# 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 PoolFormer checkpoints from the original repository. URL: https://github.com/sail-sg/poolformer"""

import argparse
import json
from collections import OrderedDict
from pathlib import Path

import torch
from PIL import Image

import requests
from huggingface_hub import hf_hub_download
from transformers import PoolFormerConfig, PoolFormerFeatureExtractor, PoolFormerForImageClassification
from transformers.utils import logging


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


def replace_key_with_offset(key, offset, original_name, new_name):
    """
    Replaces the key by subtracting the offset from the original layer number
    """
    to_find = original_name.split(".")[0]
    key_list = key.split(".")
    orig_block_num = int(key_list[key_list.index(to_find) - 2])
    layer_num = int(key_list[key_list.index(to_find) - 1])
    new_block_num = orig_block_num - offset

    key = key.replace(f"{orig_block_num}.{layer_num}.{original_name}", f"block.{new_block_num}.{layer_num}.{new_name}")
    return key


def rename_keys(state_dict):
    new_state_dict = OrderedDict()
    total_embed_found, patch_emb_offset = 0, 0
    for key, value in state_dict.items():
        if key.startswith("network"):
            key = key.replace("network", "poolformer.encoder")
        if "proj" in key:
            # Works for the first embedding as well as the internal embedding layers
            if key.endswith("bias") and "patch_embed" not in key:
                patch_emb_offset += 1
            to_replace = key[: key.find("proj")]
            key = key.replace(to_replace, f"patch_embeddings.{total_embed_found}.")
            key = key.replace("proj", "projection")
            if key.endswith("bias"):
                total_embed_found += 1
        if "patch_embeddings" in key:
            key = "poolformer.encoder." + key
        if "mlp.fc1" in key:
            key = replace_key_with_offset(key, patch_emb_offset, "mlp.fc1", "output.conv1")
        if "mlp.fc2" in key:
            key = replace_key_with_offset(key, patch_emb_offset, "mlp.fc2", "output.conv2")
        if "norm1" in key:
            key = replace_key_with_offset(key, patch_emb_offset, "norm1", "before_norm")
        if "norm2" in key:
            key = replace_key_with_offset(key, patch_emb_offset, "norm2", "after_norm")
        if "layer_scale_1" in key:
            key = replace_key_with_offset(key, patch_emb_offset, "layer_scale_1", "layer_scale_1")
        if "layer_scale_2" in key:
            key = replace_key_with_offset(key, patch_emb_offset, "layer_scale_2", "layer_scale_2")
        if "head" in key:
            key = key.replace("head", "classifier")
        new_state_dict[key] = value
    return new_state_dict


# We will verify our results on a COCO image
def prepare_img():
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)

    return image


@torch.no_grad()
def convert_poolformer_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path):
    """
    Copy/paste/tweak model's weights to our PoolFormer structure.
    """

    # load default PoolFormer configuration
    config = PoolFormerConfig()

    # set attributes based on model_name
    repo_id = "datasets/huggingface/label-files"
    size = model_name[-3:]
    config.num_labels = 1000
    filename = "imagenet-1k-id2label.json"
    expected_shape = (1, 1000)

    # set config attributes
    id2label = json.load(open(hf_hub_download(repo_id, filename), "r"))
    id2label = {int(k): v for k, v in id2label.items()}
    config.id2label = id2label
    config.label2id = {v: k for k, v in id2label.items()}
    if size == "s12":
        config.depths = [2, 2, 6, 2]
        config.hidden_sizes = [64, 128, 320, 512]
        config.mlp_ratio = 4.0
        crop_pct = 0.9
    elif size == "s24":
        config.depths = [4, 4, 12, 4]
        config.hidden_sizes = [64, 128, 320, 512]
        config.mlp_ratio = 4.0
        crop_pct = 0.9
    elif size == "s36":
        config.depths = [6, 6, 18, 6]
        config.hidden_sizes = [64, 128, 320, 512]
        config.mlp_ratio = 4.0
        config.layer_scale_init_value = 1e-6
        crop_pct = 0.9
    elif size == "m36":
        config.depths = [6, 6, 18, 6]
        config.hidden_sizes = [96, 192, 384, 768]
        config.mlp_ratio = 4.0
        config.layer_scale_init_value = 1e-6
        crop_pct = 0.95
    elif size == "m48":
        config.depths = [8, 8, 24, 8]
        config.hidden_sizes = [96, 192, 384, 768]
        config.mlp_ratio = 4.0
        config.layer_scale_init_value = 1e-6
        crop_pct = 0.95
    else:
        raise ValueError(f"Size {size} not supported")

    # load feature extractor
    feature_extractor = PoolFormerFeatureExtractor(crop_pct=crop_pct)

    # Prepare image
    image = prepare_img()
    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values

    logger.info(f"Converting model {model_name}...")

    # load original state dict
    state_dict = torch.load(checkpoint_path, map_location=torch.device("cpu"))

    # rename keys
    state_dict = rename_keys(state_dict)

    # create HuggingFace model and load state dict
    model = PoolFormerForImageClassification(config)
    model.load_state_dict(state_dict)
    model.eval()

    # Define feature extractor
    feature_extractor = PoolFormerFeatureExtractor(crop_pct=crop_pct)
    pixel_values = feature_extractor(images=prepare_img(), return_tensors="pt").pixel_values

    # forward pass
    outputs = model(pixel_values)
    logits = outputs.logits

    # define expected logit slices for different models
    if size == "s12":
        expected_slice = torch.tensor([-0.3045, -0.6758, -0.4869])
    elif size == "s24":
        expected_slice = torch.tensor([0.4402, -0.1374, -0.8045])
    elif size == "s36":
        expected_slice = torch.tensor([-0.6080, -0.5133, -0.5898])
    elif size == "m36":
        expected_slice = torch.tensor([0.3952, 0.2263, -1.2668])
    elif size == "m48":
        expected_slice = torch.tensor([0.1167, -0.0656, -0.3423])
    else:
        raise ValueError(f"Size {size} not supported")

    # verify logits
    assert logits.shape == expected_shape
    assert torch.allclose(logits[0, :3], expected_slice, atol=1e-2)

    # finally, save model and feature extractor
    logger.info(f"Saving PyTorch model and feature extractor to {pytorch_dump_folder_path}...")
    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
    model.save_pretrained(pytorch_dump_folder_path)
    print(f"Saving feature extractor to {pytorch_dump_folder_path}")
    feature_extractor.save_pretrained(pytorch_dump_folder_path)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "--model_name",
        default="poolformer_s12",
        type=str,
        help="Name of the model you'd like to convert.",
    )
    parser.add_argument(
        "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
    )
    args = parser.parse_args()
    convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
