





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


a__: Union[str, Any]							   =						16
a__: Tuple							   =						32


def       UpperCamelCase__(			UpperCamelCase__     :			Accelerator					,	UpperCamelCase__     :			int = 16    )->str:
       A__		   =		AutoTokenizer.from_pretrained('''bert-base-cased'''    )
       A__		   =		load_dataset('''glue'''					,	'''mrpc'''    )

       def tokenize_function(UpperCamelCase__     :			Tuple    ):
              # max_length=None => use the model max length (it's actually the default)
              A__		   =		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():
              A__		   =		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
       A__		   =		tokenized_datasets.rename_column('''label'''					,	'''labels'''    )

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

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

       # Instantiate dataloaders.
       A__		   =		DataLoader(
           tokenized_datasets['''train''']					,	shuffle=UpperCamelCase__					,	collate_fn=UpperCamelCase__					,	batch_size=UpperCamelCase__    )
       A__		   =		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

       a__: Union[str, Any]							   =						mocked_dataloaders  # noqa: F811


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

       A__		   =		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__     :			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(UpperCamelCase__    )

              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              A__		   =		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).
              A__		   =		model.to(accelerator.device    )

              # Instantiate optimizer
              A__		   =		AdamW(params=model.parameters()					,	lr=UpperCamelCase__    )
              A__   ,      A__		   =		get_dataloaders(UpperCamelCase__					,	UpperCamelCase__    )

              # Instantiate scheduler
              A__		   =		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.
              A__   ,      A__   ,      A__   ,      A__   ,      A__		   =		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    )
                            A__		   =		model(**UpperCamelCase__    )
                            A__		   =		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():
                                   A__		   =		model(**UpperCamelCase__    )
                            A__		   =		outputs.logits.argmax(dim=-1    )
                            A__   ,      A__		   =		accelerator.gather_for_metrics((predictions, batch['''labels'''])    )
                            metric.add_batch(
                                predictions=UpperCamelCase__					,	references=UpperCamelCase__					,	)

                     A__		   =		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       UpperCamelCase__(			)->List[str]:
       A__		   =		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.'''    )
       A__		   =		parser.parse_args()
       A__		   =		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(UpperCamelCase__					,	UpperCamelCase__    )


if __name__ == "__main__":
       main()












from typing import TYPE_CHECKING

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


a__: int							   =						{
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: List[Any]							   =						[
           'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
           'M2M100ForConditionalGeneration',
           'M2M100Model',
           'M2M100PreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
       from .tokenization_mam_aaa import MaMaaaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mam_aaa import (
                  M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MaMaaaForConditionalGeneration,
                  MaMaaaModel,
                  MaMaaaPreTrainedModel,
              )


else:
       import sys

       a__: List[str]							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





