# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
import numpy as np

from safetensors.torch import load_file

import os

class LyraSdVaeModel():
    r"""
    A VAE model with KL loss for encoding images into latents and decoding latent representations into images.

    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
    for all models (such as downloading or saving).

    Parameters:
        in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
        out_channels (int,  *optional*, defaults to 3): Number of channels in the output.
        down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
            Tuple of downsample block types.
        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
            Tuple of upsample block types.
        block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
            Tuple of block output channels.
        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
        latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.
        sample_size (`int`, *optional*, defaults to `32`): Sample input size.
        scaling_factor (`float`, *optional*, defaults to 0.18215):
            The component-wise standard deviation of the trained latent space computed using the first batch of the
            training set. This is used to scale the latent space to have unit variance when training the diffusion
            model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
            diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
            / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
            Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
        force_upcast (`bool`, *optional*, default to `True`):
            If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
            can be fine-tuned / trained to a lower range without loosing too much precision in which case
            `force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
    """

    _supports_gradient_checkpointing = True

    def __init__(
        self,
        dtype: str = "fp16",
        scaling_factor: float = 0.18215,
        scale_factor: int = 8,
        is_upcast: bool = False
    ):
        super().__init__()
        self.is_upcast = is_upcast
        self.scaling_factor = scaling_factor
        self.scale_factor = scale_factor
        self.model = torch.classes.lyrasd.VaeModelOp(
            dtype,
            is_upcast
        )

        self.vae_cache = {}

        self.use_slicing = False
        self.use_tiling = False

        self.tile_latent_min_size = 512
        self.tile_sample_min_size = 64
        self.tile_overlap_factor = 0.25

    def reload_vae_model(self, vae_path, vae_file_format='fp32'):
        if len(vae_path) > 0 and vae_path[-1] != "/":
            vae_path = vae_path + "/"
        return self.model.reload_vae_model(vae_path, vae_file_format)

    def reload_vae_model_v2(self, model_path):
        checkpoint_file = os.path.join(model_path, "vae/diffusion_pytorch_model.bin")
        if not os.path.exists(checkpoint_file):
            checkpoint_file = os.path.join(model_path, "vae/diffusion_pytorch_model.safetensors")
        if checkpoint_file in self.vae_cache:
            state_dict = self.vae_cache[checkpoint_file]
        else:
            if "safetensors" in checkpoint_file:
                state_dict = load_file(checkpoint_file)
            else:
                state_dict = torch.load(checkpoint_file, map_location="cpu")

            # replace deprecated weights
            for path in ["encoder.mid_block.attentions.0", "decoder.mid_block.attentions.0"]:
                # group_norm path stays the same

                # query -> to_q
                if f"{path}.query.weight" in state_dict:
                    state_dict[f"{path}.to_q.weight"] = state_dict.pop(f"{path}.query.weight")
                if f"{path}.query.bias" in state_dict:
                    state_dict[f"{path}.to_q.bias"] = state_dict.pop(f"{path}.query.bias")

                # key -> to_k
                if f"{path}.key.weight" in state_dict:
                    state_dict[f"{path}.to_k.weight"] = state_dict.pop(f"{path}.key.weight")
                if f"{path}.key.bias" in state_dict:
                    state_dict[f"{path}.to_k.bias"] = state_dict.pop(f"{path}.key.bias")

                # value -> to_v
                if f"{path}.value.weight" in state_dict:
                    state_dict[f"{path}.to_v.weight"] = state_dict.pop(f"{path}.value.weight")
                if f"{path}.value.bias" in state_dict:
                    state_dict[f"{path}.to_v.bias"] = state_dict.pop(f"{path}.value.bias")

                # proj_attn -> to_out.0
                if f"{path}.proj_attn.weight" in state_dict:
                    state_dict[f"{path}.to_out.0.weight"] = state_dict.pop(f"{path}.proj_attn.weight")
                if f"{path}.proj_attn.bias" in state_dict:
                    state_dict[f"{path}.to_out.0.bias"] = state_dict.pop(f"{path}.proj_attn.bias")

            for key in state_dict:
                # print(key)
                if len(state_dict[key].shape) == 4:
                    state_dict[key] = state_dict[key].permute(0,2,3,1).contiguous()
                else:
                    state_dict[key] = state_dict[key]
                if self.is_upcast and (key.startswith("decoder.up_blocks.2") or key.startswith("decoder.up_blocks.3") or key.startswith("decoder.conv_norm_out")):
                    # print(key)
                    state_dict[key] = state_dict[key].to(torch.float32)
                else:
                    state_dict[key] = state_dict[key].to(torch.float16)

            self.vae_cache[checkpoint_file] = state_dict

        return self.model.reload_vae_model_from_cache(state_dict, "cpu")

    def enable_tiling(self, use_tiling: bool = True):
        r"""
        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
        processing larger images.
        """
        self.use_tiling = use_tiling

    def disable_tiling(self):
        r"""
        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
        decoding in one step.
        """
        self.enable_tiling(False)

    def enable_slicing(self):
        r"""
        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
        """
        self.use_slicing = True

    def disable_slicing(self):
        r"""
        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
        decoding in one step.
        """
        self.use_slicing = False

    def lyra_decode(self, x: torch.FloatTensor) -> torch.FloatTensor:
        x = x.permute(0, 2, 3, 1).contiguous()
        x = self.model.vae_decode(x)
        return x.permute(0, 3, 1, 2)

    def lyra_encode(self, x: torch.FloatTensor) -> torch.FloatTensor:
        x = x.permute(0, 2, 3, 1).contiguous()
        x = self.model.vae_encode(x)
        return x.permute(0, 3, 1, 2)

    def encode(
        self, x: torch.FloatTensor, return_dict: bool = True
    ) -> DiagonalGaussianDistribution:
        """
        Encode a batch of images into latents.

        Args:
            x (`torch.FloatTensor`): Input batch of images.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.

        Returns:
                The latent representations of the encoded images. If `return_dict` is True, a
                [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
        """
        if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
            return self.tiled_encode(x, return_dict=return_dict)

        if self.use_slicing and x.shape[0] > 1:
            encoded_slices = [self.lyra_encode(
                x_slice) for x_slice in x.split(1)]
            h = torch.cat(encoded_slices)
            posterior = DiagonalGaussianDistribution(h)
        else:
            moments = self.lyra_encode(x)
            posterior = DiagonalGaussianDistribution(moments)

        return posterior

    def _decode(self, z: torch.FloatTensor, return_dict: bool = True) -> torch.FloatTensor:
        if self.use_tiling and (z.shape[2] > self.tile_latent_min_size or z.shape[3] > self.tile_latent_min_size):
            return self.tiled_decode(z, return_dict=return_dict)

        dec = self.lyra_decode(z)

        return dec

    def decode(
        self, z: torch.FloatTensor, return_dict: bool = True, generator=None
    ) -> torch.FloatTensor:
        """
        Decode a batch of images.

        Args:
            z (`torch.FloatTensor`): Input batch of latent vectors.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.

        Returns:
            [`~models.vae.DecoderOutput`] or `tuple`:
                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
                returned.

        """
        if self.use_slicing and z.shape[0] > 1:
            decoded_slices = [self._decode(
                z_slice) for z_slice in z.split(1)]
            decoded = torch.cat(decoded_slices)
        else:
            decoded = self._decode(z)

        return decoded

    def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
        blend_extent = min(a.shape[2], b.shape[2], blend_extent)
        for y in range(blend_extent):
            b[:, :, y, :] = a[:, :, -blend_extent + y, :] * \
                (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
        return b

    def blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
        blend_extent = min(a.shape[3], b.shape[3], blend_extent)
        for x in range(blend_extent):
            b[:, :, :, x] = a[:, :, :, -blend_extent + x] * \
                (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
        return b

    def tiled_encode(self, x: torch.FloatTensor, return_dict: bool = True) -> DiagonalGaussianDistribution:
        r"""Encode a batch of images using a tiled encoder.

        When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several
        steps. This is useful to keep memory use constant regardless of image size. The end result of tiled encoding is
        different from non-tiled encoding because each tile uses a different encoder. To avoid tiling artifacts, the
        tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the
        output, but they should be much less noticeable.

        Args:
            x (`torch.FloatTensor`): Input batch of images.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.

        Returns:
            [`~models.autoencoder_kl.AutoencoderKLOutput`] or `tuple`:
                If return_dict is True, a [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain
                `tuple` is returned.
        """
        overlap_size = int(self.tile_sample_min_size *
                           (1 - self.tile_overlap_factor))
        blend_extent = int(self.tile_latent_min_size *
                           self.tile_overlap_factor)
        row_limit = self.tile_latent_min_size - blend_extent

        # Split the image into 512x512 tiles and encode them separately.
        rows = []
        for i in range(0, x.shape[2], overlap_size):
            row = []
            for j in range(0, x.shape[3], overlap_size):
                tile = x[:, :, i: i + self.tile_sample_min_size,
                         j: j + self.tile_sample_min_size]
                tile = self.lyra_encode(tile)
                row.append(tile)
            rows.append(row)
        result_rows = []
        for i, row in enumerate(rows):
            result_row = []
            for j, tile in enumerate(row):
                # blend the above tile and the left tile
                # to the current tile and add the current tile to the result row
                if i > 0:
                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
                if j > 0:
                    tile = self.blend_h(row[j - 1], tile, blend_extent)
                result_row.append(tile[:, :, :row_limit, :row_limit])
            result_rows.append(torch.cat(result_row, dim=3))

        moments = torch.cat(result_rows, dim=2)
        posterior = DiagonalGaussianDistribution(moments)

        return posterior

    def tiled_decode(self, z: torch.FloatTensor, return_dict: bool = True) -> torch.FloatTensor:
        r"""
        Decode a batch of images using a tiled decoder.

        Args:
            z (`torch.FloatTensor`): Input batch of latent vectors.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.

        Returns:
            [`~models.vae.DecoderOutput`] or `tuple`:
                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
                returned.
        """
        overlap_size = int(self.tile_latent_min_size *
                           (1 - self.tile_overlap_factor))
        blend_extent = int(self.tile_sample_min_size *
                           self.tile_overlap_factor)
        row_limit = self.tile_sample_min_size - blend_extent

        # Split z into overlapping 64x64 tiles and decode them separately.
        # The tiles have an overlap to avoid seams between tiles.
        rows = []
        for i in range(0, z.shape[2], overlap_size):
            row = []
            for j in range(0, z.shape[3], overlap_size):
                tile = z[:, :, i: i + self.tile_latent_min_size,
                         j: j + self.tile_latent_min_size]
                decoded = self.lyra_decode(tile)
                row.append(decoded)
            rows.append(row)
        result_rows = []
        for i, row in enumerate(rows):
            result_row = []
            for j, tile in enumerate(row):
                # blend the above tile and the left tile
                # to the current tile and add the current tile to the result row
                if i > 0:
                    tile = self.blend_v(rows[i - 1][j], tile, blend_extent)
                if j > 0:
                    tile = self.blend_h(row[j - 1], tile, blend_extent)
                result_row.append(tile[:, :, :row_limit, :row_limit])
            result_rows.append(torch.cat(result_row, dim=3))

        dec = torch.cat(result_rows, dim=2)
        if not return_dict:
            return (dec,)

        return dec