






"""simple docstring"""





import argparse
import json
import os

import evaluate
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   a    (     __UpperCAmelCase      :    Accelerator      ,   __UpperCAmelCase      :    int = 1_6      ,   __UpperCAmelCase      :    str = "bert-base-cased"  )    ->							str:
						__magic_name__:	Tuple							=       AutoTokenizer.from_pretrained(__UpperCAmelCase  )
						__magic_name__:	Optional[Any]							=       load_dataset("""glue"""      ,   """mrpc"""  )

						def tokenize_function(__UpperCAmelCase      :    Dict  ):
												# max_length=None => use the model max length (it's actually the default)
												__magic_name__:	Union[str, Any]							=       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
						__magic_name__:	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
						__magic_name__:	int							=       tokenized_datasets.rename_column("""label"""      ,   """labels"""  )

						def collate_fn(__UpperCAmelCase      :    Optional[Any]  ):
												# 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_2_8      ,   return_tensors="""pt"""  )
												return tokenizer.pad(__UpperCAmelCase      ,   padding="""longest"""      ,   return_tensors="""pt"""  )

						# Instantiate dataloaders.
						__magic_name__:	Optional[Any]							=       DataLoader(
						    tokenized_datasets["""train"""]      ,   shuffle=__UpperCAmelCase      ,   collate_fn=__UpperCAmelCase      ,   batch_size=__UpperCAmelCase  )
						__magic_name__:	Any							=       DataLoader(
						    tokenized_datasets["""validation"""]      ,   shuffle=__UpperCAmelCase      ,   collate_fn=__UpperCAmelCase      ,   batch_size=__UpperCAmelCase  )

						return train_dataloader, eval_dataloader



def   a    (     __UpperCAmelCase      :    Optional[Any]      ,   __UpperCAmelCase      :    List[Any]      ,   __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    int  )    ->							Any:
						model.eval()
						__magic_name__:	List[str]							=       0
						for step, batch in enumerate(__UpperCAmelCase  ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device  )
												with torch.no_grad():
																		__magic_name__:	Union[str, Any]							=       model(**__UpperCAmelCase  )
												__magic_name__:	Optional[Any]							=       outputs.logits.argmax(dim=-1  )
												# It is slightly faster to call this once, than multiple times
												__magic_name__,			__magic_name__:	str							=       accelerator.gather(
												    (predictions, batch["""labels"""])  )  # If we are in a multiprocess environment, the last batch has duplicates
												if accelerator.use_distributed:
																		if step == len(__UpperCAmelCase  ) - 1:
																								__magic_name__:	List[str]							=       predictions[: len(eval_dataloader.dataset  ) - samples_seen]
																								__magic_name__:	Dict							=       references[: len(eval_dataloader.dataset  ) - samples_seen]
																		else:
																								samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=__UpperCAmelCase      ,   references=__UpperCAmelCase      ,   )

						__magic_name__:	Any							=       metric.compute()
						return eval_metric["accuracy"]



def   a    (     __UpperCAmelCase      :    Tuple      ,   __UpperCAmelCase      :    Dict  )    ->							Optional[Any]:
						# Initialize accelerator
						__magic_name__:	List[Any]							=       Accelerator()

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

						set_seed(__UpperCAmelCase  )
						__magic_name__,			__magic_name__:	Tuple							=       get_dataloaders(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )

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

						# Instantiate optimizer
						__magic_name__:	List[str]							=       (
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						__magic_name__:	Optional[int]							=       optimizer_cls(params=model.parameters()      ,   lr=__UpperCAmelCase  )

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

						# We need to keep track of how many total steps we have iterated over
						__magic_name__:	str							=       0
						# We also need to keep track of the stating epoch so files are named properly
						__magic_name__:	List[str]							=       0
						__magic_name__:	Tuple							=       evaluate.load("""glue"""      ,   """mrpc"""  )
						__magic_name__:	Tuple							=       num_epochs

						if args.partial_train_epoch is not None:
												__magic_name__:	int							=       args.partial_train_epoch

						if args.resume_from_checkpoint:
												accelerator.load_state(args.resume_from_checkpoint  )
												__magic_name__:	Any							=       args.resume_from_checkpoint.split("""epoch_"""  )[1]
												__magic_name__:	Optional[Any]							=       """"""
												for char in epoch_string:
																		if char.isdigit():
																								state_epoch_num += char
																		else:
																								break
												__magic_name__:	Union[str, Any]							=       int(__UpperCAmelCase  ) + 1
												__magic_name__:	Union[str, Any]							=       evaluation_loop(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
												accelerator.print("""resumed checkpoint performance:"""      ,   __UpperCAmelCase  )
												accelerator.print("""resumed checkpoint's scheduler's lr:"""      ,   lr_scheduler.get_lr()[0]  )
												accelerator.print("""resumed optimizers's lr:"""      ,   optimizer.param_groups[0]["""lr"""]  )
												with open(os.path.join(args.output_dir      ,   f'state_{starting_epoch-1}.json'  )      ,   """r"""  ) as f:
																		__magic_name__:	Any							=       json.load(__UpperCAmelCase  )
																		assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["lr"] == lr_scheduler.get_lr()[0]
																		), "Scheduler learning rate mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
																		), "Optimizer learning rate mismatch, loading from checkpoint failed"
																		assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
																		return

    # Now we train the model
						__magic_name__:	Optional[int]							=       {}
						for epoch in range(__UpperCAmelCase      ,   __UpperCAmelCase  ):
												model.train()
												for step, batch in enumerate(__UpperCAmelCase  ):
																		__magic_name__:	Union[str, Any]							=       model(**__UpperCAmelCase  )
																		__magic_name__:	int							=       outputs.loss
																		__magic_name__:	Optional[int]							=       loss / gradient_accumulation_steps
																		accelerator.backward(__UpperCAmelCase  )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1
												__magic_name__:	int							=       f'epoch_{epoch}'
												__magic_name__:	Tuple							=       os.path.join(args.output_dir      ,   __UpperCAmelCase  )
												accelerator.save_state(__UpperCAmelCase  )
												__magic_name__:	Optional[Any]							=       evaluation_loop(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
												__magic_name__:	str							=       accuracy
												__magic_name__:	Tuple							=       lr_scheduler.get_lr()[0]
												__magic_name__:	List[Any]							=       optimizer.param_groups[0]["""lr"""]
												__magic_name__:	Any							=       epoch
												__magic_name__:	List[str]							=       overall_step
												accelerator.print(f'epoch {epoch}:'      ,   __UpperCAmelCase  )

												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		with open(os.path.join(args.output_dir      ,   f'state_{epoch}.json'  )      ,   """w"""  ) as f:
																								json.dump(__UpperCAmelCase      ,   __UpperCAmelCase  )



def   a    (     )    ->							Any:
						__magic_name__:	Tuple							=       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(
						    """--resume_from_checkpoint"""      ,   type=__UpperCAmelCase      ,   default=__UpperCAmelCase      ,   help="""If the training should continue from a checkpoint folder."""      ,   )
						parser.add_argument(
						    """--partial_train_epoch"""      ,   type=__UpperCAmelCase      ,   default=__UpperCAmelCase      ,   help="""If passed, the training will stop after this number of epochs."""      ,   )
						parser.add_argument(
						    """--num_epochs"""      ,   type=__UpperCAmelCase      ,   default=2      ,   help="""Number of train epochs."""      ,   )
						__magic_name__:	Optional[int]							=       parser.parse_args()
						__magic_name__:	Optional[Any]							=       {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}

						training_function(__UpperCAmelCase      ,   __UpperCAmelCase  )


if __name__ == "__main__":
							main()











"""simple docstring"""





def   a    (     __UpperCAmelCase      :    list[int]  )    ->							float:
						if not nums:  # Makes sure that the list is not empty
												raise ValueError("""List is empty"""  )

						__magic_name__:	Dict							=       sum(__UpperCAmelCase  ) / len(__UpperCAmelCase  )  # Calculate the average
						return sum(abs(x - average  ) for x in nums  ) / len(__UpperCAmelCase  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()



