

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
#
########################################################################


UpperCamelCase__		 =     16
UpperCamelCase__		 =     32







def 						lowerCamelCase__      ( __A    :Accelerator	,__A    :int = 1_6		):


       """simple docstring"""




       __snake_case       	=							AutoTokenizer.from_pretrained("""bert-base-cased"""		)
       __snake_case       	=							load_dataset("""glue"""	,"""mrpc"""		)

       def tokenize_function(__A    :List[str]		):
              # max_length=None => use the model max length (it's actually the default)
              __snake_case       	=							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
       # starting with the main process first:
       with accelerator.main_process_first():
              __snake_case       	=							datasets.map(
                  __A	,batched=__A	,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
       __snake_case       	=							tokenized_datasets.rename_column("""label"""	,"""labels"""		)

       def collate_fn(__A    :Optional[Any]		):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              __snake_case       	=							1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     __snake_case       	=							1_6
              elif accelerator.mixed_precision != "no":
                     __snake_case       	=							8
              else:
                     __snake_case       	=							None

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

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

       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

    UpperCamelCase__		 =     mocked_dataloaders  # noqa: F811







def 						lowerCamelCase__      ( __A    :Dict	,__A    :int		):


       """simple docstring"""




       if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,__A		) == "1":
              __snake_case       	=							2
       # New Code #
       __snake_case       	=							int(args.gradient_accumulation_steps		)
       # Initialize accelerator
       __snake_case       	=							Accelerator(
           cpu=args.cpu	,mixed_precision=args.mixed_precision	,gradient_accumulation_steps=__A		)
       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
       __snake_case       	=							config["""lr"""]
       __snake_case       	=							int(config["""num_epochs"""]		)
       __snake_case       	=							int(config["""seed"""]		)
       __snake_case       	=							int(config["""batch_size"""]		)

       __snake_case       	=							evaluate.load("""glue"""	,"""mrpc"""		)

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

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

       # Instantiate optimizer
       __snake_case       	=							AdamW(params=model.parameters()	,lr=__A		)

       # Instantiate scheduler
       __snake_case       	=							get_linear_schedule_with_warmup(
           optimizer=__A	,num_warmup_steps=1_0_0	,num_training_steps=(len(__A		) * 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.
       __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case       	=							accelerator.prepare(
           __A	,__A	,__A	,__A	,__A		)

       # Now we train the model
       for epoch in range(__A		):
              model.train()
              for step, batch in enumerate(__A		):
                     # 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(__A		):
                            __snake_case       	=							model(**__A		)
                            __snake_case       	=							output.loss
                            accelerator.backward(__A		)
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              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():
                            __snake_case       	=							model(**__A		)
                     __snake_case       	=							outputs.logits.argmax(dim=-1		)
                     __snake_case	,    __snake_case       	=							accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
                     metric.add_batch(
                         predictions=__A	,references=__A	,)

              __snake_case       	=							metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'epoch {epoch}:'	,__A		)







def 						lowerCamelCase__      ( ):


       """simple docstring"""




       __snake_case       	=							argparse.ArgumentParser(description="""Simple example of training script."""		)
       parser.add_argument(
           """--mixed_precision"""	,type=__A	,default=__A	,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=__A	,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."""		)
       __snake_case       	=							parser.parse_args()
       __snake_case       	=							{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
       training_function(__A	,__A		)


if __name__ == "__main__":
    main()




def 						lowerCamelCase__      ( __A    :int	,__A    :float	,__A    :float		):


       """simple docstring"""




       return round(float(moles / volume		) * nfactor		)







def 						lowerCamelCase__      ( __A    :float	,__A    :float	,__A    :float		):


       """simple docstring"""




       return round(float((moles * 0.0_821 * temperature) / (volume)		)		)







def 						lowerCamelCase__      ( __A    :float	,__A    :float	,__A    :float		):


       """simple docstring"""




       return round(float((moles * 0.0_821 * temperature) / (pressure)		)		)







def 						lowerCamelCase__      ( __A    :float	,__A    :float	,__A    :float		):


       """simple docstring"""




       return round(float((pressure * volume) / (0.0_821 * moles)		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

