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import numpy as np
import torch
import torch.nn as nn
from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
import os
import torch.nn.functional as F
from transformers.modeling_utils import PreTrainedModel
from transformers.configuration_utils import PretrainedConfig
from transformers import AutoConfig
from collections import OrderedDict
class HybridTowerConfig(PretrainedConfig):
model_type = "hybrid_vision_tower"
def __init__(self, configs=None, **kwargs):
"""
Initializes the HybridTowerConfig.
Args:
configs (dict, optional): A dictionary where keys are component names and values are
instances of configurations that have a `to_dict()` method.
**kwargs: Additional keyword arguments that are passed to the superclass.
"""
super().__init__(**kwargs)
self.configs = {}
if configs is not None:
if not isinstance(configs, dict):
raise TypeError("configs must be a dictionary where keys are component names and values are configuration objects.")
for component_name, config in configs.items():
if hasattr(config, 'to_dict'):
self.configs[component_name] = config.to_dict()
else:
raise TypeError(f"The configuration for '{component_name}' does not have a to_dict() method and cannot be serialized.")
def to_dict(self):
"""
Serializes this instance to a Python dictionary.
Returns:
dict: A dictionary containing all the keys and values of this configuration instance.
"""
config_dict = super().to_dict()
config_dict['configs'] = self.configs
return config_dict
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