"""simple docstring"""





import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


_UpperCamelCase     :		Optional[int]											=					16
_UpperCamelCase     :		List[str]											=					32





def        a_  (   _lowerCAmelCase						:				Accelerator     ,				_lowerCAmelCase						:				int = 16     ,				_lowerCAmelCase						:				str = "bert-base-cased"  ):





       '''simple docstring'''

       lowercase__    :	Dict     					=			AutoTokenizer.from_pretrained(_lowerCAmelCase  )
       lowercase__    :	List[str]     					=			load_dataset('glue'     ,				'mrpc'  )

       def tokenize_function(_lowerCAmelCase						:				Optional[int]  ):
              # max_length=None => use the model max length (it's actually the default)
              lowercase__    :	Optional[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
       lowercase__    :	Optional[Any]     					=			datasets.map(
           _lowerCAmelCase     ,				batched=_lowerCAmelCase     ,				remove_columns=['idx', 'sentence1', 'sentence2']     ,				load_from_cache_file=_lowerCAmelCase  )

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       lowercase__    :	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.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(_lowerCAmelCase     ,				padding='max_length'     ,				max_length=128     ,				return_tensors='pt'  )
              return tokenizer.pad(_lowerCAmelCase     ,				padding='longest'     ,				return_tensors='pt'  )

       # Instantiate dataloaders.
       lowercase__    :	List[Any]     					=			DataLoader(
           tokenized_datasets['train']     ,				shuffle=_lowerCAmelCase     ,				collate_fn=_lowerCAmelCase     ,				batch_size=_lowerCAmelCase  )
       lowercase__    :	str     					=			DataLoader(
           tokenized_datasets['validation']     ,				shuffle=_lowerCAmelCase     ,				collate_fn=_lowerCAmelCase     ,				batch_size=_lowerCAmelCase  )

       return train_dataloader, eval_dataloader





def        a_  (   _lowerCAmelCase						:				Tuple     ,				_lowerCAmelCase						:				List[Any]  ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			Accelerator()

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

       set_seed(_lowerCAmelCase  )
       lowercase__					,   lowercase__    :	Dict     					=			get_dataloaders(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase__    :	List[str]     					=			AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase     ,				return_dict=_lowerCAmelCase  )

       # Instantiate optimizer
       lowercase__    :	Dict     					=			(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       lowercase__    :	Optional[int]     					=			optimizer_cls(params=model.parameters()     ,				lr=_lowerCAmelCase  )

       if accelerator.state.deepspeed_plugin is not None:
              lowercase__    :	Dict     					=			accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              lowercase__    :	Optional[Any]     					=			1
       lowercase__    :	Optional[Any]     					=			(len(_lowerCAmelCase  ) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              lowercase__    :	List[str]     					=			get_linear_schedule_with_warmup(
                  optimizer=_lowerCAmelCase     ,				num_warmup_steps=0     ,				num_training_steps=_lowerCAmelCase     ,				)
       else:
              lowercase__    :	List[Any]     					=			DummyScheduler(_lowerCAmelCase     ,				total_num_steps=_lowerCAmelCase     ,				warmup_num_steps=0  )

       # 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__    :	str     					=			accelerator.prepare(
           _lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )

       # We need to keep track of how many total steps we have iterated over
       lowercase__    :	Union[str, Any]     					=			0
       # We also need to keep track of the stating epoch so files are named properly
       lowercase__    :	Dict     					=			0

       # Now we train the model
       lowercase__    :	List[Any]     					=			evaluate.load('glue'     ,				'mrpc'  )
       lowercase__    :	Dict     					=			0
       lowercase__    :	Union[str, Any]     					=			{}
       for epoch in range(_lowerCAmelCase     ,				_lowerCAmelCase  ):
              model.train()
              for step, batch in enumerate(_lowerCAmelCase  ):
                     lowercase__    :	Dict     					=			model(**_lowerCAmelCase  )
                     lowercase__    :	Optional[Any]     					=			outputs.loss
                     lowercase__    :	Dict     					=			loss / gradient_accumulation_steps
                     accelerator.backward(_lowerCAmelCase  )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1

              model.eval()
              lowercase__    :	Tuple     					=			0
              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():
                            lowercase__    :	Dict     					=			model(**_lowerCAmelCase  )
                     lowercase__    :	Optional[int]     					=			outputs.logits.argmax(dim=-1  )
                     # It is slightly faster to call this once, than multiple times
                     lowercase__					,   lowercase__    :	Dict     					=			accelerator.gather(
                         (predictions, batch['labels'])  )  # If we are in a multiprocess environment, the last batch has duplicates
                     if accelerator.use_distributed:
                            if step == len(_lowerCAmelCase  ) - 1:
                                   lowercase__    :	Optional[int]     					=			predictions[: len(eval_dataloader.dataset  ) - samples_seen]
                                   lowercase__    :	Union[str, Any]     					=			references[: len(eval_dataloader.dataset  ) - samples_seen]
                            else:
                                   samples_seen += references.shape[0]
                     metric.add_batch(
                         predictions=_lowerCAmelCase     ,				references=_lowerCAmelCase     ,				)

              lowercase__    :	Optional[Any]     					=			metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"""epoch {epoch}:"""     ,				_lowerCAmelCase  )
              lowercase__    :	List[str]     					=			eval_metric['accuracy']

              if best_performance < eval_metric["accuracy"]:
                     lowercase__    :	Optional[Any]     					=			eval_metric['accuracy']

       if args.performance_lower_bound is not None:
              assert (
                  args.performance_lower_bound <= best_performance
              ), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

       accelerator.wait_for_everyone()
       if accelerator.is_main_process:
              with open(os.path.join(args.output_dir     ,				'all_results.json'  )     ,				'w'  ) as f:
                     json.dump(_lowerCAmelCase     ,				_lowerCAmelCase  )





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	List[Any]     					=			argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'  )
       parser.add_argument(
           '--model_name_or_path'     ,				type=_lowerCAmelCase     ,				default='bert-base-cased'     ,				help='Path to pretrained model or model identifier from huggingface.co/models.'     ,				required=_lowerCAmelCase     ,				)
       parser.add_argument(
           '--output_dir'     ,				type=_lowerCAmelCase     ,				default='.'     ,				help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'     ,				)
       parser.add_argument(
           '--performance_lower_bound'     ,				type=_lowerCAmelCase     ,				default=_lowerCAmelCase     ,				help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'     ,				)
       parser.add_argument(
           '--num_epochs'     ,				type=_lowerCAmelCase     ,				default=3     ,				help='Number of train epochs.'     ,				)
       lowercase__    :	Tuple     					=			parser.parse_args()
       lowercase__    :	int     					=			{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
       training_function(_lowerCAmelCase     ,				_lowerCAmelCase  )


if __name__ == "__main__":
       main()


"""simple docstring"""





from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments

