# 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 2020 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
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeq2SeqDataset, Seq2SeqDataset


BERT_BASE_CASED = "bert-base-cased"
PEGASUS_XSUM = "google/pegasus-xsum"
ARTICLES = [" Sam ate lunch today.", "Sams lunch ingredients."]
SUMMARIES = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
T5_TINY = "patrickvonplaten/t5-tiny-random"
BART_TINY = "sshleifer/bart-tiny-random"
MBART_TINY = "sshleifer/tiny-mbart"
MARIAN_TINY = "sshleifer/tiny-marian-en-de"


def _dump_articles(path: Path, articles: list):
    content = "\n".join(articles)
    Path(path).open("w").writelines(content)


def make_test_data_dir(tmp_dir):
    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(tmp_dir, f"{split}.source"), ARTICLES)
        _dump_articles(os.path.join(tmp_dir, f"{split}.target"), SUMMARIES)
    return tmp_dir


class TestAll(TestCasePlus):
    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ],
    )
    @slow
    def test_seq2seq_dataset_truncation(self, tok_name):
        tokenizer = AutoTokenizer.from_pretrained(tok_name)
        tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
        max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
        max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
        max_src_len = 4
        max_tgt_len = 8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        src_lang, tgt_lang = "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
        train_dataset = Seq2SeqDataset(
            tokenizer,
            data_dir=tmp_dir,
            type_path="train",
            max_source_length=max_src_len,
            max_target_length=max_tgt_len,  # ignored
            src_lang=src_lang,
            tgt_lang=tgt_lang,
        )
        dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
        for batch in dataloader:
            assert isinstance(batch, dict)
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_src_len
            # show that targets are the same len
            assert batch["labels"].shape[1] == max_tgt_len
            if tok_name != MBART_TINY:
                continue
            # check language codes in correct place
            batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], tokenizer.pad_token_id)
            assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
            assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

            break  # No need to test every batch

    @parameterized.expand([BART_TINY, BERT_BASE_CASED])
    def test_legacy_dataset_truncation(self, tok):
        tokenizer = AutoTokenizer.from_pretrained(tok)
        tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
        max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
        max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
        trunc_target = 4
        train_dataset = LegacySeq2SeqDataset(
            tokenizer,
            data_dir=tmp_dir,
            type_path="train",
            max_source_length=20,
            max_target_length=trunc_target,
        )
        dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
        for batch in dataloader:
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_len_source
            assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
            # show that targets were truncated
            assert batch["labels"].shape[1] == trunc_target  # Truncated
            assert max_len_target > trunc_target  # Truncated
            break  # No need to test every batch

    def test_pack_dataset(self):
        tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")

        tmp_dir = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
        orig_examples = tmp_dir.joinpath("train.source").open().readlines()
        save_dir = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
        pack_data_dir(tokenizer, tmp_dir, 128, save_dir)
        orig_paths = {x.name for x in tmp_dir.iterdir()}
        new_paths = {x.name for x in save_dir.iterdir()}
        packed_examples = save_dir.joinpath("train.source").open().readlines()
        # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
        # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
        assert len(packed_examples) < len(orig_examples)
        assert len(packed_examples) == 1
        assert len(packed_examples[0]) == sum(len(x) for x in orig_examples)
        assert orig_paths == new_paths

    @pytest.mark.skipif(not FAIRSEQ_AVAILABLE, reason="This test requires fairseq")
    def test_dynamic_batch_size(self):
        if not FAIRSEQ_AVAILABLE:
            return
        ds, max_tokens, tokenizer = self._get_dataset(max_len=64)
        required_batch_size_multiple = 64
        batch_sampler = ds.make_dynamic_sampler(max_tokens, required_batch_size_multiple=required_batch_size_multiple)
        batch_sizes = [len(x) for x in batch_sampler]
        assert len(set(batch_sizes)) > 1  # it's not dynamic batch size if every batch is the same length
        assert sum(batch_sizes) == len(ds)  # no dropped or added examples
        data_loader = DataLoader(ds, batch_sampler=batch_sampler, collate_fn=ds.collate_fn, num_workers=2)
        failures = []
        num_src_per_batch = []
        for batch in data_loader:
            src_shape = batch["input_ids"].shape
            bs = src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            num_src_tokens = np.product(batch["input_ids"].shape)
            num_src_per_batch.append(num_src_tokens)
            if num_src_tokens > (max_tokens * 1.1):
                failures.append(num_src_tokens)
        assert num_src_per_batch[0] == max(num_src_per_batch)
        if failures:
            raise AssertionError(f"too many tokens in {len(failures)} batches")

    def test_sortish_sampler_reduces_padding(self):
        ds, _, tokenizer = self._get_dataset(max_len=512)
        bs = 2
        sortish_sampler = ds.make_sortish_sampler(bs, shuffle=False)

        naive_dl = DataLoader(ds, batch_size=bs, collate_fn=ds.collate_fn, num_workers=2)
        sortish_dl = DataLoader(ds, batch_size=bs, collate_fn=ds.collate_fn, num_workers=2, sampler=sortish_sampler)

        pad = tokenizer.pad_token_id

        def count_pad_tokens(data_loader, k="input_ids"):
            return [batch[k].eq(pad).sum().item() for batch in data_loader]

        assert sum(count_pad_tokens(sortish_dl, k="labels")) < sum(count_pad_tokens(naive_dl, k="labels"))
        assert sum(count_pad_tokens(sortish_dl)) < sum(count_pad_tokens(naive_dl))
        assert len(sortish_dl) == len(naive_dl)

    def _get_dataset(self, n_obs=1000, max_len=128):
        if os.getenv("USE_REAL_DATA", False):
            data_dir = "examples/seq2seq/wmt_en_ro"
            max_tokens = max_len * 2 * 64
            if not Path(data_dir).joinpath("train.len").exists():
                save_len_file(MARIAN_TINY, data_dir)
        else:
            data_dir = "examples/seq2seq/test_data/wmt_en_ro"
            max_tokens = max_len * 4
            save_len_file(MARIAN_TINY, data_dir)

        tokenizer = AutoTokenizer.from_pretrained(MARIAN_TINY)
        ds = Seq2SeqDataset(
            tokenizer,
            data_dir=data_dir,
            type_path="train",
            max_source_length=max_len,
            max_target_length=max_len,
            n_obs=n_obs,
        )
        return ds, max_tokens, tokenizer

    def test_distributed_sortish_sampler_splits_indices_between_procs(self):
        ds, max_tokens, tokenizer = self._get_dataset()
        ids1 = set(DistributedSortishSampler(ds, 256, num_replicas=2, rank=0, add_extra_examples=False))
        ids2 = set(DistributedSortishSampler(ds, 256, num_replicas=2, rank=1, add_extra_examples=False))
        assert ids1.intersection(ids2) == set()

    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ],
    )
    def test_dataset_kwargs(self, tok_name):
        tokenizer = AutoTokenizer.from_pretrained(tok_name, use_fast=False)
        if tok_name == MBART_TINY:
            train_dataset = Seq2SeqDataset(
                tokenizer,
                data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()),
                type_path="train",
                max_source_length=4,
                max_target_length=8,
                src_lang="EN",
                tgt_lang="FR",
            )
            kwargs = train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
            train_dataset = Seq2SeqDataset(
                tokenizer,
                data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()),
                type_path="train",
                max_source_length=4,
                max_target_length=8,
            )
            kwargs = train_dataset.dataset_kwargs
            assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
            assert len(kwargs) == 1 if tok_name == BART_TINY else len(kwargs) == 0

# Copyright 2020 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
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeq2SeqDataset, Seq2SeqDataset


BERT_BASE_CASED = "bert-base-cased"
PEGASUS_XSUM = "google/pegasus-xsum"
ARTICLES = [" Sam ate lunch today.", "Sams lunch ingredients."]
SUMMARIES = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
T5_TINY = "patrickvonplaten/t5-tiny-random"
BART_TINY = "sshleifer/bart-tiny-random"
MBART_TINY = "sshleifer/tiny-mbart"
MARIAN_TINY = "sshleifer/tiny-marian-en-de"


def _dump_articles(path: Path, articles: list):
    content = "\n".join(articles)
    Path(path).open("w").writelines(content)


def make_test_data_dir(tmp_dir):
    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(tmp_dir, f"{split}.source"), ARTICLES)
        _dump_articles(os.path.join(tmp_dir, f"{split}.target"), SUMMARIES)
    return tmp_dir


class TestAll(TestCasePlus):
    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ],
    )
    @slow
    def test_seq2seq_dataset_truncation(self, tok_name):
        tokenizer = AutoTokenizer.from_pretrained(tok_name)
        tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
        max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
        max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
        max_src_len = 4
        max_tgt_len = 8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        src_lang, tgt_lang = "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
        train_dataset = Seq2SeqDataset(
            tokenizer,
            data_dir=tmp_dir,
            type_path="train",
            max_source_length=max_src_len,
            max_target_length=max_tgt_len,  # ignored
            src_lang=src_lang,
            tgt_lang=tgt_lang,
        )
        dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
        for batch in dataloader:
            assert isinstance(batch, dict)
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_src_len
            # show that targets are the same len
            assert batch["labels"].shape[1] == max_tgt_len
            if tok_name != MBART_TINY:
                continue
            # check language codes in correct place
            batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], tokenizer.pad_token_id)
            assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
            assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
            assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

            break  # No need to test every batch

    @parameterized.expand([BART_TINY, BERT_BASE_CASED])
    def test_legacy_dataset_truncation(self, tok):
        tokenizer = AutoTokenizer.from_pretrained(tok)
        tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
        max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
        max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
        trunc_target = 4
        train_dataset = LegacySeq2SeqDataset(
            tokenizer,
            data_dir=tmp_dir,
            type_path="train",
            max_source_length=20,
            max_target_length=trunc_target,
        )
        dataloader = DataLoader(train_dataset, batch_size=2, collate_fn=train_dataset.collate_fn)
        for batch in dataloader:
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_len_source
            assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
            # show that targets were truncated
            assert batch["labels"].shape[1] == trunc_target  # Truncated
            assert max_len_target > trunc_target  # Truncated
            break  # No need to test every batch

    def test_pack_dataset(self):
        tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")

        tmp_dir = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
        orig_examples = tmp_dir.joinpath("train.source").open().readlines()
        save_dir = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
        pack_data_dir(tokenizer, tmp_dir, 128, save_dir)
        orig_paths = {x.name for x in tmp_dir.iterdir()}
        new_paths = {x.name for x in save_dir.iterdir()}
        packed_examples = save_dir.joinpath("train.source").open().readlines()
        # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
        # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
        assert len(packed_examples) < len(orig_examples)
        assert len(packed_examples) == 1
        assert len(packed_examples[0]) == sum(len(x) for x in orig_examples)
        assert orig_paths == new_paths

    @pytest.mark.skipif(not FAIRSEQ_AVAILABLE, reason="This test requires fairseq")
    def test_dynamic_batch_size(self):
        if not FAIRSEQ_AVAILABLE:
            return
        ds, max_tokens, tokenizer = self._get_dataset(max_len=64)
        required_batch_size_multiple = 64
        batch_sampler = ds.make_dynamic_sampler(max_tokens, required_batch_size_multiple=required_batch_size_multiple)
        batch_sizes = [len(x) for x in batch_sampler]
        assert len(set(batch_sizes)) > 1  # it's not dynamic batch size if every batch is the same length
        assert sum(batch_sizes) == len(ds)  # no dropped or added examples
        data_loader = DataLoader(ds, batch_sampler=batch_sampler, collate_fn=ds.collate_fn, num_workers=2)
        failures = []
        num_src_per_batch = []
        for batch in data_loader:
            src_shape = batch["input_ids"].shape
            bs = src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            num_src_tokens = np.product(batch["input_ids"].shape)
            num_src_per_batch.append(num_src_tokens)
            if num_src_tokens > (max_tokens * 1.1):
                failures.append(num_src_tokens)
        assert num_src_per_batch[0] == max(num_src_per_batch)
        if failures:
            raise AssertionError(f"too many tokens in {len(failures)} batches")

    def test_sortish_sampler_reduces_padding(self):
        ds, _, tokenizer = self._get_dataset(max_len=512)
        bs = 2
        sortish_sampler = ds.make_sortish_sampler(bs, shuffle=False)

        naive_dl = DataLoader(ds, batch_size=bs, collate_fn=ds.collate_fn, num_workers=2)
        sortish_dl = DataLoader(ds, batch_size=bs, collate_fn=ds.collate_fn, num_workers=2, sampler=sortish_sampler)

        pad = tokenizer.pad_token_id

        def count_pad_tokens(data_loader, k="input_ids"):
            return [batch[k].eq(pad).sum().item() for batch in data_loader]

        assert sum(count_pad_tokens(sortish_dl, k="labels")) < sum(count_pad_tokens(naive_dl, k="labels"))
        assert sum(count_pad_tokens(sortish_dl)) < sum(count_pad_tokens(naive_dl))
        assert len(sortish_dl) == len(naive_dl)

    def _get_dataset(self, n_obs=1000, max_len=128):
        if os.getenv("USE_REAL_DATA", False):
            data_dir = "examples/seq2seq/wmt_en_ro"
            max_tokens = max_len * 2 * 64
            if not Path(data_dir).joinpath("train.len").exists():
                save_len_file(MARIAN_TINY, data_dir)
        else:
            data_dir = "examples/seq2seq/test_data/wmt_en_ro"
            max_tokens = max_len * 4
            save_len_file(MARIAN_TINY, data_dir)

        tokenizer = AutoTokenizer.from_pretrained(MARIAN_TINY)
        ds = Seq2SeqDataset(
            tokenizer,
            data_dir=data_dir,
            type_path="train",
            max_source_length=max_len,
            max_target_length=max_len,
            n_obs=n_obs,
        )
        return ds, max_tokens, tokenizer

    def test_distributed_sortish_sampler_splits_indices_between_procs(self):
        ds, max_tokens, tokenizer = self._get_dataset()
        ids1 = set(DistributedSortishSampler(ds, 256, num_replicas=2, rank=0, add_extra_examples=False))
        ids2 = set(DistributedSortishSampler(ds, 256, num_replicas=2, rank=1, add_extra_examples=False))
        assert ids1.intersection(ids2) == set()

    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ],
    )
    def test_dataset_kwargs(self, tok_name):
        tokenizer = AutoTokenizer.from_pretrained(tok_name, use_fast=False)
        if tok_name == MBART_TINY:
            train_dataset = Seq2SeqDataset(
                tokenizer,
                data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()),
                type_path="train",
                max_source_length=4,
                max_target_length=8,
                src_lang="EN",
                tgt_lang="FR",
            )
            kwargs = train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
            train_dataset = Seq2SeqDataset(
                tokenizer,
                data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()),
                type_path="train",
                max_source_length=4,
                max_target_length=8,
            )
            kwargs = train_dataset.dataset_kwargs
            assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
            assert len(kwargs) == 1 if tok_name == BART_TINY else len(kwargs) == 0
