


"""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,
# specifically showcasing the experiment tracking capability,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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    =				16
__lowercase    =				32


def 					lowercase      (    A_  ,       A_ = 16	)->				int:
  '''simple docstring'''







  a						:		int											=      AutoTokenizer.from_pretrained("bert-base-cased"	)
  a						:		List[str]											=      load_dataset("glue"  ,       "mrpc"	)

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

  def collate_fn(A_	):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    a						:		Dict											=      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":
      a						:		List[str]											=      16
    elif accelerator.mixed_precision != "no":
      a						:		int											=      8
    else:
      a						:		Tuple											=      None

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

  # Instantiate dataloaders.
  a						:		List[Any]											=      DataLoader(
      tokenized_datasets["train"]  ,       shuffle=A_  ,       collate_fn=A_  ,       batch_size=A_	)
  a						:		str											=      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

 __lowercase    =				mocked_dataloaders  # noqa: F811


def 					lowercase      (    A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  if os.environ.get("TESTING_MOCKED_DATALOADERS"  ,       A_	) == "1":
    a						:		Optional[int]											=      2
  # Initialize Accelerator

  # New Code #
  # We pass in "all" to `log_with` to grab all available trackers in the environment
  # Note: If using a custom `Tracker` class, should be passed in here such as:
  # >>> log_with = ["all", MyCustomTrackerClassInstance()]
  if args.with_tracking:
    a						:		List[Any]											=      Accelerator(
        cpu=args.cpu  ,       mixed_precision=args.mixed_precision  ,       log_with="all"  ,       project_dir=args.project_dir	)
  else:
    a						:		Optional[Any]											=      Accelerator(cpu=args.cpu  ,       mixed_precision=args.mixed_precision	)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  a						:		Union[str, Any]											=      config["lr"]
  a						:		Dict											=      int(config["num_epochs"]	)
  a						:		str											=      int(config["seed"]	)
  a						:		Union[str, Any]											=      int(config["batch_size"]	)
  set_seed(A_	)

  a						:		Optional[Any]											=      get_dataloaders(A_  ,       A_	)
  a						:		Optional[Any]											=      evaluate.load("glue"  ,       "mrpc"	)

  # If the batch size is too big we use gradient accumulation
  a						:		Optional[int]											=      1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    a						:		Optional[int]											=      batch_size // MAX_GPU_BATCH_SIZE
    a						:		List[Any]											=      MAX_GPU_BATCH_SIZE

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  a						:		Any											=      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).
  a						:		Union[str, Any]											=      model.to(accelerator.device	)

  # Instantiate optimizer
  a						:		Optional[int]											=      AdamW(params=model.parameters()  ,       lr=A_	)

  # Instantiate scheduler
  a						:		List[Any]											=      get_linear_schedule_with_warmup(
      optimizer=A_  ,       num_warmup_steps=100  ,       num_training_steps=(len(A_	) * 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.
  a						:		int											=      accelerator.prepare(
      A_  ,       A_  ,       A_  ,       A_  ,       A_	)

  # New Code #
  # We need to initialize the trackers we use. Overall configurations can also be stored
  if args.with_tracking:
    a						:		str											=      os.path.split(A_	)[-1].split("."	)[0]
    accelerator.init_trackers(A_  ,       A_	)

  # Now we train the model
  for epoch in range(A_	):
    model.train()
    # New Code #
    # For our tracking example, we will log the total loss of each epoch
    if args.with_tracking:
      a						:		Optional[Any]											=      0
    for step, batch in enumerate(A_	):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device	)
      a						:		int											=      model(**A_	)
      a						:		List[Any]											=      outputs.loss
      # New Code #
      if args.with_tracking:
        total_loss += loss.detach().float()
      a						:		Dict											=      loss / gradient_accumulation_steps
      accelerator.backward(A_	)
      if step % gradient_accumulation_steps == 0:
        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` (the default).
      batch.to(accelerator.device	)
      with torch.no_grad():
        a						:		Dict											=      model(**A_	)
      a						:		int											=      outputs.logits.argmax(dim=-1	)
      a						:		str											=      accelerator.gather_for_metrics((predictions, batch["labels"])	)
      metric.add_batch(
          predictions=A_  ,       references=A_  ,       )

    a						:		Optional[int]											=      metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F'''epoch {epoch}:'''  ,       A_	)

    # New Code #
    # To actually log, we call `Accelerator.log`
    # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
    if args.with_tracking:
      accelerator.log(
          {
              "accuracy": eval_metric["accuracy"],
              "f1": eval_metric["f1"],
              "train_loss": total_loss.item() / len(A_	),
              "epoch": epoch,
          }  ,       step=A_  ,       )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
  if args.with_tracking:
    accelerator.end_training()


def 					lowercase      (    )->				Any:
  '''simple docstring'''







  a						:		Union[str, Any]											=      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."  ,       )
  parser.add_argument("--cpu"  ,       action="store_true"  ,       help="If passed, will train on the CPU."	)
  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(
      "--project_dir"  ,       type=A_  ,       default="logs"  ,       help="Location on where to store experiment tracking logs` and relevent project information"  ,       )
  a						:		List[Any]											=      parser.parse_args()
  a						:		str											=      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(A_  ,       A_	)


if __name__ == "__main__":
 main()










"""simple docstring"""







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


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """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 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Union[str, Any]     =	"""gpt_neox"""






    def __init__( self    :   List[str]    ,			__UpperCAmelCase    :   Tuple=50432    ,			__UpperCAmelCase    :   str=6144    ,			__UpperCAmelCase    :   Any=44    ,			__UpperCAmelCase    :   Union[str, Any]=64    ,			__UpperCAmelCase    :   Dict=24576    ,			__UpperCAmelCase    :   Any="gelu"    ,			__UpperCAmelCase    :   List[Any]=0.25    ,			__UpperCAmelCase    :   Optional[Any]=10000    ,			__UpperCAmelCase    :   Tuple=0.0    ,			__UpperCAmelCase    :   Union[str, Any]=0.0    ,			__UpperCAmelCase    :   Optional[int]=0.1    ,			__UpperCAmelCase    :   List[Any]=2048    ,			__UpperCAmelCase    :   Optional[Any]=0.02    ,			__UpperCAmelCase    :   Optional[Any]=1e-5    ,			__UpperCAmelCase    :   Dict=True    ,			__UpperCAmelCase    :   Union[str, Any]=0    ,			__UpperCAmelCase    :   Union[str, Any]=2    ,			__UpperCAmelCase    :   Any=False    ,			__UpperCAmelCase    :   List[Any]=True    ,			__UpperCAmelCase    :   int=None    ,			**__UpperCAmelCase    :   Tuple    ,			):
      super().__init__(bos_token_id=__UpperCAmelCase    ,			eos_token_id=__UpperCAmelCase    ,			**__UpperCAmelCase)
      a						:		List[Any]											=      vocab_size
      a						:		Optional[int]											=      max_position_embeddings
      a						:		List[Any]											=      hidden_size
      a						:		Union[str, Any]											=      num_hidden_layers
      a						:		int											=      num_attention_heads
      a						:		Union[str, Any]											=      intermediate_size
      a						:		Optional[Any]											=      hidden_act
      a						:		Dict											=      rotary_pct
      a						:		Any											=      rotary_emb_base
      a						:		Dict											=      attention_dropout
      a						:		List[str]											=      hidden_dropout
      a						:		List[str]											=      classifier_dropout
      a						:		Any											=      initializer_range
      a						:		Union[str, Any]											=      layer_norm_eps
      a						:		int											=      use_cache
      a						:		int											=      tie_word_embeddings
      a						:		str											=      use_parallel_residual
      a						:		Union[str, Any]											=      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      __snake_case							( self    :   Any):
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling    ,			__UpperCAmelCase) 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}''')
      a						:		str											=      self.rope_scaling.get("type"    ,			__UpperCAmelCase)
      a						:		List[str]											=      self.rope_scaling.get("factor"    ,			__UpperCAmelCase)
      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(__UpperCAmelCase    ,			__UpperCAmelCase) or rope_scaling_factor <= 1.0:
        raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''')






