



import argparse
import gc
import json
import os

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_			=					16
a_			=					32





def 		a__			(		_UpperCamelCase			: Dict							):
   return int(x / 2**20							)



class  __lowerCAmelCase     :




 def __enter__(						self				):



    '''simple docstring'''
    gc.collect()
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
    __lowerCamelCase													=    torch.cuda.memory_allocated()
    return self




 def __exit__(						self	,				*__UpperCAmelCase				):



    '''simple docstring'''
    gc.collect()
    torch.cuda.empty_cache()
    __lowerCamelCase													=    torch.cuda.memory_allocated()
    __lowerCamelCase													=    torch.cuda.max_memory_allocated()
    __lowerCamelCase													=    bamb(self.end - self.begin				)
    __lowerCamelCase													=    bamb(self.peak - self.begin				)
    # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")





def 		a__			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: Dict = 16 ,_UpperCamelCase			: Union[str, Any] = "bert-base-cased" ,_UpperCamelCase			: Dict = 3_20 ,_UpperCamelCase			: int = 1_60 ,):
   __lowerCamelCase													=    AutoTokenizer.from_pretrained(__UpperCamelCase							)
   __lowerCamelCase													=    load_dataset(
       '''glue''' ,'''mrpc''' ,split={'''train''': F"""train[:{n_train}]""", '''validation''': F"""validation[:{n_val}]"""}							)

   def tokenize_function(_UpperCamelCase			: List[str]							):
      # max_length=None => use the model max length (it's actually the default)
      __lowerCamelCase													=    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
   __lowerCamelCase													=    datasets.map(
       __UpperCamelCase ,batched=__UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,load_from_cache_file=__UpperCamelCase							)

   # 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(_UpperCamelCase			: 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(__UpperCamelCase ,padding='''max_length''' ,max_length=1_28 ,return_tensors='''pt'''							)
      return tokenizer.pad(__UpperCamelCase ,padding='''longest''' ,return_tensors='''pt'''							)

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

   return train_dataloader, eval_dataloader





def 		a__			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: 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(__UpperCamelCase							)
   __lowerCamelCase							,__lowerCamelCase													=    get_dataloaders(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,args.n_train ,args.n_val							)

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

   # 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=__UpperCamelCase							)

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

   # 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													=    {}
   for epoch in range(__UpperCamelCase ,__UpperCamelCase							):
      with TorchTracemalloc() as tracemalloc:
         model.train()
         for step, batch in enumerate(__UpperCamelCase							):
            __lowerCamelCase													=    model(**__UpperCamelCase							)
            __lowerCamelCase													=    outputs.loss
            __lowerCamelCase													=    loss / gradient_accumulation_steps
            accelerator.backward(__UpperCamelCase							)
            if step % gradient_accumulation_steps == 0:
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()

            overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
      accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin							)							)							)
      accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used							)							)
      accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked							)							)
      accelerator.print(
          '''Total Peak Memory consumed during the train (max): {}'''.format(
              tracemalloc.peaked + bamb(tracemalloc.begin							)							)							)
      __lowerCamelCase													=    tracemalloc.peaked + bamb(tracemalloc.begin							)
      if args.peak_memory_upper_bound is not None:
         assert (
             train_total_peak_memory[F"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
         ), "Peak memory usage exceeded the upper bound"

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





def 		a__			(		):
   __lowerCamelCase													=    argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''							)
   parser.add_argument(
       '''--model_name_or_path''' ,type=__UpperCamelCase ,default='''bert-base-cased''' ,help='''Path to pretrained model or model identifier from huggingface.co/models.''' ,required=__UpperCamelCase ,)
   parser.add_argument(
       '''--output_dir''' ,type=__UpperCamelCase ,default='''.''' ,help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' ,)
   parser.add_argument(
       '''--peak_memory_upper_bound''' ,type=__UpperCamelCase ,default=__UpperCamelCase ,help='''The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.''' ,)
   parser.add_argument(
       '''--n_train''' ,type=__UpperCamelCase ,default=3_20 ,help='''Number of training examples to use.''' ,)
   parser.add_argument(
       '''--n_val''' ,type=__UpperCamelCase ,default=1_60 ,help='''Number of validation examples to use.''' ,)
   parser.add_argument(
       '''--num_epochs''' ,type=__UpperCamelCase ,default=1 ,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(__UpperCamelCase ,__UpperCamelCase							)


if __name__ == "__main__":
  main()











import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed



class  __lowerCAmelCase     (	lowerCAmelCase__   ):




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase=1024	,				__UpperCAmelCase=1024	,				__UpperCAmelCase=3.6				):



    '''simple docstring'''
    __lowerCamelCase													=    tokenizer
    __lowerCamelCase													=    tokenizer.bos_token_id
    __lowerCamelCase													=    dataset
    __lowerCamelCase													=    seq_length
    __lowerCamelCase													=    seq_length * chars_per_token * num_of_sequences




 def __iter__(						self				):



    '''simple docstring'''
    __lowerCamelCase													=    iter(self.dataset				)
    __lowerCamelCase													=    True
    while more_examples:
       __lowerCamelCase							,__lowerCamelCase													=    [], 0
       while True:
          if buffer_len >= self.input_characters:
             break
          try:
             buffer.append(next(__UpperCAmelCase				)['''content''']				)
             buffer_len += len(buffer[-1]				)
          except StopIteration:
             __lowerCamelCase													=    False
             break
       __lowerCamelCase													=    tokenizer(__UpperCAmelCase	,				truncation=__UpperCAmelCase				)['''input_ids''']
       __lowerCamelCase													=    []
       for tokenized_input in tokenized_inputs:
          all_token_ids.extend(tokenized_input + [self.concat_token_id]				)
       for i in range(0	,				len(__UpperCAmelCase				)	,				self.seq_length				):
          __lowerCamelCase													=    all_token_ids[i : i + self.seq_length]
          if len(__UpperCAmelCase				) == self.seq_length:
             yield torch.tensor(__UpperCAmelCase				)





def 		a__			(		_UpperCamelCase			: List[Any]							):
   __lowerCamelCase													=    {'''streaming''': True}
   __lowerCamelCase													=    load_dataset(args.dataset_name ,split='''train''' ,**_UpperCamelCase							)
   __lowerCamelCase													=    ConstantLengthDataset(_UpperCamelCase ,_UpperCamelCase ,seq_length=args.seq_length							)
   __lowerCamelCase													=    DataLoader(_UpperCamelCase ,batch_size=args.batch_size							)
   return eval_dataloader





def 		a__			(		_UpperCamelCase			: str							):
   model.eval()
   __lowerCamelCase													=    []
   for step, batch in enumerate(_UpperCamelCase							):
      with torch.no_grad():
         __lowerCamelCase													=    model(_UpperCamelCase ,labels=_UpperCamelCase							)
      __lowerCamelCase													=    outputs.loss.repeat(args.batch_size							)
      losses.append(accelerator.gather(_UpperCamelCase							)							)

      if args.max_eval_steps > 0 and step >= args.max_eval_steps:
         break
   __lowerCamelCase													=    torch.mean(torch.cat(_UpperCamelCase							)							)
   try:
      __lowerCamelCase													=    torch.exp(_UpperCamelCase							)
   except OverflowError:
      __lowerCamelCase													=    float('''inf'''							)
   return loss.item(), perplexity.item()


# Setup Accelerator
a_			=					Accelerator()

# Parse configuration
a_			=					HfArgumentParser(EvaluationArguments)
a_			=					parser.parse_args()
set_seed(args.seed)

# Logging
a_			=					logging.getLogger(__name__)
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)

# Load model and tokenizer
a_			=					AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a_			=					AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
a_			=					create_dataloader(args)

# Prepare everything with our `accelerator`.
a_		,						a_			=					accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
a_		,						a_			=					evaluate(args)





logger.info(f"loss/eval: {eval_loss}, perplexity: {perplexity}")






