

'''simple docstring'''
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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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         =  16
_snake_case         =  32







def  _A		(     snake_case		,							snake_case = 16					)      ->     Tuple:
   _lowercase      :				int      					=       AutoTokenizer.from_pretrained("bert-base-cased"					)
   _lowercase      :				Optional[int]      					=       load_dataset("glue"		,							"mrpc"					)

   def tokenize_function(snake_case					):
      # max_length=None => use the model max length (it's actually the default)
      _lowercase      :				Any      					=       tokenizer(examples["sentence1"]		,							examples["sentence2"]		,							truncation=A__		,							max_length=A__					)
      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():
      _lowercase      :				int      					=       datasets.map(
          A__		,							batched=A__		,							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
   _lowercase      :				int      					=       tokenized_datasets.rename_column("label"		,							"labels"					)

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

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

   # Instantiate dataloaders.
   _lowercase      :				Optional[Any]      					=       DataLoader(
       tokenized_datasets["train"]		,							shuffle=A__		,							collate_fn=A__		,							batch_size=A__					)
   _lowercase      :				Any      					=       DataLoader(
       tokenized_datasets["validation"]		,							shuffle=A__		,							collate_fn=A__		,							batch_size=A__					)

   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

       _snake_case         =  mocked_dataloaders  # noqa: F811







def  _A		(     snake_case		,							snake_case					)      ->     Union[str, Any]:



   if os.environ.get("TESTING_MOCKED_DATALOADERS"		,							A__					) == "1":
      _lowercase      :				List[str]      					=       2
   # New Code #
   _lowercase      :				Union[str, Any]      					=       int(args.gradient_accumulation_steps					)
   _lowercase      :				Union[str, Any]      					=       int(args.local_sgd_steps					)
   # Initialize accelerator
   _lowercase      :				str      					=       Accelerator(
       cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							gradient_accumulation_steps=A__					)
   if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
      raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"					)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _lowercase      :				Any      					=       config["lr"]
   _lowercase      :				str      					=       int(config["num_epochs"]					)
   _lowercase      :				int      					=       int(config["seed"]					)
   _lowercase      :				List[Any]      					=       int(config["batch_size"]					)

   _lowercase      :				List[Any]      					=       evaluate.load("glue"		,							"mrpc"					)

   set_seed(A__					)
   _lowercase					,				_lowercase      :				List[str]      					=       get_dataloaders(A__		,							A__					)
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   _lowercase      :				Optional[int]      					=       AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,							return_dict=A__					)

   # 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).
   _lowercase      :				List[Any]      					=       model.to(accelerator.device					)

   # Instantiate optimizer
   _lowercase      :				List[Any]      					=       AdamW(params=model.parameters()		,							lr=A__					)

   # Instantiate scheduler
   _lowercase      :				List[Any]      					=       get_linear_schedule_with_warmup(
       optimizer=A__		,							num_warmup_steps=1_00		,							num_training_steps=(len(A__					) * 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.
   _lowercase					,				_lowercase					,				_lowercase					,				_lowercase					,				_lowercase      :				Tuple      					=       accelerator.prepare(
       A__		,							A__		,							A__		,							A__		,							A__					)

   # Now we train the model
   for epoch in range(A__					):
      model.train()
      with LocalSGD(
          accelerator=A__		,							model=A__		,							local_sgd_steps=A__		,							enabled=local_sgd_steps is not None					) as local_sgd:
         for step, batch in enumerate(A__					):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device					)
            # New code #
            # We use the new `accumulate` context manager to perform gradient accumulation
            # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
            with accelerator.accumulate(A__					):
               _lowercase      :				int      					=       model(**A__					)
               _lowercase      :				Optional[Any]      					=       output.loss
               accelerator.backward(A__					)
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               # LocalSGD-specific line
               local_sgd.step()

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

      _lowercase      :				Union[str, Any]      					=       metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''		,							A__					)







def  _A		(     )      ->     List[str]:
   _lowercase      :				List[Any]      					=       argparse.ArgumentParser(description="Simple example of training script."					)
   parser.add_argument(
       "--mixed_precision"		,							type=A__		,							default=A__		,							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."		,							)
   # New Code #
   parser.add_argument(
       "--gradient_accumulation_steps"		,							type=A__		,							default=1		,							help="The number of minibatches to be ran before gradients are accumulated."		,							)
   parser.add_argument(
       "--local_sgd_steps"		,							type=A__		,							default=8		,							help="Number of local SGD steps or None to disable local SGD"					)
   parser.add_argument("--cpu"		,							action="store_true"		,							help="If passed, will train on the CPU."					)
   _lowercase      :				Optional[int]      					=       parser.parse_args()
   _lowercase      :				List[Any]      					=       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(A__		,							A__					)


if __name__ == "__main__":
       main()









'''simple docstring'''
from sklearn.metrics import mean_squared_error

import datasets


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

_snake_case         =  '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


_snake_case         =  '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'


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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      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_squared_error.html"
          ]      ,    )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      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 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase=None      ,    _UpperCamelCase="uniform_average"      ,    _UpperCamelCase=True  ):





      """simple docstring"""





      _lowercase      :				str      					=       mean_squared_error(
          _UpperCamelCase      ,    _UpperCamelCase      ,    sample_weight=_UpperCamelCase      ,    multioutput=_UpperCamelCase      ,    squared=_UpperCamelCase  )

      return {"mse": mse}






