# 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.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Based on [AnyText: Multilingual Visual Text Generation And Editing](https://huggingface.co/papers/2311.03054).
# Authors: Yuxiang Tuo, Wangmeng Xiang, Jun-Yan He, Yifeng Geng, Xuansong Xie
# Code: https://github.com/tyxsspa/AnyText with Apache-2.0 license
#
# Adapted to Diffusers by [M. Tolga Cangöz](https://github.com/tolgacangoz).


from typing import Any, Dict, Optional, Tuple, Union

import torch
from torch import nn

from diffusers.configuration_utils import register_to_config
from diffusers.models.controlnets.controlnet import (
    ControlNetModel,
    ControlNetOutput,
)
from diffusers.utils import logging


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


class AnyTextControlNetConditioningEmbedding(nn.Module):
    """
    Quoting from https://huggingface.co/papers/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN
    [11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized
    training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the
    convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides
    (activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full
    model) to encode image-space conditions ... into feature maps ..."
    """

    def __init__(
        self,
        conditioning_embedding_channels: int,
        glyph_channels=1,
        position_channels=1,
    ):
        super().__init__()

        self.glyph_block = nn.Sequential(
            nn.Conv2d(glyph_channels, 8, 3, padding=1),
            nn.SiLU(),
            nn.Conv2d(8, 8, 3, padding=1),
            nn.SiLU(),
            nn.Conv2d(8, 16, 3, padding=1, stride=2),
            nn.SiLU(),
            nn.Conv2d(16, 16, 3, padding=1),
            nn.SiLU(),
            nn.Conv2d(16, 32, 3, padding=1, stride=2),
            nn.SiLU(),
            nn.Conv2d(32, 32, 3, padding=1),
            nn.SiLU(),
            nn.Conv2d(32, 96, 3, padding=1, stride=2),
            nn.SiLU(),
            nn.Conv2d(96, 96, 3, padding=1),
            nn.SiLU(),
            nn.Conv2d(96, 256, 3, padding=1, stride=2),
            nn.SiLU(),
        )

        self.position_block = nn.Sequential(
            nn.Conv2d(position_channels, 8, 3, padding=1),
            nn.SiLU(),
            nn.Conv2d(8, 8, 3, padding=1),
            nn.SiLU(),
            nn.Conv2d(8, 16, 3, padding=1, stride=2),
            nn.SiLU(),
            nn.Conv2d(16, 16, 3, padding=1),
            nn.SiLU(),
            nn.Conv2d(16, 32, 3, padding=1, stride=2),
            nn.SiLU(),
            nn.Conv2d(32, 32, 3, padding=1),
            nn.SiLU(),
            nn.Conv2d(32, 64, 3, padding=1, stride=2),
            nn.SiLU(),
        )

        self.fuse_block = nn.Conv2d(256 + 64 + 4, conditioning_embedding_channels, 3, padding=1)

    def forward(self, glyphs, positions, text_info):
        glyph_embedding = self.glyph_block(glyphs.to(self.glyph_block[0].weight.device))
        position_embedding = self.position_block(positions.to(self.position_block[0].weight.device))
        guided_hint = self.fuse_block(torch.cat([glyph_embedding, position_embedding, text_info["masked_x"]], dim=1))

        return guided_hint


class AnyTextControlNetModel(ControlNetModel):
    """
    A AnyTextControlNetModel model.

    Args:
        in_channels (`int`, defaults to 4):
            The number of channels in the input sample.
        flip_sin_to_cos (`bool`, defaults to `True`):
            Whether to flip the sin to cos in the time embedding.
        freq_shift (`int`, defaults to 0):
            The frequency shift to apply to the time embedding.
        down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
            The tuple of downsample blocks to use.
        only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`):
        block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`):
            The tuple of output channels for each block.
        layers_per_block (`int`, defaults to 2):
            The number of layers per block.
        downsample_padding (`int`, defaults to 1):
            The padding to use for the downsampling convolution.
        mid_block_scale_factor (`float`, defaults to 1):
            The scale factor to use for the mid block.
        act_fn (`str`, defaults to "silu"):
            The activation function to use.
        norm_num_groups (`int`, *optional*, defaults to 32):
            The number of groups to use for the normalization. If None, normalization and activation layers is skipped
            in post-processing.
        norm_eps (`float`, defaults to 1e-5):
            The epsilon to use for the normalization.
        cross_attention_dim (`int`, defaults to 1280):
            The dimension of the cross attention features.
        transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
        encoder_hid_dim (`int`, *optional*, defaults to None):
            If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
            dimension to `cross_attention_dim`.
        encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
            If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
            embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
        attention_head_dim (`Union[int, Tuple[int]]`, defaults to 8):
            The dimension of the attention heads.
        use_linear_projection (`bool`, defaults to `False`):
        class_embed_type (`str`, *optional*, defaults to `None`):
            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from None,
            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
        addition_embed_type (`str`, *optional*, defaults to `None`):
            Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
            "text". "text" will use the `TextTimeEmbedding` layer.
        num_class_embeds (`int`, *optional*, defaults to 0):
            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
            class conditioning with `class_embed_type` equal to `None`.
        upcast_attention (`bool`, defaults to `False`):
        resnet_time_scale_shift (`str`, defaults to `"default"`):
            Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`.
        projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`):
            The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when
            `class_embed_type="projection"`.
        controlnet_conditioning_channel_order (`str`, defaults to `"rgb"`):
            The channel order of conditional image. Will convert to `rgb` if it's `bgr`.
        conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(16, 32, 96, 256)`):
            The tuple of output channel for each block in the `conditioning_embedding` layer.
        global_pool_conditions (`bool`, defaults to `False`):
            TODO(Patrick) - unused parameter.
        addition_embed_type_num_heads (`int`, defaults to 64):
            The number of heads to use for the `TextTimeEmbedding` layer.
    """

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        in_channels: int = 4,
        conditioning_channels: int = 1,
        flip_sin_to_cos: bool = True,
        freq_shift: int = 0,
        down_block_types: Tuple[str, ...] = (
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "DownBlock2D",
        ),
        mid_block_type: str | None = "UNetMidBlock2DCrossAttn",
        only_cross_attention: Union[bool, Tuple[bool]] = False,
        block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
        layers_per_block: int = 2,
        downsample_padding: int = 1,
        mid_block_scale_factor: float = 1,
        act_fn: str = "silu",
        norm_num_groups: Optional[int] = 32,
        norm_eps: float = 1e-5,
        cross_attention_dim: int = 1280,
        transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1,
        encoder_hid_dim: Optional[int] = None,
        encoder_hid_dim_type: str | None = None,
        attention_head_dim: Union[int, Tuple[int, ...]] = 8,
        num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None,
        use_linear_projection: bool = False,
        class_embed_type: str | None = None,
        addition_embed_type: str | None = None,
        addition_time_embed_dim: Optional[int] = None,
        num_class_embeds: Optional[int] = None,
        upcast_attention: bool = False,
        resnet_time_scale_shift: str = "default",
        projection_class_embeddings_input_dim: Optional[int] = None,
        controlnet_conditioning_channel_order: str = "rgb",
        conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256),
        global_pool_conditions: bool = False,
        addition_embed_type_num_heads: int = 64,
    ):
        super().__init__(
            in_channels,
            conditioning_channels,
            flip_sin_to_cos,
            freq_shift,
            down_block_types,
            mid_block_type,
            only_cross_attention,
            block_out_channels,
            layers_per_block,
            downsample_padding,
            mid_block_scale_factor,
            act_fn,
            norm_num_groups,
            norm_eps,
            cross_attention_dim,
            transformer_layers_per_block,
            encoder_hid_dim,
            encoder_hid_dim_type,
            attention_head_dim,
            num_attention_heads,
            use_linear_projection,
            class_embed_type,
            addition_embed_type,
            addition_time_embed_dim,
            num_class_embeds,
            upcast_attention,
            resnet_time_scale_shift,
            projection_class_embeddings_input_dim,
            controlnet_conditioning_channel_order,
            conditioning_embedding_out_channels,
            global_pool_conditions,
            addition_embed_type_num_heads,
        )

        # control net conditioning embedding
        self.controlnet_cond_embedding = AnyTextControlNetConditioningEmbedding(
            conditioning_embedding_channels=block_out_channels[0],
            glyph_channels=conditioning_channels,
            position_channels=conditioning_channels,
        )

    def forward(
        self,
        sample: torch.Tensor,
        timestep: Union[torch.Tensor, float, int],
        encoder_hidden_states: torch.Tensor,
        controlnet_cond: torch.Tensor,
        conditioning_scale: float = 1.0,
        class_labels: Optional[torch.Tensor] = None,
        timestep_cond: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        guess_mode: bool = False,
        return_dict: bool = True,
    ) -> Union[ControlNetOutput, Tuple[Tuple[torch.Tensor, ...], torch.Tensor]]:
        """
        The [`~PromptDiffusionControlNetModel`] forward method.

        Args:
            sample (`torch.Tensor`):
                The noisy input tensor.
            timestep (`Union[torch.Tensor, float, int]`):
                The number of timesteps to denoise an input.
            encoder_hidden_states (`torch.Tensor`):
                The encoder hidden states.
            #controlnet_cond (`torch.Tensor`):
            #    The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
            conditioning_scale (`float`, defaults to `1.0`):
                The scale factor for ControlNet outputs.
            class_labels (`torch.Tensor`, *optional*, defaults to `None`):
                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
            timestep_cond (`torch.Tensor`, *optional*, defaults to `None`):
                Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the
                timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep
                embeddings.
            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
                negative values to the attention scores corresponding to "discard" tokens.
            added_cond_kwargs (`dict`):
                Additional conditions for the Stable Diffusion XL UNet.
            cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`):
                A kwargs dictionary that if specified is passed along to the `AttnProcessor`.
            guess_mode (`bool`, defaults to `False`):
                In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
                you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended.
            return_dict (`bool`, defaults to `True`):
                Whether or not to return a [`~models.controlnet.ControlNetOutput`] instead of a plain tuple.

        Returns:
            [`~models.controlnet.ControlNetOutput`] **or** `tuple`:
                If `return_dict` is `True`, a [`~models.controlnet.ControlNetOutput`] is returned, otherwise a tuple is
                returned where the first element is the sample tensor.
        """
        # check channel order
        channel_order = self.config.controlnet_conditioning_channel_order

        if channel_order == "rgb":
            # in rgb order by default
            ...
        # elif channel_order == "bgr":
        #    controlnet_cond = torch.flip(controlnet_cond, dims=[1])
        else:
            raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")

        # prepare attention_mask
        if attention_mask is not None:
            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
            attention_mask = attention_mask.unsqueeze(1)

        # 1. time
        timesteps = timestep
        if not torch.is_tensor(timesteps):
            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
            # This would be a good case for the `match` statement (Python 3.10+)
            is_mps = sample.device.type == "mps"
            if isinstance(timestep, float):
                dtype = torch.float32 if is_mps else torch.float64
            else:
                dtype = torch.int32 if is_mps else torch.int64
            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
        elif len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(sample.device)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        timesteps = timesteps.expand(sample.shape[0])

        t_emb = self.time_proj(timesteps)

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        t_emb = t_emb.to(dtype=sample.dtype)

        emb = self.time_embedding(t_emb, timestep_cond)
        aug_emb = None

        if self.class_embedding is not None:
            if class_labels is None:
                raise ValueError("class_labels should be provided when num_class_embeds > 0")

            if self.config.class_embed_type == "timestep":
                class_labels = self.time_proj(class_labels)

            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
            emb = emb + class_emb

        if self.config.addition_embed_type is not None:
            if self.config.addition_embed_type == "text":
                aug_emb = self.add_embedding(encoder_hidden_states)

            elif self.config.addition_embed_type == "text_time":
                if "text_embeds" not in added_cond_kwargs:
                    raise ValueError(
                        f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
                    )
                text_embeds = added_cond_kwargs.get("text_embeds")
                if "time_ids" not in added_cond_kwargs:
                    raise ValueError(
                        f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
                    )
                time_ids = added_cond_kwargs.get("time_ids")
                time_embeds = self.add_time_proj(time_ids.flatten())
                time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))

                add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
                add_embeds = add_embeds.to(emb.dtype)
                aug_emb = self.add_embedding(add_embeds)

        emb = emb + aug_emb if aug_emb is not None else emb

        # 2. pre-process
        sample = self.conv_in(sample)

        controlnet_cond = self.controlnet_cond_embedding(*controlnet_cond)
        sample = sample + controlnet_cond

        # 3. down
        down_block_res_samples = (sample,)
        for downsample_block in self.down_blocks:
            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
                sample, res_samples = downsample_block(
                    hidden_states=sample,
                    temb=emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=attention_mask,
                    cross_attention_kwargs=cross_attention_kwargs,
                )
            else:
                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)

            down_block_res_samples += res_samples

        # 4. mid
        if self.mid_block is not None:
            if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
                sample = self.mid_block(
                    sample,
                    emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=attention_mask,
                    cross_attention_kwargs=cross_attention_kwargs,
                )
            else:
                sample = self.mid_block(sample, emb)

        # 5. Control net blocks
        controlnet_down_block_res_samples = ()

        for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
            down_block_res_sample = controlnet_block(down_block_res_sample)
            controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,)

        down_block_res_samples = controlnet_down_block_res_samples

        mid_block_res_sample = self.controlnet_mid_block(sample)

        # 6. scaling
        if guess_mode and not self.config.global_pool_conditions:
            scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device)  # 0.1 to 1.0
            scales = scales * conditioning_scale
            down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
            mid_block_res_sample = mid_block_res_sample * scales[-1]  # last one
        else:
            down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
            mid_block_res_sample = mid_block_res_sample * conditioning_scale

        if self.config.global_pool_conditions:
            down_block_res_samples = [
                torch.mean(sample, dim=(2, 3), keepdim=True) for sample in down_block_res_samples
            ]
            mid_block_res_sample = torch.mean(mid_block_res_sample, dim=(2, 3), keepdim=True)

        if not return_dict:
            return (down_block_res_samples, mid_block_res_sample)

        return ControlNetOutput(
            down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
        )


# Copied from diffusers.models.controlnet.zero_module
def zero_module(module):
    for p in module.parameters():
        nn.init.zeros_(p)
    return module
