





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


_snake_case  :    str						    =  16
_snake_case  :    Optional[int]						    =  32

def   a_    ( lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			Tuple = 16,	lowerCAmelCase_      :			Any = "bert-base-cased"	):
   __lowerCAmelCase								=	AutoTokenizer.from_pretrained(lowerCAmelCase_	)
   __lowerCAmelCase								=	load_dataset('glue',	'mrpc'	)

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

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

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   __lowerCAmelCase								=	tokenized_datasets.rename_column('label',	'labels'	)

   def collate_fn(lowerCAmelCase_      :			Optional[int]	):
      # 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(lowerCAmelCase_,	padding='max_length',	max_length=128,	return_tensors='pt'	)
      return tokenizer.pad(lowerCAmelCase_,	padding='longest',	return_tensors='pt'	)

   # Instantiate dataloaders.
   __lowerCAmelCase								=	DataLoader(
       tokenized_datasets['train'],	shuffle=lowerCAmelCase_,	collate_fn=lowerCAmelCase_,	batch_size=lowerCAmelCase_	)
   __lowerCAmelCase								=	DataLoader(
       tokenized_datasets['validation'],	shuffle=lowerCAmelCase_,	collate_fn=lowerCAmelCase_,	batch_size=lowerCAmelCase_	)

   return train_dataloader, eval_dataloader

def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			Optional[Any]	):
   # Initialize accelerator
   __lowerCAmelCase								=	Accelerator()

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   __lowerCAmelCase								=	config['lr']
   __lowerCAmelCase								=	int(config['num_epochs']	)
   __lowerCAmelCase								=	int(config['seed']	)
   __lowerCAmelCase								=	int(config['batch_size']	)
   __lowerCAmelCase								=	args.model_name_or_path

   set_seed(lowerCAmelCase_	)
   __lowerCAmelCase								=	get_dataloaders(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	)

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __lowerCAmelCase								=	AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_,	return_dict=lowerCAmelCase_	)

   # Instantiate optimizer
   __lowerCAmelCase								=	(
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   __lowerCAmelCase								=	optimizer_cls(params=model.parameters(),	lr=lowerCAmelCase_	)

   if accelerator.state.deepspeed_plugin is not None:
      __lowerCAmelCase								=	accelerator.state.deepspeed_plugin.deepspeed_config[
          'gradient_accumulation_steps'
      ]
   else:
      __lowerCAmelCase								=	1
   __lowerCAmelCase								=	(len(lowerCAmelCase_	) * 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								=	get_linear_schedule_with_warmup(
          optimizer=lowerCAmelCase_,	num_warmup_steps=0,	num_training_steps=lowerCAmelCase_,	)
   else:
      __lowerCAmelCase								=	DummyScheduler(lowerCAmelCase_,	total_num_steps=lowerCAmelCase_,	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								=	accelerator.prepare(
       lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	)

   # We need to keep track of how many total steps we have iterated over
   __lowerCAmelCase								=	0
   # We also need to keep track of the stating epoch so files are named properly
   __lowerCAmelCase								=	0

   # Now we train the model
   __lowerCAmelCase								=	evaluate.load('glue',	'mrpc'	)
   __lowerCAmelCase								=	0
   __lowerCAmelCase								=	{}
   for epoch in range(lowerCAmelCase_,	lowerCAmelCase_	):
      model.train()
      for step, batch in enumerate(lowerCAmelCase_	):
         __lowerCAmelCase								=	model(**lowerCAmelCase_	)
         __lowerCAmelCase								=	outputs.loss
         __lowerCAmelCase								=	loss / gradient_accumulation_steps
         accelerator.backward(lowerCAmelCase_	)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

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

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

      if best_performance < eval_metric["accuracy"]:
         __lowerCAmelCase								=	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(lowerCAmelCase_,	lowerCAmelCase_	)

def   a_    ( ):
   __lowerCAmelCase								=	argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'	)
   parser.add_argument(
       '--model_name_or_path',	type=lowerCAmelCase_,	default='bert-base-cased',	help='Path to pretrained model or model identifier from huggingface.co/models.',	required=lowerCAmelCase_,	)
   parser.add_argument(
       '--output_dir',	type=lowerCAmelCase_,	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=lowerCAmelCase_,	default=lowerCAmelCase_,	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=lowerCAmelCase_,	default=3,	help='Number of train epochs.',	)
   __lowerCAmelCase								=	parser.parse_args()
   __lowerCAmelCase								=	{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
   training_function(lowerCAmelCase_,	lowerCAmelCase_	)


if __name__ == "__main__":
     main()





import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger





class   __a			(    __UpperCamelCase      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__ )					->				Any:




        '''simple docstring'''
        lowercase__:   Any  			=   params

        lowercase__:   List[Any]  			=   np.array(lowerCAmelCase__ )
        lowercase__:   Optional[Any]  			=   np.array([len(lowerCAmelCase__ ) for t in data] )

        self.check()
        self.remove_long_sequences()
        self.remove_empty_sequences()
        self.remove_unknown_sequences()
        self.check()
        self.print_statistics()
 def __getitem__(   self				, lowerCAmelCase__ )					->				List[Any]:




        '''simple docstring'''
        return (self.token_ids[index], self.lengths[index])
 def __len__(   self )					->				List[Any]:




        '''simple docstring'''
        return len(self.lengths )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        assert len(self.token_ids ) == len(self.lengths )
        assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        lowercase__:   Any  			=   self.params.max_model_input_size
        lowercase__:   Dict  			=   self.lengths > max_len
        logger.info(F'Splitting {sum(lowerCAmelCase__ )} too long sequences.' )

        def divide_chunks(lowerCAmelCase__				, lowerCAmelCase__ ):
               return [l[i : i + n] for i in range(0				, len(lowerCAmelCase__ )				, lowerCAmelCase__ )]

        lowercase__:   str  			=   []
        lowercase__:   List[str]  			=   []
        if self.params.mlm:
               lowercase__			,						lowercase__:   str  			=   self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
        else:
               lowercase__			,						lowercase__:   int  			=   self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']

        for seq_, len_ in zip(self.token_ids				, self.lengths ):
               assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
               if len_ <= max_len:
                      new_tok_ids.append(seq_ )
                      new_lengths.append(len_ )
               else:
                      lowercase__:   Optional[Any]  			=   []
                      for sub_s in divide_chunks(seq_				, max_len - 2 ):
                             if sub_s[0] != cls_id:
                                    lowercase__:   int  			=   np.insert(lowerCAmelCase__				, 0				, lowerCAmelCase__ )
                             if sub_s[-1] != sep_id:
                                    lowercase__:   Union[str, Any]  			=   np.insert(lowerCAmelCase__				, len(lowerCAmelCase__ )				, lowerCAmelCase__ )
                             assert len(lowerCAmelCase__ ) <= max_len
                             assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                             sub_seqs.append(lowerCAmelCase__ )

                      new_tok_ids.extend(lowerCAmelCase__ )
                      new_lengths.extend([len(lowerCAmelCase__ ) for l in sub_seqs] )

        lowercase__:   Union[str, Any]  			=   np.array(lowerCAmelCase__ )
        lowercase__:   Union[str, Any]  			=   np.array(lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   Dict  			=   len(self )
        lowercase__:   Union[str, Any]  			=   self.lengths > 11
        lowercase__:   List[Any]  			=   self.token_ids[indices]
        lowercase__:   Optional[Any]  			=   self.lengths[indices]
        lowercase__:   List[str]  			=   len(self )
        logger.info(F'Remove {init_size - new_size} too short (<=11 tokens) sequences.' )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        if "unk_token" not in self.params.special_tok_ids:
               return
        else:
               lowercase__:   List[str]  			=   self.params.special_tok_ids['unk_token']
        lowercase__:   str  			=   len(self )
        lowercase__:   Tuple  			=   np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
        lowercase__:   Any  			=   (unk_occs / self.lengths) < 0.5
        lowercase__:   Tuple  			=   self.token_ids[indices]
        lowercase__:   str  			=   self.lengths[indices]
        lowercase__:   Dict  			=   len(self )
        logger.info(F'Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).' )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        if not self.params.is_master:
               return
        logger.info(F'{len(self )} sequences' )
        # data_len = sum(self.lengths)
        # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
        # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

        # unk_idx = self.params.special_tok_ids['unk_token']
        # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
        # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        lowercase__:   Union[str, Any]  			=   [t[0] for t in batch]
        lowercase__:   Dict  			=   [t[1] for t in batch]
        assert len(lowerCAmelCase__ ) == len(lowerCAmelCase__ )

        # Max for paddings
        lowercase__:   List[Any]  			=   max(lowerCAmelCase__ )

        # Pad token ids
        if self.params.mlm:
               lowercase__:   Dict  			=   self.params.special_tok_ids['pad_token']
        else:
               lowercase__:   Optional[Any]  			=   self.params.special_tok_ids['unk_token']
        lowercase__:   int  			=   [list(t.astype(lowerCAmelCase__ ) ) + [pad_idx] * (max_seq_len_ - len(lowerCAmelCase__ )) for t in token_ids]
        assert len(tk_ ) == len(lowerCAmelCase__ )
        assert all(len(lowerCAmelCase__ ) == max_seq_len_ for t in tk_ )

        lowercase__:   Tuple  			=   torch.tensor(tk_ )  # (bs, max_seq_len_)
        lowercase__:   Optional[Any]  			=   torch.tensor(lowerCAmelCase__ )  # (bs)
        return tk_t, lg_t


