# coding=utf-8
# Copyright 2024 TikTok and The HuggingFace Inc. team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""PyTorch Depth Anything model."""

from typing import Optional, Union

import torch
from torch import nn

from ...modeling_outputs import DepthEstimatorOutput
from ...modeling_utils import PreTrainedModel
from ...utils import auto_docstring, logging
from ...utils.backbone_utils import load_backbone
from .configuration_depth_anything import DepthAnythingConfig


logger = logging.get_logger(__name__)

# General docstring


class DepthAnythingReassembleLayer(nn.Module):
    def __init__(self, config, channels, factor):
        super().__init__()
        self.projection = nn.Conv2d(in_channels=config.reassemble_hidden_size, out_channels=channels, kernel_size=1)

        # up/down sampling depending on factor
        if factor > 1:
            self.resize = nn.ConvTranspose2d(channels, channels, kernel_size=factor, stride=factor, padding=0)
        elif factor == 1:
            self.resize = nn.Identity()
        elif factor < 1:
            # so should downsample
            self.resize = nn.Conv2d(channels, channels, kernel_size=3, stride=int(1 / factor), padding=1)

    # Copied from transformers.models.dpt.modeling_dpt.DPTReassembleLayer.forward
    def forward(self, hidden_state):
        hidden_state = self.projection(hidden_state)
        hidden_state = self.resize(hidden_state)

        return hidden_state


class DepthAnythingReassembleStage(nn.Module):
    """
    This class reassembles the hidden states of the backbone into image-like feature representations at various
    resolutions.

    This happens in 3 stages:
    1. Take the patch embeddings and reshape them to image-like feature representations.
    2. Project the channel dimension of the hidden states according to `config.neck_hidden_sizes`.
    3. Resizing the spatial dimensions (height, width).

    Args:
        config (`[DepthAnythingConfig]`):
            Model configuration class defining the model architecture.
    """

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

        self.config = config
        self.layers = nn.ModuleList()
        for channels, factor in zip(config.neck_hidden_sizes, config.reassemble_factors):
            self.layers.append(DepthAnythingReassembleLayer(config, channels=channels, factor=factor))

    def forward(self, hidden_states: list[torch.Tensor], patch_height=None, patch_width=None) -> list[torch.Tensor]:
        """
        Args:
            hidden_states (`list[torch.FloatTensor]`, each of shape `(batch_size, sequence_length + 1, hidden_size)`):
                List of hidden states from the backbone.
        """
        out = []

        for i, hidden_state in enumerate(hidden_states):
            # reshape to (batch_size, num_channels, height, width)
            hidden_state = hidden_state[:, 1:]
            batch_size, _, num_channels = hidden_state.shape
            hidden_state = hidden_state.reshape(batch_size, patch_height, patch_width, num_channels)
            hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous()
            hidden_state = self.layers[i](hidden_state)
            out.append(hidden_state)

        return out


class DepthAnythingPreActResidualLayer(nn.Module):
    """
    ResidualConvUnit, pre-activate residual unit.

    Args:
        config (`[DepthAnythingConfig]`):
            Model configuration class defining the model architecture.
    """

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

        self.activation1 = nn.ReLU()
        self.convolution1 = nn.Conv2d(
            config.fusion_hidden_size,
            config.fusion_hidden_size,
            kernel_size=3,
            stride=1,
            padding=1,
            bias=True,
        )

        self.activation2 = nn.ReLU()
        self.convolution2 = nn.Conv2d(
            config.fusion_hidden_size,
            config.fusion_hidden_size,
            kernel_size=3,
            stride=1,
            padding=1,
            bias=True,
        )

    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
        residual = hidden_state
        hidden_state = self.activation1(hidden_state)
        hidden_state = self.convolution1(hidden_state)
        hidden_state = self.activation2(hidden_state)
        hidden_state = self.convolution2(hidden_state)

        return hidden_state + residual


class DepthAnythingFeatureFusionLayer(nn.Module):
    """Feature fusion layer, merges feature maps from different stages.

    Args:
        config (`[DepthAnythingConfig]`):
            Model configuration class defining the model architecture.
    """

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

        self.projection = nn.Conv2d(config.fusion_hidden_size, config.fusion_hidden_size, kernel_size=1, bias=True)

        self.residual_layer1 = DepthAnythingPreActResidualLayer(config)
        self.residual_layer2 = DepthAnythingPreActResidualLayer(config)

    def forward(self, hidden_state, residual=None, size=None):
        if residual is not None:
            if hidden_state.shape != residual.shape:
                residual = nn.functional.interpolate(
                    residual, size=(hidden_state.shape[2], hidden_state.shape[3]), mode="bilinear", align_corners=False
                )
            hidden_state = hidden_state + self.residual_layer1(residual)

        hidden_state = self.residual_layer2(hidden_state)

        modifier = {"scale_factor": 2} if size is None else {"size": size}

        hidden_state = nn.functional.interpolate(
            hidden_state,
            **modifier,
            mode="bilinear",
            align_corners=True,
        )
        hidden_state = self.projection(hidden_state)

        return hidden_state


class DepthAnythingFeatureFusionStage(nn.Module):
    # Copied from transformers.models.dpt.modeling_dpt.DPTFeatureFusionStage.__init__ with DPT->DepthAnything
    def __init__(self, config: DepthAnythingConfig):
        super().__init__()
        self.layers = nn.ModuleList()
        for _ in range(len(config.neck_hidden_sizes)):
            self.layers.append(DepthAnythingFeatureFusionLayer(config))

    def forward(self, hidden_states, size=None):
        # reversing the hidden_states, we start from the last
        hidden_states = hidden_states[::-1]

        fused_hidden_states = []
        fused_hidden_state = None

        for idx, (hidden_state, layer) in enumerate(zip(hidden_states, self.layers)):
            size = hidden_states[idx + 1].shape[2:] if idx != (len(hidden_states) - 1) else None

            if fused_hidden_state is None:
                # first layer only uses the last hidden_state
                fused_hidden_state = layer(hidden_state, size=size)
            else:
                fused_hidden_state = layer(fused_hidden_state, hidden_state, size=size)

            fused_hidden_states.append(fused_hidden_state)

        return fused_hidden_states


# Modified from transformers.models.dpt.modeling_dpt.DPTPreTrainedModel with DPT->DepthAnything,dpt->depth_anything
# avoiding sdpa and flash_attn_2 support, it's done in the backend
@auto_docstring
class DepthAnythingPreTrainedModel(PreTrainedModel):
    config: DepthAnythingConfig
    base_model_prefix = "depth_anything"
    main_input_name = "pixel_values"
    input_modalities = "image"
    supports_gradient_checkpointing = True

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, (nn.Linear, nn.Conv2d, nn.ConvTranspose2d)):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)


class DepthAnythingNeck(nn.Module):
    """
    DepthAnythingNeck. A neck is a module that is normally used between the backbone and the head. It takes a list of tensors as
    input and produces another list of tensors as output. For DepthAnything, it includes 2 stages:

    * DepthAnythingReassembleStage
    * DepthAnythingFeatureFusionStage.

    Args:
        config (dict): config dict.
    """

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

        self.reassemble_stage = DepthAnythingReassembleStage(config)

        self.convs = nn.ModuleList()
        for channel in config.neck_hidden_sizes:
            self.convs.append(nn.Conv2d(channel, config.fusion_hidden_size, kernel_size=3, padding=1, bias=False))

        # fusion
        self.fusion_stage = DepthAnythingFeatureFusionStage(config)

    def forward(self, hidden_states: list[torch.Tensor], patch_height=None, patch_width=None) -> list[torch.Tensor]:
        """
        Args:
            hidden_states (`list[torch.FloatTensor]`, each of shape `(batch_size, sequence_length, hidden_size)` or `(batch_size, hidden_size, height, width)`):
                List of hidden states from the backbone.
        """
        if not isinstance(hidden_states, (tuple, list)):
            raise TypeError("hidden_states should be a tuple or list of tensors")

        if len(hidden_states) != len(self.config.neck_hidden_sizes):
            raise ValueError("The number of hidden states should be equal to the number of neck hidden sizes.")

        # postprocess hidden states
        hidden_states = self.reassemble_stage(hidden_states, patch_height, patch_width)

        features = [self.convs[i](feature) for i, feature in enumerate(hidden_states)]

        # fusion blocks
        output = self.fusion_stage(features)

        return output


class DepthAnythingDepthEstimationHead(nn.Module):
    """
    Output head consisting of 3 convolutional layers. It progressively halves the feature dimension and upsamples
    the predictions to the input resolution after the first convolutional layer (details can be found in the DPT paper's
    supplementary material). The final activation function is either ReLU or Sigmoid, depending on the depth estimation
    type (relative or metric). For metric depth estimation, the output is scaled by the maximum depth used during pretraining.
    """

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

        self.head_in_index = config.head_in_index
        self.patch_size = config.patch_size

        features = config.fusion_hidden_size
        self.conv1 = nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(features // 2, config.head_hidden_size, kernel_size=3, stride=1, padding=1)
        self.activation1 = nn.ReLU()
        self.conv3 = nn.Conv2d(config.head_hidden_size, 1, kernel_size=1, stride=1, padding=0)
        if config.depth_estimation_type == "relative":
            self.activation2 = nn.ReLU()
        elif config.depth_estimation_type == "metric":
            self.activation2 = nn.Sigmoid()
        else:
            raise ValueError(f"Unknown depth estimation type: {config.depth_estimation_type}")
        self.max_depth = config.max_depth

    def forward(self, hidden_states: list[torch.Tensor], patch_height, patch_width) -> torch.Tensor:
        hidden_states = hidden_states[self.head_in_index]

        predicted_depth = self.conv1(hidden_states)
        predicted_depth = nn.functional.interpolate(
            predicted_depth,
            (int(patch_height * self.patch_size), int(patch_width * self.patch_size)),
            mode="bilinear",
            align_corners=True,
        )
        predicted_depth = self.conv2(predicted_depth)
        predicted_depth = self.activation1(predicted_depth)
        predicted_depth = self.conv3(predicted_depth)
        predicted_depth = self.activation2(predicted_depth) * self.max_depth
        predicted_depth = predicted_depth.squeeze(dim=1)  # shape (batch_size, height, width)

        return predicted_depth


@auto_docstring(
    custom_intro="""
    Depth Anything Model with a depth estimation head on top (consisting of 3 convolutional layers) e.g. for KITTI, NYUv2.
    """
)
class DepthAnythingForDepthEstimation(DepthAnythingPreTrainedModel):
    _no_split_modules = ["DPTViTEmbeddings"]

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

        self.backbone = load_backbone(config)
        self.neck = DepthAnythingNeck(config)
        self.head = DepthAnythingDepthEstimationHead(config)

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

    @auto_docstring
    def forward(
        self,
        pixel_values: torch.FloatTensor,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[tuple[torch.Tensor], DepthEstimatorOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
            Ground truth depth estimation maps for computing the loss.

        Examples:
        ```python
        >>> from transformers import AutoImageProcessor, AutoModelForDepthEstimation
        >>> import torch
        >>> import numpy as np
        >>> from PIL import Image
        >>> import requests

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

        >>> image_processor = AutoImageProcessor.from_pretrained("LiheYoung/depth-anything-small-hf")
        >>> model = AutoModelForDepthEstimation.from_pretrained("LiheYoung/depth-anything-small-hf")

        >>> # prepare image for the model
        >>> inputs = image_processor(images=image, return_tensors="pt")

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

        >>> # interpolate to original size
        >>> post_processed_output = image_processor.post_process_depth_estimation(
        ...     outputs,
        ...     target_sizes=[(image.height, image.width)],
        ... )

        >>> # visualize the prediction
        >>> predicted_depth = post_processed_output[0]["predicted_depth"]
        >>> depth = predicted_depth * 255 / predicted_depth.max()
        >>> depth = depth.detach().cpu().numpy()
        >>> depth = Image.fromarray(depth.astype("uint8"))
        ```"""
        loss = None
        if labels is not None:
            raise NotImplementedError("Training is not implemented yet")

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

        outputs = self.backbone.forward_with_filtered_kwargs(
            pixel_values, output_hidden_states=output_hidden_states, output_attentions=output_attentions
        )
        hidden_states = outputs.feature_maps

        _, _, height, width = pixel_values.shape
        patch_size = self.config.patch_size
        patch_height = height // patch_size
        patch_width = width // patch_size

        hidden_states = self.neck(hidden_states, patch_height, patch_width)

        predicted_depth = self.head(hidden_states, patch_height, patch_width)

        if not return_dict:
            if output_hidden_states:
                output = (predicted_depth,) + outputs[1:]
            else:
                output = (predicted_depth,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return DepthEstimatorOutput(
            loss=loss,
            predicted_depth=predicted_depth,
            hidden_states=outputs.hidden_states if output_hidden_states else None,
            attentions=outputs.attentions,
        )


__all__ = ["DepthAnythingForDepthEstimation", "DepthAnythingPreTrainedModel"]
