

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


lowercase_   							=  1_6
lowercase_   							=  3_2
def 	a (     A__      :  Accelerator ,  A__      :  int = 16 ,  A__      :  str = "bert-base-cased"	)       -> Optional[Any]:





      """simple docstring"""



      _lowercase      			=AutoTokenizer.from_pretrained(A__	)
      _lowercase      			=load_dataset('glue' ,  'mrpc'	)

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

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      _lowercase      			=tokenized_datasets.rename_column('label' ,  'labels'	)

      def collate_fn(A__      :  Union[str, Any]	):
            # 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(A__ ,  padding='max_length' ,  max_length=128 ,  return_tensors='pt'	)
            return tokenizer.pad(A__ ,  padding='longest' ,  return_tensors='pt'	)

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

      return train_dataloader, eval_dataloader
def 	a (     A__      :  Tuple ,  A__      :  Optional[int]	)       -> Tuple:





      """simple docstring"""



      _lowercase      			=Accelerator()

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

      set_seed(A__	)
      _lowercase			,			_lowercase      			=get_dataloaders(A__ ,  A__ ,  A__	)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _lowercase      			=AutoModelForSequenceClassification.from_pretrained(A__ ,  return_dict=A__	)

      # Instantiate optimizer
      _lowercase      			=(
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      _lowercase      			=optimizer_cls(params=model.parameters() ,  lr=A__	)

      if accelerator.state.deepspeed_plugin is not None:
            _lowercase      			=accelerator.state.deepspeed_plugin.deepspeed_config[
                'gradient_accumulation_steps'
            ]
      else:
            _lowercase      			=1
      _lowercase      			=(len(A__	) * 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      			=get_linear_schedule_with_warmup(
                optimizer=A__ ,  num_warmup_steps=0 ,  num_training_steps=A__ ,  )
      else:
            _lowercase      			=DummyScheduler(A__ ,  total_num_steps=A__ ,  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      			=accelerator.prepare(
          A__ ,  A__ ,  A__ ,  A__ ,  A__	)

      # We need to keep track of how many total steps we have iterated over
      _lowercase      			=0
      # We also need to keep track of the stating epoch so files are named properly
      _lowercase      			=0

      # Now we train the model
      _lowercase      			=evaluate.load('glue' ,  'mrpc'	)
      _lowercase      			=0
      _lowercase      			={}
      for epoch in range(A__ ,  A__	):
            model.train()
            for step, batch in enumerate(A__	):
                  _lowercase      			=model(**A__	)
                  _lowercase      			=outputs.loss
                  _lowercase      			=loss / gradient_accumulation_steps
                  accelerator.backward(A__	)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1

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

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

            if best_performance < eval_metric["accuracy"]:
                  _lowercase      			=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(A__ ,  A__	)
def 	a (     )       -> str:





      """simple docstring"""



      _lowercase      			=argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'	)
      parser.add_argument(
          '--model_name_or_path' ,  type=A__ ,  default='bert-base-cased' ,  help='Path to pretrained model or model identifier from huggingface.co/models.' ,  required=A__ ,  )
      parser.add_argument(
          '--output_dir' ,  type=A__ ,  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=A__ ,  default=A__ ,  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=A__ ,  default=3 ,  help='Number of train epochs.' ,  )
      _lowercase      			=parser.parse_args()
      _lowercase      			={'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
      training_function(A__ ,  A__	)


if __name__ == "__main__":
  main()





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_   							=  5_0_0_0_0_0

lowercase_							,		lowercase_   							=  os.path.split(__file__)
lowercase_   							=  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[str]	)       -> Optional[Any]:





      """simple docstring"""



      _lowercase      			=dataset.map(**A__	)
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[Any]	)       -> List[str]:





      """simple docstring"""



      _lowercase      			=dataset.filter(**A__	)
def 	a (     )       -> Union[str, Any]:





      """simple docstring"""



      _lowercase      			={'num examples': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            _lowercase      			=datasets.Features({'text': datasets.Value('string'	), 'numbers': datasets.Value('float32'	)}	)
            _lowercase      			=generate_example_dataset(
                os.path.join(A__ ,  'dataset.arrow'	) ,  A__ ,  num_examples=A__	)

            _lowercase      			=transformers.AutoTokenizer.from_pretrained('bert-base-cased' ,  use_fast=A__	)

            def tokenize(A__      :  int	):
                  return tokenizer(examples['text']	)

            _lowercase      			=map(A__	)

            _lowercase      			=map(A__ ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='numpy'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='pandas'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='torch' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='tensorflow' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=A__ ,  batched=A__	)

            _lowercase      			=filter(A__	)

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

      with open(A__ ,  'wb'	) as f:
            f.write(json.dumps(A__	).encode('utf-8'	)	)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_map_filter()


