"""HuggingFace-compatible port of TalkieModel.

Mirrors talkie/src/talkie/model.py exactly:
- F.rms_norm everywhere (no learnable RMSNorm scale)
- RoPE with base=1e6
- Attention with QK-norm and per-head HeadGain on Q
- SwiGLU MLP
- Per-layer ActGain on attn / mlp / embed_skip residuals
- WeightGain on the lm_head matrix

Linear projections are renamed to Llama conventions (q_proj, k_proj, v_proj,
o_proj, gate_proj, up_proj, down_proj) so GPTQModel auto-detects them.
"""

from __future__ import annotations

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.generation import GenerationMixin
from transformers.modeling_outputs import CausalLMOutput

from .configuration_talkie import TalkieConfig


def _apply_rotary_emb(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
    # x: [B, T, H, D]; cos/sin: [1, T, 1, D/2]
    d = x.shape[-1] // 2
    x1, x2 = x[..., :d], x[..., d:]
    y1 = x1 * cos + x2 * sin
    y2 = -x1 * sin + x2 * cos
    return torch.cat([y1, y2], dim=-1).type_as(x)


class HeadGain(nn.Module):
    def __init__(self, n_head: int):
        super().__init__()
        self.head_g = nn.Parameter(torch.ones(n_head))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return x * self.head_g.type_as(x).view(1, 1, -1, 1)


class WeightGain(nn.Module):
    def __init__(self):
        super().__init__()
        self.w_g = nn.Parameter(torch.ones(1))

    def forward(self, w: torch.Tensor) -> torch.Tensor:
        return w * self.w_g.type_as(w)


class ActGain(nn.Module):
    def __init__(self, init_value: float):
        super().__init__()
        self.a_g = nn.Parameter(torch.ones(1) * init_value)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return x * self.a_g.type_as(x)


class TalkieAttention(nn.Module):
    def __init__(self, config: TalkieConfig):
        super().__init__()
        self.n_head = config.num_attention_heads
        self.head_dim = config.head_dim
        h = config.hidden_size
        self.q_proj = nn.Linear(h, h, bias=False)
        self.k_proj = nn.Linear(h, h, bias=False)
        self.v_proj = nn.Linear(h, h, bias=False)
        self.o_proj = nn.Linear(h, h, bias=False)
        self.head_gain = HeadGain(config.num_attention_heads)

    def forward(self, x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
        bsz, seq_len, _ = x.size()
        q = self.q_proj(x).view(bsz, seq_len, self.n_head, self.head_dim)
        k = self.k_proj(x).view(bsz, seq_len, self.n_head, self.head_dim)
        v = self.v_proj(x).view(bsz, seq_len, self.n_head, self.head_dim)

        q = _apply_rotary_emb(q, cos, sin)
        k = _apply_rotary_emb(k, cos, sin)
        q = F.rms_norm(q, (q.size(-1),))
        k = F.rms_norm(k, (k.size(-1),))
        q = self.head_gain(q)

        # SDPA expects [B, H, T, D]
        y = F.scaled_dot_product_attention(
            q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), is_causal=True
        )
        y = y.transpose(1, 2).contiguous().view(bsz, seq_len, -1)
        return self.o_proj(y)


class TalkieMLP(nn.Module):
    def __init__(self, config: TalkieConfig):
        super().__init__()
        self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
        self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))


class TalkieDecoderLayer(nn.Module):
    def __init__(self, config: TalkieConfig):
        super().__init__()
        self.self_attn = TalkieAttention(config)
        self.attn_gain = ActGain((2 * config.num_hidden_layers) ** -0.5)
        self.mlp = TalkieMLP(config)
        self.mlp_gain = ActGain((2 * config.num_hidden_layers) ** -0.5)
        self.embed_skip = ActGain(0.0)

    def forward(
        self,
        hidden_states: torch.Tensor,
        e_x: torch.Tensor = None,
        cos: torch.Tensor = None,
        sin: torch.Tensor = None,
        **kwargs,  # absorb attention_mask / position_ids / etc that HF tooling injects
    ) -> torch.Tensor:
        x = hidden_states
        x = x + self.attn_gain(self.self_attn(F.rms_norm(x, (x.shape[-1],)), cos, sin))
        x = x + self.mlp_gain(self.mlp(F.rms_norm(x, (x.shape[-1],))))
        x = x + self.embed_skip(e_x)
        return x


class TalkiePreTrainedModel(PreTrainedModel):
    config_class = TalkieConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = False
    _no_split_modules = ["TalkieDecoderLayer"]

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=0.02)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=0.02)


class TalkieModel(TalkiePreTrainedModel):
    def __init__(self, config: TalkieConfig):
        super().__init__(config)
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
        self.layers = nn.ModuleList(
            [TalkieDecoderLayer(config) for _ in range(config.num_hidden_layers)]
        )
        # cos/sin are computed lazily in forward — see _rope. Avoid register_buffer
        # so HF's meta-init / low_cpu_mem_usage loading path does not leave us
        # holding meta tensors that we then try to slice (which raises).
        self._rope_cache: tuple[torch.Tensor, torch.Tensor, torch.device, torch.dtype, int] | None = None
        self.post_init()

    @staticmethod
    def _build_rope(
        seq_len: int, head_dim: int, base: float, device, dtype
    ) -> tuple[torch.Tensor, torch.Tensor]:
        ch = torch.arange(0, head_dim, 2, dtype=torch.float32, device=device)
        inv_freq = 1.0 / (base ** (ch / head_dim))
        t = torch.arange(seq_len, dtype=torch.float32, device=device)
        freqs = torch.outer(t, inv_freq)
        cos, sin = freqs.cos().to(dtype), freqs.sin().to(dtype)
        return cos[None, :, None, :], sin[None, :, None, :]

    def _rope(self, seq_len: int, device, dtype) -> tuple[torch.Tensor, torch.Tensor]:
        cache = self._rope_cache
        if (cache is None or cache[2] != device or cache[3] != dtype or cache[4] < seq_len):
            cap = max(seq_len, self.config.max_position_embeddings)
            cos, sin = self._build_rope(cap, self.config.head_dim, self.config.rope_theta, device, dtype)
            self._rope_cache = (cos, sin, device, dtype, cap)
        cos, sin, _, _, _ = self._rope_cache
        return cos[:, :seq_len], sin[:, :seq_len]

    def forward(self, input_ids: torch.LongTensor, **kwargs) -> torch.Tensor:
        _, seq_len = input_ids.shape
        x = self.embed_tokens(input_ids)
        x = F.rms_norm(x, (x.shape[-1],))
        e_x = x  # post-RMSNorm input embeddings; reused as embed_skip source at every layer
        cos, sin = self._rope(seq_len, x.device, x.dtype)
        for layer in self.layers:
            # Pass e_x/cos/sin as kwargs so HF tooling (GPTQModel etc) captures
            # and replays them per-sample when iterating layers individually.
            x = layer(x, e_x=e_x, cos=cos, sin=sin)
        x = F.rms_norm(x, (x.shape[-1],))
        return x


class TalkieForCausalLM(TalkiePreTrainedModel, GenerationMixin):
    _tied_weights_keys = []
    _supports_cache_class = False
    _supports_static_cache = False
    # Talkie has no KV cache implementation — every generate step recomputes
    # the full sequence. Mirror the reference talkie inference behavior.

    def __init__(self, config: TalkieConfig):
        super().__init__(config)
        # Force use_cache=False so HF generate doesn't try to feed only the
        # last token via past_key_values (which we don't support).
        config.use_cache = False
        self.model = TalkieModel(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.lm_head_gain = WeightGain()
        self.post_init()
        # Belt-and-suspenders: also force the generation_config to not cache.
        if hasattr(self, "generation_config") and self.generation_config is not None:
            self.generation_config.use_cache = False

    def prepare_inputs_for_generation(self, input_ids, **kwargs):
        # Strip past_key_values and always feed the full sequence — talkie
        # has no incremental state.
        kwargs.pop("past_key_values", None)
        kwargs.pop("cache_position", None)
        kwargs["use_cache"] = False
        return {"input_ids": input_ids, **kwargs}

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: torch.Tensor = None,  # accepted but unused (causal-only)
        labels: torch.LongTensor = None,
        **kwargs,
    ) -> CausalLMOutput:
        hidden = self.model(input_ids)
        # WeightGain is scalar-broadcast over the lm_head matrix, so applying
        # it on the linear's output is mathematically identical to pre-scaling
        # the weight (and avoids a cross-module tensor passing pattern that
        # confuses accelerate's device-map hooks).
        logits = self.lm_head(hidden).float() * self.lm_head_gain.w_g.float()
        loss = None
        if labels is not None:
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss = F.cross_entropy(
                shift_logits.view(-1, shift_logits.size(-1)),
                shift_labels.view(-1),
            )
        return CausalLMOutput(loss=loss, logits=logits)