AutoAndroidController/py/venv/Lib/site-packages/transformers/models/emu3/modular_emu3.py

# coding=utf-8
# Copyright 2024 HuggingFace Inc. 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.

import math
from functools import cached_property
from typing import Optional, Union

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

from ...cache_utils import Cache
from ...generation import GenerationMixin
from ...modeling_outputs import CausalLMOutputWithPast
from ...modeling_utils import PreTrainedModel
from ...processing_utils import Unpack
from ...utils import auto_docstring, can_return_tuple, logging
from ...utils.deprecation import deprecate_kwarg
from ..chameleon.modeling_chameleon import (
    ChameleonPreTrainedModel,
    ChameleonVQVAEEncoderConvDownsample,
)
from ..llama.modeling_llama import LlamaAttention, LlamaDecoderLayer, LlamaForCausalLM, LlamaModel, TransformersKwargs
from ..siglip.modeling_siglip import SiglipAttention
from .configuration_emu3 import Emu3Config, Emu3TextConfig, Emu3VQVAEConfig


logger = logging.get_logger(__name__)


class Emu3Attention(LlamaAttention):
    pass


# Has extra dropout which no other model in the library has
class Emu3DecoderLayer(LlamaDecoderLayer):
    def __init__(self, config: Emu3Config, layer_idx: int):
        super().__init__(config, layer_idx)
        self.dropout = nn.Dropout(config.attention_dropout)

    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)

        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            use_cache=use_cache,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            **kwargs,
        )
        hidden_states = residual + self.dropout(hidden_states)

        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + self.dropout(hidden_states)
        return hidden_states


class Emu3VQVAEVectorQuantizer(nn.Module):
    """
    A module for vector quantization using learned embedding vectors.

    This module implements the quantization process similar to te one described in
    the VQ-VAE (Vector Quantized Variational AutoEncoder) paper. It quantizes continuous
    input vectors into discrete codebook vectors, which are learned during training.
    Current implementation improves over previous ones by avoiding costly matrix multiplications
    and allowing for post-hoc remapping of indices.
    """

    def __init__(self, config: Emu3VQVAEConfig):
        super().__init__()
        self.embedding = nn.Embedding(config.codebook_size, config.embed_dim)
        self.embedding.weight.data.uniform_(-1.0 / config.codebook_size, 1.0 / config.codebook_size)

    def forward(self, hidden_state: torch.Tensor):
        batch_size, temporal, channels, height, width = hidden_state.shape
        hidden_state = hidden_state.permute(0, 1, 3, 4, 2).contiguous()
        hidden_state_flattened = hidden_state.view(-1, channels)

        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
        hidden_state_sum = torch.sum(hidden_state_flattened**2, dim=1, keepdim=True)
        embedding_sum = torch.sum(self.embedding.weight**2, dim=1)

        # "bd,dn->bn",
        distances = 2 * torch.matmul(hidden_state_flattened, self.embedding.weight.transpose(0, 1))
        distances = hidden_state_sum + embedding_sum - distances

        min_encoding_indices = torch.argmin(distances, dim=1)
        min_encoding_indices = min_encoding_indices.view(batch_size, temporal, height, width)
        return min_encoding_indices


class Emu3VQVAEEncoderConvDownsample(ChameleonVQVAEEncoderConvDownsample):
    pass


class Emu3VQVAEEncoderConvUpsample(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)

    def forward(self, hidden_states):
        hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
        hidden_states = self.conv(hidden_states)
        return hidden_states


class Emu3VQVAEConv3d(nn.Module):
    def __init__(
        self,
        in_channel: int,
        out_channel: int,
        kernel_size: tuple[int],
        stride: tuple[int],
    ):
        super().__init__()

        padding_sizes = [one_kernel - one_stride for one_kernel, one_stride in zip(kernel_size[1:], stride[1:])]
        self.padding = ()
        for pad_size in padding_sizes[::-1]:
            self.padding += (pad_size // 2 + pad_size % 2, pad_size // 2)
        self.padding += (2, 0)

        self.conv = nn.Conv3d(
            in_channel,
            out_channel,
            kernel_size,
            stride=stride,
        )

    def forward(self, hidden_states: torch.Tensor):
        hidden_states = F.pad(hidden_states, self.padding)
        hidden_states = self.conv(hidden_states)
        return hidden_states


class Emu3VQVAESpatialNorm(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
    ):
        super().__init__()
        self.norm_layer = nn.GroupNorm(
            num_channels=out_channels,
            num_groups=32,
            eps=1e-6,
            affine=True,
        )

        self.conv_y = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size=1,
            stride=1,
            padding=0,
        )
        self.conv_b = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size=1,
            stride=1,
            padding=0,
        )

    def forward(self, hidden_states: torch.Tensor, quant_states: torch.Tensor):
        quant_states = F.interpolate(quant_states, size=hidden_states.shape[-2:], mode="nearest")
        hidden_states = self.norm_layer(hidden_states)
        hidden_states = hidden_states * self.conv_y(quant_states) + self.conv_b(quant_states)
        return hidden_states


class Emu3VQVAETemporalUpsample(nn.Module):
    def __init__(
        self,
        in_channel: int,
        out_channel: int,
    ):
        super().__init__()
        self.conv = Emu3VQVAEConv3d(
            in_channel,
            out_channel,
            kernel_size=(3, 3, 3),
            stride=(1, 1, 1),
        )

    def forward(self, hidden_states: torch.Tensor):
        batch_size, channels, temporal, height, width = hidden_states.shape
        hidden_states = hidden_states.permute(0, 1, 3, 4, 2).contiguous().view(batch_size, -1, temporal)
        hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
        hidden_states = hidden_states.view(batch_size, channels, height, width, -1).permute(0, 1, 4, 2, 3).contiguous()
        hidden_states = self.conv(hidden_states)
        return hidden_states


class Emu3VQVAETemporalDownsample(nn.Module):
    def __init__(
        self,
        in_channel: int,
        out_channel: int,
    ):
        super().__init__()
        self.conv = Emu3VQVAEConv3d(
            in_channel,
            out_channel,
            kernel_size=(4, 3, 3),
            stride=(2, 1, 1),
        )

    def forward(self, hidden_states: torch.Tensor):
        hidden_states = self.conv(hidden_states)
        return hidden_states


class Emu3VQVAETemporalResnetBlock(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels=None,
    ):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = in_channels if out_channels is None else out_channels

        self.norm1 = nn.BatchNorm3d(in_channels)
        self.conv1 = Emu3VQVAEConv3d(
            in_channels,
            out_channels,
            kernel_size=(3, 3, 3),
            stride=(1, 1, 1),
        )
        self.norm2 = nn.BatchNorm3d(out_channels)
        self.conv2 = Emu3VQVAEConv3d(
            out_channels,
            out_channels,
            kernel_size=(3, 3, 3),
            stride=(1, 1, 1),
        )
        if self.in_channels != self.out_channels:
            self.nin_shortcut = nn.Conv3d(
                in_channels,
                out_channels,
                kernel_size=1,
                stride=1,
                padding=0,
            )

    def forward(self, hidden_states):
        residual = hidden_states
        hidden_states = self.norm1(hidden_states)
        hidden_states *= torch.sigmoid(hidden_states)
        hidden_states = self.conv1(hidden_states)

        hidden_states = self.norm2(hidden_states)
        hidden_states *= torch.sigmoid(hidden_states)
        hidden_states = self.conv2(hidden_states)

        if self.in_channels != self.out_channels:
            residual = self.nin_shortcut(residual)

        return residual + hidden_states


class Emu3VQVAEResnetBlock(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: Optional[int] = None,
        quant_channels: Optional[int] = None,
    ):
        super().__init__()
        self.in_channels = in_channels
        out_channels = in_channels if out_channels is None else out_channels
        self.out_channels = out_channels
        self.quant_channels = quant_channels

        if quant_channels is None:
            self.norm1 = nn.GroupNorm(num_channels=in_channels, num_groups=32, eps=1e-6, affine=True)
            self.norm2 = nn.GroupNorm(num_channels=out_channels, num_groups=32, eps=1e-6, affine=True)
        else:
            self.norm1 = Emu3VQVAESpatialNorm(quant_channels, in_channels)
            self.norm2 = Emu3VQVAESpatialNorm(quant_channels, out_channels)

        self.conv1 = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size=3,
            stride=1,
            padding=1,
        )

        self.conv2 = nn.Conv2d(
            out_channels,
            out_channels,
            kernel_size=3,
            stride=1,
            padding=1,
        )

        if self.in_channels != self.out_channels:
            self.nin_shortcut = nn.Conv2d(
                in_channels,
                out_channels,
                kernel_size=1,
                stride=1,
                padding=0,
            )

    def forward(self, hidden_states: torch.Tensor, quant_channels: Optional[torch.Tensor] = None):
        norm_args = () if self.quant_channels is None else (quant_channels,)

        residual = hidden_states
        hidden_states = self.norm1(hidden_states, *norm_args)
        hidden_states *= torch.sigmoid(hidden_states)
        hidden_states = self.conv1(hidden_states)

        hidden_states = self.norm2(hidden_states, *norm_args)
        hidden_states *= torch.sigmoid(hidden_states)
        hidden_states = self.conv2(hidden_states)

        if self.in_channels != self.out_channels:
            residual = self.nin_shortcut(residual)

        return residual + hidden_states


class Emu3VQVAEAttentionBlock(SiglipAttention):
    def __init__(self, config: Emu3VQVAEConfig):
        super().__init__(config)

        # for compatibility with the attention interface
        self.num_key_value_groups = 1


class Emu3VQVAEGroupNorm(nn.GroupNorm):
    """
    Same as the torch GroupNorm with the only difference that this ones accepts
    an optional kwarg `quant_states` which is not used. This class makes it easier to
    use SpatialNorm or GroupNorm without conditionals
    """

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def forward(self, input, quant_states=None):
        return F.group_norm(input, self.num_groups, self.weight, self.bias, self.eps)


class Emu3VQVAEMiddleBlock(nn.Module):
    def __init__(self, config, in_channels, quant_channels=None):
        super().__init__()

        self.block_1 = Emu3VQVAEResnetBlock(
            in_channels=in_channels,
            out_channels=in_channels,
            quant_channels=quant_channels,
        )
        self.attn_1 = Emu3VQVAEAttentionBlock(config)
        if quant_channels is None:
            self.attn_norm = Emu3VQVAEGroupNorm(num_channels=in_channels, num_groups=32, eps=1e-6, affine=True)
        else:
            self.attn_norm = Emu3VQVAESpatialNorm(quant_channels, in_channels)

        self.block_2 = Emu3VQVAEResnetBlock(
            in_channels=in_channels,
            out_channels=in_channels,
            quant_channels=quant_channels,
        )

    def forward(self, hidden_states: torch.FloatTensor, quant_states: Optional[torch.FloatTensor] = None):
        hidden_states = self.block_1(hidden_states, quant_states)
        residual = hidden_states
        hidden_states = self.attn_norm(hidden_states, quant_states)
        batch_size, channels, height, width = hidden_states.shape
        hidden_states = hidden_states.view(batch_size, channels, height * width).transpose(1, 2)
        hidden_states = self.attn_1(hidden_states)[0]
        hidden_states = hidden_states.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2)
        hidden_states = residual + hidden_states
        hidden_states = self.block_2(hidden_states, quant_states)
        return hidden_states


class Emu3VQVAEDownBlock(nn.Module):
    def __init__(self, config):
        super().__init__()

        self.num_resolutions = len(config.channel_multiplier)
        self.num_res_blocks = config.num_res_blocks
        base_channels = config.base_channels
        channel_multiplier = config.channel_multiplier

        in_channel_multiplier = (1,) + tuple(channel_multiplier)
        self.in_channel_multiplier = in_channel_multiplier
        self.down = nn.ModuleList()
        for i_level in range(self.num_resolutions):
            block = nn.ModuleList()
            attn = nn.ModuleList()
            attn_norms = nn.ModuleList()
            block_in = base_channels * in_channel_multiplier[i_level]
            block_out = base_channels * channel_multiplier[i_level]
            for i_block in range(self.num_res_blocks):
                block.append(
                    Emu3VQVAEResnetBlock(
                        in_channels=block_in,
                        out_channels=block_out,
                    )
                )
                block_in = block_out
                if config.attn_resolutions is not None and i_level in config.attn_resolutions:
                    attn.append(Emu3VQVAEAttentionBlock(config))
                    attn_norms.append(nn.GroupNorm(num_channels=block_in, num_groups=32, eps=1e-6, affine=True))

            down = nn.Module()
            down.block = block
            down.attn = attn
            down.attn_norms = attn_norms
            if i_level != self.num_resolutions - 1:
                down.downsample = Emu3VQVAEEncoderConvDownsample(block_in)
            self.down.append(down)

    def forward(self, hidden_states: torch.FloatTensor):
        for i_level, blocks in enumerate(self.down):
            for i_block in range(self.num_res_blocks):
                hidden_states = blocks.block[i_block](hidden_states)
                if len(blocks.attn) > 0:
                    residual = hidden_states
                    hidden_states = blocks.attn_norms[i_block](hidden_states)

                    batch_size, channels, height, width = hidden_states.shape
                    hidden_states = hidden_states.view(batch_size, channels, height * width).transpose(1, 2)
                    hidden_states = blocks.attn[i_block](hidden_states)[0]

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

            if i_level != self.num_resolutions - 1:
                hidden_states = blocks.downsample(hidden_states)

        return hidden_states


class Emu3VQVAEUpBlock(nn.Module):
    def __init__(self, config):
        super().__init__()

        self.num_resolutions = len(config.channel_multiplier)
        self.num_res_blocks = config.num_res_blocks

        quant_channels = config.embed_dim
        block_in = config.base_channels * config.channel_multiplier[-1]

        self.up = nn.ModuleList()
        for i_level in reversed(range(self.num_resolutions)):
            block = nn.ModuleList()
            attn = nn.ModuleList()
            attn_norms = nn.ModuleList()
            block_out = config.base_channels * config.channel_multiplier[i_level]
            for i_block in range(self.num_res_blocks + 1):
                block.append(
                    Emu3VQVAEResnetBlock(
                        in_channels=block_in,
                        out_channels=block_out,
                        quant_channels=quant_channels,
                    )
                )
                block_in = block_out
                if i_level in config.attn_resolutions:
                    attn.append(Emu3VQVAEAttentionBlock(config))
                    attn_norms.append(Emu3VQVAESpatialNorm(quant_channels, block_in))

            up = nn.Module()
            up.block = block
            up.attn = attn
            up.attn_norms = attn_norms
            if i_level != 0:
                up.upsample = Emu3VQVAEEncoderConvUpsample(block_in)

            self.up.insert(0, up)

    def forward(self, hidden_states: torch.FloatTensor, quant_states: torch.FloatTensor):
        for i_level, blocks in enumerate(self.up[::-1]):
            for i_block in range(self.num_res_blocks + 1):
                hidden_states = blocks.block[i_block](hidden_states, quant_states)
                if len(blocks.attn) > 0:
                    residual = hidden_states
                    hidden_states = blocks.attn_norms[i_block](hidden_states, quant_states)

                    batch_size, channels, height, width = hidden_states.shape
                    hidden_states = hidden_states.view(batch_size, channels, height * width).transpose(1, 2)
                    hidden_states = blocks.attn[i_block](hidden_states)[0]

                    hidden_states = hidden_states.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2)
                    hidden_states = residual + hidden_states
            if i_level != len(self.up) - 1:
                hidden_states = blocks.upsample(hidden_states)

        return hidden_states


class Emu3VQVAEEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()

        base_channels = config.base_channels
        in_channels = config.in_channels
        double_latent = config.double_latent
        latent_channels = config.latent_channels
        channel_multiplier = config.channel_multiplier
        out_channels = 2 * latent_channels if double_latent else latent_channels
        block_in = base_channels * channel_multiplier[-1]

        self.conv_in = torch.nn.Conv2d(in_channels, base_channels, kernel_size=3, stride=1, padding=1)
        self.down_block = Emu3VQVAEDownBlock(config)
        self.middle_block = Emu3VQVAEMiddleBlock(config, block_in)

        self.norm_out = torch.nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
        self.conv_out = torch.nn.Conv2d(
            block_in,
            out_channels,
            kernel_size=3,
            stride=1,
            padding=1,
        )

        temporal_down_blocks = int(math.log2(config.temporal_downsample_factor))
        self.time_conv = nn.ModuleList()
        self.time_res_stack = nn.ModuleList()

        for i in range(temporal_down_blocks):
            conv = Emu3VQVAETemporalDownsample(out_channels, out_channels)
            self.time_conv.append(conv)

        for _ in range(config.num_res_blocks):
            time_res_conv = Emu3VQVAETemporalResnetBlock(
                in_channels=out_channels,
                out_channels=out_channels,
            )
            self.time_res_stack.append(time_res_conv)

    def forward(self, pixel_values: torch.LongTensor):
        temporal_dim = pixel_values.shape[1]
        pixel_values = pixel_values.reshape(-1, *pixel_values.shape[2:])

        # downsampling & middle
        hidden_states = self.conv_in(pixel_values)
        hidden_states = self.down_block(hidden_states)
        hidden_states = self.middle_block(hidden_states)

        # end
        hidden_states = self.norm_out(hidden_states)
        hidden_states *= torch.sigmoid(hidden_states)
        hidden_states = self.conv_out(hidden_states)

        hidden_states = hidden_states.reshape(-1, temporal_dim, *hidden_states.shape[1:])
        hidden_states = hidden_states.permute(0, 2, 1, 3, 4)

        # temporal convs
        for conv in self.time_conv:
            hidden_states = conv(hidden_states)
            hidden_states *= torch.sigmoid(hidden_states)

        for layer in self.time_res_stack:
            hidden_states = layer(hidden_states)

        hidden_states = hidden_states.permute(0, 2, 1, 3, 4)

        return hidden_states


class Emu3VQVAEDecoder(nn.Module):
    def __init__(self, config: Emu3VQVAEConfig):
        super().__init__()

        quant_channels = config.embed_dim
        block_in = config.base_channels * config.channel_multiplier[-1]
        self.time_res_stack = nn.ModuleList()
        for _ in range(config.num_res_blocks):
            time_res_conv = Emu3VQVAETemporalResnetBlock(
                in_channels=config.latent_channels, out_channels=config.latent_channels
            )
            self.time_res_stack.append(time_res_conv)

        temp_upsample_block_num = int(math.log2(config.temporal_downsample_factor))
        self.time_conv = nn.ModuleList()
        for i in range(temp_upsample_block_num):
            conv = Emu3VQVAETemporalUpsample(config.latent_channels, config.latent_channels)
            self.time_conv.append(conv)

        self.conv_in = nn.Conv2d(
            config.latent_channels,
            block_in,
            kernel_size=3,
            stride=1,
            padding=1,
        )

        self.middle_block = Emu3VQVAEMiddleBlock(config, block_in, quant_channels=quant_channels)
        self.up_block = Emu3VQVAEUpBlock(config)

        block_in = config.base_channels * config.channel_multiplier[0]
        self.norm_out = Emu3VQVAESpatialNorm(quant_channels, block_in)
        self.conv_out = nn.Conv2d(
            block_in,
            config.out_channels,
            kernel_size=3,
            stride=1,
            padding=1,
        )

    def forward(self, hidden_states: torch.Tensor, quant_states: torch.Tensor):
        hidden_quant_states = torch.cat((hidden_states, quant_states), dim=0)
        hidden_quant_states = hidden_quant_states.permute(0, 2, 1, 3, 4)

        # temporal convs
        for layer in self.time_res_stack:
            hidden_quant_states = layer(hidden_quant_states)

        for layer in self.time_conv:
            hidden_quant_states = layer(hidden_quant_states)
            hidden_quant_states *= torch.sigmoid(hidden_quant_states)

        hidden_quant_states = hidden_quant_states.permute(0, 2, 1, 3, 4)
        hidden_states, quant_states = torch.chunk(hidden_quant_states, 2, dim=0)
        hidden_states = hidden_states.reshape(-1, *hidden_states.shape[2:])
        quant_states = quant_states.reshape(-1, *quant_states.shape[2:])

        hidden_states = self.conv_in(hidden_states)

        # middle & upsampling
        hidden_states = self.middle_block(hidden_states, quant_states)
        hidden_states = self.up_block(hidden_states, quant_states)

        hidden_states = self.norm_out(hidden_states, quant_states)
        hidden_states *= torch.sigmoid(hidden_states)
        hidden_states = self.conv_out(hidden_states)

        return hidden_states


@auto_docstring(
    custom_intro="""
    The VQ-VAE model used in Emu3 for encoding/decoding images into discrete tokens.
    This model follows the "Make-a-scene: Scene-based text-to-image generation with human priors" paper from
    [ Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, and Yaniv
    Taigman](https://huggingface.co/papers/2203.13131).
    """
)
class Emu3VQVAE(PreTrainedModel):
    config: Emu3VQVAEConfig
    base_model_prefix = "emuvideovq"
    main_input_name = "pixel_values"
    _supports_sdpa = True
    _supports_flash_attn = True
    _supports_flex_attn = True
    _supports_attention_backend = True
    _no_split_modules = [
        "Emu3VQVAETemporalResnetBlock",
        "Emu3VQVAEAttentionBlock",
        "Emu3VQVAEResnetBlock",
        "Emu3VQVAEVectorQuantizer",
    ]

    def _init_weights(self, module):
        if isinstance(module, (nn.Conv2d, nn.Conv3d)):
            nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
            if module.bias is not None:
                fan_in, _ = nn.init._calculate_fan_in_and_fan_out(module.weight)
                bound = 1 / math.sqrt(fan_in)
                nn.init.uniform_(module.bias, -bound, bound)
        elif isinstance(module, nn.Linear):
            nn.init.kaiming_uniform_(module.weight, a=math.sqrt(5))
            if module.bias is not None:
                fan_in, _ = nn.init._calculate_fan_in_and_fan_out(module.weight)
                bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
                nn.init.uniform_(module.bias, -bound, bound)
        elif isinstance(module, (nn.BatchNorm2d, nn.BatchNorm3d, nn.GroupNorm)):
            nn.init.constant_(module.weight, 1.0)
            nn.init.constant_(module.bias, 0.0)
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_()
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

    def __init__(self, config: Emu3VQVAEConfig):
        super().__init__(config)

        self.config = config

        self.encoder = Emu3VQVAEEncoder(config)
        self.decoder = Emu3VQVAEDecoder(config)
        self.quantize = Emu3VQVAEVectorQuantizer(config)
        self.vision_spatial_factor = 2 ** (len(config.channel_multiplier) - 1)

        self.quant_conv = Emu3VQVAEConv3d(
            config.latent_channels, config.embed_dim, kernel_size=(3, 1, 1), stride=(1, 1, 1)
        )
        self.post_quant_conv = Emu3VQVAEConv3d(
            config.embed_dim, config.latent_channels, kernel_size=(3, 1, 1), stride=(1, 1, 1)
        )
        self.spatial_scale_factor = 2 ** (len(config.channel_multiplier) - 1)
        self.eval()  # Emu3's VQ model is frozen

        self.post_init()

    def encode(self, pixel_values: torch.Tensor, image_sizes: torch.Tensor):
        is_image = pixel_values.ndim == 4
        if is_image:
            temporal = self.config.temporal_downsample_factor
            batch_size, channels, height, width = pixel_values.shape
            pixel_values = pixel_values.unsqueeze(1).repeat(1, temporal, 1, 1, 1)
        else:
            batch_size, temporal, channels, height, width = pixel_values.shape

        hidden_states = self.encoder(pixel_values)

        # b t c h w -> b c t h w
        hidden_states = hidden_states.permute(0, 2, 1, 3, 4)
        hidden_states = self.quant_conv(hidden_states)

        # b c t h w -> b t c h w
        hidden_states = hidden_states.permute(0, 2, 1, 3, 4)
        codes = self.quantize(hidden_states)

        image_tokens = codes.squeeze(1) if is_image else codes

        image_tokens = [
            single_image[: int(size[0] / self.vision_spatial_factor), : int(size[1] / self.vision_spatial_factor)]
            for single_image, size in zip(image_tokens, image_sizes)
        ]

        return image_tokens

    def decode(self, hidden_states: torch.Tensor):
        is_image = hidden_states.ndim == 3
        if is_image:
            hidden_states = hidden_states.unsqueeze(1)

        batch_size, temporal, height, width = hidden_states.shape
        quant = self.quantize.embedding(hidden_states.flatten())

        channels = quant.shape[-1]
        quant = quant.view(batch_size, temporal, height, width, channels).permute(0, 4, 1, 2, 3).contiguous()
        post_quant = self.post_quant_conv(quant)

        quant = quant.permute(0, 2, 1, 3, 4)
        post_quant = post_quant.permute(0, 2, 1, 3, 4)

        video = self.decoder(post_quant, quant)
        video = video.reshape(
            batch_size,
            temporal * self.config.temporal_downsample_factor,
            self.config.out_channels,
            height * self.spatial_scale_factor,
            width * self.spatial_scale_factor,
        )
        return video[:, 0] if is_image else video


class Emu3ImageVocabularyMapping:
    """
    A class for mapping discrete image tokens from VQGAN to BPE tokens.
    """

    def __init__(self, vocab_map):
        self.vocab_map = vocab_map
        self.eol_token_id = vocab_map.get("<|extra_200|>")
        self.image_token_id = vocab_map.get("<image>")

    @cached_property
    def image_tokens(self):
        return sorted([val for name, val in self.vocab_map.items() if name.startswith("<|visual token")])

    @cached_property
    def image_tokens_str(self):
        return sorted([name for name, val in self.vocab_map.items() if name.startswith("<|visual token")])

    @cached_property
    def img2bpe(self):
        return {int(token[-8:-2]): self.vocab_map[token] for token in self.image_tokens_str}

    @cached_property
    def bpe2img(self):
        return {v: k for k, v in self.img2bpe.items()}

    @cached_property
    def bpe2img_mapping_tensor(self):
        mapping = torch.zeros(max(self.bpe2img.keys()) + 1, dtype=torch.int)
        for k, v in self.bpe2img.items():
            mapping[k] = v
        return mapping

    @cached_property
    def img2bpe_mapping_tensor(self):
        mapping = torch.zeros(max(self.img2bpe.keys()) + 1, dtype=torch.int)
        for k, v in self.img2bpe.items():
            mapping[k] = v
        return mapping

    def convert_img2bpe(self, img_batch: list[torch.Tensor]) -> torch.Tensor:
        device = img_batch.device
        eol_row = torch.ones((img_batch.shape[0], 1), dtype=torch.int) * self.eol_token_id
        img_tokens = self.img2bpe_mapping_tensor[img_batch.to("cpu")]
        img_tokens = torch.cat([img_tokens, eol_row], dim=-1)
        return img_tokens.to(device)

    def convert_bpe2img(self, img_batch: torch.Tensor) -> torch.Tensor:
        device = img_batch.device
        img_batch = img_batch[..., :-1]  # remove last row of EOL tokens
        img_tokens = self.bpe2img_mapping_tensor[img_batch.to("cpu")]
        return img_tokens.to(device)


class Emu3PreTrainedModel(ChameleonPreTrainedModel, Emu3VQVAE):
    _no_split_modules = [
        "Emu3DecoderLayer",
    ]
    _supports_flex_attn = True
    _supports_attention_backend = True


class Emu3TextModel(LlamaModel, Emu3PreTrainedModel):
    _can_record_outputs = {
        "hidden_states": Emu3DecoderLayer,
        "attentions": Emu3Attention,
    }

    def __init__(self, config: Emu3Config):
        super().__init__(config)
        self.layers = nn.ModuleList(
            [Emu3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )


class Emu3ForCausalLM(LlamaForCausalLM, Emu3PreTrainedModel, GenerationMixin):
    config: Emu3TextConfig

    def __init__(self, config):
        super().__init__(config)
        self.model = Emu3TextModel(config)

    def forward(**super_kwargs):
        r"""
        Example:

        ```python
        >>> from transformers import Emu3Processor, Emu3ForConditionalGeneration
        >>> import torch
        >>> import requests
        >>> from PIL import Image

        >>> model = Emu3ForCausalLM.from_pretrained("BAAI/Emu3-Chat-hf", dtype=torch.bfloat16)
        >>> processor = Emu3Processor.from_pretrained("BAAI/Emu3-Chat-hf")

        >>> inputs = processor(text=["Can you write me a poem about winter."], return_tensors="pt").to(model.device)

        >>> generated_ids = model.generate(**inputs, max_new_tokens=100, do_sample=False)
        >>> processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
        ```"""
        super().forward()


class Emu3Model(Emu3PreTrainedModel):
    _checkpoint_conversion_mapping = {"text_model.model": "text_model"}

    def __init__(self, config):
        super().__init__(config)
        self.text_model = Emu3TextModel._from_config(config.text_config)
        self.vqmodel = Emu3VQVAE(config.vq_config)
        self.vocabulary_mapping = Emu3ImageVocabularyMapping(config.vocabulary_map)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.text_model.get_input_embeddings()

    def set_input_embeddings(self, value):
        self.text_model.set_input_embeddings(value)

    def set_decoder(self, decoder):
        self.text_model = decoder

    def get_decoder(self):
        return self.text_model

    def get_image_tokens(self, pixel_values: torch.FloatTensor, image_sizes: torch.LongTensor):
        """
        Tokenizes images into discrete tokens with VQGAN module. Converts
        obtained image tokens into BPE tokens and wraps with "boi" and "eoi"
        special tokens.

        Args:
            pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
                The tensors corresponding to the input images.
            image_sizes (`torch.LongTensor` of shape `(batch_size, 2)`):
                The sizes of the images in the batch, being (height, width) for each image.
        """
        image_tokens_list = self.vqmodel.encode(pixel_values, image_sizes)
        bpe_tokens_list = [self.vocabulary_mapping.convert_img2bpe(tokens).flatten() for tokens in image_tokens_list]
        bpe_tokens = torch.cat(bpe_tokens_list)
        return bpe_tokens

    def get_image_features(self, pixel_values: torch.FloatTensor, image_sizes: torch.LongTensor):
        """
        Tokenizes images into discrete tokens with VQGAN module and embeds
        them with text embeddings layer

        Args:
            pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
                The tensors corresponding to the input images.
        """
        image_tokens = self.get_image_tokens(pixel_values, image_sizes)
        split_sizes = [
            (height // self.vqmodel.vision_spatial_factor) * (width // self.vqmodel.vision_spatial_factor + 1)
            for height, width in image_sizes
        ]
        image_features = self.get_input_embeddings()(image_tokens)
        image_features = torch.split(image_features, split_sizes)
        return image_features

    @torch.no_grad
    def decode_image_tokens(self, image_tokens: torch.LongTensor, height: int, width: int):
        """
        Decodes generated image tokens from language model to continuous pixel values
        with VQGAN module via upsampling.

        Args:
            image_tokens (`torch.LongTensor` of shape `(batch_size, num_of_tokens)`):
                The tensors corresponding to the input images.
            height (`int`):
                Height of the generated image before upsampling.
            width (`int`):
                Width of the generated image before upsampling.
        """
        sequences = image_tokens[:, :-3].view(-1, height, width + 1)
        image_tokens = self.vocabulary_mapping.convert_bpe2img(sequences)
        image = self.vqmodel.decode(image_tokens)
        return image

    def get_placeholder_mask(
        self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor
    ):
        """
        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
        equal to the length of multimodal features. If the lengths are different, an error is raised.
        """
        if input_ids is None:
            special_image_mask = inputs_embeds == self.get_input_embeddings()(
                torch.tensor(self.vocabulary_mapping.image_token_id, dtype=torch.long, device=inputs_embeds.device)
            )
            special_image_mask = special_image_mask.all(-1)
        else:
            special_image_mask = input_ids == self.vocabulary_mapping.image_token_id

        n_image_tokens = special_image_mask.sum()
        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
        n_image_features = image_features.shape[0] * image_features.shape[1]
        if inputs_embeds[special_image_mask].numel() != image_features.numel():
            raise ValueError(
                f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
            )
        return special_image_mask

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        pixel_values: Optional[torch.FloatTensor] = None,
        image_sizes: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> Union[tuple, CausalLMOutputWithPast]:
        r"""
        image_sizes (`torch.LongTensor` of shape `(batch_size, 2)`):
            The sizes of the images in the batch, being (height, width) for each image. Image sizes can be obtained using
            [`AutoImageProcessor`]. See [`Emu3ImageProcessor.__call__`] for details ([]`Emu3Processor`] uses
            [`Emu3ImageProcessor`] for processing images).
        """
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError(
                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
            )

        if inputs_embeds is None:
            inputs_embeds = self.get_input_embeddings()(input_ids)

        if pixel_values is not None:
            image_embeds = self.get_image_features(pixel_values, image_sizes)
            image_embeds = torch.cat(image_embeds, dim=0)
            special_image_mask = self.get_placeholder_mask(
                input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds
            )
            inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_embeds)

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.text_model(
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )

        return outputs


class Emu3ForConditionalGeneration(Emu3PreTrainedModel, GenerationMixin):
    base_model_prefix = ""
    _tied_weights_keys = ["lm_head.weight"]
    _checkpoint_conversion_mapping = {
        "^text_model.model": "model.text_model",
        "^vqmodel": "model.vqmodel",
        "^text_model.lm_head": "lm_head",
    }

    def __init__(self, config):
        super().__init__(config)
        self.model = Emu3Model(config)
        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)

        self.post_init()

    def get_input_embeddings(self):
        return self.model.get_input_embeddings()

    def set_input_embeddings(self, value):
        self.model.set_input_embeddings(value)

    def get_output_embeddings(self) -> nn.Module:
        return self.lm_head

    def set_decoder(self, decoder):
        self.model.set_decoder(decoder)

    def get_decoder(self):
        return self.model.get_decoder()

    # Make modules available through conditional class for BC
    @property
    def text_model(self):
        return self.model.text_model

    @property
    def vqmodel(self):
        return self.model.vqmodel

    @property
    def vocabulary_mapping(self):
        return self.model.vocabulary_mapping

    def decode_image_tokens(self, **kwargs):
        return self.model.decode_image_tokens(**kwargs)

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        pixel_values: Optional[torch.FloatTensor] = None,
        image_sizes: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        logits_to_keep: Union[int, torch.Tensor] = 0,
        **kwargs: Unpack[TransformersKwargs],
    ) -> Union[tuple, CausalLMOutputWithPast]:
        r"""
        image_sizes (`torch.LongTensor` of shape `(batch_size, 2)`):
            The sizes of the images in the batch, being (height, width) for each image. Image sizes can be obtained using
            [`AutoImageProcessor`]. See [`Emu3ImageProcessor.__call__`] for details ([]`Emu3Processor`] uses
            [`Emu3ImageProcessor`] for processing images).
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Example:

        ```python
        >>> from transformers import Emu3Processor, Emu3ForConditionalGeneration
        >>> import torch
        >>> import requests
        >>> from PIL import Image

        >>> model = Emu3ForConditionalGeneration.from_pretrained("BAAI/Emu3-Chat-hf", dtype=torch.bfloat16)
        >>> processor = Emu3Processor.from_pretrained("BAAI/Emu3-Chat-hf")

        >>> conversation = [
        ...     {
        ...     "role": "system",
        ...     "content": [
        ...         {"type": "text", "text": "You are a helpful assistant."},
        ...         ],
        ...     },
        ...     {
        ...     "role": "user",
        ...     "content": [
        ...         {"type": "image"},
        ...         {"type": "text", "text": "Please describe the image."},
        ...         ],
        ...     },
        ... ]

        >>> prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
        >>> image = Image.open(requests.get("https://www.ilankelman.org/stopsigns/australia.jpg", stream=True).raw)

        >>> inputs = processor(images=[image], text=[prompt], return_tensors="pt").to(model.device, torch.bfloat16)

        >>> generated_ids = model.generate(**inputs, max_new_tokens=100, do_sample=False)
        >>> processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
        ```"""
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )

        hidden_states = outputs[0]
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        logits = self.lm_head(hidden_states[:, slice_indices, :])

        loss = None
        if labels is not None:
            loss = self.loss_function(
                logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
            )

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        attention_mask=None,
        inputs_embeds=None,
        cache_position=None,
        position_ids=None,
        use_cache=True,
        pixel_values=None,
        **kwargs,
    ):
        # Overwritten -- in specific circumstances we don't want to forward image inputs to the model

        model_inputs = super().prepare_inputs_for_generation(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            cache_position=cache_position,
            position_ids=position_ids,
            pixel_values=pixel_values,
            use_cache=use_cache,
            **kwargs,
        )

        if cache_position[0] != 0:
            model_inputs["pixel_values"] = None

        return model_inputs


__all__ = [
    "Emu3ForConditionalGeneration",
    "Emu3ForCausalLM",
    "Emu3TextModel",
    "Emu3PreTrainedModel",
    "Emu3VQVAE",
    "Emu3Model",
]