


'''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


__magic_name__			=      16
__magic_name__			=      32




def      lowerCamelCase					(      lowerCamelCase  :      Accelerator							,						lowerCamelCase  :      int = 16							,						lowerCamelCase  :      str = "bert-base-cased"):
      A_						:  Optional[int]          =				AutoTokenizer.from_pretrained(lowerCamelCase)
      A_						:  Tuple          =				load_dataset("""glue"""							,						"""mrpc""")

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

      def collate_fn(lowerCamelCase  :      str):
            # 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.
      A_						:  List[str]          =				DataLoader(
          tokenized_datasets["""train"""]							,						shuffle=lowerCamelCase							,						collate_fn=lowerCamelCase							,						batch_size=lowerCamelCase)
      A_						:  Dict          =				DataLoader(
          tokenized_datasets["""validation"""]							,						shuffle=lowerCamelCase							,						collate_fn=lowerCamelCase							,						batch_size=lowerCamelCase)

      return train_dataloader, eval_dataloader




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

      A_						:  str          =				metric.compute()
      return eval_metric["accuracy"]




def      lowerCamelCase					(      lowerCamelCase  :      str							,						lowerCamelCase  :      str):
      # Initialize accelerator
      A_						:  Optional[Any]          =				Accelerator()

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

      set_seed(lowerCamelCase)
      A_						:  Optional[Any]          =				get_dataloaders(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      A_						:  str          =				AutoModelForSequenceClassification.from_pretrained(lowerCamelCase							,						return_dict=lowerCamelCase)

      # Instantiate optimizer
      A_						:  Union[str, Any]          =				(
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      A_						:  Any          =				optimizer_cls(params=model.parameters()							,						lr=lowerCamelCase)

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

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

      if args.partial_train_epoch is not None:
            A_						:  Optional[Any]          =				args.partial_train_epoch

      if args.resume_from_checkpoint:
            accelerator.load_state(args.resume_from_checkpoint)
            A_						:  List[str]          =				args.resume_from_checkpoint.split("""epoch_""")[1]
            A_						:  Optional[Any]          =				""""""
            for char in epoch_string:
                  if char.isdigit():
                        state_epoch_num += char
                  else:
                        break
            A_						:  Optional[Any]          =				int(lowerCamelCase) + 1
            A_						:  Dict          =				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:
                  A_						:  Union[str, Any]          =				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
      A_						:  Tuple          =				{}
      for epoch in range(lowerCamelCase							,						lowerCamelCase):
            model.train()
            for step, batch in enumerate(lowerCamelCase):
                  A_						:  Dict          =				model(**lowerCamelCase)
                  A_						:  str          =				outputs.loss
                  A_						:  List[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
            A_						:  List[str]          =				F'epoch_{epoch}'
            A_						:  Optional[Any]          =				os.path.join(args.output_dir							,						lowerCamelCase)
            accelerator.save_state(lowerCamelCase)
            A_						:  List[Any]          =				evaluation_loop(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)
            A_						:  str          =				accuracy
            A_						:  List[Any]          =				lr_scheduler.get_lr()[0]
            A_						:  int          =				optimizer.param_groups[0]["""lr"""]
            A_						:  Any          =				epoch
            A_						:  Optional[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      lowerCamelCase					(      ):
      A_						:  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(
          """--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."""							,						)
      A_						:  Tuple          =				parser.parse_args()
      A_						:  Union[str, Any]          =				{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

      training_function(lowerCamelCase							,						lowerCamelCase)


if __name__ == "__main__":
  main()




'''simple docstring'''


from ..utils import DummyObject, requires_backends



class 	__lowerCAmelCase		(							metaclass=__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""torch""", """torchsde"""]






  def __init__(							self :       Any	,*_a :       Union[str, Any]	,**_a :       Optional[int] ):

        '''simple docstring'''




        requires_backends(self	,["""torch""", """torchsde"""] )






  @classmethod
  def        _a							(							cls :       Optional[int]	,*_a :       List[Any]	,**_a :       Any ):

        '''simple docstring'''




        requires_backends(cls	,["""torch""", """torchsde"""] )






  @classmethod
  def        _a							(							cls :       List[Any]	,*_a :       Tuple	,**_a :       Union[str, Any] ):

        '''simple docstring'''




        requires_backends(cls	,["""torch""", """torchsde"""] )
