



'''simple docstring'''





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      						=	16
_lowerCAmelCase      						=	32
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return int(x / 2**20			)






class        lowerCAmelCase_:
  '''simple docstring'''




  def __enter__(    self      )    ->		str:
      gc.collect()
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
      lowerCAmelCase__ :  Tuple							      =					torch.cuda.memory_allocated()
      return self






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







def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase = 16							,    UpperCamelCase = "bert-base-cased"							,    UpperCamelCase = 320							,    UpperCamelCase = 160							,    ):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[Any]							      =					AutoTokenizer.from_pretrained(__lowerCAmelCase			)
    lowerCAmelCase__ :  List[Any]							      =					load_dataset(
        """glue"""							,    """mrpc"""							,    split={"""train""": f"""train[:{n_train}]""", """validation""": f"""validation[:{n_val}]"""}			)

    def tokenize_function(UpperCamelCase			):
        # max_length=None => use the model max length (it's actually the default)
        lowerCAmelCase__ :  Tuple							      =					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__ :  int							      =					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__ :  Optional[int]							      =					tokenized_datasets.rename_column("""label"""							,    """labels"""			)

    def collate_fn(UpperCamelCase			):
        # 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__ :  str							      =					DataLoader(
        tokenized_datasets["""train"""]							,    shuffle=__lowerCAmelCase							,    collate_fn=__lowerCAmelCase							,    batch_size=__lowerCAmelCase			)
    lowerCAmelCase__ :  List[str]							      =					DataLoader(
        tokenized_datasets["""validation"""]							,    shuffle=__lowerCAmelCase							,    collate_fn=__lowerCAmelCase							,    batch_size=__lowerCAmelCase			)

    return train_dataloader, eval_dataloader
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					Accelerator()

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

    set_seed(__lowerCAmelCase			)
    lowerCAmelCase__ :  Union[str, Any]							      =					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__ :  Any							      =					AutoModelForSequenceClassification.from_pretrained(__lowerCAmelCase							,    return_dict=__lowerCAmelCase			)

    # Instantiate optimizer
    lowerCAmelCase__ :  Optional[int]							      =					(
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    lowerCAmelCase__ :  List[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__ :  Any							      =					1
    lowerCAmelCase__ :  Union[str, 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__ :  Dict							      =					get_linear_schedule_with_warmup(
            optimizer=__lowerCAmelCase							,    num_warmup_steps=0							,    num_training_steps=__lowerCAmelCase							,    )
    else:
        lowerCAmelCase__ :  int							      =					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__ :  str							      =					accelerator.prepare(
        __lowerCAmelCase							,    __lowerCAmelCase							,    __lowerCAmelCase							,    __lowerCAmelCase							,    __lowerCAmelCase			)

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

    # Now we train the model
    lowerCAmelCase__ :  Tuple							      =					{}
    for epoch in range(__lowerCAmelCase							,    __lowerCAmelCase			):
        with TorchTracemalloc() as tracemalloc:
            model.train()
            for step, batch in enumerate(__lowerCAmelCase			):
                lowerCAmelCase__ :  str							      =					model(**__lowerCAmelCase			)
                lowerCAmelCase__ :  str							      =					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__ :  int							      =					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    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    lowerCAmelCase__ :  Tuple							      =					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__ :  Optional[Any]							      =					parser.parse_args()
    lowerCAmelCase__ :  List[str]							      =					{"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







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	np.array:
     return 1 / (1 + np.exp(-vector			))







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	np.array:
     return vector * sigmoid(1.7_0_2 * vector			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()