



from sklearn.metrics import mean_absolute_error

import datasets


lowercase_  =   '''\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
'''

lowercase_  =   '''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


lowercase_  =   '''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 "raw_values" : Returns a full set of errors in case of multioutput input.

                 "uniform_average" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric("mae")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.5}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric("mae", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mae\': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results)
    {\'mae\': array([0.5, 1. ])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,		_KWARGS_DESCRIPTION)
class  _snake_case	(    datasets.Metric):




				def 							A__					(  self    :  int  ):
							return datasets.MetricInfo(
							    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(self._get_feature_types()  ),    reference_urls=[
							        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"
							    ],    )




				def 							A__					(  self    :  List[Any]  ):
							if self.config_name == "multilist":
										return {
										    "predictions": datasets.Sequence(datasets.Value("float"  )  ),
										    "references": datasets.Sequence(datasets.Value("float"  )  ),
										}
							else:
										return {
										    "predictions": datasets.Value("float"  ),
										    "references": datasets.Value("float"  ),
										}




				def 							A__					(  self    :  Optional[int],    __lowercase    :  Any,    __lowercase    :  List[Any],    __lowercase    :  Dict=None,    __lowercase    :  Any="uniform_average"  ):
							lowercase__         = mean_absolute_error(UpperCAmelCase_,    UpperCAmelCase_,    sample_weight=UpperCAmelCase_,    multioutput=UpperCAmelCase_  )

							return {"mae": mae_score}











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 checkpointing 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__  :	Union[str, Any]     =16
UpperCAmelCase__  :	List[Any]     =32





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase = 16 )  ->     Dict:
				lowerCamelCase		       =AutoTokenizer.from_pretrained("""bert-base-cased""" )
				lowerCamelCase		       =load_dataset("""glue"""	,					"""mrpc""" )

				def tokenize_function(_UpperCAmelCase ):
								# max_length=None => use the model max length (it's actually the default)
								lowerCamelCase		       =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		       =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		       =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		       =1_28 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		       =16
								elif accelerator.mixed_precision != "no":
												lowerCamelCase		       =8
								else:
												lowerCamelCase		       =None

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

				# Instantiate dataloaders.
				lowerCamelCase		       =DataLoader(
				    tokenized_datasets["""train"""]	,					shuffle=_UpperCAmelCase	,					collate_fn=_UpperCAmelCase	,					batch_size=_UpperCAmelCase )
				lowerCamelCase		       =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__  :	Union[str, Any]     =mocked_dataloaders  # noqa: F811





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase )  ->     List[Any]:
				# For testing only
				if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,					_UpperCAmelCase ) == "1":
								lowerCamelCase		       =2
				# Initialize accelerator
				lowerCamelCase		       =Accelerator(cpu=args.cpu	,					mixed_precision=args.mixed_precision )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCamelCase		       =config["""lr"""]
				lowerCamelCase		       =int(config["""num_epochs"""] )
				lowerCamelCase		       =int(config["""seed"""] )
				lowerCamelCase		       =int(config["""batch_size"""] )

				# New Code #
				# Parse out whether we are saving every epoch or after a certain number of batches
				if hasattr(args.checkpointing_steps	,					"""isdigit""" ):
								if args.checkpointing_steps == "epoch":
												lowerCamelCase		       =args.checkpointing_steps
								elif args.checkpointing_steps.isdigit():
												lowerCamelCase		       =int(args.checkpointing_steps )
								else:
												raise ValueError(
												    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.""" )
				else:
								lowerCamelCase		       =None

				set_seed(_UpperCAmelCase )

				lowerCamelCase       ,     lowerCamelCase		       =get_dataloaders(_UpperCAmelCase	,					_UpperCAmelCase )
				lowerCamelCase		       =evaluate.load("""glue"""	,					"""mrpc""" )

				# If the batch size is too big we use gradient accumulation
				lowerCamelCase		       =1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								lowerCamelCase		       =batch_size // MAX_GPU_BATCH_SIZE
								lowerCamelCase		       =MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCamelCase		       =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		       =model.to(accelerator.device )

				# Instantiate optimizer
				lowerCamelCase		       =AdamW(params=model.parameters()	,					lr=_UpperCAmelCase )

				# Instantiate scheduler
				lowerCamelCase		       =get_linear_schedule_with_warmup(
				    optimizer=_UpperCAmelCase	,					num_warmup_steps=1_00	,					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		       =accelerator.prepare(
				    _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )

				# New Code #
				# We need to keep track of how many total steps we have iterated over
				lowerCamelCase		       =0
				# We also need to keep track of the stating epoch so files are named properly
				lowerCamelCase		       =0

				# We need to load the checkpoint back in before training here with `load_state`
				# The total number of epochs is adjusted based on where the state is being loaded from,
				# as we assume continuation of the same training script
				if args.resume_from_checkpoint:
								if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
												accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}""" )
												accelerator.load_state(args.resume_from_checkpoint )
												lowerCamelCase		       =os.path.basename(args.resume_from_checkpoint )
								else:
												# Get the most recent checkpoint
												lowerCamelCase		       =[f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
												dirs.sort(key=os.path.getctime )
												lowerCamelCase		       =dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
								# Extract `epoch_{i}` or `step_{i}`
								lowerCamelCase		       =os.path.splitext(_UpperCAmelCase )[0]

								if "epoch" in training_difference:
												lowerCamelCase		       =int(training_difference.replace("""epoch_"""	,					"""""" ) ) + 1
												lowerCamelCase		       =None
								else:
												lowerCamelCase		       =int(training_difference.replace("""step_"""	,					"""""" ) )
												lowerCamelCase		       =resume_step // len(_UpperCAmelCase )
												resume_step -= starting_epoch * len(_UpperCAmelCase )

    # Now we train the model
				for epoch in range(_UpperCAmelCase	,					_UpperCAmelCase ):
								model.train()
								# New Code #
								if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
												# We need to skip steps until we reach the resumed step
												lowerCamelCase		       =accelerator.skip_first_batches(_UpperCAmelCase	,					_UpperCAmelCase )
												overall_step += resume_step
								else:
												# After the first iteration though, we need to go back to the original dataloader
												lowerCamelCase		       =train_dataloader
								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		       =model(**_UpperCAmelCase )
												lowerCamelCase		       =outputs.loss
												lowerCamelCase		       =loss / gradient_accumulation_steps
												accelerator.backward(_UpperCAmelCase )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()
												# New Code #
												overall_step += 1

												# New Code #
												# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
												# These are saved to folders named `step_{overall_step}`
												# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
												# If mixed precision was used, will also save a "scalar.bin" file
												if isinstance(_UpperCAmelCase	,					_UpperCAmelCase ):
																lowerCamelCase		       =F"""step_{overall_step}"""
																if overall_step % checkpointing_steps == 0:
																				if args.output_dir is not None:
																								lowerCamelCase		       =os.path.join(args.output_dir	,					_UpperCAmelCase )
																				accelerator.save_state(_UpperCAmelCase )

								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		       =model(**_UpperCAmelCase )
												lowerCamelCase		       =outputs.logits.argmax(dim=-1 )
												lowerCamelCase       ,     lowerCamelCase		       =accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
												metric.add_batch(
												    predictions=_UpperCAmelCase	,					references=_UpperCAmelCase	,					)

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

								# New Code #
								# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
								# These are saved to folders named `epoch_{epoch}`
								# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
								# If mixed precision was used, will also save a "scalar.bin" file
								if checkpointing_steps == "epoch":
												lowerCamelCase		       =F"""epoch_{epoch}"""
												if args.output_dir is not None:
																lowerCamelCase		       =os.path.join(args.output_dir	,					_UpperCAmelCase )
												accelerator.save_state(_UpperCAmelCase )





def    _lowercase    (  )  ->     str:
				lowerCamelCase		       =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(
				    """--checkpointing_steps"""	,					type=_UpperCAmelCase	,					default=_UpperCAmelCase	,					help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""	,					)
				parser.add_argument(
				    """--output_dir"""	,					type=_UpperCAmelCase	,					default="""."""	,					help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""	,					)
				parser.add_argument(
				    """--resume_from_checkpoint"""	,					type=_UpperCAmelCase	,					default=_UpperCAmelCase	,					help="""If the training should continue from a checkpoint folder."""	,					)
				lowerCamelCase		       =parser.parse_args()
				lowerCamelCase		       ={"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(_UpperCAmelCase	,					_UpperCAmelCase )


if __name__ == "__main__":
							main()





