




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


_lowerCamelCase	:		Tuple							       =   16
_lowerCamelCase	:		Optional[int]							       =   32







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Union[str, Any]:



       """simple docstring"""

       return int(x / 2**20						)


class 				UpperCamelCase_    :

    '''simple docstring'''






    def __enter__(			self      :	Tuple)						->int:





           '''simple docstring'''

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





    def __exit__(			self      :	Optional[int]					, *UpperCAmelCase__      :	int)						->Dict:





           '''simple docstring'''

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







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ = 16 ,				lowercase_ = "bert-base-cased" ,				lowercase_ = 320 ,				lowercase_ = 160 ,				)   ->       int:



       """simple docstring"""

       A__       =    AutoTokenizer.from_pretrained(lowercase_						)
       A__       =    load_dataset(
           '''glue''' ,				'''mrpc''' ,				split={'''train''': f"""train[:{n_train}]""", '''validation''': f"""validation[:{n_val}]"""}						)

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

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

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

       def collate_fn(lowercase_						):
              # 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(lowercase_ ,				padding='''max_length''' ,				max_length=128 ,				return_tensors='''pt'''						)
              return tokenizer.pad(lowercase_ ,				padding='''longest''' ,				return_tensors='''pt'''						)

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

       return train_dataloader, eval_dataloader







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Tuple:



       """simple docstring"""

       A__       =    Accelerator()

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

       set_seed(lowercase_						)
       A__ ,							A__       =    get_dataloaders(lowercase_ ,				lowercase_ ,				lowercase_ ,				args.n_train ,				args.n_val						)

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

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

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

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

       # Now we train the model
       A__       =    {}
       for epoch in range(lowercase_ ,				lowercase_						):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(lowercase_						):
                            A__       =    model(**lowercase_						)
                            A__       =    outputs.loss
                            A__       =    loss / gradient_accumulation_steps
                            accelerator.backward(lowercase_						)
                            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__       =    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(lowercase_ ,				lowercase_						)







def SCREAMING_SNAKE_CASE				(       )   ->       List[str]:



       """simple docstring"""

       A__       =    argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''						)
       parser.add_argument(
           '''--model_name_or_path''' ,				type=lowercase_ ,				default='''bert-base-cased''' ,				help='''Path to pretrained model or model identifier from huggingface.co/models.''' ,				required=lowercase_ ,				)
       parser.add_argument(
           '''--output_dir''' ,				type=lowercase_ ,				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=lowercase_ ,				default=lowercase_ ,				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=lowercase_ ,				default=320 ,				help='''Number of training examples to use.''' ,				)
       parser.add_argument(
           '''--n_val''' ,				type=lowercase_ ,				default=160 ,				help='''Number of validation examples to use.''' ,				)
       parser.add_argument(
           '''--num_epochs''' ,				type=lowercase_ ,				default=1 ,				help='''Number of train epochs.''' ,				)
       A__       =    parser.parse_args()
       A__       =    {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
       training_function(lowercase_ ,				lowercase_						)


if __name__ == "__main__":
  main()








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase	:		Any							       =   logging.get_logger(__name__)


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''encoder-decoder'''
    UpperCAmelCase__							  =      True


    def __init__(			self      :	List[str]					, **UpperCAmelCase__      :	Union[str, Any])						->List[Any]:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)
           assert (
               "encoder" in kwargs and "decoder" in kwargs
           ), "Config has to be initialized with encoder and decoder config"
           A__       =    kwargs.pop('''encoder''')
           A__       =    encoder_config.pop('''model_type''')
           A__       =    kwargs.pop('''decoder''')
           A__       =    decoder_config.pop('''model_type''')

           from ..auto.configuration_auto import AutoConfig

           A__       =    AutoConfig.for_model(UpperCAmelCase__					, **UpperCAmelCase__)
           A__       =    AutoConfig.for_model(UpperCAmelCase__					, **UpperCAmelCase__)
           A__       =    True


    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	Union[str, Any]					, UpperCAmelCase__      :	PretrainedConfig					, UpperCAmelCase__      :	PretrainedConfig					, **UpperCAmelCase__      :	Union[str, Any])						->PretrainedConfig:





           '''simple docstring'''

           logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''')
           A__       =    True
           A__       =    True

           return cls(encoder=encoder_config.to_dict()					, decoder=decoder_config.to_dict()					, **UpperCAmelCase__)





    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Optional[Any]:





           '''simple docstring'''

           A__       =    copy.deepcopy(self.__dict__)
           A__       =    self.encoder.to_dict()
           A__       =    self.decoder.to_dict()
           A__       =    self.__class__.model_type
           return output


