




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


__UpperCamelCase   :			Tuple          = 16
__UpperCamelCase   :			Dict          = 32





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase = 16				,     __lowerCamelCase = "bert-base-cased"       ) ->			str:
	a									=				AutoTokenizer.from_pretrained(__UpperCAmelCase       )
	a									=				load_dataset("""glue"""				,     """mrpc"""       )

	def tokenize_function(__lowerCamelCase       ):
		# max_length=None => use the model max length (it's actually the default)
		a									=				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
	a									=				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
	a									=				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(__UpperCAmelCase				,     padding="""max_length"""				,     max_length=128				,     return_tensors="""pt"""       )
		return tokenizer.pad(__UpperCAmelCase				,     padding="""longest"""				,     return_tensors="""pt"""       )

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

	return train_dataloader, eval_dataloader





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Any:
	model.eval()
	a									=				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():
			a									=				model(**__UpperCAmelCase       )
		a									=				outputs.logits.argmax(dim=-1       )
		# It is slightly faster to call this once, than multiple times
		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(__UpperCAmelCase       ) - 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=__UpperCAmelCase				,     references=__UpperCAmelCase				,     )

	a									=				metric.compute()
	return eval_metric["accuracy"]





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			Optional[Any]:
	# 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(__UpperCAmelCase       )
	a									=				get_dataloaders(__UpperCAmelCase				,     __UpperCAmelCase				,     __UpperCAmelCase       )

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

	# 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=__UpperCAmelCase       )

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

	# 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
	a									=				evaluate.load("""glue"""				,     """mrpc"""       )
	a									=				num_epochs

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

	if args.resume_from_checkpoint:
		accelerator.load_state(args.resume_from_checkpoint       )
		a									=				args.resume_from_checkpoint.split("""epoch_"""       )[1]
		a									=				""""""
		for char in epoch_string:
			if char.isdigit():
				state_epoch_num += char
			else:
				break
		a									=				int(__UpperCAmelCase       ) + 1
		a									=				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:
			a									=				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
	a									=				{}
	for epoch in range(__UpperCAmelCase				,     __UpperCAmelCase       ):
		model.train()
		for step, batch in enumerate(__UpperCAmelCase       ):
			a									=				model(**__UpperCAmelCase       )
			a									=				outputs.loss
			a									=				loss / gradient_accumulation_steps
			accelerator.backward(__UpperCAmelCase       )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1
		a									=				f'epoch_{epoch}'
		a									=				os.path.join(args.output_dir				,     __UpperCAmelCase       )
		accelerator.save_state(__UpperCAmelCase       )
		a									=				evaluation_loop(__UpperCAmelCase				,     __UpperCAmelCase				,     __UpperCAmelCase				,     __UpperCAmelCase       )
		a									=				accuracy
		a									=				lr_scheduler.get_lr()[0]
		a									=				optimizer.param_groups[0]["""lr"""]
		a									=				epoch
		a									=				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:
	a									=				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."""				,     )
	a									=				parser.parse_args()
	a									=				{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

	training_function(__UpperCAmelCase				,     __UpperCAmelCase       )


if __name__ == "__main__":
		main()









"""simple docstring"""





def   a    (     __UpperCAmelCase      :    List[Any]  )    ->							str:
						__magic_name__:	Optional[int]							=       [0] * len(__UpperCAmelCase  )
						__magic_name__:	str							=       []
						__magic_name__:	Any							=       []
						__magic_name__:	Union[str, Any]							=       0

						for values in graph.values():
												for i in values:
																		indegree[i] += 1

						for i in range(len(__UpperCAmelCase  )  ):
												if indegree[i] == 0:
																		queue.append(__UpperCAmelCase  )

						while queue:
												__magic_name__:	Optional[Any]							=       queue.pop(0  )
												cnt += 1
												topo.append(__UpperCAmelCase  )
												for x in graph[vertex]:
																		indegree[x] -= 1
																		if indegree[x] == 0:
																								queue.append(__UpperCAmelCase  )

						if cnt != len(__UpperCAmelCase  ):
												print("""Cycle exists"""  )
						else:
												print(__UpperCAmelCase  )


# Adjacency List of Graph
__lowerCamelCase							=  {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}

topological_sort(graph)



