

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	( UpperCamelCase__      ):







							'''simple docstring'''






							return int(x / 2**20      )







class  lowercase				:




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




				def __exit__(					self      ,   *snake_case       ):
											gc.collect()
											torch.cuda.empty_cache()
											snake_case_							=    torch.cuda.memory_allocated()
											snake_case_							=    torch.cuda.max_memory_allocated()
											snake_case_							=    bamb(self.end - self.begin       )
											snake_case_							=    bamb(self.peak - self.begin       )
											# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__ = 16   ,							UpperCamelCase__ = "bert-base-cased"   ,							UpperCamelCase__ = 320   ,							UpperCamelCase__ = 160   ,							):







							'''simple docstring'''






							snake_case_							=    AutoTokenizer.from_pretrained(UpperCamelCase__      )
							snake_case_							=    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)
														snake_case_							=    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
							snake_case_							=    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
							snake_case_							=    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=128   ,							return_tensors='pt'      )
														return tokenizer.pad(UpperCamelCase__   ,							padding='longest'   ,							return_tensors='pt'      )

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

							return train_dataloader, eval_dataloader





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    Accelerator()

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

							set_seed(UpperCamelCase__      )
							snake_case_				,    snake_case_							=    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)
							snake_case_							=    AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__   ,							return_dict=UpperCamelCase__      )

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

							if accelerator.state.deepspeed_plugin is not None:
														snake_case_							=    accelerator.state.deepspeed_plugin.deepspeed_config[
														    'gradient_accumulation_steps'
														]
							else:
														snake_case_							=    1
							snake_case_							=    (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
							):
														snake_case_							=    get_linear_schedule_with_warmup(
														    optimizer=UpperCamelCase__   ,							num_warmup_steps=0   ,							num_training_steps=UpperCamelCase__   ,							)
							else:
														snake_case_							=    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.
							snake_case_				,    snake_case_				,    snake_case_				,    snake_case_				,    snake_case_							=    accelerator.prepare(
							    UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )

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

							# Now we train the model
							snake_case_							=    {}
							for epoch in range(UpperCamelCase__   ,							UpperCamelCase__      ):
														with TorchTracemalloc() as tracemalloc:
																					model.train()
																					for step, batch in enumerate(UpperCamelCase__      ):
																												snake_case_							=    model(**UpperCamelCase__      )
																												snake_case_							=    outputs.loss
																												snake_case_							=    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      )      )      )
														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(UpperCamelCase__   ,							UpperCamelCase__      )





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    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.'   ,							)
							snake_case_							=    parser.parse_args()
							snake_case_							=    {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
							training_function(UpperCamelCase__   ,							UpperCamelCase__      )


if __name__ == "__main__":
					main()







import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict





@pytest.mark.parametrize(
    'files'   ,							[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]   ,							)
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    tmp_path_factory.mktemp('dset_infos_dir'      )
							if "full:README.md" in files:
														with open(dataset_infos_dir / 'README.md'   ,							'w'      ) as f:
																					f.write('---\ndataset_info:\n  dataset_size: 42\n---'      )
							if "empty:README.md" in files:
														with open(dataset_infos_dir / 'README.md'   ,							'w'      ) as f:
																					f.write(''      )
    # we want to support dataset_infos.json for backward compatibility
							if "dataset_infos.json" in files:
														with open(dataset_infos_dir / 'dataset_infos.json'   ,							'w'      ) as f:
																					f.write('{"default": {"dataset_size": 42}}'      )
							snake_case_							=    DatasetInfosDict.from_directory(UpperCamelCase__      )
							assert dataset_infos
							assert dataset_infos["default"].dataset_size == 42





@pytest.mark.parametrize(
    'dataset_info'   ,							[
        DatasetInfo(),
        DatasetInfo(
            description='foo'   ,							features=Features({'a': Value('int32'      )}      )   ,							builder_name='builder'   ,							config_name='config'   ,							version='1.0.0'   ,							splits=[{'name': 'train'}]   ,							download_size=42   ,							),
    ]   ,							)
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    str(UpperCamelCase__      )
							dataset_info.write_to_directory(UpperCamelCase__      )
							snake_case_							=    DatasetInfo.from_directory(UpperCamelCase__      )
							assert dataset_info == reloaded
							assert os.path.exists(os.path.join(UpperCamelCase__   ,							'dataset_info.json'      )      )





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    DatasetInfo(
							    description='foo'   ,							citation='bar'   ,							homepage='https://foo.bar'   ,							license='CC0'   ,							features=Features({'a': Value('int32'      )}      )   ,							post_processed={}   ,							supervised_keys=()   ,							task_templates=[]   ,							builder_name='builder'   ,							config_name='config'   ,							version='1.0.0'   ,							splits=[{'name': 'train', 'num_examples': 42}]   ,							download_checksums={}   ,							download_size=1337   ,							post_processing_size=442   ,							dataset_size=1234   ,							size_in_bytes=1337 + 442 + 1234   ,							)
							snake_case_							=    dataset_info._to_yaml_dict()
							assert sorted(UpperCamelCase__      ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML      )
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														assert key in dataset_info_yaml_dict
														assert isinstance(dataset_info_yaml_dict[key]   ,							(list, dict, int, str)      )
							snake_case_							=    yaml.safe_dump(UpperCamelCase__      )
							snake_case_							=    yaml.safe_load(UpperCamelCase__      )
							assert dataset_info_yaml_dict == reloaded





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    DatasetInfo()
							snake_case_							=    dataset_info._to_yaml_dict()
							assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    'dataset_infos_dict'   ,							[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}      ),
        DatasetInfosDict({'my_config_name': DatasetInfo()}      ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'   ,							features=Features({'a': Value('int32'      )}      )   ,							builder_name='builder'   ,							config_name='config'   ,							version='1.0.0'   ,							splits=[{'name': 'train'}]   ,							download_size=42   ,							)
            }      ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42      ),
                'v2': DatasetInfo(dataset_size=1337      ),
            }      ),
    ]   ,							)
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    str(UpperCamelCase__      )
							dataset_infos_dict.write_to_directory(UpperCamelCase__      )
							snake_case_							=    DatasetInfosDict.from_directory(UpperCamelCase__      )

							# the config_name of the dataset_infos_dict take over the attribute
							for config_name, dataset_info in dataset_infos_dict.items():
														snake_case_							=    config_name
														# the yaml representation doesn't include fields like description or citation
														# so we just test that we can recover what we can from the yaml
														snake_case_							=    DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()      )
							assert dataset_infos_dict == reloaded

							if dataset_infos_dict:
														assert os.path.exists(os.path.join(UpperCamelCase__   ,							'README.md'      )      )




