# 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.
from importlib import import_module
from typing import Callable, Optional, Union

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

from diffusers.utils import USE_PEFT_BACKEND, deprecate, logging
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.torch_utils import maybe_allow_in_graph
from diffusers.models.lora import LoRACompatibleLinear, LoRALinearLayer


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


if is_xformers_available():
    import xformers
    import xformers.ops
else:
    xformers = None


@maybe_allow_in_graph
class Attention(nn.Module):
    r"""
    A cross attention layer.

    Parameters:
        query_dim (`int`):
            The number of channels in the query.
        cross_attention_dim (`int`, *optional*):
            The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
        heads (`int`,  *optional*, defaults to 8):
            The number of heads to use for multi-head attention.
        dim_head (`int`,  *optional*, defaults to 64):
            The number of channels in each head.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability to use.
        bias (`bool`, *optional*, defaults to False):
            Set to `True` for the query, key, and value linear layers to contain a bias parameter.
        upcast_attention (`bool`, *optional*, defaults to False):
            Set to `True` to upcast the attention computation to `float32`.
        upcast_softmax (`bool`, *optional*, defaults to False):
            Set to `True` to upcast the softmax computation to `float32`.
        cross_attention_norm (`str`, *optional*, defaults to `None`):
            The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`.
        cross_attention_norm_num_groups (`int`, *optional*, defaults to 32):
            The number of groups to use for the group norm in the cross attention.
        added_kv_proj_dim (`int`, *optional*, defaults to `None`):
            The number of channels to use for the added key and value projections. If `None`, no projection is used.
        norm_num_groups (`int`, *optional*, defaults to `None`):
            The number of groups to use for the group norm in the attention.
        spatial_norm_dim (`int`, *optional*, defaults to `None`):
            The number of channels to use for the spatial normalization.
        out_bias (`bool`, *optional*, defaults to `True`):
            Set to `True` to use a bias in the output linear layer.
        scale_qk (`bool`, *optional*, defaults to `True`):
            Set to `True` to scale the query and key by `1 / sqrt(dim_head)`.
        only_cross_attention (`bool`, *optional*, defaults to `False`):
            Set to `True` to only use cross attention and not added_kv_proj_dim. Can only be set to `True` if
            `added_kv_proj_dim` is not `None`.
        eps (`float`, *optional*, defaults to 1e-5):
            An additional value added to the denominator in group normalization that is used for numerical stability.
        rescale_output_factor (`float`, *optional*, defaults to 1.0):
            A factor to rescale the output by dividing it with this value.
        residual_connection (`bool`, *optional*, defaults to `False`):
            Set to `True` to add the residual connection to the output.
        _from_deprecated_attn_block (`bool`, *optional*, defaults to `False`):
            Set to `True` if the attention block is loaded from a deprecated state dict.
        processor (`AttnProcessor`, *optional*, defaults to `None`):
            The attention processor to use. If `None`, defaults to `AttnProcessor2_0` if `torch 2.x` is used and
            `AttnProcessor` otherwise.
    """

    def __init__(
        self,
        query_dim: int,
        cross_attention_dim: Optional[int] = None,
        heads: int = 8,
        dim_head: int = 64,
        dropout: float = 0.0,
        bias: bool = False,
        upcast_attention: bool = False,
        upcast_softmax: bool = False,
        cross_attention_norm: Optional[str] = None,
        cross_attention_norm_num_groups: int = 32,
        added_kv_proj_dim: Optional[int] = None,
        norm_num_groups: Optional[int] = None,
        spatial_norm_dim: Optional[int] = None,
        out_bias: bool = True,
        scale_qk: bool = True,
        only_cross_attention: bool = False,
        eps: float = 1e-5,
        rescale_output_factor: float = 1.0,
        residual_connection: bool = False,
        _from_deprecated_attn_block: bool = False,
        processor: Optional["AttnProcessor"] = None,
    ):
        super().__init__()
        self.inner_dim = dim_head * heads
        self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
        self.upcast_attention = upcast_attention
        self.upcast_softmax = upcast_softmax
        self.rescale_output_factor = rescale_output_factor
        self.residual_connection = residual_connection
        self.dropout = dropout

        # we make use of this private variable to know whether this class is loaded
        # with an deprecated state dict so that we can convert it on the fly
        self._from_deprecated_attn_block = _from_deprecated_attn_block

        self.scale_qk = scale_qk
        self.scale = dim_head**-0.5 if self.scale_qk else 1.0

        self.heads = heads
        # for slice_size > 0 the attention score computation
        # is split across the batch axis to save memory
        # You can set slice_size with `set_attention_slice`
        self.sliceable_head_dim = heads

        self.added_kv_proj_dim = added_kv_proj_dim
        self.only_cross_attention = only_cross_attention

        if self.added_kv_proj_dim is None and self.only_cross_attention:
            raise ValueError(
                "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`."
            )

        if norm_num_groups is not None:
            self.group_norm = nn.GroupNorm(num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True)
        else:
            self.group_norm = None

        if spatial_norm_dim is not None:
            self.spatial_norm = SpatialNorm(f_channels=query_dim, zq_channels=spatial_norm_dim)
        else:
            self.spatial_norm = None

        if cross_attention_norm is None:
            self.norm_cross = None
        elif cross_attention_norm == "layer_norm":
            self.norm_cross = nn.LayerNorm(self.cross_attention_dim)
        elif cross_attention_norm == "group_norm":
            if self.added_kv_proj_dim is not None:
                # The given `encoder_hidden_states` are initially of shape
                # (batch_size, seq_len, added_kv_proj_dim) before being projected
                # to (batch_size, seq_len, cross_attention_dim). The norm is applied
                # before the projection, so we need to use `added_kv_proj_dim` as
                # the number of channels for the group norm.
                norm_cross_num_channels = added_kv_proj_dim
            else:
                norm_cross_num_channels = self.cross_attention_dim

            self.norm_cross = nn.GroupNorm(
                num_channels=norm_cross_num_channels, num_groups=cross_attention_norm_num_groups, eps=1e-5, affine=True
            )
        else:
            raise ValueError(
                f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'"
            )

        if USE_PEFT_BACKEND:
            linear_cls = nn.Linear
        else:
            linear_cls = LoRACompatibleLinear

        self.to_q = linear_cls(query_dim, self.inner_dim, bias=bias)

        if not self.only_cross_attention:
            # only relevant for the `AddedKVProcessor` classes
            self.to_k = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
            self.to_v = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
        else:
            self.to_k = None
            self.to_v = None

        if self.added_kv_proj_dim is not None:
            self.add_k_proj = linear_cls(added_kv_proj_dim, self.inner_dim)
            self.add_v_proj = linear_cls(added_kv_proj_dim, self.inner_dim)

        self.to_out = nn.ModuleList([])
        self.to_out.append(linear_cls(self.inner_dim, query_dim, bias=out_bias))
        self.to_out.append(nn.Dropout(dropout))

        # set attention processor
        # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
        # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
        # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
        if processor is None:
            processor = (
                AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
            )
        self.set_processor(processor)

    def set_use_memory_efficient_attention_xformers(
        self, use_memory_efficient_attention_xformers: bool, attention_op: Optional[Callable] = None
    ) -> None:
        r"""
        Set whether to use memory efficient attention from `xformers` or not.

        Args:
            use_memory_efficient_attention_xformers (`bool`):
                Whether to use memory efficient attention from `xformers` or not.
            attention_op (`Callable`, *optional*):
                The attention operation to use. Defaults to `None` which uses the default attention operation from
                `xformers`.
        """
        is_lora = hasattr(self, "processor") and isinstance(
            self.processor,
            LORA_ATTENTION_PROCESSORS,
        )
        is_custom_diffusion = hasattr(self, "processor") and isinstance(
            self.processor,
            (CustomDiffusionAttnProcessor, CustomDiffusionXFormersAttnProcessor, CustomDiffusionAttnProcessor2_0),
        )
        is_added_kv_processor = hasattr(self, "processor") and isinstance(
            self.processor,
            (
                AttnAddedKVProcessor,
                AttnAddedKVProcessor2_0,
                SlicedAttnAddedKVProcessor,
                XFormersAttnAddedKVProcessor,
                LoRAAttnAddedKVProcessor,
            ),
        )

        if use_memory_efficient_attention_xformers:
            if is_added_kv_processor and (is_lora or is_custom_diffusion):
                raise NotImplementedError(
                    f"Memory efficient attention is currently not supported for LoRA or custom diffusion for attention processor type {self.processor}"
                )
            if not is_xformers_available():
                raise ModuleNotFoundError(
                    (
                        "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
                        " xformers"
                    ),
                    name="xformers",
                )
            elif not torch.cuda.is_available():
                raise ValueError(
                    "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is"
                    " only available for GPU "
                )
            else:
                try:
                    # Make sure we can run the memory efficient attention
                    _ = xformers.ops.memory_efficient_attention(
                        torch.randn((1, 2, 40), device="cuda"),
                        torch.randn((1, 2, 40), device="cuda"),
                        torch.randn((1, 2, 40), device="cuda"),
                    )
                except Exception as e:
                    raise e

            if is_lora:
                # TODO (sayakpaul): should we throw a warning if someone wants to use the xformers
                # variant when using PT 2.0 now that we have LoRAAttnProcessor2_0?
                processor = LoRAXFormersAttnProcessor(
                    hidden_size=self.processor.hidden_size,
                    cross_attention_dim=self.processor.cross_attention_dim,
                    rank=self.processor.rank,
                    attention_op=attention_op,
                )
                processor.load_state_dict(self.processor.state_dict())
                processor.to(self.processor.to_q_lora.up.weight.device)
            elif is_custom_diffusion:
                processor = CustomDiffusionXFormersAttnProcessor(
                    train_kv=self.processor.train_kv,
                    train_q_out=self.processor.train_q_out,
                    hidden_size=self.processor.hidden_size,
                    cross_attention_dim=self.processor.cross_attention_dim,
                    attention_op=attention_op,
                )
                processor.load_state_dict(self.processor.state_dict())
                if hasattr(self.processor, "to_k_custom_diffusion"):
                    processor.to(self.processor.to_k_custom_diffusion.weight.device)
            elif is_added_kv_processor:
                # TODO(Patrick, Suraj, William) - currently xformers doesn't work for UnCLIP
                # which uses this type of cross attention ONLY because the attention mask of format
                # [0, ..., -10.000, ..., 0, ...,] is not supported
                # throw warning
                logger.info(
                    "Memory efficient attention with `xformers` might currently not work correctly if an attention mask is required for the attention operation."
                )
                processor = XFormersAttnAddedKVProcessor(attention_op=attention_op)
            else:
                processor = XFormersAttnProcessor(attention_op=attention_op)
        else:
            if is_lora:
                attn_processor_class = (
                    LoRAAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else LoRAAttnProcessor
                )
                processor = attn_processor_class(
                    hidden_size=self.processor.hidden_size,
                    cross_attention_dim=self.processor.cross_attention_dim,
                    rank=self.processor.rank,
                )
                processor.load_state_dict(self.processor.state_dict())
                processor.to(self.processor.to_q_lora.up.weight.device)
            elif is_custom_diffusion:
                attn_processor_class = (
                    CustomDiffusionAttnProcessor2_0
                    if hasattr(F, "scaled_dot_product_attention")
                    else CustomDiffusionAttnProcessor
                )
                processor = attn_processor_class(
                    train_kv=self.processor.train_kv,
                    train_q_out=self.processor.train_q_out,
                    hidden_size=self.processor.hidden_size,
                    cross_attention_dim=self.processor.cross_attention_dim,
                )
                processor.load_state_dict(self.processor.state_dict())
                if hasattr(self.processor, "to_k_custom_diffusion"):
                    processor.to(self.processor.to_k_custom_diffusion.weight.device)
            else:
                # set attention processor
                # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
                # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
                # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
                processor = (
                    AttnProcessor2_0()
                    if hasattr(F, "scaled_dot_product_attention") and self.scale_qk
                    else AttnProcessor()
                )

        self.set_processor(processor)

    def set_attention_slice(self, slice_size: int) -> None:
        r"""
        Set the slice size for attention computation.

        Args:
            slice_size (`int`):
                The slice size for attention computation.
        """
        if slice_size is not None and slice_size > self.sliceable_head_dim:
            raise ValueError(f"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.")

        if slice_size is not None and self.added_kv_proj_dim is not None:
            processor = SlicedAttnAddedKVProcessor(slice_size)
        elif slice_size is not None:
            processor = SlicedAttnProcessor(slice_size)
        elif self.added_kv_proj_dim is not None:
            processor = AttnAddedKVProcessor()
        else:
            # set attention processor
            # We use the AttnProcessor2_0 by default when torch 2.x is used which uses
            # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
            # but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
            processor = (
                AttnProcessor2_0() if hasattr(F, "scaled_dot_product_attention") and self.scale_qk else AttnProcessor()
            )

        self.set_processor(processor)

    def set_processor(self, processor: "AttnProcessor", _remove_lora: bool = False) -> None:
        r"""
        Set the attention processor to use.

        Args:
            processor (`AttnProcessor`):
                The attention processor to use.
            _remove_lora (`bool`, *optional*, defaults to `False`):
                Set to `True` to remove LoRA layers from the model.
        """
        if not USE_PEFT_BACKEND and hasattr(self, "processor") and _remove_lora and self.to_q.lora_layer is not None:
            deprecate(
                "set_processor to offload LoRA",
                "0.26.0",
                "In detail, removing LoRA layers via calling `set_default_attn_processor` is deprecated. Please make sure to call `pipe.unload_lora_weights()` instead.",
            )
            # TODO(Patrick, Sayak) - this can be deprecated once PEFT LoRA integration is complete
            # We need to remove all LoRA layers
            # Don't forget to remove ALL `_remove_lora` from the codebase
            for module in self.modules():
                if hasattr(module, "set_lora_layer"):
                    module.set_lora_layer(None)

        # if current processor is in `self._modules` and if passed `processor` is not, we need to
        # pop `processor` from `self._modules`
        if (
            hasattr(self, "processor")
            and isinstance(self.processor, torch.nn.Module)
            and not isinstance(processor, torch.nn.Module)
        ):
            logger.info(f"You are removing possibly trained weights of {self.processor} with {processor}")
            self._modules.pop("processor")

        self.processor = processor

    def get_processor(self, return_deprecated_lora: bool = False) -> "AttentionProcessor":
        r"""
        Get the attention processor in use.

        Args:
            return_deprecated_lora (`bool`, *optional*, defaults to `False`):
                Set to `True` to return the deprecated LoRA attention processor.

        Returns:
            "AttentionProcessor": The attention processor in use.
        """
        if not return_deprecated_lora:
            return self.processor

        # TODO(Sayak, Patrick). The rest of the function is needed to ensure backwards compatible
        # serialization format for LoRA Attention Processors. It should be deleted once the integration
        # with PEFT is completed.
        is_lora_activated = {
            name: module.lora_layer is not None
            for name, module in self.named_modules()
            if hasattr(module, "lora_layer")
        }

        # 1. if no layer has a LoRA activated we can return the processor as usual
        if not any(is_lora_activated.values()):
            return self.processor

        # If doesn't apply LoRA do `add_k_proj` or `add_v_proj`
        is_lora_activated.pop("add_k_proj", None)
        is_lora_activated.pop("add_v_proj", None)
        # 2. else it is not posssible that only some layers have LoRA activated
        if not all(is_lora_activated.values()):
            raise ValueError(
                f"Make sure that either all layers or no layers have LoRA activated, but have {is_lora_activated}"
            )

        # 3. And we need to merge the current LoRA layers into the corresponding LoRA attention processor
        non_lora_processor_cls_name = self.processor.__class__.__name__
        lora_processor_cls = getattr(import_module(__name__), "LoRA" + non_lora_processor_cls_name)

        hidden_size = self.inner_dim

        # now create a LoRA attention processor from the LoRA layers
        if lora_processor_cls in [LoRAAttnProcessor, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor]:
            kwargs = {
                "cross_attention_dim": self.cross_attention_dim,
                "rank": self.to_q.lora_layer.rank,
                "network_alpha": self.to_q.lora_layer.network_alpha,
                "q_rank": self.to_q.lora_layer.rank,
                "q_hidden_size": self.to_q.lora_layer.out_features,
                "k_rank": self.to_k.lora_layer.rank,
                "k_hidden_size": self.to_k.lora_layer.out_features,
                "v_rank": self.to_v.lora_layer.rank,
                "v_hidden_size": self.to_v.lora_layer.out_features,
                "out_rank": self.to_out[0].lora_layer.rank,
                "out_hidden_size": self.to_out[0].lora_layer.out_features,
            }

            if hasattr(self.processor, "attention_op"):
                kwargs["attention_op"] = self.processor.attention_op

            lora_processor = lora_processor_cls(hidden_size, **kwargs)
            lora_processor.to_q_lora.load_state_dict(self.to_q.lora_layer.state_dict())
            lora_processor.to_k_lora.load_state_dict(self.to_k.lora_layer.state_dict())
            lora_processor.to_v_lora.load_state_dict(self.to_v.lora_layer.state_dict())
            lora_processor.to_out_lora.load_state_dict(self.to_out[0].lora_layer.state_dict())
        elif lora_processor_cls == LoRAAttnAddedKVProcessor:
            lora_processor = lora_processor_cls(
                hidden_size,
                cross_attention_dim=self.add_k_proj.weight.shape[0],
                rank=self.to_q.lora_layer.rank,
                network_alpha=self.to_q.lora_layer.network_alpha,
            )
            lora_processor.to_q_lora.load_state_dict(self.to_q.lora_layer.state_dict())
            lora_processor.to_k_lora.load_state_dict(self.to_k.lora_layer.state_dict())
            lora_processor.to_v_lora.load_state_dict(self.to_v.lora_layer.state_dict())
            lora_processor.to_out_lora.load_state_dict(self.to_out[0].lora_layer.state_dict())

            # only save if used
            if self.add_k_proj.lora_layer is not None:
                lora_processor.add_k_proj_lora.load_state_dict(self.add_k_proj.lora_layer.state_dict())
                lora_processor.add_v_proj_lora.load_state_dict(self.add_v_proj.lora_layer.state_dict())
            else:
                lora_processor.add_k_proj_lora = None
                lora_processor.add_v_proj_lora = None
        else:
            raise ValueError(f"{lora_processor_cls} does not exist.")

        return lora_processor

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        **cross_attention_kwargs,
    ) -> torch.Tensor:
        r"""
        The forward method of the `Attention` class.

        Args:
            hidden_states (`torch.Tensor`):
                The hidden states of the query.
            encoder_hidden_states (`torch.Tensor`, *optional*):
                The hidden states of the encoder.
            attention_mask (`torch.Tensor`, *optional*):
                The attention mask to use. If `None`, no mask is applied.
            **cross_attention_kwargs:
                Additional keyword arguments to pass along to the cross attention.

        Returns:
            `torch.Tensor`: The output of the attention layer.
        """
        # The `Attention` class can call different attention processors / attention functions
        # here we simply pass along all tensors to the selected processor class
        # For standard processors that are defined here, `**cross_attention_kwargs` is empty
        return self.processor(
            self,
            hidden_states,
            encoder_hidden_states=encoder_hidden_states,
            attention_mask=attention_mask,
            **cross_attention_kwargs,
        )

    def batch_to_head_dim(self, tensor: torch.Tensor) -> torch.Tensor:
        r"""
        Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads`
        is the number of heads initialized while constructing the `Attention` class.

        Args:
            tensor (`torch.Tensor`): The tensor to reshape.

        Returns:
            `torch.Tensor`: The reshaped tensor.
        """
        head_size = self.heads
        batch_size, seq_len, dim = tensor.shape
        tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
        return tensor

    def head_to_batch_dim(self, tensor: torch.Tensor, out_dim: int = 3) -> torch.Tensor:
        r"""
        Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is
        the number of heads initialized while constructing the `Attention` class.

        Args:
            tensor (`torch.Tensor`): The tensor to reshape.
            out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is
                reshaped to `[batch_size * heads, seq_len, dim // heads]`.

        Returns:
            `torch.Tensor`: The reshaped tensor.
        """
        head_size = self.heads
        batch_size, seq_len, dim = tensor.shape
        tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
        tensor = tensor.permute(0, 2, 1, 3)

        if out_dim == 3:
            tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size)

        return tensor

    def get_attention_scores(
        self, query: torch.Tensor, key: torch.Tensor, attention_mask: torch.Tensor = None
    ) -> torch.Tensor:
        r"""
        Compute the attention scores.

        Args:
            query (`torch.Tensor`): The query tensor.
            key (`torch.Tensor`): The key tensor.
            attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied.

        Returns:
            `torch.Tensor`: The attention probabilities/scores.
        """
        dtype = query.dtype
        if self.upcast_attention:
            query = query.float()
            key = key.float()

        if attention_mask is None:
            baddbmm_input = torch.empty(
                query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device
            )
            beta = 0
        else:
            baddbmm_input = attention_mask
            beta = 1

        attention_scores = torch.baddbmm(
            baddbmm_input,
            query,
            key.transpose(-1, -2),
            beta=beta,
            alpha=self.scale,
        )
        del baddbmm_input

        if self.upcast_softmax:
            attention_scores = attention_scores.float()

        attention_probs = attention_scores.softmax(dim=-1)
        del attention_scores

        attention_probs = attention_probs.to(dtype)

        return attention_probs

    def prepare_attention_mask(
        self, attention_mask: torch.Tensor, target_length: int, batch_size: int, out_dim: int = 3
    ) -> torch.Tensor:
        r"""
        Prepare the attention mask for the attention computation.

        Args:
            attention_mask (`torch.Tensor`):
                The attention mask to prepare.
            target_length (`int`):
                The target length of the attention mask. This is the length of the attention mask after padding.
            batch_size (`int`):
                The batch size, which is used to repeat the attention mask.
            out_dim (`int`, *optional*, defaults to `3`):
                The output dimension of the attention mask. Can be either `3` or `4`.

        Returns:
            `torch.Tensor`: The prepared attention mask.
        """
        head_size = self.heads
        if attention_mask is None:
            return attention_mask

        current_length: int = attention_mask.shape[-1]
        if current_length != target_length:
            if attention_mask.device.type == "mps":
                # HACK: MPS: Does not support padding by greater than dimension of input tensor.
                # Instead, we can manually construct the padding tensor.
                padding_shape = (attention_mask.shape[0], attention_mask.shape[1], target_length)
                padding = torch.zeros(padding_shape, dtype=attention_mask.dtype, device=attention_mask.device)
                attention_mask = torch.cat([attention_mask, padding], dim=2)
            else:
                # TODO: for pipelines such as stable-diffusion, padding cross-attn mask:
                #       we want to instead pad by (0, remaining_length), where remaining_length is:
                #       remaining_length: int = target_length - current_length
                # TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding
                attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)

        if out_dim == 3:
            if attention_mask.shape[0] < batch_size * head_size:
                attention_mask = attention_mask.repeat_interleave(head_size, dim=0)
        elif out_dim == 4:
            attention_mask = attention_mask.unsqueeze(1)
            attention_mask = attention_mask.repeat_interleave(head_size, dim=1)

        return attention_mask

    def norm_encoder_hidden_states(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
        r"""
        Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the
        `Attention` class.

        Args:
            encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder.

        Returns:
            `torch.Tensor`: The normalized encoder hidden states.
        """
        assert self.norm_cross is not None, "self.norm_cross must be defined to call self.norm_encoder_hidden_states"

        if isinstance(self.norm_cross, nn.LayerNorm):
            encoder_hidden_states = self.norm_cross(encoder_hidden_states)
        elif isinstance(self.norm_cross, nn.GroupNorm):
            # Group norm norms along the channels dimension and expects
            # input to be in the shape of (N, C, *). In this case, we want
            # to norm along the hidden dimension, so we need to move
            # (batch_size, sequence_length, hidden_size) ->
            # (batch_size, hidden_size, sequence_length)
            encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
            encoder_hidden_states = self.norm_cross(encoder_hidden_states)
            encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
        else:
            assert False

        return encoder_hidden_states


class AttnProcessor:
    r"""
    Default processor for performing attention-related computations.
    """

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        temb: Optional[torch.FloatTensor] = None,
        scale: float = 1.0,
    ) -> torch.Tensor:
        residual = hidden_states

        args = () if USE_PEFT_BACKEND else (scale,)

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states, *args)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states, *args)
        value = attn.to_v(encoder_hidden_states, *args)

        # Record the Q,K,V for PCA guidance
        self.key = key
        # self.query = query
        # self.value = value
        self.hidden_state = hidden_states.detach()

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states, *args)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


class CustomDiffusionAttnProcessor(nn.Module):
    r"""
    Processor for implementing attention for the Custom Diffusion method.

    Args:
        train_kv (`bool`, defaults to `True`):
            Whether to newly train the key and value matrices corresponding to the text features.
        train_q_out (`bool`, defaults to `True`):
            Whether to newly train query matrices corresponding to the latent image features.
        hidden_size (`int`, *optional*, defaults to `None`):
            The hidden size of the attention layer.
        cross_attention_dim (`int`, *optional*, defaults to `None`):
            The number of channels in the `encoder_hidden_states`.
        out_bias (`bool`, defaults to `True`):
            Whether to include the bias parameter in `train_q_out`.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability to use.
    """

    def __init__(
        self,
        train_kv: bool = True,
        train_q_out: bool = True,
        hidden_size: Optional[int] = None,
        cross_attention_dim: Optional[int] = None,
        out_bias: bool = True,
        dropout: float = 0.0,
    ):
        super().__init__()
        self.train_kv = train_kv
        self.train_q_out = train_q_out

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim

        # `_custom_diffusion` id for easy serialization and loading.
        if self.train_kv:
            self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
            self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
        if self.train_q_out:
            self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False)
            self.to_out_custom_diffusion = nn.ModuleList([])
            self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias))
            self.to_out_custom_diffusion.append(nn.Dropout(dropout))

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
    ) -> torch.Tensor:
        batch_size, sequence_length, _ = hidden_states.shape
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
        if self.train_q_out:
            query = self.to_q_custom_diffusion(hidden_states).to(attn.to_q.weight.dtype)
        else:
            query = attn.to_q(hidden_states.to(attn.to_q.weight.dtype))

        if encoder_hidden_states is None:
            crossattn = False
            encoder_hidden_states = hidden_states
        else:
            crossattn = True
            if attn.norm_cross:
                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        if self.train_kv:
            key = self.to_k_custom_diffusion(encoder_hidden_states.to(self.to_k_custom_diffusion.weight.dtype))
            value = self.to_v_custom_diffusion(encoder_hidden_states.to(self.to_v_custom_diffusion.weight.dtype))
            key = key.to(attn.to_q.weight.dtype)
            value = value.to(attn.to_q.weight.dtype)
        else:
            key = attn.to_k(encoder_hidden_states)
            value = attn.to_v(encoder_hidden_states)

        if crossattn:
            detach = torch.ones_like(key)
            detach[:, :1, :] = detach[:, :1, :] * 0.0
            key = detach * key + (1 - detach) * key.detach()
            value = detach * value + (1 - detach) * value.detach()

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        if self.train_q_out:
            # linear proj
            hidden_states = self.to_out_custom_diffusion[0](hidden_states)
            # dropout
            hidden_states = self.to_out_custom_diffusion[1](hidden_states)
        else:
            # linear proj
            hidden_states = attn.to_out[0](hidden_states)
            # dropout
            hidden_states = attn.to_out[1](hidden_states)

        return hidden_states


class AttnAddedKVProcessor:
    r"""
    Processor for performing attention-related computations with extra learnable key and value matrices for the text
    encoder.
    """

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        scale: float = 1.0,
    ) -> torch.Tensor:
        residual = hidden_states

        args = () if USE_PEFT_BACKEND else (scale,)

        hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
        batch_size, sequence_length, _ = hidden_states.shape

        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states, *args)
        query = attn.head_to_batch_dim(query)

        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states, *args)
        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states, *args)
        encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
        encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)

        if not attn.only_cross_attention:
            key = attn.to_k(hidden_states, *args)
            value = attn.to_v(hidden_states, *args)
            key = attn.head_to_batch_dim(key)
            value = attn.head_to_batch_dim(value)
            key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
            value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
        else:
            key = encoder_hidden_states_key_proj
            value = encoder_hidden_states_value_proj

        attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states, *args)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
        hidden_states = hidden_states + residual

        return hidden_states


class AttnAddedKVProcessor2_0:
    r"""
    Processor for performing scaled dot-product attention (enabled by default if you're using PyTorch 2.0), with extra
    learnable key and value matrices for the text encoder.
    """

    def __init__(self):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError(
                "AttnAddedKVProcessor2_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: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        scale: float = 1.0,
    ) -> torch.Tensor:
        residual = hidden_states

        args = () if USE_PEFT_BACKEND else (scale,)

        hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
        batch_size, sequence_length, _ = hidden_states.shape

        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size, out_dim=4)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states, *args)
        query = attn.head_to_batch_dim(query, out_dim=4)

        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
        encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj, out_dim=4)
        encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj, out_dim=4)

        if not attn.only_cross_attention:
            key = attn.to_k(hidden_states, *args)
            value = attn.to_v(hidden_states, *args)
            key = attn.head_to_batch_dim(key, out_dim=4)
            value = attn.head_to_batch_dim(value, out_dim=4)
            key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
            value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
        else:
            key = encoder_hidden_states_key_proj
            value = encoder_hidden_states_value_proj

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
        )
        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, residual.shape[1])

        # linear proj
        hidden_states = attn.to_out[0](hidden_states, *args)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
        hidden_states = hidden_states + residual

        return hidden_states


class XFormersAttnAddedKVProcessor:
    r"""
    Processor for implementing memory efficient attention using xFormers.

    Args:
        attention_op (`Callable`, *optional*, defaults to `None`):
            The base
            [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
            use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
            operator.
    """

    def __init__(self, attention_op: Optional[Callable] = None):
        self.attention_op = attention_op

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
    ) -> torch.Tensor:
        residual = hidden_states
        hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)
        batch_size, sequence_length, _ = hidden_states.shape

        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)
        query = attn.head_to_batch_dim(query)

        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
        encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
        encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)

        if not attn.only_cross_attention:
            key = attn.to_k(hidden_states)
            value = attn.to_v(hidden_states)
            key = attn.head_to_batch_dim(key)
            value = attn.head_to_batch_dim(value)
            key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
            value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
        else:
            key = encoder_hidden_states_key_proj
            value = encoder_hidden_states_value_proj

        hidden_states = xformers.ops.memory_efficient_attention(
            query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
        )
        hidden_states = hidden_states.to(query.dtype)
        hidden_states = attn.batch_to_head_dim(hidden_states)

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

        hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
        hidden_states = hidden_states + residual

        return hidden_states


class XFormersAttnProcessor:
    r"""
    Processor for implementing memory efficient attention using xFormers.

    Args:
        attention_op (`Callable`, *optional*, defaults to `None`):
            The base
            [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
            use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
            operator.
    """

    def __init__(self, attention_op: Optional[Callable] = None):
        self.attention_op = attention_op

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        temb: Optional[torch.FloatTensor] = None,
        scale: float = 1.0,
    ) -> torch.FloatTensor:
        residual = hidden_states

        args = () if USE_PEFT_BACKEND else (scale,)

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, key_tokens, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        attention_mask = attn.prepare_attention_mask(attention_mask, key_tokens, batch_size)
        if attention_mask is not None:
            # expand our mask's singleton query_tokens dimension:
            #   [batch*heads,            1, key_tokens] ->
            #   [batch*heads, query_tokens, key_tokens]
            # so that it can be added as a bias onto the attention scores that xformers computes:
            #   [batch*heads, query_tokens, key_tokens]
            # we do this explicitly because xformers doesn't broadcast the singleton dimension for us.
            _, query_tokens, _ = hidden_states.shape
            attention_mask = attention_mask.expand(-1, query_tokens, -1)

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states, *args)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states, *args)
        value = attn.to_v(encoder_hidden_states, *args)

        # Record the Q,K,V for PCA guidance
        self.key = key
        # self.query = query
        # self.value = value
        self.hidden_state = hidden_states.detach()

        query = attn.head_to_batch_dim(query).contiguous()
        key = attn.head_to_batch_dim(key).contiguous()
        value = attn.head_to_batch_dim(value).contiguous()

        hidden_states = xformers.ops.memory_efficient_attention(
            query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
        )
        hidden_states = hidden_states.to(query.dtype)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states, *args)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


class AttnProcessor2_0:
    r"""
    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
    """

    def __init__(self):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("AttnProcessor2_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: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        temb: Optional[torch.FloatTensor] = None,
        scale: float = 1.0,
    ) -> torch.FloatTensor:
        residual = hidden_states

        args = () if USE_PEFT_BACKEND else (scale,)

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        args = () if USE_PEFT_BACKEND else (scale,)
        query = attn.to_q(hidden_states, *args)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states, *args)
        value = attn.to_v(encoder_hidden_states, *args)

        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)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, 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)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states, *args)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


class CustomDiffusionXFormersAttnProcessor(nn.Module):
    r"""
    Processor for implementing memory efficient attention using xFormers for the Custom Diffusion method.

    Args:
    train_kv (`bool`, defaults to `True`):
        Whether to newly train the key and value matrices corresponding to the text features.
    train_q_out (`bool`, defaults to `True`):
        Whether to newly train query matrices corresponding to the latent image features.
    hidden_size (`int`, *optional*, defaults to `None`):
        The hidden size of the attention layer.
    cross_attention_dim (`int`, *optional*, defaults to `None`):
        The number of channels in the `encoder_hidden_states`.
    out_bias (`bool`, defaults to `True`):
        Whether to include the bias parameter in `train_q_out`.
    dropout (`float`, *optional*, defaults to 0.0):
        The dropout probability to use.
    attention_op (`Callable`, *optional*, defaults to `None`):
        The base
        [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to use
        as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best operator.
    """

    def __init__(
        self,
        train_kv: bool = True,
        train_q_out: bool = False,
        hidden_size: Optional[int] = None,
        cross_attention_dim: Optional[int] = None,
        out_bias: bool = True,
        dropout: float = 0.0,
        attention_op: Optional[Callable] = None,
    ):
        super().__init__()
        self.train_kv = train_kv
        self.train_q_out = train_q_out

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.attention_op = attention_op

        # `_custom_diffusion` id for easy serialization and loading.
        if self.train_kv:
            self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
            self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
        if self.train_q_out:
            self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False)
            self.to_out_custom_diffusion = nn.ModuleList([])
            self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias))
            self.to_out_custom_diffusion.append(nn.Dropout(dropout))

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

        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if self.train_q_out:
            query = self.to_q_custom_diffusion(hidden_states).to(attn.to_q.weight.dtype)
        else:
            query = attn.to_q(hidden_states.to(attn.to_q.weight.dtype))

        if encoder_hidden_states is None:
            crossattn = False
            encoder_hidden_states = hidden_states
        else:
            crossattn = True
            if attn.norm_cross:
                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        if self.train_kv:
            key = self.to_k_custom_diffusion(encoder_hidden_states.to(self.to_k_custom_diffusion.weight.dtype))
            value = self.to_v_custom_diffusion(encoder_hidden_states.to(self.to_v_custom_diffusion.weight.dtype))
            key = key.to(attn.to_q.weight.dtype)
            value = value.to(attn.to_q.weight.dtype)
        else:
            key = attn.to_k(encoder_hidden_states)
            value = attn.to_v(encoder_hidden_states)

        if crossattn:
            detach = torch.ones_like(key)
            detach[:, :1, :] = detach[:, :1, :] * 0.0
            key = detach * key + (1 - detach) * key.detach()
            value = detach * value + (1 - detach) * value.detach()

        query = attn.head_to_batch_dim(query).contiguous()
        key = attn.head_to_batch_dim(key).contiguous()
        value = attn.head_to_batch_dim(value).contiguous()

        hidden_states = xformers.ops.memory_efficient_attention(
            query, key, value, attn_bias=attention_mask, op=self.attention_op, scale=attn.scale
        )
        hidden_states = hidden_states.to(query.dtype)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        if self.train_q_out:
            # linear proj
            hidden_states = self.to_out_custom_diffusion[0](hidden_states)
            # dropout
            hidden_states = self.to_out_custom_diffusion[1](hidden_states)
        else:
            # linear proj
            hidden_states = attn.to_out[0](hidden_states)
            # dropout
            hidden_states = attn.to_out[1](hidden_states)

        return hidden_states


class CustomDiffusionAttnProcessor2_0(nn.Module):
    r"""
    Processor for implementing attention for the Custom Diffusion method using PyTorch 2.0’s memory-efficient scaled
    dot-product attention.

    Args:
        train_kv (`bool`, defaults to `True`):
            Whether to newly train the key and value matrices corresponding to the text features.
        train_q_out (`bool`, defaults to `True`):
            Whether to newly train query matrices corresponding to the latent image features.
        hidden_size (`int`, *optional*, defaults to `None`):
            The hidden size of the attention layer.
        cross_attention_dim (`int`, *optional*, defaults to `None`):
            The number of channels in the `encoder_hidden_states`.
        out_bias (`bool`, defaults to `True`):
            Whether to include the bias parameter in `train_q_out`.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability to use.
    """

    def __init__(
        self,
        train_kv: bool = True,
        train_q_out: bool = True,
        hidden_size: Optional[int] = None,
        cross_attention_dim: Optional[int] = None,
        out_bias: bool = True,
        dropout: float = 0.0,
    ):
        super().__init__()
        self.train_kv = train_kv
        self.train_q_out = train_q_out

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim

        # `_custom_diffusion` id for easy serialization and loading.
        if self.train_kv:
            self.to_k_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
            self.to_v_custom_diffusion = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
        if self.train_q_out:
            self.to_q_custom_diffusion = nn.Linear(hidden_size, hidden_size, bias=False)
            self.to_out_custom_diffusion = nn.ModuleList([])
            self.to_out_custom_diffusion.append(nn.Linear(hidden_size, hidden_size, bias=out_bias))
            self.to_out_custom_diffusion.append(nn.Dropout(dropout))

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        batch_size, sequence_length, _ = hidden_states.shape
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
        if self.train_q_out:
            query = self.to_q_custom_diffusion(hidden_states)
        else:
            query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            crossattn = False
            encoder_hidden_states = hidden_states
        else:
            crossattn = True
            if attn.norm_cross:
                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        if self.train_kv:
            key = self.to_k_custom_diffusion(encoder_hidden_states.to(self.to_k_custom_diffusion.weight.dtype))
            value = self.to_v_custom_diffusion(encoder_hidden_states.to(self.to_v_custom_diffusion.weight.dtype))
            key = key.to(attn.to_q.weight.dtype)
            value = value.to(attn.to_q.weight.dtype)

        else:
            key = attn.to_k(encoder_hidden_states)
            value = attn.to_v(encoder_hidden_states)

        if crossattn:
            detach = torch.ones_like(key)
            detach[:, :1, :] = detach[:, :1, :] * 0.0
            key = detach * key + (1 - detach) * key.detach()
            value = detach * value + (1 - detach) * value.detach()

        inner_dim = hidden_states.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)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, 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 self.train_q_out:
            # linear proj
            hidden_states = self.to_out_custom_diffusion[0](hidden_states)
            # dropout
            hidden_states = self.to_out_custom_diffusion[1](hidden_states)
        else:
            # linear proj
            hidden_states = attn.to_out[0](hidden_states)
            # dropout
            hidden_states = attn.to_out[1](hidden_states)

        return hidden_states


class SlicedAttnProcessor:
    r"""
    Processor for implementing sliced attention.

    Args:
        slice_size (`int`, *optional*):
            The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and
            `attention_head_dim` must be a multiple of the `slice_size`.
    """

    def __init__(self, slice_size: int):
        self.slice_size = slice_size

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        residual = hidden_states

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)
        dim = query.shape[-1]
        query = attn.head_to_batch_dim(query)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        batch_size_attention, query_tokens, _ = query.shape
        hidden_states = torch.zeros(
            (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype
        )

        for i in range(batch_size_attention // self.slice_size):
            start_idx = i * self.slice_size
            end_idx = (i + 1) * self.slice_size

            query_slice = query[start_idx:end_idx]
            key_slice = key[start_idx:end_idx]
            attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None

            attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)

            attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])

            hidden_states[start_idx:end_idx] = attn_slice

        hidden_states = attn.batch_to_head_dim(hidden_states)

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

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


class SlicedAttnAddedKVProcessor:
    r"""
    Processor for implementing sliced attention with extra learnable key and value matrices for the text encoder.

    Args:
        slice_size (`int`, *optional*):
            The number of steps to compute attention. Uses as many slices as `attention_head_dim // slice_size`, and
            `attention_head_dim` must be a multiple of the `slice_size`.
    """

    def __init__(self, slice_size):
        self.slice_size = slice_size

    def __call__(
        self,
        attn: "Attention",
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        temb: Optional[torch.FloatTensor] = None,
    ) -> torch.FloatTensor:
        residual = hidden_states

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        hidden_states = hidden_states.view(hidden_states.shape[0], hidden_states.shape[1], -1).transpose(1, 2)

        batch_size, sequence_length, _ = hidden_states.shape

        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)
        dim = query.shape[-1]
        query = attn.head_to_batch_dim(query)

        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)

        encoder_hidden_states_key_proj = attn.head_to_batch_dim(encoder_hidden_states_key_proj)
        encoder_hidden_states_value_proj = attn.head_to_batch_dim(encoder_hidden_states_value_proj)

        if not attn.only_cross_attention:
            key = attn.to_k(hidden_states)
            value = attn.to_v(hidden_states)
            key = attn.head_to_batch_dim(key)
            value = attn.head_to_batch_dim(value)
            key = torch.cat([encoder_hidden_states_key_proj, key], dim=1)
            value = torch.cat([encoder_hidden_states_value_proj, value], dim=1)
        else:
            key = encoder_hidden_states_key_proj
            value = encoder_hidden_states_value_proj

        batch_size_attention, query_tokens, _ = query.shape
        hidden_states = torch.zeros(
            (batch_size_attention, query_tokens, dim // attn.heads), device=query.device, dtype=query.dtype
        )

        for i in range(batch_size_attention // self.slice_size):
            start_idx = i * self.slice_size
            end_idx = (i + 1) * self.slice_size

            query_slice = query[start_idx:end_idx]
            key_slice = key[start_idx:end_idx]
            attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None

            attn_slice = attn.get_attention_scores(query_slice, key_slice, attn_mask_slice)

            attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])

            hidden_states[start_idx:end_idx] = attn_slice

        hidden_states = attn.batch_to_head_dim(hidden_states)

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

        hidden_states = hidden_states.transpose(-1, -2).reshape(residual.shape)
        hidden_states = hidden_states + residual

        return hidden_states


class SpatialNorm(nn.Module):
    """
    Spatially conditioned normalization as defined in https://arxiv.org/abs/2209.09002.

    Args:
        f_channels (`int`):
            The number of channels for input to group normalization layer, and output of the spatial norm layer.
        zq_channels (`int`):
            The number of channels for the quantized vector as described in the paper.
    """

    def __init__(
        self,
        f_channels: int,
        zq_channels: int,
    ):
        super().__init__()
        self.norm_layer = nn.GroupNorm(num_channels=f_channels, num_groups=32, eps=1e-6, affine=True)
        self.conv_y = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)
        self.conv_b = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)

    def forward(self, f: torch.FloatTensor, zq: torch.FloatTensor) -> torch.FloatTensor:
        f_size = f.shape[-2:]
        zq = F.interpolate(zq, size=f_size, mode="nearest")
        norm_f = self.norm_layer(f)
        new_f = norm_f * self.conv_y(zq) + self.conv_b(zq)
        return new_f


## Deprecated
class LoRAAttnProcessor(nn.Module):
    r"""
    Processor for implementing the LoRA attention mechanism.

    Args:
        hidden_size (`int`, *optional*):
            The hidden size of the attention layer.
        cross_attention_dim (`int`, *optional*):
            The number of channels in the `encoder_hidden_states`.
        rank (`int`, defaults to 4):
            The dimension of the LoRA update matrices.
        network_alpha (`int`, *optional*):
            Equivalent to `alpha` but it's usage is specific to Kohya (A1111) style LoRAs.
        kwargs (`dict`):
            Additional keyword arguments to pass to the `LoRALinearLayer` layers.
    """

    def __init__(
        self,
        hidden_size: int,
        cross_attention_dim: Optional[int] = None,
        rank: int = 4,
        network_alpha: Optional[int] = None,
        **kwargs,
    ):
        super().__init__()

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.rank = rank

        q_rank = kwargs.pop("q_rank", None)
        q_hidden_size = kwargs.pop("q_hidden_size", None)
        q_rank = q_rank if q_rank is not None else rank
        q_hidden_size = q_hidden_size if q_hidden_size is not None else hidden_size

        v_rank = kwargs.pop("v_rank", None)
        v_hidden_size = kwargs.pop("v_hidden_size", None)
        v_rank = v_rank if v_rank is not None else rank
        v_hidden_size = v_hidden_size if v_hidden_size is not None else hidden_size

        out_rank = kwargs.pop("out_rank", None)
        out_hidden_size = kwargs.pop("out_hidden_size", None)
        out_rank = out_rank if out_rank is not None else rank
        out_hidden_size = out_hidden_size if out_hidden_size is not None else hidden_size

        self.to_q_lora = LoRALinearLayer(q_hidden_size, q_hidden_size, q_rank, network_alpha)
        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
        self.to_v_lora = LoRALinearLayer(cross_attention_dim or v_hidden_size, v_hidden_size, v_rank, network_alpha)
        self.to_out_lora = LoRALinearLayer(out_hidden_size, out_hidden_size, out_rank, network_alpha)

    def __call__(self, attn: Attention, hidden_states: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor:
        self_cls_name = self.__class__.__name__
        deprecate(
            self_cls_name,
            "0.26.0",
            (
                f"Make sure use {self_cls_name[4:]} instead by setting"
                "LoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using"
                " `LoraLoaderMixin.load_lora_weights`"
            ),
        )
        attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device)
        attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device)
        attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device)
        attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device)

        attn._modules.pop("processor")
        attn.processor = AttnProcessor()
        return attn.processor(attn, hidden_states, *args, **kwargs)


class LoRAAttnProcessor2_0(nn.Module):
    r"""
    Processor for implementing the LoRA attention mechanism using PyTorch 2.0's memory-efficient scaled dot-product
    attention.

    Args:
        hidden_size (`int`):
            The hidden size of the attention layer.
        cross_attention_dim (`int`, *optional*):
            The number of channels in the `encoder_hidden_states`.
        rank (`int`, defaults to 4):
            The dimension of the LoRA update matrices.
        network_alpha (`int`, *optional*):
            Equivalent to `alpha` but it's usage is specific to Kohya (A1111) style LoRAs.
        kwargs (`dict`):
            Additional keyword arguments to pass to the `LoRALinearLayer` layers.
    """

    def __init__(
        self,
        hidden_size: int,
        cross_attention_dim: Optional[int] = None,
        rank: int = 4,
        network_alpha: Optional[int] = None,
        **kwargs,
    ):
        super().__init__()
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.rank = rank

        q_rank = kwargs.pop("q_rank", None)
        q_hidden_size = kwargs.pop("q_hidden_size", None)
        q_rank = q_rank if q_rank is not None else rank
        q_hidden_size = q_hidden_size if q_hidden_size is not None else hidden_size

        v_rank = kwargs.pop("v_rank", None)
        v_hidden_size = kwargs.pop("v_hidden_size", None)
        v_rank = v_rank if v_rank is not None else rank
        v_hidden_size = v_hidden_size if v_hidden_size is not None else hidden_size

        out_rank = kwargs.pop("out_rank", None)
        out_hidden_size = kwargs.pop("out_hidden_size", None)
        out_rank = out_rank if out_rank is not None else rank
        out_hidden_size = out_hidden_size if out_hidden_size is not None else hidden_size

        self.to_q_lora = LoRALinearLayer(q_hidden_size, q_hidden_size, q_rank, network_alpha)
        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
        self.to_v_lora = LoRALinearLayer(cross_attention_dim or v_hidden_size, v_hidden_size, v_rank, network_alpha)
        self.to_out_lora = LoRALinearLayer(out_hidden_size, out_hidden_size, out_rank, network_alpha)

    def __call__(self, attn: Attention, hidden_states: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor:
        self_cls_name = self.__class__.__name__
        deprecate(
            self_cls_name,
            "0.26.0",
            (
                f"Make sure use {self_cls_name[4:]} instead by setting"
                "LoRA layers to `self.{to_q,to_k,to_v,to_out[0]}.lora_layer` respectively. This will be done automatically when using"
                " `LoraLoaderMixin.load_lora_weights`"
            ),
        )
        attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device)
        attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device)
        attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device)
        attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device)

        attn._modules.pop("processor")
        attn.processor = AttnProcessor2_0()
        return attn.processor(attn, hidden_states, *args, **kwargs)


class LoRAXFormersAttnProcessor(nn.Module):
    r"""
    Processor for implementing the LoRA attention mechanism with memory efficient attention using xFormers.

    Args:
        hidden_size (`int`, *optional*):
            The hidden size of the attention layer.
        cross_attention_dim (`int`, *optional*):
            The number of channels in the `encoder_hidden_states`.
        rank (`int`, defaults to 4):
            The dimension of the LoRA update matrices.
        attention_op (`Callable`, *optional*, defaults to `None`):
            The base
            [operator](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.AttentionOpBase) to
            use as the attention operator. It is recommended to set to `None`, and allow xFormers to choose the best
            operator.
        network_alpha (`int`, *optional*):
            Equivalent to `alpha` but it's usage is specific to Kohya (A1111) style LoRAs.
        kwargs (`dict`):
            Additional keyword arguments to pass to the `LoRALinearLayer` layers.
    """

    def __init__(
        self,
        hidden_size: int,
        cross_attention_dim: int,
        rank: int = 4,
        attention_op: Optional[Callable] = None,
        network_alpha: Optional[int] = None,
        **kwargs,
    ):
        super().__init__()

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.rank = rank
        self.attention_op = attention_op

        q_rank = kwargs.pop("q_rank", None)
        q_hidden_size = kwargs.pop("q_hidden_size", None)
        q_rank = q_rank if q_rank is not None else rank
        q_hidden_size = q_hidden_size if q_hidden_size is not None else hidden_size

        v_rank = kwargs.pop("v_rank", None)
        v_hidden_size = kwargs.pop("v_hidden_size", None)
        v_rank = v_rank if v_rank is not None else rank
        v_hidden_size = v_hidden_size if v_hidden_size is not None else hidden_size

        out_rank = kwargs.pop("out_rank", None)
        out_hidden_size = kwargs.pop("out_hidden_size", None)
        out_rank = out_rank if out_rank is not None else rank
        out_hidden_size = out_hidden_size if out_hidden_size is not None else hidden_size

        self.to_q_lora = LoRALinearLayer(q_hidden_size, q_hidden_size, q_rank, network_alpha)
        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
        self.to_v_lora = LoRALinearLayer(cross_attention_dim or v_hidden_size, v_hidden_size, v_rank, network_alpha)
        self.to_out_lora = LoRALinearLayer(out_hidden_size, out_hidden_size, out_rank, network_alpha)

    def __call__(self, attn: Attention, hidden_states: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor:
        self_cls_name = self.__class__.__name__
        deprecate(
            self_cls_name,
            "0.26.0",
            (
                f"Make sure use {self_cls_name[4:]} instead by setting"
                "LoRA layers to `self.{to_q,to_k,to_v,add_k_proj,add_v_proj,to_out[0]}.lora_layer` respectively. This will be done automatically when using"
                " `LoraLoaderMixin.load_lora_weights`"
            ),
        )
        attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device)
        attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device)
        attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device)
        attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device)

        attn._modules.pop("processor")
        attn.processor = XFormersAttnProcessor()
        return attn.processor(attn, hidden_states, *args, **kwargs)


class LoRAAttnAddedKVProcessor(nn.Module):
    r"""
    Processor for implementing the LoRA attention mechanism with extra learnable key and value matrices for the text
    encoder.

    Args:
        hidden_size (`int`, *optional*):
            The hidden size of the attention layer.
        cross_attention_dim (`int`, *optional*, defaults to `None`):
            The number of channels in the `encoder_hidden_states`.
        rank (`int`, defaults to 4):
            The dimension of the LoRA update matrices.
        network_alpha (`int`, *optional*):
            Equivalent to `alpha` but it's usage is specific to Kohya (A1111) style LoRAs.
        kwargs (`dict`):
            Additional keyword arguments to pass to the `LoRALinearLayer` layers.
    """

    def __init__(
        self,
        hidden_size: int,
        cross_attention_dim: Optional[int] = None,
        rank: int = 4,
        network_alpha: Optional[int] = None,
    ):
        super().__init__()

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.rank = rank

        self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
        self.add_k_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
        self.add_v_proj_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
        self.to_k_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
        self.to_v_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
        self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)

    def __call__(self, attn: Attention, hidden_states: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor:
        self_cls_name = self.__class__.__name__
        deprecate(
            self_cls_name,
            "0.26.0",
            (
                f"Make sure use {self_cls_name[4:]} instead by setting"
                "LoRA layers to `self.{to_q,to_k,to_v,add_k_proj,add_v_proj,to_out[0]}.lora_layer` respectively. This will be done automatically when using"
                " `LoraLoaderMixin.load_lora_weights`"
            ),
        )
        attn.to_q.lora_layer = self.to_q_lora.to(hidden_states.device)
        attn.to_k.lora_layer = self.to_k_lora.to(hidden_states.device)
        attn.to_v.lora_layer = self.to_v_lora.to(hidden_states.device)
        attn.to_out[0].lora_layer = self.to_out_lora.to(hidden_states.device)

        attn._modules.pop("processor")
        attn.processor = AttnAddedKVProcessor()
        return attn.processor(attn, hidden_states, *args, **kwargs)


class IPAdapterAttnProcessor(nn.Module):
    r"""
    Attention processor for IP-Adapater.

    Args:
        hidden_size (`int`):
            The hidden size of the attention layer.
        cross_attention_dim (`int`):
            The number of channels in the `encoder_hidden_states`.
        num_tokens (`int`, defaults to 4):
            The context length of the image features.
        scale (`float`, defaults to 1.0):
            the weight scale of image prompt.
    """

    def __init__(self, hidden_size, cross_attention_dim=None, num_tokens=4, scale=1.0):
        super().__init__()

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.num_tokens = num_tokens
        self.scale = scale

        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)

    def __call__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
        temb=None,
        scale=1.0,
    ):
        if scale != 1.0:
            logger.warning("`scale` of IPAttnProcessor should be set with `set_ip_adapter_scale`.")
        residual = hidden_states

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )
        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        # split hidden states
        end_pos = encoder_hidden_states.shape[1] - self.num_tokens
        encoder_hidden_states, ip_hidden_states = (
            encoder_hidden_states[:, :end_pos, :],
            encoder_hidden_states[:, end_pos:, :],
        )

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        query = attn.head_to_batch_dim(query)
        key = attn.head_to_batch_dim(key)
        value = attn.head_to_batch_dim(value)

        attention_probs = attn.get_attention_scores(query, key, attention_mask)
        hidden_states = torch.bmm(attention_probs, value)
        hidden_states = attn.batch_to_head_dim(hidden_states)

        # for ip-adapter
        ip_key = self.to_k_ip(ip_hidden_states)
        ip_value = self.to_v_ip(ip_hidden_states)

        ip_key = attn.head_to_batch_dim(ip_key)
        ip_value = attn.head_to_batch_dim(ip_value)

        ip_attention_probs = attn.get_attention_scores(query, ip_key, None)
        ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
        ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)

        hidden_states = hidden_states + self.scale * ip_hidden_states

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

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


class IPAdapterAttnProcessor2_0(torch.nn.Module):
    r"""
    Attention processor for IP-Adapater for PyTorch 2.0.

    Args:
        hidden_size (`int`):
            The hidden size of the attention layer.
        cross_attention_dim (`int`):
            The number of channels in the `encoder_hidden_states`.
        num_tokens (`int`, defaults to 4):
            The context length of the image features.
        scale (`float`, defaults to 1.0):
            the weight scale of image prompt.
    """

    def __init__(self, hidden_size, cross_attention_dim=None, num_tokens=4, scale=1.0):
        super().__init__()

        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError(
                f"{self.__class__.__name__} requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
            )

        self.hidden_size = hidden_size
        self.cross_attention_dim = cross_attention_dim
        self.num_tokens = num_tokens
        self.scale = scale

        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)

    def __call__(
        self,
        attn,
        hidden_states,
        encoder_hidden_states=None,
        attention_mask=None,
        temb=None,
        scale=1.0,
    ):
        if scale != 1.0:
            logger.warning("`scale` of IPAttnProcessor should be set by `set_ip_adapter_scale`.")
        residual = hidden_states

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        # split hidden states
        end_pos = encoder_hidden_states.shape[1] - self.num_tokens
        encoder_hidden_states, ip_hidden_states = (
            encoder_hidden_states[:, :end_pos, :],
            encoder_hidden_states[:, end_pos:, :],
        )

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        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)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        hidden_states = F.scaled_dot_product_attention(
            query, key, value, attn_mask=attention_mask, 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)

        # for ip-adapter
        ip_key = self.to_k_ip(ip_hidden_states)
        ip_value = self.to_v_ip(ip_hidden_states)

        ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # TODO: add support for attn.scale when we move to Torch 2.1
        ip_hidden_states = F.scaled_dot_product_attention(
            query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
        )

        ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        ip_hidden_states = ip_hidden_states.to(query.dtype)

        hidden_states = hidden_states + self.scale * ip_hidden_states

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

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


# TODO(Yiyi): This class should not exist, we can replace it with a normal attention processor I believe
# this way torch.compile and co. will work as well
class Kandi3AttnProcessor:
    r"""
    Default kandinsky3 proccesor for performing attention-related computations.
    """

    @staticmethod
    def _reshape(hid_states, h):
        b, n, f = hid_states.shape
        d = f // h
        return hid_states.unsqueeze(-1).reshape(b, n, h, d).permute(0, 2, 1, 3)

    def __call__(
        self,
        attn,
        x,
        context,
        context_mask=None,
    ):
        query = self._reshape(attn.to_q(x), h=attn.num_heads)
        key = self._reshape(attn.to_k(context), h=attn.num_heads)
        value = self._reshape(attn.to_v(context), h=attn.num_heads)

        attention_matrix = einsum("b h i d, b h j d -> b h i j", query, key)

        if context_mask is not None:
            max_neg_value = -torch.finfo(attention_matrix.dtype).max
            context_mask = context_mask.unsqueeze(1).unsqueeze(1)
            attention_matrix = attention_matrix.masked_fill(~(context_mask != 0), max_neg_value)
        attention_matrix = (attention_matrix * attn.scale).softmax(dim=-1)

        out = einsum("b h i j, b h j d -> b h i d", attention_matrix, value)
        out = out.permute(0, 2, 1, 3).reshape(out.shape[0], out.shape[2], -1)
        out = attn.to_out[0](out)
        return out


LORA_ATTENTION_PROCESSORS = (
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    LoRAAttnAddedKVProcessor,
)

ADDED_KV_ATTENTION_PROCESSORS = (
    AttnAddedKVProcessor,
    SlicedAttnAddedKVProcessor,
    AttnAddedKVProcessor2_0,
    XFormersAttnAddedKVProcessor,
    LoRAAttnAddedKVProcessor,
)

CROSS_ATTENTION_PROCESSORS = (
    AttnProcessor,
    AttnProcessor2_0,
    XFormersAttnProcessor,
    SlicedAttnProcessor,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    IPAdapterAttnProcessor,
    IPAdapterAttnProcessor2_0,
    Kandi3AttnProcessor,
)

AttentionProcessor = Union[
    AttnProcessor,
    AttnProcessor2_0,
    XFormersAttnProcessor,
    SlicedAttnProcessor,
    AttnAddedKVProcessor,
    SlicedAttnAddedKVProcessor,
    AttnAddedKVProcessor2_0,
    XFormersAttnAddedKVProcessor,
    CustomDiffusionAttnProcessor,
    CustomDiffusionXFormersAttnProcessor,
    CustomDiffusionAttnProcessor2_0,
    # deprecated
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    LoRAAttnAddedKVProcessor,
]
