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# coding=utf-8
# Copyright The HuggingFace Inc. team. All rights reserved.
#
# 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.
""" ZhClip model configuration"""
import copy
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
from transformers.models.auto.configuration_auto import AutoConfig
from transformers.models.clip.configuration_clip import CLIPVisionConfig
from typing import Union, Dict
logger = logging.get_logger(__name__)
class ZhCLIPConfig(PretrainedConfig):
model_type = "zhclip"
is_composition = True
def __init__(
self,
text_config: Union[PretrainedConfig, Dict],
vision_config: Union[PretrainedConfig, Dict],
num_token_types=2,
hidden_size=768,
num_hidden_layers=6,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
layer_norm_eps=1e-12,
classifier_dropout=None,
**kwargs):
super().__init__(**kwargs)
if not isinstance(text_config, PretrainedConfig):
text_model_type = text_config.pop('model_type')
text_config = AutoConfig.for_model(text_model_type, **text_config)
self.text_config = text_config
if not isinstance(vision_config, PretrainedConfig):
vision_model_type = vision_config.pop('model_type')
if vision_model_type == "clip":
vision_config = AutoConfig.for_model(vision_model_type, **vision_config).vision_config
elif vision_model_type == "clip_vision_model":
vision_config = CLIPVisionConfig(**vision_config)
else:
vision_config = AutoConfig.for_model(vision_model_type, **vision_config)
self.vision_config = vision_config
else:
vision_model_type = vision_config.model_type
if vision_model_type== "clip":
vision_config = vision_config.vision_config
self.vision_config = vision_config
# co-attention
self.num_token_types=num_token_types
self.hidden_size=hidden_size
self.num_hidden_layers=num_hidden_layers
self.num_attention_heads=num_attention_heads
self.intermediate_size=intermediate_size
self.hidden_act=hidden_act
self.hidden_dropout_prob=hidden_dropout_prob
self.attention_probs_dropout_prob=attention_probs_dropout_prob
self.initializer_range=initializer_range
self.layer_norm_eps=layer_norm_eps
self.classifier_dropout=classifier_dropout
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["text_config"] = self.text_config.to_dict()
output["model_type"] = self.__class__.model_type
return output |