





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


_SCREAMING_SNAKE_CASE  :						str         =    16
_SCREAMING_SNAKE_CASE  :						int         =    32
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return int(x / 2**20       )






class  UpperCAmelCase__	:






  """simple docstring"""


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







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



def        UpperCAmelCase_     (		_A   ,	_A = 16   ,	_A = "bert-base-cased"   ,	_A = 3_20   ,	_A = 1_60   ,	):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE       )
     SCREAMING_SNAKE_CASE__					=					load_dataset(
         '''glue'''   ,	'''mrpc'''   ,	split={'''train''': F'''train[:{n_train}]''', '''validation''': F'''validation[:{n_val}]'''}       )

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

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

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

     def collate_fn(_A       ):
          # 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(_SCREAMING_SNAKE_CASE   ,	padding='''max_length'''   ,	max_length=1_28   ,	return_tensors='''pt'''       )
          return tokenizer.pad(_SCREAMING_SNAKE_CASE   ,	padding='''longest'''   ,	return_tensors='''pt'''       )

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

     return train_dataloader, eval_dataloader
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					Accelerator()

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

     set_seed(_SCREAMING_SNAKE_CASE       )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					get_dataloaders(_SCREAMING_SNAKE_CASE   ,	_SCREAMING_SNAKE_CASE   ,	_SCREAMING_SNAKE_CASE   ,	args.n_train   ,	args.n_val       )

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

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

     if accelerator.state.deepspeed_plugin is not None:
          SCREAMING_SNAKE_CASE__					=					accelerator.state.deepspeed_plugin.deepspeed_config[
              '''gradient_accumulation_steps'''
          ]
     else:
          SCREAMING_SNAKE_CASE__					=					1
     SCREAMING_SNAKE_CASE__					=					(len(_SCREAMING_SNAKE_CASE       ) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          SCREAMING_SNAKE_CASE__					=					get_linear_schedule_with_warmup(
              optimizer=_SCREAMING_SNAKE_CASE   ,	num_warmup_steps=0   ,	num_training_steps=_SCREAMING_SNAKE_CASE   ,	)
     else:
          SCREAMING_SNAKE_CASE__					=					DummyScheduler(_SCREAMING_SNAKE_CASE   ,	total_num_steps=_SCREAMING_SNAKE_CASE   ,	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.
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					accelerator.prepare(
         _SCREAMING_SNAKE_CASE   ,	_SCREAMING_SNAKE_CASE   ,	_SCREAMING_SNAKE_CASE   ,	_SCREAMING_SNAKE_CASE   ,	_SCREAMING_SNAKE_CASE       )

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

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





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''       )
     parser.add_argument(
         '''--model_name_or_path'''   ,	type=_SCREAMING_SNAKE_CASE   ,	default='''bert-base-cased'''   ,	help='''Path to pretrained model or model identifier from huggingface.co/models.'''   ,	required=_SCREAMING_SNAKE_CASE   ,	)
     parser.add_argument(
         '''--output_dir'''   ,	type=_SCREAMING_SNAKE_CASE   ,	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=_SCREAMING_SNAKE_CASE   ,	default=_SCREAMING_SNAKE_CASE   ,	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=_SCREAMING_SNAKE_CASE   ,	default=3_20   ,	help='''Number of training examples to use.'''   ,	)
     parser.add_argument(
         '''--n_val'''   ,	type=_SCREAMING_SNAKE_CASE   ,	default=1_60   ,	help='''Number of validation examples to use.'''   ,	)
     parser.add_argument(
         '''--num_epochs'''   ,	type=_SCREAMING_SNAKE_CASE   ,	default=1   ,	help='''Number of train epochs.'''   ,	)
     SCREAMING_SNAKE_CASE__					=					parser.parse_args()
     SCREAMING_SNAKE_CASE__					=					{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
     training_function(_SCREAMING_SNAKE_CASE   ,	_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
       main()





from collections.abc import Callable

import numpy as np
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					int(np.ceil((x_end - xa) / step_size       )       )
     SCREAMING_SNAKE_CASE__					=					np.zeros((n + 1,)       )
     SCREAMING_SNAKE_CASE__					=					ya
     SCREAMING_SNAKE_CASE__					=					xa

     for k in range(_A       ):
          SCREAMING_SNAKE_CASE__					=					y[k] + step_size * ode_func(_A   ,	y[k]       )
          SCREAMING_SNAKE_CASE__					=					y[k] + (
              (step_size / 2) * (ode_func(_A   ,	y[k]       ) + ode_func(x + step_size   ,	_A       ))
          )
          x += step_size

     return y


if __name__ == "__main__":
       import doctest

       doctest.testmod()


