def convert_tf_weight_name_to_pt_weight_name(
    tf_name, start_prefix_to_remove="", tf_weight_shape=None, name_scope=None
):
    """
    Convert a TF 2.0 model variable name in a pytorch model weight name.

    Conventions for TF2.0 scopes -> PyTorch attribute names conversions:

        - '$1___$2' is replaced by $2 (can be used to duplicate or remove layers in TF2.0 vs PyTorch)
        - '_._' is replaced by a new level separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList)

    return tuple with:

        - pytorch model weight name
        - transpose: `TransposeType` member indicating whether and how TF2.0 and PyTorch weights matrices should be
          transposed with regards to each other
    """
    if name_scope is not None:
        if not tf_name.startswith(name_scope):
            raise ValueError(
                f"Weight name {tf_name} does not start with name_scope {name_scope}. This is an internal error "
                "in Transformers, so (unless you were doing something really evil) please open an issue to report it!"
            )
        tf_name = tf_name[len(name_scope) :]
        tf_name = tf_name.lstrip("/")
    tf_name = tf_name.replace(":0", "")  # device ids
    tf_name = re.sub(
        r"/[^/]*___([^/]*)/", r"/\1/", tf_name
    )  # '$1___$2' is replaced by $2 (can be used to duplicate or remove layers in TF2.0 vs PyTorch)
    tf_name = tf_name.replace(
        "_._", "/"
    )  # '_._' is replaced by a level separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList)
    tf_name = re.sub(r"//+", "/", tf_name)  # Remove empty levels at the end
    tf_name = tf_name.split("/")  # Convert from TF2.0 '/' separators to PyTorch '.' separators
    # Some weights have a single name without "/" such as final_logits_bias in BART
    if len(tf_name) > 1:
        tf_name = tf_name[1:]  # Remove level zero

    tf_weight_shape = list(tf_weight_shape)

    # When should we transpose the weights
    if tf_name[-1] == "kernel" and tf_weight_shape is not None and len(tf_weight_shape) == 4:
        transpose = TransposeType.CONV2D
    elif tf_name[-1] == "kernel" and tf_weight_shape is not None and len(tf_weight_shape) == 3:
        transpose = TransposeType.CONV1D
    elif bool(
        tf_name[-1] in ["kernel", "pointwise_kernel", "depthwise_kernel"]
        or "emb_projs" in tf_name
        or "out_projs" in tf_name
    ):
        transpose = TransposeType.SIMPLE
    else:
        transpose = TransposeType.NO

    # Convert standard TF2.0 names in PyTorch names
    if tf_name[-1] == "kernel" or tf_name[-1] == "embeddings" or tf_name[-1] == "gamma":
        tf_name[-1] = "weight"
    if tf_name[-1] == "beta":
        tf_name[-1] = "bias"

    # The SeparableConv1D TF layer contains two weights that are translated to PyTorch Conv1D here
    if tf_name[-1] == "pointwise_kernel" or tf_name[-1] == "depthwise_kernel":
        tf_name[-1] = tf_name[-1].replace("_kernel", ".weight")

    # Remove prefix if needed
    tf_name = ".".join(tf_name)
    if start_prefix_to_remove:
        tf_name = tf_name.replace(start_prefix_to_remove, "", 1)

    return tf_name, transpose

    Convert a TF 2.0 model variable name in a pytorch model weight name.

    Conventions for TF2.0 scopes -> PyTorch attribute names conversions:

        - '$1___$2' is replaced by $2 (can be used to duplicate or remove layers in TF2.0 vs PyTorch)
        - '_._' is replaced by a new level separation (can be used to convert TF2.0 lists in PyTorch nn.ModulesList)

    return tuple with:

        - pytorch model weight name
        - transpose: `TransposeType` member indicating whether and how TF2.0 and PyTorch weights matrices should be
          transposed with regards to each other
    
dpkd/transformers/src/transformers/modeling_tf_pytorch_utils.py