


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
# and perform gradient accumulation
#
# 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)
#
# 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
#
########################################################################


__a       =					16
__a       =					32




def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase = 16   )			->Optional[int]:







      """simple docstring"""

      lowercase	:      List[Any]          =						AutoTokenizer.from_pretrained('''bert-base-cased'''   )
      lowercase	:      Any          =						load_dataset('''glue''',			'''mrpc'''   )

      def tokenize_function(_UpperCamelCase   ):
            # max_length=None => use the model max length (it's actually the default)
            lowercase	:      int          =						tokenizer(examples['''sentence1'''],			examples['''sentence2'''],			truncation=snake_case_,			max_length=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():
            lowercase	:      Dict          =						datasets.map(
                snake_case_,			batched=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
      lowercase	:      List[Any]          =						tokenized_datasets.rename_column('''label''',			'''labels'''   )

      def collate_fn(_UpperCamelCase   ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            lowercase	:      Optional[Any]          =						128 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  lowercase	:      Any          =						16
            elif accelerator.mixed_precision != "no":
                  lowercase	:      int          =						8
            else:
                  lowercase	:      str          =						None

            return tokenizer.pad(
                snake_case_,			padding='''longest''',			max_length=snake_case_,			pad_to_multiple_of=snake_case_,			return_tensors='''pt''',			)

      # Instantiate dataloaders.
      lowercase	:      Dict          =						DataLoader(
          tokenized_datasets['''train'''],			shuffle=snake_case_,			collate_fn=snake_case_,			batch_size=snake_case_   )
      lowercase	:      str          =						DataLoader(
          tokenized_datasets['''validation'''],			shuffle=snake_case_,			collate_fn=snake_case_,			batch_size=snake_case_   )

      return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
  from accelerate.test_utils.training import mocked_dataloaders

  __a       =					mocked_dataloaders  # noqa: F811




def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase   )			->int:







      """simple docstring"""

      if os.environ.get('''TESTING_MOCKED_DATALOADERS''',			snake_case_   ) == "1":
            lowercase	:      Optional[Any]          =						2
      # New Code #
      lowercase	:      str          =						int(args.gradient_accumulation_steps   )
      # Initialize accelerator
      lowercase	:      Dict          =						Accelerator(
          cpu=args.cpu,			mixed_precision=args.mixed_precision,			gradient_accumulation_steps=snake_case_   )
      if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
            raise NotImplementedError(
                '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''   )
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowercase	:      int          =						config["""lr"""]
      lowercase	:      str          =						int(config['''num_epochs''']   )
      lowercase	:      Tuple          =						int(config['''seed''']   )
      lowercase	:      Optional[Any]          =						int(config['''batch_size''']   )

      lowercase	:      int          =						evaluate.load('''glue''',			'''mrpc'''   )

      set_seed(snake_case_   )
      lowercase	:      Dict          =						get_dataloaders(snake_case_,			snake_case_   )
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowercase	:      Dict          =						AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''',			return_dict=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).
      lowercase	:      List[Any]          =						model.to(accelerator.device   )

      # Instantiate optimizer
      lowercase	:      Any          =						AdamW(params=model.parameters(),			lr=snake_case_   )

      # Instantiate scheduler
      lowercase	:      Optional[Any]          =						get_linear_schedule_with_warmup(
          optimizer=snake_case_,			num_warmup_steps=100,			num_training_steps=(len(snake_case_   ) * num_epochs),			)

      # 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	:      List[Any]          =						accelerator.prepare(
          snake_case_,			snake_case_,			snake_case_,			snake_case_,			snake_case_   )

      # Now we train the model
      for epoch in range(snake_case_   ):
            model.train()
            for step, batch in enumerate(snake_case_   ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device   )
                  # New code #
                  # We use the new `accumulate` context manager to perform gradient accumulation
                  # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                  with accelerator.accumulate(snake_case_   ):
                        lowercase	:      List[Any]          =						model(**snake_case_   )
                        lowercase	:      Dict          =						output.loss
                        accelerator.backward(snake_case_   )
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(snake_case_   ):
                  # 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(**snake_case_   )
                  lowercase	:      Any          =						outputs.logits.argmax(dim=-1   )
                  lowercase	:      Tuple          =						accelerator.gather_for_metrics((predictions, batch['''labels'''])   )
                  metric.add_batch(
                      predictions=snake_case_,			references=snake_case_,			)

            lowercase	:      int          =						metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"""epoch {epoch}:""",			snake_case_   )




def 	__lowercase		(  )			->Union[str, Any]:







      """simple docstring"""

      lowercase	:      Any          =						argparse.ArgumentParser(description='''Simple example of training script.'''   )
      parser.add_argument(
          '''--mixed_precision''',			type=snake_case_,			default=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.''',			)
      # New Code #
      parser.add_argument(
          '''--gradient_accumulation_steps''',			type=snake_case_,			default=1,			help='''The number of minibatches to be ran before gradients are accumulated.''',			)
      parser.add_argument('''--cpu''',			action='''store_true''',			help='''If passed, will train on the CPU.'''   )
      lowercase	:      Tuple          =						parser.parse_args()
      lowercase	:      List[str]          =						{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(snake_case_,			snake_case_   )


if __name__ == "__main__":
  main()







from __future__ import annotations

import numpy as np







def 				lowerCAmelCase_	(     snake_case_     ):
      return np.maximum(0,snake_case_     )


if __name__ == "__main__":
  print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]

