# Copyright 2025 the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ...configuration_utils import PreTrainedConfig, PretrainedConfig from ...modeling_rope_utils import RopeParameters from ...utils import logging from ..auto import CONFIG_MAPPING, AutoConfig from ..timm_wrapper import TimmWrapperConfig logger = logging.get_logger(__name__) class PeVideoEncoderConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`PeVideoEncoder`]. It is used to instantiate a PeVideoEncoder model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of pe-av-large. e.g. [facebook/pe-av-large](https://huggingface.co/facebook/pe-av-large) 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[PreTrainedConfig, dict]`, *optional*): Configuration for the vision backbone used to extract frame embeddings. If a dictionary is provided, it is used to instantiate a [`~transformers.TimmWrapperConfig`] with the PE default arguments. hidden_size (`int`, *optional*, defaults to 1792): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 4800): Dimension of the feedforward layers in the Transformer blocks. num_hidden_layers (`int`, *optional*, defaults to 6): Number of Transformer encoder blocks. num_attention_heads (`int`, *optional*, defaults to 14): Number of attention heads used in each attention layer. num_key_value_heads (`int`, *optional*): Number of key and value heads for grouped-query attention. If unset, this defaults to `num_attention_heads`. head_dim (`int`, *optional*, defaults to 128): Dimension of each attention head for query, key, and value projections. hidden_act (`str`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the Transformer blocks. max_position_embeddings (`int`, *optional*, defaults to 10000): Maximum sequence length supported by the rotary position embeddings. initializer_range (`float`, *optional*, defaults to 0.02): Standard deviation of the truncated normal initializer for weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-05): Epsilon used by the RMS normalization layers. rope_parameters (`Union[RopeParameters, dict]`, *optional*, defaults to `{'rope_theta': 20000}`): Parameters for the rotary position embeddings, such as the base `rope_theta`. attention_bias (`bool`, *optional*, defaults to `False`): Whether to use bias terms in the query, key, value, and output projections. attention_dropout (`float`, *optional*, defaults to 0.0): Dropout ratio applied to attention probabilities. ```python >>> from transformers import PeAudioEncoder, PeAudioEncoderConfig >>> # Initializing a PeAudioEncoder style configuration >>> configuration = PeAudioEncoderConfig() >>> # Initializing a model from the pe-av-large style configuration >>> model = PeAudioEncoder(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "pe_video_encoder" sub_configs = {"vision_config": TimmWrapperConfig} base_config_key = "audio_video_config" _default_vision_config_kwargs = { "architecture": "vit_pe_core_large_patch14_336", "do_pooling": True, "num_classes": 1024, "global_pool": "map", "initializer_range": 0.02, } def __init__( self, vision_config: dict | PreTrainedConfig | None = None, hidden_size: int | None = 1792, intermediate_size: int | None = 4800, num_hidden_layers: int | None = 6, num_attention_heads: int | None = 14, num_key_value_heads: int | None = None, head_dim: int | None = 128, hidden_act: str | None = "silu", max_position_embeddings: int | None = 10000, initializer_range: float | None = 0.02, rms_norm_eps: float | None = 1e-5, rope_parameters: RopeParameters | dict | None = {"rope_theta": 20000}, attention_bias: bool | None = False, attention_dropout: float | None = 0.0, **kwargs, ): self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.head_dim = head_dim self.hidden_act = hidden_act self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.rope_parameters = rope_parameters self.attention_bias = attention_bias self.attention_dropout = attention_dropout if isinstance(vision_config, dict): vision_config["model_type"] = vision_config.get("model_type", "timm_wrapper") vision_config = CONFIG_MAPPING[vision_config["model_type"]].from_dict( {**self._default_vision_config_kwargs, **vision_config} ) elif vision_config is None: vision_config = CONFIG_MAPPING["timm_wrapper"].from_dict(self._default_vision_config_kwargs) self.vision_config = vision_config super().__init__(**kwargs) class PeVideoConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`PeVideoModel`]. It is used to instantiate a PeVideoModel model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of pe-av-large. e.g. [facebook/pe-av-large](https://huggingface.co/facebook/pe-av-large) Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: text_config (`dict` or `PreTrainedConfig`, *optional*): Configuration for the text model component. video_config (`dict` or `PreTrainedConfig`, *optional*): Configuration for the video encoder component. ```python >>> from transformers import PeVideoModel, PeVideoConfig >>> # Initializing a PeVideoModel style configuration >>> configuration = PeVideoConfig() >>> # Initializing a model from the pe-av-large style configuration >>> model = PeVideoModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "pe_video" sub_configs = {"text_config": AutoConfig, "video_config": PeVideoEncoderConfig} base_config_key = "audio_video_config" _default_text_config_kwargs = { "model_type": "modernbert", "hidden_size": 1024, "intermediate_size": 2624, "num_hidden_layers": 22, "num_attention_heads": 16, } def __init__( self, text_config=None, video_config=None, **kwargs, ): if isinstance(text_config, dict): text_config["model_type"] = text_config.get("model_type", "modernbert") text_config = CONFIG_MAPPING[text_config["model_type"]]( **{**self._default_text_config_kwargs, **text_config} ) elif text_config is None: text_config = CONFIG_MAPPING["modernbert"](**self._default_text_config_kwargs) if isinstance(video_config, dict): video_config = PeVideoEncoderConfig(**video_config) elif video_config is None: video_config = PeVideoEncoderConfig() self.text_config = text_config self.video_config = video_config super().__init__(**kwargs) __all__ = ["PeVideoEncoderConfig", "PeVideoConfig"]