Add files using upload-large-folder tool

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# 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 ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

EXCLUDE_EXAMPLES = [
    "cross_validation.py",
    "gradient_accumulation.py",
    "multi_process_metrics.py",
    "memory.py",
    "automatic_gradient_accumulation.py",
    "fsdp_with_peak_mem_tracking.py",
    "deepspeed_with_config_support.py",
    "megatron_lm_gpt_pretraining.py",
]


class ExampleDifferenceTests(unittest.TestCase):
    """
    This TestCase checks that all of the `complete_*` scripts contain all of the
    information found in the `by_feature` scripts, line for line. If one fails,
    then a complete example does not contain all of the features in the features
    scripts, and should be updated.
    Each example script should be a single test (such as `test_nlp_example`),
    and should run `one_complete_example` twice: once with `parser_only=True`,
    and the other with `parser_only=False`. This is so that when the test
    failures are returned to the user, they understand if the discrepancy lies in
    the `main` function, or the `training_loop` function. Otherwise it will be
    unclear.
    Also, if there are any expected differences between the base script used and
    `complete_nlp_example.py` (the canonical base script), these should be included in
    `special_strings`. These would be differences in how something is logged, print statements,
    etc (such as calls to `Accelerate.log()`)
    """

    def one_complete_example(
        self, complete_file_name: str, parser_only: bool, secondary_filename: str = None, special_strings: list = None
    ):
        """
        Tests a single `complete` example against all of the implemented `by_feature` scripts
        Args:
            complete_file_name (`str`):
                The filename of a complete example
            parser_only (`bool`):
                Whether to look at the main training function, or the argument parser
            secondary_filename (`str`, *optional*):
                A potential secondary base file to strip all script information not relevant for checking,
                such as "cv_example.py" when testing "complete_cv_example.py"
            special_strings (`list`, *optional*):
                A list of strings to potentially remove before checking no differences are left. These should be
                diffs that are file specific, such as different logging variations between files.
        """
        self.maxDiff = None
        by_feature_path = os.path.abspath(os.path.join("examples", "by_feature"))
        examples_path = os.path.abspath("examples")
        for item in os.listdir(by_feature_path):
            if item not in EXCLUDE_EXAMPLES:
                item_path = os.path.join(by_feature_path, item)
                if os.path.isfile(item_path) and ".py" in item_path:
                    with self.subTest(
                        tested_script=complete_file_name,
                        feature_script=item,
                        tested_section="main()" if parser_only else "training_function()",
                    ):
                        diff = compare_against_test(
                            os.path.join(examples_path, complete_file_name), item_path, parser_only, secondary_filename
                        )
                        diff = "\n".join(diff)
                        if special_strings is not None:
                            for string in special_strings:
                                diff = diff.replace(string, "")
                        self.assertEqual(diff, "")

    def test_nlp_examples(self):
        self.one_complete_example("complete_nlp_example.py", True)
        self.one_complete_example("complete_nlp_example.py", False)

    def test_cv_examples(self):
        cv_path = os.path.abspath(os.path.join("examples", "cv_example.py"))
        special_strings = [
            " " * 16 + "{\n\n",
            " " * 20 + '"accuracy": eval_metric["accuracy"],\n\n',
            " " * 20 + '"f1": eval_metric["f1"],\n\n',
            " " * 20 + '"train_loss": total_loss.item() / len(train_dataloader),\n\n',
            " " * 20 + '"epoch": epoch,\n\n',
            " " * 16 + "},\n\n",
            " " * 16 + "step=epoch,\n",
            " " * 12,
        ]
        self.one_complete_example("complete_cv_example.py", True, cv_path, special_strings)
        self.one_complete_example("complete_cv_example.py", False, cv_path, special_strings)


@mock.patch.dict(os.environ, {"TESTING_MOCKED_DATALOADERS": "1"})
class FeatureExamplesTests(TempDirTestCase):
    clear_on_setup = False

    @classmethod
    def setUpClass(cls):
        super().setUpClass()
        cls._tmpdir = tempfile.mkdtemp()
        cls.configPath = os.path.join(cls._tmpdir, "default_config.yml")

        write_basic_config(save_location=cls.configPath)
        cls._launch_args = ["accelerate", "launch", "--config_file", cls.configPath]

    @classmethod
    def tearDownClass(cls):
        super().tearDownClass()
        shutil.rmtree(cls._tmpdir)

    def test_checkpointing_by_epoch(self):
        testargs = f"""
        examples/by_feature/checkpointing.py
        --checkpointing_steps epoch
        --output_dir {self.tmpdir}
        """.split()
        run_command(self._launch_args + testargs)
        self.assertTrue(os.path.exists(os.path.join(self.tmpdir, "epoch_0")))

    def test_checkpointing_by_steps(self):
        testargs = f"""
        examples/by_feature/checkpointing.py
        --checkpointing_steps 1
        --output_dir {self.tmpdir}
        """.split()
        _ = run_command(self._launch_args + testargs)
        self.assertTrue(os.path.exists(os.path.join(self.tmpdir, "step_2")))

    def test_load_states_by_epoch(self):
        testargs = f"""
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir, "epoch_0")}
        """.split()
        output = run_command(self._launch_args + testargs, return_stdout=True)
        self.assertNotIn("epoch 0:", output)
        self.assertIn("epoch 1:", output)

    def test_load_states_by_steps(self):
        testargs = f"""
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir, "step_2")}
        """.split()
        output = run_command(self._launch_args + testargs, return_stdout=True)
        if torch.cuda.is_available():
            num_processes = torch.cuda.device_count()
        else:
            num_processes = 1
        if num_processes > 1:
            self.assertNotIn("epoch 0:", output)
            self.assertIn("epoch 1:", output)
        else:
            self.assertIn("epoch 0:", output)
            self.assertIn("epoch 1:", output)

    @slow
    def test_cross_validation(self):
        testargs = """
        examples/by_feature/cross_validation.py
        --num_folds 2
        """.split()
        with mock.patch.dict(os.environ, {"TESTING_MOCKED_DATALOADERS": "0"}):
            output = run_command(self._launch_args + testargs, return_stdout=True)
            results = ast.literal_eval(re.findall("({.+})", output)[-1])
            self.assertGreaterEqual(results["accuracy"], 0.75)

    def test_multi_process_metrics(self):
        testargs = ["examples/by_feature/multi_process_metrics.py"]
        run_command(self._launch_args + testargs)

    @require_trackers
    @mock.patch.dict(os.environ, {"WANDB_MODE": "offline"})
    def test_tracking(self):
        with tempfile.TemporaryDirectory() as tmpdir:
            testargs = f"""
            examples/by_feature/tracking.py
            --with_tracking
            --logging_dir {tmpdir}
            """.split()
            run_command(self._launch_args + testargs)
            self.assertTrue(os.path.exists(os.path.join(tmpdir, "tracking")))

    def test_gradient_accumulation(self):
        testargs = ["examples/by_feature/gradient_accumulation.py"]
        run_command(self._launch_args + testargs)

	# 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 ast
	import os
	import re
	import shutil
	import tempfile
	import unittest
	from unittest import mock

	import torch

	from accelerate.test_utils.examples import compare_against_test
	from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
	from accelerate.utils import write_basic_config


	# DataLoaders built from `test_samples/MRPC` for quick testing
	# Should mock `{script_name}.get_dataloaders` via:
	# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

	EXCLUDE_EXAMPLES = [
	"cross_validation.py",
	"gradient_accumulation.py",
	"multi_process_metrics.py",
	"memory.py",
	"automatic_gradient_accumulation.py",
	"fsdp_with_peak_mem_tracking.py",
	"deepspeed_with_config_support.py",
	"megatron_lm_gpt_pretraining.py",
	]


	class ExampleDifferenceTests(unittest.TestCase):
	"""
	This TestCase checks that all of the `complete_*` scripts contain all of the
	information found in the `by_feature` scripts, line for line. If one fails,
	then a complete example does not contain all of the features in the features
	scripts, and should be updated.

	Each example script should be a single test (such as `test_nlp_example`),
	and should run `one_complete_example` twice: once with `parser_only=True`,
	and the other with `parser_only=False`. This is so that when the test
	failures are returned to the user, they understand if the discrepancy lies in
	the `main` function, or the `training_loop` function. Otherwise it will be
	unclear.

	Also, if there are any expected differences between the base script used and
	`complete_nlp_example.py` (the canonical base script), these should be included in
	`special_strings`. These would be differences in how something is logged, print statements,
	etc (such as calls to `Accelerate.log()`)
	"""

	def one_complete_example(
	self, complete_file_name: str, parser_only: bool, secondary_filename: str = None, special_strings: list = None
	):
	"""
	Tests a single `complete` example against all of the implemented `by_feature` scripts

	Args:
	complete_file_name (`str`):
	The filename of a complete example
	parser_only (`bool`):
	Whether to look at the main training function, or the argument parser
	secondary_filename (`str`, optional):
	A potential secondary base file to strip all script information not relevant for checking,
	such as "cv_example.py" when testing "complete_cv_example.py"
	special_strings (`list`, optional):
	A list of strings to potentially remove before checking no differences are left. These should be
	diffs that are file specific, such as different logging variations between files.
	"""
	self.maxDiff = None
	by_feature_path = os.path.abspath(os.path.join("examples", "by_feature"))
	examples_path = os.path.abspath("examples")
	for item in os.listdir(by_feature_path):
	if item not in EXCLUDE_EXAMPLES:
	item_path = os.path.join(by_feature_path, item)
	if os.path.isfile(item_path) and ".py" in item_path:
	with self.subTest(
	tested_script=complete_file_name,
	feature_script=item,
	tested_section="main()" if parser_only else "training_function()",
	):
	diff = compare_against_test(
	os.path.join(examples_path, complete_file_name), item_path, parser_only, secondary_filename
	)
	diff = "\n".join(diff)
	if special_strings is not None:
	for string in special_strings:
	diff = diff.replace(string, "")
	self.assertEqual(diff, "")

	def test_nlp_examples(self):
	self.one_complete_example("complete_nlp_example.py", True)
	self.one_complete_example("complete_nlp_example.py", False)

	def test_cv_examples(self):
	cv_path = os.path.abspath(os.path.join("examples", "cv_example.py"))
	special_strings = [
	" " * 16 + "{\n\n",
	" " * 20 + '"accuracy": eval_metric["accuracy"],\n\n',
	" " * 20 + '"f1": eval_metric["f1"],\n\n',
	" " * 20 + '"train_loss": total_loss.item() / len(train_dataloader),\n\n',
	" " * 20 + '"epoch": epoch,\n\n',
	" " * 16 + "},\n\n",
	" " * 16 + "step=epoch,\n",
	" " * 12,
	]
	self.one_complete_example("complete_cv_example.py", True, cv_path, special_strings)
	self.one_complete_example("complete_cv_example.py", False, cv_path, special_strings)


	@mock.patch.dict(os.environ, {"TESTING_MOCKED_DATALOADERS": "1"})
	class FeatureExamplesTests(TempDirTestCase):
	clear_on_setup = False

	@classmethod
	def setUpClass(cls):
	super().setUpClass()
	cls._tmpdir = tempfile.mkdtemp()
	cls.configPath = os.path.join(cls._tmpdir, "default_config.yml")

	write_basic_config(save_location=cls.configPath)
	cls._launch_args = ["accelerate", "launch", "--config_file", cls.configPath]

	@classmethod
	def tearDownClass(cls):
	super().tearDownClass()
	shutil.rmtree(cls._tmpdir)

	def test_checkpointing_by_epoch(self):
	testargs = f"""
	examples/by_feature/checkpointing.py
	--checkpointing_steps epoch
	--output_dir {self.tmpdir}
	""".split()
	run_command(self._launch_args + testargs)
	self.assertTrue(os.path.exists(os.path.join(self.tmpdir, "epoch_0")))

	def test_checkpointing_by_steps(self):
	testargs = f"""
	examples/by_feature/checkpointing.py
	--checkpointing_steps 1
	--output_dir {self.tmpdir}
	""".split()
	_ = run_command(self._launch_args + testargs)
	self.assertTrue(os.path.exists(os.path.join(self.tmpdir, "step_2")))

	def test_load_states_by_epoch(self):
	testargs = f"""
	examples/by_feature/checkpointing.py
	--resume_from_checkpoint {os.path.join(self.tmpdir, "epoch_0")}
	""".split()
	output = run_command(self._launch_args + testargs, return_stdout=True)
	self.assertNotIn("epoch 0:", output)
	self.assertIn("epoch 1:", output)

	def test_load_states_by_steps(self):
	testargs = f"""
	examples/by_feature/checkpointing.py
	--resume_from_checkpoint {os.path.join(self.tmpdir, "step_2")}
	""".split()
	output = run_command(self._launch_args + testargs, return_stdout=True)
	if torch.cuda.is_available():
	num_processes = torch.cuda.device_count()
	else:
	num_processes = 1
	if num_processes > 1:
	self.assertNotIn("epoch 0:", output)
	self.assertIn("epoch 1:", output)
	else:
	self.assertIn("epoch 0:", output)
	self.assertIn("epoch 1:", output)

	@slow
	def test_cross_validation(self):
	testargs = """
	examples/by_feature/cross_validation.py
	--num_folds 2
	""".split()
	with mock.patch.dict(os.environ, {"TESTING_MOCKED_DATALOADERS": "0"}):
	output = run_command(self._launch_args + testargs, return_stdout=True)
	results = ast.literal_eval(re.findall("({.+})", output)[-1])
	self.assertGreaterEqual(results["accuracy"], 0.75)

	def test_multi_process_metrics(self):
	testargs = ["examples/by_feature/multi_process_metrics.py"]
	run_command(self._launch_args + testargs)

	@require_trackers
	@mock.patch.dict(os.environ, {"WANDB_MODE": "offline"})
	def test_tracking(self):
	with tempfile.TemporaryDirectory() as tmpdir:
	testargs = f"""
	examples/by_feature/tracking.py
	--with_tracking
	--logging_dir {tmpdir}
	""".split()
	run_command(self._launch_args + testargs)
	self.assertTrue(os.path.exists(os.path.join(tmpdir, "tracking")))

	def test_gradient_accumulation(self):
	testargs = ["examples/by_feature/gradient_accumulation.py"]
	run_command(self._launch_args + testargs)