





"""simple docstring"""





import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


SCREAMING_SNAKE_CASE__ =       16
SCREAMING_SNAKE_CASE__ =       32


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Optional[int]							,						SCREAMING_SNAKE_CASE					:						Tuple      ):







      '''simple docstring'''




      if args.with_tracking:
            lowerCAmelCase  						=	Accelerator(
                cpu=args.cpu							,						mixed_precision=args.mixed_precision							,						log_with="""all"""							,						project_dir=args.project_dir      )
      else:
            lowerCAmelCase  						=	Accelerator(cpu=args.cpu							,						mixed_precision=args.mixed_precision      )

      if hasattr(args.checkpointing_steps							,						"""isdigit"""      ):
            if args.checkpointing_steps == "epoch":
                  lowerCAmelCase  						=	args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                  lowerCAmelCase  						=	int(args.checkpointing_steps      )
            else:
                  raise ValueError(
                      F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'      )
      else:
            lowerCAmelCase  						=	None
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCAmelCase  						=	config["""lr"""]
      lowerCAmelCase  						=	int(config["""num_epochs"""]      )
      lowerCAmelCase  						=	int(config["""seed"""]      )
      lowerCAmelCase  						=	int(config["""batch_size"""]      )

      # We need to initialize the trackers we use, and also store our configuration
      if args.with_tracking:
            lowerCAmelCase  						=	os.path.split(SCREAMING_SNAKE_CASE      )[-1].split("""."""      )[0]
            accelerator.init_trackers(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      )

      lowerCAmelCase  						=	AutoTokenizer.from_pretrained("""bert-base-cased"""      )
      lowerCAmelCase  						=	load_dataset("""glue"""							,						"""mrpc"""      )
      lowerCAmelCase  						=	evaluate.load("""glue"""							,						"""mrpc"""      )

      def tokenize_function(SCREAMING_SNAKE_CASE					:						Optional[Any]      ):
            # max_length=None => use the model max length (it's actually the default)
            lowerCAmelCase  						=	tokenizer(examples["""sentence1"""]							,						examples["""sentence2"""]							,						truncation=SCREAMING_SNAKE_CASE							,						max_length=SCREAMING_SNAKE_CASE      )
            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():
            lowerCAmelCase  						=	datasets.map(
                SCREAMING_SNAKE_CASE							,						batched=SCREAMING_SNAKE_CASE							,						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
      lowerCAmelCase  						=	tokenized_datasets.rename_column("""label"""							,						"""labels"""      )

      # If the batch size is too big we use gradient accumulation
      lowerCAmelCase  						=	1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            lowerCAmelCase  						=	batch_size // MAX_GPU_BATCH_SIZE
            lowerCAmelCase  						=	MAX_GPU_BATCH_SIZE

      def collate_fn(SCREAMING_SNAKE_CASE					:						Optional[Any]      ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            lowerCAmelCase  						=	1_28 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  lowerCAmelCase  						=	16
            elif accelerator.mixed_precision != "no":
                  lowerCAmelCase  						=	8
            else:
                  lowerCAmelCase  						=	None

            return tokenizer.pad(
                SCREAMING_SNAKE_CASE							,						padding="""longest"""							,						max_length=SCREAMING_SNAKE_CASE							,						pad_to_multiple_of=SCREAMING_SNAKE_CASE							,						return_tensors="""pt"""							,						)

      # Instantiate dataloaders.
      lowerCAmelCase  						=	DataLoader(
          tokenized_datasets["""train"""]							,						shuffle=SCREAMING_SNAKE_CASE							,						collate_fn=SCREAMING_SNAKE_CASE							,						batch_size=SCREAMING_SNAKE_CASE      )
      lowerCAmelCase  						=	DataLoader(
          tokenized_datasets["""validation"""]							,						shuffle=SCREAMING_SNAKE_CASE							,						collate_fn=SCREAMING_SNAKE_CASE							,						batch_size=SCREAMING_SNAKE_CASE      )

      set_seed(SCREAMING_SNAKE_CASE      )

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCAmelCase  						=	AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""							,						return_dict=SCREAMING_SNAKE_CASE      )

      # 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).
      lowerCAmelCase  						=	model.to(accelerator.device      )

      # Instantiate optimizer
      lowerCAmelCase  						=	AdamW(params=model.parameters()							,						lr=SCREAMING_SNAKE_CASE      )

      # Instantiate scheduler
      lowerCAmelCase  						=	get_linear_schedule_with_warmup(
          optimizer=SCREAMING_SNAKE_CASE							,						num_warmup_steps=1_00							,						num_training_steps=(len(SCREAMING_SNAKE_CASE      ) * num_epochs) // gradient_accumulation_steps							,						)

      # 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  						=	accelerator.prepare(
          SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      )

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

      # Potentially load in the weights and states from a previous save
      if args.resume_from_checkpoint:
            if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
                  accelerator.print(F'Resumed from checkpoint: {args.resume_from_checkpoint}'      )
                  accelerator.load_state(args.resume_from_checkpoint      )
                  lowerCAmelCase  						=	os.path.basename(args.resume_from_checkpoint      )
            else:
                  # Get the most recent checkpoint
                  lowerCAmelCase  						=	[f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
                  dirs.sort(key=os.path.getctime      )
                  lowerCAmelCase  						=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            # Extract `epoch_{i}` or `step_{i}`
            lowerCAmelCase  						=	os.path.splitext(SCREAMING_SNAKE_CASE      )[0]

            if "epoch" in training_difference:
                  lowerCAmelCase  						=	int(training_difference.replace("""epoch_"""							,						""""""      )      ) + 1
                  lowerCAmelCase  						=	None
            else:
                  lowerCAmelCase  						=	int(training_difference.replace("""step_"""							,						""""""      )      )
                  lowerCAmelCase  						=	resume_step // len(SCREAMING_SNAKE_CASE      )
                  resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE      )

    # Now we train the model
      for epoch in range(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      ):
            model.train()
            if args.with_tracking:
                  lowerCAmelCase  						=	0
            if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                  # We need to skip steps until we reach the resumed step
                  lowerCAmelCase  						=	accelerator.skip_first_batches(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      )
                  overall_step += resume_step
            else:
                  # After the first iteration though, we need to go back to the original dataloader
                  lowerCAmelCase  						=	train_dataloader
            for step, batch in enumerate(SCREAMING_SNAKE_CASE      ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device      )
                  lowerCAmelCase  						=	model(**SCREAMING_SNAKE_CASE      )
                  lowerCAmelCase  						=	outputs.loss
                  lowerCAmelCase  						=	loss / gradient_accumulation_steps
                  # We keep track of the loss at each epoch
                  if args.with_tracking:
                        total_loss += loss.detach().float()
                  accelerator.backward(SCREAMING_SNAKE_CASE      )
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1

                  if isinstance(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      ):
                        lowerCAmelCase  						=	F'step_{overall_step}'
                        if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                    lowerCAmelCase  						=	os.path.join(args.output_dir							,						SCREAMING_SNAKE_CASE      )
                              accelerator.save_state(SCREAMING_SNAKE_CASE      )

            model.eval()
            for step, batch in enumerate(SCREAMING_SNAKE_CASE      ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device      )
                  with torch.no_grad():
                        lowerCAmelCase  						=	model(**SCREAMING_SNAKE_CASE      )
                  lowerCAmelCase  						=	outputs.logits.argmax(dim=-1      )
                  lowerCAmelCase						,		lowerCAmelCase  						=	accelerator.gather_for_metrics((predictions, batch["""labels"""])      )
                  metric.add_batch(
                      predictions=SCREAMING_SNAKE_CASE							,						references=SCREAMING_SNAKE_CASE							,						)

            lowerCAmelCase  						=	metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'epoch {epoch}:'							,						SCREAMING_SNAKE_CASE      )
            if args.with_tracking:
                  accelerator.log(
                      {
                          """accuracy""": eval_metric["""accuracy"""],
                          """f1""": eval_metric["""f1"""],
                          """train_loss""": total_loss.item() / len(SCREAMING_SNAKE_CASE      ),
                          """epoch""": epoch,
                      }							,						step=SCREAMING_SNAKE_CASE							,						)

            if checkpointing_steps == "epoch":
                  lowerCAmelCase  						=	F'epoch_{epoch}'
                  if args.output_dir is not None:
                        lowerCAmelCase  						=	os.path.join(args.output_dir							,						SCREAMING_SNAKE_CASE      )
                  accelerator.save_state(SCREAMING_SNAKE_CASE      )

      if args.with_tracking:
            accelerator.end_training()


def       UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=	argparse.ArgumentParser(description="""Simple example of training script."""      )
      parser.add_argument(
          """--mixed_precision"""							,						type=SCREAMING_SNAKE_CASE							,						default=SCREAMING_SNAKE_CASE							,						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."""      )
      parser.add_argument(
          """--checkpointing_steps"""							,						type=SCREAMING_SNAKE_CASE							,						default=SCREAMING_SNAKE_CASE							,						help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""							,						)
      parser.add_argument(
          """--resume_from_checkpoint"""							,						type=SCREAMING_SNAKE_CASE							,						default=SCREAMING_SNAKE_CASE							,						help="""If the training should continue from a checkpoint folder."""							,						)
      parser.add_argument(
          """--with_tracking"""							,						action="""store_true"""							,						help="""Whether to load in all available experiment trackers from the environment and use them for logging."""							,						)
      parser.add_argument(
          """--output_dir"""							,						type=SCREAMING_SNAKE_CASE							,						default="""."""							,						help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""							,						)
      parser.add_argument(
          """--project_dir"""							,						type=SCREAMING_SNAKE_CASE							,						default="""logs"""							,						help="""Location on where to store experiment tracking logs` and relevent project information"""							,						)
      lowerCAmelCase  						=	parser.parse_args()
      lowerCAmelCase  						=	{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
       main()













"""simple docstring"""





import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput

@dataclass
class 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				42
      _SCREAMING_SNAKE_CASE         =				None
      _SCREAMING_SNAKE_CASE         =				None







class 		lowercase ( unittest.TestCase ):





      def    _snake_case      (	self			)		->					Optional[Any]:
            lowerCAmelCase  						=	ModelOutputTest(a=30			)
            self.assertEqual(x.a				,						30			)
            self.assertIsNone(x.b			)
            self.assertIsNone(x.c			)
            with self.assertRaises(lowercase			):
                  lowerCAmelCase  						=	x.d





      def    _snake_case      (	self			)		->					Optional[Any]:
            lowerCAmelCase  						=	ModelOutputTest(a=30				,						b=10			)
            self.assertEqual(x[0]				,						30			)
            self.assertEqual(x[1]				,						10			)
            self.assertEqual(x[:2]				,						(30, 10)			)
            self.assertEqual(x[:]				,						(30, 10)			)

            lowerCAmelCase  						=	ModelOutputTest(a=30				,						c=10			)
            self.assertEqual(x[0]				,						30			)
            self.assertEqual(x[1]				,						10			)
            self.assertEqual(x[:2]				,						(30, 10)			)
            self.assertEqual(x[:]				,						(30, 10)			)





      def    _snake_case      (	self			)		->					Tuple:
            lowerCAmelCase  						=	ModelOutputTest(a=30				,						b=10			)
            self.assertEqual(x["""a"""]				,						30			)
            self.assertEqual(x["""b"""]				,						10			)
            with self.assertRaises(lowercase			):
                  lowerCAmelCase  						=	x["""c"""]

            lowerCAmelCase  						=	ModelOutputTest(a=30				,						c=10			)
            self.assertEqual(x["""a"""]				,						30			)
            self.assertEqual(x["""c"""]				,						10			)
            with self.assertRaises(lowercase			):
                  lowerCAmelCase  						=	x["""b"""]





      def    _snake_case      (	self			)		->					Union[str, Any]:
            lowerCAmelCase  						=	ModelOutputTest(a=30			)
            self.assertEqual(list(x.keys()			)				,						["""a"""]			)
            self.assertEqual(list(x.values()			)				,						[30]			)
            self.assertEqual(list(x.items()			)				,						[("""a""", 30)]			)
            self.assertEqual(list(lowercase			)				,						["""a"""]			)

            lowerCAmelCase  						=	ModelOutputTest(a=30				,						b=10			)
            self.assertEqual(list(x.keys()			)				,						["""a""", """b"""]			)
            self.assertEqual(list(x.values()			)				,						[30, 10]			)
            self.assertEqual(list(x.items()			)				,						[("""a""", 30), ("""b""", 10)]			)
            self.assertEqual(list(lowercase			)				,						["""a""", """b"""]			)

            lowerCAmelCase  						=	ModelOutputTest(a=30				,						c=10			)
            self.assertEqual(list(x.keys()			)				,						["""a""", """c"""]			)
            self.assertEqual(list(x.values()			)				,						[30, 10]			)
            self.assertEqual(list(x.items()			)				,						[("""a""", 30), ("""c""", 10)]			)
            self.assertEqual(list(lowercase			)				,						["""a""", """c"""]			)

            with self.assertRaises(lowercase			):
                  lowerCAmelCase  						=	x.update({"""d""": 20}			)
            with self.assertRaises(lowercase			):
                  del x["a"]
            with self.assertRaises(lowercase			):
                  lowerCAmelCase  						=	x.pop("""a"""			)
            with self.assertRaises(lowercase			):
                  lowerCAmelCase  						=	x.setdefault("""d"""				,						32			)





      def    _snake_case      (	self			)		->					Dict:
            lowerCAmelCase  						=	ModelOutputTest(a=30			)
            lowerCAmelCase  						=	10
            self.assertEqual(x.a				,						10			)
            self.assertEqual(x["""a"""]				,						10			)





      def    _snake_case      (	self			)		->					List[str]:
            lowerCAmelCase  						=	ModelOutputTest(a=30			)
            lowerCAmelCase  						=	10
            self.assertEqual(x.a				,						10			)
            self.assertEqual(x["""a"""]				,						10			)





      def    _snake_case      (	self			)		->					str:
            lowerCAmelCase  						=	ModelOutputTest({"""a""": 30, """b""": 10}			)
            self.assertEqual(list(x.keys()			)				,						["""a""", """b"""]			)
            self.assertEqual(x.a				,						30			)
            self.assertEqual(x.b				,						10			)





      def    _snake_case      (	self			)		->					Dict:
            lowerCAmelCase  						=	ModelOutputTest([("""a""", 30), ("""b""", 10)]			)
            self.assertEqual(list(x.keys()			)				,						["""a""", """b"""]			)
            self.assertEqual(x.a				,						30			)
            self.assertEqual(x.b				,						10			)

            with self.assertRaises(lowercase			):
                  lowerCAmelCase  						=	ModelOutputTest([("""a""", 30), (10, 10)]			)

            lowerCAmelCase  						=	ModelOutputTest(a=(30, 30)			)
            self.assertEqual(list(x.keys()			)				,						["""a"""]			)
            self.assertEqual(x.a				,						(30, 30)			)






