





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__						:      str	=    16
lowercase__						:      int	=    32







def  a__    (    lowercase :					Accelerator, lowercase :					int = 16				)		->		List[str]:





     """simple docstring"""

     _UpperCamelCase       =	AutoTokenizer.from_pretrained('''bert-base-cased'''				)
     _UpperCamelCase       =	load_dataset('''glue''', '''mrpc'''				)

     def tokenize_function(lowercase :					Union[str, Any]				):
          # max_length=None => use the model max length (it's actually the default)
          _UpperCamelCase       =	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():
          _UpperCamelCase       =	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
     _UpperCamelCase       =	tokenized_datasets.rename_column('''label''', '''labels'''				)

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

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

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

   lowercase__						:      str	=    mocked_dataloaders  # noqa: F811







def  a__    (    lowercase :					List[Any], lowercase :					List[str]				)		->		Any:





     """simple docstring"""

     if os.environ.get('''TESTING_MOCKED_DATALOADERS''', lowercase				) == "1":
          _UpperCamelCase       =	2
     # Initialize accelerator
     _UpperCamelCase       =	Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision				)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     _UpperCamelCase       =	config['''lr''']
     _UpperCamelCase       =	int(config['''num_epochs''']				)
     _UpperCamelCase       =	int(config['''seed''']				)
     _UpperCamelCase       =	int(config['''batch_size''']				)

     _UpperCamelCase       =	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=lowercase				)
     def inner_training_loop(lowercase :					List[Any]				):
          # 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(lowercase				)

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

          # Instantiate optimizer
          _UpperCamelCase       =	AdamW(params=model.parameters(), lr=lowercase				)
          _UpperCamelCase   ,    _UpperCamelCase       =	get_dataloaders(lowercase, lowercase				)

          # Instantiate scheduler
          _UpperCamelCase       =	get_linear_schedule_with_warmup(
              optimizer=lowercase, num_warmup_steps=100, 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.
          _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	accelerator.prepare(
              lowercase, lowercase, lowercase, lowercase, lowercase				)

          # Now we train the model
          for epoch in range(lowercase				):
               model.train()
               for step, batch in enumerate(lowercase				):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device				)
                    _UpperCamelCase       =	model(**lowercase				)
                    _UpperCamelCase       =	outputs.loss
                    accelerator.backward(lowercase				)
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               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():
                         _UpperCamelCase       =	model(**lowercase				)
                    _UpperCamelCase       =	outputs.logits.argmax(dim=-1				)
                    _UpperCamelCase   ,    _UpperCamelCase       =	accelerator.gather_for_metrics((predictions, batch['''labels'''])				)
                    metric.add_batch(
                        predictions=lowercase, references=lowercase, )

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

    # 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  a__    (    )		->		str:





     """simple docstring"""

     _UpperCamelCase       =	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.''', )
     parser.add_argument('''--cpu''', action='''store_true''', help='''If passed, will train on the CPU.'''				)
     _UpperCamelCase       =	parser.parse_args()
     _UpperCamelCase       =	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(lowercase, lowercase				)


if __name__ == "__main__":
   main()










'''simple docstring'''

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowercase__						:      Any	=    logging.getLogger(__name__)







def  a__    (    lowercase :					Optional[Any], lowercase :					Tuple				)		->		Any:





     """simple docstring"""

     return (preds == labels).mean()
@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					str						    =   field(
           metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Pretrained config name or path if not the same as model_name'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,						)

@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					str						    =   field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()			)}			)
       _snake_case     :					str						    =   field(metadata={'help': 'Should contain the data files for the task.'}			)
       _snake_case     :					int						    =   field(
           default=1_2_8						,						metadata={
               'help': (
                   'The maximum total input sequence length after tokenization. Sequences longer '
                   'than this will be truncated, sequences shorter will be padded.'
               )
           }						,						)
       _snake_case     :					bool						    =   field(
           default=__magic_name__						,						metadata={'help': 'Overwrite the cached training and evaluation sets'}			)







def  a__    (    )		->		Optional[Any]:





     """simple docstring"""

     _UpperCamelCase       =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
     _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir				)
         and os.listdir(training_args.output_dir				)
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
              ''' --overwrite_output_dir to overcome.'''				)

     # Setup logging
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, )
     logger.warning(
         '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''', training_args.local_rank, training_args.device, training_args.n_gpu, bool(training_args.local_rank != -1				), training_args.fpaa, )
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank				):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info('''Training/evaluation parameters %s''', lowercase				)

     # Set seed
     set_seed(training_args.seed				)

     try:
          _UpperCamelCase       =	processors[data_args.task_name]()
          _UpperCamelCase       =	processor.get_labels()
          _UpperCamelCase       =	len(lowercase				)
     except KeyError:
          raise ValueError('''Task not found: %s''' % (data_args.task_name)				)

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     _UpperCamelCase       =	AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=lowercase, finetuning_task=data_args.task_name, cache_dir=model_args.cache_dir, )
     _UpperCamelCase       =	AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, )
     _UpperCamelCase       =	AutoModelForMultipleChoice.from_pretrained(
         model_args.model_name_or_path, from_tf=bool('''.ckpt''' in model_args.model_name_or_path				), config=lowercase, cache_dir=model_args.cache_dir, )

     # Get datasets
     _UpperCamelCase       =	(
         MultipleChoiceDataset(
             data_dir=data_args.data_dir, tokenizer=lowercase, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.train, )
         if training_args.do_train
         else None
     )
     _UpperCamelCase       =	(
         MultipleChoiceDataset(
             data_dir=data_args.data_dir, tokenizer=lowercase, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.dev, )
         if training_args.do_eval
         else None
     )

     def compute_metrics(lowercase :					EvalPrediction				) -> Dict:
          _UpperCamelCase       =	np.argmax(p.predictions, axis=1				)
          return {"acc": simple_accuracy(lowercase, p.label_ids				)}

     # Data collator
     _UpperCamelCase       =	DataCollatorWithPadding(lowercase, pad_to_multiple_of=8				) if training_args.fpaa else None

     # Initialize our Trainer
     _UpperCamelCase       =	Trainer(
         model=lowercase, args=lowercase, train_dataset=lowercase, eval_dataset=lowercase, compute_metrics=lowercase, data_collator=lowercase, )

     # Training
     if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_master():
               tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
     _UpperCamelCase       =	{}
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''				)

          _UpperCamelCase       =	trainer.evaluate()

          _UpperCamelCase       =	os.path.join(training_args.output_dir, '''eval_results.txt'''				)
          if trainer.is_world_master():
               with open(lowercase, '''w'''				) as writer:
                    logger.info('''***** Eval results *****'''				)
                    for key, value in result.items():
                         logger.info('''  %s = %s''', lowercase, lowercase				)
                         writer.write('''%s = %s\n''' % (key, value)				)

                    results.update(lowercase				)

     return results







def  a__    (    lowercase :					Tuple				)		->		List[Any]:





     """simple docstring"""

     main()


if __name__ == "__main__":
   main()



