


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


_UpperCAmelCase			:  List[str]     =16
_UpperCAmelCase			:  Union[str, Any]     =32



def 			lowerCAmelCase (		lowerCAmelCase_ )-> List[Any]:
    return int(x / 2**20 )







class 				snake_case__:







      '''simple docstring'''




      def __enter__(				self							)					->    List[str]:
          gc.collect()
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
          lowerCAmelCase_ :      Optional[int]    				=				torch.cuda.memory_allocated()
          return self




      def __exit__(				self   ,						*__lowercase							)					->    Dict:
          gc.collect()
          torch.cuda.empty_cache()
          lowerCAmelCase_ :      Any    				=				torch.cuda.memory_allocated()
          lowerCAmelCase_ :      Union[str, Any]    				=				torch.cuda.max_memory_allocated()
          lowerCAmelCase_ :      Any    				=				bamb(self.end - self.begin							)
          lowerCAmelCase_ :      Tuple    				=				bamb(self.peak - self.begin							)
          # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ = 16 ,   lowerCAmelCase_ = "bert-base-cased" ,   lowerCAmelCase_ = 320 ,   lowerCAmelCase_ = 160 ,   )-> Dict:
    lowerCAmelCase_ :      List[str]    				=				AutoTokenizer.from_pretrained(lowerCAmelCase_ )
    lowerCAmelCase_ :      List[str]    				=				load_dataset(
        '''glue''' ,   '''mrpc''' ,   split={'''train''': f"""train[:{n_train}]""", '''validation''': f"""validation[:{n_val}]"""} )

    def tokenize_function(lowerCAmelCase_ ):
        # max_length=None => use the model max length (it's actually the default)
        lowerCAmelCase_ :      int    				=				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_ :      Tuple    				=				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_ :      Dict    				=				tokenized_datasets.rename_column('''label''' ,   '''labels''' )

    def collate_fn(lowerCAmelCase_ ):
        # 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_ :      List[str]    				=				DataLoader(
        tokenized_datasets['''train'''] ,   shuffle=lowerCAmelCase_ ,   collate_fn=lowerCAmelCase_ ,   batch_size=lowerCAmelCase_ )
    lowerCAmelCase_ :      Any    				=				DataLoader(
        tokenized_datasets['''validation'''] ,   shuffle=lowerCAmelCase_ ,   collate_fn=lowerCAmelCase_ ,   batch_size=lowerCAmelCase_ )

    return train_dataloader, eval_dataloader



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> Any:
    # Initialize accelerator
    lowerCAmelCase_ :      Optional[int]    				=				Accelerator()

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

    set_seed(lowerCAmelCase_ )
    lowerCAmelCase_ :      List[str]    				=				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)
    lowerCAmelCase_ :      Optional[int]    				=				AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_ ,   return_dict=lowerCAmelCase_ )

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

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

    # 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_ :      Optional[Any]    				=				0

    # Now we train the model
    lowerCAmelCase_ :      Any    				=				{}
    for epoch in range(lowerCAmelCase_ ,   lowerCAmelCase_ ):
        with TorchTracemalloc() as tracemalloc:
            model.train()
            for step, batch in enumerate(lowerCAmelCase_ ):
                lowerCAmelCase_ :      List[Any]    				=				model(**lowerCAmelCase_ )
                lowerCAmelCase_ :      Dict    				=				outputs.loss
                lowerCAmelCase_ :      Optional[Any]    				=				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 ) ) )
        lowerCAmelCase_ :      List[Any]    				=				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 			lowerCAmelCase (		)-> Dict:
    lowerCAmelCase_ :      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=320 ,   help='''Number of training examples to use.''' ,   )
    parser.add_argument(
        '''--n_val''' ,   type=lowerCAmelCase_ ,   default=160 ,   help='''Number of validation examples to use.''' ,   )
    parser.add_argument(
        '''--num_epochs''' ,   type=lowerCAmelCase_ ,   default=1 ,   help='''Number of train epochs.''' ,   )
    lowerCAmelCase_ :      Dict    				=				parser.parse_args()
    lowerCAmelCase_ :      Dict    				=				{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
    training_function(lowerCAmelCase_ ,   lowerCAmelCase_ )


if __name__ == "__main__":
      main()



import copy

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


_UpperCAmelCase			:  str     =logging.get_logger(__name__)







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Optional[int]					=							"""encoder-decoder"""
						SCREAMING_SNAKE_CASE__      :  str					=							True




						def __init__(				self   ,						**__lowercase							)					->    Union[str, Any]:
										super().__init__(**__lowercase							)
										assert (
										    "encoder" in kwargs and "decoder" in kwargs
										), "Config has to be initialized with encoder and decoder config"
										lowerCAmelCase_ :      str    				=				kwargs.pop('''encoder'''							)
										lowerCAmelCase_ :      int    				=				encoder_config.pop('''model_type'''							)
										lowerCAmelCase_ :      Optional[Any]    				=				kwargs.pop('''decoder'''							)
										lowerCAmelCase_ :      Optional[Any]    				=				decoder_config.pop('''model_type'''							)

										from ..auto.configuration_auto import AutoConfig

										lowerCAmelCase_ :      Union[str, Any]    				=				AutoConfig.for_model(__lowercase   ,						**__lowercase							)
										lowerCAmelCase_ :      List[str]    				=				AutoConfig.for_model(__lowercase   ,						**__lowercase							)
										lowerCAmelCase_ :      Any    				=				True




						@classmethod
						def 						lowercase_ (				cls   ,						__lowercase   ,						__lowercase   ,						**__lowercase							)					->    PretrainedConfig:
										logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''							)
										lowerCAmelCase_ :      int    				=				True
										lowerCAmelCase_ :      List[Any]    				=				True

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




						def 						lowercase_ (				self							)					->    Any:
										lowerCAmelCase_ :      Optional[Any]    				=				copy.deepcopy(self.__dict__							)
										lowerCAmelCase_ :      List[str]    				=				self.encoder.to_dict()
										lowerCAmelCase_ :      Dict    				=				self.decoder.to_dict()
										lowerCAmelCase_ :      Optional[Any]    				=				self.__class__.model_type
										return output