# 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 json
import os

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
logger = logging.get_logger(__name__)


def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path):
    """Load tf pre-trained weights in a pytorch model (from NumPy arrays here)"""
    import re

    import numpy as np

    if ".ckpt" in openai_checkpoint_folder_path:
        openai_checkpoint_folder_path = os.path.dirname(openai_checkpoint_folder_path)

    logger.info(f"Loading weights from {openai_checkpoint_folder_path}")

    with open(openai_checkpoint_folder_path + "/parameters_names.json", "r", encoding="utf-8") as names_handle:
        names = json.load(names_handle)
    with open(openai_checkpoint_folder_path + "/params_shapes.json", "r", encoding="utf-8") as shapes_handle:
        shapes = json.load(shapes_handle)
    offsets = np.cumsum([np.prod(shape) for shape in shapes])
    init_params = [np.load(openai_checkpoint_folder_path + f"/params_{n}.npy") for n in range(10)]
    init_params = np.split(np.concatenate(init_params, 0), offsets)[:-1]
    init_params = [param.reshape(shape) for param, shape in zip(init_params, shapes)]

    # This was used when we had a single embedding matrix for positions and tokens
    # init_params[0] = np.concatenate([init_params[1], init_params[0]], 0)
    # del init_params[1]
    init_params = [arr.squeeze() for arr in init_params]

    # Check that the token and position embeddings weight dimensions map those of the init parameters.
    if model.tokens_embed.weight.shape != init_params[1].shape:
        raise ValueError(
            f"tokens_embed.weight.shape: {model.tokens_embed.weight.shape} does not match init_param[1].shape:"
            f" {init_params[1].shape}"
        )

    if model.positions_embed.weight.shape != init_params[0].shape:
        raise ValueError(
            f"positions_embed.weight.shape: {model.positions_embed.weight.shape} does not match init_param[0].shape:"
            f" {init_params[0].shape}"
        )

    model.tokens_embed.weight.data = torch.from_numpy(init_params[1])
    model.positions_embed.weight.data = torch.from_numpy(init_params[0])
    names.pop(0)
    # Pop position and token embedding arrays
    init_params.pop(0)
    init_params.pop(0)

    for name, array in zip(names, init_params):  # names[1:n_transfer], init_params[1:n_transfer]):
        name = name[6:]  # skip "model/"
        if name[-2:] != ":0":
            raise ValueError(f"Layer {name} does not end with :0")
        name = name[:-2]
        name = name.split("/")
        pointer = model
        for m_name in name:
            if re.fullmatch(r"[A-Za-z]+\d+", m_name):
                scope_names = re.split(r"(\d+)", m_name)
            else:
                scope_names = [m_name]
            if scope_names[0] == "g":
                pointer = getattr(pointer, "weight")
            elif scope_names[0] == "b":
                pointer = getattr(pointer, "bias")
            elif scope_names[0] == "w":
                pointer = getattr(pointer, "weight")
            else:
                pointer = getattr(pointer, scope_names[0])
            if len(scope_names) >= 2:
                num = int(scope_names[1])
                pointer = pointer[num]

        # Ensure that the pointer and array have compatible shapes.
        if pointer.shape != array.shape:
            raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")

        logger.info(f"Initialize PyTorch weight {name}")
        pointer.data = torch.from_numpy(array)
    return model


def convert_openai_checkpoint_to_pytorch(openai_checkpoint_folder_path, openai_config_file, pytorch_dump_folder_path):
    # Construct model
    if openai_config_file == "":
        config = OpenAIGPTConfig()
    else:
        config = OpenAIGPTConfig.from_json_file(openai_config_file)
    model = OpenAIGPTModel(config)

    # Load weights from numpy
    load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_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(
        "--openai_checkpoint_folder_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(
        "--openai_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_openai_checkpoint_to_pytorch(
        args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
    )
