

import argparse
from copy import deepcopy

import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)



def 			_A	(      ):
	UpperCAmelCase__:	Tuple									= argparse.ArgumentParser()
	parser.add_argument("--model_ckpt"	,type=SCREAMING_SNAKE_CASE	,default="microsoft/unixcoder-base-nine"      )
	parser.add_argument("--num_epochs"	,type=SCREAMING_SNAKE_CASE	,default=5      )
	parser.add_argument("--batch_size"	,type=SCREAMING_SNAKE_CASE	,default=6      )
	parser.add_argument("--gradient_accumulation_steps"	,type=SCREAMING_SNAKE_CASE	,default=1      )
	parser.add_argument("--freeze"	,type=SCREAMING_SNAKE_CASE	,default=SCREAMING_SNAKE_CASE      )
	parser.add_argument("--learning_rate"	,type=SCREAMING_SNAKE_CASE	,default=5e-4      )
	parser.add_argument("--seed"	,type=SCREAMING_SNAKE_CASE	,default=0      )
	parser.add_argument("--lr_scheduler_type"	,type=SCREAMING_SNAKE_CASE	,default="cosine"      )
	parser.add_argument("--num_warmup_steps"	,type=SCREAMING_SNAKE_CASE	,default=1_0      )
	parser.add_argument("--weight_decay"	,type=SCREAMING_SNAKE_CASE	,default=0.01      )
	parser.add_argument("--output_dir"	,type=SCREAMING_SNAKE_CASE	,default="./results"      )
	return parser.parse_args()


_lowerCAmelCase  :      int			 =load("""accuracy""")



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= eval_pred
	UpperCAmelCase__:	str									= np.argmax(SCREAMING_SNAKE_CASE	,axis=1      )
	return metric.compute(predictions=SCREAMING_SNAKE_CASE	,references=SCREAMING_SNAKE_CASE      )

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__						):
						super().__init__()
						UpperCAmelCase__:	Union[str, Any]									= trainer





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						):
						if control.should_evaluate:
							UpperCAmelCase__:	int									= deepcopy(lowerCamelCase__						)
							self._trainer.evaluate(eval_dataset=self._trainer.train_dataset				,      metric_key_prefix="train"						)
							return control_copy



def 			_A	(      ):
	UpperCAmelCase__:	int									= get_args()
	set_seed(args.seed      )

	UpperCAmelCase__:	Optional[int]									= load_dataset("codeparrot/codecomplex"	,split="train"      )
	UpperCAmelCase__:	int									= dataset.train_test_split(test_size=0.2      )
	UpperCAmelCase__:	Union[str, Any]									= train_test["test"].train_test_split(test_size=0.5      )
	UpperCAmelCase__:	Dict									= DatasetDict(
	    {
	        "train": train_test["train"],
	        "test": test_validation["train"],
	        "valid": test_validation["test"],
	    }      )

	print("Loading tokenizer and model"      )
	UpperCAmelCase__:	str									= AutoTokenizer.from_pretrained(args.model_ckpt      )
	UpperCAmelCase__:	Dict									= tokenizer.eos_token
	UpperCAmelCase__:	Union[str, Any]									= AutoModelForSequenceClassification.from_pretrained(args.model_ckpt	,num_labels=7      )
	UpperCAmelCase__:	Dict									= model.config.eos_token_id

	if args.freeze:
		for param in model.roberta.parameters():
			UpperCAmelCase__:	Dict									= False

	UpperCAmelCase__:	Tuple									= ClassLabel(num_classes=7	,names=list(set(train_test_validation["train"]["complexity"]      )      )      )

	def tokenize(SCREAMING_SNAKE_CASE      ):
		UpperCAmelCase__:	Union[str, Any]									= tokenizer(example["src"]	,truncation=SCREAMING_SNAKE_CASE	,max_length=1_0_2_4      )
		UpperCAmelCase__:	List[Any]									= labels.straint(example["complexity"]      )
		return {
		    "input_ids": inputs["input_ids"],
		    "attention_mask": inputs["attention_mask"],
		    "label": label,
		}

	UpperCAmelCase__:	Dict									= train_test_validation.map(
	    SCREAMING_SNAKE_CASE	,batched=SCREAMING_SNAKE_CASE	,remove_columns=train_test_validation["train"].column_names	,)
	UpperCAmelCase__:	List[str]									= DataCollatorWithPadding(tokenizer=SCREAMING_SNAKE_CASE      )

	UpperCAmelCase__:	List[str]									= TrainingArguments(
	    output_dir=args.output_dir	,learning_rate=args.learning_rate	,lr_scheduler_type=args.lr_scheduler_type	,evaluation_strategy="epoch"	,save_strategy="epoch"	,logging_strategy="epoch"	,per_device_train_batch_size=args.batch_size	,per_device_eval_batch_size=args.batch_size	,num_train_epochs=args.num_epochs	,gradient_accumulation_steps=args.gradient_accumulation_steps	,weight_decay=0.01	,metric_for_best_model="accuracy"	,run_name="complexity-java"	,report_to="wandb"	,)

	UpperCAmelCase__:	Union[str, Any]									= Trainer(
	    model=SCREAMING_SNAKE_CASE	,args=SCREAMING_SNAKE_CASE	,train_dataset=tokenized_datasets["train"]	,eval_dataset=tokenized_datasets["valid"]	,tokenizer=SCREAMING_SNAKE_CASE	,data_collator=SCREAMING_SNAKE_CASE	,compute_metrics=SCREAMING_SNAKE_CASE	,)

	print("Training..."      )
	trainer.add_callback(CustomCallback(SCREAMING_SNAKE_CASE      )      )
	trainer.train()


if __name__ == "__main__":
					main()


def 			_A	(      SCREAMING_SNAKE_CASE      ):  # noqa: E741
	UpperCAmelCase__:	int									= len(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Dict									= 0
	UpperCAmelCase__:	Optional[int]									= [0] * n
	UpperCAmelCase__:	List[str]									= [False] * n
	UpperCAmelCase__:	List[str]									= [False] * n

	def dfs(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
		if parent == root:
			out_edge_count += 1
		UpperCAmelCase__:	List[str]									= True
		UpperCAmelCase__:	List[Any]									= at

		for to in l[at]:
			if to == parent:
				pass
			elif not visited[to]:
				UpperCAmelCase__:	str									= dfs(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
				UpperCAmelCase__:	Optional[Any]									= min(low[at]	,low[to]      )

				# AP found via bridge
				if at < low[to]:
					UpperCAmelCase__:	List[Any]									= True
				# AP found via cycle
				if at == low[to]:
					UpperCAmelCase__:	str									= True
			else:
				UpperCAmelCase__:	Union[str, Any]									= min(low[at]	,SCREAMING_SNAKE_CASE      )
		return out_edge_count

	for i in range(SCREAMING_SNAKE_CASE      ):
		if not visited[i]:
			UpperCAmelCase__:	Optional[Any]									= 0
			UpperCAmelCase__:	Union[str, Any]									= dfs(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,-1	,SCREAMING_SNAKE_CASE      )
			UpperCAmelCase__:	Tuple									= out_edge_count > 1

	for x in range(len(SCREAMING_SNAKE_CASE      )      ):
		if is_art[x] is True:
			print(SCREAMING_SNAKE_CASE      )


# Adjacency list of graph
_lowerCAmelCase  :      Any			 ={
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}
compute_ap(data)