# 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 MobileNetV2 checkpoints from the tensorflow/models library."""


import argparse
import json
import re
from pathlib import Path

import torch
from PIL import Image

import requests
from huggingface_hub import hf_hub_download
from transformers import (
    MobileNetV2Config,
    MobileNetV2ForImageClassification,
    MobileNetV2ForSemanticSegmentation,
    MobileNetV2ImageProcessor,
    load_tf_weights_in_mobilenet_v2,
)
from transformers.utils import logging


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


def get_mobilenet_v2_config(model_name):
    config = MobileNetV2Config(layer_norm_eps=0.001)

    if "quant" in model_name:
        raise ValueError("Quantized models are not supported.")

    matches = re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$", model_name)
    if matches:
        config.depth_multiplier = float(matches[1])
        config.image_size = int(matches[2])

    if model_name.startswith("deeplabv3_"):
        config.output_stride = 8
        config.num_labels = 21
        filename = "pascal-voc-id2label.json"
    else:
        # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
        # of the usual 1000. The first class (index 0) is "background".
        config.num_labels = 1001
        filename = "imagenet-1k-id2label.json"

    repo_id = "huggingface/label-files"
    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))

    if config.num_labels == 1001:
        id2label = {int(k) + 1: v for k, v in id2label.items()}
        id2label[0] = "background"
    else:
        id2label = {int(k): v for k, v in id2label.items()}

    config.id2label = id2label
    config.label2id = {v: k for k, v in id2label.items()}

    return config


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


@torch.no_grad()
def convert_movilevit_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub=False):
    """
    Copy/paste/tweak model's weights to our MobileNetV2 structure.
    """
    config = get_mobilenet_v2_config(model_name)

    # Load 🤗 model
    if model_name.startswith("deeplabv3_"):
        model = MobileNetV2ForSemanticSegmentation(config).eval()
    else:
        model = MobileNetV2ForImageClassification(config).eval()

    # Load weights from TensorFlow checkpoint
    load_tf_weights_in_mobilenet_v2(model, config, checkpoint_path)

    # Check outputs on an image, prepared by MobileNetV2ImageProcessor
    feature_extractor = MobileNetV2ImageProcessor(
        crop_size={"width": config.image_size, "height": config.image_size},
        size={"shortest_edge": config.image_size + 32},
    )
    encoding = feature_extractor(images=prepare_img(), return_tensors="pt")
    outputs = model(**encoding)
    logits = outputs.logits

    if model_name.startswith("deeplabv3_"):
        assert logits.shape == (1, 21, 65, 65)

        if model_name == "deeplabv3_mobilenet_v2_1.0_513":
            expected_logits = torch.tensor(
                [
                    [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
                    [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
                    [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
                ]
            )

        else:
            raise ValueError(f"Unknown model name: {model_name}")

        assert torch.allclose(logits[0, :3, :3, :3], expected_logits, atol=1e-4)
    else:
        assert logits.shape == (1, 1001)

        if model_name == "mobilenet_v2_1.4_224":
            expected_logits = torch.tensor([0.0181, -1.0015, 0.4688])
        elif model_name == "mobilenet_v2_1.0_224":
            expected_logits = torch.tensor([0.2445, -1.1993, 0.1905])
        elif model_name == "mobilenet_v2_0.75_160":
            expected_logits = torch.tensor([0.2482, 0.4136, 0.6669])
        elif model_name == "mobilenet_v2_0.35_96":
            expected_logits = torch.tensor([0.1451, -0.4624, 0.7192])
        else:
            expected_logits = None

        if expected_logits is not None:
            assert torch.allclose(logits[0, :3], expected_logits, atol=1e-4)

    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
    print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
    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 push_to_hub:
        print("Pushing to the hub...")
        repo_id = "google/" + model_name
        feature_extractor.push_to_hub(repo_id)
        model.push_to_hub(repo_id)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--model_name",
        default="mobilenet_v2_1.0_224",
        type=str,
        help="Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<depth>_<size>'.",
    )
    parser.add_argument(
        "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
    )
    parser.add_argument(
        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
    )

    args = parser.parse_args()
    convert_movilevit_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )
