
'''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


lowercase__											=16
lowercase__											=32






def      UpperCamelCase_				(      A__  ,      A__ = 16  ,      A__ = "bert-base-cased"						):
	a_      				=						AutoTokenizer.from_pretrained(A__						)
	a_      				=						load_dataset("""glue"""  ,      """mrpc"""						)

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

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

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

	def collate_fn(A__						):
		# 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(A__  ,      padding="""max_length"""  ,      max_length=1_28  ,      return_tensors="""pt"""						)
		return tokenizer.pad(A__  ,      padding="""longest"""  ,      return_tensors="""pt"""						)

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

	return train_dataloader, eval_dataloader






def      UpperCamelCase_				(      A__  ,      A__						):
	# Initialize accelerator
	a_      				=						Accelerator()

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

	set_seed(A__						)
	a_				,  a_      				=						get_dataloaders(A__  ,      A__  ,      A__						)

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

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

	if accelerator.state.deepspeed_plugin is not None:
		a_      				=						accelerator.state.deepspeed_plugin.deepspeed_config[
		    """gradient_accumulation_steps"""
		]
	else:
		a_      				=						1
	a_      				=						(len(A__						) * num_epochs) // gradient_accumulation_steps

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

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

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

			overall_step += 1

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

		a_      				=						metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'''epoch {epoch}:'''  ,      A__						)
		a_      				=						eval_metric["""accuracy"""]

		if best_performance < eval_metric["accuracy"]:
			a_      				=						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(A__  ,      A__						)






def      UpperCamelCase_				(      ):
	a_      				=						argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""						)
	parser.add_argument(
	    """--model_name_or_path"""  ,      type=A__  ,      default="""bert-base-cased"""  ,      help="""Path to pretrained model or model identifier from huggingface.co/models."""  ,      required=A__  ,      )
	parser.add_argument(
	    """--output_dir"""  ,      type=A__  ,      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=A__  ,      default=A__  ,      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=A__  ,      default=3  ,      help="""Number of train epochs."""  ,      )
	a_      				=						parser.parse_args()
	a_      				=						{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
	training_function(A__  ,      A__						)


if __name__ == "__main__":
		main()


'''simple docstring'''
def      UpperCamelCase_				(      A__						):
	if len(A__						) <= 1:
		return lst

	a_      				=						1

	while i < len(A__						):
		if lst[i - 1] <= lst[i]:
			i += 1
		else:
			a_				,  a_      				=						lst[i], lst[i - 1]
			i -= 1
			if i == 0:
				a_      				=						1

	return lst


if __name__ == "__main__":
		lowercase__											=input('Enter numbers separated by a comma:\n').strip()
		lowercase__											=[int(item) for item in user_input.split(',')]
		print(gnome_sort(unsorted))
