# What does this PR do?

[Sorry for the spams, GitHub had some issues]

Same as #24075, but that PR got freezed after I force pushed (after rebase), and my changes to address the comments were not able to appear)

(amy have already approved #24075)

----

Byebye!

EOL of python 3.7 is `2023/06/27`.

https://endoflife.date/python
⚠️ Time to say goodbye to py37. # What does this PR do?

[Sorry for the spams, GitHub had some issues]

Same as #24075, but that PR got freezed after I force pushed (after rebase), and my changes to address the comments were not able to appear)

(amy have already approved #24075)

----

Byebye!

EOL of python 3.7 is `2023/06/27`.

https://endoflife.date/python
# coding=utf-8
# Copyright 2018 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 OpenAI GPT checkpoint."""


import argparse

import torch

from transformers import GPT2Config, GPT2Model, load_tf_weights_in_gpt2
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, pytorch_dump_folder_path):
    # Construct model
    if gpt2_config_file == "":
        config = GPT2Config()
    else:
        config = GPT2Config.from_json_file(gpt2_config_file)
    model = GPT2Model(config)

    # Load weights from numpy
    load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path)

    # Save pytorch-model
    pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
    pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
    print(f"Save PyTorch model to {pytorch_weights_dump_path}")
    torch.save(model.state_dict(), pytorch_weights_dump_path)
    print(f"Save configuration file to {pytorch_config_dump_path}")
    with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
        f.write(config.to_json_string())


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    parser.add_argument(
        "--gpt2_config_file",
        default="",
        type=str,
        help=(
            "An optional config json file corresponding to the pre-trained OpenAI model. \n"
            "This specifies the model architecture."
        ),
    )
    args = parser.parse_args()
    convert_gpt2_checkpoint_to_pytorch(args.gpt2_checkpoint_path, args.gpt2_config_file, args.pytorch_dump_folder_path)

# coding=utf-8
# Copyright 2018 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 OpenAI GPT checkpoint."""


import argparse

import torch

from transformers import GPT2Config, GPT2Model, load_tf_weights_in_gpt2
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, pytorch_dump_folder_path):
    # Construct model
    if gpt2_config_file == "":
        config = GPT2Config()
    else:
        config = GPT2Config.from_json_file(gpt2_config_file)
    model = GPT2Model(config)

    # Load weights from numpy
    load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path)

    # Save pytorch-model
    pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
    pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
    print(f"Save PyTorch model to {pytorch_weights_dump_path}")
    torch.save(model.state_dict(), pytorch_weights_dump_path)
    print(f"Save configuration file to {pytorch_config_dump_path}")
    with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
        f.write(config.to_json_string())


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    parser.add_argument(
        "--gpt2_config_file",
        default="",
        type=str,
        help=(
            "An optional config json file corresponding to the pre-trained OpenAI model. \n"
            "This specifies the model architecture."
        ),
    )
    args = parser.parse_args()
    convert_gpt2_checkpoint_to_pytorch(args.gpt2_checkpoint_path, args.gpt2_config_file, args.pytorch_dump_folder_path)
