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#                          modular_edgetam_video.py file directly. One of our CI enforces this.
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# coding=utf-8
# 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 ...configuration_utils import PretrainedConfig
from ..auto import CONFIG_MAPPING, AutoConfig


class EdgeTamVideoPromptEncoderConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`EdgeTamVideoPromptEncoder`]. The [`EdgeTamVideoPromptEncoder`]
    module is used to encode the input 2D points and bounding boxes.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        hidden_size (`int`, *optional*, defaults to 256):
            Dimensionality of the hidden states.
        image_size (`int`, *optional*, defaults to 1024):
            The expected output resolution of the image.
        patch_size (`int`, *optional*, defaults to 16):
            The size (resolution) of each patch.
        mask_input_channels (`int`, *optional*, defaults to 16):
            The number of channels to be fed to the `MaskDecoder` module.
        num_point_embeddings (`int`, *optional*, defaults to 4):
            The number of point embeddings to be used.
        hidden_act (`str`, *optional*, defaults to `"gelu"`):
            The non-linear activation function in the encoder and pooler.
        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the layer normalization layers.
        scale (`float`, *optional*, defaults to 1):
            The scale factor for the prompt encoder.
    """

    base_config_key = "prompt_encoder_config"

    def __init__(
        self,
        hidden_size=256,
        image_size=1024,
        patch_size=16,
        mask_input_channels=16,
        num_point_embeddings=4,
        hidden_act="gelu",
        layer_norm_eps=1e-6,
        scale=1,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.hidden_size = hidden_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.mask_input_channels = mask_input_channels
        self.num_point_embeddings = num_point_embeddings
        self.hidden_act = hidden_act
        self.layer_norm_eps = layer_norm_eps
        self.scale = scale


class EdgeTamVideoMaskDecoderConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`EdgeTamVideoMaskDecoder`]. It is used to instantiate a EDGETAM_VIDEO
    memory encoder according to the specified arguments, defining the model architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        hidden_size (`int`, *optional*, defaults to 256):
            Dimensionality of the hidden states.
        hidden_act (`str`, *optional*, defaults to `"gelu"`):
            The non-linear activation function in the EDGETAM_VIDEO mask decoder.
        mlp_dim (`int`, *optional*, defaults to 2048):
            The dimension of the MLP in the two-way transformer.
        num_hidden_layers (`int`, *optional*, defaults to 2):
            The number of hidden layers in the two-way transformer.
        num_attention_heads (`int`, *optional*, defaults to 8):
            The number of attention heads in the two-way transformer.
        attention_downsample_rate (`int`, *optional*, defaults to 2):
            The downsample rate for the attention layers.
        num_multimask_outputs (`int`, *optional*, defaults to 3):
            The number of multimask outputs.
        iou_head_depth (`int`, *optional*, defaults to 3):
            The depth of the IoU head.
        iou_head_hidden_dim (`int`, *optional*, defaults to 256):
            The hidden dimension of the IoU head.
        dynamic_multimask_via_stability (`bool`, *optional*, defaults to `True`):
            Whether to use dynamic multimask via stability.
        dynamic_multimask_stability_delta (`float`, *optional*, defaults to 0.05):
            The stability delta for the dynamic multimask.
        dynamic_multimask_stability_thresh (`float`, *optional*, defaults to 0.98):
            The stability threshold for the dynamic multimask.

    """

    base_config_key = "mask_decoder_config"

    def __init__(
        self,
        hidden_size=256,
        hidden_act="gelu",
        mlp_dim=2048,
        num_hidden_layers=2,
        num_attention_heads=8,
        attention_downsample_rate=2,
        num_multimask_outputs=3,
        iou_head_depth=3,
        iou_head_hidden_dim=256,
        dynamic_multimask_via_stability=True,
        dynamic_multimask_stability_delta=0.05,
        dynamic_multimask_stability_thresh=0.98,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.num_multimask_outputs = num_multimask_outputs
        self.hidden_act = hidden_act
        self.iou_head_depth = iou_head_depth
        self.iou_head_hidden_dim = iou_head_hidden_dim
        self.dynamic_multimask_via_stability = dynamic_multimask_via_stability
        self.dynamic_multimask_stability_delta = dynamic_multimask_stability_delta
        self.dynamic_multimask_stability_thresh = dynamic_multimask_stability_thresh

        # TwoWayTransformer configuration
        self.num_hidden_layers = num_hidden_layers
        self.hidden_size = hidden_size
        self.num_attention_heads = num_attention_heads
        self.mlp_dim = mlp_dim
        self.attention_downsample_rate = attention_downsample_rate


class EdgeTamVideoConfig(PretrainedConfig):
    r"""
    [`EdgeTamVideoConfig`] is the configuration class to store the configuration of a [`EdgeTamVideoModel`]. It is used to instantiate a
    EDGETAM model according to the specified arguments, defining the memory attention, memory encoder, and image encoder
    configs. Instantiating a configuration defaults will yield a similar configuration to that of the SAM 2.1 Hiera-tiny
    [facebook/EdgeTAM](https://huggingface.co/facebook/EdgeTAM) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        vision_config (Union[`dict`, `EdgeTamVideoVisionConfig`], *optional*):
            Dictionary of configuration options used to initialize [`EdgeTamVideoVisionConfig`].
        prompt_encoder_config (Union[`dict`, `EdgeTamVideoPromptEncoderConfig`], *optional*):
            Dictionary of configuration options used to initialize [`EdgeTamVideoPromptEncoderConfig`].
        mask_decoder_config (Union[`dict`, `EdgeTamVideoMaskDecoderConfig`], *optional*):
            Dictionary of configuration options used to initialize [`EdgeTamMaskDecoderConfig`].
        initializer_range (`float`, *optional*, defaults to 0.02):
            Standard deviation for parameter initialization.
        num_maskmem (`int`, *optional*, defaults to 7):
            The number of memory slots for the mask memory.
        image_size (`int`, *optional*, defaults to 1024):
            The size of the input images.
        sigmoid_scale_for_mem_enc (`float`, *optional*, defaults to 20.0):
            Scale factor for the sigmoid function in the memory encoder.
        sigmoid_bias_for_mem_enc (`float`, *optional*, defaults to -10.0):
            Bias for the sigmoid function in the memory encoder.
        enable_occlusion_spatial_embedding (`bool`, *optional*, defaults to `True`):
            Whether to enable spatial embedding for occlusions.
        multimask_output_in_sam (`bool`, *optional*, defaults to `True`):
            Whether to output multiple masks from the SAM head.
        multimask_min_pt_num (`int`, *optional*, defaults to 0):
            The minimum number of points to trigger multimask output.
        multimask_max_pt_num (`int`, *optional*, defaults to 1):
            The maximum number of points to trigger multimask output.
        multimask_output_for_tracking (`bool`, *optional*, defaults to `True`):
            Whether to use multimask output for tracking.
        max_object_pointers_in_encoder (`int`, *optional*, defaults to 16):
            The maximum number of object pointers in the encoder.
        enable_temporal_pos_encoding_for_object_pointers (`bool`, *optional*, defaults to `True`):
            Whether to enable temporal positional encoding for object pointers.
        memory_attention_hidden_size (`int`, *optional*, defaults to 256):
            Dimensionality of the memory attention hidden states.
        memory_attention_num_layers (`int`, *optional*, defaults to 2):
            The number of layers in the memory attention module.
        memory_attention_num_attention_heads (`int`, *optional*, defaults to 1):
            Number of attention heads for each attention layer in the memory attention.
        memory_attention_downsample_rate (`int`, *optional*, defaults to 1):
            The downsample rate for the attention layers.
        memory_attention_mlp_hidden_size (`int`, *optional*, defaults to 2048):
            The dimension of the feedforward network in the memory attention module.
        memory_attention_mlp_hidden_act (`str`, *optional*, defaults to `"relu"`):
            The non-linear activation function in the feedforward network in the memory attention module.
        memory_attention_dropout (`float`, *optional*, defaults to 0.1):
            The dropout rate for the memory attention module.
        memory_attention_rope_theta (`float`, *optional*, defaults to 10000):
            The Rope theta parameter.
        memory_attention_rope_feat_sizes (`Tuple[int, int]`, *optional*, defaults to `[64, 64]`):
            The feature sizes for the Rope positional encoding.
        memory_attention_rope_k_sizes (`List[int]`, *optional*, defaults to `[16, 16]`):
            The key feature sizes for the RoPE positional encoding in memory attention.
        memory_attention_rope_dropout (`float`, *optional*, defaults to 0.1):
            The dropout rate for the Rope positional encoding.
        perceiver_resampler_num_latents (`int`, *optional*, defaults to 256):
            The number of 1D latent tokens in the perceiver resampler.
        perceiver_resampler_num_latents_2d (`int`, *optional*, defaults to 256):
            The number of 2D latent tokens in the perceiver resampler.
        perceiver_resampler_hidden_size (`int`, *optional*, defaults to 64):
            The hidden size of the perceiver resampler.
        perceiver_resampler_mlp_intermediate_size (`int`, *optional*, defaults to 256):
            The intermediate size of the feedforward network in the perceiver resampler.
        perceiver_resampler_num_attention_heads (`int`, *optional*, defaults to 1):
            The number of attention heads in the perceiver resampler.
        perceiver_resampler_attention_head_dim (`int`, *optional*, defaults to 64):
            The dimension of each attention head in the perceiver resampler.
        perceiver_resampler_num_layers (`int`, *optional*, defaults to 2):
            The number of layers in the perceiver resampler.
        perceiver_resampler_hidden_dropout (`float`, *optional*, defaults to 0.0):
            The dropout rate for the hidden layers in the perceiver resampler.
        perceiver_resampler_attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout rate for the attention layers in the perceiver resampler.
        memory_encoder_hidden_size (`int`, *optional*, defaults to 256):
            Dimensionality of the memory encoder hidden states.
        memory_encoder_output_channels (`int`, *optional*, defaults to 64):
            The number of output channels for the memory encoder.
        mask_downsampler_embed_dim (`int`, *optional*, defaults to 256):
            The dimension of the mask downsampler embedding.
        memory_fuser_intermediate_dim (`int`, *optional*, defaults to 1024):
            The intermediate dimension of the memory fuser feedforward network.
        mask_downsampler_kernel_size (`int`, *optional*, defaults to 3):
            The kernel size for the mask downsampler.
        mask_downsampler_stride (`int`, *optional*, defaults to 2):
            The stride for the mask downsampler.
        mask_downsampler_padding (`int`, *optional*, defaults to 1):
            The padding for the mask downsampler.
        mask_downsampler_total_stride (`int`, *optional*, defaults to 16):
            The total stride for the mask downsampler.
        mask_downsampler_hidden_act (`str`, *optional*, defaults to `"gelu"`):
            The non-linear activation function in the mask downsampler.
        memory_fuser_num_layers (`int`, *optional*, defaults to 2):
            The number of layers in the memory fuser.
        memory_fuser_embed_dim (`int`, *optional*, defaults to 256):
            The dimension of the memory fuser embedding.
        memory_fuser_kernel_size (`int`, *optional*, defaults to 7):
            The kernel size for the memory fuser.
        memory_fuser_padding (`int`, *optional*, defaults to 3):
            The padding for the memory fuser.
        memory_fuser_layer_scale_init_value (`float`, *optional*, defaults to 1e-06):
            The initial value for the layer scale in the memory fuser.
        memory_fuser_hidden_act (`str`, *optional*, defaults to `"gelu"`):
            The non-linear activation function in the memory fuser.

    Example:

    ```python
    >>> from transformers import (
    ...     EdgeTamVisionConfig,
    ...     EdgeTamVideoPromptEncoderConfig,
    ...     EdgeTamVideoMaskDecoderConfig,
    ...     EdgeTamVideoModel,
    ...     EdgeTamVideoConfig,
    ... )

    >>> # Initializing a EdgeTamVideoConfig with `"facebook/edgetam.1_hiera_tiny"` style configuration
    >>> configuration = EdgeTamVideoConfig()

    >>> # Initializing a EdgeTamVideoModel (with random weights) from the `"facebook/edgetam.1_hiera_tiny"` style configuration
    >>> model = EdgeTamVideoModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config

    >>> # We can also initialize a EdgeTamConfig from a EdgeTamVisionConfig, EdgeTamPromptEncoderConfig, and EdgeTamMaskDecoderConfig

    >>> # Initializing EDGETAM vision encoder, memory attention, and memory encoder configurations
    >>> vision_config = EdgeTamVisionConfig()
    >>> prompt_encoder_config = EdgeTamVideoPromptEncoderConfig()
    >>> mask_decoder_config = EdgeTamVideoMaskDecoderConfig()

    >>> config = EdgeTamVideoConfig(vision_config, prompt_encoder_config, mask_decoder_config)
    ```"""

    model_type = "edgetam_video"
    sub_configs = {
        "vision_config": AutoConfig,
        "prompt_encoder_config": EdgeTamVideoPromptEncoderConfig,
        "mask_decoder_config": EdgeTamVideoMaskDecoderConfig,
    }

    def __init__(
        self,
        vision_config=None,
        prompt_encoder_config=None,
        mask_decoder_config=None,
        initializer_range=0.02,
        num_maskmem=7,
        image_size=1024,
        sigmoid_scale_for_mem_enc=20.0,
        sigmoid_bias_for_mem_enc=-10.0,
        enable_occlusion_spatial_embedding=True,
        multimask_output_in_sam=True,
        multimask_min_pt_num=0,
        multimask_max_pt_num=1,
        multimask_output_for_tracking=True,
        max_object_pointers_in_encoder=16,
        enable_temporal_pos_encoding_for_object_pointers=True,
        # memory attention
        memory_attention_hidden_size=256,
        memory_attention_num_layers=2,
        memory_attention_num_attention_heads=1,
        memory_attention_downsample_rate=1,
        memory_attention_mlp_hidden_size=2048,
        memory_attention_mlp_hidden_act="relu",
        memory_attention_dropout=0.1,
        memory_attention_rope_theta=10000,
        memory_attention_rope_feat_sizes=None,
        memory_attention_rope_k_sizes=None,
        memory_attention_rope_dropout=0.1,
        # spatial perceiver resampler
        perceiver_resampler_num_latents=256,
        perceiver_resampler_num_latents_2d=256,
        perceiver_resampler_hidden_size=64,
        perceiver_resampler_mlp_intermediate_size=256,
        perceiver_resampler_num_attention_heads=1,
        perceiver_resampler_attention_head_dim=64,
        perceiver_resampler_num_layers=2,
        perceiver_resampler_hidden_dropout=0.0,
        perceiver_resampler_attention_dropout=0.0,
        # memory encoder
        memory_encoder_hidden_size=256,
        memory_encoder_output_channels=64,
        mask_downsampler_embed_dim=256,
        memory_fuser_intermediate_dim=1024,
        mask_downsampler_kernel_size=3,
        mask_downsampler_stride=2,
        mask_downsampler_padding=1,
        mask_downsampler_total_stride=16,
        mask_downsampler_hidden_act="gelu",
        memory_fuser_num_layers=2,
        memory_fuser_embed_dim=256,
        memory_fuser_kernel_size=7,
        memory_fuser_padding=3,
        memory_fuser_layer_scale_init_value=1e-6,
        memory_fuser_hidden_act="gelu",
        **kwargs,
    ):
        super().__init__(**kwargs)
        vision_config = vision_config if vision_config is not None else {}
        prompt_encoder_config = prompt_encoder_config if prompt_encoder_config is not None else {}
        mask_decoder_config = mask_decoder_config if mask_decoder_config is not None else {}
        memory_attention_rope_feat_sizes = (
            [64, 64] if memory_attention_rope_feat_sizes is None else memory_attention_rope_feat_sizes
        )
        memory_attention_rope_k_sizes = (
            [16, 16] if memory_attention_rope_k_sizes is None else memory_attention_rope_k_sizes
        )

        if isinstance(vision_config, dict):
            vision_config["model_type"] = vision_config.get("model_type", "sam2_vision_model")
            vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
        if isinstance(prompt_encoder_config, EdgeTamVideoPromptEncoderConfig):
            prompt_encoder_config = prompt_encoder_config.to_dict()
        if isinstance(mask_decoder_config, EdgeTamVideoMaskDecoderConfig):
            mask_decoder_config = mask_decoder_config.to_dict()

        self.vision_config = vision_config
        self.prompt_encoder_config = EdgeTamVideoPromptEncoderConfig(**prompt_encoder_config)
        self.mask_decoder_config = EdgeTamVideoMaskDecoderConfig(**mask_decoder_config)

        self.initializer_range = initializer_range
        self.num_maskmem = num_maskmem  # default 1 input frame + 6 previous frames
        self.image_size = image_size
        self.sigmoid_scale_for_mem_enc = sigmoid_scale_for_mem_enc  # scale factor for mask sigmoid prob
        self.sigmoid_bias_for_mem_enc = sigmoid_bias_for_mem_enc  # bias factor for mask sigmoid prob
        self.enable_occlusion_spatial_embedding = enable_occlusion_spatial_embedding
        self.multimask_output_in_sam = multimask_output_in_sam
        self.multimask_min_pt_num = multimask_min_pt_num
        self.multimask_max_pt_num = multimask_max_pt_num
        self.multimask_output_for_tracking = multimask_output_for_tracking
        self.max_object_pointers_in_encoder = max_object_pointers_in_encoder
        self.enable_temporal_pos_encoding_for_object_pointers = enable_temporal_pos_encoding_for_object_pointers

        # memory attention
        self.memory_attention_hidden_size = memory_attention_hidden_size
        self.memory_attention_num_layers = memory_attention_num_layers
        self.memory_attention_num_attention_heads = memory_attention_num_attention_heads
        self.memory_attention_downsample_rate = memory_attention_downsample_rate
        self.memory_attention_mlp_hidden_size = memory_attention_mlp_hidden_size
        self.memory_attention_mlp_hidden_act = memory_attention_mlp_hidden_act
        self.memory_attention_dropout = memory_attention_dropout
        self.memory_attention_rope_theta = memory_attention_rope_theta
        self.memory_attention_rope_feat_sizes = memory_attention_rope_feat_sizes
        self.memory_attention_rope_k_sizes = memory_attention_rope_k_sizes
        self.memory_attention_rope_dropout = memory_attention_rope_dropout

        # spatial perceiver resampler
        self.perceiver_resampler_num_latents = perceiver_resampler_num_latents
        self.perceiver_resampler_num_latents_2d = perceiver_resampler_num_latents_2d
        self.perceiver_resampler_hidden_size = perceiver_resampler_hidden_size
        self.perceiver_resampler_mlp_intermediate_size = perceiver_resampler_mlp_intermediate_size
        self.perceiver_resampler_attention_head_dim = perceiver_resampler_attention_head_dim
        self.perceiver_resampler_num_attention_heads = perceiver_resampler_num_attention_heads
        self.perceiver_resampler_num_layers = perceiver_resampler_num_layers
        self.perceiver_resampler_hidden_dropout = perceiver_resampler_hidden_dropout
        self.perceiver_resampler_attention_dropout = perceiver_resampler_attention_dropout

        # memory encoder
        self.memory_encoder_hidden_size = memory_encoder_hidden_size
        self.memory_encoder_output_channels = memory_encoder_output_channels
        self.mask_downsampler_embed_dim = mask_downsampler_embed_dim
        self.mask_downsampler_kernel_size = mask_downsampler_kernel_size
        self.mask_downsampler_stride = mask_downsampler_stride
        self.mask_downsampler_padding = mask_downsampler_padding
        self.mask_downsampler_total_stride = mask_downsampler_total_stride
        self.mask_downsampler_hidden_act = mask_downsampler_hidden_act
        self.memory_fuser_num_layers = memory_fuser_num_layers
        self.memory_fuser_embed_dim = memory_fuser_embed_dim
        self.memory_fuser_intermediate_dim = memory_fuser_intermediate_dim
        self.memory_fuser_kernel_size = memory_fuser_kernel_size
        self.memory_fuser_padding = memory_fuser_padding
        self.memory_fuser_layer_scale_init_value = memory_fuser_layer_scale_init_value
        self.memory_fuser_hidden_act = memory_fuser_hidden_act


__all__ = ["EdgeTamVideoMaskDecoderConfig", "EdgeTamVideoPromptEncoderConfig", "EdgeTamVideoConfig"]
