





import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_lowerCamelCase			       =      16
_lowerCamelCase			       =      32




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ = 16 ,    lowercase_ = "bert-base-cased"      ):
       _lowerCamelCase							:     Optional[int]	=		AutoTokenizer.from_pretrained(lowercase_      )
       _lowerCamelCase							:     Dict	=		load_dataset('''glue''' ,    '''mrpc'''      )

       def tokenize_function(lowercase_      ):
              # max_length=None => use the model max length (it's actually the default)
              _lowerCamelCase							:     str	=		tokenizer(examples['''sentence1'''] ,    examples['''sentence2'''] ,    truncation=lowercase_ ,    max_length=lowercase_      )
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       _lowerCamelCase							:     str	=		datasets.map(
           lowercase_ ,    batched=lowercase_ ,    remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,    load_from_cache_file=lowercase_      )

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       _lowerCamelCase							:     List[Any]	=		tokenized_datasets.rename_column('''label''' ,    '''labels'''      )

       def collate_fn(lowercase_      ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(lowercase_ ,    padding='''max_length''' ,    max_length=1_28 ,    return_tensors='''pt'''      )
              return tokenizer.pad(lowercase_ ,    padding='''longest''' ,    return_tensors='''pt'''      )

       # Instantiate dataloaders.
       _lowerCamelCase							:     Optional[int]	=		DataLoader(
           tokenized_datasets['''train'''] ,    shuffle=lowercase_ ,    collate_fn=lowercase_ ,    batch_size=lowercase_      )
       _lowerCamelCase							:     Any	=		DataLoader(
           tokenized_datasets['''validation'''] ,    shuffle=lowercase_ ,    collate_fn=lowercase_ ,    batch_size=lowercase_      )

       return train_dataloader, eval_dataloader




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_      ):
       model.eval()
       _lowerCamelCase							:     Dict	=		0
       for step, batch in enumerate(lowercase_      ):
              # We could avoid this line since we set the accelerator with `device_placement=True`.
              batch.to(accelerator.device      )
              with torch.no_grad():
                     _lowerCamelCase							:     Dict	=		model(**lowercase_      )
              _lowerCamelCase							:     int	=		outputs.logits.argmax(dim=-1      )
              # It is slightly faster to call this once, than multiple times
              _lowerCamelCase,  _lowerCamelCase							:     List[Any]	=		accelerator.gather(
                  (predictions, batch['''labels'''])      )  # If we are in a multiprocess environment, the last batch has duplicates
              if accelerator.use_distributed:
                     if step == len(lowercase_      ) - 1:
                            _lowerCamelCase							:     List[Any]	=		predictions[: len(eval_dataloader.dataset      ) - samples_seen]
                            _lowerCamelCase							:     Union[str, Any]	=		references[: len(eval_dataloader.dataset      ) - samples_seen]
                     else:
                            samples_seen += references.shape[0]
              metric.add_batch(
                  predictions=lowercase_ ,    references=lowercase_ ,    )

       _lowerCamelCase							:     Optional[Any]	=		metric.compute()
       return eval_metric["accuracy"]




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       # Initialize accelerator
       _lowerCamelCase							:     List[Any]	=		Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _lowerCamelCase							:     Optional[int]	=		config['''lr''']
       _lowerCamelCase							:     Union[str, Any]	=		int(config['''num_epochs''']      )
       _lowerCamelCase							:     Union[str, Any]	=		int(config['''seed''']      )
       _lowerCamelCase							:     Tuple	=		int(config['''batch_size''']      )
       _lowerCamelCase							:     List[str]	=		args.model_name_or_path

       set_seed(lowercase_      )
       _lowerCamelCase,  _lowerCamelCase							:     Dict	=		get_dataloaders(lowercase_ ,    lowercase_ ,    lowercase_      )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _lowerCamelCase							:     Tuple	=		AutoModelForSequenceClassification.from_pretrained(lowercase_ ,    return_dict=lowercase_      )

       # Instantiate optimizer
       _lowerCamelCase							:     Optional[int]	=		(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       _lowerCamelCase							:     str	=		optimizer_cls(params=model.parameters() ,    lr=lowercase_      )

       if accelerator.state.deepspeed_plugin is not None:
              _lowerCamelCase							:     Tuple	=		accelerator.state.deepspeed_plugin.deepspeed_config[
                  '''gradient_accumulation_steps'''
              ]
       else:
              _lowerCamelCase							:     int	=		1
       _lowerCamelCase							:     Tuple	=		(len(lowercase_      ) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              _lowerCamelCase							:     str	=		get_linear_schedule_with_warmup(
                  optimizer=lowercase_ ,    num_warmup_steps=0 ,    num_training_steps=lowercase_ ,    )
       else:
              _lowerCamelCase							:     int	=		DummyScheduler(lowercase_ ,    total_num_steps=lowercase_ ,    warmup_num_steps=0      )

       # 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							:     Dict	=		accelerator.prepare(
           lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_      )

       # We need to keep track of how many total steps we have iterated over
       _lowerCamelCase							:     Tuple	=		0
       # We also need to keep track of the stating epoch so files are named properly
       _lowerCamelCase							:     int	=		0
       _lowerCamelCase							:     Union[str, Any]	=		evaluate.load('''glue''' ,    '''mrpc'''      )
       _lowerCamelCase							:     Optional[int]	=		num_epochs

       if args.partial_train_epoch is not None:
              _lowerCamelCase							:     str	=		args.partial_train_epoch

       if args.resume_from_checkpoint:
              accelerator.load_state(args.resume_from_checkpoint      )
              _lowerCamelCase							:     Union[str, Any]	=		args.resume_from_checkpoint.split('''epoch_'''      )[1]
              _lowerCamelCase							:     List[Any]	=		''''''
              for char in epoch_string:
                     if char.isdigit():
                            state_epoch_num += char
                     else:
                            break
              _lowerCamelCase							:     int	=		int(lowercase_      ) + 1
              _lowerCamelCase							:     Optional[Any]	=		evaluation_loop(lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_      )
              accelerator.print('''resumed checkpoint performance:''' ,    lowercase_      )
              accelerator.print('''resumed checkpoint\'s scheduler\'s lr:''' ,    lr_scheduler.get_lr()[0]      )
              accelerator.print('''resumed optimizers\'s lr:''' ,    optimizer.param_groups[0]['''lr''']      )
              with open(os.path.join(args.output_dir ,    F"""state_{starting_epoch-1}.json"""      ) ,    '''r'''      ) as f:
                     _lowerCamelCase							:     Union[str, Any]	=		json.load(lowercase_      )
                     assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["lr"] == lr_scheduler.get_lr()[0]
                     ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                     assert (
                         resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                     ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                     assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                     return

    # Now we train the model
       _lowerCamelCase							:     Optional[Any]	=		{}
       for epoch in range(lowercase_ ,    lowercase_      ):
              model.train()
              for step, batch in enumerate(lowercase_      ):
                     _lowerCamelCase							:     int	=		model(**lowercase_      )
                     _lowerCamelCase							:     Tuple	=		outputs.loss
                     _lowerCamelCase							:     Tuple	=		loss / gradient_accumulation_steps
                     accelerator.backward(lowercase_      )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1
              _lowerCamelCase							:     str	=		F"""epoch_{epoch}"""
              _lowerCamelCase							:     Optional[int]	=		os.path.join(args.output_dir ,    lowercase_      )
              accelerator.save_state(lowercase_      )
              _lowerCamelCase							:     str	=		evaluation_loop(lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_      )
              _lowerCamelCase							:     Union[str, Any]	=		accuracy
              _lowerCamelCase							:     Dict	=		lr_scheduler.get_lr()[0]
              _lowerCamelCase							:     Union[str, Any]	=		optimizer.param_groups[0]['''lr''']
              _lowerCamelCase							:     Any	=		epoch
              _lowerCamelCase							:     Any	=		overall_step
              accelerator.print(F"""epoch {epoch}:""" ,    lowercase_      )

              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                     with open(os.path.join(args.output_dir ,    F"""state_{epoch}.json"""      ) ,    '''w'''      ) as f:
                            json.dump(lowercase_ ,    lowercase_      )




def    __UpperCAmelCase(      ):
       _lowerCamelCase							:     Union[str, Any]	=		argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''      )
       parser.add_argument(
           '''--model_name_or_path''' ,    type=lowercase_ ,    default='''bert-base-cased''' ,    help='''Path to pretrained model or model identifier from huggingface.co/models.''' ,    required=lowercase_ ,    )
       parser.add_argument(
           '''--output_dir''' ,    type=lowercase_ ,    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=lowercase_ ,    default=lowercase_ ,    help='''If the training should continue from a checkpoint folder.''' ,    )
       parser.add_argument(
           '''--partial_train_epoch''' ,    type=lowercase_ ,    default=lowercase_ ,    help='''If passed, the training will stop after this number of epochs.''' ,    )
       parser.add_argument(
           '''--num_epochs''' ,    type=lowercase_ ,    default=2 ,    help='''Number of train epochs.''' ,    )
       _lowerCamelCase							:     str	=		parser.parse_args()
       _lowerCamelCase							:     str	=		{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

       training_function(lowercase_ ,    lowercase_      )


if __name__ == "__main__":
       main()








from typing import TYPE_CHECKING

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


_lowerCamelCase			       =      {
    'configuration_roberta_prelayernorm': [
        'ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'RobertaPreLayerNormConfig',
        'RobertaPreLayerNormOnnxConfig',
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RobertaPreLayerNormForCausalLM',
           'RobertaPreLayerNormForMaskedLM',
           'RobertaPreLayerNormForMultipleChoice',
           'RobertaPreLayerNormForQuestionAnswering',
           'RobertaPreLayerNormForSequenceClassification',
           'RobertaPreLayerNormForTokenClassification',
           'RobertaPreLayerNormModel',
           'RobertaPreLayerNormPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRobertaPreLayerNormForCausalLM',
           'TFRobertaPreLayerNormForMaskedLM',
           'TFRobertaPreLayerNormForMultipleChoice',
           'TFRobertaPreLayerNormForQuestionAnswering',
           'TFRobertaPreLayerNormForSequenceClassification',
           'TFRobertaPreLayerNormForTokenClassification',
           'TFRobertaPreLayerNormMainLayer',
           'TFRobertaPreLayerNormModel',
           'TFRobertaPreLayerNormPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'FlaxRobertaPreLayerNormForCausalLM',
           'FlaxRobertaPreLayerNormForMaskedLM',
           'FlaxRobertaPreLayerNormForMultipleChoice',
           'FlaxRobertaPreLayerNormForQuestionAnswering',
           'FlaxRobertaPreLayerNormForSequenceClassification',
           'FlaxRobertaPreLayerNormForTokenClassification',
           'FlaxRobertaPreLayerNormModel',
           'FlaxRobertaPreLayerNormPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roberta_prelayernorm import (
           ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
           RobertaPreLayerNormConfig,
           RobertaPreLayerNormOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta_prelayernorm import (
                  ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaPreLayerNormForCausalLM,
                  RobertaPreLayerNormForMaskedLM,
                  RobertaPreLayerNormForMultipleChoice,
                  RobertaPreLayerNormForQuestionAnswering,
                  RobertaPreLayerNormForSequenceClassification,
                  RobertaPreLayerNormForTokenClassification,
                  RobertaPreLayerNormModel,
                  RobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta_prelayernorm import (
                  TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaPreLayerNormForCausalLM,
                  TFRobertaPreLayerNormForMaskedLM,
                  TFRobertaPreLayerNormForMultipleChoice,
                  TFRobertaPreLayerNormForQuestionAnswering,
                  TFRobertaPreLayerNormForSequenceClassification,
                  TFRobertaPreLayerNormForTokenClassification,
                  TFRobertaPreLayerNormMainLayer,
                  TFRobertaPreLayerNormModel,
                  TFRobertaPreLayerNormPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta_prelayernorm import (
                  FlaxRobertaPreLayerNormForCausalLM,
                  FlaxRobertaPreLayerNormForMaskedLM,
                  FlaxRobertaPreLayerNormForMultipleChoice,
                  FlaxRobertaPreLayerNormForQuestionAnswering,
                  FlaxRobertaPreLayerNormForSequenceClassification,
                  FlaxRobertaPreLayerNormForTokenClassification,
                  FlaxRobertaPreLayerNormModel,
                  FlaxRobertaPreLayerNormPreTrainedModel,
              )

else:
       import sys

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

