


'''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[Any]     =							16
__UpperCamelCase    :						Optional[Any]     =							32





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Any = 16						,	UpperCAmelCase      :			Any = "bert-base-cased"      ):
      """simple docstring"""




      __lowerCamelCase     :      List[str]       						=						AutoTokenizer.from_pretrained(lowerCAmelCase__      )
      __lowerCamelCase     :      Optional[int]       						=						load_dataset("""glue"""						,	"""mrpc"""      )

      def tokenize_function(UpperCAmelCase      :			str      ):
            # max_length=None => use the model max length (it's actually the default)
            __lowerCamelCase     :      Union[str, 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
      __lowerCamelCase     :      Optional[int]       						=						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
      __lowerCamelCase     :      int       						=						tokenized_datasets.rename_column("""label"""						,	"""labels"""      )

      def collate_fn(UpperCAmelCase      :			Dict      ):
            # 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.
      __lowerCamelCase     :      Dict       						=						DataLoader(
          tokenized_datasets["""train"""]						,	shuffle=lowerCAmelCase__						,	collate_fn=lowerCAmelCase__						,	batch_size=lowerCAmelCase__      )
      __lowerCamelCase     :      Union[str, Any]       						=						DataLoader(
          tokenized_datasets["""validation"""]						,	shuffle=lowerCAmelCase__						,	collate_fn=lowerCAmelCase__						,	batch_size=lowerCAmelCase__      )

      return train_dataloader, eval_dataloader





def   _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			str						,	UpperCAmelCase      :			List[str]      ):
      """simple docstring"""




      model.eval()
      __lowerCamelCase     :      Optional[int]       						=						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():
                  __lowerCamelCase     :      Any       						=						model(**lowerCAmelCase__      )
            __lowerCamelCase     :      Tuple       						=						outputs.logits.argmax(dim=-1      )
            # It is slightly faster to call this once, than multiple times
            __lowerCamelCase						, __lowerCamelCase     :      List[str]       						=						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:
                        __lowerCamelCase     :      Any       						=						predictions[: len(eval_dataloader.dataset      ) - samples_seen]
                        __lowerCamelCase     :      int       						=						references[: len(eval_dataloader.dataset      ) - samples_seen]
                  else:
                        samples_seen += references.shape[0]
            metric.add_batch(
                predictions=lowerCAmelCase__						,	references=lowerCAmelCase__						,	)

      __lowerCamelCase     :      Union[str, Any]       						=						metric.compute()
      return eval_metric["accuracy"]





def   _UpperCAmelCase       (     UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			int      ):
      """simple docstring"""




      __lowerCamelCase     :      Dict       						=						Accelerator()

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

      set_seed(lowerCAmelCase__      )
      __lowerCamelCase						, __lowerCamelCase     :      Tuple       						=						get_dataloaders(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__      )

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

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

      if accelerator.state.deepspeed_plugin is not None:
            __lowerCamelCase     :      Any       						=						accelerator.state.deepspeed_plugin.deepspeed_config[
                """gradient_accumulation_steps"""
            ]
      else:
            __lowerCamelCase     :      Any       						=						1
      __lowerCamelCase     :      int       						=						(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
      ):
            __lowerCamelCase     :      Dict       						=						get_linear_schedule_with_warmup(
                optimizer=lowerCAmelCase__						,	num_warmup_steps=0						,	num_training_steps=lowerCAmelCase__						,	)
      else:
            __lowerCamelCase     :      int       						=						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.
      __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      List[Any]       						=						accelerator.prepare(
          lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__      )

      # We need to keep track of how many total steps we have iterated over
      __lowerCamelCase     :      Union[str, Any]       						=						0
      # We also need to keep track of the stating epoch so files are named properly
      __lowerCamelCase     :      Union[str, Any]       						=						0
      __lowerCamelCase     :      Any       						=						evaluate.load("""glue"""						,	"""mrpc"""      )
      __lowerCamelCase     :      Tuple       						=						num_epochs

      if args.partial_train_epoch is not None:
            __lowerCamelCase     :      Dict       						=						args.partial_train_epoch

      if args.resume_from_checkpoint:
            accelerator.load_state(args.resume_from_checkpoint      )
            __lowerCamelCase     :      int       						=						args.resume_from_checkpoint.split("""epoch_"""      )[1]
            __lowerCamelCase     :      Dict       						=						""""""
            for char in epoch_string:
                  if char.isdigit():
                        state_epoch_num += char
                  else:
                        break
            __lowerCamelCase     :      Dict       						=						int(lowerCAmelCase__      ) + 1
            __lowerCamelCase     :      Optional[int]       						=						evaluation_loop(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__      )
            accelerator.print("""resumed checkpoint performance:"""						,	lowerCAmelCase__      )
            accelerator.print("""resumed checkpoint's scheduler's lr:"""						,	lr_scheduler.get_lr()[0]      )
            accelerator.print("""resumed optimizers's lr:"""						,	optimizer.param_groups[0]["""lr"""]      )
            with open(os.path.join(args.output_dir						,	f"""state_{starting_epoch-1}.json"""      )						,	"""r"""      ) as f:
                  __lowerCamelCase     :      List[str]       						=						json.load(lowerCAmelCase__      )
                  assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["lr"] == lr_scheduler.get_lr()[0]
                  ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                  ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                  assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                  return

    # Now we train the model
      __lowerCamelCase     :      Tuple       						=						{}
      for epoch in range(lowerCAmelCase__						,	lowerCAmelCase__      ):
            model.train()
            for step, batch in enumerate(lowerCAmelCase__      ):
                  __lowerCamelCase     :      Any       						=						model(**lowerCAmelCase__      )
                  __lowerCamelCase     :      Dict       						=						outputs.loss
                  __lowerCamelCase     :      str       						=						loss / gradient_accumulation_steps
                  accelerator.backward(lowerCAmelCase__      )
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1
            __lowerCamelCase     :      Dict       						=						f"""epoch_{epoch}"""
            __lowerCamelCase     :      Dict       						=						os.path.join(args.output_dir						,	lowerCAmelCase__      )
            accelerator.save_state(lowerCAmelCase__      )
            __lowerCamelCase     :      Dict       						=						evaluation_loop(lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__						,	lowerCAmelCase__      )
            __lowerCamelCase     :      str       						=						accuracy
            __lowerCamelCase     :      Optional[int]       						=						lr_scheduler.get_lr()[0]
            __lowerCamelCase     :      int       						=						optimizer.param_groups[0]["""lr"""]
            __lowerCamelCase     :      Optional[int]       						=						epoch
            __lowerCamelCase     :      Union[str, Any]       						=						overall_step
            accelerator.print(f"""epoch {epoch}:"""						,	lowerCAmelCase__      )

            accelerator.wait_for_everyone()
            if accelerator.is_main_process:
                  with open(os.path.join(args.output_dir						,	f"""state_{epoch}.json"""      )						,	"""w"""      ) as f:
                        json.dump(lowerCAmelCase__						,	lowerCAmelCase__      )





def   _UpperCAmelCase       (     ):
      """simple docstring"""




      __lowerCamelCase     :      int       						=						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(
          """--resume_from_checkpoint"""						,	type=lowerCAmelCase__						,	default=lowerCAmelCase__						,	help="""If the training should continue from a checkpoint folder."""						,	)
      parser.add_argument(
          """--partial_train_epoch"""						,	type=lowerCAmelCase__						,	default=lowerCAmelCase__						,	help="""If passed, the training will stop after this number of epochs."""						,	)
      parser.add_argument(
          """--num_epochs"""						,	type=lowerCAmelCase__						,	default=2						,	help="""Number of train epochs."""						,	)
      __lowerCamelCase     :      List[str]       						=						parser.parse_args()
      __lowerCamelCase     :      Union[str, Any]       						=						{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

      training_function(lowerCAmelCase__						,	lowerCAmelCase__      )


if __name__ == "__main__":
    main()







def   _UpperCAmelCase       (     UpperCAmelCase      :			str      ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						hex_num.strip()
      if not hex_num:
            raise ValueError("""No value was passed to the function"""      )

      __lowerCamelCase     :      Dict       						=						hex_num[0] == """-"""
      if is_negative:
            __lowerCamelCase     :      Optional[Any]       						=						hex_num[1:]

      try:
            __lowerCamelCase     :      Any       						=						int(UpperCAmelCase						,	16      )
      except ValueError:
            raise ValueError("""Invalid value was passed to the function"""      )

      __lowerCamelCase     :      List[str]       						=						""""""
      while int_num > 0:
            __lowerCamelCase     :      Dict       						=						str(int_num % 2      ) + bin_str
            int_num >>= 1

      return int(("""-""" + bin_str) if is_negative else bin_str      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()



