Delete configuration_internvl_chat copy.py

configuration_internvl_chat copy.py

@@ -1,97 +0,0 @@
-# --------------------------------------------------------
-# InternVL
-# Copyright (c) 2024 OpenGVLab
-# Licensed under The MIT License [see LICENSE for details]
-# --------------------------------------------------------
-
-import copy
-
-from transformers import AutoConfig, LlamaConfig, Qwen2Config
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-from .configuration_intern_vit import InternVisionConfig
-
-logger = logging.get_logger(__name__)
-
-
-class InternVLChatConfig(PretrainedConfig):
-    model_type = 'internvl_chat'
-    is_composition = True
-
-    def __init__(
-            self,
-            vision_config=None,
-            llm_config=None,
-            use_backbone_lora=0,
-            use_llm_lora=0,
-            select_layer=-1,
-            force_image_size=None,
-            downsample_ratio=0.5,
-            template=None,
-            dynamic_image_size=False,
-            use_thumbnail=False,
-            ps_version='v1',
-            min_dynamic_patch=1,
-            max_dynamic_patch=6,
-            **kwargs):
-        super().__init__(**kwargs)
-
-        if vision_config is None:
-            vision_config = {'architectures': ['InternVisionModel']}
-            logger.info('vision_config is None. Initializing the InternVisionConfig with default values.')
-
-        if llm_config is None:
-            llm_config = {'architectures': ['Qwen2ForCausalLM']}
-            logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
-
-        self.vision_config = InternVisionConfig(**vision_config)
-        if llm_config.get('architectures')[0] == 'LlamaForCausalLM':
-            self.llm_config = LlamaConfig(**llm_config)
-        elif llm_config.get('architectures')[0] == 'Qwen2ForCausalLM':
-            self.llm_config = Qwen2Config(**llm_config)
-        else:
-            raise ValueError('Unsupported architecture: {}'.format(llm_config.get('architectures')[0]))
-        self.use_backbone_lora = use_backbone_lora
-        self.use_llm_lora = use_llm_lora
-        self.select_layer = select_layer
-        self.force_image_size = force_image_size
-        self.downsample_ratio = downsample_ratio
-        self.template = template
-        self.dynamic_image_size = dynamic_image_size
-        self.use_thumbnail = use_thumbnail
-        self.ps_version = ps_version  # pixel shuffle version
-        self.min_dynamic_patch = min_dynamic_patch
-        self.max_dynamic_patch = max_dynamic_patch
-        # By default, we use tie_word_embeddings=False for models of all sizes.
-        self.tie_word_embeddings = self.llm_config.tie_word_embeddings
-
-        logger.info(f'vision_select_layer: {self.select_layer}')
-        logger.info(f'ps_version: {self.ps_version}')
-        logger.info(f'min_dynamic_patch: {self.min_dynamic_patch}')
-        logger.info(f'max_dynamic_patch: {self.max_dynamic_patch}')
-
-    def to_dict(self):
-        """
-        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
-
-        Returns:
-            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
-        """
-        output = copy.deepcopy(self.__dict__)
-        output['vision_config'] = self.vision_config.to_dict()
-        output['llm_config'] = self.llm_config.to_dict()
-        output['model_type'] = self.__class__.model_type
-        output['use_backbone_lora'] = self.use_backbone_lora
-        output['use_llm_lora'] = self.use_llm_lora
-        output['select_layer'] = self.select_layer
-        output['force_image_size'] = self.force_image_size
-        output['downsample_ratio'] = self.downsample_ratio
-        output['template'] = self.template
-        output['dynamic_image_size'] = self.dynamic_image_size
-        output['use_thumbnail'] = self.use_thumbnail
-        output['ps_version'] = self.ps_version
-        output['min_dynamic_patch'] = self.min_dynamic_patch
-        output['max_dynamic_patch'] = self.max_dynamic_patch
-
-        return output