# coding=utf-8
# Copyright 2021 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 Image GPT checkpoints."""

import argparse
import os

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


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


def load_tf_weights_in_imagegpt(model, config, imagegpt_checkpoint_path):
    """
    Load tf checkpoints in a pytorch model
    """
    try:
        import re

        import tensorflow as tf
    except ImportError:
        logger.error(
            "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
            "https://www.tensorflow.org/install/ for installation instructions."
        )
        raise
    tf_path = os.path.abspath(imagegpt_checkpoint_path)
    logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
    # Load weights from TF model
    init_vars = tf.train.list_variables(tf_path)
    names = []
    arrays = []

    for name, shape in init_vars:
        logger.info(f"Loading TF weight {name} with shape {shape}")
        array = tf.train.load_variable(tf_path, name)
        names.append(name)
        arrays.append(array.squeeze())

    for name, array in zip(names, arrays):
        name = name[6:]  # skip "model/"
        name = name.split("/")

        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
        # which are not required for using pretrained model
        if (
            any(
                n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
                for n in name
            )
            or name[-1] == "_step"
        ):
            logger.info("Skipping {}".format("/".join(name)))
            continue

        pointer = model
        if name[-1] != "wtet":
            pointer = getattr(pointer, "transformer")

        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] == "w" or scope_names[0] == "g":
                pointer = getattr(pointer, "weight")
            elif scope_names[0] == "b":
                pointer = getattr(pointer, "bias")
            elif scope_names[0] == "wpe" or scope_names[0] == "wte":
                pointer = getattr(pointer, scope_names[0])
                pointer = getattr(pointer, "weight")
            elif scope_names[0] in ["q_proj", "k_proj", "v_proj"]:
                pointer = getattr(pointer, "c_attn")
                pointer = getattr(pointer, "weight")
            elif len(name) == 3 and name[1] == "attn" and scope_names[0] == "c_proj":
                pointer = getattr(pointer, scope_names[0])
                pointer = getattr(pointer, "weight")
            elif scope_names[0] == "wtet":
                pointer = getattr(pointer, "lm_head")
                pointer = getattr(pointer, "weight")
            elif scope_names[0] == "sos":
                pointer = getattr(pointer, "wte")
                pointer = getattr(pointer, "weight")
            else:
                pointer = getattr(pointer, scope_names[0])
            if len(scope_names) >= 2:
                num = int(scope_names[1])
                pointer = pointer[num]

        if len(name) > 1 and name[1] == "attn" or name[-1] == "wtet" or name[-1] == "sos" or name[-1] == "wte":
            pass  # array is used to initialize only part of the pointer so sizes won't match
        else:
            try:
                assert pointer.shape == array.shape
            except AssertionError as e:
                e.args += (pointer.shape, array.shape)
                raise

        logger.info(f"Initialize PyTorch weight {name}")

        if name[-1] == "q_proj":
            pointer.data[:, : config.n_embd] = torch.from_numpy(array.reshape(config.n_embd, config.n_embd)).T
        elif name[-1] == "k_proj":
            pointer.data[:, config.n_embd : 2 * config.n_embd] = torch.from_numpy(
                array.reshape(config.n_embd, config.n_embd)
            ).T
        elif name[-1] == "v_proj":
            pointer.data[:, 2 * config.n_embd :] = torch.from_numpy(array.reshape(config.n_embd, config.n_embd)).T
        elif len(name) == 3 and name[1] == "attn" and name[2] == "c_proj":
            pointer.data = torch.from_numpy(array.reshape(config.n_embd, config.n_embd))
        elif name[-1] == "wtet":
            pointer.data = torch.from_numpy(array)
        elif name[-1] == "wte":
            pointer.data[: config.vocab_size - 1, :] = torch.from_numpy(array)
        elif name[-1] == "sos":
            pointer.data[-1] = torch.from_numpy(array)
        else:
            pointer.data = torch.from_numpy(array)

    return model


def convert_imagegpt_checkpoint_to_pytorch(imagegpt_checkpoint_path, model_size, pytorch_dump_folder_path):
    # Construct configuration depending on size
    MODELS = {"small": (512, 8, 24), "medium": (1024, 8, 36), "large": (1536, 16, 48)}
    n_embd, n_head, n_layer = MODELS[model_size]  # set model hyperparameters
    config = ImageGPTConfig(n_embd=n_embd, n_layer=n_layer, n_head=n_head)
    model = ImageGPTForCausalLM(config)

    # Load weights from numpy
    load_tf_weights_in_imagegpt(model, config, imagegpt_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(
        "--imagegpt_checkpoint_path",
        default=None,
        type=str,
        required=True,
        help="Path to the TensorFlow checkpoint path.",
    )
    parser.add_argument(
        "--model_size",
        default=None,
        type=str,
        required=True,
        help="Size of the model (can be either 'small', 'medium' or 'large').",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    args = parser.parse_args()
    convert_imagegpt_checkpoint_to_pytorch(
        args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
    )
