import base64
import gzip
from dataclasses import dataclass
from typing import Dict, Iterable, Optional

import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor, nn

from .decoding import decode as decode_function
from .decoding import detect_language as detect_language_function
from .transcribe import transcribe as transcribe_function


@dataclass
class ModelDimensions:
    n_mels: int
    n_audio_ctx: int
    n_audio_state: int
    n_audio_head: int
    n_audio_layer: int
    n_vocab: int
    n_text_ctx: int
    n_text_state: int
    n_text_head: int
    n_text_layer: int
    att_type: str = "default"


# class LayerNorm(nn.LayerNorm):
#     def forward(self, x: Tensor) -> Tensor:
#         return super().forward(x.float()).type(x.dtype)


class LayerNorm(nn.LayerNorm):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def forward(self, input):
        output = F.layer_norm(
            input.float(),
            self.normalized_shape,
            self.weight.float() if self.weight is not None else None,
            self.bias.float() if self.bias is not None else None,
            self.eps,
        )
        return output.type_as(input)


class Linear(nn.Linear):
    def forward(self, x: Tensor) -> Tensor:
        return F.linear(
            x,
            self.weight.to(x.dtype),
            None if self.bias is None else self.bias.to(x.dtype),
        )


class Conv1d(nn.Conv1d):
    def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor:
        return super()._conv_forward(
            x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
        )


def sinusoids(length, channels, max_timescale=10000):
    """Returns sinusoids for positional embedding"""
    assert channels % 2 == 0
    log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
    inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
    scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
    return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)


class MultiHeadAttention(nn.Module):
    def __init__(self, n_state: int, n_head: int):
        super().__init__()
        self.n_head = n_head
        self.query = Linear(n_state, n_state)
        self.key = Linear(n_state, n_state, bias=False)
        self.value = Linear(n_state, n_state)
        self.out = Linear(n_state, n_state)

    def forward(
        self,
        x: Tensor,
        xa: Optional[Tensor] = None,
        mask: Optional[Tensor] = None,
        kv_cache: Optional[dict] = None,
        **kwargs,
    ):
        is_pad_mask = kwargs.get("is_pad_mask", False)

        q = self.query(x)

        if kv_cache is None or xa is None or self.key not in kv_cache:
            # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
            # otherwise, perform key/value projections for self- or cross-attention as usual.
            k = self.key(x if xa is None else xa)
            v = self.value(x if xa is None else xa)
        else:
            # for cross-attention, calculate keys and values once and reuse in subsequent calls.
            k = kv_cache[self.key]
            v = kv_cache[self.value]

        wv, qk = self.qkv_attention(q, k, v, mask, is_pad_mask=is_pad_mask)
        return self.out(wv), qk

    def qkv_attention(
        self,
        q: Tensor,
        k: Tensor,
        v: Tensor,
        mask: Optional[Tensor] = None,
        **kwargs,
    ):
        is_pad_mask = kwargs.get("is_pad_mask", False)
        n_batch, n_ctx, n_state = q.shape
        scale = (n_state // self.n_head) ** -0.25
        q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
        k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
        v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)

        qk = q @ k
        if mask is not None:
            if not is_pad_mask:
                qk = qk + mask[:n_ctx, :n_ctx]
            else:
                mask = mask.unsqueeze(1).eq(0)  # (batch, 1, t, 1)
                min_value = -float(
                    "inf"
                )  # min_value = float(np.finfo(torch.tensor(0, dtype=qk.dtype).numpy().dtype).min)
                qk = qk.masked_fill(mask, min_value)

        qk = qk.float()

        w = F.softmax(qk, dim=-1).to(q.dtype)
        if mask is not None and is_pad_mask:
            w = w.masked_fill(mask, 0.0)
        return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()


class MultiHeadAttentionSdpa(nn.Module):
    def __init__(self, n_state: int, n_head: int):
        super().__init__()
        self.n_head = n_head
        self.query = Linear(n_state, n_state)
        self.key = Linear(n_state, n_state, bias=False)
        self.value = Linear(n_state, n_state)
        self.out = Linear(n_state, n_state)

    def forward(
        self,
        x: Tensor,
        xa: Optional[Tensor] = None,
        mask: Optional[Tensor] = None,
        kv_cache: Optional[dict] = None,
        **kwargs,
    ):
        is_pad_mask = kwargs.get("is_pad_mask", False)

        q = self.query(x)

        if kv_cache is None or xa is None or self.key not in kv_cache:
            # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
            # otherwise, perform key/value projections for self- or cross-attention as usual.
            k = self.key(x if xa is None else xa)
            v = self.value(x if xa is None else xa)
        else:
            # for cross-attention, calculate keys and values once and reuse in subsequent calls.
            k = kv_cache[self.key]
            v = kv_cache[self.value]

        wv, qk = self.qkv_attention(q, k, v, mask, is_pad_mask=is_pad_mask, is_causal=False)
        return self.out(wv), qk

    def qkv_attention(
        self,
        q: Tensor,
        k: Tensor,
        v: Tensor,
        mask: Optional[Tensor] = None,
        **kwargs,
    ):
        is_pad_mask = kwargs.get("is_pad_mask", False)
        is_causal = kwargs.get("is_causal", False)
        n_batch, n_ctx, n_state = q.shape
        scale = (n_state // self.n_head) ** -0.5
        q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
        k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
        v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)

        if mask is not None:
            if not is_pad_mask:
                mask = None
                is_causal = True
            else:
                mask = mask.unsqueeze(1).to(torch.bool)  # (batch, 1, 1, t)

        attn_output = torch.nn.functional.scaled_dot_product_attention(
            q,
            k,
            v,
            attn_mask=mask,
            dropout_p=0.0,
            is_causal=is_causal,
            scale=scale,
        )
        if mask is not None:
            attn_output = attn_output.masked_fill(mask.transpose(2, 3).logical_not(), 0.0)
        attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.flatten(start_dim=2)
        return attn_output, None


att_type_dict = {
    "default": MultiHeadAttention,
    "sdpa": MultiHeadAttentionSdpa,
}


class ResidualAttentionBlock(nn.Module):
    def __init__(self, n_state: int, n_head: int, cross_attention: bool = False, **kwargs):
        super().__init__()

        att_type = kwargs.get("att_type", "default")
        self.attn = att_type_dict[att_type](n_state, n_head)  # MultiHeadAttention(n_state, n_head)
        self.attn_ln = LayerNorm(n_state)

        self.cross_attn = (
            att_type_dict[att_type](n_state, n_head) if cross_attention else None
        )  # MultiHeadAttention(n_state, n_head) if cross_attention else None
        self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None

        n_mlp = n_state * 4
        self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state))
        self.mlp_ln = LayerNorm(n_state)

    def forward(
        self,
        x: Tensor,
        xa: Optional[Tensor] = None,
        mask: Optional[Tensor] = None,
        kv_cache: Optional[dict] = None,
        **kwargs,
    ):
        is_pad_mask = kwargs.get("is_pad_mask", False)
        is_pad_memory_mask = kwargs.get("is_pad_memory_mask", False)
        memory_mask = kwargs.get("memory_mask", None)
        x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache, is_pad_mask=is_pad_mask)[0]
        if self.cross_attn:
            x = (
                x
                + self.cross_attn(
                    self.cross_attn_ln(x),
                    xa,
                    mask=memory_mask,
                    kv_cache=kv_cache,
                    is_pad_mask=is_pad_memory_mask,
                )[0]
            )
        x = x + self.mlp(self.mlp_ln(x))
        return x


class AudioEncoder(nn.Module):
    def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int, **kwargs):
        super().__init__()
        self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, stride=2, padding=1)
        self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
        self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))

        self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
            [
                ResidualAttentionBlock(n_state, n_head, att_type=kwargs.get("att_type", "default"))
                for _ in range(n_layer)
            ]
        )
        self.ln_post = LayerNorm(n_state)

    def forward(self, x: Tensor):
        """
        x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
            the mel spectrogram of the audio
        """
        x = F.gelu(self.conv1(x))
        x = F.gelu(self.conv2(x))
        x = x.permute(0, 2, 1)

        # assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape"
        # x = (x + self.positional_embedding).to(x.dtype)
        x = (x + self.positional_embedding[: x.size(1), :]).to(x.dtype)

        for block in self.blocks:
            x = block(x)

        x = self.ln_post(x)
        return x


class TextDecoder(nn.Module):
    def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int, **kwargs):
        super().__init__()

        self.token_embedding = nn.Embedding(n_vocab, n_state)
        self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))

        self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
            [
                ResidualAttentionBlock(
                    n_state,
                    n_head,
                    cross_attention=True,
                    att_type="default",  # kwargs.get("att_type", "default"),
                )
                for _ in range(n_layer)
            ]
        )
        self.ln = LayerNorm(n_state)

        mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
        self.register_buffer("mask", mask, persistent=False)

    def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None):
        """
        x : torch.LongTensor, shape = (batch_size, <= n_ctx)
            the text tokens
        xa : torch.Tensor, shape = (batch_size, n_audio_ctx, n_audio_state)
            the encoded audio features to be attended on
        """
        offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
        x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]]
        x = x.to(xa.dtype)

        for block in self.blocks:
            x = block(x, xa, mask=self.mask, kv_cache=kv_cache)

        x = self.ln(x)
        logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float()

        return logits

    def init_state(self, x):
        state = {}

        return state

    def final_score(self, state) -> float:
        """Score eos (optional).

        Args:
            state: Scorer state for prefix tokens

        Returns:
            float: final score

        """
        return 0.0

    def score(self, ys, state, x):
        """Score."""
        # ys_mask = subsequent_mask(len(ys), device=x.device).unsqueeze(0)
        # print(ys.unsqueeze(0).size())
        logp = self.forward(ys, x.unsqueeze(0), cache=state)
        logp = torch.log_softmax(logp, dim=-1)
        return logp[:, -1, :], state


class Whisper(nn.Module):
    def __init__(self, dims: ModelDimensions):
        super().__init__()
        self.dims = dims
        self.encoder = AudioEncoder(
            self.dims.n_mels,
            self.dims.n_audio_ctx,
            self.dims.n_audio_state,
            self.dims.n_audio_head,
            self.dims.n_audio_layer,
            att_type=self.dims.att_type,
        )
        self.decoder = TextDecoder(
            self.dims.n_vocab,
            self.dims.n_text_ctx,
            self.dims.n_text_state,
            self.dims.n_text_head,
            self.dims.n_text_layer,
            att_type=self.dims.att_type,
        )
        # use the last half among the decoder layers for time alignment by default;
        # to use a specific set of heads, see `set_alignment_heads()` below.
        all_heads = torch.zeros(self.dims.n_text_layer, self.dims.n_text_head, dtype=torch.bool)
        all_heads[self.dims.n_text_layer // 2 :] = True
        # self.register_buffer("alignment_heads", all_heads.to_sparse(), persistent=False)
        # alignment_heads_dense = model.get_buffer("alignment_heads").to_dense()
        # model.register_buffer("alignment_heads", alignment_heads_dense, persistent=False)

    def set_alignment_heads(self, dump: bytes):
        array = np.frombuffer(gzip.decompress(base64.b85decode(dump)), dtype=bool).copy()
        mask = torch.from_numpy(array).reshape(self.dims.n_text_layer, self.dims.n_text_head)
        self.register_buffer("alignment_heads", mask.to_sparse(), persistent=False)

    def embed_audio(self, mel: torch.Tensor):
        return self.encoder(mel)

    def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor):
        return self.decoder(tokens, audio_features)

    def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]:
        return self.decoder(tokens, self.encoder(mel))

    @property
    def device(self):
        return next(self.parameters()).device

    @property
    def is_multilingual(self):
        return self.dims.n_vocab >= 51865

    @property
    def num_languages(self):
        return self.dims.n_vocab - 51765 - int(self.is_multilingual)

    def install_kv_cache_hooks(self, cache: Optional[dict] = None):
        """
        The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value
        tensors calculated for the previous positions. This method returns a dictionary that stores
        all caches, and the necessary hooks for the key and value projection modules that save the
        intermediate tensors to be reused during later calculations.

        Returns
        -------
        cache : Dict[nn.Module, torch.Tensor]
            A dictionary object mapping the key/value projection modules to its cache
        hooks : List[RemovableHandle]
            List of PyTorch RemovableHandle objects to stop the hooks to be called
        """
        cache = {**cache} if cache is not None else {}
        hooks = []

        def save_to_cache(module, _, output):
            if module not in cache or output.shape[1] > self.dims.n_text_ctx:
                # save as-is, for the first token or cross attention
                cache[module] = output
            else:
                cache[module] = torch.cat([cache[module], output], dim=1).detach()
            return cache[module]

        def install_hooks(layer: nn.Module):
            if isinstance(layer, MultiHeadAttention):
                hooks.append(layer.key.register_forward_hook(save_to_cache))
                hooks.append(layer.value.register_forward_hook(save_to_cache))

        self.decoder.apply(install_hooks)
        return cache, hooks

    detect_language = detect_language_function
    transcribe = transcribe_function
    decode = decode_function