



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 					__lowerCAmelCase		(  __lowerCamelCase	:    Accelerator				,   __lowerCamelCase	:    int = 16				,   __lowerCamelCase	:    str = "bert-base-cased"	)  ->						Tuple:
					__lowerCAmelCase	   =AutoTokenizer.from_pretrained(__lowerCamelCase	)
					__lowerCAmelCase	   =load_dataset("""glue"""				,   """mrpc"""	)

					def tokenize_function(__lowerCamelCase	:    str	):
										# 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	:    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(__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 					__lowerCAmelCase		(  __lowerCamelCase	:    str				,   __lowerCamelCase	:    Optional[Any]				,   __lowerCamelCase	:    Any				,   __lowerCamelCase	:    Optional[Any]	)  ->						str:
					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()
					return eval_metric["accuracy"]






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str				,   __lowerCamelCase	:    List[Any]	)  ->						Tuple:
					# 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
					__lowerCAmelCase	   =evaluate.load("""glue"""				,   """mrpc"""	)
					__lowerCAmelCase	   =num_epochs

					if args.partial_train_epoch is not None:
										__lowerCAmelCase	   =args.partial_train_epoch

					if args.resume_from_checkpoint:
										accelerator.load_state(args.resume_from_checkpoint	)
										__lowerCAmelCase	   =args.resume_from_checkpoint.split("""epoch_"""	)[1]
										__lowerCAmelCase	   =""""""
										for char in epoch_string:
															if char.isdigit():
																				state_epoch_num += char
															else:
																				break
										__lowerCAmelCase	   =int(__lowerCamelCase	) + 1
										__lowerCAmelCase	   =evaluation_loop(__lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase	)
										accelerator.print("""resumed checkpoint performance:"""				,   __lowerCamelCase	)
										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:
															__lowerCAmelCase	   =json.load(__lowerCamelCase	)
															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
					__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
										__lowerCAmelCase	   =f"""epoch_{epoch}"""
										__lowerCAmelCase	   =os.path.join(args.output_dir				,   __lowerCamelCase	)
										accelerator.save_state(__lowerCamelCase	)
										__lowerCAmelCase	   =evaluation_loop(__lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase	)
										__lowerCAmelCase	   =accuracy
										__lowerCAmelCase	   =lr_scheduler.get_lr()[0]
										__lowerCAmelCase	   =optimizer.param_groups[0]["""lr"""]
										__lowerCAmelCase	   =epoch
										__lowerCAmelCase	   =overall_step
										accelerator.print(f"""epoch {epoch}:"""				,   __lowerCamelCase	)

										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(__lowerCamelCase				,   __lowerCamelCase	)






def 					__lowerCAmelCase		(  )  ->						Optional[Any]:
					__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(
					    """--resume_from_checkpoint"""				,   type=__lowerCamelCase				,   default=__lowerCamelCase				,   help="""If the training should continue from a checkpoint folder."""				,   )
					parser.add_argument(
					    """--partial_train_epoch"""				,   type=__lowerCamelCase				,   default=__lowerCamelCase				,   help="""If passed, the training will stop after this number of epochs."""				,   )
					parser.add_argument(
					    """--num_epochs"""				,   type=__lowerCamelCase				,   default=2				,   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()









def 					__lowerCAmelCase		(  __lowerCamelCase	:    list	)  ->						list:
					if len(__lowerCamelCase	) <= 1:
										return lst

					__lowerCAmelCase	   =1

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




