# What does this PR do?

This PR migrates the internal of the push to Hub strategy inside the Trainer to the upload methods instead of the old git approach. The goal is to benefit from the improvements in terms of speed (for instance `upload_folder` is faster than `Repository` on Colab) and avoid some weird sync errors we have seen happen in the past.

This migration breaks a few things. I'm listing them for the sake of completeness but I don't think those are important breaking changes.
1. the return of `Trainer.push_to_hub` will change: in the blocking case it's the url of the repo instead of the URL of the commit. In the non-blocking case, it's the Future object returned by the `huggingface_hub` libreary (instead of a tuple commit address, job in progress)
2. before the migration, it was instant to cancel the push in progress when at the end of the training, to then push the final version of the Hub (but we also had some weird bugs). After the migration we will have to wait for the last push to be completed before pusing the final version of the model.
3. the `output_dir` of the `Trainer` won't be a git repository which is an exact clone of the model repo anymore.

Note that I chose to push checkpoint jobs in two `upload_folder`: one for the model and one for the optimizer state (roughly). This way I can cancel the second one if it hasn't started when at the end of training.
Migrate Trainer from `Repository` to `upload_folder`. # What does this PR do?

This PR migrates the internal of the push to Hub strategy inside the Trainer to the upload methods instead of the old git approach. The goal is to benefit from the improvements in terms of speed (for instance `upload_folder` is faster than `Repository` on Colab) and avoid some weird sync errors we have seen happen in the past.

This migration breaks a few things. I'm listing them for the sake of completeness but I don't think those are important breaking changes.
1. the return of `Trainer.push_to_hub` will change: in the blocking case it's the url of the repo instead of the URL of the commit. In the non-blocking case, it's the Future object returned by the `huggingface_hub` libreary (instead of a tuple commit address, job in progress)
2. before the migration, it was instant to cancel the push in progress when at the end of the training, to then push the final version of the Hub (but we also had some weird bugs). After the migration we will have to wait for the last push to be completed before pusing the final version of the model.
3. the `output_dir` of the `Trainer` won't be a git repository which is an exact clone of the model repo anymore.

Note that I chose to push checkpoint jobs in two `upload_folder`: one for the model and one for the optimizer state (roughly). This way I can cancel the second one if it hasn't started when at the end of training.
# 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 requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

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
    image_processor = MobileNetV2ImageProcessor(
        crop_size={"width": config.image_size, "height": config.image_size},
        size={"shortest_edge": config.image_size + 32},
    )
    encoding = image_processor(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 image processor to {pytorch_dump_folder_path}")
    image_processor.save_pretrained(pytorch_dump_folder_path)

    if push_to_hub:
        print("Pushing to the hub...")
        repo_id = "google/" + model_name
        image_processor.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
    )

# 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 requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

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
    image_processor = MobileNetV2ImageProcessor(
        crop_size={"width": config.image_size, "height": config.image_size},
        size={"shortest_edge": config.image_size + 32},
    )
    encoding = image_processor(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 image processor to {pytorch_dump_folder_path}")
    image_processor.save_pretrained(pytorch_dump_folder_path)

    if push_to_hub:
        print("Pushing to the hub...")
        repo_id = "google/" + model_name
        image_processor.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
    )
