





"""simple docstring"""





import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


lowercase__				:Tuple				   =       1_6
lowercase__				:Union[str, Any]				   =       3_2


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_ = 16  )						->Dict:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[Any]       =						AutoTokenizer.from_pretrained('''bert-base-cased'''  )
  __UpperCAmelCase    :						Optional[int]       =						load_dataset('''glue'''  ,			'''mrpc'''  )

  def tokenize_function(UpperCAmelCase_  ):
    # max_length=None => use the model max length (it's actually the default)
    __UpperCAmelCase    :						Optional[int]       =						tokenizer(examples['''sentence1''']  ,			examples['''sentence2''']  ,			truncation=UpperCAmelCase_  ,			max_length=UpperCAmelCase_  )
    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():
    __UpperCAmelCase    :						Union[str, Any]       =						datasets.map(
        UpperCAmelCase_  ,			batched=UpperCAmelCase_  ,			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
  __UpperCAmelCase    :						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.
    __UpperCAmelCase    :						Optional[Any]       =						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":
      __UpperCAmelCase    :						Optional[int]       =						16
    elif accelerator.mixed_precision != "no":
      __UpperCAmelCase    :						List[str]       =						8
    else:
      __UpperCAmelCase    :						Optional[int]       =						None

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

  # Instantiate dataloaders.
  __UpperCAmelCase    :						Union[str, Any]       =						DataLoader(
      tokenized_datasets['''train''']  ,			shuffle=UpperCAmelCase_  ,			collate_fn=UpperCAmelCase_  ,			batch_size=UpperCAmelCase_  )
  __UpperCAmelCase    :						List[str]       =						DataLoader(
      tokenized_datasets['''validation''']  ,			shuffle=UpperCAmelCase_  ,			collate_fn=UpperCAmelCase_  ,			batch_size=UpperCAmelCase_  )

  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

  lowercase__				:Dict				   =       mocked_dataloaders  # noqa: F811


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Dict:





  """simple docstring"""


  if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  ,			UpperCAmelCase_  ) == "1":
    __UpperCAmelCase    :						Union[str, Any]       =						2
  # Initialize accelerator
  __UpperCAmelCase    :						str       =						Accelerator(cpu=args.cpu  ,			mixed_precision=args.mixed_precision  )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  __UpperCAmelCase    :						Optional[int]       =						config['''lr''']
  __UpperCAmelCase    :						Tuple       =						int(config['''num_epochs''']  )
  __UpperCAmelCase    :						Optional[int]       =						int(config['''seed''']  )
  __UpperCAmelCase    :						Tuple       =						int(config['''batch_size''']  )

  __UpperCAmelCase    :						List[Any]       =						evaluate.load('''glue'''  ,			'''mrpc'''  )

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=UpperCAmelCase_  )
  def inner_training_loop(UpperCAmelCase_  ):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(UpperCAmelCase_  )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __UpperCAmelCase    :						List[str]       =						AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''  ,			return_dict=UpperCAmelCase_  )

    # 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).
    __UpperCAmelCase    :						str       =						model.to(accelerator.device  )

    # Instantiate optimizer
    __UpperCAmelCase    :						List[Any]       =						AdamW(params=model.parameters()  ,			lr=UpperCAmelCase_  )
    __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[int]       =						get_dataloaders(UpperCAmelCase_  ,			UpperCAmelCase_  )

    # Instantiate scheduler
    __UpperCAmelCase    :						Tuple       =						get_linear_schedule_with_warmup(
        optimizer=UpperCAmelCase_  ,			num_warmup_steps=1_00  ,			num_training_steps=(len(UpperCAmelCase_  ) * 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.
    __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						Dict       =						accelerator.prepare(
        UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )

    # Now we train the model
    for epoch in range(UpperCAmelCase_  ):
      model.train()
      for step, batch in enumerate(UpperCAmelCase_  ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device  )
        __UpperCAmelCase    :						Optional[Any]       =						model(**UpperCAmelCase_  )
        __UpperCAmelCase    :						Any       =						outputs.loss
        accelerator.backward(UpperCAmelCase_  )
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

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

      __UpperCAmelCase    :						int       =						metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''  ,			UpperCAmelCase_  )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()


def       lowerCamelCase_       (   )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						List[str]       =						argparse.ArgumentParser(description='''Simple example of training script.'''  )
  parser.add_argument(
      '''--mixed_precision'''  ,			type=UpperCAmelCase_  ,			default=UpperCAmelCase_  ,			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.'''  )
  __UpperCAmelCase    :						Union[str, Any]       =						parser.parse_args()
  __UpperCAmelCase    :						str       =						{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(UpperCAmelCase_  ,			UpperCAmelCase_  )


if __name__ == "__main__":
  main()






"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__				:int				   =       logging.get_logger(__name__)

lowercase__				:List[str]				   =       {
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       List[Any]         =       'gpt_neox'

      def __init__(							self       :      Union[str, Any]						,				__lowercase       :      Any=50_432						,				__lowercase       :      int=6_144						,				__lowercase       :      List[str]=44						,				__lowercase       :      List[Any]=64						,				__lowercase       :      int=24_576						,				__lowercase       :      List[str]="gelu"						,				__lowercase       :      Optional[int]=0.2_5						,				__lowercase       :      Any=10_000						,				__lowercase       :      Union[str, Any]=0.0						,				__lowercase       :      List[str]=0.0						,				__lowercase       :      Union[str, Any]=0.1						,				__lowercase       :      Optional[int]=2_048						,				__lowercase       :      Union[str, Any]=0.0_2						,				__lowercase       :      Optional[int]=1e-5						,				__lowercase       :      str=True						,				__lowercase       :      str=0						,				__lowercase       :      Tuple=2						,				__lowercase       :      Tuple=False						,				__lowercase       :      int=True						,				__lowercase       :      Any=None						,				**__lowercase       :      List[str]						,				):


        '''simple docstring'''




        super().__init__(bos_token_id=__lowercase						,				eos_token_id=__lowercase						,				**__lowercase )
        __UpperCAmelCase    :						int       =						vocab_size
        __UpperCAmelCase    :						Union[str, Any]       =						max_position_embeddings
        __UpperCAmelCase    :						List[str]       =						hidden_size
        __UpperCAmelCase    :						Tuple       =						num_hidden_layers
        __UpperCAmelCase    :						Any       =						num_attention_heads
        __UpperCAmelCase    :						Union[str, Any]       =						intermediate_size
        __UpperCAmelCase    :						str       =						hidden_act
        __UpperCAmelCase    :						str       =						rotary_pct
        __UpperCAmelCase    :						str       =						rotary_emb_base
        __UpperCAmelCase    :						Dict       =						attention_dropout
        __UpperCAmelCase    :						str       =						hidden_dropout
        __UpperCAmelCase    :						Any       =						classifier_dropout
        __UpperCAmelCase    :						Tuple       =						initializer_range
        __UpperCAmelCase    :						Any       =						layer_norm_eps
        __UpperCAmelCase    :						Optional[int]       =						use_cache
        __UpperCAmelCase    :						List[Any]       =						tie_word_embeddings
        __UpperCAmelCase    :						Any       =						use_parallel_residual
        __UpperCAmelCase    :						List[str]       =						rope_scaling
        self._rope_scaling_validation()

        if self.hidden_size % self.num_attention_heads != 0:
          raise ValueError(
              '''The hidden size is not divisble by the number of attention heads! Make sure to update them!''' )







      def       A_					(							self       :      int ):


        '''simple docstring'''




        if self.rope_scaling is None:
          return

        if not isinstance(self.rope_scaling						,				__lowercase ) or len(self.rope_scaling ) != 2:
          raise ValueError(
              '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
              f'''got {self.rope_scaling}''' )
        __UpperCAmelCase    :						Union[str, Any]       =						self.rope_scaling.get('''type'''						,				__lowercase )
        __UpperCAmelCase    :						List[Any]       =						self.rope_scaling.get('''factor'''						,				__lowercase )
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
          raise ValueError(
              f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''' )
        if rope_scaling_factor is None or not isinstance(__lowercase						,				__lowercase ) or rope_scaling_factor <= 1.0:
          raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )