
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


lowercase__          =16
lowercase__          =32




def        __UpperCamelCase					(    lowerCAmelCase__							:    Dict  ):
 return int(x / 2**2_0  )



class 		UpperCamelCase__  :
   def __enter__(self			: str       ):
    gc.collect()
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
    __a			:  Union[str, Any]		    =  torch.cuda.memory_allocated()
    return self







   def __exit__(self			: Optional[int] ,    *snake_case_			: int       ):
    gc.collect()
    torch.cuda.empty_cache()
    __a			:  List[str]		    =  torch.cuda.memory_allocated()
    __a			:  str		    =  torch.cuda.max_memory_allocated()
    __a			:  Optional[Any]		    =  bamb(self.end - self.begin       )
    __a			:  Any		    =  bamb(self.peak - self.begin       )
    # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")





def        __UpperCamelCase					(    lowerCAmelCase__							:    Accelerator  ,       lowerCAmelCase__							:    int = 1_6  ,       lowerCAmelCase__							:    str = "bert-base-cased"  ,       lowerCAmelCase__							:    int = 3_2_0  ,       lowerCAmelCase__							:    int = 1_6_0  ,       ):
 __a			:  Dict		    =  AutoTokenizer.from_pretrained(lowerCAmelCase__  )
 __a			:  str		    =  load_dataset(
     '''glue'''  ,       '''mrpc'''  ,       split={'''train''': f"train[:{n_train}]", '''validation''': f"validation[:{n_val}]"}  )

 def tokenize_function(lowerCAmelCase__							:    Dict  ):
  # max_length=None => use the model max length (it's actually the default)
  __a			:  Any		    =  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
 __a			:  List[Any]		    =  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
 __a			:  Any		    =  tokenized_datasets.rename_column('''label'''  ,       '''labels'''  )

 def collate_fn(lowerCAmelCase__							:    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(lowerCAmelCase__  ,       padding='''max_length'''  ,       max_length=1_2_8  ,       return_tensors='''pt'''  )
  return tokenizer.pad(lowerCAmelCase__  ,       padding='''longest'''  ,       return_tensors='''pt'''  )

 # Instantiate dataloaders.
 __a			:  Dict		    =  DataLoader(
     tokenized_datasets['''train''']  ,       shuffle=lowerCAmelCase__  ,       collate_fn=lowerCAmelCase__  ,       batch_size=lowerCAmelCase__  )
 __a			:  str		    =  DataLoader(
     tokenized_datasets['''validation''']  ,       shuffle=lowerCAmelCase__  ,       collate_fn=lowerCAmelCase__  ,       batch_size=lowerCAmelCase__  )

 return train_dataloader, eval_dataloader




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    Tuple  ):
 # Initialize accelerator
 __a			:  List[Any]		    =  Accelerator()

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

 set_seed(lowerCAmelCase__  )
 __a    ,	__a			:  int		    =  get_dataloaders(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       args.n_train  ,       args.n_val  )

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

 # 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			:  Union[str, Any]		    =  optimizer_cls(params=model.parameters()  ,       lr=lowerCAmelCase__  )

 if accelerator.state.deepspeed_plugin is not None:
  __a			:  Dict		    =  accelerator.state.deepspeed_plugin.deepspeed_config[
      '''gradient_accumulation_steps'''
  ]
 else:
  __a			:  List[str]		    =  1
 __a			:  Tuple		    =  (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
 ):
  __a			:  Any		    =  get_linear_schedule_with_warmup(
      optimizer=lowerCAmelCase__  ,       num_warmup_steps=0  ,       num_training_steps=lowerCAmelCase__  ,       )
 else:
  __a			:  List[str]		    =  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.
 __a    ,	__a    ,	__a    ,	__a    ,	__a			:  List[Any]		    =  accelerator.prepare(
     lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )

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

 # Now we train the model
 __a			:  Dict		    =  {}
 for epoch in range(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  with TorchTracemalloc() as tracemalloc:
   model.train()
   for step, batch in enumerate(lowerCAmelCase__  ):
    __a			:  Union[str, Any]		    =  model(**lowerCAmelCase__  )
    __a			:  Optional[int]		    =  outputs.loss
    __a			:  Dict		    =  loss / gradient_accumulation_steps
    accelerator.backward(lowerCAmelCase__  )
    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  )  )  )
  __a			:  List[str]		    =  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(lowerCAmelCase__  ,       lowerCAmelCase__  )




def        __UpperCamelCase					(    ):
 __a			:  List[Any]		    =  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(
     '''--peak_memory_upper_bound'''  ,       type=lowerCAmelCase__  ,       default=lowerCAmelCase__  ,       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=lowerCAmelCase__  ,       default=3_2_0  ,       help='''Number of training examples to use.'''  ,       )
 parser.add_argument(
     '''--n_val'''  ,       type=lowerCAmelCase__  ,       default=1_6_0  ,       help='''Number of validation examples to use.'''  ,       )
 parser.add_argument(
     '''--num_epochs'''  ,       type=lowerCAmelCase__  ,       default=1  ,       help='''Number of train epochs.'''  ,       )
 __a			:  Optional[int]		    =  parser.parse_args()
 __a			:  Tuple		    =  {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
 training_function(lowerCAmelCase__  ,       lowerCAmelCase__  )


if __name__ == "__main__":
      main()


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
      from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__  (	unittest.TestCase   ):
   @slow
   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  List[str]		    =  AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' ,    return_dict=snake_case_       ).to(snake_case_       )
    __a			:  List[Any]		    =  AutoTokenizer.from_pretrained('''google/mt5-small'''       )

    __a			:  Optional[int]		    =  tokenizer('''Hello there''' ,    return_tensors='''pt'''       ).input_ids
    __a			:  Dict		    =  tokenizer('''Hi I am''' ,    return_tensors='''pt'''       ).input_ids

    __a			:  Optional[Any]		    =  model(input_ids.to(snake_case_       ) ,    labels=labels.to(snake_case_       )       ).loss
    __a			:  Tuple		    =  -(labels.shape[-1] * loss.item())

    __a			:  Dict		    =  -84.9127
    self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1E-4       )
