
"""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


SCREAMING_SNAKE_CASE_					     =	16
SCREAMING_SNAKE_CASE_					     =	32

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase = 16 ,    _lowerCAmelCase = "bert-base-cased"						):
						__lowerCAmelCase     =  AutoTokenizer.from_pretrained(_lowerCAmelCase						)
						__lowerCAmelCase     =  load_dataset("""glue""" ,    """mrpc"""						)

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

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

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

						def collate_fn(_lowerCAmelCase						):
												# 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(_lowerCAmelCase ,    padding="""max_length""" ,    max_length=128 ,    return_tensors="""pt"""						)
												return tokenizer.pad(_lowerCAmelCase ,    padding="""longest""" ,    return_tensors="""pt"""						)

						# Instantiate dataloaders.
						__lowerCAmelCase     =  DataLoader(
						    tokenized_datasets["""train"""] ,    shuffle=_lowerCAmelCase ,    collate_fn=_lowerCAmelCase ,    batch_size=_lowerCAmelCase						)
						__lowerCAmelCase     =  DataLoader(
						    tokenized_datasets["""validation"""] ,    shuffle=_lowerCAmelCase ,    collate_fn=_lowerCAmelCase ,    batch_size=_lowerCAmelCase						)

						return train_dataloader, eval_dataloader

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						# Initialize accelerator
						__lowerCAmelCase     =  Accelerator()

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

						set_seed(_lowerCAmelCase						)
						__lowerCAmelCase   ,     __lowerCAmelCase     =  get_dataloaders(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)

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

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

						if accelerator.state.deepspeed_plugin is not None:
												__lowerCAmelCase     =  accelerator.state.deepspeed_plugin.deepspeed_config[
												    """gradient_accumulation_steps"""
												]
						else:
												__lowerCAmelCase     =  1
						__lowerCAmelCase     =  (len(_lowerCAmelCase						) * 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     =  get_linear_schedule_with_warmup(
												    optimizer=_lowerCAmelCase ,    num_warmup_steps=0 ,    num_training_steps=_lowerCAmelCase ,    )
						else:
												__lowerCAmelCase     =  DummyScheduler(_lowerCAmelCase ,    total_num_steps=_lowerCAmelCase ,    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     =  accelerator.prepare(
						    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)

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

						# Now we train the model
						__lowerCAmelCase     =  evaluate.load("""glue""" ,    """mrpc"""						)
						__lowerCAmelCase     =  0
						__lowerCAmelCase     =  {}
						for epoch in range(_lowerCAmelCase ,    _lowerCAmelCase						):
												model.train()
												for step, batch in enumerate(_lowerCAmelCase						):
																		__lowerCAmelCase     =  model(**_lowerCAmelCase						)
																		__lowerCAmelCase     =  outputs.loss
																		__lowerCAmelCase     =  loss / gradient_accumulation_steps
																		accelerator.backward(_lowerCAmelCase						)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

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

												__lowerCAmelCase     =  metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"""epoch {epoch}:""" ,    _lowerCAmelCase						)
												__lowerCAmelCase     =  eval_metric["""accuracy"""]

												if best_performance < eval_metric["accuracy"]:
																		__lowerCAmelCase     =  eval_metric["""accuracy"""]

						if args.performance_lower_bound is not None:
												assert (
												    args.performance_lower_bound <= best_performance
												), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
												with open(os.path.join(args.output_dir ,    """all_results.json"""						) ,    """w"""						) as f:
																		json.dump(_lowerCAmelCase ,    _lowerCAmelCase						)

def  lowercase     ():
						__lowerCAmelCase     =  argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""						)
						parser.add_argument(
						    """--model_name_or_path""" ,    type=_lowerCAmelCase ,    default="""bert-base-cased""" ,    help="""Path to pretrained model or model identifier from huggingface.co/models.""" ,    required=_lowerCAmelCase ,    )
						parser.add_argument(
						    """--output_dir""" ,    type=_lowerCAmelCase ,    default=""".""" ,    help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" ,    )
						parser.add_argument(
						    """--performance_lower_bound""" ,    type=_lowerCAmelCase ,    default=_lowerCAmelCase ,    help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.""" ,    )
						parser.add_argument(
						    """--num_epochs""" ,    type=_lowerCAmelCase ,    default=3 ,    help="""Number of train epochs.""" ,    )
						__lowerCAmelCase     =  parser.parse_args()
						__lowerCAmelCase     =  {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
						training_function(_lowerCAmelCase ,    _lowerCAmelCase						)


if __name__ == "__main__":
			main()


"""simple docstring"""
from __future__ import annotations

def  lowercase     (_lowerCAmelCase						):
						__lowerCAmelCase     =  2
						__lowerCAmelCase     =  []
						while i * i <= n:
												if n % i:
																		i += 1
												else:
																		n //= i
																		factors.append(_lowerCAmelCase						)
						if n > 1:
												factors.append(_lowerCAmelCase						)
						return factors


if __name__ == "__main__":
			import doctest

			doctest.testmod()
