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