def load_flax_weights_in_pytorch_model(pt_model, flax_state):
    """Load flax checkpoints in a PyTorch model"""

    try:
        import torch  # noqa: F401
    except (ImportError, ModuleNotFoundError):
        logger.error(
            "Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
            " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
            " instructions."
        )
        raise

    # check if we have bf16 weights
    is_type_bf16 = flatten_dict(jax.tree_util.tree_map(lambda x: x.dtype == jnp.bfloat16, flax_state)).values()
    if any(is_type_bf16):
        # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
        # and bf16 is not fully supported in PT yet.
        logger.warning(
            "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
            "before loading those in PyTorch model."
        )
        flax_state = jax.tree_util.tree_map(
            lambda params: params.astype(np.float32) if params.dtype == jnp.bfloat16 else params, flax_state
        )

    flax_state_dict = flatten_dict(flax_state)
    pt_model_dict = pt_model.state_dict()

    load_model_with_head_into_base_model = (pt_model.base_model_prefix in flax_state) and (
        pt_model.base_model_prefix not in {k.split(".")[0] for k in pt_model_dict.keys()}
    )
    load_base_model_into_model_with_head = (pt_model.base_model_prefix not in flax_state) and (
        pt_model.base_model_prefix in {k.split(".")[0] for k in pt_model_dict.keys()}
    )

    # keep track of unexpected & missing keys
    unexpected_keys = []
    missing_keys = set(pt_model_dict.keys())

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        has_base_model_prefix = flax_key_tuple[0] == pt_model.base_model_prefix
        require_base_model_prefix = ".".join((pt_model.base_model_prefix,) + flax_key_tuple) in pt_model_dict

        # adapt flax_key to prepare for loading from/to base model only
        if load_model_with_head_into_base_model and has_base_model_prefix:
            flax_key_tuple = flax_key_tuple[1:]
        elif load_base_model_into_model_with_head and require_base_model_prefix:
            flax_key_tuple = (pt_model.base_model_prefix,) + flax_key_tuple

        # rename flax weights to PyTorch format
        if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(flax_key_tuple) not in pt_model_dict:
            # conv layer
            flax_key_tuple = flax_key_tuple[:-1] + ("weight",)
            flax_tensor = jnp.transpose(flax_tensor, (3, 2, 0, 1))
        elif flax_key_tuple[-1] == "kernel" and ".".join(flax_key_tuple) not in pt_model_dict:
            # linear layer
            flax_key_tuple = flax_key_tuple[:-1] + ("weight",)
            flax_tensor = flax_tensor.T
        elif flax_key_tuple[-1] in ["scale", "embedding"]:
            flax_key_tuple = flax_key_tuple[:-1] + ("weight",)

        # adding batch stats from flax batch norm to pt
        elif "mean" in flax_key_tuple[-1]:
            flax_key_tuple = flax_key_tuple[:-1] + ("running_mean",)
        elif "var" in flax_key_tuple[-1]:
            flax_key_tuple = flax_key_tuple[:-1] + ("running_var",)

        if "batch_stats" in flax_state:
            flax_key = ".".join(flax_key_tuple[1:])  # Remove the params/batch_stats header
        else:
            flax_key = ".".join(flax_key_tuple)

        # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
        special_pt_names = {}
        # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
        for key in pt_model_dict:
            key_components = key.split(".")
            name = None
            if key_components[-3::2] == ["parametrizations", "original0"]:
                name = key_components[-2] + "_g"
            elif key_components[-3::2] == ["parametrizations", "original1"]:
                name = key_components[-2] + "_v"
            if name is not None:
                key_components = key_components[:-3] + [name]
                key_to_check = ".".join(key_components)
                special_pt_names[key_to_check] = key

        if flax_key in special_pt_names:
            flax_key = special_pt_names[flax_key]

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    f"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
                    f"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."
                )
            else:
                # add weight to pytorch dict
                flax_tensor = np.asarray(flax_tensor) if not isinstance(flax_tensor, np.ndarray) else flax_tensor
                pt_model_dict[flax_key] = torch.from_numpy(flax_tensor)
                # remove from missing keys
                missing_keys.remove(flax_key)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(flax_key)

    pt_model.load_state_dict(pt_model_dict)

    # re-transform missing_keys to list
    missing_keys = list(missing_keys)

    if len(unexpected_keys) > 0:
        logger.warning(
            "Some weights of the Flax model were not used when initializing the PyTorch model"
            f" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
            f" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
            " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
            f" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
            " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
            " FlaxBertForSequenceClassification model)."
        )
    else:
        logger.warning(f"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n")
    if len(missing_keys) > 0:
        logger.warning(
            f"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
            f" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
            " use it for predictions and inference."
        )
    else:
        logger.warning(
            f"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
            "If your task is similar to the task the model of the checkpoint was trained on, "
            f"you can already use {pt_model.__class__.__name__} for predictions without further training."
        )

    return pt_model
Load flax checkpoints in a PyTorch model
dpkd/transformers/src/transformers/modeling_flax_pytorch_utils.py