# Based on implementations from the 4M repo: https://github.com/apple/ml-4m/
from functools import partial
from typing import Dict, List, Optional, Tuple, Union

import torch
import torch.nn as nn
from einops import repeat

from .fm_utils import build_1d_sincos_posemb, build_2d_sincos_posemb, pair


class SequenceDecoderEmbedding(nn.Module):
    """Embedding module for sequence inputs, like captions or a sequence of objects.

    Args:
        vocab_size: Vocabulary size
        max_length: Maximum number of tokens in the sequence
        dim_tokens: Dimension of output tokens. Can be set using init method.
        sincos_pos_emb: Set to True (default) to use fixed 1D sin-cos positional embeddings
        padding_idx: Padding index for word embedding
        share_embedding: Set to True to share input and output embedding weights
    """
    def __init__(self,
                 vocab_size: int,
                 max_length: int,
                 dim_tokens: Optional[int] = None,
                 sincos_pos_emb: bool = True,
                 max_sincos_pos_emb: int = 512,
                 padding_idx: int = 0,
                 share_embedding: bool = True,
                 **kwargs):
        super().__init__()
        self.vocab_size = vocab_size
        self.max_length = max_length
        self.dim_tokens = dim_tokens
        self.sincos_pos_emb = sincos_pos_emb
        self.padding_idx = padding_idx
        self.max_sincos_pos_emb = max_sincos_pos_emb
        self.share_embedding = share_embedding

        if self.dim_tokens is not None:
            self.init(dim_tokens=dim_tokens)

    def init(self, dim_tokens: int = 768, init_std=0.02):
        """
        Initialize parts of embedding module that are dependent on dimension of tokens.
        Should be called when setting up FourM.

        Args:
            dim_tokens: Dimension of tokens
            init_std: Standard deviation of init
        """
        self.dim_tokens = dim_tokens

        # Task embedding identifying from which task a given token comes from
        # Fixed-size positional embeddings. Can be interpolated to different input sizes

        if self.sincos_pos_emb:
            if self.max_length > self.max_sincos_pos_emb:
                raise ValueError(f"Max length ({self.max_length}) is greater than the number of posembs ({self.max_sincos_pos_emb}")
            # Get all posembs, than truncate up to max length
            pos_emb = build_1d_sincos_posemb(max_len=self.max_sincos_pos_emb, embed_dim=self.dim_tokens)[:self.max_length]
            self.register_buffer("pos_emb", pos_emb)
        else:
            self.pos_emb = nn.Parameter(torch.zeros(1, self.max_length, self.dim_tokens))
            nn.init.normal_(self.pos_emb, std=init_std)

        self.mod_emb = nn.Parameter(torch.zeros(1, 1, self.dim_tokens))
        nn.init.normal_(self.mod_emb, std=init_std)

        # Token embedding
        self.token_emb = nn.Embedding(num_embeddings=self.vocab_size, embedding_dim=self.dim_tokens, padding_idx=self.padding_idx)

        # Output projection layer
        self.to_logits = nn.Linear(self.dim_tokens, self.vocab_size, bias=False)

        if self.share_embedding:
            # Share input and output embedding weights
            self.to_logits.weight = self.token_emb.weight


    @torch.jit.ignore
    def no_weight_decay(self):
        return set()

    def forward_embed(self, d: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
        """
        Forward pass through embedding module, transforming sequence of ids to sequence of embeddings.
        Creates corresponding modality and positional embeddings and adds them to the dict.

        Args:
            d (Dict[str, torch.Tensor]): Modality dict, with at least the following keys:
                - 'tensor' (torch.Tensor): Token sequence for each batch. Shape (B, L) where B is the batch size and L is the sequence length.
                - 'target_mask' (torch.Tensor): Mask for valid tokens in the target sequence (set to 0 for valid tokens and 1 otherwise). Shape (B, L).

        Returns:
            Dict[str, torch.Tensor]: Modality dict with added keys:
                - 'x' (torch.Tensor): Embedded token sequence. Shape (B, L, D) where D is the embedding dimension.
                - 'emb' (torch.Tensor): Sum of positional and modality embeddings for the target sequence. Shape (B, L, D).
                - 'ids' (torch.Tensor): Original token sequence from input dict. Shape (B, L).
        """
        ids = d['tensor']
        B = ids.shape[0]
        assert self.dim_tokens is not None, 'Need to call init(dim_tokens) function first'

        # Map to embedding
        x = self.token_emb(ids)

        expanded_pos_emb = repeat(self.pos_emb, "() n d -> b n d", b=B)

        # Target pos encoding
        target_mask = d['target_mask']
        target_pos_id = (~target_mask).int().cumsum(dim=1) - 1
        target_pos_id[target_mask] = 0
        # Sometimes target sequence is over max length, it will be truncated in decoder
        target_pos_id[target_pos_id >= self.max_length] = 0
        target_pos_emb = torch.gather(expanded_pos_emb, dim=1, index=repeat(target_pos_id, "b n -> b n d", d=expanded_pos_emb.shape[2]))
        target_pos_emb[target_mask] = 0

        x_emb = target_pos_emb + self.mod_emb


        d['x'] = x
        d['emb'] = x_emb
        d['ids'] = d['tensor']

        return d

    def forward_logits(self, x: torch.Tensor) -> torch.Tensor:
        """
        Forward pass through output projection layer, transforming sequence of embeddings to logits.

        Args:
            x (torch.Tensor): Output tokens from the decoder. Shape (B, M, D)
        
        Returns:
            torch.Tensor: Logits for each token in the sequence. Shape (B, M, V)
        """
        logits = self.to_logits(x)
        return logits



class ImageTokenDecoderEmbedding(nn.Module):
    """Embedding module for tokenized spatial inputs.

    Args:
        vocab_size: Vocabulary size
        patch_size: Int or tuple of the patch size over the full image size.
        dim_tokens: Dimension of output tokens. Can be set using init method.
        sincos_pos_emb: Set to True (default) to use fixed 2D sin-cos positional embeddings
        image_size: Default image size. Used to initialize size of positional embeddings.
        share_embedding: Set to True to share input and output embedding weights
    """
    def __init__(self,
                 vocab_size: int,
                 patch_size: Union[int, Tuple[int,int]] = 16,
                 dim_tokens: Optional[int] = None,
                 sincos_pos_emb: bool = True,
                 image_size: Union[int, Tuple[int]] = 224,
                 share_embedding: bool = True,
                 **kwargs):
        super().__init__()
        self.vocab_size = vocab_size
        self.patch_size = pair(patch_size)
        self.dim_tokens = dim_tokens
        self.sincos_pos_emb = sincos_pos_emb
        self.image_size = pair(image_size)
        self.num_patches = (self.image_size[0] // self.patch_size[0]) * (self.image_size[1] // self.patch_size[1])
        self.share_embedding = share_embedding

        if self.dim_tokens is not None:
            self.init(dim_tokens=dim_tokens)

    def init(self, dim_tokens: int = 768, init_std=0.02):
        """
        Initialize parts of module that are dependent on dimension of tokens.
        Should be called when setting up FourM.

        Args:
            dim_tokens: Dimension of tokens
            init_std: Standard deviation of init
        """
        self.dim_tokens = dim_tokens

        # Task embedding identifying from which task a given token comes from
        # Fixed-size positional embeddings. Can be interpolated to different input sizes
        h_posemb = self.image_size[0] // self.patch_size[0]
        w_posemb = self.image_size[1] // self.patch_size[1]
        if self.sincos_pos_emb:
            pos_emb = build_2d_sincos_posemb(h=h_posemb, w=w_posemb, embed_dim=self.dim_tokens)
            self.register_buffer("pos_emb", pos_emb)
        else:
            self.pos_emb = nn.Parameter(torch.zeros(1, (h_posemb * w_posemb), self.dim_tokens))
            nn.init.normal_(self.pos_emb, std=init_std)

        self.mod_emb = nn.Parameter(torch.zeros(1, 1, self.dim_tokens))
        nn.init.normal_(self.mod_emb, std=init_std)

        # Token embedding (not needed if only masked tokens are given as input, but can be useful to train Token Critic)
        self.token_emb = nn.Embedding(num_embeddings=self.vocab_size, embedding_dim=self.dim_tokens)

        # Output projection layer
        self.to_logits = nn.Linear(self.dim_tokens, self.vocab_size, bias=False)

        if self.share_embedding:
            # Share input and output embedding weights
            self.to_logits.weight = self.token_emb.weight

    @torch.jit.ignore
    def no_weight_decay(self):
        return set()

    def forward_embed(self, d: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
        """
        Forward pass through the embedding module, transforming tokenized spatial inputs to embeddings.
        Creates corresponding modality and positional embeddings and adds them to the dict.

        Args:
            d (Dict[str, torch.Tensor]): Modality dict, with at least the following key:
                - 'tensor' (torch.Tensor): Modality tokens for each batch (e.g. from tokenized images). Shape (B, H, W) where B is the batch size, H and W are height and width after tokenization.


        Returns:
            Dict[str, torch.Tensor]: Modality dict with added keys:
                - 'x' (torch.Tensor): Embedded token sequence, which is replaced by mask tokens in the 4M decoder. Shape (B, H*W, D) where D is the embedding dimension.
                - 'emb' (torch.Tensor): Sum of positional and modality embeddings for the token sequence. Shape (B, H*W, D).
                - 'ids' (torch.Tensor): Reshaped token sequence from input dict, flattened in the spatial dimensions. Shape (B, H*W).
        """
        ids = d['tensor']
        B = ids.shape[0]
        ids = ids.reshape(B, -1)

        # Map to embedding
        x = self.token_emb(ids)

        # Create positional embedding + modality embedding
        x_emb = repeat(self.pos_emb + self.mod_emb, '() n d -> b n d', b=B)

        d['x'] = x
        d['emb'] = x_emb
        d['ids'] = ids
        return d

    def forward_logits(self, x: torch.Tensor) -> torch.Tensor:
        """
        Forward pass through output projection layer, transforming sequence of embeddings to logits.

        Args:
            x (torch.Tensor): Output tokens from the decoder. Shape (B, M, D)
        
        Returns:
            torch.Tensor: Logits for each token in the sequence. Shape (B, M, V)
        """
        logits = self.to_logits(x)
        return logits
