Errors while importing FlaxHybridCLIP checkpoints to FlaxCLIPModel or CLIPModel 
## Environment info
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- `transformers` version: 4.12.2
- Platform: Linux-5.4.0-80-generic-x86_64-with-debian-buster-sid
- Python version: 3.7.9
- PyTorch version (GPU?): 1.10.0+cu113 (True)
- Tensorflow version (GPU?): 2.4.1 (True)
- Flax version (CPU?/GPU?/TPU?): 0.3.6 (cpu)
- Jax version: 0.2.25
- JaxLib version: 0.1.73
- Using GPU in script?: no
- Using distributed or parallel set-up in script?: no

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Models:

- ALBERT, BERT, XLM, DeBERTa, DeBERTa-v2, ELECTRA, MobileBert, SqueezeBert: @LysandreJik
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- Benchmarks: @patrickvonplaten
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- Tokenizers: @LysandreJik
- Trainer: @sgugger
- Pipelines: @Narsil
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- Vision: @NielsRogge, @sgugger

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@patil-suraj @patrickvonplaten 

## Information

During the last Flax/JAX Community Week we trained a fine-tuned version of [CLIP for the Italian language](https://github.com/clip-italian/clip-italian). We used the [provided script](https://github.com/clip-italian/clip-italian/tree/master/hybrid_clip), so we trained a *FlaxHybridCLIP* model with Open AI's ViT and `"dbmdz/bert-base-italian-xxl-uncased"` BERT as encoders.

Now, I'm trying to use that model with the transformers' official API classes, either FlaxCLIPModel or CLIPModel (my final goal would be to port it to pytorch and publish it to the hub). However, I am having a hard time loading our weights into any of the two.

I tried different workarounds (see below) but none of them seems to be working. 

## To reproduce 

I assume these imports
```python
from modeling_hybrid_clip import FlaxHybridCLIP
from configuration_hybrid_clip import HybridCLIPConfig
from transformers import CLIPModel, CLIPConfig, FlaxCLIPModel, CLIPVisionConfig, CLIPTextConfig
import jax
import jax.numpy as jnp
```

Steps to reproduce the behavior:

1. My first tests were:
```python
model = FlaxCLIPModel.from_pretrained("clip-italian/clip-italian")
# or
model = CLIPModel.from_pretrained("clip-italian/clip-italian", from_flax=True)

# Output
You are using a model of type hybrid-clip to instantiate a model of type clip. This is not supported for all configurations of models and can yield errors.
INFO:absl:Unable to initialize backend 'tpu_driver': NOT_FOUND: Unable to find driver in registry given worker: 
INFO:absl:Unable to initialize backend 'gpu': NOT_FOUND: Could not find registered platform with name: "cuda". Available platform names are: Interpreter Host
INFO:absl:Unable to initialize backend 'tpu': INVALID_ARGUMENT: TpuPlatform is not available.
WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-3-52c9dab549d0> in <module>
----> 1 model = FlaxCLIPModel.from_pretrained("clip-italian/clip-italian")

~/venvs/unbias_venv/lib/python3.7/site-packages/transformers/modeling_flax_utils.py in from_pretrained(cls, pretrained_model_name_or_path, dtype, *model_args, **kwargs)
    402                 else:
    403                     raise ValueError(
--> 404                         f"Trying to load the pretrained weight for {key} failed: checkpoint has shape "
    405                         f"{state[key].shape} which is incompatible with the model shape {random_state[key].shape}. "
    406                         "Using `ignore_mismatched_sizes=True` if you really want to load this checkpoint inside this "

ValueError: Trying to load the pretrained weight for ('text_projection', 'kernel') failed: checkpoint has shape (768, 512) which is incompatible with the model shape (512, 512). Using `ignore_mismatched_sizes=True` if you really want to load this checkpoint inside this model.
```
but for both of them, I got inconsistent shapes for the text_projection dense layer (it is expected to be (512,512) but BERT has hidden size 768, so in our checkpoints it is (768,512)).

If I try to ignore the mismatched shapes it seems to be working, but I think that many of the weights from the checkpoint are not used:
```python
model = FlaxCLIPModel.from_pretrained("clip-italian/clip-italian", ignore_mismatched_sizes=True)

# Output
You are using a model of type hybrid-clip to instantiate a model of type clip. This is not supported for all configurations of models and can yield errors.
Some weights of the model checkpoint at clip-italian/clip-italian were not used when initializing FlaxCLIPModel: {('vision_model', 'vision_model', 'encoder', 'layers', '4', 'self_attn', 'out_proj', 'bias'), ('vision_model', 'vision_model', 'encoder', 'layers', '9', 'self_attn', 'out_proj', 'bias'), ('text_model', 'encoder', 'layer', '7', 'attention', 'output', 'LayerNorm', 'scale'), ('text_model', 'embeddings', 'token_type_embeddings', 'embedding'), ('vision_model', 'vision_model', 'encoder', 'layers', '2', 'layer_norm2', 'scale'), ('vision_model', 'vision_model', 'embeddings', 'patch_embedding', 'kernel'), ('text_model', 'encoder', 'layer', '3', 'attention', 'output', 'dense', 'kernel'), ('text_model', 'encoder', 'layer', '10', 'attention', 'self', 'key', 'bias'), ('vision_model', 'vision_model', 'post_layernorm', 'bias'), ('text_model', 'encoder', 'layer', '0', 'intermediate', 'dense', 'bias'), ('vision_model', 'vision_model', 'encoder', 'layers', '3', 'layer_norm2', 'scale'), ('text_model', 'encoder', 'layer', '2', 'intermediate', 'dense', 'bias'), ('vision_model', 'vision_model', 'encoder', 'layers', '3', 'self_attn', 'v_proj', 'kernel'), ('text_model', 'encoder', 'layer', '8', 'attention', 'self', 'value', 'kernel'), ('text_model', 'encoder', 'layer', '5', 'attention', 'output', 'LayerNorm', 'scale'), ('text_model', 'encoder', 'layer', '4', 'attention', 'output', 'LayerNorm', 'bias'), ('text_model', 'encoder', 'layer', '8', 'output', 'dense', 'bias'), ('vision_model', 'vision_model', 'pre_layrnorm', 'scale'), ('text_model', 'encoder', 'layer', '4', 'output', 'LayerNorm', 'bias'), ('vision_model', 'vision_model', 'encoder', 'layers', '8', 'mlp', 'fc2', 'kernel'), ('vision_model', 'vision_model', 'encoder', 'layers', '1', 'self_attn', 'k_proj', 'kernel'), ('vision_model', 'vision_model', 'encoder', 'layers', '10', 'self_attn', 'out_proj', 'bias'), ('text_model', 'encoder', 'layer', '7', 'attention', 'output', 'dense', 'kernel'), ('vision_model', 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- This IS expected if you are initializing FlaxCLIPModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing FlaxCLIPModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of FlaxCLIPModel were not initialized from the model checkpoint at clip-italian/clip-italian and are newly initialized: {('text_model', 'final_layer_norm', 'scale'), ('vision_model', 'encoder', 'layers', '7', 'self_attn', 'v_proj', 'bias'), ('vision_model', 'encoder', 'layers', '6', 'layer_norm1', 'bias'), ('vision_model', 'encoder', 'layers', '1', 'self_attn', 'out_proj', 'bias'), ('text_model', 'encoder', 'layers', '10', 'self_attn', 'q_proj', 'bias'), ('vision_model', 'encoder', 'layers', '11', 'self_attn', 'v_proj', 'kernel'), ('text_model', 'encoder', 'layers', '7', 'layer_norm1', 'bias'), ('text_model', 'encoder', 'layers', '9', 'self_attn', 'out_proj', 'kernel'), ('vision_model', 'encoder', 'layers', '7', 'self_attn', 'out_proj', 'kernel'), ('text_model', 'encoder', 'layers', '5', 'mlp', 'fc1', 'bias'), ('vision_model', 'encoder', 'layers', '9', 'layer_norm1', 'scale'), ('vision_model', 'encoder', 'layers', '8', 'self_attn', 'k_proj', 'bias'), ('vision_model', 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'encoder', 'layers', '1', 'mlp', 'fc1', 'bias'), ('vision_model', 'encoder', 'layers', '9', 'layer_norm2', 'bias')}
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Some weights of FlaxCLIPModel were not initialized from the model checkpoint at clip-italian/clip-italian and are newly initialized because the shapes did not match:
- ('text_projection', 'kernel'): found shape (768, 512) in the checkpoint and (512, 512) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```

2. Next, I tried to force our configuration, so that it would force `config.text_config.hidden_size == 768` and let the shapes match at loading time:
```python
config = HybridCLIPConfig.from_pretrained("clip-italian/clip-italian")
config.logit_scale_init_value = 20  # required by FlaxCLIPModel
config.text_config.attention_dropout = 0.0  # required by FlaxCLIPModel

model = FlaxCLIPModel.from_pretrained("clip-italian/clip-italian", config=config)

# Output
Some weights of the model checkpoint at clip-italian/clip-italian were not used when initializing FlaxCLIPModel: {('vision_model', 'vision_model', 'encoder', 'layers', '4', 'self_attn', 'out_proj', 'bias'), ('vision_model', 'vision_model', 'encoder', 'layers', '9', 'self_attn', 'out_proj', 'bias'), ('text_model', 'encoder', 'layer', '7', 'attention', 'output', 'LayerNorm', 'scale'), ('text_model', 'embeddings', 'token_type_embeddings', 'embedding'), ('vision_model', 'vision_model', 'encoder', 'layers', '2', 'layer_norm2', 'scale'), ('vision_model', 'vision_model', 'embeddings', 'patch_embedding', 'kernel'), ('text_model', 'encoder', 'layer', '3', 'attention', 'output', 'dense', 'kernel'), ('text_model', 'encoder', 'layer', '10', 'attention', 'self', 'key', 'bias'), ('vision_model', 'vision_model', 'post_layernorm', 'bias'), ('text_model', 'encoder', 'layer', '0', 'intermediate', 'dense', 'bias'), ('vision_model', 'vision_model', 'encoder', 'layers', '3', 'layer_norm2', 'scale'), 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- This IS expected if you are initializing FlaxCLIPModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing FlaxCLIPModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of FlaxCLIPModel were not initialized from the model checkpoint at clip-italian/clip-italian and are newly initialized: {('text_model', 'final_layer_norm', 'scale'), ('vision_model', 'encoder', 'layers', '7', 'self_attn', 'v_proj', 'bias'), ('vision_model', 'encoder', 'layers', '6', 'layer_norm1', 'bias'), ('vision_model', 'encoder', 'layers', '1', 'self_attn', 'out_proj', 'bias'), ('text_model', 'encoder', 'layers', '10', 'self_attn', 'q_proj', 'bias'), ('vision_model', 'encoder', 'layers', '11', 'self_attn', 'v_proj', 'kernel'), ('text_model', 'encoder', 'layers', '7', 'layer_norm1', 'bias'), ('text_model', 'encoder', 'layers', '9', 'self_attn', 'out_proj', 'kernel'), ('vision_model', 'encoder', 'layers', '7', 'self_attn', 'out_proj', 'kernel'), ('text_model', 'encoder', 'layers', '5', 'mlp', 'fc1', 'bias'), ('vision_model', 'encoder', 'layers', '9', 'layer_norm1', 'scale'), ('vision_model', 'encoder', 'layers', '8', 'self_attn', 'k_proj', 'bias'), ('vision_model', 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'encoder', 'layers', '1', 'mlp', 'fc1', 'bias'), ('vision_model', 'encoder', 'layers', '9', 'layer_norm2', 'bias')}
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```

In this case, I don't have mismatching sizes but still many weights from our checkpoint are not used.  

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3. My last resource was to load the checkpoint with the hybrid class, transform its weights into f32, save it locally, and load it as pytorch model, but still, I had the same wrong initialization:
```python
model = FlaxHybridCLIP.from_pretrained("clip-italian/clip-italian")
def to_f32(t):
    return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, t)

model.params = to_f32(model.params)
model.save_pretrained("./clip-italian-f32")

vision_config = CLIPVisionConfig.from_pretrained("openai/clip-vit-base-patch32") 
text_config = CLIPTextConfig.from_pretrained("dbmdz/bert-base-italian-xxl-uncased")
config = CLIPConfig.from_text_vision_configs(text_config=text_config, vision_config=vision_config)
pt_model = CLIPModel.from_pretrained("./clip-italian-f32/", from_flax=True, config=config)

# Output: same as before (no errors, many layers initialized as new), I just didn't copy it here :)
```

## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->

This code to run flawlessly:

```python
model = FlaxCLIPModel.from_pretrained("clip-italian/clip-italian")

# or

model = CLIPModel.from_pretrained("clip-italian/clip-italian", from_flax=True)
```

Thank you in advance!
Hey @g8a9

It's not possible to load `FlaxHybridCLIP`  into `FlaxCLIP` since the module structure is different as the hybrid can use any pre-trained text and vision models. 

The hybrid clip model will soon be officially supported in `transformers` (see #13511), we are now calling it `VisionTextDualEncoder`. This will be available in both torch and flax. Stay tuned!<|||||>Nice to know, thanks! 
Do you think that once the `VisionTextDualEncoder` is out, we will be able to load our checkpoint with it?

(actually, my final goal is to have access to our two fine-tuned encoders, ViT and BERT, in pytorch)<|||||>>Do you think that once the VisionTextDualEncoder is out, we will be able to load our checkpoint with it?

The module structure is pretty much similar, so yes! If not I'll share a script to convert the old hybrid clip weights to this new class.<|||||>Hey @g8a9 

clip-italian (or any hybrid clip) model can now be loaded using the new `VisionTextDualEncoderModel`. Converting from flax to pt should also work.

```python3
from transformers import FlaxVisionTextDualEncoderModel, VisionTextDualEncoderModel

# `logit_scale` can be initialized using `config.logit_scale_init_value` attribute
model = FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian", logit_scale_init_value=1)
model.save_pretrained("clip-italin")

model_pt = VisionTextDualEncoderModel.from_pretrained("clip-italian", from_flax=True)
```

Let me know if this works for you and if you see any discrepancies in the result. I would like to use clip-Italian to feature this new model class :) <|||||>Do you think you could push the PT checkpoint and the processor (tokenizer/feature-extractor) for `clip-italian`? Going to use this model in doc examples :) <|||||>> Hey @g8a9
> 
> clip-italian (or any hybrid clip) model can now be loaded using the new `VisionTextDualEncoderModel`. Converting from flax to pt should also work.
> 
> ```python
> from transformers import FlaxVisionTextDualEncoderModel, VisionTextDualEncoderModel
> 
> # `logit_scale` can be initialized using `config.logit_scale_init_value` attribute
> model = FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian", logit_scale_init_value=1)
> model.save_pretrained("clip-italin")
> 
> model_pt = VisionTextDualEncoderModel.from_pretrained("clip-italian", from_flax=True)
> ```
> 
> Let me know if this works for you and if you see any discrepancies in the result. I would like to use clip-Italian to feature this new model class :)

this worked for me. Thanks for the solution.