'''simple docstring'''


import argparse

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
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


snake_case__					:      int				      =  16
snake_case__					:      Optional[Any]				      =  32


def  _lowerCamelCase   (				lowerCamelCase_ :							Accelerator     ,					lowerCamelCase_ :							int = 16       ):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[int]       =				AutoTokenizer.from_pretrained('bert-base-cased'       )
     UpperCAmelCase_							:       int       =				load_dataset('glue'     ,					'mrpc'       )

     def tokenize_function(lowerCamelCase_ :							Tuple       ):
          # max_length=None => use the model max length (it's actually the default)
          UpperCAmelCase_							:       Union[str, Any]       =				tokenizer(examples['sentence1']     ,					examples['sentence2']     ,					truncation=lowerCamelCase_     ,					max_length=lowerCamelCase_       )
          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():
          UpperCAmelCase_							:       Any       =				datasets.map(
              lowerCamelCase_     ,					batched=lowerCamelCase_     ,					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
     UpperCAmelCase_							:       List[Any]       =				tokenized_datasets.rename_column('label'     ,					'labels'       )

     def collate_fn(lowerCamelCase_ :							int       ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          UpperCAmelCase_							:       Optional[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":
               UpperCAmelCase_							:       Tuple       =				16
          elif accelerator.mixed_precision != "no":
               UpperCAmelCase_							:       Tuple       =				8
          else:
               UpperCAmelCase_							:       List[str]       =				None

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

     # Instantiate dataloaders.
     UpperCAmelCase_							:       List[str]       =				DataLoader(
         tokenized_datasets['train']     ,					shuffle=lowerCamelCase_     ,					collate_fn=lowerCamelCase_     ,					batch_size=lowerCamelCase_     ,					drop_last=lowerCamelCase_       )
     UpperCAmelCase_							:       Union[str, Any]       =				DataLoader(
         tokenized_datasets['validation']     ,					shuffle=lowerCamelCase_     ,					collate_fn=lowerCamelCase_     ,					batch_size=lowerCamelCase_     ,					drop_last=(accelerator.mixed_precision == 'fp8')     ,					)

     return train_dataloader, eval_dataloader


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[Any]     ,					lowerCamelCase_ :							str       ):



     """simple docstring"""





     UpperCAmelCase_							:       str       =				Accelerator(cpu=args.cpu     ,					mixed_precision=args.mixed_precision       )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     UpperCAmelCase_							:       str       =				config['lr']
     UpperCAmelCase_							:       Optional[Any]       =				int(config['num_epochs']       )
     UpperCAmelCase_							:       List[str]       =				int(config['seed']       )
     UpperCAmelCase_							:       List[Any]       =				int(config['batch_size']       )

     UpperCAmelCase_							:       int       =				evaluate.load('glue'     ,					'mrpc'       )

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

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

     # 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).
     UpperCAmelCase_							:       Tuple       =				model.to(accelerator.device       )
     # Instantiate optimizer
     UpperCAmelCase_							:       str       =				AdamW(params=model.parameters()     ,					lr=lowerCamelCase_       )

     # Instantiate scheduler
     UpperCAmelCase_							:       int       =				get_linear_schedule_with_warmup(
         optimizer=lowerCamelCase_     ,					num_warmup_steps=100     ,					num_training_steps=(len(lowerCamelCase_       ) * 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.

     UpperCAmelCase_							:       List[str]       =				accelerator.prepare(
         lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_       )

     # Now we train the model
     for epoch in range(lowerCamelCase_       ):
          model.train()
          for step, batch in enumerate(lowerCamelCase_       ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device       )
               UpperCAmelCase_							:       str       =				model(**lowerCamelCase_       )
               UpperCAmelCase_							:       List[str]       =				outputs.loss
               UpperCAmelCase_							:       int       =				loss / gradient_accumulation_steps
               accelerator.backward(lowerCamelCase_       )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

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

          UpperCAmelCase_							:       Optional[int]       =				metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'''epoch {epoch}:'''     ,					lowerCamelCase_       )


def  _lowerCamelCase   (				):



     """simple docstring"""





     UpperCAmelCase_							:       Union[str, Any]       =				argparse.ArgumentParser(description='Simple example of training script.'       )
     parser.add_argument(
         '--mixed_precision'     ,					type=lowerCamelCase_     ,					default=lowerCamelCase_     ,					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.'       )
     UpperCAmelCase_							:       Any       =				parser.parse_args()
     UpperCAmelCase_							:       str       =				{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(lowerCamelCase_     ,					lowerCamelCase_       )


if __name__ == "__main__":
 main()







'''simple docstring'''


from sklearn.metrics import fa_score

import datasets


snake_case__					:      str				      =  '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


snake_case__					:      int				      =  '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


snake_case__					:      Tuple				      =  '''
@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}
}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,     _KWARGS_DESCRIPTION	)
class 					__SCREAMING_SNAKE_CASE						(	datasets.Metric	):







  '''simple docstring'''






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return datasets.MetricInfo(
           description=_DESCRIPTION				,		citation=_CITATION				,		inputs_description=_KWARGS_DESCRIPTION				,		features=datasets.Features(
               {
                   'predictions': datasets.Sequence(datasets.Value('int32'						)						),
                   'references': datasets.Sequence(datasets.Value('int32'						)						),
               }
               if self.config_name == 'multilabel'
               else {
                   'predictions': datasets.Value('int32'						),
                   'references': datasets.Value('int32'						),
               }						)				,		reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html']				,		)






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_=None				,		snake_case_=1				,		snake_case_="binary"				,		snake_case_=None						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				fa_score(
           snake_case_				,		snake_case_				,		labels=snake_case_				,		pos_label=snake_case_				,		average=snake_case_				,		sample_weight=snake_case_						)
       return {"f1": float(snake_case_						) if score.size == 1 else score}






