# What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
Fix last models for common tests that are too big.. # What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# 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.
"""Convert YOSO checkpoints from the original repository. URL: https://github.com/mlpen/YOSO"""

import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM


def rename_key(orig_key):
    if "model" in orig_key:
        orig_key = orig_key.replace("model.", "")
    if "norm1" in orig_key:
        orig_key = orig_key.replace("norm1", "attention.output.LayerNorm")
    if "norm2" in orig_key:
        orig_key = orig_key.replace("norm2", "output.LayerNorm")
    if "norm" in orig_key:
        orig_key = orig_key.replace("norm", "LayerNorm")
    if "transformer" in orig_key:
        layer_num = orig_key.split(".")[0].split("_")[-1]
        orig_key = orig_key.replace(f"transformer_{layer_num}", f"encoder.layer.{layer_num}")
    if "mha.attn" in orig_key:
        orig_key = orig_key.replace("mha.attn", "attention.self")
    if "mha" in orig_key:
        orig_key = orig_key.replace("mha", "attention")
    if "W_q" in orig_key:
        orig_key = orig_key.replace("W_q", "self.query")
    if "W_k" in orig_key:
        orig_key = orig_key.replace("W_k", "self.key")
    if "W_v" in orig_key:
        orig_key = orig_key.replace("W_v", "self.value")
    if "ff1" in orig_key:
        orig_key = orig_key.replace("ff1", "intermediate.dense")
    if "ff2" in orig_key:
        orig_key = orig_key.replace("ff2", "output.dense")
    if "ff" in orig_key:
        orig_key = orig_key.replace("ff", "output.dense")
    if "mlm_class" in orig_key:
        orig_key = orig_key.replace("mlm.mlm_class", "cls.predictions.decoder")
    if "mlm" in orig_key:
        orig_key = orig_key.replace("mlm", "cls.predictions.transform")
    if "cls" not in orig_key:
        orig_key = "yoso." + orig_key

    return orig_key


def convert_checkpoint_helper(max_position_embeddings, orig_state_dict):
    for key in orig_state_dict.copy().keys():
        val = orig_state_dict.pop(key)

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            orig_state_dict[rename_key(key)] = val

    orig_state_dict["cls.predictions.bias"] = orig_state_dict["cls.predictions.decoder.bias"]
    orig_state_dict["yoso.embeddings.position_ids"] = torch.arange(max_position_embeddings).expand((1, -1)) + 2

    return orig_state_dict


def convert_yoso_checkpoint(checkpoint_path, yoso_config_file, pytorch_dump_path):
    orig_state_dict = torch.load(checkpoint_path, map_location="cpu")["model_state_dict"]
    config = YosoConfig.from_json_file(yoso_config_file)
    model = YosoForMaskedLM(config)

    new_state_dict = convert_checkpoint_helper(config.max_position_embeddings, orig_state_dict)

    print(model.load_state_dict(new_state_dict))
    model.eval()
    model.save_pretrained(pytorch_dump_path)

    print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
    )
    parser.add_argument(
        "--config_file",
        default=None,
        type=str,
        required=True,
        help="The json file for YOSO model config.",
    )
    parser.add_argument(
        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    args = parser.parse_args()
    convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)

# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# 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.
"""Convert YOSO checkpoints from the original repository. URL: https://github.com/mlpen/YOSO"""

import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM


def rename_key(orig_key):
    if "model" in orig_key:
        orig_key = orig_key.replace("model.", "")
    if "norm1" in orig_key:
        orig_key = orig_key.replace("norm1", "attention.output.LayerNorm")
    if "norm2" in orig_key:
        orig_key = orig_key.replace("norm2", "output.LayerNorm")
    if "norm" in orig_key:
        orig_key = orig_key.replace("norm", "LayerNorm")
    if "transformer" in orig_key:
        layer_num = orig_key.split(".")[0].split("_")[-1]
        orig_key = orig_key.replace(f"transformer_{layer_num}", f"encoder.layer.{layer_num}")
    if "mha.attn" in orig_key:
        orig_key = orig_key.replace("mha.attn", "attention.self")
    if "mha" in orig_key:
        orig_key = orig_key.replace("mha", "attention")
    if "W_q" in orig_key:
        orig_key = orig_key.replace("W_q", "self.query")
    if "W_k" in orig_key:
        orig_key = orig_key.replace("W_k", "self.key")
    if "W_v" in orig_key:
        orig_key = orig_key.replace("W_v", "self.value")
    if "ff1" in orig_key:
        orig_key = orig_key.replace("ff1", "intermediate.dense")
    if "ff2" in orig_key:
        orig_key = orig_key.replace("ff2", "output.dense")
    if "ff" in orig_key:
        orig_key = orig_key.replace("ff", "output.dense")
    if "mlm_class" in orig_key:
        orig_key = orig_key.replace("mlm.mlm_class", "cls.predictions.decoder")
    if "mlm" in orig_key:
        orig_key = orig_key.replace("mlm", "cls.predictions.transform")
    if "cls" not in orig_key:
        orig_key = "yoso." + orig_key

    return orig_key


def convert_checkpoint_helper(max_position_embeddings, orig_state_dict):
    for key in orig_state_dict.copy().keys():
        val = orig_state_dict.pop(key)

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            orig_state_dict[rename_key(key)] = val

    orig_state_dict["cls.predictions.bias"] = orig_state_dict["cls.predictions.decoder.bias"]
    orig_state_dict["yoso.embeddings.position_ids"] = torch.arange(max_position_embeddings).expand((1, -1)) + 2

    return orig_state_dict


def convert_yoso_checkpoint(checkpoint_path, yoso_config_file, pytorch_dump_path):
    orig_state_dict = torch.load(checkpoint_path, map_location="cpu")["model_state_dict"]
    config = YosoConfig.from_json_file(yoso_config_file)
    model = YosoForMaskedLM(config)

    new_state_dict = convert_checkpoint_helper(config.max_position_embeddings, orig_state_dict)

    print(model.load_state_dict(new_state_dict))
    model.eval()
    model.save_pretrained(pytorch_dump_path)

    print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
    )
    parser.add_argument(
        "--config_file",
        default=None,
        type=str,
        required=True,
        help="The json file for YOSO model config.",
    )
    parser.add_argument(
        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    args = parser.parse_args()
    convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
