



from scipy.stats import pearsonr

import datasets


_lowercase						  =		"\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n"


_lowercase						  =		"\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        ['p-value', 'pearsonr']\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n        >>> print(round(results['p-value'], 2))\n        0.15\n"


_lowercase						  =		"\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n"



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




     def 		snake_case_		(				self):
         return datasets.MetricInfo(
             description=_DESCRIPTION ,						citation=_CITATION ,						inputs_description=_KWARGS_DESCRIPTION ,						features=datasets.Features(
                 {
                     '''predictions''': datasets.Value('''float'''),
                     '''references''': datasets.Value('''float'''),
                 }) ,						reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html'''] ,						)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__=False):
         if return_pvalue:
             A__		 =					pearsonr(a__ ,						a__)
             return {"pearsonr": results[0], "p-value": results[1]}
         else:
             return {"pearsonr": float(pearsonr(a__ ,						a__)[0])}





import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


_lowercase						  =		16
_lowercase						  =		32



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Accelerator ,       UpperCamelCase_				:       int = 1_6	)->  List[str]:
    A__		 =					AutoTokenizer.from_pretrained('''bert-base-cased'''	)
    A__		 =					load_dataset('''glue''' ,       '''mrpc'''	)

    def tokenize_function(UpperCamelCase_				:       Any	):
        # max_length=None => use the model max length (it's actually the default)
        A__		 =					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():
        A__		 =					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
    A__		 =					tokenized_datasets.rename_column('''label''' ,       '''labels'''	)

    def collate_fn(UpperCamelCase_				:       str	):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        A__		 =					1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            A__		 =					1_6
        elif accelerator.mixed_precision != "no":
            A__		 =					8
        else:
            A__		 =					None

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

    # Instantiate dataloaders.
    A__		 =					DataLoader(
        tokenized_datasets['''train'''] ,       shuffle=UpperCamelCase_ ,       collate_fn=UpperCamelCase_ ,       batch_size=UpperCamelCase_	)
    A__		 =					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

   _lowercase						  =		mocked_dataloaders  # noqa: F811



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

    A__		 =					evaluate.load('''glue''' ,       '''mrpc'''	)

    # New Code #
    # We now can define an inner training loop function. It should take a batch size as the only parameter,
    # and build the dataloaders in there.
    # It also gets our decorator
    @find_executable_batch_size(starting_batch_size=UpperCamelCase_	)
    def inner_training_loop(UpperCamelCase_				:       Optional[int]	):
        # And now just move everything below under this function
        # We need to bring in the Accelerator object from earlier
        nonlocal accelerator
        # And reset all of its attributes that could hold onto any memory:
        accelerator.free_memory()

        # Then we can declare the model, optimizer, and everything else:
        set_seed(UpperCamelCase_	)

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

        # Instantiate optimizer
        A__		 =					AdamW(params=model.parameters() ,       lr=UpperCamelCase_	)
        A__ ,	A__		 =					get_dataloaders(UpperCamelCase_ ,       UpperCamelCase_	)

        # Instantiate scheduler
        A__		 =					get_linear_schedule_with_warmup(
            optimizer=UpperCamelCase_ ,       num_warmup_steps=1_0_0 ,       num_training_steps=(len(UpperCamelCase_	) * num_epochs) ,       )

        # 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.
        A__ ,	A__ ,	A__ ,	A__ ,	A__		 =					accelerator.prepare(
            UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_	)

        # Now we train the model
        for epoch in range(UpperCamelCase_	):
            model.train()
            for step, batch in enumerate(UpperCamelCase_	):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device	)
                A__		 =					model(**UpperCamelCase_	)
                A__		 =					outputs.loss
                accelerator.backward(UpperCamelCase_	)
                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`.
                batch.to(accelerator.device	)
                with torch.no_grad():
                    A__		 =					model(**UpperCamelCase_	)
                A__		 =					outputs.logits.argmax(dim=-1	)
                A__ ,	A__		 =					accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
                metric.add_batch(
                    predictions=UpperCamelCase_ ,       references=UpperCamelCase_ ,       )

            A__		 =					metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:" ,       UpperCamelCase_	)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
    inner_training_loop()



def 		lowerCAmelCase__    (					)->  Optional[Any]:
    A__		 =					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.'''	)
    A__		 =					parser.parse_args()
    A__		 =					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
    training_function(UpperCamelCase_ ,       UpperCamelCase_	)


if __name__ == "__main__":
   main()
