# Copyright 2024 Black Forest Labs, The HuggingFace Team, The InstantX Team and The MeissonFlow 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.


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

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
from diffusers.models.attention import FeedForward, BasicTransformerBlock, SkipFFTransformerBlock
from diffusers.models.attention_processor import (
    Attention,
    AttentionProcessor,
    FluxAttnProcessor2_0,
    # FusedFluxAttnProcessor2_0,
)
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.normalization import AdaLayerNormContinuous, AdaLayerNormZero, AdaLayerNormZeroSingle, GlobalResponseNorm, RMSNorm
from diffusers.utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
from diffusers.utils.torch_utils import maybe_allow_in_graph
from diffusers.models.embeddings import CombinedTimestepGuidanceTextProjEmbeddings, CombinedTimestepTextProjEmbeddings,TimestepEmbedding, get_timestep_embedding #,FluxPosEmbed
from diffusers.models.modeling_outputs import Transformer2DModelOutput
from diffusers.models.resnet import Downsample2D, Upsample2D

from typing import List

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



def get_3d_rotary_pos_embed(
    embed_dim, crops_coords, grid_size, temporal_size, theta: int = 10000, use_real: bool = True
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
    """
    RoPE for video tokens with 3D structure.

    Args:
    embed_dim: (`int`):
        The embedding dimension size, corresponding to hidden_size_head.
    crops_coords (`Tuple[int]`):
        The top-left and bottom-right coordinates of the crop.
    grid_size (`Tuple[int]`):
        The grid size of the spatial positional embedding (height, width).
    temporal_size (`int`):
        The size of the temporal dimension.
    theta (`float`):
        Scaling factor for frequency computation.
    use_real (`bool`):
        If True, return real part and imaginary part separately. Otherwise, return complex numbers.

    Returns:
        `torch.Tensor`: positional embedding with shape `(temporal_size * grid_size[0] * grid_size[1], embed_dim/2)`.
    """
    start, stop = crops_coords
    grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32)
    grid_w = np.linspace(start[1], stop[1], grid_size[1], endpoint=False, dtype=np.float32)
    grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32)

    # Compute dimensions for each axis
    dim_t = embed_dim // 4
    dim_h = embed_dim // 8 * 3
    dim_w = embed_dim // 8 * 3

    # Temporal frequencies
    freqs_t = 1.0 / (theta ** (torch.arange(0, dim_t, 2).float() / dim_t))
    grid_t = torch.from_numpy(grid_t).float()
    freqs_t = torch.einsum("n , f -> n f", grid_t, freqs_t)
    freqs_t = freqs_t.repeat_interleave(2, dim=-1)

    # Spatial frequencies for height and width
    freqs_h = 1.0 / (theta ** (torch.arange(0, dim_h, 2).float() / dim_h))
    freqs_w = 1.0 / (theta ** (torch.arange(0, dim_w, 2).float() / dim_w))
    grid_h = torch.from_numpy(grid_h).float()
    grid_w = torch.from_numpy(grid_w).float()
    freqs_h = torch.einsum("n , f -> n f", grid_h, freqs_h)
    freqs_w = torch.einsum("n , f -> n f", grid_w, freqs_w)
    freqs_h = freqs_h.repeat_interleave(2, dim=-1)
    freqs_w = freqs_w.repeat_interleave(2, dim=-1)

    # Broadcast and concatenate tensors along specified dimension
    def broadcast(tensors, dim=-1):
        num_tensors = len(tensors)
        shape_lens = {len(t.shape) for t in tensors}
        assert len(shape_lens) == 1, "tensors must all have the same number of dimensions"
        shape_len = list(shape_lens)[0]
        dim = (dim + shape_len) if dim < 0 else dim
        dims = list(zip(*(list(t.shape) for t in tensors)))
        expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim]
        assert all(
            [*(len(set(t[1])) <= 2 for t in expandable_dims)]
        ), "invalid dimensions for broadcastable concatenation"
        max_dims = [(t[0], max(t[1])) for t in expandable_dims]
        expanded_dims = [(t[0], (t[1],) * num_tensors) for t in max_dims]
        expanded_dims.insert(dim, (dim, dims[dim]))
        expandable_shapes = list(zip(*(t[1] for t in expanded_dims)))
        tensors = [t[0].expand(*t[1]) for t in zip(tensors, expandable_shapes)]
        return torch.cat(tensors, dim=dim)

    freqs = broadcast((freqs_t[:, None, None, :], freqs_h[None, :, None, :], freqs_w[None, None, :, :]), dim=-1)

    t, h, w, d = freqs.shape
    freqs = freqs.view(t * h * w, d)

    # Generate sine and cosine components
    sin = freqs.sin()
    cos = freqs.cos()

    if use_real:
        return cos, sin
    else:
        freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
        return freqs_cis


def get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size, use_real=True):
    """
    RoPE for image tokens with 2d structure.

    Args:
    embed_dim: (`int`):
        The embedding dimension size
    crops_coords (`Tuple[int]`)
        The top-left and bottom-right coordinates of the crop.
    grid_size (`Tuple[int]`):
        The grid size of the positional embedding.
    use_real (`bool`):
        If True, return real part and imaginary part separately. Otherwise, return complex numbers.

    Returns:
        `torch.Tensor`: positional embedding with shape `( grid_size * grid_size, embed_dim/2)`.
    """
    start, stop = crops_coords
    grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32)
    grid_w = np.linspace(start[1], stop[1], grid_size[1], endpoint=False, dtype=np.float32)
    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
    grid = np.stack(grid, axis=0)  # [2, W, H]

    grid = grid.reshape([2, 1, *grid.shape[1:]])
    pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real)
    return pos_embed


def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False):
    assert embed_dim % 4 == 0

    # use half of dimensions to encode grid_h
    emb_h = get_1d_rotary_pos_embed(
        embed_dim // 2, grid[0].reshape(-1), use_real=use_real
    )  # (H*W, D/2) if use_real else (H*W, D/4)
    emb_w = get_1d_rotary_pos_embed(
        embed_dim // 2, grid[1].reshape(-1), use_real=use_real
    )  # (H*W, D/2) if use_real else (H*W, D/4)

    if use_real:
        cos = torch.cat([emb_h[0], emb_w[0]], dim=1)  # (H*W, D)
        sin = torch.cat([emb_h[1], emb_w[1]], dim=1)  # (H*W, D)
        return cos, sin
    else:
        emb = torch.cat([emb_h, emb_w], dim=1)  # (H*W, D/2)
        return emb


def get_2d_rotary_pos_embed_lumina(embed_dim, len_h, len_w, linear_factor=1.0, ntk_factor=1.0):
    assert embed_dim % 4 == 0

    emb_h = get_1d_rotary_pos_embed(
        embed_dim // 2, len_h, linear_factor=linear_factor, ntk_factor=ntk_factor
    )  # (H, D/4)
    emb_w = get_1d_rotary_pos_embed(
        embed_dim // 2, len_w, linear_factor=linear_factor, ntk_factor=ntk_factor
    )  # (W, D/4)
    emb_h = emb_h.view(len_h, 1, embed_dim // 4, 1).repeat(1, len_w, 1, 1)  # (H, W, D/4, 1)
    emb_w = emb_w.view(1, len_w, embed_dim // 4, 1).repeat(len_h, 1, 1, 1)  # (H, W, D/4, 1)

    emb = torch.cat([emb_h, emb_w], dim=-1).flatten(2)  # (H, W, D/2)
    return emb


def get_1d_rotary_pos_embed(
    dim: int,
    pos: Union[np.ndarray, int],
    theta: float = 10000.0,
    use_real=False,
    linear_factor=1.0,
    ntk_factor=1.0,
    repeat_interleave_real=True,
    freqs_dtype=torch.float32,  # torch.float32 (hunyuan, stable audio), torch.float64 (flux)
):
    """
    Precompute the frequency tensor for complex exponentials (cis) with given dimensions.

    This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' and the end
    index 'end'. The 'theta' parameter scales the frequencies. The returned tensor contains complex values in complex64
    data type.

    Args:
        dim (`int`): Dimension of the frequency tensor.
        pos (`np.ndarray` or `int`): Position indices for the frequency tensor. [S] or scalar
        theta (`float`, *optional*, defaults to 10000.0):
            Scaling factor for frequency computation. Defaults to 10000.0.
        use_real (`bool`, *optional*):
            If True, return real part and imaginary part separately. Otherwise, return complex numbers.
        linear_factor (`float`, *optional*, defaults to 1.0):
            Scaling factor for the context extrapolation. Defaults to 1.0.
        ntk_factor (`float`, *optional*, defaults to 1.0):
            Scaling factor for the NTK-Aware RoPE. Defaults to 1.0.
        repeat_interleave_real (`bool`, *optional*, defaults to `True`):
            If `True` and `use_real`, real part and imaginary part are each interleaved with themselves to reach `dim`.
            Otherwise, they are concateanted with themselves.
        freqs_dtype (`torch.float32` or `torch.float64`, *optional*, defaults to `torch.float32`):
            the dtype of the frequency tensor.
    Returns:
        `torch.Tensor`: Precomputed frequency tensor with complex exponentials. [S, D/2]
    """
    assert dim % 2 == 0

    if isinstance(pos, int):
        pos = np.arange(pos)
    theta = theta * ntk_factor
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=freqs_dtype)[: (dim // 2)] / dim)) / linear_factor  # [D/2]
    t = torch.from_numpy(pos).to(freqs.device)  # type: ignore  # [S]
    freqs = torch.outer(t, freqs)  # type: ignore   # [S, D/2]
    if use_real and repeat_interleave_real:
        freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float()  # [S, D]
        freqs_sin = freqs.sin().repeat_interleave(2, dim=1).float()  # [S, D]
        return freqs_cos, freqs_sin
    elif use_real:
        freqs_cos = torch.cat([freqs.cos(), freqs.cos()], dim=-1).float()  # [S, D]
        freqs_sin = torch.cat([freqs.sin(), freqs.sin()], dim=-1).float()  # [S, D]
        return freqs_cos, freqs_sin
    else:
        freqs_cis = torch.polar(torch.ones_like(freqs), freqs).float()  # complex64     # [S, D/2]
        return freqs_cis


class FluxPosEmbed(nn.Module):
    # modified from https://github.com/black-forest-labs/flux/blob/c00d7c60b085fce8058b9df845e036090873f2ce/src/flux/modules/layers.py#L11
    def __init__(self, theta: int, axes_dim: List[int]):
        super().__init__()
        self.theta = theta
        self.axes_dim = axes_dim

    def forward(self, ids: torch.Tensor) -> torch.Tensor:
        n_axes = ids.shape[-1]
        cos_out = []
        sin_out = []
        pos = ids.squeeze().float().cpu().numpy()
        is_mps = ids.device.type == "mps"
        freqs_dtype = torch.float32 if is_mps else torch.float64
        for i in range(n_axes):
            cos, sin = get_1d_rotary_pos_embed(
                self.axes_dim[i], pos[:, i], repeat_interleave_real=True, use_real=True, freqs_dtype=freqs_dtype
            )
            cos_out.append(cos)
            sin_out.append(sin)
        freqs_cos = torch.cat(cos_out, dim=-1).to(ids.device)
        freqs_sin = torch.cat(sin_out, dim=-1).to(ids.device)
        return freqs_cos, freqs_sin



class FusedFluxAttnProcessor2_0:
    """Attention processor used typically in processing the SD3-like self-attention projections."""

    def __init__(self):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError(
                "FusedFluxAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
            )

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: torch.FloatTensor = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        image_rotary_emb: Optional[torch.Tensor] = None,
    ) -> torch.FloatTensor:
        batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape

        # `sample` projections.
        qkv = attn.to_qkv(hidden_states)
        split_size = qkv.shape[-1] // 3
        query, key, value = torch.split(qkv, split_size, dim=-1)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        if attn.norm_q is not None:
            query = attn.norm_q(query)
        if attn.norm_k is not None:
            key = attn.norm_k(key)

        # the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
        # `context` projections.
        if encoder_hidden_states is not None:
            encoder_qkv = attn.to_added_qkv(encoder_hidden_states)
            split_size = encoder_qkv.shape[-1] // 3
            (
                encoder_hidden_states_query_proj,
                encoder_hidden_states_key_proj,
                encoder_hidden_states_value_proj,
            ) = torch.split(encoder_qkv, split_size, dim=-1)

            encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
                batch_size, -1, attn.heads, head_dim
            ).transpose(1, 2)
            encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
                batch_size, -1, attn.heads, head_dim
            ).transpose(1, 2)
            encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
                batch_size, -1, attn.heads, head_dim
            ).transpose(1, 2)

            if attn.norm_added_q is not None:
                encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
            if attn.norm_added_k is not None:
                encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)

            # attention
            query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
            key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
            value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)

        hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states = hidden_states.to(query.dtype)

        if encoder_hidden_states is not None:
            encoder_hidden_states, hidden_states = (
                hidden_states[:, : encoder_hidden_states.shape[1]],
                hidden_states[:, encoder_hidden_states.shape[1] :],
            )

            # linear proj
            hidden_states = attn.to_out[0](hidden_states)
            # dropout
            hidden_states = attn.to_out[1](hidden_states)
            encoder_hidden_states = attn.to_add_out(encoder_hidden_states)

            return hidden_states, encoder_hidden_states
        else:
            return hidden_states



@maybe_allow_in_graph
class   SingleTransformerBlock(nn.Module):
    r"""
    A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3.

    Reference: https://arxiv.org/abs/2403.03206

    Parameters:
        dim (`int`): The number of channels in the input and output.
        num_attention_heads (`int`): The number of heads to use for multi-head attention.
        attention_head_dim (`int`): The number of channels in each head.
        context_pre_only (`bool`): Boolean to determine if we should add some blocks associated with the
            processing of `context` conditions.
    """

    def __init__(self, dim, num_attention_heads, attention_head_dim, mlp_ratio=4.0):
        super().__init__()
        self.mlp_hidden_dim = int(dim * mlp_ratio)

        self.norm = AdaLayerNormZeroSingle(dim)
        self.proj_mlp = nn.Linear(dim, self.mlp_hidden_dim)
        self.act_mlp = nn.GELU(approximate="tanh")
        self.proj_out = nn.Linear(dim + self.mlp_hidden_dim, dim)

        processor = FluxAttnProcessor2_0()
        self.attn = Attention(
            query_dim=dim,
            cross_attention_dim=None,
            dim_head=attention_head_dim,
            heads=num_attention_heads,
            out_dim=dim,
            bias=True,
            processor=processor,
            qk_norm="rms_norm",
            eps=1e-6,
            pre_only=True,
        )

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        temb: torch.FloatTensor,
        image_rotary_emb=None,
    ):
        residual = hidden_states
        norm_hidden_states, gate = self.norm(hidden_states, emb=temb)
        mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states))

        attn_output = self.attn(
            hidden_states=norm_hidden_states,
            image_rotary_emb=image_rotary_emb,
        )

        hidden_states = torch.cat([attn_output, mlp_hidden_states], dim=2)
        gate = gate.unsqueeze(1)
        hidden_states = gate * self.proj_out(hidden_states)
        hidden_states = residual + hidden_states
        if hidden_states.dtype == torch.float16:
            hidden_states = hidden_states.clip(-65504, 65504)

        return hidden_states

@maybe_allow_in_graph
class TransformerBlock(nn.Module):
    r"""
    A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3.

    Reference: https://arxiv.org/abs/2403.03206

    Parameters:
        dim (`int`): The number of channels in the input and output.
        num_attention_heads (`int`): The number of heads to use for multi-head attention.
        attention_head_dim (`int`): The number of channels in each head.
        context_pre_only (`bool`): Boolean to determine if we should add some blocks associated with the
            processing of `context` conditions.
    """

    def __init__(self, dim, num_attention_heads, attention_head_dim, qk_norm="rms_norm", eps=1e-6):
        super().__init__()

        self.norm1 = AdaLayerNormZero(dim)

        self.norm1_context = AdaLayerNormZero(dim)

        if hasattr(F, "scaled_dot_product_attention"):
            processor = FluxAttnProcessor2_0()
        else:
            raise ValueError(
                "The current PyTorch version does not support the `scaled_dot_product_attention` function."
            )
        self.attn = Attention(
            query_dim=dim,
            cross_attention_dim=None,
            added_kv_proj_dim=dim,
            dim_head=attention_head_dim,
            heads=num_attention_heads,
            out_dim=dim,
            context_pre_only=False,
            bias=True,
            processor=processor,
            qk_norm=qk_norm,
            eps=eps,
        )

        self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
        self.ff = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
        # self.ff = FeedForward(dim=dim, dim_out=dim, activation_fn="swiglu")

        self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
        self.ff_context = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
        # self.ff_context = FeedForward(dim=dim, dim_out=dim, activation_fn="swiglu")

        # let chunk size default to None
        self._chunk_size = None
        self._chunk_dim = 0

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: torch.FloatTensor,
        temb: torch.FloatTensor,
        image_rotary_emb=None,
    ):
        norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)

        norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
            encoder_hidden_states, emb=temb
        )
        # Attention.
        attn_output, context_attn_output = self.attn(
            hidden_states=norm_hidden_states,
            encoder_hidden_states=norm_encoder_hidden_states,
            image_rotary_emb=image_rotary_emb,
        )

        # Process attention outputs for the `hidden_states`.
        attn_output = gate_msa.unsqueeze(1) * attn_output
        hidden_states = hidden_states + attn_output

        norm_hidden_states = self.norm2(hidden_states)
        norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

        ff_output = self.ff(norm_hidden_states)
        ff_output = gate_mlp.unsqueeze(1) * ff_output

        hidden_states = hidden_states + ff_output

        # Process attention outputs for the `encoder_hidden_states`.

        context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output
        encoder_hidden_states = encoder_hidden_states + context_attn_output

        norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
        norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]

        context_ff_output = self.ff_context(norm_encoder_hidden_states)
        encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
        if encoder_hidden_states.dtype == torch.float16:
            encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)

        return encoder_hidden_states, hidden_states


class UVit2DConvEmbed(nn.Module):
    def __init__(self, in_channels, block_out_channels, vocab_size, elementwise_affine, eps, bias):
        super().__init__()
        self.embeddings = nn.Embedding(vocab_size, in_channels)
        self.layer_norm = RMSNorm(in_channels, eps, elementwise_affine)
        self.conv = nn.Conv2d(in_channels, block_out_channels, kernel_size=1, bias=bias)

    def forward(self, input_ids):
        embeddings = self.embeddings(input_ids)
        embeddings = self.layer_norm(embeddings)
        embeddings = embeddings.permute(0, 3, 1, 2)
        embeddings = self.conv(embeddings)
        return embeddings

class ConvMlmLayer(nn.Module):
    def __init__(
        self,
        block_out_channels: int,
        in_channels: int,
        use_bias: bool,
        ln_elementwise_affine: bool,
        layer_norm_eps: float,
        codebook_size: int,
    ):
        super().__init__()
        self.conv1 = nn.Conv2d(block_out_channels, in_channels, kernel_size=1, bias=use_bias)
        self.layer_norm = RMSNorm(in_channels, layer_norm_eps, ln_elementwise_affine)
        self.conv2 = nn.Conv2d(in_channels, codebook_size, kernel_size=1, bias=use_bias)

    def forward(self, hidden_states):
        hidden_states = self.conv1(hidden_states)
        hidden_states = self.layer_norm(hidden_states.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
        logits = self.conv2(hidden_states)
        return logits

class SwiGLU(nn.Module):
    r"""
    A [variant](https://arxiv.org/abs/2002.05202) of the gated linear unit activation function. It's similar to `GEGLU`
    but uses SiLU / Swish instead of GeLU.

    Parameters:
        dim_in (`int`): The number of channels in the input.
        dim_out (`int`): The number of channels in the output.
        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
    """

    def __init__(self, dim_in: int, dim_out: int, bias: bool = True):
        super().__init__()
        self.proj = nn.Linear(dim_in, dim_out * 2, bias=bias)
        self.activation = nn.SiLU()

    def forward(self, hidden_states):
        hidden_states = self.proj(hidden_states)
        hidden_states, gate = hidden_states.chunk(2, dim=-1)
        return hidden_states * self.activation(gate)

class ConvNextBlock(nn.Module):
    def __init__(
        self, channels, layer_norm_eps, ln_elementwise_affine, use_bias, hidden_dropout, hidden_size, res_ffn_factor=4
    ):
        super().__init__()
        self.depthwise = nn.Conv2d(
            channels,
            channels,
            kernel_size=3,
            padding=1,
            groups=channels,
            bias=use_bias,
        )
        self.norm = RMSNorm(channels, layer_norm_eps, ln_elementwise_affine)
        self.channelwise_linear_1 = nn.Linear(channels, int(channels * res_ffn_factor), bias=use_bias)
        self.channelwise_act = nn.GELU()
        self.channelwise_norm = GlobalResponseNorm(int(channels * res_ffn_factor))
        self.channelwise_linear_2 = nn.Linear(int(channels * res_ffn_factor), channels, bias=use_bias)
        self.channelwise_dropout = nn.Dropout(hidden_dropout)
        self.cond_embeds_mapper = nn.Linear(hidden_size, channels * 2, use_bias)

    def forward(self, x, cond_embeds):
        x_res = x

        x = self.depthwise(x)

        x = x.permute(0, 2, 3, 1)
        x = self.norm(x)

        x = self.channelwise_linear_1(x)
        x = self.channelwise_act(x)
        x = self.channelwise_norm(x)
        x = self.channelwise_linear_2(x)
        x = self.channelwise_dropout(x)

        x = x.permute(0, 3, 1, 2)

        x = x + x_res

        scale, shift = self.cond_embeds_mapper(F.silu(cond_embeds)).chunk(2, dim=1)
        x = x * (1 + scale[:, :, None, None]) + shift[:, :, None, None]

        return x

class Simple_UVitBlock(nn.Module):
    def __init__(
        self,
        channels,
        ln_elementwise_affine,
        layer_norm_eps,
        use_bias,
        downsample: bool,
        upsample: bool,
    ):
        super().__init__()

        if downsample:
            self.downsample = Downsample2D(
                channels,
                use_conv=True,
                padding=0,
                name="Conv2d_0",
                kernel_size=2,
                norm_type="rms_norm",
                eps=layer_norm_eps,
                elementwise_affine=ln_elementwise_affine,
                bias=use_bias,
            )
        else:
            self.downsample = None

        if upsample:
            self.upsample = Upsample2D(
                channels,
                use_conv_transpose=True,
                kernel_size=2,
                padding=0,
                name="conv",
                norm_type="rms_norm",
                eps=layer_norm_eps,
                elementwise_affine=ln_elementwise_affine,
                bias=use_bias,
                interpolate=False,
            )
        else:
            self.upsample = None

    def forward(self, x):
        if self.downsample is not None:
            x = self.downsample(x)

        if self.upsample is not None:
            x = self.upsample(x)
        return x


class UVitBlock(nn.Module):
    def __init__(
        self,
        channels,
        num_res_blocks: int,
        hidden_size,
        hidden_dropout,
        ln_elementwise_affine,
        layer_norm_eps,
        use_bias,
        block_num_heads,
        attention_dropout,
        downsample: bool,
        upsample: bool,
    ):
        super().__init__()

        if downsample:
            self.downsample = Downsample2D(
                channels,
                use_conv=True,
                padding=0,
                name="Conv2d_0",
                kernel_size=2,
                norm_type="rms_norm",
                eps=layer_norm_eps,
                elementwise_affine=ln_elementwise_affine,
                bias=use_bias,
            )
        else:
            self.downsample = None

        self.res_blocks = nn.ModuleList(
            [
                ConvNextBlock(
                    channels,
                    layer_norm_eps,
                    ln_elementwise_affine,
                    use_bias,
                    hidden_dropout,
                    hidden_size,
                )
                for i in range(num_res_blocks)
            ]
        )

        self.attention_blocks = nn.ModuleList(
            [
                SkipFFTransformerBlock(
                    channels,
                    block_num_heads,
                    channels // block_num_heads,
                    hidden_size,
                    use_bias,
                    attention_dropout,
                    channels,
                    attention_bias=use_bias,
                    attention_out_bias=use_bias,
                )
                for _ in range(num_res_blocks)
            ]
        )

        if upsample:
            self.upsample = Upsample2D(
                channels,
                use_conv_transpose=True,
                kernel_size=2,
                padding=0,
                name="conv",
                norm_type="rms_norm",
                eps=layer_norm_eps,
                elementwise_affine=ln_elementwise_affine,
                bias=use_bias,
                interpolate=False,
            )
        else:
            self.upsample = None

    def forward(self, x, pooled_text_emb, encoder_hidden_states, cross_attention_kwargs):
        if self.downsample is not None:
            x = self.downsample(x)

        for res_block, attention_block in zip(self.res_blocks, self.attention_blocks):
            x = res_block(x, pooled_text_emb)

            batch_size, channels, height, width = x.shape
            x = x.view(batch_size, channels, height * width).permute(0, 2, 1)
            x = attention_block(
                x, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs
            )
            x = x.permute(0, 2, 1).view(batch_size, channels, height, width)

        if self.upsample is not None:
            x = self.upsample(x)

        return x

class Transformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
    """
    The Transformer model introduced in Flux.

    Reference: https://blackforestlabs.ai/announcing-black-forest-labs/

    Parameters:
        patch_size (`int`): Patch size to turn the input data into small patches.
        in_channels (`int`, *optional*, defaults to 16): The number of channels in the input.
        num_layers (`int`, *optional*, defaults to 18): The number of layers of MMDiT blocks to use.
        num_single_layers (`int`, *optional*, defaults to 18): The number of layers of single DiT blocks to use.
        attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
        num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention.
        joint_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
        pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`.
        guidance_embeds (`bool`, defaults to False): Whether to use guidance embeddings.
    """

    _supports_gradient_checkpointing = False #True
    # Due to NotImplementedError: DDPOptimizer backend: Found a higher order op in the graph. This is not supported. Please turn off DDP optimizer using torch._dynamo.config.optimize_ddp=False. Note that this can cause performance degradation because there will be one bucket for the entire Dynamo graph.
    # Please refer to this issue - https://github.com/pytorch/pytorch/issues/104674.
    _no_split_modules = ["TransformerBlock", "SingleTransformerBlock"]

    @register_to_config
    def __init__(
        self,
        patch_size: int = 1,
        in_channels: int = 64,
        num_layers: int = 19,
        num_single_layers: int = 38,
        attention_head_dim: int = 128,
        num_attention_heads: int = 24,
        joint_attention_dim: int = 4096,
        pooled_projection_dim: int = 768,
        guidance_embeds: bool = False, # unused in our implementation
        axes_dims_rope: Tuple[int] = (16, 56, 56),
        vocab_size: int = 8256,
        codebook_size: int = 8192,
        downsample: bool = False,
        upsample: bool = False,
    ):
        super().__init__()
        self.out_channels = in_channels
        self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim

        self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_dims_rope)
        text_time_guidance_cls = (
            CombinedTimestepGuidanceTextProjEmbeddings if guidance_embeds else CombinedTimestepTextProjEmbeddings
        )
        self.time_text_embed = text_time_guidance_cls(
            embedding_dim=self.inner_dim, pooled_projection_dim=self.config.pooled_projection_dim
        )

        self.context_embedder = nn.Linear(self.config.joint_attention_dim, self.inner_dim)

        self.transformer_blocks = nn.ModuleList(
            [
                TransformerBlock(
                    dim=self.inner_dim,
                    num_attention_heads=self.config.num_attention_heads,
                    attention_head_dim=self.config.attention_head_dim,
                )
                for i in range(self.config.num_layers)
            ]
        )

        self.single_transformer_blocks = nn.ModuleList(
            [
                SingleTransformerBlock(
                    dim=self.inner_dim,
                    num_attention_heads=self.config.num_attention_heads,
                    attention_head_dim=self.config.attention_head_dim,
                )
                for i in range(self.config.num_single_layers)
            ]
        )


        self.gradient_checkpointing = False

        in_channels_embed = self.inner_dim
        ln_elementwise_affine = True
        layer_norm_eps = 1e-06
        use_bias = False
        micro_cond_embed_dim = 1280
        self.embed = UVit2DConvEmbed(
            in_channels_embed, self.inner_dim, self.config.vocab_size, ln_elementwise_affine, layer_norm_eps, use_bias
        )
        self.mlm_layer = ConvMlmLayer(
            self.inner_dim, in_channels_embed, use_bias, ln_elementwise_affine, layer_norm_eps, self.config.codebook_size
        )
        self.cond_embed = TimestepEmbedding(
            micro_cond_embed_dim + self.config.pooled_projection_dim, self.inner_dim, sample_proj_bias=use_bias
        )
        self.encoder_proj_layer_norm = RMSNorm(self.inner_dim, layer_norm_eps, ln_elementwise_affine)
        self.project_to_hidden_norm = RMSNorm(in_channels_embed, layer_norm_eps, ln_elementwise_affine)
        self.project_to_hidden = nn.Linear(in_channels_embed, self.inner_dim, bias=use_bias)
        self.project_from_hidden_norm = RMSNorm(self.inner_dim, layer_norm_eps, ln_elementwise_affine)
        self.project_from_hidden = nn.Linear(self.inner_dim, in_channels_embed, bias=use_bias)

        self.down_block = Simple_UVitBlock(
            self.inner_dim,
            ln_elementwise_affine,
            layer_norm_eps,
            use_bias,
            downsample,
            False,
        )
        self.up_block = Simple_UVitBlock(
            self.inner_dim, #block_out_channels,
            ln_elementwise_affine,
            layer_norm_eps,
            use_bias,
            False,
            upsample=upsample,
        )

        # self.fuse_qkv_projections()

    @property
    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
    def attn_processors(self) -> Dict[str, AttentionProcessor]:
        r"""
        Returns:
            `dict` of attention processors: A dictionary containing all attention processors used in the model with
            indexed by its weight name.
        """
        # set recursively
        processors = {}

        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
            if hasattr(module, "get_processor"):
                processors[f"{name}.processor"] = module.get_processor()

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(name, module, processors)

        return processors

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
        r"""
        Sets the attention processor to use to compute attention.

        Parameters:
            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
                The instantiated processor class or a dictionary of processor classes that will be set as the processor
                for **all** `Attention` layers.

                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
                processor. This is strongly recommended when setting trainable attention processors.

        """
        count = len(self.attn_processors.keys())

        if isinstance(processor, dict) and len(processor) != count:
            raise ValueError(
                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
            )

        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
            if hasattr(module, "set_processor"):
                if not isinstance(processor, dict):
                    module.set_processor(processor)
                else:
                    module.set_processor(processor.pop(f"{name}.processor"))

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

        for name, module in self.named_children():
            fn_recursive_attn_processor(name, module, processor)

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedFluxAttnProcessor2_0
    def fuse_qkv_projections(self):
        """
        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
        are fused. For cross-attention modules, key and value projection matrices are fused.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>
        """
        self.original_attn_processors = None

        for _, attn_processor in self.attn_processors.items():
            if "Added" in str(attn_processor.__class__.__name__):
                raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")

        self.original_attn_processors = self.attn_processors

        for module in self.modules():
            if isinstance(module, Attention):
                module.fuse_projections(fuse=True)

        self.set_attn_processor(FusedFluxAttnProcessor2_0())

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
    def unfuse_qkv_projections(self):
        """Disables the fused QKV projection if enabled.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>

        """
        if self.original_attn_processors is not None:
            self.set_attn_processor(self.original_attn_processors)

    def _set_gradient_checkpointing(self, module, value=False):
        if hasattr(module, "gradient_checkpointing"):
            module.gradient_checkpointing = value

    def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor = None,
        pooled_projections: torch.Tensor = None,
        timestep: torch.LongTensor = None,
        img_ids: torch.Tensor = None,
        txt_ids: torch.Tensor = None,
        guidance: torch.Tensor = None,
        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
        controlnet_block_samples= None,
        controlnet_single_block_samples=None,
        return_dict: bool = True,
        micro_conds: torch.Tensor = None,
    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
        """
        The [`FluxTransformer2DModel`] forward method.

        Args:
            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
                Input `hidden_states`.
            encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
            pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
                from the embeddings of input conditions.
            timestep ( `torch.LongTensor`):
                Used to indicate denoising step.
            block_controlnet_hidden_states: (`list` of `torch.Tensor`):
                A list of tensors that if specified are added to the residuals of transformer blocks.
            joint_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
                tuple.

        Returns:
            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
            `tuple` where the first element is the sample tensor.
        """
        micro_cond_encode_dim = 256 # same as self.config.micro_cond_encode_dim = 256 from amused
        micro_cond_embeds = get_timestep_embedding(
            micro_conds.flatten(), micro_cond_encode_dim, flip_sin_to_cos=True, downscale_freq_shift=0
        )
        micro_cond_embeds = micro_cond_embeds.reshape((hidden_states.shape[0], -1))

        pooled_projections = torch.cat([pooled_projections, micro_cond_embeds], dim=1)
        pooled_projections = pooled_projections.to(dtype=self.dtype)
        pooled_projections = self.cond_embed(pooled_projections).to(encoder_hidden_states.dtype)


        hidden_states = self.embed(hidden_states)

        encoder_hidden_states = self.context_embedder(encoder_hidden_states)
        encoder_hidden_states = self.encoder_proj_layer_norm(encoder_hidden_states)
        hidden_states = self.down_block(hidden_states)

        batch_size, channels, height, width = hidden_states.shape
        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels)
        hidden_states = self.project_to_hidden_norm(hidden_states)
        hidden_states = self.project_to_hidden(hidden_states)


        if joint_attention_kwargs is not None:
            joint_attention_kwargs = joint_attention_kwargs.copy()
            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
        else:
            lora_scale = 1.0

        if USE_PEFT_BACKEND:
            # weight the lora layers by setting `lora_scale` for each PEFT layer
            scale_lora_layers(self, lora_scale)
        else:
            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
                logger.warning(
                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
                )

        timestep = timestep.to(hidden_states.dtype) * 1000
        if guidance is not None:
            guidance = guidance.to(hidden_states.dtype) * 1000
        else:
            guidance = None
        temb = (
            self.time_text_embed(timestep, pooled_projections)
            if guidance is None
            else self.time_text_embed(timestep, guidance, pooled_projections)
        )

        if txt_ids.ndim == 3:
            logger.warning(
                "Passing `txt_ids` 3d torch.Tensor is deprecated."
                "Please remove the batch dimension and pass it as a 2d torch Tensor"
            )
            txt_ids = txt_ids[0]
        if img_ids.ndim == 3:
            logger.warning(
                "Passing `img_ids` 3d torch.Tensor is deprecated."
                "Please remove the batch dimension and pass it as a 2d torch Tensor"
            )
            img_ids = img_ids[0]
        ids = torch.cat((txt_ids, img_ids), dim=0)

        image_rotary_emb = self.pos_embed(ids)

        for index_block, block in enumerate(self.transformer_blocks):
            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module, return_dict=None):
                    def custom_forward(*inputs):
                        if return_dict is not None:
                            return module(*inputs, return_dict=return_dict)
                        else:
                            return module(*inputs)

                    return custom_forward

                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    hidden_states,
                    encoder_hidden_states,
                    temb,
                    image_rotary_emb,
                    **ckpt_kwargs,
                )

            else:
                encoder_hidden_states, hidden_states = block(
                    hidden_states=hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    temb=temb,
                    image_rotary_emb=image_rotary_emb,
                )


            # controlnet residual
            if controlnet_block_samples is not None:
                interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
                interval_control = int(np.ceil(interval_control))
                hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]

        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)

        for index_block, block in enumerate(self.single_transformer_blocks):
            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module, return_dict=None):
                    def custom_forward(*inputs):
                        if return_dict is not None:
                            return module(*inputs, return_dict=return_dict)
                        else:
                            return module(*inputs)

                    return custom_forward

                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    hidden_states,
                    temb,
                    image_rotary_emb,
                    **ckpt_kwargs,
                )

            else:
                hidden_states = block(
                    hidden_states=hidden_states,
                    temb=temb,
                    image_rotary_emb=image_rotary_emb,
                )

            # controlnet residual
            if controlnet_single_block_samples is not None:
                interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
                interval_control = int(np.ceil(interval_control))
                hidden_states[:, encoder_hidden_states.shape[1] :, ...] = (
                    hidden_states[:, encoder_hidden_states.shape[1] :, ...]
                    + controlnet_single_block_samples[index_block // interval_control]
                )

        hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]


        hidden_states = self.project_from_hidden_norm(hidden_states)
        hidden_states = self.project_from_hidden(hidden_states)


        hidden_states = hidden_states.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2)

        hidden_states = self.up_block(hidden_states)

        if USE_PEFT_BACKEND:
            # remove `lora_scale` from each PEFT layer
            unscale_lora_layers(self, lora_scale)

        output = self.mlm_layer(hidden_states)
        # self.unfuse_qkv_projections()
        if not return_dict:
            return (output,)


        return output
