

'''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


lowercase__         =      16
lowercase__         =      32




def 				UpperCamelCase(					UpperCAmelCase_     ):
 return int(x / 2**20     )




class 	A_			:






     '''simple docstring'''



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




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




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_ = 16  ,	UpperCAmelCase_ = "bert-base-cased"  ,	UpperCAmelCase_ = 3_20  ,	UpperCAmelCase_ = 1_60  ,	):
 UpperCAmelCase	:     Union[str, Any]						 =			AutoTokenizer.from_pretrained(UpperCAmelCase_     )
 UpperCAmelCase	:     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)
  UpperCAmelCase	:     Dict						 =			tokenizer(examples['sentence1']  ,	examples['sentence2']  ,	truncation=UpperCAmelCase_  ,	max_length=UpperCAmelCase_     )
  return outputs

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

 # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
 # transformers library
 UpperCAmelCase	:     Optional[Any]						 =			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(UpperCAmelCase_  ,	padding='max_length'  ,	max_length=1_28  ,	return_tensors='pt'     )
  return tokenizer.pad(UpperCAmelCase_  ,	padding='longest'  ,	return_tensors='pt'     )

 # Instantiate dataloaders.
 UpperCAmelCase	:     Any						 =			DataLoader(
     tokenized_datasets['train']  ,	shuffle=UpperCAmelCase_  ,	collate_fn=UpperCAmelCase_  ,	batch_size=UpperCAmelCase_     )
 UpperCAmelCase	:     Any						 =			DataLoader(
     tokenized_datasets['validation']  ,	shuffle=UpperCAmelCase_  ,	collate_fn=UpperCAmelCase_  ,	batch_size=UpperCAmelCase_     )

 return train_dataloader, eval_dataloader




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 # Initialize accelerator
 UpperCAmelCase	:     Tuple						 =			Accelerator()

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

 set_seed(UpperCAmelCase_     )
 UpperCAmelCase ,			UpperCAmelCase	:     str						 =			get_dataloaders(UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	args.n_train  ,	args.n_val     )

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 UpperCAmelCase	:     Union[str, Any]						 =			AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase_  ,	return_dict=UpperCAmelCase_     )

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

 if accelerator.state.deepspeed_plugin is not None:
  UpperCAmelCase	:     Any						 =			accelerator.state.deepspeed_plugin.deepspeed_config[
      'gradient_accumulation_steps'
  ]
 else:
  UpperCAmelCase	:     Optional[int]						 =			1
 UpperCAmelCase	:     int						 =			(len(UpperCAmelCase_     ) * num_epochs) // gradient_accumulation_steps

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

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

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




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


if __name__ == "__main__":
 main()







'''simple docstring'''

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 assert isinstance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 assert dataset.num_rows == 4
 assert dataset.num_columns == 1
 assert dataset.column_names == ["text"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('keep_in_memory'  ,	[False, True]     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			tmp_path / 'cache'
 UpperCAmelCase	:     List[str]						 =			{'text': 'string'}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  UpperCAmelCase	:     Tuple						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_  ,	keep_in_memory=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize(
    'features'  ,	[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]  ,	)
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[int]						 =			tmp_path / 'cache'
 UpperCAmelCase	:     List[str]						 =			{'text': 'string'}
 UpperCAmelCase	:     Optional[int]						 =			features.copy() if features else default_expected_features
 UpperCAmelCase	:     int						 =			(
     Features({feature: Value(UpperCAmelCase_     ) for feature, dtype in features.items()}     ) if features is not None else None
 )
 UpperCAmelCase	:     Union[str, Any]						 =			TextDatasetReader(UpperCAmelCase_  ,	features=UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize('split'  ,	[None, NamedSplit('train'     ), 'train', 'test']     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[int]						 =			tmp_path / 'cache'
 UpperCAmelCase	:     Tuple						 =			{'text': 'string'}
 UpperCAmelCase	:     List[str]						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_  ,	split=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )
 assert dataset.split == split if split else "train"




@pytest.mark.parametrize('path_type'  ,	[str, list]     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if issubclass(UpperCAmelCase_  ,	UpperCAmelCase_     ):
  UpperCAmelCase	:     Tuple						 =			text_path
 elif issubclass(UpperCAmelCase_  ,	UpperCAmelCase_     ):
  UpperCAmelCase	:     Optional[Any]						 =			[text_path]
 UpperCAmelCase	:     List[Any]						 =			tmp_path / 'cache'
 UpperCAmelCase	:     Union[str, Any]						 =			{'text': 'string'}
 UpperCAmelCase	:     List[Any]						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=("train",)     ):
 assert isinstance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 for split in splits:
  UpperCAmelCase	:     Union[str, Any]						 =			dataset_dict[split]
  assert dataset.num_rows == 4
  assert dataset.num_columns == 1
  assert dataset.column_names == ["text"]
  for feature, expected_dtype in expected_features.items():
   assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('keep_in_memory'  ,	[False, True]     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Any						 =			tmp_path / 'cache'
 UpperCAmelCase	:     List[str]						 =			{'text': 'string'}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  UpperCAmelCase	:     int						 =			TextDatasetReader({'train': text_path}  ,	cache_dir=UpperCAmelCase_  ,	keep_in_memory=UpperCAmelCase_     ).read()
 _check_text_datasetdict(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize(
    'features'  ,	[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]  ,	)
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Union[str, Any]						 =			tmp_path / 'cache'
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 UpperCAmelCase	:     Tuple						 =			{'text': 'string'}
 UpperCAmelCase	:     Union[str, Any]						 =			features.copy() if features else default_expected_features
 UpperCAmelCase	:     int						 =			(
     Features({feature: Value(UpperCAmelCase_     ) for feature, dtype in features.items()}     ) if features is not None else None
 )
 UpperCAmelCase	:     List[Any]						 =			TextDatasetReader({'train': text_path}  ,	features=UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_datasetdict(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize('split'  ,	[None, NamedSplit('train'     ), 'train', 'test']     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if split:
  UpperCAmelCase	:     int						 =			{split: text_path}
 else:
  UpperCAmelCase	:     int						 =			'train'
  UpperCAmelCase	:     Any						 =			{'train': text_path, 'test': text_path}
 UpperCAmelCase	:     Dict						 =			tmp_path / 'cache'
 UpperCAmelCase	:     Any						 =			{'text': 'string'}
 UpperCAmelCase	:     List[str]						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_datasetdict(UpperCAmelCase_  ,	UpperCAmelCase_  ,	splits=list(path.keys()     )     )
 assert all(dataset[split].split == split for split in path.keys()     )




