AutoAndroidController/py/venv/Lib/site-packages/transformers/models/clipseg/modeling_clipseg.py

# coding=utf-8
# Copyright 2022 The OpenAI Team Authors and 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
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# 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.
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"""PyTorch CLIPSeg model."""

import copy
import math
from dataclasses import dataclass
from typing import Any, Callable, Optional, Union

import torch
from torch import nn

from ...activations import ACT2FN
from ...modeling_attn_mask_utils import _create_4d_causal_attention_mask, _prepare_4d_attention_mask
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...utils import ModelOutput, auto_docstring, can_return_tuple, filter_out_non_signature_kwargs, logging, torch_int
from .configuration_clipseg import CLIPSegConfig, CLIPSegTextConfig, CLIPSegVisionConfig


logger = logging.get_logger(__name__)


# contrastive loss function, adapted from
# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
    return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device))


# Copied from transformers.models.clip.modeling_clip.clip_loss with clip->clipseg
def clipseg_loss(similarity: torch.Tensor) -> torch.Tensor:
    caption_loss = contrastive_loss(similarity)
    image_loss = contrastive_loss(similarity.t())
    return (caption_loss + image_loss) / 2.0


@dataclass
@auto_docstring
# Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->CLIPSeg
class CLIPSegOutput(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
        Contrastive loss for image-text similarity.
    logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
        The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
        similarity scores.
    logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
        The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
        similarity scores.
    text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
        The text embeddings obtained by applying the projection layer to the pooled output of [`CLIPSegTextModel`].
    image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
        The image embeddings obtained by applying the projection layer to the pooled output of [`CLIPSegVisionModel`].
    text_model_output (`BaseModelOutputWithPooling`):
        The output of the [`CLIPSegTextModel`].
    vision_model_output (`BaseModelOutputWithPooling`):
        The output of the [`CLIPSegVisionModel`].
    """

    loss: Optional[torch.FloatTensor] = None
    logits_per_image: Optional[torch.FloatTensor] = None
    logits_per_text: Optional[torch.FloatTensor] = None
    text_embeds: Optional[torch.FloatTensor] = None
    image_embeds: Optional[torch.FloatTensor] = None
    text_model_output: BaseModelOutputWithPooling = None
    vision_model_output: BaseModelOutputWithPooling = None

    def to_tuple(self) -> tuple[Any]:
        return tuple(
            self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
            for k in self.keys()
        )


@dataclass
@auto_docstring
class CLIPSegDecoderOutput(ModelOutput):
    r"""
    logits (`torch.FloatTensor` of shape `(batch_size, height, width)`):
        Classification scores for each pixel.
    """

    logits: Optional[torch.FloatTensor] = None
    hidden_states: Optional[tuple[torch.FloatTensor]] = None
    attentions: Optional[tuple[torch.FloatTensor]] = None


@dataclass
@auto_docstring
class CLIPSegImageSegmentationOutput(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
        Binary cross entropy loss for segmentation.
    logits (`torch.FloatTensor` of shape `(batch_size, height, width)`):
        Classification scores for each pixel.
    conditional_embeddings (`torch.FloatTensor` of shape `(batch_size, projection_dim)`):
        Conditional embeddings used for segmentation.
    pooled_output (`torch.FloatTensor` of shape `(batch_size, embed_dim)`):
        Pooled output of the [`CLIPSegVisionModel`].
    vision_model_output (`BaseModelOutputWithPooling`):
        The output of the [`CLIPSegVisionModel`].
    decoder_output (`CLIPSegDecoderOutput`):
        The output of the [`CLIPSegDecoder`].
    """

    loss: Optional[torch.FloatTensor] = None
    logits: Optional[torch.FloatTensor] = None
    conditional_embeddings: Optional[torch.FloatTensor] = None
    pooled_output: Optional[torch.FloatTensor] = None
    vision_model_output: BaseModelOutputWithPooling = None
    decoder_output: CLIPSegDecoderOutput = None

    def to_tuple(self) -> tuple[Any]:
        return tuple(
            self[k] if k not in ["vision_model_output", "decoder_output"] else getattr(self, k).to_tuple()
            for k in self.keys()
        )


class CLIPSegVisionEmbeddings(nn.Module):
    # Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings.__init__ with CLIP->CLIPSeg
    def __init__(self, config: CLIPSegVisionConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.image_size = config.image_size
        self.patch_size = config.patch_size

        self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))

        self.patch_embedding = nn.Conv2d(
            in_channels=config.num_channels,
            out_channels=self.embed_dim,
            kernel_size=self.patch_size,
            stride=self.patch_size,
            bias=False,
        )

        self.num_patches = (self.image_size // self.patch_size) ** 2
        self.num_positions = self.num_patches + 1
        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
        self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False)

    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
        """
        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
        images. This method is also adapted to support torch.jit tracing.

        Adapted from:
        - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
        - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
        """

        num_patches = embeddings.shape[1] - 1
        position_embedding = self.position_embedding.weight.unsqueeze(0)
        num_positions = position_embedding.shape[1] - 1

        # always interpolate when tracing to ensure the exported model works for dynamic input shapes
        if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
            return self.position_embedding(self.position_ids)

        class_pos_embed = position_embedding[:, :1]
        patch_pos_embed = position_embedding[:, 1:]

        dim = embeddings.shape[-1]

        new_height = height // self.patch_size
        new_width = width // self.patch_size

        sqrt_num_positions = torch_int(num_positions**0.5)
        patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)

        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed,
            size=(new_height, new_width),
            mode="bicubic",
            align_corners=False,
        )

        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)

        return torch.cat((class_pos_embed, patch_pos_embed), dim=1)

    def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding=True) -> torch.Tensor:
        batch_size, _, height, width = pixel_values.shape
        if not interpolate_pos_encoding and (height != self.image_size or width != self.image_size):
            raise ValueError(
                f"Input image size ({height}*{width}) doesn't match model ({self.image_size}*{self.image_size})."
            )
        patch_embeds = self.patch_embedding(pixel_values)  # shape = [*, width, grid, grid]
        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)

        class_embeds = self.class_embedding.expand(batch_size, 1, -1)
        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
        if interpolate_pos_encoding:
            embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
        else:
            embeddings = embeddings + self.position_embedding(self.position_ids)
        return embeddings


# Copied from transformers.models.clip.modeling_clip.CLIPTextEmbeddings with CLIP->CLIPSeg
class CLIPSegTextEmbeddings(nn.Module):
    def __init__(self, config: CLIPSegTextConfig):
        super().__init__()
        embed_dim = config.hidden_size

        self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
        self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim)

        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.register_buffer(
            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
        )

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
    ) -> torch.Tensor:
        seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
        max_position_embedding = self.position_embedding.weight.shape[0]

        if seq_length > max_position_embedding:
            raise ValueError(
                f"Sequence length must be less than max_position_embeddings (got `sequence length`: "
                f"{seq_length} and max_position_embeddings: {max_position_embedding}"
            )

        if position_ids is None:
            position_ids = self.position_ids[:, :seq_length]

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

        position_embeddings = self.position_embedding(position_ids)
        embeddings = inputs_embeds + position_embeddings

        return embeddings


# Copied from transformers.models.siglip.modeling_siglip.eager_attention_forward
def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: Optional[torch.Tensor],
    scaling: float,
    dropout: float = 0.0,
    **kwargs,
):
    attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling
    if attention_mask is not None:
        attn_weights = attn_weights + attention_mask

    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)

    attn_output = torch.matmul(attn_weights, value)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


class CLIPSegAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: Union[CLIPSegVisionConfig, CLIPSegTextConfig]):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.embed_dim // self.num_heads
        if self.head_dim * self.num_heads != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
                f" {self.num_heads})."
            )
        self.scale = self.head_dim**-0.5
        self.dropout = config.attention_dropout
        self.is_causal = False

        self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
        self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        causal_attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = False,
    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
        """Input shape: Batch x Time x Channel"""

        batch_size, seq_length, embed_dim = hidden_states.shape

        queries = self.q_proj(hidden_states)
        keys = self.k_proj(hidden_states)
        values = self.v_proj(hidden_states)

        queries = queries.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
        keys = keys.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
        values = values.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
        # CLIP text model uses both `causal_attention_mask` and `attention_mask`
        # in case FA2 kernel is called, `is_causal` should be inferred from `causal_attention_mask`
        if self.config._attn_implementation != "flash_attention_2":
            if attention_mask is not None and causal_attention_mask is not None:
                attention_mask = attention_mask + causal_attention_mask
            elif causal_attention_mask is not None:
                attention_mask = causal_attention_mask
        else:
            self.is_causal = causal_attention_mask is not None

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

        attn_output, attn_weights = attention_interface(
            self,
            queries,
            keys,
            values,
            attention_mask,
            is_causal=self.is_causal,
            scaling=self.scale,
            dropout=0.0 if not self.training else self.dropout,
        )

        attn_output = attn_output.reshape(batch_size, seq_length, embed_dim).contiguous()
        attn_output = self.out_proj(attn_output)
        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights


# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->CLIPSeg
class CLIPSegMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.activation_fn = ACT2FN[config.hidden_act]
        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.fc1(hidden_states)
        hidden_states = self.activation_fn(hidden_states)
        hidden_states = self.fc2(hidden_states)
        return hidden_states


# Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoderLayer with AltCLIP->CLIPSeg
class CLIPSegEncoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: CLIPSegConfig):
        super().__init__()
        self.embed_dim = config.hidden_size
        self.self_attn = CLIPSegAttention(config)
        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
        self.mlp = CLIPSegMLP(config)
        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        causal_attention_mask: torch.Tensor,
        output_attentions: Optional[bool] = False,
    ) -> tuple[torch.FloatTensor]:
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
                `(config.encoder_attention_heads,)`.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states

        hidden_states = self.layer_norm1(hidden_states)
        hidden_states, attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            causal_attention_mask=causal_attention_mask,
            output_attentions=output_attentions,
        )
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.layer_norm2(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs


@auto_docstring
class CLIPSegPreTrainedModel(PreTrainedModel):
    config: CLIPSegConfig
    base_model_prefix = "clip"
    supports_gradient_checkpointing = True

    def _init_weights(self, module):
        """Initialize the weights"""
        factor = self.config.initializer_factor
        if isinstance(module, CLIPSegTextEmbeddings):
            module.token_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
            module.position_embedding.weight.data.normal_(mean=0.0, std=factor * 0.02)
        elif isinstance(module, CLIPSegVisionEmbeddings):
            factor = self.config.initializer_factor
            nn.init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor)
            nn.init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor)
            nn.init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor)
        elif isinstance(module, CLIPSegAttention):
            factor = self.config.initializer_factor
            in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
            out_proj_std = (module.embed_dim**-0.5) * factor
            nn.init.normal_(module.q_proj.weight, std=in_proj_std)
            nn.init.normal_(module.k_proj.weight, std=in_proj_std)
            nn.init.normal_(module.v_proj.weight, std=in_proj_std)
            nn.init.normal_(module.out_proj.weight, std=out_proj_std)
        elif isinstance(module, CLIPSegMLP):
            factor = self.config.initializer_factor
            in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor
            fc_std = (2 * module.config.hidden_size) ** -0.5 * factor
            nn.init.normal_(module.fc1.weight, std=fc_std)
            nn.init.normal_(module.fc2.weight, std=in_proj_std)
        elif isinstance(module, CLIPSegModel):
            nn.init.normal_(
                module.text_projection.weight,
                std=module.text_embed_dim**-0.5 * self.config.initializer_factor,
            )
            nn.init.normal_(
                module.visual_projection.weight,
                std=module.vision_embed_dim**-0.5 * self.config.initializer_factor,
            )

        if isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        if isinstance(module, nn.Linear) and module.bias is not None:
            module.bias.data.zero_()


# Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoder with AltCLIP->CLIPSeg
class CLIPSegEncoder(nn.Module):
    """
    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
    [`CLIPSegEncoderLayer`].

    Args:
        config: CLIPSegConfig
    """

    def __init__(self, config: CLIPSegConfig):
        super().__init__()
        self.config = config
        self.layers = nn.ModuleList([CLIPSegEncoderLayer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False

    @can_return_tuple
    def forward(
        self,
        inputs_embeds,
        attention_mask: Optional[torch.Tensor] = None,
        causal_attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, BaseModelOutput]:
        r"""
        Args:
            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
                than the model's internal embedding lookup matrix.
            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.

                [What are attention masks?](../glossary#attention-mask)
            causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Causal mask for the text model. Mask values selected in `[0, 1]`:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.

                [What are attention masks?](../glossary#attention-mask)
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        encoder_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

        hidden_states = inputs_embeds
        for idx, encoder_layer in enumerate(self.layers):
            if output_hidden_states:
                encoder_states = encoder_states + (hidden_states,)
            layer_outputs = encoder_layer(
                hidden_states,
                attention_mask,
                causal_attention_mask,
                output_attentions=output_attentions,
            )
            hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

        if output_hidden_states:
            encoder_states = encoder_states + (hidden_states,)

        return BaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
        )


class CLIPSegTextTransformer(nn.Module):
    def __init__(self, config: CLIPSegTextConfig):
        super().__init__()
        self.config = config
        embed_dim = config.hidden_size
        self.embeddings = CLIPSegTextEmbeddings(config)
        self.encoder = CLIPSegEncoder(config)
        self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)

        # For `pooled_output` computation
        self.eos_token_id = config.eos_token_id

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, BaseModelOutputWithPooling]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if input_ids is None:
            raise ValueError("You have to specify input_ids")

        input_shape = input_ids.size()
        input_ids = input_ids.view(-1, input_shape[-1])

        hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids)

        # CLIPSeg's text model uses causal mask, prepare it here.
        # https://github.com/openai/CLIPSeg/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clipseg/model.py#L324
        causal_attention_mask = _create_4d_causal_attention_mask(
            input_shape, hidden_states.dtype, device=hidden_states.device
        )
        # expand attention_mask
        if attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)

        encoder_outputs = self.encoder(
            inputs_embeds=hidden_states,
            attention_mask=attention_mask,
            causal_attention_mask=causal_attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        last_hidden_state = encoder_outputs[0]
        last_hidden_state = self.final_layer_norm(last_hidden_state)

        if self.eos_token_id == 2:
            # The `eos_token_id` was incorrect before PR #24773: Let's keep what have been done here.
            # A CLIPSeg model with such `eos_token_id` in the config can't work correctly with extra new tokens added
            # ------------------------------------------------------------
            # text_embeds.shape = [batch_size, sequence_length, transformer.width]
            # take features from the eot embedding (eot_token is the highest number in each sequence)
            # casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14
            pooled_output = last_hidden_state[
                torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
                input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax(dim=-1),
            ]
        else:
            # The config gets updated `eos_token_id` from PR #24773 (so the use of extra new tokens is possible)
            pooled_output = last_hidden_state[
                torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
                # We need to get the first position of `eos_token_id` value (`pad_token_ids` might equal to `eos_token_id`)
                # Note: we assume each sequence (along batch dim.) contains an  `eos_token_id` (e.g. prepared by the tokenizer)
                (input_ids.to(dtype=torch.int, device=last_hidden_state.device) == self.eos_token_id)
                .int()
                .argmax(dim=-1),
            ]

        if not return_dict:
            return (last_hidden_state, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPooling(
            last_hidden_state=last_hidden_state,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


class CLIPSegTextModel(CLIPSegPreTrainedModel):
    config: CLIPSegTextConfig

    _no_split_modules = ["CLIPSegTextEmbeddings", "CLIPSegEncoderLayer"]

    def __init__(self, config: CLIPSegTextConfig):
        super().__init__(config)
        self.text_model = CLIPSegTextTransformer(config)
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self) -> nn.Module:
        return self.text_model.embeddings.token_embedding

    def set_input_embeddings(self, value):
        self.text_model.embeddings.token_embedding = value

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, BaseModelOutputWithPooling]:
        r"""
        Examples:

        ```python
        >>> from transformers import AutoTokenizer, CLIPSegTextModel

        >>> tokenizer = AutoTokenizer.from_pretrained("CIDAS/clipseg-rd64-refined")
        >>> model = CLIPSegTextModel.from_pretrained("CIDAS/clipseg-rd64-refined")

        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")

        >>> outputs = model(**inputs)
        >>> last_hidden_state = outputs.last_hidden_state
        >>> pooled_output = outputs.pooler_output  # pooled (EOS token) states
        ```"""
        return self.text_model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )


class CLIPSegVisionTransformer(nn.Module):
    # Copied from transformers.models.altclip.modeling_altclip.AltCLIPVisionTransformer.__init__ with AltCLIP->CLIPSeg
    def __init__(self, config: CLIPSegVisionConfig):
        super().__init__()
        self.config = config
        embed_dim = config.hidden_size

        self.embeddings = CLIPSegVisionEmbeddings(config)
        self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
        self.encoder = CLIPSegEncoder(config)
        self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)

    @auto_docstring
    def forward(
        self,
        pixel_values: Optional[torch.FloatTensor],
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        interpolate_pos_encoding: Optional[bool] = True,
    ) -> Union[tuple, BaseModelOutputWithPooling]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
        hidden_states = self.pre_layrnorm(hidden_states)

        encoder_outputs = self.encoder(
            inputs_embeds=hidden_states,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        last_hidden_state = encoder_outputs[0]
        pooled_output = last_hidden_state[:, 0, :]
        pooled_output = self.post_layernorm(pooled_output)

        if not return_dict:
            return (last_hidden_state, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPooling(
            last_hidden_state=last_hidden_state,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


class CLIPSegVisionModel(CLIPSegPreTrainedModel):
    config: CLIPSegVisionConfig
    main_input_name = "pixel_values"

    def __init__(self, config: CLIPSegVisionConfig):
        super().__init__(config)
        self.vision_model = CLIPSegVisionTransformer(config)
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self) -> nn.Module:
        return self.vision_model.embeddings.patch_embedding

    @auto_docstring
    def forward(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        interpolate_pos_encoding: Optional[bool] = True,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, BaseModelOutputWithPooling]:
        r"""
        Examples:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, CLIPSegVisionModel

        >>> processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
        >>> model = CLIPSegVisionModel.from_pretrained("CIDAS/clipseg-rd64-refined")

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> inputs = processor(images=image, return_tensors="pt")

        >>> outputs = model(**inputs)
        >>> last_hidden_state = outputs.last_hidden_state
        >>> pooled_output = outputs.pooler_output  # pooled CLS states
        ```"""
        return self.vision_model(
            pixel_values=pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            interpolate_pos_encoding=interpolate_pos_encoding,
            return_dict=return_dict,
        )


@auto_docstring
class CLIPSegModel(CLIPSegPreTrainedModel):
    config: CLIPSegConfig

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

        if not isinstance(config.text_config, CLIPSegTextConfig):
            raise TypeError(
                "config.text_config is expected to be of type CLIPSegTextConfig but is of type"
                f" {type(config.text_config)}."
            )

        if not isinstance(config.vision_config, CLIPSegVisionConfig):
            raise TypeError(
                "config.vision_config is expected to be of type CLIPSegVisionConfig but is of type"
                f" {type(config.vision_config)}."
            )

        text_config = config.text_config
        vision_config = config.vision_config
        # The module using it is not a PreTrainedModel subclass so we need this
        text_config._attn_implementation = config._attn_implementation
        # The module using it is not a PreTrainedModel subclass so we need this
        vision_config._attn_implementation = config._attn_implementation

        self.projection_dim = config.projection_dim
        self.text_embed_dim = text_config.hidden_size
        self.vision_embed_dim = vision_config.hidden_size

        self.text_model = CLIPSegTextTransformer(text_config)
        self.vision_model = CLIPSegVisionTransformer(vision_config)

        self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)
        self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False)
        self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))

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

    @filter_out_non_signature_kwargs()
    @auto_docstring
    def get_text_features(
        self,
        input_ids: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
    ) -> torch.FloatTensor:
        r"""
        Returns:
            text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
            applying the projection layer to the pooled output of [`CLIPSegTextModel`].

        Examples:

        ```python
        >>> import torch
        >>> from transformers import AutoTokenizer, CLIPSegModel

        >>> tokenizer = AutoTokenizer.from_pretrained("CIDAS/clipseg-rd64-refined")
        >>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined")

        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
        >>> with torch.inference_mode():
        ...     text_features = model.get_text_features(**inputs)
        ```"""
        text_outputs: BaseModelOutputWithPooling = self.text_model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
        )
        pooled_output = text_outputs.pooler_output
        text_features = self.text_projection(pooled_output)

        return text_features

    @filter_out_non_signature_kwargs()
    @auto_docstring
    def get_image_features(
        self,
        pixel_values: torch.FloatTensor,
        interpolate_pos_encoding: bool = True,
    ) -> torch.FloatTensor:
        r"""
        Returns:
            image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
            applying the projection layer to the pooled output of [`CLIPSegVisionModel`].

        Examples:

        ```python
        >>> import torch
        >>> from transformers import AutoProcessor, CLIPSegModel
        >>> from transformers.image_utils import load_image

        >>> processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
        >>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined")

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = load_image(url)

        >>> inputs = processor(images=image, return_tensors="pt")

        >>> with torch.inference_mode():
        ...     image_features = model.get_image_features(**inputs)
        ```"""
        vision_outputs: BaseModelOutputWithPooling = self.vision_model(
            pixel_values=pixel_values,
            interpolate_pos_encoding=interpolate_pos_encoding,
        )
        pooled_output = vision_outputs.pooler_output
        image_features = self.visual_projection(pooled_output)

        return image_features

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        pixel_values: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        return_loss: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        interpolate_pos_encoding: bool = True,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, CLIPSegOutput]:
        r"""
        return_loss (`bool`, *optional*):
            Whether or not to return the contrastive loss.

        Examples:

        ```python
        >>> import torch
        >>> from transformers import AutoProcessor, CLIPSegModel
        >>> from transformers.image_utils import load_image

        >>> processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
        >>> model = CLIPSegModel.from_pretrained("CIDAS/clipseg-rd64-refined")

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = load_image(url)

        >>> inputs = processor(
        ...     text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
        ... )

        >>> with torch.inference_mode():
        ...     outputs = model(**inputs)
        >>> logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
        >>> probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
        ```"""
        # Use CLIPSEG model's config for some fields (if specified) instead of those of vision & text components.
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        vision_outputs = self.vision_model(
            pixel_values=pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            interpolate_pos_encoding=interpolate_pos_encoding,
            return_dict=return_dict,
        )

        text_outputs = self.text_model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        image_embeds = vision_outputs[1]
        image_embeds = self.visual_projection(image_embeds)

        text_embeds = text_outputs[1]
        text_embeds = self.text_projection(text_embeds)

        # normalized features
        image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)
        text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)

        # cosine similarity as logits
        logit_scale = self.logit_scale.exp()
        logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * logit_scale
        logits_per_image = logits_per_text.t()

        loss = None
        if return_loss:
            loss = clipseg_loss(logits_per_text)

        if not return_dict:
            output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
            return ((loss,) + output) if loss is not None else output

        return CLIPSegOutput(
            loss=loss,
            logits_per_image=logits_per_image,
            logits_per_text=logits_per_text,
            text_embeds=text_embeds,
            image_embeds=image_embeds,
            text_model_output=text_outputs,
            vision_model_output=vision_outputs,
        )


class CLIPSegDecoderLayer(nn.Module):
    """
    CLIPSeg decoder layer, which is identical to `CLIPSegEncoderLayer`, except that normalization is applied after
    self-attention/MLP, rather than before.
    """

    # Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoderLayer.__init__ with AltCLIP->CLIPSeg
    def __init__(self, config: CLIPSegConfig):
        super().__init__()
        self.embed_dim = config.hidden_size
        self.self_attn = CLIPSegAttention(config)
        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
        self.mlp = CLIPSegMLP(config)
        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        causal_attention_mask: torch.Tensor,
        output_attentions: Optional[bool] = False,
    ) -> tuple[torch.FloatTensor]:
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
                `(config.encoder_attention_heads,)`.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states

        hidden_states, attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            causal_attention_mask=causal_attention_mask,
            output_attentions=output_attentions,
        )

        hidden_states = residual + hidden_states
        hidden_states = self.layer_norm1(hidden_states)

        residual = hidden_states
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states
        hidden_states = self.layer_norm2(hidden_states)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        return outputs


class CLIPSegDecoder(CLIPSegPreTrainedModel):
    def __init__(self, config: CLIPSegConfig):
        super().__init__(config)

        self.conditional_layer = config.conditional_layer

        self.film_mul = nn.Linear(config.projection_dim, config.reduce_dim)
        self.film_add = nn.Linear(config.projection_dim, config.reduce_dim)

        if config.use_complex_transposed_convolution:
            transposed_kernels = (config.vision_config.patch_size // 4, config.vision_config.patch_size // 4)

            self.transposed_convolution = nn.Sequential(
                nn.Conv2d(config.reduce_dim, config.reduce_dim, kernel_size=3, padding=1),
                nn.ReLU(),
                nn.ConvTranspose2d(
                    config.reduce_dim,
                    config.reduce_dim // 2,
                    kernel_size=transposed_kernels[0],
                    stride=transposed_kernels[0],
                ),
                nn.ReLU(),
                nn.ConvTranspose2d(
                    config.reduce_dim // 2, 1, kernel_size=transposed_kernels[1], stride=transposed_kernels[1]
                ),
            )
        else:
            self.transposed_convolution = nn.ConvTranspose2d(
                config.reduce_dim, 1, config.vision_config.patch_size, stride=config.vision_config.patch_size
            )

        depth = len(config.extract_layers)
        self.reduces = nn.ModuleList(
            [nn.Linear(config.vision_config.hidden_size, config.reduce_dim) for _ in range(depth)]
        )

        decoder_config = copy.deepcopy(config.vision_config)
        decoder_config.hidden_size = config.reduce_dim
        decoder_config.num_attention_heads = config.decoder_num_attention_heads
        decoder_config.intermediate_size = config.decoder_intermediate_size
        decoder_config.hidden_act = "relu"
        self.layers = nn.ModuleList([CLIPSegDecoderLayer(decoder_config) for _ in range(len(config.extract_layers))])

    def forward(
        self,
        hidden_states: tuple[torch.Tensor],
        conditional_embeddings: torch.Tensor,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = True,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

        activations = hidden_states[::-1]

        output = None
        for i, (activation, layer, reduce) in enumerate(zip(activations, self.layers, self.reduces)):
            if output is not None:
                output = reduce(activation) + output
            else:
                output = reduce(activation)

            if i == self.conditional_layer:
                output = self.film_mul(conditional_embeddings) * output.permute(1, 0, 2) + self.film_add(
                    conditional_embeddings
                )
                output = output.permute(1, 0, 2)

            layer_outputs = layer(
                output, attention_mask=None, causal_attention_mask=None, output_attentions=output_attentions
            )

            output = layer_outputs[0]

            if output_hidden_states:
                all_hidden_states += (output,)

            if output_attentions:
                all_attentions += (layer_outputs[1],)

        output = output[:, 1:, :].permute(0, 2, 1)  # remove cls token and reshape to [batch_size, reduce_dim, seq_len]

        size = int(math.sqrt(output.shape[2]))

        batch_size = conditional_embeddings.shape[0]
        output = output.view(batch_size, output.shape[1], size, size)

        logits = self.transposed_convolution(output).squeeze(1)

        if not return_dict:
            return tuple(v for v in [logits, all_hidden_states, all_attentions] if v is not None)

        return CLIPSegDecoderOutput(
            logits=logits,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
        )


@auto_docstring(
    custom_intro="""
    CLIPSeg model with a Transformer-based decoder on top for zero-shot and one-shot image segmentation.
    """
)
class CLIPSegForImageSegmentation(CLIPSegPreTrainedModel):
    config: CLIPSegConfig

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

        self.config = config

        self.clip = CLIPSegModel(config)
        self.extract_layers = config.extract_layers

        self.decoder = CLIPSegDecoder(config)

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

    def get_conditional_embeddings(
        self,
        batch_size: Optional[int] = None,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        conditional_pixel_values: Optional[torch.Tensor] = None,
    ):
        if input_ids is not None:
            # compute conditional embeddings from texts
            if len(input_ids) != batch_size:
                raise ValueError("Make sure to pass as many prompt texts as there are query images")
            with torch.no_grad():
                conditional_embeddings = self.clip.get_text_features(
                    input_ids, attention_mask=attention_mask, position_ids=position_ids
                )
        elif conditional_pixel_values is not None:
            # compute conditional embeddings from images
            if len(conditional_pixel_values) != batch_size:
                raise ValueError("Make sure to pass as many prompt images as there are query images")
            with torch.no_grad():
                conditional_embeddings = self.clip.get_image_features(conditional_pixel_values)
        else:
            raise ValueError(
                "Invalid conditional, should be either provided as `input_ids` or `conditional_pixel_values`"
            )

        return conditional_embeddings

    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.FloatTensor] = None,
        pixel_values: Optional[torch.FloatTensor] = None,
        conditional_pixel_values: Optional[torch.FloatTensor] = None,
        conditional_embeddings: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        interpolate_pos_encoding: bool = True,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple, CLIPSegOutput]:
        r"""
        conditional_pixel_values (`torch.FloatTensor`, *optional*):
            The pixel values of the conditional images.
        conditional_embeddings (`torch.FloatTensor` of shape `(batch_size, config.projection_dim)`, *optional*):
            The conditional embeddings for the query images. If provided, the model will use this instead of computing
            the embeddings from the conditional_pixel_values.
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Examples:

        ```python
        >>> import torch
        >>> from transformers import AutoProcessor, CLIPSegForImageSegmentation
        >>> from transformers.image_utils import load_image

        >>> processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
        >>> model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = load_image(url)

        >>> texts = ["a cat", "a remote", "a blanket"]
        >>> inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt")

        >>> with torch.inference_mode():
        ...     outputs = model(**inputs)

        >>> logits = outputs.logits
        >>> print(logits.shape)
        torch.Size([3, 352, 352])
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # step 1: forward the query images through the frozen CLIP vision encoder
        with torch.no_grad():
            vision_outputs = self.clip.vision_model(
                pixel_values=pixel_values,
                output_attentions=output_attentions,
                output_hidden_states=True,  # we need the intermediate hidden states
                interpolate_pos_encoding=interpolate_pos_encoding,
                return_dict=return_dict,
            )
            pooled_output = self.clip.visual_projection(vision_outputs[1])

            hidden_states = vision_outputs.hidden_states if return_dict else vision_outputs[2]
            # we add +1 here as the hidden states also include the initial embeddings
            activations = [hidden_states[i + 1] for i in self.extract_layers]

            # update vision_outputs
            if return_dict:
                vision_outputs = BaseModelOutputWithPooling(
                    last_hidden_state=vision_outputs.last_hidden_state,
                    pooler_output=vision_outputs.pooler_output,
                    hidden_states=vision_outputs.hidden_states if output_hidden_states else None,
                    attentions=vision_outputs.attentions,
                )
            else:
                vision_outputs = (
                    vision_outputs[:2] + vision_outputs[3:] if not output_hidden_states else vision_outputs
                )

        # step 2: compute conditional embeddings, either from text, images or an own provided embedding
        if conditional_embeddings is None:
            conditional_embeddings = self.get_conditional_embeddings(
                batch_size=pixel_values.shape[0],
                input_ids=input_ids,
                attention_mask=attention_mask,
                position_ids=position_ids,
                conditional_pixel_values=conditional_pixel_values,
            )
        else:
            if conditional_embeddings.shape[0] != pixel_values.shape[0]:
                raise ValueError(
                    "Make sure to pass as many conditional embeddings as there are query images in the batch"
                )
            if conditional_embeddings.shape[1] != self.config.projection_dim:
                raise ValueError(
                    "Make sure that the feature dimension of the conditional embeddings matches"
                    " `config.projection_dim`."
                )

        # step 3: forward both the pooled output and the activations through the lightweight decoder to predict masks
        decoder_outputs = self.decoder(
            activations,
            conditional_embeddings,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        logits = decoder_outputs.logits if return_dict else decoder_outputs[0]

        loss = None
        if labels is not None:
            # move labels to the correct device to enable PP
            labels = labels.to(logits.device)
            loss_fn = nn.BCEWithLogitsLoss()
            loss = loss_fn(logits, labels)

        if not return_dict:
            output = (logits, conditional_embeddings, pooled_output, vision_outputs, decoder_outputs)
            return ((loss,) + output) if loss is not None else output

        return CLIPSegImageSegmentationOutput(
            loss=loss,
            logits=logits,
            conditional_embeddings=conditional_embeddings,
            pooled_output=pooled_output,
            vision_model_output=vision_outputs,
            decoder_output=decoder_outputs,
        )


__all__ = [
    "CLIPSegModel",
    "CLIPSegPreTrainedModel",
    "CLIPSegTextModel",
    "CLIPSegVisionModel",
    "CLIPSegForImageSegmentation",
]