



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


__snake_case       :	Union[str, Any]            =1_6
__snake_case       :	List[str]            =3_2






def 			lowerCAmelCase__	(					lowerCamelCase_	:	str):





				'''simple docstring'''

				return int(x / 2**20)



class  lowerCamelCase__    :




		'''simple docstring'''



		def __enter__(self				)  ->    Tuple:

						"""simple docstring"""






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



		def __exit__(self	,*__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						gc.collect()
						torch.cuda.empty_cache()
						lowerCAmelCase__  :       List[str]				=  torch.cuda.memory_allocated()
						lowerCAmelCase__  :       Tuple				=  torch.cuda.max_memory_allocated()
						lowerCAmelCase__  :       Optional[Any]				=  bamb(self.end - self.begin				)
						lowerCAmelCase__  :       List[Any]				=  bamb(self.peak - self.begin				)
						# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Accelerator ,lowerCamelCase_	:	int = 16 ,lowerCamelCase_	:	str = "bert-base-cased" ,lowerCamelCase_	:	int = 320 ,lowerCamelCase_	:	int = 160 ,):





				'''simple docstring'''

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

				def tokenize_function(lowerCamelCase_	:	Optional[Any]):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase__  :       str				=  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
				lowerCAmelCase__  :       List[Any]				=  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
				lowerCAmelCase__  :       List[Any]				=  tokenized_datasets.rename_column('''label''' ,'''labels''')

				def collate_fn(lowerCamelCase_	:	str):
								# 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=128 ,return_tensors='''pt''')
								return tokenizer.pad(UpperCamelCase__ ,padding='''longest''' ,return_tensors='''pt''')

				# Instantiate dataloaders.
				lowerCAmelCase__  :       int				=  DataLoader(
				    tokenized_datasets['''train'''] ,shuffle=UpperCamelCase__ ,collate_fn=UpperCamelCase__ ,batch_size=UpperCamelCase__)
				lowerCAmelCase__  :       int				=  DataLoader(
				    tokenized_datasets['''validation'''] ,shuffle=UpperCamelCase__ ,collate_fn=UpperCamelCase__ ,batch_size=UpperCamelCase__)

				return train_dataloader, eval_dataloader






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	Tuple):





				'''simple docstring'''

				lowerCAmelCase__  :       List[str]				=  Accelerator()

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

				set_seed(UpperCamelCase__)
				lowerCAmelCase__		,	lowerCAmelCase__  :       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)
				lowerCAmelCase__  :       Dict				=  AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__ ,return_dict=UpperCamelCase__)

				# Instantiate optimizer
				lowerCAmelCase__  :       Dict				=  (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				lowerCAmelCase__  :       Optional[int]				=  optimizer_cls(params=model.parameters() ,lr=UpperCamelCase__)

				if accelerator.state.deepspeed_plugin is not None:
								lowerCAmelCase__  :       Optional[int]				=  accelerator.state.deepspeed_plugin.deepspeed_config[
								    '''gradient_accumulation_steps'''
								]
				else:
								lowerCAmelCase__  :       Tuple				=  1
				lowerCAmelCase__  :       Optional[Any]				=  (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
				):
								lowerCAmelCase__  :       Dict				=  get_linear_schedule_with_warmup(
								    optimizer=UpperCamelCase__ ,num_warmup_steps=0 ,num_training_steps=UpperCamelCase__ ,)
				else:
								lowerCAmelCase__  :       Optional[Any]				=  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.
				lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Tuple				=  accelerator.prepare(
				    UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__)

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

				# Now we train the model
				lowerCAmelCase__  :       int				=  {}
				for epoch in range(UpperCamelCase__ ,UpperCamelCase__):
								with TorchTracemalloc() as tracemalloc:
												model.train()
												for step, batch in enumerate(UpperCamelCase__):
																lowerCAmelCase__  :       List[str]				=  model(**UpperCamelCase__)
																lowerCAmelCase__  :       Tuple				=  outputs.loss
																lowerCAmelCase__  :       Any				=  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)))
								lowerCAmelCase__  :       Dict				=  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 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       List[Any]				=  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=320 ,help='''Number of training examples to use.''' ,)
				parser.add_argument(
				    '''--n_val''' ,type=UpperCamelCase__ ,default=160 ,help='''Number of validation examples to use.''' ,)
				parser.add_argument(
				    '''--num_epochs''' ,type=UpperCamelCase__ ,default=1 ,help='''Number of train epochs.''' ,)
				lowerCAmelCase__  :       int				=  parser.parse_args()
				lowerCAmelCase__  :       Tuple				=  {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
				training_function(UpperCamelCase__ ,UpperCamelCase__)


if __name__ == "__main__":
							main()






from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


__snake_case       :	str            ='\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n'

__snake_case       :	Tuple            ='\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n'

__snake_case       :	int            ='\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "pearson": Pearson Correlation\n    "spearmanr": Spearman Correlation\n    "matthews_correlation": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})\n    {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Dict):





				'''simple docstring'''

				return float((preds == labels).mean())






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       int				=  simple_accuracy(lowerCamelCase_ ,lowerCamelCase_)
				lowerCAmelCase__  :       Any				=  float(fa_score(y_true=lowerCamelCase_ ,y_pred=lowerCamelCase_))
				return {
				    "accuracy": acc,
				    "f1": fa,
				}






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Union[str, Any]				=  float(pearsonr(lowerCamelCase_ ,lowerCamelCase_)[0])
				lowerCAmelCase__  :       int				=  float(spearmanr(lowerCamelCase_ ,lowerCamelCase_)[0])
				return {
				    "pearson": pearson_corr,
				    "spearmanr": spearman_corr,
				}



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,       _KWARGS_DESCRIPTION)
class  lowerCamelCase__    (							datasets.Metric):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						if self.config_name not in [
						    "sst2",
						    "mnli",
						    "mnli_mismatched",
						    "mnli_matched",
						    "cola",
						    "stsb",
						    "mrpc",
						    "qqp",
						    "qnli",
						    "rte",
						    "wnli",
						    "hans",
						]:
										raise KeyError(
										    '''You should supply a configuration name selected in '''
										    '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
										    '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''				)
						return datasets.MetricInfo(
						    description=_DESCRIPTION	,citation=_CITATION	,inputs_description=_KWARGS_DESCRIPTION	,features=datasets.Features(
						        {
						            '''predictions''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''				),
						            '''references''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''				),
						        }				)	,codebase_urls=[]	,reference_urls=[]	,format='''numpy'''	,)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Tuple:

						"""simple docstring"""






						if self.config_name == "cola":
										return {"matthews_correlation": matthews_corrcoef(__lowerCamelCase	,__lowerCamelCase				)}
						elif self.config_name == "stsb":
										return pearson_and_spearman(__lowerCamelCase	,__lowerCamelCase				)
						elif self.config_name in ["mrpc", "qqp"]:
										return acc_and_fa(__lowerCamelCase	,__lowerCamelCase				)
						elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
										return {"accuracy": simple_accuracy(__lowerCamelCase	,__lowerCamelCase				)}
						else:
										raise KeyError(
										    '''You should supply a configuration name selected in '''
										    '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
										    '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''				)

