# 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 2019-present, 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.
#
#
#
# this test validates that we can stack skip decorators in groups and whether
# they work correctly with other decorators
#
# since the decorators have already built their decision params (like checking
# env[], we can't mock the env and test each of the combinations), so ideally
# the following 4 should be run. But since we have different CI jobs running
# different configs, all combinations should get covered
#
# RUN_SLOW=1 pytest -rA tests/test_skip_decorators.py
# RUN_SLOW=1 CUDA_VISIBLE_DEVICES="" pytest -rA tests/test_skip_decorators.py
# RUN_SLOW=0 pytest -rA tests/test_skip_decorators.py
# RUN_SLOW=0 CUDA_VISIBLE_DEVICES="" pytest -rA tests/test_skip_decorators.py

import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

params = [(1,)]


# test that we can stack our skip decorators with 3rd party decorators
def check_slow():
    run_slow = bool(os.getenv("RUN_SLOW", 0))
    if run_slow:
        assert True
    else:
        assert False, "should have been skipped"


# test that we can stack our skip decorators
def check_slow_torch_cuda():
    run_slow = bool(os.getenv("RUN_SLOW", 0))
    if run_slow and torch_device == "cuda":
        assert True
    else:
        assert False, "should have been skipped"


@require_torch
class SkipTester(unittest.TestCase):
    @slow
    @require_torch_gpu
    def test_2_skips_slow_first(self):
        check_slow_torch_cuda()

    @require_torch_gpu
    @slow
    def test_2_skips_slow_last(self):
        check_slow_torch_cuda()

    # The combination of any skip decorator, followed by parameterized fails to skip the tests
    # 1. @slow manages to correctly skip `test_param_slow_first`
    # 2. but then `parameterized` creates new tests, with a unique name for each parameter groups.
    #    It has no idea that they are to be skipped and so they all run, ignoring @slow
    # Therefore skip decorators must come after `parameterized`
    #
    # @slow
    # @parameterized.expand(params)
    # def test_param_slow_first(self, param=None):
    #     check_slow()

    # This works as expected:
    # 1. `parameterized` creates new tests with unique names
    # 2. each of them gets an opportunity to be skipped
    @parameterized.expand(params)
    @slow
    def test_param_slow_last(self, param=None):
        check_slow()


# skipping in non-unittest tests
# no problem at all here


@slow
@require_torch_gpu
def test_pytest_2_skips_slow_first():
    check_slow_torch_cuda()


@require_torch_gpu
@slow
def test_pytest_2_skips_slow_last():
    check_slow_torch_cuda()


@slow
@pytest.mark.parametrize("param", [1])
def test_pytest_param_slow_first(param):
    check_slow()


@pytest.mark.parametrize("param", [1])
@slow
def test_pytest_param_slow_last(param):
    check_slow()

# coding=utf-8
# Copyright 2019-present, 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.
#
#
#
# this test validates that we can stack skip decorators in groups and whether
# they work correctly with other decorators
#
# since the decorators have already built their decision params (like checking
# env[], we can't mock the env and test each of the combinations), so ideally
# the following 4 should be run. But since we have different CI jobs running
# different configs, all combinations should get covered
#
# RUN_SLOW=1 pytest -rA tests/test_skip_decorators.py
# RUN_SLOW=1 CUDA_VISIBLE_DEVICES="" pytest -rA tests/test_skip_decorators.py
# RUN_SLOW=0 pytest -rA tests/test_skip_decorators.py
# RUN_SLOW=0 CUDA_VISIBLE_DEVICES="" pytest -rA tests/test_skip_decorators.py

import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

params = [(1,)]


# test that we can stack our skip decorators with 3rd party decorators
def check_slow():
    run_slow = bool(os.getenv("RUN_SLOW", 0))
    if run_slow:
        assert True
    else:
        assert False, "should have been skipped"


# test that we can stack our skip decorators
def check_slow_torch_cuda():
    run_slow = bool(os.getenv("RUN_SLOW", 0))
    if run_slow and torch_device == "cuda":
        assert True
    else:
        assert False, "should have been skipped"


@require_torch
class SkipTester(unittest.TestCase):
    @slow
    @require_torch_gpu
    def test_2_skips_slow_first(self):
        check_slow_torch_cuda()

    @require_torch_gpu
    @slow
    def test_2_skips_slow_last(self):
        check_slow_torch_cuda()

    # The combination of any skip decorator, followed by parameterized fails to skip the tests
    # 1. @slow manages to correctly skip `test_param_slow_first`
    # 2. but then `parameterized` creates new tests, with a unique name for each parameter groups.
    #    It has no idea that they are to be skipped and so they all run, ignoring @slow
    # Therefore skip decorators must come after `parameterized`
    #
    # @slow
    # @parameterized.expand(params)
    # def test_param_slow_first(self, param=None):
    #     check_slow()

    # This works as expected:
    # 1. `parameterized` creates new tests with unique names
    # 2. each of them gets an opportunity to be skipped
    @parameterized.expand(params)
    @slow
    def test_param_slow_last(self, param=None):
        check_slow()


# skipping in non-unittest tests
# no problem at all here


@slow
@require_torch_gpu
def test_pytest_2_skips_slow_first():
    check_slow_torch_cuda()


@require_torch_gpu
@slow
def test_pytest_2_skips_slow_last():
    check_slow_torch_cuda()


@slow
@pytest.mark.parametrize("param", [1])
def test_pytest_param_slow_first(param):
    check_slow()


@pytest.mark.parametrize("param", [1])
@slow
def test_pytest_param_slow_last(param):
    check_slow()
