





import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_UpperCAmelCase				:       List[Any]	   =				16
_UpperCAmelCase				:       Any	   =				32


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase = 16 )			->     List[str]:
    lowerCamelCase__    :      Any					 =		AutoTokenizer.from_pretrained('bert-base-cased' )
    lowerCamelCase__    :      List[Any]					 =		load_dataset('glue'						,	'mrpc' )

    def tokenize_function(_UpperCAmelCase ):
        # max_length=None => use the model max length (it's actually the default)
        lowerCamelCase__    :      Optional[int]					 =		tokenizer(examples['sentence1']						,	examples['sentence2']						,	truncation=_UpperCAmelCase						,	max_length=_UpperCAmelCase )
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        lowerCamelCase__    :      List[Any]					 =		datasets.map(
            _UpperCAmelCase						,	batched=_UpperCAmelCase						,	remove_columns=['idx', 'sentence1', 'sentence2']						,	)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    lowerCamelCase__    :      List[Any]					 =		tokenized_datasets.rename_column('label'						,	'labels' )

    def collate_fn(_UpperCAmelCase ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        lowerCamelCase__    :      int					 =		128 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            lowerCamelCase__    :      int					 =		16
        elif accelerator.mixed_precision != "no":
            lowerCamelCase__    :      Dict					 =		8
        else:
            lowerCamelCase__    :      Optional[int]					 =		None

        return tokenizer.pad(
            _UpperCAmelCase						,	padding='longest'						,	max_length=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_tensors='pt'						,	)

    # Instantiate dataloaders.
    lowerCamelCase__    :      Optional[int]					 =		DataLoader(
        tokenized_datasets['train']						,	shuffle=_UpperCAmelCase						,	collate_fn=_UpperCAmelCase						,	batch_size=_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		DataLoader(
        tokenized_datasets['validation']						,	shuffle=_UpperCAmelCase						,	collate_fn=_UpperCAmelCase						,	batch_size=_UpperCAmelCase )

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    _UpperCAmelCase				:       Optional[Any]	   =				mocked_dataloaders  # noqa: F811


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     str:
    # For testing only
    if os.environ.get('TESTING_MOCKED_DATALOADERS'						,	_UpperCAmelCase ) == "1":
        lowerCamelCase__    :      Optional[int]					 =		2
    # Initialize Accelerator

    # New Code #
    # We pass in "all" to `log_with` to grab all available trackers in the environment
    # Note: If using a custom `Tracker` class, should be passed in here such as:
    # >>> log_with = ["all", MyCustomTrackerClassInstance()]
    if args.with_tracking:
        lowerCamelCase__    :      List[Any]					 =		Accelerator(
            cpu=args.cpu						,	mixed_precision=args.mixed_precision						,	log_with='all'						,	project_dir=args.project_dir )
    else:
        lowerCamelCase__    :      Optional[int]					 =		Accelerator(cpu=args.cpu						,	mixed_precision=args.mixed_precision )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    lowerCamelCase__    :      Optional[Any]					 =		config['lr']
    lowerCamelCase__    :      Tuple					 =		int(config['num_epochs'] )
    lowerCamelCase__    :      Union[str, Any]					 =		int(config['seed'] )
    lowerCamelCase__    :      str					 =		int(config['batch_size'] )
    set_seed(_UpperCAmelCase )

    lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		get_dataloaders(_UpperCAmelCase						,	_UpperCAmelCase )
    lowerCamelCase__    :      Optional[int]					 =		evaluate.load('glue'						,	'mrpc' )

    # If the batch size is too big we use gradient accumulation
    lowerCamelCase__    :      Optional[Any]					 =		1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        lowerCamelCase__    :      Any					 =		batch_size // MAX_GPU_BATCH_SIZE
        lowerCamelCase__    :      str					 =		MAX_GPU_BATCH_SIZE

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    lowerCamelCase__    :      Union[str, Any]					 =		AutoModelForSequenceClassification.from_pretrained('bert-base-cased'						,	return_dict=_UpperCAmelCase )

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    lowerCamelCase__    :      Optional[Any]					 =		model.to(accelerator.device )

    # Instantiate optimizer
    lowerCamelCase__    :      List[str]					 =		AdamW(params=model.parameters()						,	lr=_UpperCAmelCase )

    # Instantiate scheduler
    lowerCamelCase__    :      List[Any]					 =		get_linear_schedule_with_warmup(
        optimizer=_UpperCAmelCase						,	num_warmup_steps=100						,	num_training_steps=(len(_UpperCAmelCase ) * num_epochs) // gradient_accumulation_steps						,	)

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      Optional[Any]					 =		accelerator.prepare(
        _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )

    # New Code #
    # We need to initialize the trackers we use. Overall configurations can also be stored
    if args.with_tracking:
        lowerCamelCase__    :      Optional[Any]					 =		os.path.split(_UpperCAmelCase )[-1].split('.' )[0]
        accelerator.init_trackers(_UpperCAmelCase						,	_UpperCAmelCase )

    # Now we train the model
    for epoch in range(_UpperCAmelCase ):
        model.train()
        # New Code #
        # For our tracking example, we will log the total loss of each epoch
        if args.with_tracking:
            lowerCamelCase__    :      Union[str, Any]					 =		0
        for step, batch in enumerate(_UpperCAmelCase ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device )
            lowerCamelCase__    :      Any					 =		model(**_UpperCAmelCase )
            lowerCamelCase__    :      Dict					 =		outputs.loss
            # New Code #
            if args.with_tracking:
                total_loss += loss.detach().float()
            lowerCamelCase__    :      Dict					 =		loss / gradient_accumulation_steps
            accelerator.backward(_UpperCAmelCase )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        for step, batch in enumerate(_UpperCAmelCase ):
            # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
            batch.to(accelerator.device )
            with torch.no_grad():
                lowerCamelCase__    :      str					 =		model(**_UpperCAmelCase )
            lowerCamelCase__    :      List[str]					 =		outputs.logits.argmax(dim=-1 )
            lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		accelerator.gather_for_metrics((predictions, batch['labels']) )
            metric.add_batch(
                predictions=_UpperCAmelCase						,	references=_UpperCAmelCase						,	)

        lowerCamelCase__    :      Tuple					 =		metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F"""epoch {epoch}:"""						,	_UpperCAmelCase )

        # New Code #
        # To actually log, we call `Accelerator.log`
        # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
        if args.with_tracking:
            accelerator.log(
                {
                    'accuracy': eval_metric['accuracy'],
                    'f1': eval_metric['f1'],
                    'train_loss': total_loss.item() / len(_UpperCAmelCase ),
                    'epoch': epoch,
                }						,	step=_UpperCAmelCase						,	)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
    if args.with_tracking:
        accelerator.end_training()


def        SCREAMING_SNAKE_CASE      (       )			->     List[str]:
    lowerCamelCase__    :      Any					 =		argparse.ArgumentParser(description='Simple example of training script.' )
    parser.add_argument(
        '--mixed_precision'						,	type=_UpperCAmelCase						,	default=_UpperCAmelCase						,	choices=['no', 'fp16', 'bf16', 'fp8']						,	help='Whether to use mixed precision. Choose'
        'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
        'and an Nvidia Ampere GPU.'						,	)
    parser.add_argument('--cpu'						,	action='store_true'						,	help='If passed, will train on the CPU.' )
    parser.add_argument(
        '--with_tracking'						,	action='store_true'						,	help='Whether to load in all available experiment trackers from the environment and use them for logging.'						,	)
    parser.add_argument(
        '--project_dir'						,	type=_UpperCAmelCase						,	default='logs'						,	help='Location on where to store experiment tracking logs` and relevent project information'						,	)
    lowerCamelCase__    :      Union[str, Any]					 =		parser.parse_args()
    lowerCamelCase__    :      str					 =		{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
    training_function(_UpperCAmelCase						,	_UpperCAmelCase )


if __name__ == "__main__":
    main()












# Copyright 2021 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 argparse
import os

from accelerate.test_utils import execute_subprocess_async


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase=None )			->     Tuple:
    if subparsers is not None:
        lowerCamelCase__    :      Any					 =		subparsers.add_parser('test' )
    else:
        lowerCamelCase__    :      int					 =		argparse.ArgumentParser('Accelerate test command' )

    parser.add_argument(
        '--config_file'						,	default=_UpperCAmelCase						,	help=(
            'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
            'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
            'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
            'with \'huggingface\'.'
        )						,	)

    if subparsers is not None:
        parser.set_defaults(func=_UpperCAmelCase )
    return parser


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Union[str, Any]:
    lowerCamelCase__    :      Tuple					 =		os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['test_utils', 'scripts', 'test_script.py'] )

    if args.config_file is None:
        lowerCamelCase__    :      List[str]					 =		script_name
    else:
        lowerCamelCase__    :      List[Any]					 =		F"""--config_file={args.config_file} {script_name}"""

    lowerCamelCase__    :      str					 =		['accelerate-launch'] + test_args.split()
    lowerCamelCase__    :      Dict					 =		execute_subprocess_async(_UpperCAmelCase						,	env=os.environ.copy() )
    if result.returncode == 0:
        print('Test is a success! You are ready for your distributed training!' )


def        SCREAMING_SNAKE_CASE      (       )			->     Any:
    lowerCamelCase__    :      Any					 =		test_command_parser()
    lowerCamelCase__    :      List[Any]					 =		parser.parse_args()
    test_command(_UpperCAmelCase )


if __name__ == "__main__":
    main()





