






"""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 checkpointing 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
#
########################################################################

lowerCAmelCase_		:			List[Any]				   =					1_6
lowerCAmelCase_		:			Dict				   =					3_2






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase = 16						):







       '''simple docstring'''
       UpperCAmelCase					=	AutoTokenizer.from_pretrained("""bert-base-cased"""						)
       UpperCAmelCase					=	load_dataset("""glue"""		,					"""mrpc"""						)

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

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

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

       # Instantiate dataloaders.
       UpperCAmelCase					=	DataLoader(
           tokenized_datasets["""train"""]		,					shuffle=lowerCAmelCase		,					collate_fn=lowerCAmelCase		,					batch_size=lowerCAmelCase						)
       UpperCAmelCase					=	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

 lowerCAmelCase_		:			List[Any]				   =					mocked_dataloaders  # noqa: F811






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       # For testing only
       if os.environ.get("""TESTING_MOCKED_DATALOADERS"""		,					lowerCAmelCase						) == "1":
              UpperCAmelCase					=	2
       # Initialize accelerator
       UpperCAmelCase					=	Accelerator(cpu=args.cpu		,					mixed_precision=args.mixed_precision						)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       UpperCAmelCase					=	config["""lr"""]
       UpperCAmelCase					=	int(config["""num_epochs"""]						)
       UpperCAmelCase					=	int(config["""seed"""]						)
       UpperCAmelCase					=	int(config["""batch_size"""]						)

       # New Code #
       # Parse out whether we are saving every epoch or after a certain number of batches
       if hasattr(args.checkpointing_steps		,					"""isdigit"""						):
              if args.checkpointing_steps == "epoch":
                     UpperCAmelCase					=	args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     UpperCAmelCase					=	int(args.checkpointing_steps						)
              else:
                     raise ValueError(
                         F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''						)
       else:
              UpperCAmelCase					=	None

       set_seed(lowerCAmelCase						)

       UpperCAmelCase			,				UpperCAmelCase					=	get_dataloaders(lowerCAmelCase		,					lowerCAmelCase						)
       UpperCAmelCase					=	evaluate.load("""glue"""		,					"""mrpc"""						)

       # If the batch size is too big we use gradient accumulation
       UpperCAmelCase					=	1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              UpperCAmelCase					=	batch_size // MAX_GPU_BATCH_SIZE
              UpperCAmelCase					=	MAX_GPU_BATCH_SIZE

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

       # Instantiate optimizer
       UpperCAmelCase					=	AdamW(params=model.parameters()		,					lr=lowerCAmelCase						)

       # Instantiate scheduler
       UpperCAmelCase					=	get_linear_schedule_with_warmup(
           optimizer=lowerCAmelCase		,					num_warmup_steps=100		,					num_training_steps=(len(lowerCAmelCase						) * 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.
       UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase					=	accelerator.prepare(
           lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       # New Code #
       # We need to keep track of how many total steps we have iterated over
       UpperCAmelCase					=	0
       # We also need to keep track of the stating epoch so files are named properly
       UpperCAmelCase					=	0

       # We need to load the checkpoint back in before training here with `load_state`
       # The total number of epochs is adjusted based on where the state is being loaded from,
       # as we assume continuation of the same training script
       if args.resume_from_checkpoint:
              if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
                     accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''						)
                     accelerator.load_state(args.resume_from_checkpoint						)
                     UpperCAmelCase					=	os.path.basename(args.resume_from_checkpoint						)
              else:
                     # Get the most recent checkpoint
                     UpperCAmelCase					=	[f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
                     dirs.sort(key=os.path.getctime						)
                     UpperCAmelCase					=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              UpperCAmelCase					=	os.path.splitext(lowerCAmelCase						)[0]

              if "epoch" in training_difference:
                     UpperCAmelCase					=	int(training_difference.replace("""epoch_"""		,					""""""						)						) + 1
                     UpperCAmelCase					=	None
              else:
                     UpperCAmelCase					=	int(training_difference.replace("""step_"""		,					""""""						)						)
                     UpperCAmelCase					=	resume_step // len(lowerCAmelCase						)
                     resume_step -= starting_epoch * len(lowerCAmelCase						)

    # Now we train the model
       for epoch in range(lowerCAmelCase		,					lowerCAmelCase						):
              model.train()
              # New Code #
              if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                     # We need to skip steps until we reach the resumed step
                     UpperCAmelCase					=	accelerator.skip_first_batches(lowerCAmelCase		,					lowerCAmelCase						)
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     UpperCAmelCase					=	train_dataloader
              for step, batch in enumerate(lowerCAmelCase						):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device						)
                     UpperCAmelCase					=	model(**lowerCAmelCase						)
                     UpperCAmelCase					=	outputs.loss
                     UpperCAmelCase					=	loss / gradient_accumulation_steps
                     accelerator.backward(lowerCAmelCase						)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()
                     # New Code #
                     overall_step += 1

                     # New Code #
                     # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                     # These are saved to folders named `step_{overall_step}`
                     # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                     # If mixed precision was used, will also save a "scalar.bin" file
                     if isinstance(lowerCAmelCase		,					lowerCAmelCase						):
                            UpperCAmelCase					=	F'''step_{overall_step}'''
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          UpperCAmelCase					=	os.path.join(args.output_dir		,					lowerCAmelCase						)
                                   accelerator.save_state(lowerCAmelCase						)

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

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

              # New Code #
              # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
              # These are saved to folders named `epoch_{epoch}`
              # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
              # If mixed precision was used, will also save a "scalar.bin" file
              if checkpointing_steps == "epoch":
                     UpperCAmelCase					=	F'''epoch_{epoch}'''
                     if args.output_dir is not None:
                            UpperCAmelCase					=	os.path.join(args.output_dir		,					lowerCAmelCase						)
                     accelerator.save_state(lowerCAmelCase						)






def 					_lowerCAmelCase				(					):







       '''simple docstring'''
       UpperCAmelCase					=	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."""						)
       parser.add_argument(
           """--checkpointing_steps"""		,					type=lowerCAmelCase		,					default=lowerCAmelCase		,					help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""		,					)
       parser.add_argument(
           """--output_dir"""		,					type=lowerCAmelCase		,					default="""."""		,					help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""		,					)
       parser.add_argument(
           """--resume_from_checkpoint"""		,					type=lowerCAmelCase		,					default=lowerCAmelCase		,					help="""If the training should continue from a checkpoint folder."""		,					)
       UpperCAmelCase					=	parser.parse_args()
       UpperCAmelCase					=	{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
       training_function(lowerCAmelCase		,					lowerCAmelCase						)


if __name__ == "__main__":
 main()





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A				    =						{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A				    =						['''MPNetTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A				    =						[
	    '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''MPNetForMaskedLM''',
	    '''MPNetForMultipleChoice''',
	    '''MPNetForQuestionAnswering''',
	    '''MPNetForSequenceClassification''',
	    '''MPNetForTokenClassification''',
	    '''MPNetLayer''',
	    '''MPNetModel''',
	    '''MPNetPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A				    =						[
	    '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFMPNetEmbeddings''',
	    '''TFMPNetForMaskedLM''',
	    '''TFMPNetForMultipleChoice''',
	    '''TFMPNetForQuestionAnswering''',
	    '''TFMPNetForSequenceClassification''',
	    '''TFMPNetForTokenClassification''',
	    '''TFMPNetMainLayer''',
	    '''TFMPNetModel''',
	    '''TFMPNetPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
	from .tokenization_mpnet import MPNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mpnet_fast import MPNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mpnet import (
		    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MPNetForMaskedLM,
		    MPNetForMultipleChoice,
		    MPNetForQuestionAnswering,
		    MPNetForSequenceClassification,
		    MPNetForTokenClassification,
		    MPNetLayer,
		    MPNetModel,
		    MPNetPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_mpnet import (
		    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFMPNetEmbeddings,
		    TFMPNetForMaskedLM,
		    TFMPNetForMultipleChoice,
		    TFMPNetForQuestionAnswering,
		    TFMPNetForSequenceClassification,
		    TFMPNetForTokenClassification,
		    TFMPNetMainLayer,
		    TFMPNetModel,
		    TFMPNetPreTrainedModel,
		)

else:
	import sys

	__A				    =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




