





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
#
########################################################################


_lowerCamelCase			       =      16
_lowerCamelCase			       =      32




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ = 16      ):
       _lowerCamelCase							:     int	=		AutoTokenizer.from_pretrained('''bert-base-cased'''      )
       _lowerCamelCase							:     Optional[int]	=		load_dataset('''glue''' ,    '''mrpc'''      )

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

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

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

       # Instantiate dataloaders.
       _lowerCamelCase							:     List[str]	=		DataLoader(
           tokenized_datasets['''train'''] ,    shuffle=lowercase_ ,    collate_fn=lowercase_ ,    batch_size=lowercase_      )
       _lowerCamelCase							:     Dict	=		DataLoader(
           tokenized_datasets['''validation'''] ,    shuffle=lowercase_ ,    collate_fn=lowercase_ ,    batch_size=lowercase_      )

       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

       _lowerCamelCase			       =      mocked_dataloaders  # noqa: F811




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       # For testing only
       if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,    lowercase_      ) == "1":
              _lowerCamelCase							:     Any	=		2
       # New Code #
       _lowerCamelCase							:     Optional[Any]	=		int(args.gradient_accumulation_steps      )
       _lowerCamelCase							:     Optional[int]	=		int(args.local_sgd_steps      )
       # Initialize accelerator
       _lowerCamelCase							:     List[str]	=		Accelerator(
           cpu=args.cpu ,    mixed_precision=args.mixed_precision ,    gradient_accumulation_steps=lowercase_      )
       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
       _lowerCamelCase							:     Dict	=		config['''lr''']
       _lowerCamelCase							:     str	=		int(config['''num_epochs''']      )
       _lowerCamelCase							:     Dict	=		int(config['''seed''']      )
       _lowerCamelCase							:     List[str]	=		int(config['''batch_size''']      )

       _lowerCamelCase							:     Tuple	=		evaluate.load('''glue''' ,    '''mrpc'''      )

       set_seed(lowercase_      )
       _lowerCamelCase,  _lowerCamelCase							:     int	=		get_dataloaders(lowercase_ ,    lowercase_      )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _lowerCamelCase							:     Dict	=		AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,    return_dict=lowercase_      )

       # 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							:     int	=		model.to(accelerator.device      )

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

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

       # Now we train the model
       for epoch in range(lowercase_      ):
              model.train()
              with LocalSGD(
                  accelerator=lowercase_ ,    model=lowercase_ ,    local_sgd_steps=lowercase_ ,    enabled=local_sgd_steps is not None      ) as local_sgd:
                     for step, batch in enumerate(lowercase_      ):
                            # 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(lowercase_      ):
                                   _lowerCamelCase							:     Union[str, Any]	=		model(**lowercase_      )
                                   _lowerCamelCase							:     Optional[Any]	=		output.loss
                                   accelerator.backward(lowercase_      )
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()
                                   # LocalSGD-specific line
                                   local_sgd.step()

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

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




def    __UpperCAmelCase(      ):
       _lowerCamelCase							:     Dict	=		argparse.ArgumentParser(description='''Simple example of training script.'''      )
       parser.add_argument(
           '''--mixed_precision''' ,    type=lowercase_ ,    default=lowercase_ ,    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=lowercase_ ,    default=1 ,    help='''The number of minibatches to be ran before gradients are accumulated.''' ,    )
       parser.add_argument(
           '''--local_sgd_steps''' ,    type=lowercase_ ,    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.'''      )
       _lowerCamelCase							:     int	=		parser.parse_args()
       _lowerCamelCase							:     Tuple	=		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(lowercase_ ,    lowercase_      )


if __name__ == "__main__":
       main()








import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


_lowerCamelCase			       =      logging.get_logger(__name__)







@dataclass
class 			__A       :




      """simple docstring"""







      UpperCAmelCase__						=      field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()  )}  )
      UpperCAmelCase__						=      field(
          metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}  )
      UpperCAmelCase__						=      field(
          default=128 ,metadata={
              """help""": (
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )
          } ,)
      UpperCAmelCase__						=      field(
          default=lowerCamelCase__ ,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		self.task_name.lower()







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      """train"""
      UpperCAmelCase__						=      """dev"""
      UpperCAmelCase__						=      """test"""







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      42
      UpperCAmelCase__						=      42
      UpperCAmelCase__						=      42


      def __init__(		self      ,							a__      ,							a__      ,							a__ = None      ,							a__ = Split.train      ,							a__ = None      ,							):



             """simple docstring"""





             warnings.warn(
                 '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
                 '''library. You can have a look at this example script for pointers: '''
                 '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''      ,							a__      ,							)
             _lowerCamelCase							:     Optional[Any]	=		args
             _lowerCamelCase							:     Tuple	=		glue_processors[args.task_name]()
             _lowerCamelCase							:     Any	=		glue_output_modes[args.task_name]
             if isinstance(a__      ,							a__):
                    try:
                           _lowerCamelCase							:     List[Any]	=		Split[mode]
                    except KeyError:
                           raise KeyError('''mode is not a valid split name''')
        # Load data features from cache or dataset file
             _lowerCamelCase							:     Tuple	=		os.path.join(
                 cache_dir if cache_dir is not None else args.data_dir      ,							F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""      ,							)
             _lowerCamelCase							:     int	=		self.processor.get_labels()
             if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
                 "RobertaTokenizer",
                 "RobertaTokenizerFast",
                 "XLMRobertaTokenizer",
                 "BartTokenizer",
                 "BartTokenizerFast",
             ):
                    # HACK(label indices are swapped in RoBERTa pretrained model)
                    _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		label_list[2], label_list[1]
             _lowerCamelCase							:     str	=		label_list

             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
             _lowerCamelCase							:     Any	=		cached_features_file + '''.lock'''
             with FileLock(a__):
                    if os.path.exists(a__) and not args.overwrite_cache:
                           _lowerCamelCase							:     Any	=		time.time()
                           _lowerCamelCase							:     Optional[Any]	=		torch.load(a__)
                           logger.info(
                               F"""Loading features from cached file {cached_features_file} [took %.3f s]"""      ,							time.time() - start)
                    else:
                           logger.info(F"""Creating features from dataset file at {args.data_dir}""")

                           if mode == Split.dev:
                                  _lowerCamelCase							:     List[str]	=		self.processor.get_dev_examples(args.data_dir)
                           elif mode == Split.test:
                                  _lowerCamelCase							:     str	=		self.processor.get_test_examples(args.data_dir)
                           else:
                                  _lowerCamelCase							:     List[Any]	=		self.processor.get_train_examples(args.data_dir)
                           if limit_length is not None:
                                  _lowerCamelCase							:     List[Any]	=		examples[:limit_length]
                           _lowerCamelCase							:     List[str]	=		glue_convert_examples_to_features(
                               a__      ,							a__      ,							max_length=args.max_seq_length      ,							label_list=a__      ,							output_mode=self.output_mode      ,							)
                           _lowerCamelCase							:     int	=		time.time()
                           torch.save(self.features      ,							a__)
                           # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                           logger.info(
                               F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]""")


      def __len__(		self):



             """simple docstring"""





             return len(self.features)


      def __getitem__(		self      ,							a__):



             """simple docstring"""





             return self.features[i]



      def 				__snake_case			(		self):



             """simple docstring"""





             return self.label_list

