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from dataclasses import dataclass

from typing import Optional, Union

import torch

import torch.nn.functional as F

from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config

from ..utils import BaseOutput

from .attention import BasicTransformerBlock

from .embeddings import TimestepEmbedding, Timesteps

from .modeling_utils import ModelMixin

@dataclass

class PriorTransformerOutput(BaseOutput):

"""

Args:

predicted_image_embedding (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`):

The predicted CLIP image embedding conditioned on the CLIP text embedding input.

"""

predicted_image_embedding: torch.FloatTensor

class PriorTransformer(ModelMixin, ConfigMixin):

"""

The prior transformer from unCLIP is used to predict CLIP image embeddings from CLIP text embeddings. Note that the

transformer predicts the image embeddings through a denoising diffusion process.

This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library

implements for all the models (such as downloading or saving, etc.)

For more details, see the original paper: https://arxiv.org/abs/2204.06125

Parameters:

num_attention_heads (`int`, *optional*, defaults to 32): The number of heads to use for multi-head attention.

attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.

num_layers (`int`, *optional*, defaults to 20): The number of layers of Transformer blocks to use.

embedding_dim (`int`, *optional*, defaults to 768): The dimension of the CLIP embeddings. Note that CLIP

image embeddings and text embeddings are both the same dimension.

num_embeddings (`int`, *optional*, defaults to 77): The max number of clip embeddings allowed. I.e. the

length of the prompt after it has been tokenized.

additional_embeddings (`int`, *optional*, defaults to 4): The number of additional tokens appended to the

projected hidden_states. The actual length of the used hidden_states is `num_embeddings +

additional_embeddings`.

dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.

"""

@register_to_config

def __init__(

self,

num_attention_heads: int = 32,

attention_head_dim: int = 64,

num_layers: int = 20,

embedding_dim: int = 768,

num_embeddings=77,

additional_embeddings=4,

dropout: float = 0.0,

):

super().__init__()

self.num_attention_heads = num_attention_heads

self.attention_head_dim = attention_head_dim

inner_dim = num_attention_heads * attention_head_dim

self.additional_embeddings = additional_embeddings

self.time_proj = Timesteps(inner_dim, True, 0)

self.time_embedding = TimestepEmbedding(inner_dim, inner_dim)

self.proj_in = nn.Linear(embedding_dim, inner_dim)

self.embedding_proj = nn.Linear(embedding_dim, inner_dim)

self.encoder_hidden_states_proj = nn.Linear(embedding_dim, inner_dim)

self.positional_embedding = nn.Parameter(torch.zeros(1, num_embeddings + additional_embeddings, inner_dim))

self.prd_embedding = nn.Parameter(torch.zeros(1, 1, inner_dim))

self.transformer_blocks = nn.ModuleList(

[

BasicTransformerBlock(

inner_dim,

num_attention_heads,

attention_head_dim,

dropout=dropout,

activation_fn="gelu",

attention_bias=True,

)

for d in range(num_layers)

]

)

self.norm_out = nn.LayerNorm(inner_dim)

self.proj_to_clip_embeddings = nn.Linear(inner_dim, embedding_dim)

causal_attention_mask = torch.full(

[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings], -10000.0

)

causal_attention_mask.triu_(1)

causal_attention_mask = causal_attention_mask[None, ...]

self.register_buffer("causal_attention_mask", causal_attention_mask, persistent=False)

self.clip_mean = nn.Parameter(torch.zeros(1, embedding_dim))

self.clip_std = nn.Parameter(torch.zeros(1, embedding_dim))

def forward(

self,

hidden_states,

timestep: Union[torch.Tensor, float, int],

proj_embedding: torch.FloatTensor,

encoder_hidden_states: torch.FloatTensor,

attention_mask: Optional[torch.BoolTensor] = None,

return_dict: bool = True,

):

"""

Args:

hidden_states (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`):

x_t, the currently predicted image embeddings.

timestep (`torch.long`):

Current denoising step.

proj_embedding (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`):

Projected embedding vector the denoising process is conditioned on.

encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, num_embeddings, embedding_dim)`):

Hidden states of the text embeddings the denoising process is conditioned on.

attention_mask (`torch.BoolTensor` of shape `(batch_size, num_embeddings)`):

Text mask for the text embeddings.

return_dict (`bool`, *optional*, defaults to `True`):

Whether or not to return a [`models.prior_transformer.PriorTransformerOutput`] instead of a plain

tuple.

Returns:

[`~models.prior_transformer.PriorTransformerOutput`] or `tuple`:

[`~models.prior_transformer.PriorTransformerOutput`] if `return_dict` is True, otherwise a `tuple`. When

returning a tuple, the first element is the sample tensor.

"""

batch_size = hidden_states.shape[0]

timesteps = timestep

if not torch.is_tensor(timesteps):

timesteps = torch.tensor([timesteps], dtype=torch.long, device=hidden_states.device)

elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:

timesteps = timesteps[None].to(hidden_states.device)

# broadcast to batch dimension in a way that's compatible with ONNX/Core ML

timesteps = timesteps * torch.ones(batch_size, dtype=timesteps.dtype, device=timesteps.device)

timesteps_projected = self.time_proj(timesteps)

# timesteps does not contain any weights and will always return f32 tensors

# but time_embedding might be fp16, so we need to cast here.

timesteps_projected = timesteps_projected.to(dtype=self.dtype)

time_embeddings = self.time_embedding(timesteps_projected)

proj_embeddings = self.embedding_proj(proj_embedding)

encoder_hidden_states = self.encoder_hidden_states_proj(encoder_hidden_states)

hidden_states = self.proj_in(hidden_states)

prd_embedding = self.prd_embedding.to(hidden_states.dtype).expand(batch_size, -1, -1)

positional_embeddings = self.positional_embedding.to(hidden_states.dtype)

hidden_states = torch.cat(

[

encoder_hidden_states,

proj_embeddings[:, None, :],

time_embeddings[:, None, :],

hidden_states[:, None, :],

prd_embedding,

],

dim=1,

)

hidden_states = hidden_states + positional_embeddings

if attention_mask is not None:

attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0

attention_mask = F.pad(attention_mask, (0, self.additional_embeddings), value=0.0)

attention_mask = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype)

attention_mask = attention_mask.repeat_interleave(self.config.num_attention_heads, dim=0)

for block in self.transformer_blocks:

hidden_states = block(hidden_states, attention_mask=attention_mask)

hidden_states = self.norm_out(hidden_states)

hidden_states = hidden_states[:, -1]

predicted_image_embedding = self.proj_to_clip_embeddings(hidden_states)

if not return_dict:

return (predicted_image_embedding,)

return PriorTransformerOutput(predicted_image_embedding=predicted_image_embedding)

def post_process_latents(self, prior_latents):

prior_latents = (prior_latents * self.clip_std) + self.clip_mean

return prior_latents