# 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.
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# 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.

import os
import sys
import unittest


git_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
check_dummies.PATH_TO_TRANSFORMERS = os.path.join(git_repo_path, "src", "transformers")

DUMMY_CONSTANT = """
{0} = None
"""

DUMMY_CLASS = """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


DUMMY_FUNCTION = """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""


class CheckDummiesTester(unittest.TestCase):
    def test_find_backend(self):
        no_backend = find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")')
        self.assertIsNone(no_backend)

        simple_backend = find_backend("    if not is_tokenizers_available():")
        self.assertEqual(simple_backend, "tokenizers")

        backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
        self.assertEqual(backend_with_underscore, "tensorflow_text")

        double_backend = find_backend("    if not (is_sentencepiece_available() and is_tokenizers_available()):")
        self.assertEqual(double_backend, "sentencepiece_and_tokenizers")

        double_backend_with_underscore = find_backend(
            "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
        )
        self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

        triple_backend = find_backend(
            "    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"
        )
        self.assertEqual(triple_backend, "sentencepiece_and_tokenizers_and_vision")

    def test_read_init(self):
        objects = read_init()
        # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
        self.assertIn("torch", objects)
        self.assertIn("tensorflow_text", objects)
        self.assertIn("sentencepiece_and_tokenizers", objects)

        # Likewise, we can't assert on the exact content of a key
        self.assertIn("BertModel", objects["torch"])
        self.assertIn("TFBertModel", objects["tf"])
        self.assertIn("FlaxBertModel", objects["flax"])
        self.assertIn("BertModel", objects["torch"])
        self.assertIn("TFBertTokenizer", objects["tensorflow_text"])
        self.assertIn("convert_slow_tokenizer", objects["sentencepiece_and_tokenizers"])

    def test_create_dummy_object(self):
        dummy_constant = create_dummy_object("CONSTANT", "'torch'")
        self.assertEqual(dummy_constant, "\nCONSTANT = None\n")

        dummy_function = create_dummy_object("function", "'torch'")
        self.assertEqual(
            dummy_function, "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"
        )

        expected_dummy_class = """
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
        dummy_class = create_dummy_object("FakeClass", "'torch'")
        self.assertEqual(dummy_class, expected_dummy_class)

    def test_create_dummy_files(self):
        expected_dummy_pytorch_file = """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
"""
        dummy_files = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]})
        self.assertEqual(dummy_files["torch"], expected_dummy_pytorch_file)

# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# 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.

import os
import sys
import unittest


git_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
check_dummies.PATH_TO_TRANSFORMERS = os.path.join(git_repo_path, "src", "transformers")

DUMMY_CONSTANT = """
{0} = None
"""

DUMMY_CLASS = """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


DUMMY_FUNCTION = """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""


class CheckDummiesTester(unittest.TestCase):
    def test_find_backend(self):
        no_backend = find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")')
        self.assertIsNone(no_backend)

        simple_backend = find_backend("    if not is_tokenizers_available():")
        self.assertEqual(simple_backend, "tokenizers")

        backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
        self.assertEqual(backend_with_underscore, "tensorflow_text")

        double_backend = find_backend("    if not (is_sentencepiece_available() and is_tokenizers_available()):")
        self.assertEqual(double_backend, "sentencepiece_and_tokenizers")

        double_backend_with_underscore = find_backend(
            "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
        )
        self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

        triple_backend = find_backend(
            "    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"
        )
        self.assertEqual(triple_backend, "sentencepiece_and_tokenizers_and_vision")

    def test_read_init(self):
        objects = read_init()
        # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
        self.assertIn("torch", objects)
        self.assertIn("tensorflow_text", objects)
        self.assertIn("sentencepiece_and_tokenizers", objects)

        # Likewise, we can't assert on the exact content of a key
        self.assertIn("BertModel", objects["torch"])
        self.assertIn("TFBertModel", objects["tf"])
        self.assertIn("FlaxBertModel", objects["flax"])
        self.assertIn("BertModel", objects["torch"])
        self.assertIn("TFBertTokenizer", objects["tensorflow_text"])
        self.assertIn("convert_slow_tokenizer", objects["sentencepiece_and_tokenizers"])

    def test_create_dummy_object(self):
        dummy_constant = create_dummy_object("CONSTANT", "'torch'")
        self.assertEqual(dummy_constant, "\nCONSTANT = None\n")

        dummy_function = create_dummy_object("function", "'torch'")
        self.assertEqual(
            dummy_function, "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"
        )

        expected_dummy_class = """
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
        dummy_class = create_dummy_object("FakeClass", "'torch'")
        self.assertEqual(dummy_class, expected_dummy_class)

    def test_create_dummy_files(self):
        expected_dummy_pytorch_file = """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
"""
        dummy_files = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]})
        self.assertEqual(dummy_files["torch"], expected_dummy_pytorch_file)
