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modeling_fgclip2.py

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+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/fgclip2/modular_fgclip2.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_fgclip2.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# 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 transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class Fgclip2TextConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Fgclip2TextModel`]. It is used to instantiate a
+    Fgclip2 text encoder according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the text encoder of the Fgclip2
+    [qihoo360/fg-clip2-base](https://huggingface.co/qihoo360/fg-clip2-base) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the Fgclip2 text model. Defines the number of different tokens that can be represented by
+            the `inputs_ids` passed when calling [`Fgclip2Model`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        max_position_embeddings (`int`, *optional*, defaults to 64):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the layer normalization layers.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        pad_token_id (`int`, *optional*, defaults to 1):
+            The id of the padding token in the vocabulary.
+        bos_token_id (`int`, *optional*, defaults to 49406):
+            The id of the beginning-of-sequence token in the vocabulary.
+        eos_token_id (`int`, *optional*, defaults to 49407):
+            The id of the end-of-sequence token in the vocabulary.
+        projection_size (`int`, *optional*, defaults to `hidden_size`):
+            The size of the projection head.
+        keep_len (`int`, *optional*, defaults to 20):
+            When processing long texts, the retained tokens are used for handling short text lengths.
+            For details, please refer to the FG-CLIP 'https://arxiv.org/abs/2505.05071' paper.
+        longtext_len (`int`, *optional*, defaults to 196):
+            The maximum number of tokens in long texts that can be processed
+
+
+    Example:
+
+    ```python
+    >>> from transformers import Fgclip2TextConfig, Fgclip2TextModel
+
+    >>> # Initializing a Fgclip2TextConfig with qihoo/fgclip2-base-patch16 style configuration
+    >>> configuration = Fgclip2TextConfig()
+
+    >>> # Initializing a Fgclip2TextModel (with random weights) from the qihoo/fgclip2-base-patch16 style configuration
+    >>> model = Fgclip2TextModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "fgclip2_text_model"
+    base_config_key = "text_config"
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=768,
+        intermediate_size=3072,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        max_position_embeddings=64,
+        hidden_act="gelu_pytorch_tanh",
+        layer_norm_eps=1e-6,
+        attention_dropout=0.0,
+        # This differs from `CLIPTokenizer`'s default and from openai/fgclip2
+        # See https://github.com/huggingface/transformers/pull/24773#issuecomment-1632287538
+        pad_token_id=1,
+        bos_token_id=49406,
+        eos_token_id=49407,
+        projection_size=None,
+        keep_len=20,
+        longtext_len=196,
+        **kwargs,
+    ):
+        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.max_position_embeddings = max_position_embeddings
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        self.attention_dropout = attention_dropout
+        self.projection_size = projection_size if projection_size is not None else hidden_size
+        self.keep_len = keep_len
+        self.longtext_len = longtext_len
+
+
+class Fgclip2VisionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Fgclip2VisionModel`]. It is used to instantiate a
+    Fgclip2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the vision encoder of the Fgclip2
+    [qihoo/fgclip2-base-patch16](https://huggingface.co/qihoo/fgclip2-base-patch16) 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 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_channels (`int`, *optional*, defaults to 3):
+            Number of channels in the input images.
+        num_patches (`int`, *optional*, defaults to 256):
+            The number of patches in the image with the size of (`patch_size`, `patch_size`).
+            The image is resized to fill maximum of this number of patches, and to preserve
+            the aspect ratio. In case the resulted number of patches is lower, the image is
+            padded in "patch" dimension.
+        patch_size (`int`, *optional*, defaults to 16):
+            The size (resolution) of each patch.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the layer normalization layers.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    Example:
+
+    ```python
+    >>> from transformers import Fgclip2VisionConfig, Fgclip2VisionModel
+
+    >>> # Initializing a Fgclip2VisionConfig with qihoo/fgclip2-base-patch16 style configuration
+    >>> configuration = Fgclip2VisionConfig()
+
+    >>> # Initializing a Fgclip2VisionModel (with random weights) from the qihoo/fgclip2-base-patch16 style configuration
+    >>> model = Fgclip2VisionModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "fgclip2_vision_model"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        hidden_size=768,
+        intermediate_size=3072,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        num_channels=3,
+        num_patches=256,
+        patch_size=16,
+        hidden_act="gelu_pytorch_tanh",
+        layer_norm_eps=1e-6,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        super().__init__(**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
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        self.num_patches = num_patches
+
+
+class Fgclip2Config(PretrainedConfig):
+    r"""
+    [`Fgclip2Config`] is the configuration class to store the configuration of a [`Fgclip2Model`]. It is used to
+    instantiate a Fgclip2 model according to the specified arguments, defining the text model and vision model configs.
+    Instantiating a configuration with the defaults will yield a similar configuration to that of the Fgclip2
+    [qihoo/fgclip2-base-patch16](https://huggingface.co/qihoo/fgclip2-base-patch16) architecture.
+
+    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`, *optional*):
+            Dictionary of configuration options used to initialize [`Fgclip2TextConfig`].
+        vision_config (`dict`, *optional*):
+            Dictionary of configuration options used to initialize [`Fgclip2VisionConfig`].
+        kwargs (*optional*):
+            Dictionary of keyword arguments.
+
+    Example:
+
+    ```python
+    >>> from transformers import Fgclip2Config, Fgclip2Model
+
+    >>> # Initializing a Fgclip2Config with qihoo/fgclip2-base-patch16 style configuration
+    >>> configuration = Fgclip2Config()
+
+    >>> # Initializing a Fgclip2Model (with random weights) from the qihoo/fgclip2-base-patch16 style configuration
+    >>> model = Fgclip2Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+
+    >>> # We can also initialize a Fgclip2Config from a Fgclip2TextConfig and a Fgclip2VisionConfig
+    >>> from transformers import Fgclip2TextConfig, Fgclip2VisionConfig
+
+    >>> # Initializing a Fgclip2Text and Fgclip2Vision configuration
+    >>> config_text = Fgclip2TextConfig()
+    >>> config_vision = Fgclip2VisionConfig()
+
+    >>> config = Fgclip2Config.from_text_vision_configs(config_text, config_vision)
+    ```"""
+
+    model_type = "fgclip2"
+    sub_configs = {"text_config": Fgclip2TextConfig, "vision_config": Fgclip2VisionConfig}
+
+    def __init__(self, text_config=None, vision_config=None, **kwargs):
+        super().__init__(**kwargs)
+
+        if text_config is None:
+            text_config = {}
+            logger.info("`text_config` is `None`. Initializing the `Fgclip2TextConfig` with default values.")
+
+        if vision_config is None:
+            vision_config = {}
+            logger.info("`vision_config` is `None`. initializing the `Fgclip2VisionConfig` with default values.")
+
+        self.text_config = Fgclip2TextConfig(**text_config)
+        self.vision_config = Fgclip2VisionConfig(**vision_config)
+
+        self.initializer_factor = 1.0
+
+
+__all__ = ["Fgclip2Config", "Fgclip2TextConfig", "Fgclip2VisionConfig"]