Delete configuration_intern_vit copy.py

configuration_intern_vit copy.py

@@ -1,120 +0,0 @@
-# --------------------------------------------------------
-# InternVL
-# Copyright (c) 2024 OpenGVLab
-# Licensed under The MIT License [see LICENSE for details]
-# --------------------------------------------------------
-
-import os
-from typing import Union
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-
-class InternVisionConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
-    instantiate a vision 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:
-        num_channels (`int`, *optional*, defaults to 3):
-            Number of color channels in the input images (e.g., 3 for RGB).
-        patch_size (`int`, *optional*, defaults to 14):
-            The size (resolution) of each patch.
-        image_size (`int`, *optional*, defaults to 224):
-            The size (resolution) of each image.
-        qkv_bias (`bool`, *optional*, defaults to `False`):
-            Whether to add a bias to the queries and values in the self-attention layers.
-        hidden_size (`int`, *optional*, defaults to 3200):
-            Dimensionality of the encoder layers and the pooler layer.
-        num_attention_heads (`int`, *optional*, defaults to 25):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        intermediate_size (`int`, *optional*, defaults to 12800):
-            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
-        qk_normalization (`bool`, *optional*, defaults to `True`):
-            Whether to normalize the queries and keys in the self-attention layers.
-        num_hidden_layers (`int`, *optional*, defaults to 48):
-            Number of hidden layers in the Transformer encoder.
-        use_flash_attn (`bool`, *optional*, defaults to `True`):
-            Whether to use flash attention mechanism.
-        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
-            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
-            `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
-        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
-            The epsilon used by the layer normalization layers.
-        dropout (`float`, *optional*, defaults to 0.0):
-            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        drop_path_rate (`float`, *optional*, defaults to 0.0):
-            Dropout rate for stochastic depth.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        initializer_factor (`float`, *optional*, defaults to 0.1):
-            A factor for layer scale.
-    """
-
-    model_type = 'intern_vit_6b'
-
-    def __init__(
-            self,
-            num_channels=3,
-            patch_size=14,
-            image_size=224,
-            qkv_bias=False,
-            hidden_size=3200,
-            num_attention_heads=25,
-            intermediate_size=12800,
-            qk_normalization=True,
-            num_hidden_layers=48,
-            use_flash_attn=True,
-            hidden_act='gelu',
-            norm_type='rms_norm',
-            layer_norm_eps=1e-6,
-            dropout=0.0,
-            drop_path_rate=0.0,
-            attention_dropout=0.0,
-            initializer_range=0.02,
-            initializer_factor=0.1,
-            **kwargs,
-    ):
-        super().__init__(**kwargs)
-
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.dropout = dropout
-        self.drop_path_rate = drop_path_rate
-        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.image_size = image_size
-        self.initializer_range = initializer_range
-        self.initializer_factor = initializer_factor
-        self.attention_dropout = attention_dropout
-        self.layer_norm_eps = layer_norm_eps
-        self.hidden_act = hidden_act
-        self.norm_type = norm_type
-        self.qkv_bias = qkv_bias
-        self.qk_normalization = qk_normalization
-        self.use_flash_attn = use_flash_attn
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
-        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-
-        if 'vision_config' in config_dict:
-            config_dict = config_dict['vision_config']
-
-        if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type:
-            logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
-            )
-
-        return cls.from_dict(config_dict, **kwargs)