# What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
Fix last models for common tests that are too big.. # What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
# 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 CANINE checkpoint."""


import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()


def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, pytorch_dump_path):
    # Initialize PyTorch model
    config = CanineConfig()
    model = CanineModel(config)
    model.eval()

    print(f"Building PyTorch model from configuration: {config}")

    # Load weights from tf checkpoint
    load_tf_weights_in_canine(model, config, tf_checkpoint_path)

    # Save pytorch-model (weights and configuration)
    print(f"Save PyTorch model to {pytorch_dump_path}")
    model.save_pretrained(pytorch_dump_path)

    # Save tokenizer files
    tokenizer = CanineTokenizer()
    print(f"Save tokenizer files to {pytorch_dump_path}")
    tokenizer.save_pretrained(pytorch_dump_path)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--tf_checkpoint_path",
        default=None,
        type=str,
        required=True,
        help="Path to the TensorFlow checkpoint. Should end with model.ckpt",
    )
    parser.add_argument(
        "--pytorch_dump_path",
        default=None,
        type=str,
        required=True,
        help="Path to a folder where the PyTorch model will be placed.",
    )
    args = parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)

# 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 CANINE checkpoint."""


import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()


def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, pytorch_dump_path):
    # Initialize PyTorch model
    config = CanineConfig()
    model = CanineModel(config)
    model.eval()

    print(f"Building PyTorch model from configuration: {config}")

    # Load weights from tf checkpoint
    load_tf_weights_in_canine(model, config, tf_checkpoint_path)

    # Save pytorch-model (weights and configuration)
    print(f"Save PyTorch model to {pytorch_dump_path}")
    model.save_pretrained(pytorch_dump_path)

    # Save tokenizer files
    tokenizer = CanineTokenizer()
    print(f"Save tokenizer files to {pytorch_dump_path}")
    tokenizer.save_pretrained(pytorch_dump_path)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--tf_checkpoint_path",
        default=None,
        type=str,
        required=True,
        help="Path to the TensorFlow checkpoint. Should end with model.ckpt",
    )
    parser.add_argument(
        "--pytorch_dump_path",
        default=None,
        type=str,
        required=True,
        help="Path to a folder where the PyTorch model will be placed.",
    )
    args = parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
