# Copy from diffusers.models.transformers.transformer_2d.py

# Copyright 2024 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.
from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils import BaseOutput, deprecate, is_torch_version, logging
from diffusers.models.attention import BasicTransformerBlock
from diffusers.models.embeddings import ImagePositionalEmbeddings, PatchEmbed, PixArtAlphaTextProjection
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.normalization import AdaLayerNormSingle

from module.attention import ExtractKVTransformerBlock


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


@dataclass
class ExtractKVTransformer2DModelOutput(BaseOutput):
    """
    The output of [`ExtractKVTransformer2DModel`].

    Args:
        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
            The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
            distributions for the unnoised latent pixels.
    """

    sample: torch.FloatTensor
    cached_kvs: Dict[str, Any] = None


class ExtractKVTransformer2DModel(ModelMixin, ConfigMixin):
    """
    A 2D Transformer model for image-like data which also outputs CrossAttention KV metrics.

    Parameters:
        num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
        attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
        in_channels (`int`, *optional*):
            The number of channels in the input and output (specify if the input is **continuous**).
        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
        sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
            This is fixed during training since it is used to learn a number of position embeddings.
        num_vector_embeds (`int`, *optional*):
            The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).
            Includes the class for the masked latent pixel.
        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
        num_embeds_ada_norm ( `int`, *optional*):
            The number of diffusion steps used during training. Pass if at least one of the norm_layers is
            `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are
            added to the hidden states.

            During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
        attention_bias (`bool`, *optional*):
            Configure if the `TransformerBlocks` attention should contain a bias parameter.
    """

    _supports_gradient_checkpointing = True
    _no_split_modules = ["BasicTransformerBlock"]

    @register_to_config
    def __init__(
        self,
        num_attention_heads: int = 16,
        attention_head_dim: int = 88,
        in_channels: Optional[int] = None,
        out_channels: Optional[int] = None,
        num_layers: int = 1,
        dropout: float = 0.0,
        norm_num_groups: int = 32,
        cross_attention_dim: Optional[int] = None,
        attention_bias: bool = False,
        sample_size: Optional[int] = None,
        num_vector_embeds: Optional[int] = None,
        patch_size: Optional[int] = None,
        activation_fn: str = "geglu",
        num_embeds_ada_norm: Optional[int] = None,
        use_linear_projection: bool = False,
        only_cross_attention: bool = False,
        double_self_attention: bool = False,
        upcast_attention: bool = False,
        norm_type: str = "layer_norm",  # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single', 'ada_norm_continuous', 'layer_norm_i2vgen'
        norm_elementwise_affine: bool = True,
        norm_eps: float = 1e-5,
        attention_type: str = "default",
        caption_channels: int = None,
        interpolation_scale: float = None,
        use_additional_conditions: Optional[bool] = None,
        extract_self_attention_kv: bool = False,
        extract_cross_attention_kv: bool = False,
    ):
        super().__init__()

        # Validate inputs.
        if patch_size is not None:
            if norm_type not in ["ada_norm", "ada_norm_zero", "ada_norm_single"]:
                raise NotImplementedError(
                    f"Forward pass is not implemented when `patch_size` is not None and `norm_type` is '{norm_type}'."
                )
            elif norm_type in ["ada_norm", "ada_norm_zero"] and num_embeds_ada_norm is None:
                raise ValueError(
                    f"When using a `patch_size` and this `norm_type` ({norm_type}), `num_embeds_ada_norm` cannot be None."
                )

        # Set some common variables used across the board.
        self.use_linear_projection = use_linear_projection
        self.interpolation_scale = interpolation_scale
        self.caption_channels = caption_channels
        self.num_attention_heads = num_attention_heads
        self.attention_head_dim = attention_head_dim
        self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
        self.in_channels = in_channels
        self.out_channels = in_channels if out_channels is None else out_channels
        self.gradient_checkpointing = False
        if use_additional_conditions is None:
            if norm_type == "ada_norm_single" and sample_size == 128:
                use_additional_conditions = True
            else:
                use_additional_conditions = False
        self.use_additional_conditions = use_additional_conditions
        self.extract_self_attention_kv = extract_self_attention_kv
        self.extract_cross_attention_kv = extract_cross_attention_kv

        # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
        # Define whether input is continuous or discrete depending on configuration
        self.is_input_continuous = (in_channels is not None) and (patch_size is None)
        self.is_input_vectorized = num_vector_embeds is not None
        self.is_input_patches = in_channels is not None and patch_size is not None

        if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
            deprecation_message = (
                f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
                " incorrectly set to `'layer_norm'`. Make sure to set `norm_type` to `'ada_norm'` in the config."
                " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"
                " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"
                " would be very nice if you could open a Pull request for the `transformer/config.json` file"
            )
            deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False)
            norm_type = "ada_norm"

        if self.is_input_continuous and self.is_input_vectorized:
            raise ValueError(
                f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
                " sure that either `in_channels` or `num_vector_embeds` is None."
            )
        elif self.is_input_vectorized and self.is_input_patches:
            raise ValueError(
                f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
                " sure that either `num_vector_embeds` or `num_patches` is None."
            )
        elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
            raise ValueError(
                f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
                f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."
            )

        # 2. Initialize the right blocks.
        # These functions follow a common structure:
        # a. Initialize the input blocks. b. Initialize the transformer blocks.
        # c. Initialize the output blocks and other projection blocks when necessary.
        if self.is_input_continuous:
            self._init_continuous_input(norm_type=norm_type)
        elif self.is_input_vectorized:
            self._init_vectorized_inputs(norm_type=norm_type)
        elif self.is_input_patches:
            self._init_patched_inputs(norm_type=norm_type)

    def _init_continuous_input(self, norm_type):
        self.norm = torch.nn.GroupNorm(
            num_groups=self.config.norm_num_groups, num_channels=self.in_channels, eps=1e-6, affine=True
        )
        if self.use_linear_projection:
            self.proj_in = torch.nn.Linear(self.in_channels, self.inner_dim)
        else:
            self.proj_in = torch.nn.Conv2d(self.in_channels, self.inner_dim, kernel_size=1, stride=1, padding=0)

        self.transformer_blocks = nn.ModuleList(
            [
                ExtractKVTransformerBlock(
                    self.inner_dim,
                    self.config.num_attention_heads,
                    self.config.attention_head_dim,
                    dropout=self.config.dropout,
                    cross_attention_dim=self.config.cross_attention_dim,
                    activation_fn=self.config.activation_fn,
                    num_embeds_ada_norm=self.config.num_embeds_ada_norm,
                    attention_bias=self.config.attention_bias,
                    only_cross_attention=self.config.only_cross_attention,
                    double_self_attention=self.config.double_self_attention,
                    upcast_attention=self.config.upcast_attention,
                    norm_type=norm_type,
                    norm_elementwise_affine=self.config.norm_elementwise_affine,
                    norm_eps=self.config.norm_eps,
                    attention_type=self.config.attention_type,
                    extract_self_attention_kv=self.config.extract_self_attention_kv,
                    extract_cross_attention_kv=self.config.extract_cross_attention_kv,
                )
                for _ in range(self.config.num_layers)
            ]
        )

        if self.use_linear_projection:
            self.proj_out = torch.nn.Linear(self.inner_dim, self.out_channels)
        else:
            self.proj_out = torch.nn.Conv2d(self.inner_dim, self.out_channels, kernel_size=1, stride=1, padding=0)

    def _init_vectorized_inputs(self, norm_type):
        assert self.config.sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
        assert (
            self.config.num_vector_embeds is not None
        ), "Transformer2DModel over discrete input must provide num_embed"

        self.height = self.config.sample_size
        self.width = self.config.sample_size
        self.num_latent_pixels = self.height * self.width

        self.latent_image_embedding = ImagePositionalEmbeddings(
            num_embed=self.config.num_vector_embeds, embed_dim=self.inner_dim, height=self.height, width=self.width
        )

        self.transformer_blocks = nn.ModuleList(
            [
                ExtractKVTransformerBlock(
                    self.inner_dim,
                    self.config.num_attention_heads,
                    self.config.attention_head_dim,
                    dropout=self.config.dropout,
                    cross_attention_dim=self.config.cross_attention_dim,
                    activation_fn=self.config.activation_fn,
                    num_embeds_ada_norm=self.config.num_embeds_ada_norm,
                    attention_bias=self.config.attention_bias,
                    only_cross_attention=self.config.only_cross_attention,
                    double_self_attention=self.config.double_self_attention,
                    upcast_attention=self.config.upcast_attention,
                    norm_type=norm_type,
                    norm_elementwise_affine=self.config.norm_elementwise_affine,
                    norm_eps=self.config.norm_eps,
                    attention_type=self.config.attention_type,
                    extract_self_attention_kv=self.config.extract_self_attention_kv,
                    extract_cross_attention_kv=self.config.extract_cross_attention_kv,
                )
                for _ in range(self.config.num_layers)
            ]
        )

        self.norm_out = nn.LayerNorm(self.inner_dim)
        self.out = nn.Linear(self.inner_dim, self.config.num_vector_embeds - 1)

    def _init_patched_inputs(self, norm_type):
        assert self.config.sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

        self.height = self.config.sample_size
        self.width = self.config.sample_size

        self.patch_size = self.config.patch_size
        interpolation_scale = (
            self.config.interpolation_scale
            if self.config.interpolation_scale is not None
            else max(self.config.sample_size // 64, 1)
        )
        self.pos_embed = PatchEmbed(
            height=self.config.sample_size,
            width=self.config.sample_size,
            patch_size=self.config.patch_size,
            in_channels=self.in_channels,
            embed_dim=self.inner_dim,
            interpolation_scale=interpolation_scale,
        )

        self.transformer_blocks = nn.ModuleList(
            [
                ExtractKVTransformerBlock(
                    self.inner_dim,
                    self.config.num_attention_heads,
                    self.config.attention_head_dim,
                    dropout=self.config.dropout,
                    cross_attention_dim=self.config.cross_attention_dim,
                    activation_fn=self.config.activation_fn,
                    num_embeds_ada_norm=self.config.num_embeds_ada_norm,
                    attention_bias=self.config.attention_bias,
                    only_cross_attention=self.config.only_cross_attention,
                    double_self_attention=self.config.double_self_attention,
                    upcast_attention=self.config.upcast_attention,
                    norm_type=norm_type,
                    norm_elementwise_affine=self.config.norm_elementwise_affine,
                    norm_eps=self.config.norm_eps,
                    attention_type=self.config.attention_type,
                    extract_self_attention_kv=self.config.extract_self_attention_kv,
                    extract_cross_attention_kv=self.config.extract_cross_attention_kv,
                )
                for _ in range(self.config.num_layers)
            ]
        )

        if self.config.norm_type != "ada_norm_single":
            self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6)
            self.proj_out_1 = nn.Linear(self.inner_dim, 2 * self.inner_dim)
            self.proj_out_2 = nn.Linear(
                self.inner_dim, self.config.patch_size * self.config.patch_size * self.out_channels
            )
        elif self.config.norm_type == "ada_norm_single":
            self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6)
            self.scale_shift_table = nn.Parameter(torch.randn(2, self.inner_dim) / self.inner_dim**0.5)
            self.proj_out = nn.Linear(
                self.inner_dim, self.config.patch_size * self.config.patch_size * self.out_channels
            )

        # PixArt-Alpha blocks.
        self.adaln_single = None
        if self.config.norm_type == "ada_norm_single":
            # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use
            # additional conditions until we find better name
            self.adaln_single = AdaLayerNormSingle(
                self.inner_dim, use_additional_conditions=self.use_additional_conditions
            )

        self.caption_projection = None
        if self.caption_channels is not None:
            self.caption_projection = PixArtAlphaTextProjection(
                in_features=self.caption_channels, hidden_size=self.inner_dim
            )

    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: Optional[torch.Tensor] = None,
        timestep: Optional[torch.LongTensor] = None,
        added_cond_kwargs: Dict[str, torch.Tensor] = None,
        class_labels: Optional[torch.LongTensor] = None,
        cross_attention_kwargs: Dict[str, Any] = None,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        return_dict: bool = True,
    ):
        """
        The [`Transformer2DModel`] forward method.

        Args:
            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
                Input `hidden_states`.
            encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
                self-attention.
            timestep ( `torch.LongTensor`, *optional*):
                Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
            class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
                Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
                `AdaLayerZeroNorm`.
            cross_attention_kwargs ( `Dict[str, Any]`, *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).
            attention_mask ( `torch.Tensor`, *optional*):
                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.
            encoder_attention_mask ( `torch.Tensor`, *optional*):
                Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:

                    * Mask `(batch, sequence_length)` True = keep, False = discard.
                    * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.

                If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
                above. This bias will be added to the cross-attention scores.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] 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.
        """
        if cross_attention_kwargs is not None:
            if cross_attention_kwargs.get("scale", None) is not None:
                logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")
        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
        #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
        #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
        # expects mask of shape:
        #   [batch, key_tokens]
        # adds singleton query_tokens dimension:
        #   [batch,                    1, key_tokens]
        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
        if attention_mask is not None and attention_mask.ndim == 2:
            # assume that mask is expressed as:
            #   (1 = keep,      0 = discard)
            # convert mask into a bias that can be added to attention scores:
            #       (keep = +0,     discard = -10000.0)
            attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
            attention_mask = attention_mask.unsqueeze(1)

        # convert encoder_attention_mask to a bias the same way we do for attention_mask
        if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
            encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)

        # 1. Input
        if self.is_input_continuous:
            batch_size, _, height, width = hidden_states.shape
            residual = hidden_states
            hidden_states, inner_dim = self._operate_on_continuous_inputs(hidden_states)
        elif self.is_input_vectorized:
            hidden_states = self.latent_image_embedding(hidden_states)
        elif self.is_input_patches:
            height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size
            hidden_states, encoder_hidden_states, timestep, embedded_timestep = self._operate_on_patched_inputs(
                hidden_states, encoder_hidden_states, timestep, added_cond_kwargs
            )

        # 2. Blocks
        extracted_kvs = {}
        for block in 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 {}
                hidden_states, extracted_kv = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    hidden_states,
                    attention_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    timestep,
                    cross_attention_kwargs,
                    class_labels,
                    **ckpt_kwargs,
                )
            else:
                hidden_states, extracted_kv = block(
                    hidden_states,
                    attention_mask=attention_mask,
                    encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=encoder_attention_mask,
                    timestep=timestep,
                    cross_attention_kwargs=cross_attention_kwargs,
                    class_labels=class_labels,
                )

            if extracted_kv:
                extracted_kvs[block.full_name] = extracted_kv

        # 3. Output
        if self.is_input_continuous:
            output = self._get_output_for_continuous_inputs(
                hidden_states=hidden_states,
                residual=residual,
                batch_size=batch_size,
                height=height,
                width=width,
                inner_dim=inner_dim,
            )
        elif self.is_input_vectorized:
            output = self._get_output_for_vectorized_inputs(hidden_states)
        elif self.is_input_patches:
            output = self._get_output_for_patched_inputs(
                hidden_states=hidden_states,
                timestep=timestep,
                class_labels=class_labels,
                embedded_timestep=embedded_timestep,
                height=height,
                width=width,
            )

        if not return_dict:
            return (output, extracted_kvs)

        return ExtractKVTransformer2DModelOutput(sample=output, cached_kvs=extracted_kvs)

    def init_kv_extraction(self):
        for block in self.transformer_blocks:
            block.init_kv_extraction()

    def _operate_on_continuous_inputs(self, hidden_states):
        batch, _, height, width = hidden_states.shape
        hidden_states = self.norm(hidden_states)

        if not self.use_linear_projection:
            hidden_states = self.proj_in(hidden_states)
            inner_dim = hidden_states.shape[1]
            hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
        else:
            inner_dim = hidden_states.shape[1]
            hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
            hidden_states = self.proj_in(hidden_states)

        return hidden_states, inner_dim

    def _operate_on_patched_inputs(self, hidden_states, encoder_hidden_states, timestep, added_cond_kwargs):
        batch_size = hidden_states.shape[0]
        hidden_states = self.pos_embed(hidden_states)
        embedded_timestep = None

        if self.adaln_single is not None:
            if self.use_additional_conditions and added_cond_kwargs is None:
                raise ValueError(
                    "`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`."
                )
            timestep, embedded_timestep = self.adaln_single(
                timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype
            )

        if self.caption_projection is not None:
            encoder_hidden_states = self.caption_projection(encoder_hidden_states)
            encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1])

        return hidden_states, encoder_hidden_states, timestep, embedded_timestep

    def _get_output_for_continuous_inputs(self, hidden_states, residual, batch_size, height, width, inner_dim):
        if not self.use_linear_projection:
            hidden_states = (
                hidden_states.reshape(batch_size, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
            )
            hidden_states = self.proj_out(hidden_states)
        else:
            hidden_states = self.proj_out(hidden_states)
            hidden_states = (
                hidden_states.reshape(batch_size, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
            )

        output = hidden_states + residual
        return output

    def _get_output_for_vectorized_inputs(self, hidden_states):
        hidden_states = self.norm_out(hidden_states)
        logits = self.out(hidden_states)
        # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
        logits = logits.permute(0, 2, 1)
        # log(p(x_0))
        output = F.log_softmax(logits.double(), dim=1).float()
        return output

    def _get_output_for_patched_inputs(
        self, hidden_states, timestep, class_labels, embedded_timestep, height=None, width=None
    ):
        if self.config.norm_type != "ada_norm_single":
            conditioning = self.transformer_blocks[0].norm1.emb(
                timestep, class_labels, hidden_dtype=hidden_states.dtype
            )
            shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
            hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
            hidden_states = self.proj_out_2(hidden_states)
        elif self.config.norm_type == "ada_norm_single":
            shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1)
            hidden_states = self.norm_out(hidden_states)
            # Modulation
            hidden_states = hidden_states * (1 + scale) + shift
            hidden_states = self.proj_out(hidden_states)
            hidden_states = hidden_states.squeeze(1)

        # unpatchify
        if self.adaln_single is None:
            height = width = int(hidden_states.shape[1] ** 0.5)
        hidden_states = hidden_states.reshape(
            shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)
        )
        hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
        output = hidden_states.reshape(
            shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)
        )
        return output