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# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import Callable, Optional, Union

import numpy as np
import torch
from torch import nn
from torch.nn.utils.rnn import pad_sequence

from ...activations import ACT2FN
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import ModelOutput, TransformersKwargs, auto_docstring
from ...utils.generic import can_return_tuple
from ..auto.modeling_auto import AutoModelForKeypointDetection
from .configuration_lightglue import LightGlueConfig


@dataclass
@auto_docstring(
    custom_intro="""
    Base class for outputs of LightGlue keypoint matching models. Due to the nature of keypoint detection and matching,
    the number of keypoints is not fixed and can vary from image to image, which makes batching non-trivial. In the
    batch of images, the maximum number of matches is set as the dimension of the matches and matching scores. The mask
    tensor is used to indicate which values in the keypoints, matches, matching_scores and prune tensors are keypoint
    matching information.
    """
)
class LightGlueKeypointMatchingOutput(ModelOutput):
    r"""
    loss (`torch.FloatTensor` of shape `(1,)`, *optional*):
        Loss computed during training.
    matches (`torch.FloatTensor` of shape `(batch_size, 2, num_matches)`):
        Index of keypoint matched in the other image.
    matching_scores (`torch.FloatTensor` of shape `(batch_size, 2, num_matches)`):
        Scores of predicted matches.
    keypoints (`torch.FloatTensor` of shape `(batch_size, num_keypoints, 2)`):
        Absolute (x, y) coordinates of predicted keypoints in a given image.
    prune (`torch.IntTensor` of shape `(batch_size, num_keypoints)`):
        Pruning mask indicating which keypoints are removed and at which layer.
    mask (`torch.BoolTensor` of shape `(batch_size, num_keypoints)`):
        Mask indicating which values in matches, matching_scores, keypoints and prune are keypoint matching
        information.
    hidden_states (`Tuple[torch.FloatTensor, ...]`, *optional*):
        Tuple of `torch.FloatTensor` (one for the output of each stage) of shape `(batch_size, 2, num_channels,
        num_keypoints)` returned when `output_hidden_states=True` is passed or when
        `config.output_hidden_states=True`
    attentions (`Tuple[torch.FloatTensor, ...]`, *optional*):
        Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, 2, num_heads, num_keypoints,
        num_keypoints)` returned when `output_attentions=True` is passed or when
        `config.output_attentions=True`
    """

    loss: Optional[torch.FloatTensor] = None
    matches: Optional[torch.FloatTensor] = None
    matching_scores: Optional[torch.FloatTensor] = None
    keypoints: Optional[torch.FloatTensor] = None
    prune: Optional[torch.IntTensor] = None
    mask: Optional[torch.FloatTensor] = None
    hidden_states: Optional[tuple[torch.FloatTensor]] = None
    attentions: Optional[tuple[torch.FloatTensor]] = None


class LightGluePositionalEncoder(nn.Module):
    def __init__(self, config: LightGlueConfig):
        super().__init__()
        self.projector = nn.Linear(2, config.descriptor_dim // config.num_attention_heads // 2, bias=False)

    def forward(
        self, keypoints: torch.Tensor, output_hidden_states: Optional[bool] = False
    ) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:
        projected_keypoints = self.projector(keypoints)
        embeddings = projected_keypoints.repeat_interleave(2, dim=-1)
        cosines = torch.cos(embeddings)
        sines = torch.sin(embeddings)
        embeddings = (cosines, sines)
        output = (embeddings, projected_keypoints) if output_hidden_states else (embeddings,)
        return output


def rotate_half(x):
    # Split and rotate. Note that this function is different from e.g. Llama.
    x1 = x[..., ::2]
    x2 = x[..., 1::2]
    rot_x = torch.stack([-x2, x1], dim=-1).flatten(-2)
    return rot_x


def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        position_ids (`torch.Tensor`, *optional*):
            Deprecated and unused.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    dtype = q.dtype
    q = q.float()
    k = k.float()
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed.to(dtype=dtype), k_embed.to(dtype=dtype)


def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


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: Unpack[TransformersKwargs],
):
    key_states = repeat_kv(key, module.num_key_value_groups)
    value_states = repeat_kv(value, module.num_key_value_groups)

    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
    if attention_mask is not None:
        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
        attn_weights = attn_weights + causal_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_states)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


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

    def __init__(self, config: LightGlueConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.scaling = self.head_dim**-0.5
        self.attention_dropout = config.attention_dropout
        self.is_causal = True

        self.q_proj = nn.Linear(
            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
        )
        self.k_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.v_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.o_proj = nn.Linear(
            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)

        is_cross_attention = encoder_hidden_states is not None
        current_states = encoder_hidden_states if is_cross_attention else hidden_states
        current_attention_mask = encoder_attention_mask if is_cross_attention else attention_mask

        key_states = self.k_proj(current_states).view(hidden_shape).transpose(1, 2)
        value_states = self.v_proj(current_states).view(hidden_shape).transpose(1, 2)

        if position_embeddings is not None:
            cos, sin = position_embeddings
            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        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,
            query_states,
            key_states,
            value_states,
            current_attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            **kwargs,
        )

        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights


class LightGlueMLP(nn.Module):
    def __init__(self, config: LightGlueConfig):
        super().__init__()
        self.config = config
        self.activation_fn = ACT2FN[config.hidden_act]
        self.fc1 = nn.Linear(config.intermediate_size, config.intermediate_size)
        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
        self.layer_norm = nn.LayerNorm(config.intermediate_size, elementwise_affine=True)

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


class LightGlueTransformerLayer(nn.Module):
    def __init__(self, config: LightGlueConfig, layer_idx: int):
        super().__init__()
        self.self_attention = LightGlueAttention(config, layer_idx)
        self.self_mlp = LightGlueMLP(config)
        self.cross_attention = LightGlueAttention(config, layer_idx)
        self.cross_mlp = LightGlueMLP(config)

    def forward(
        self,
        descriptors: torch.Tensor,
        keypoints: torch.Tensor,
        attention_mask: torch.Tensor,
        output_hidden_states: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
    ) -> tuple[torch.Tensor, Optional[tuple[torch.Tensor]], Optional[tuple[torch.Tensor]]]:
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (descriptors,)

        batch_size, num_keypoints, descriptor_dim = descriptors.shape

        # Self attention block
        attention_output, self_attentions = self.self_attention(
            descriptors,
            position_embeddings=keypoints,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
        )
        intermediate_states = torch.cat([descriptors, attention_output], dim=-1)
        output_states = self.self_mlp(intermediate_states)
        self_attention_descriptors = descriptors + output_states

        if output_hidden_states:
            self_attention_hidden_states = (intermediate_states, output_states)

        # Reshape hidden_states to group by image_pairs :
        #   (batch_size, num_keypoints, descriptor_dim) -> (batch_size, 2, num_keypoints, descriptor_dim)
        # Flip dimension 1 to perform cross attention :
        #   (image0, image1) -> (image1, image0)
        # Reshape back to original shape :
        #   (batch_size, 2, num_keypoints, descriptor_dim) -> (batch_size, num_keypoints, descriptor_dim)
        encoder_hidden_states = (
            self_attention_descriptors.reshape(-1, 2, num_keypoints, descriptor_dim)
            .flip(1)
            .reshape(batch_size, num_keypoints, descriptor_dim)
        )
        # Same for mask
        encoder_attention_mask = (
            attention_mask.reshape(-1, 2, 1, 1, num_keypoints).flip(1).reshape(batch_size, 1, 1, num_keypoints)
            if attention_mask is not None
            else None
        )

        # Cross attention block
        cross_attention_output, cross_attentions = self.cross_attention(
            self_attention_descriptors,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_attentions=output_attentions,
        )
        cross_intermediate_states = torch.cat([self_attention_descriptors, cross_attention_output], dim=-1)
        cross_output_states = self.cross_mlp(cross_intermediate_states)
        descriptors = self_attention_descriptors + cross_output_states

        if output_hidden_states:
            cross_attention_hidden_states = (cross_intermediate_states, cross_output_states)
            all_hidden_states = (
                all_hidden_states
                + (self_attention_descriptors.reshape(batch_size, num_keypoints, descriptor_dim),)
                + self_attention_hidden_states
                + (descriptors.reshape(batch_size, num_keypoints, descriptor_dim),)
                + cross_attention_hidden_states
            )

        if output_attentions:
            all_attentions = all_attentions + (self_attentions,) + (cross_attentions,)

        return descriptors, all_hidden_states, all_attentions


def sigmoid_log_double_softmax(
    similarity: torch.Tensor, matchability0: torch.Tensor, matchability1: torch.Tensor
) -> torch.Tensor:
    """create the log assignment matrix from logits and similarity"""
    batch_size, num_keypoints_0, num_keypoints_1 = similarity.shape
    certainties = nn.functional.logsigmoid(matchability0) + nn.functional.logsigmoid(matchability1).transpose(1, 2)
    scores0 = nn.functional.log_softmax(similarity, 2)
    scores1 = nn.functional.log_softmax(similarity.transpose(-1, -2).contiguous(), 2).transpose(-1, -2)
    scores = similarity.new_full((batch_size, num_keypoints_0 + 1, num_keypoints_1 + 1), 0)
    scores[:, :num_keypoints_0, :num_keypoints_1] = scores0 + scores1 + certainties
    scores[:, :-1, -1] = nn.functional.logsigmoid(-matchability0.squeeze(-1))
    scores[:, -1, :-1] = nn.functional.logsigmoid(-matchability1.squeeze(-1))
    return scores


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

        self.descriptor_dim = config.descriptor_dim
        self.final_projection = nn.Linear(self.descriptor_dim, self.descriptor_dim, bias=True)
        self.matchability = nn.Linear(self.descriptor_dim, 1, bias=True)

    def forward(self, descriptors: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
        batch_size, num_keypoints, descriptor_dim = descriptors.shape
        # Final projection and similarity computation
        m_descriptors = self.final_projection(descriptors)
        m_descriptors = m_descriptors / torch.tensor(self.descriptor_dim, device=m_descriptors.device) ** 0.25
        m_descriptors = m_descriptors.reshape(batch_size // 2, 2, num_keypoints, descriptor_dim)
        m_descriptors0 = m_descriptors[:, 0]
        m_descriptors1 = m_descriptors[:, 1]
        similarity = m_descriptors0 @ m_descriptors1.transpose(-1, -2)
        if mask is not None:
            mask = mask.reshape(batch_size // 2, 2, num_keypoints)
            mask0 = mask[:, 0].unsqueeze(-1)
            mask1 = mask[:, 1].unsqueeze(-1).transpose(-1, -2)
            mask = mask0 * mask1
            similarity = similarity.masked_fill(mask == 0, torch.finfo(similarity.dtype).min)

        # Compute matchability of descriptors
        matchability = self.matchability(descriptors)
        matchability = matchability.reshape(batch_size // 2, 2, num_keypoints, 1)
        matchability_0 = matchability[:, 0]
        matchability_1 = matchability[:, 1]

        # Compute scores from similarity and matchability
        scores = sigmoid_log_double_softmax(similarity, matchability_0, matchability_1)
        return scores

    def get_matchability(self, descriptors: torch.Tensor) -> torch.Tensor:
        """Get matchability of descriptors as a probability"""
        matchability = self.matchability(descriptors)
        matchability = nn.functional.sigmoid(matchability).squeeze(-1)
        return matchability


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

        self.token = nn.Linear(config.descriptor_dim, 1)

    def forward(self, descriptors: torch.Tensor) -> torch.Tensor:
        token = self.token(descriptors.detach())
        token = nn.functional.sigmoid(token).squeeze(-1)
        return token


@auto_docstring
class LightGluePreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config: LightGlueConfig
    base_model_prefix = "lightglue"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = False
    _supports_flash_attn = True
    _supports_sdpa = True


def get_matches_from_scores(scores: torch.Tensor, threshold: float) -> tuple[torch.Tensor, torch.Tensor]:
    """obtain matches from a score matrix [Bx M+1 x N+1]"""
    batch_size, _, _ = scores.shape
    # For each keypoint, get the best match
    max0 = scores[:, :-1, :-1].max(2)
    max1 = scores[:, :-1, :-1].max(1)
    matches0 = max0.indices
    matches1 = max1.indices

    # Mutual check for matches
    indices0 = torch.arange(matches0.shape[1], device=matches0.device)[None]
    indices1 = torch.arange(matches1.shape[1], device=matches1.device)[None]
    mutual0 = indices0 == matches1.gather(1, matches0)
    mutual1 = indices1 == matches0.gather(1, matches1)

    # Get matching scores and filter based on mutual check and thresholding
    max0 = max0.values.exp()
    zero = max0.new_tensor(0)
    matching_scores0 = torch.where(mutual0, max0, zero)
    matching_scores1 = torch.where(mutual1, matching_scores0.gather(1, matches1), zero)
    valid0 = mutual0 & (matching_scores0 > threshold)
    valid1 = mutual1 & valid0.gather(1, matches1)

    # Filter matches based on mutual check and thresholding of scores
    matches0 = torch.where(valid0, matches0, -1)
    matches1 = torch.where(valid1, matches1, -1)
    matches = torch.stack([matches0, matches1]).transpose(0, 1).reshape(batch_size * 2, -1)
    matching_scores = torch.stack([matching_scores0, matching_scores1]).transpose(0, 1).reshape(batch_size * 2, -1)

    return matches, matching_scores


def normalize_keypoints(keypoints: torch.Tensor, height: int, width: int) -> torch.Tensor:
    """
    Normalize keypoints locations based on image image_shape

    Args:
        keypoints (`torch.Tensor` of shape `(batch_size, num_keypoints, 2)`):
            Keypoints locations in (x, y) format.
        height (`int`):
            Image height.
        width (`int`):
            Image width.

    Returns:
        Normalized keypoints locations of shape (`torch.Tensor` of shape `(batch_size, num_keypoints, 2)`).
    """
    size = torch.tensor([width, height], device=keypoints.device, dtype=keypoints.dtype)[None]
    shift = size / 2
    scale = size.max(-1).values / 2
    keypoints = (keypoints - shift[..., None, :]) / scale[..., None, None]
    return keypoints


@auto_docstring(
    custom_intro="""
    LightGlue model taking images as inputs and outputting the matching of them.
    """
)
class LightGlueForKeypointMatching(LightGluePreTrainedModel):
    """
    LightGlue is a model matching keypoints in images by leveraging detections from a keypoint detector such as
    SuperPoint. It is based on the SuperGlue architecture and is designed to be lightweight and efficient.
    It consists of :
        1. Keypoint Encoder
        2. A Graph Neural Network with self and cross attention layers
        3. Matching Assignment layers

    The correspondence ids use -1 to indicate non-matching points.

    Philipp Lindenberger, Paul-Edouard Sarlin and Marc Pollefeys. LightGlue: Local Feature Matching at Light Speed.
    In ICCV 2023. https://arxiv.org/pdf/2306.13643.pdf
    """

    def __init__(self, config: LightGlueConfig):
        super().__init__(config)
        self.keypoint_detector = AutoModelForKeypointDetection.from_config(
            config.keypoint_detector_config, trust_remote_code=config.trust_remote_code
        )

        self.keypoint_detector_descriptor_dim = config.keypoint_detector_config.descriptor_decoder_dim
        self.descriptor_dim = config.descriptor_dim
        self.num_layers = config.num_hidden_layers
        self.filter_threshold = config.filter_threshold
        self.depth_confidence = config.depth_confidence
        self.width_confidence = config.width_confidence

        if self.descriptor_dim != self.keypoint_detector_descriptor_dim:
            self.input_projection = nn.Linear(self.keypoint_detector_descriptor_dim, self.descriptor_dim, bias=True)
        else:
            self.input_projection = nn.Identity()

        self.positional_encoder = LightGluePositionalEncoder(config)

        self.transformer_layers = nn.ModuleList(
            [LightGlueTransformerLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)]
        )
        self.match_assignment_layers = nn.ModuleList(
            [LightGlueMatchAssignmentLayer(config) for _ in range(config.num_hidden_layers)]
        )
        self.token_confidence = nn.ModuleList(
            [LightGlueTokenConfidenceLayer(config) for _ in range(config.num_hidden_layers - 1)]
        )

        self.post_init()

    def _get_confidence_threshold(self, layer_index: int) -> float:
        """scaled confidence threshold for a given layer"""
        threshold = 0.8 + 0.1 * np.exp(-4.0 * layer_index / self.num_layers)
        return np.clip(threshold, 0, 1)

    def _keypoint_processing(
        self, descriptors: torch.Tensor, keypoints: torch.Tensor, output_hidden_states: Optional[bool] = False
    ) -> tuple[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
        descriptors = descriptors.detach().contiguous()
        projected_descriptors = self.input_projection(descriptors)
        keypoint_encoding_output = self.positional_encoder(keypoints, output_hidden_states=output_hidden_states)
        return projected_descriptors, keypoint_encoding_output

    def _get_early_stopped_image_pairs(
        self, keypoint_confidences: torch.Tensor, layer_index: int, mask: torch.Tensor, num_points: torch.Tensor
    ) -> torch.Tensor:
        """evaluate whether we should stop inference based on the confidence of the keypoints"""
        batch_size, _ = mask.shape
        if layer_index < self.num_layers - 1:
            # If the current layer is not the last layer, we compute the confidence of the keypoints and check
            # if we should stop the forward pass through the transformer layers for each pair of images.
            keypoint_confidences = keypoint_confidences.masked_fill(mask == 0, 1)
            keypoint_confidences = keypoint_confidences.reshape(batch_size // 2, -1)
            threshold = self._get_confidence_threshold(layer_index)
            ratio_confident = 1.0 - (keypoint_confidences < threshold).float().sum(dim=1) / num_points
            early_stopped_pairs = ratio_confident > self.depth_confidence
        else:
            # If the current layer is the last layer, we stop the forward pass through the transformer layers for
            # all pairs of images.
            early_stopped_pairs = torch.ones(batch_size, dtype=torch.bool)
        return early_stopped_pairs

    def _get_keypoint_matching(self, descriptors, mask, layer_index, early_stops=None):
        if early_stops is not None:
            descriptors = descriptors[early_stops]
            mask = mask[early_stops]
        scores = self.match_assignment_layers[layer_index](descriptors, mask)
        matches, matching_scores = get_matches_from_scores(scores, self.filter_threshold)
        return matches, matching_scores

    def _get_pruning_mask(self, confidences: torch.Tensor, scores: torch.Tensor, layer_index: int) -> torch.Tensor:
        """mask points which should be removed"""
        keep = scores > (1 - self.width_confidence)
        if confidences is not None:  # Low-confidence points are never pruned.
            keep |= confidences <= self._get_confidence_threshold(layer_index)
        return keep

    def _do_layer_keypoint_pruning(
        self,
        descriptors: torch.Tensor,
        keypoints: torch.Tensor,
        mask: torch.Tensor,
        indices: torch.Tensor,
        prune_output: torch.Tensor,
        keypoint_confidences: torch.Tensor,
        layer_index: int,
    ):
        """
        For a given layer, prune keypoints based on the confidence of the keypoints and the matchability of the
        descriptors.
        """
        batch_size, _, _ = descriptors.shape
        descriptors_matchability = self.match_assignment_layers[layer_index].get_matchability(descriptors)
        pruned_keypoints_mask = self._get_pruning_mask(keypoint_confidences, descriptors_matchability, layer_index)
        pruned_keypoints_mask = pruned_keypoints_mask.masked_fill(mask == 0, torch.tensor(False))

        # For each image, we extract the pruned indices and the corresponding descriptors and keypoints.
        pruned_descriptors, pruned_keypoints_0, pruned_keypoints_1, pruned_mask, pruned_indices = (
            [t[mask] for t, mask in zip(tensor, pruned_keypoints_mask)]
            for tensor in [descriptors, keypoints[0], keypoints[1], pruned_keypoints_mask, indices]
        )
        for i in range(batch_size):
            prune_output[i, pruned_indices[i]] += 1

        # Pad the pruned descriptors, keypoints, indices and mask to have the same shape across the batch.
        pruned_descriptors, pruned_keypoints_0, pruned_keypoints_1, pruned_mask = (
            pad_sequence(pruned_tensor, batch_first=True)
            for pruned_tensor in [pruned_descriptors, pruned_keypoints_0, pruned_keypoints_1, pruned_mask]
        )
        pruned_keypoints = (pruned_keypoints_0, pruned_keypoints_1)
        pruned_indices = pad_sequence(pruned_indices, batch_first=True, padding_value=-1)

        return pruned_descriptors, pruned_keypoints, pruned_indices, pruned_mask, prune_output

    def _concat_early_stopped_outputs(
        self,
        early_stops_indices,
        final_pruned_keypoints_indices,
        final_pruned_keypoints_iterations,
        matches,
        matching_scores,
    ):
        early_stops_indices = torch.stack(early_stops_indices)
        matches, final_pruned_keypoints_indices = (
            pad_sequence(tensor, batch_first=True, padding_value=-1)
            for tensor in [matches, final_pruned_keypoints_indices]
        )
        matching_scores, final_pruned_keypoints_iterations = (
            pad_sequence(tensor, batch_first=True, padding_value=0)
            for tensor in [matching_scores, final_pruned_keypoints_iterations]
        )
        matches, matching_scores, final_pruned_keypoints_indices, final_pruned_keypoints_iterations = (
            tensor[early_stops_indices]
            for tensor in [
                matches,
                matching_scores,
                final_pruned_keypoints_indices,
                final_pruned_keypoints_iterations,
            ]
        )
        return final_pruned_keypoints_indices, final_pruned_keypoints_iterations, matches, matching_scores

    def _do_final_keypoint_pruning(
        self,
        indices: torch.Tensor,
        matches: torch.Tensor,
        matching_scores: torch.Tensor,
        num_keypoints: torch.Tensor,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        # (batch_size, num_keypoints) -> (batch_size // 2, 2, num_keypoints) -> 2 * (batch_size // 2, num_keypoints) to
        # have tensors from
        batch_size, _ = indices.shape
        indices, matches, matching_scores = (
            tensor.reshape(batch_size // 2, 2, -1) for tensor in [indices, matches, matching_scores]
        )
        indices0 = indices[:, 0]
        indices1 = indices[:, 1]
        matches0 = matches[:, 0]
        matches1 = matches[:, 1]
        matching_scores0 = matching_scores[:, 0]
        matching_scores1 = matching_scores[:, 1]

        # Prepare final matches and matching scores
        _matches = torch.full((batch_size // 2, 2, num_keypoints), -1, device=indices.device, dtype=matches.dtype)
        _matching_scores = torch.zeros(
            (batch_size // 2, 2, num_keypoints), device=indices.device, dtype=matching_scores.dtype
        )
        # Fill the matches and matching scores for each image pair
        for i in range(batch_size // 2):
            _matches[i, 0, indices0[i]] = torch.where(
                matches0[i] == -1, -1, indices1[i].gather(0, matches0[i].clamp(min=0))
            )
            _matches[i, 1, indices1[i]] = torch.where(
                matches1[i] == -1, -1, indices0[i].gather(0, matches1[i].clamp(min=0))
            )
            _matching_scores[i, 0, indices0[i]] = matching_scores0[i]
            _matching_scores[i, 1, indices1[i]] = matching_scores1[i]
        return _matches, _matching_scores

    def _match_image_pair(
        self,
        keypoints: torch.Tensor,
        descriptors: torch.Tensor,
        height: int,
        width: int,
        mask: torch.Tensor = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, tuple, tuple]:
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

        if keypoints.shape[2] == 0:  # no keypoints
            shape = keypoints.shape[:-1]
            return (
                keypoints.new_full(shape, -1, dtype=torch.int),
                keypoints.new_zeros(shape),
                keypoints.new_zeros(shape),
                all_hidden_states,
                all_attentions,
            )

        device = keypoints.device
        batch_size, _, initial_num_keypoints, _ = keypoints.shape
        num_points_per_pair = torch.sum(mask.reshape(batch_size, -1), dim=1)
        # (batch_size, 2, num_keypoints, 2) -> (batch_size * 2, num_keypoints, 2)
        keypoints = keypoints.reshape(batch_size * 2, initial_num_keypoints, 2)
        mask = mask.reshape(batch_size * 2, initial_num_keypoints) if mask is not None else None
        descriptors = descriptors.reshape(batch_size * 2, initial_num_keypoints, self.keypoint_detector_descriptor_dim)
        image_indices = torch.arange(batch_size * 2, device=device)
        # Keypoint normalization
        keypoints = normalize_keypoints(keypoints, height, width)

        descriptors, keypoint_encoding_output = self._keypoint_processing(
            descriptors, keypoints, output_hidden_states=output_hidden_states
        )

        keypoints = keypoint_encoding_output[0]

        # Early stop consists of stopping the forward pass through the transformer layers when the confidence of the
        # keypoints is above a certain threshold.
        do_early_stop = self.depth_confidence > 0
        # Keypoint pruning consists of removing keypoints from the input of the transformer layers when the confidence of
        # the keypoints is below a certain threshold.
        do_keypoint_pruning = self.width_confidence > 0

        early_stops_indices = []
        matches = []
        matching_scores = []
        final_pruned_keypoints_indices = []
        final_pruned_keypoints_iterations = []

        pruned_keypoints_indices = torch.arange(0, initial_num_keypoints, device=device).expand(batch_size * 2, -1)
        pruned_keypoints_iterations = torch.ones_like(pruned_keypoints_indices)

        for layer_index in range(self.num_layers):
            input_shape = descriptors.size()
            if mask is not None:
                extended_attention_mask = self.get_extended_attention_mask(mask, input_shape)
            else:
                extended_attention_mask = torch.ones((batch_size, input_shape[-2]), device=keypoints.device)
            layer_output = self.transformer_layers[layer_index](
                descriptors,
                keypoints,
                attention_mask=extended_attention_mask,
                output_hidden_states=output_hidden_states,
                output_attentions=output_attentions,
            )
            descriptors, hidden_states, attention = layer_output
            if output_hidden_states:
                all_hidden_states = all_hidden_states + hidden_states
            if output_attentions:
                all_attentions = all_attentions + attention

            if do_early_stop:
                if layer_index < self.num_layers - 1:
                    # Get the confidence of the keypoints for the current layer
                    keypoint_confidences = self.token_confidence[layer_index](descriptors)

                    # Determine which pairs of images should be early stopped based on the confidence of the keypoints for
                    # the current layer.
                    early_stopped_pairs = self._get_early_stopped_image_pairs(
                        keypoint_confidences, layer_index, mask, num_points=num_points_per_pair
                    )
                else:
                    # Early stopping always occurs at the last layer
                    early_stopped_pairs = torch.ones(batch_size, dtype=torch.bool)

                if torch.any(early_stopped_pairs):
                    # If a pair of images is considered early stopped, we compute the matches for the remaining
                    # keypoints and stop the forward pass through the transformer layers for this pair of images.
                    early_stops = early_stopped_pairs.repeat_interleave(2)
                    early_stopped_image_indices = image_indices[early_stops]
                    early_stopped_matches, early_stopped_matching_scores = self._get_keypoint_matching(
                        descriptors, mask, layer_index, early_stops=early_stops
                    )
                    early_stops_indices.extend(list(early_stopped_image_indices))
                    matches.extend(list(early_stopped_matches))
                    matching_scores.extend(list(early_stopped_matching_scores))
                    if do_keypoint_pruning:
                        final_pruned_keypoints_indices.extend(list(pruned_keypoints_indices[early_stops]))
                        final_pruned_keypoints_iterations.extend(list(pruned_keypoints_iterations[early_stops]))

                    # Remove image pairs that have been early stopped from the forward pass
                    num_points_per_pair = num_points_per_pair[~early_stopped_pairs]
                    descriptors, keypoints_0, keypoint_1, mask, image_indices = tuple(
                        tensor[~early_stops]
                        for tensor in [descriptors, keypoints[0], keypoints[1], mask, image_indices]
                    )
                    keypoints = (keypoints_0, keypoint_1)
                    if do_keypoint_pruning:
                        pruned_keypoints_indices, pruned_keypoints_iterations, keypoint_confidences = tuple(
                            tensor[~early_stops]
                            for tensor in [
                                pruned_keypoints_indices,
                                pruned_keypoints_iterations,
                                keypoint_confidences,
                            ]
                        )
                # If all pairs of images are early stopped, we stop the forward pass through the transformer
                # layers for all pairs of images.
                if torch.all(early_stopped_pairs):
                    break

            if do_keypoint_pruning:
                # Prune keypoints from the input of the transformer layers for the next iterations if the confidence of
                # the keypoints is below a certain threshold.
                descriptors, keypoints, pruned_keypoints_indices, mask, pruned_keypoints_iterations = (
                    self._do_layer_keypoint_pruning(
                        descriptors,
                        keypoints,
                        mask,
                        pruned_keypoints_indices,
                        pruned_keypoints_iterations,
                        keypoint_confidences,
                        layer_index,
                    )
                )

        if do_early_stop and do_keypoint_pruning:
            # Concatenate early stopped outputs together and perform final keypoint pruning
            final_pruned_keypoints_indices, final_pruned_keypoints_iterations, matches, matching_scores = (
                self._concat_early_stopped_outputs(
                    early_stops_indices,
                    final_pruned_keypoints_indices,
                    final_pruned_keypoints_iterations,
                    matches,
                    matching_scores,
                )
            )
            matches, matching_scores = self._do_final_keypoint_pruning(
                final_pruned_keypoints_indices,
                matches,
                matching_scores,
                initial_num_keypoints,
            )
        else:
            matches, matching_scores = self._get_keypoint_matching(descriptors, mask, self.num_layers - 1)
            final_pruned_keypoints_iterations = torch.ones_like(matching_scores) * self.num_layers

        final_pruned_keypoints_iterations = final_pruned_keypoints_iterations.reshape(
            batch_size, 2, initial_num_keypoints
        )

        return (
            matches,
            matching_scores,
            final_pruned_keypoints_iterations,
            all_hidden_states,
            all_attentions,
        )

    @can_return_tuple
    @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,
    ) -> Union[tuple, LightGlueKeypointMatchingOutput]:
        loss = None
        if labels is not None:
            raise ValueError("LightGlue is not trainable, no labels should be provided.")

        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
        )

        if pixel_values.ndim != 5 or pixel_values.size(1) != 2:
            raise ValueError("Input must be a 5D tensor of shape (batch_size, 2, num_channels, height, width)")

        batch_size, _, channels, height, width = pixel_values.shape
        pixel_values = pixel_values.reshape(batch_size * 2, channels, height, width)
        keypoint_detections = self.keypoint_detector(pixel_values)

        keypoints, _, descriptors, mask = keypoint_detections[:4]
        keypoints = keypoints.reshape(batch_size, 2, -1, 2).to(pixel_values)
        descriptors = descriptors.reshape(batch_size, 2, -1, self.keypoint_detector_descriptor_dim).to(pixel_values)
        mask = mask.reshape(batch_size, 2, -1)

        absolute_keypoints = keypoints.clone()
        absolute_keypoints[:, :, :, 0] = absolute_keypoints[:, :, :, 0] * width
        absolute_keypoints[:, :, :, 1] = absolute_keypoints[:, :, :, 1] * height

        matches, matching_scores, prune, hidden_states, attentions = self._match_image_pair(
            absolute_keypoints,
            descriptors,
            height,
            width,
            mask=mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
        )

        return LightGlueKeypointMatchingOutput(
            loss=loss,
            matches=matches,
            matching_scores=matching_scores,
            keypoints=keypoints,
            prune=prune,
            mask=mask,
            hidden_states=hidden_states,
            attentions=attentions,
        )


__all__ = ["LightGluePreTrainedModel", "LightGlueForKeypointMatching"]