]],
        labels: Iterable[list[int]] | None = None,
        output_hidden_states: bool = False,
        output_attentions: bool = False,
    ) -> TokenClassifierOutput:
        assert not nn.parameter.is_lazy(self.classifier_head), (
            "Run init_classifier to initialise weights"
        )
        base_model_output = self.BaseModel(
            input_ids,
            attention_mask,
            output_hidden_states=output_hidden_states,
            output_attentions=output_attentions,
        )
        token_vectors = base_model_output.last_hidden_state
        span_vectors = torch.cat(
            [
                torch.vstack(
                    [
                        torch.sum(chunk, dim=0)
                        for chunk in self.split(token_vectors[i], sentence_indices)
                    ]
                )
                for (i, sentence_indices) in enumerate(lengths)
            ]
        )
        logits = torch.einsum("ij,kj->ki", span_vectors, self.classifier_head) + self.bias
        logits1 = logits - logits.min()
        mask = torch.zeros_like(logits)
        for i, concepts in enumerate(chain.from_iterable(candidates)):
            mask[concepts, i] = torch.tensor(1.0)
        logits2 = logits1 * mask
        sentence_lengths = [len(sentence_indices) for sentence_indices in lengths]
        maxlen = max(sentence_lengths)
        split_logits = torch.split(logits2, sentence_lengths, dim=1)
        logits3 = torch.stack(
            [
                self.extend_to_max_length(sentence, length, maxlen)
                for (sentence, length) in zip(split_logits, sentence_lengths, strict=True)
            ]
        )
        return TokenClassifierOutput(
            logits=logits3,
            loss=self.loss(logits3, labels) if labels is not None else None,
            hidden_states=base_model_output.hidden_states if output_hidden_states else None,
            attentions=base_model_output.attentions if output_attentions else None,
        )

    def split(self, vectors: torch.Tensor, lengths: list[int]) -> tuple[torch.Tensor, ...]:
        maxlen = vectors.shape[0]
        total_length = sum(lengths)
        is_padded = total_length < maxlen
        chunks = vectors.split((lengths + [maxlen - total_length]) if is_padded else lengths)
        return chunks[:-1] if is_padded else chunks

    def pad(self, tokens: Iterable[list[int]]) -> PaddedBatch:
        lengths = [len(sentence) for sentence in tokens]
        maxlen = max(lengths)
        input_ids = torch.tensor(
            [
                sentence + [self.config.pad_token_id] * (maxlen - length)
                for (sentence, length) in zip(tokens, lengths, strict=True)
            ]
        )
        attention_mask = torch.vstack(
            [torch.cat((torch.ones(length), torch.zeros(maxlen - length))) for length in lengths]
        )
        return PaddedBatch(input_ids, attention_mask)

    def extend_to_max_length(
        self, sentence: torch.Tensor, length: int, maxlength: int
    ) -> torch.Tensor:
        with self.BaseModel.device:
            return (
                torch.cat(
                    [
                        sentence,
                        torch.zeros((self.config.ontology_size, maxlength - length)),
                    ],
                    dim=1,
                )
                if length < maxlength
                else sentence
            )

    def pad_labels(self, labels: list[list[int]]) -> torch.Tensor:
        unk = len(self.config.entities) - 1
        lengths = [len(seq) for seq in labels]
        maxlen = max(lengths)
        with self.BaseModel.device:
            return torch.tensor(
                [
                    seq + [unk] * (maxlen - length)
                    for (seq, length) in zip(labels, lengths, strict=True)
                ]
            )

    def tokenize(
        self, batch: list[dict[str, str | list[int]]]
    ) -> dict[str, torch.Tensor | list[list[int]]]:
        all_indices = []
        all_tokens = []
        with self.BaseModel.device:
            for example in batch:
                text = example["text"]
                span_indices = example["indices"]
                indices = []
                tokens = []
                last_span = len(span_indices) - 2
                for i, position in enumerate(span_indices[:-1]):
                    span = text[position : span_indices[i + 1]]
                    span_tokens = self.tokenizer([span], padding=False)["input_ids"][0]
                    if i > 0:
                        span_tokens = span_tokens[1:]
                    if i < last_span:
                        span_tokens = span_tokens[:-1]
                    indices.append(len(span_tokens))
                    tokens.extend(span_tokens)
                all_indices.append(indices)
                all_tokens.append(tokens)
            padded = self.pad(all_tokens)
            result = {
                "input_ids": padded.input_ids,
                "attention_mask": padded.attention_mask,
                "lengths": all_indices,
                "candidates": [example["candidates"] for example in batch],
            }
            if "labels" in batch[0]:
                result["labels"] = self.pad_labels([example["labels"] for example in batch])
            return result