

'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


SCREAMING_SNAKE_CASE__     =       1_6
SCREAMING_SNAKE_CASE__     =       3_2







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase = 16   )->			int:
      UpperCamelCase      = AutoTokenizer.from_pretrained("""bert-base-cased"""   )
      UpperCamelCase      = load_dataset("""glue"""		,						"""mrpc"""   )

      def tokenize_function(__UpperCamelCase   ):
            # 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   ):
            # 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

     SCREAMING_SNAKE_CASE__     =       mocked_dataloaders  # noqa: F811







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			Dict:
      # For testing only
      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""		,						__UpperCamelCase   ) == "1":
            UpperCamelCase      = 2
      # New Code #
      UpperCamelCase      = int(args.gradient_accumulation_steps   )
      UpperCamelCase      = int(args.local_sgd_steps   )
      # Initialize accelerator
      UpperCamelCase      = Accelerator(
          cpu=args.cpu		,						mixed_precision=args.mixed_precision		,						gradient_accumulation_steps=__UpperCamelCase   )
      if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
            raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""   )
      # 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"""   )

      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)		,						)

      # 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()
            with LocalSGD(
                accelerator=__UpperCamelCase		,						model=__UpperCamelCase		,						local_sgd_steps=__UpperCamelCase		,						enabled=local_sgd_steps is not None   ) as local_sgd:
                  for step, batch in enumerate(__UpperCamelCase   ):
                        # We could avoid this line since we set the accelerator with `device_placement=True`.
                        batch.to(accelerator.device   )
                        # New code #
                        # We use the new `accumulate` context manager to perform gradient accumulation
                        # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                        with accelerator.accumulate(__UpperCamelCase   ):
                              UpperCamelCase      = model(**__UpperCamelCase   )
                              UpperCamelCase      = output.loss
                              accelerator.backward(__UpperCamelCase   )
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()
                              # LocalSGD-specific line
                              local_sgd.step()

            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():
                        UpperCamelCase      = model(**__UpperCamelCase   )
                  UpperCamelCase      = outputs.logits.argmax(dim=-1   )
                  UpperCamelCase  ,UpperCamelCase      = 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    lowercase__			(				)->			Optional[int]:
      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."""		,						)
      # New Code #
      parser.add_argument(
          """--gradient_accumulation_steps"""		,						type=__UpperCamelCase		,						default=1		,						help="""The number of minibatches to be ran before gradients are accumulated."""		,						)
      parser.add_argument(
          """--local_sgd_steps"""		,						type=__UpperCamelCase		,						default=8		,						help="""Number of local SGD steps or None to disable local SGD"""   )
      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()








'''simple docstring'''

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__     =       {
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MobileBertForMaskedLM',
         'MobileBertForMultipleChoice',
         'MobileBertForNextSentencePrediction',
         'MobileBertForPreTraining',
         'MobileBertForQuestionAnswering',
         'MobileBertForSequenceClassification',
         'MobileBertForTokenClassification',
         'MobileBertLayer',
         'MobileBertModel',
         'MobileBertPreTrainedModel',
         'load_tf_weights_in_mobilebert',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFMobileBertForMaskedLM',
         'TFMobileBertForMultipleChoice',
         'TFMobileBertForNextSentencePrediction',
         'TFMobileBertForPreTraining',
         'TFMobileBertForQuestionAnswering',
         'TFMobileBertForSequenceClassification',
         'TFMobileBertForTokenClassification',
         'TFMobileBertMainLayer',
         'TFMobileBertModel',
         'TFMobileBertPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mobilebert import (
         MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         MobileBertConfig,
         MobileBertOnnxConfig,
     )
     from .tokenization_mobilebert import MobileBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mobilebert_fast import MobileBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilebert import (
              MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileBertForMaskedLM,
              MobileBertForMultipleChoice,
              MobileBertForNextSentencePrediction,
              MobileBertForPreTraining,
              MobileBertForQuestionAnswering,
              MobileBertForSequenceClassification,
              MobileBertForTokenClassification,
              MobileBertLayer,
              MobileBertModel,
              MobileBertPreTrainedModel,
              load_tf_weights_in_mobilebert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mobilebert import (
              TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMobileBertForMaskedLM,
              TFMobileBertForMultipleChoice,
              TFMobileBertForNextSentencePrediction,
              TFMobileBertForPreTraining,
              TFMobileBertForQuestionAnswering,
              TFMobileBertForSequenceClassification,
              TFMobileBertForTokenClassification,
              TFMobileBertMainLayer,
              TFMobileBertModel,
              TFMobileBertPreTrainedModel,
          )

else:
     import sys

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