


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


_A		:				List[Any]									=       16
_A		:				Tuple									=       32



def     __lowerCAmelCase						(							snake_case	:    Accelerator						,      snake_case	:    int = 16						,      snake_case	:    str = "bert-base-cased" )       ->							Dict:
   __lowerCamelCase:				List[Any]     =	AutoTokenizer.from_pretrained(snake_case )
   __lowerCamelCase:				Dict     =	load_dataset("""glue"""						,      """mrpc""" )

   def tokenize_function(snake_case	:    List[Any] ):
      # max_length=None => use the model max length (it's actually the default)
      __lowerCamelCase:				Optional[int]     =	tokenizer(examples["""sentence1"""]						,      examples["""sentence2"""]						,      truncation=snake_case						,      max_length=snake_case )
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   __lowerCamelCase:				List[Any]     =	datasets.map(
       snake_case						,      batched=snake_case						,      remove_columns=["""idx""", """sentence1""", """sentence2"""]						,      load_from_cache_file=snake_case )

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

   def collate_fn(snake_case	:    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(snake_case						,      padding="""max_length"""						,      max_length=128						,      return_tensors="""pt""" )
      return tokenizer.pad(snake_case						,      padding="""longest"""						,      return_tensors="""pt""" )

   # Instantiate dataloaders.
   __lowerCamelCase:				List[str]     =	DataLoader(
       tokenized_datasets["""train"""]						,      shuffle=snake_case						,      collate_fn=snake_case						,      batch_size=snake_case )
   __lowerCamelCase:				List[str]     =	DataLoader(
       tokenized_datasets["""validation"""]						,      shuffle=snake_case						,      collate_fn=snake_case						,      batch_size=snake_case )

   return train_dataloader, eval_dataloader



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

   __lowerCamelCase:				int     =	metric.compute()
   return eval_metric["accuracy"]



def     __lowerCAmelCase						(							snake_case	:    Optional[int]						,      snake_case	:    Optional[int] )       ->							Dict:
   # Initialize accelerator
   __lowerCamelCase:				Union[str, Any]     =	Accelerator()

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

   set_seed(snake_case )
   __lowerCamelCase   ,     __lowerCamelCase:				Optional[Any]     =	get_dataloaders(snake_case						,      snake_case						,      snake_case )

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

   # Instantiate optimizer
   __lowerCamelCase:				List[str]     =	(
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   __lowerCamelCase:				int     =	optimizer_cls(params=model.parameters()						,      lr=snake_case )

   if accelerator.state.deepspeed_plugin is not None:
      __lowerCamelCase:				Optional[Any]     =	accelerator.state.deepspeed_plugin.deepspeed_config[
          """gradient_accumulation_steps"""
      ]
   else:
      __lowerCamelCase:				List[str]     =	1
   __lowerCamelCase:				List[str]     =	(len(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
   ):
      __lowerCamelCase:				Optional[int]     =	get_linear_schedule_with_warmup(
          optimizer=snake_case						,      num_warmup_steps=0						,      num_training_steps=snake_case						,      )
   else:
      __lowerCamelCase:				str     =	DummyScheduler(snake_case						,      total_num_steps=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.
   __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				str     =	accelerator.prepare(
       snake_case						,      snake_case						,      snake_case						,      snake_case						,      snake_case )

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

   if args.partial_train_epoch is not None:
      __lowerCamelCase:				List[str]     =	args.partial_train_epoch

   if args.resume_from_checkpoint:
      accelerator.load_state(args.resume_from_checkpoint )
      __lowerCamelCase:				str     =	args.resume_from_checkpoint.split("""epoch_""" )[1]
      __lowerCamelCase:				str     =	""""""
      for char in epoch_string:
         if char.isdigit():
            state_epoch_num += char
         else:
            break
      __lowerCamelCase:				List[str]     =	int(snake_case ) + 1
      __lowerCamelCase:				List[Any]     =	evaluation_loop(snake_case						,      snake_case						,      snake_case						,      snake_case )
      accelerator.print("""resumed checkpoint performance:"""						,      snake_case )
      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:				Any     =	json.load(snake_case )
         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:				Any     =	{}
   for epoch in range(snake_case						,      snake_case ):
      model.train()
      for step, batch in enumerate(snake_case ):
         __lowerCamelCase:				Optional[int]     =	model(**snake_case )
         __lowerCamelCase:				str     =	outputs.loss
         __lowerCamelCase:				Any     =	loss / gradient_accumulation_steps
         accelerator.backward(snake_case )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

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

      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(snake_case						,      snake_case )



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

   training_function(snake_case						,      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,
)


_A		:				List[Any]									=       {
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				int									=       ['''ConvBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				List[Any]									=       [
       '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ConvBertForMaskedLM''',
       '''ConvBertForMultipleChoice''',
       '''ConvBertForQuestionAnswering''',
       '''ConvBertForSequenceClassification''',
       '''ConvBertForTokenClassification''',
       '''ConvBertLayer''',
       '''ConvBertModel''',
       '''ConvBertPreTrainedModel''',
       '''load_tf_weights_in_convbert''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				List[Any]									=       [
       '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFConvBertForMaskedLM''',
       '''TFConvBertForMultipleChoice''',
       '''TFConvBertForQuestionAnswering''',
       '''TFConvBertForSequenceClassification''',
       '''TFConvBertForTokenClassification''',
       '''TFConvBertLayer''',
       '''TFConvBertModel''',
       '''TFConvBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

   _A		:				int									=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


