



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase__		:    Dict				   =     {
    """configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
    """tokenization_mvp""": ["""MvpTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase__		:    Any				   =     ["""MvpTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase__		:    Union[str, Any]				   =     [
					    """MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MvpForCausalLM""",
					    """MvpForConditionalGeneration""",
					    """MvpForQuestionAnswering""",
					    """MvpForSequenceClassification""",
					    """MvpModel""",
					    """MvpPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
					from .tokenization_mvp import MvpTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mvp_fast import MvpTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mvp import (
										    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MvpForCausalLM,
										    MvpForConditionalGeneration,
										    MvpForQuestionAnswering,
										    MvpForSequenceClassification,
										    MvpModel,
										    MvpPreTrainedModel,
										)

else:
					import sys

					lowercase__		:    Optional[Any]				   =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""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__		:    Optional[int]				   =     1_6
lowercase__		:    List[str]				   =     3_2

def     UpperCamelCase_   (   lowerCAmelCase__				:    Accelerator   ,       lowerCAmelCase__				:    int = 16						)						->       Dict:

  """simple docstring"""




  lowerCAmelCase_       :    Union[str, Any]								=			AutoTokenizer.from_pretrained('bert-base-cased'						)
  lowerCAmelCase_       :    Union[str, Any]								=			load_dataset('glue'   ,       'mrpc'						)

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

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

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

  # Instantiate dataloaders.
  lowerCAmelCase_       :    List[Any]								=			DataLoader(
      tokenized_datasets['train']   ,       shuffle=lowerCAmelCase__   ,       collate_fn=lowerCAmelCase__   ,       batch_size=lowerCAmelCase__						)
  lowerCAmelCase_       :    Tuple								=			DataLoader(
      tokenized_datasets['validation']   ,       shuffle=lowerCAmelCase__   ,       collate_fn=lowerCAmelCase__   ,       batch_size=lowerCAmelCase__						)

  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__		:    int				   =     mocked_dataloaders  # noqa: F811

def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Optional[Any]						)						->       Optional[int]:

  """simple docstring"""




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

  lowerCAmelCase_       :    str								=			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=lowerCAmelCase__						)
  def inner_training_loop(lowerCAmelCase__				:    Optional[int]						):
    # 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(lowerCAmelCase__						)

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

    # 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_       :    int								=			model.to(accelerator.device						)

    # Instantiate optimizer
    lowerCAmelCase_       :    List[Any]								=			AdamW(params=model.parameters()   ,       lr=lowerCAmelCase__						)
    lowerCAmelCase_						,lowerCAmelCase_       :    Any								=			get_dataloaders(lowerCAmelCase__   ,       lowerCAmelCase__						)

    # Instantiate scheduler
    lowerCAmelCase_       :    Dict								=			get_linear_schedule_with_warmup(
        optimizer=lowerCAmelCase__   ,       num_warmup_steps=100   ,       num_training_steps=(len(lowerCAmelCase__						) * 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.
    lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_       :    List[Any]								=			accelerator.prepare(
        lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)

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

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

      lowerCAmelCase_       :    str								=			metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f"epoch {epoch}:"   ,       lowerCAmelCase__						)

    # 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     UpperCamelCase_   (   )						->       Optional[Any]:

  """simple docstring"""




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


if __name__ == "__main__":
     main()





