


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


UpperCAmelCase_				:			Union[str, Any]   =	16
UpperCAmelCase_				:			Any   =	32







def 	SCREAMING_SNAKE_CASE_						(      __A			:      List[str]  ,   __A			:      List[str] = 16  ,   __A			:      str = "bert-base-cased"    )     ->		Any:

       """simple docstring"""

       a_ :		List[str]							=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__    )
       a_ :		int							=  load_dataset('glue'  ,   'mrpc'    )

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

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

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

       def collate_fn(__A			:      List[Any]    ):
              # 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(SCREAMING_SNAKE_CASE__  ,   padding='max_length'  ,   max_length=1_28  ,   return_tensors='pt'    )
              return tokenizer.pad(SCREAMING_SNAKE_CASE__  ,   padding='longest'  ,   return_tensors='pt'    )

       # Instantiate dataloaders.
       a_ :		Tuple							=  DataLoader(
           tokenized_datasets['train']  ,   shuffle=SCREAMING_SNAKE_CASE__  ,   collate_fn=SCREAMING_SNAKE_CASE__  ,   batch_size=SCREAMING_SNAKE_CASE__    )
       a_ :		str							=  DataLoader(
           tokenized_datasets['validation']  ,   shuffle=SCREAMING_SNAKE_CASE__  ,   collate_fn=SCREAMING_SNAKE_CASE__  ,   batch_size=SCREAMING_SNAKE_CASE__    )

       return train_dataloader, eval_dataloader







def 	SCREAMING_SNAKE_CASE_						(      __A			:      List[Any]  ,   __A			:      str    )     ->		Optional[int]:

       """simple docstring"""

       a_ :		Any							=  Accelerator()

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

       set_seed(SCREAMING_SNAKE_CASE__    )
       a_ :		Union[str, Any]							=  get_dataloaders(SCREAMING_SNAKE_CASE__  ,   SCREAMING_SNAKE_CASE__  ,   SCREAMING_SNAKE_CASE__    )

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

       # Instantiate optimizer
       a_ :		Tuple							=  (
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       a_ :		List[Any]							=  optimizer_cls(params=model.parameters()  ,   lr=SCREAMING_SNAKE_CASE__    )

       if accelerator.state.deepspeed_plugin is not None:
              a_ :		List[str]							=  accelerator.state.deepspeed_plugin.deepspeed_config[
                  '''gradient_accumulation_steps'''
              ]
       else:
              a_ :		Dict							=  1
       a_ :		Any							=  (len(SCREAMING_SNAKE_CASE__    ) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              a_ :		Dict							=  get_linear_schedule_with_warmup(
                  optimizer=SCREAMING_SNAKE_CASE__  ,   num_warmup_steps=0  ,   num_training_steps=SCREAMING_SNAKE_CASE__  ,   )
       else:
              a_ :		Optional[int]							=  DummyScheduler(SCREAMING_SNAKE_CASE__  ,   total_num_steps=SCREAMING_SNAKE_CASE__  ,   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.
       a_ :		Dict							=  accelerator.prepare(
           SCREAMING_SNAKE_CASE__  ,   SCREAMING_SNAKE_CASE__  ,   SCREAMING_SNAKE_CASE__  ,   SCREAMING_SNAKE_CASE__  ,   SCREAMING_SNAKE_CASE__    )

       # We need to keep track of how many total steps we have iterated over
       a_ :		Dict							=  0
       # We also need to keep track of the stating epoch so files are named properly
       a_ :		List[str]							=  0

       # Now we train the model
       a_ :		str							=  evaluate.load('glue'  ,   'mrpc'    )
       a_ :		Optional[Any]							=  0
       a_ :		List[str]							=  {}
       for epoch in range(SCREAMING_SNAKE_CASE__  ,   SCREAMING_SNAKE_CASE__    ):
              model.train()
              for step, batch in enumerate(SCREAMING_SNAKE_CASE__    ):
                     a_ :		List[str]							=  model(**SCREAMING_SNAKE_CASE__    )
                     a_ :		Union[str, Any]							=  outputs.loss
                     a_ :		Any							=  loss / gradient_accumulation_steps
                     accelerator.backward(SCREAMING_SNAKE_CASE__    )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1

              model.eval()
              a_ :		List[str]							=  0
              for step, batch in enumerate(SCREAMING_SNAKE_CASE__    ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device    )
                     with torch.no_grad():
                            a_ :		Dict							=  model(**SCREAMING_SNAKE_CASE__    )
                     a_ :		List[str]							=  outputs.logits.argmax(dim=-1    )
                     # It is slightly faster to call this once, than multiple times
                     a_ :		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(SCREAMING_SNAKE_CASE__    ) - 1:
                                   a_ :		Dict							=  predictions[: len(eval_dataloader.dataset    ) - samples_seen]
                                   a_ :		Any							=  references[: len(eval_dataloader.dataset    ) - samples_seen]
                            else:
                                   samples_seen += references.shape[0]
                     metric.add_batch(
                         predictions=SCREAMING_SNAKE_CASE__  ,   references=SCREAMING_SNAKE_CASE__  ,   )

              a_ :		Optional[Any]							=  metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:"""  ,   SCREAMING_SNAKE_CASE__    )
              a_ :		int							=  eval_metric['''accuracy''']

              if best_performance < eval_metric["accuracy"]:
                     a_ :		Optional[Any]							=  eval_metric['''accuracy''']

       if args.performance_lower_bound is not None:
              assert (
                  args.performance_lower_bound <= best_performance
              ), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

       accelerator.wait_for_everyone()
       if accelerator.is_main_process:
              with open(os.path.join(args.output_dir  ,   'all_results.json'    )  ,   'w'    ) as f:
                     json.dump(SCREAMING_SNAKE_CASE__  ,   SCREAMING_SNAKE_CASE__    )







def 	SCREAMING_SNAKE_CASE_						(      )     ->		Optional[int]:

       """simple docstring"""

       a_ :		Dict							=  argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'    )
       parser.add_argument(
           '--model_name_or_path'  ,   type=SCREAMING_SNAKE_CASE__  ,   default='bert-base-cased'  ,   help='Path to pretrained model or model identifier from huggingface.co/models.'  ,   required=SCREAMING_SNAKE_CASE__  ,   )
       parser.add_argument(
           '--output_dir'  ,   type=SCREAMING_SNAKE_CASE__  ,   default='.'  ,   help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'  ,   )
       parser.add_argument(
           '--performance_lower_bound'  ,   type=SCREAMING_SNAKE_CASE__  ,   default=SCREAMING_SNAKE_CASE__  ,   help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'  ,   )
       parser.add_argument(
           '--num_epochs'  ,   type=SCREAMING_SNAKE_CASE__  ,   default=3  ,   help='Number of train epochs.'  ,   )
       a_ :		Dict							=  parser.parse_args()
       a_ :		Union[str, Any]							=  {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
       training_function(SCREAMING_SNAKE_CASE__  ,   SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
   main()



from typing import TYPE_CHECKING

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


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

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			Union[str, Any]   =	['MobileBertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			List[str]   =	[
       '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:
   UpperCAmelCase_				:			Tuple   =	[
       '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

   UpperCAmelCase_				:			Tuple   =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


