




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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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      			=						16
_lowerCamelCase      			=						32




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Accelerator	,					__UpperCamelCase      :						int = 16				)  ->							List[Any]:
    UpperCAmelCase_										=					AutoTokenizer.from_pretrained('''bert-base-cased'''				)
    UpperCAmelCase_										=					load_dataset('''glue'''	,					'''mrpc'''				)

    def tokenize_function(__UpperCamelCase      :						Optional[int]				):
        # max_length=None => use the model max length (it's actually the default)
        UpperCAmelCase_										=					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():
        UpperCAmelCase_										=					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
    UpperCAmelCase_										=					tokenized_datasets.rename_column('''label'''	,					'''labels'''				)

    def collate_fn(__UpperCamelCase      :						Any				):
        # 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(
            __UpperCamelCase	,					padding='''longest'''	,					max_length=__UpperCamelCase	,					pad_to_multiple_of=__UpperCamelCase	,					return_tensors='''pt'''	,					)

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

 _lowerCamelCase      			=						mocked_dataloaders  # noqa: F811




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[Any]	,					__UpperCamelCase      :						Optional[Any]				)  ->							List[str]:
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,					__UpperCamelCase				) == "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''']				)

    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

    set_seed(__UpperCamelCase				)
    UpperCAmelCase_			,							UpperCAmelCase_										=					get_dataloaders(__UpperCamelCase	,					__UpperCamelCase				)
    # 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=__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).
    UpperCAmelCase_										=					model.to(accelerator.device				)

    # Instantiate optimizer
    UpperCAmelCase_										=					AdamW(params=model.parameters()	,					lr=__UpperCamelCase				)

    # Instantiate scheduler
    UpperCAmelCase_										=					get_linear_schedule_with_warmup(
        optimizer=__UpperCamelCase	,					num_warmup_steps=100	,					num_training_steps=(len(__UpperCamelCase				) * 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(
        __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				)
            UpperCAmelCase_										=					model(**__UpperCamelCase				)
            UpperCAmelCase_										=					outputs.loss
            UpperCAmelCase_										=					loss / gradient_accumulation_steps
            accelerator.backward(__UpperCamelCase				)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        UpperCAmelCase_										=					0
        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():
                UpperCAmelCase_										=					model(**__UpperCamelCase				)
            UpperCAmelCase_										=					outputs.logits.argmax(dim=-1				)
            UpperCAmelCase_			,							UpperCAmelCase_										=					accelerator.gather((predictions, batch['''labels'''])				)
            # New Code #
            # First we check if it's a distributed system
            if accelerator.use_distributed:
                # Then see if we're on the last batch of our eval dataloader
                if step == len(__UpperCamelCase				) - 1:
                    # Last batch needs to be truncated on distributed systems as it contains additional samples
                    UpperCAmelCase_										=					predictions[: len(eval_dataloader.dataset				) - samples_seen]
                    UpperCAmelCase_										=					references[: len(eval_dataloader.dataset				) - samples_seen]
                else:
                    # Otherwise we add the number of samples seen
                    samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
            metric.add_batch(
                predictions=__UpperCamelCase	,					references=__UpperCamelCase	,					)

        UpperCAmelCase_										=					metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f'epoch {epoch}:'	,					__UpperCamelCase				)




def  SCREAMING_SNAKE_CASE     (	)  ->							str:
    UpperCAmelCase_										=					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.'''				)
    UpperCAmelCase_										=					parser.parse_args()
    UpperCAmelCase_										=					{'''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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase      			=						{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'RobertaForCausalLM',
     'RobertaForMaskedLM',
     'RobertaForMultipleChoice',
     'RobertaForQuestionAnswering',
     'RobertaForSequenceClassification',
     'RobertaForTokenClassification',
     'RobertaModel',
     'RobertaPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFRobertaForCausalLM',
     'TFRobertaForMaskedLM',
     'TFRobertaForMultipleChoice',
     'TFRobertaForQuestionAnswering',
     'TFRobertaForSequenceClassification',
     'TFRobertaForTokenClassification',
     'TFRobertaMainLayer',
     'TFRobertaModel',
     'TFRobertaPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'FlaxRobertaForCausalLM',
     'FlaxRobertaForMaskedLM',
     'FlaxRobertaForMultipleChoice',
     'FlaxRobertaForQuestionAnswering',
     'FlaxRobertaForSequenceClassification',
     'FlaxRobertaForTokenClassification',
     'FlaxRobertaModel',
     'FlaxRobertaPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
 from .tokenization_roberta import RobertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_roberta_fast import RobertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roberta import (
      ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      RobertaForCausalLM,
      RobertaForMaskedLM,
      RobertaForMultipleChoice,
      RobertaForQuestionAnswering,
      RobertaForSequenceClassification,
      RobertaForTokenClassification,
      RobertaModel,
      RobertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roberta import (
      TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRobertaForCausalLM,
      TFRobertaForMaskedLM,
      TFRobertaForMultipleChoice,
      TFRobertaForQuestionAnswering,
      TFRobertaForSequenceClassification,
      TFRobertaForTokenClassification,
      TFRobertaMainLayer,
      TFRobertaModel,
      TFRobertaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roberta import (
      FlaxRobertaForCausalLM,
      FlaxRobertaForMaskedLM,
      FlaxRobertaForMultipleChoice,
      FlaxRobertaForQuestionAnswering,
      FlaxRobertaForSequenceClassification,
      FlaxRobertaForTokenClassification,
      FlaxRobertaModel,
      FlaxRobertaPreTrainedModel,
  )

else:
 import sys

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





