





'''simple docstring'''





from __future__ import annotations

import typing
from collections import Counter




def    A_(			A   ):
	UpperCAmelCase_								=     Counter()
	for base in range(1				,      max_perimeter + 1   ):
		for perpendicular in range(_lowerCAmelCase				,      max_perimeter + 1   ):
			UpperCAmelCase_								=     (base * base + perpendicular * perpendicular) ** 0.5
			if hypotenuse == int(_lowerCAmelCase   ):
				UpperCAmelCase_								=     int(base + perpendicular + hypotenuse   )
				if perimeter > max_perimeter:
					continue
				triplets[perimeter] += 1
	return triplets




def    A_(			A = 1000   ):
	UpperCAmelCase_								=     pythagorean_triple(_lowerCAmelCase   )
	return triplets.most_common(1   )[0][0]


if __name__ == "__main__":
				print(f"Perimeter {solution()} has maximum solutions")









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_								=     argparse.ArgumentParser()
	parser.add_argument("""--model_ckpt"""				,      type=A				,      default="""microsoft/unixcoder-base-nine"""   )
	parser.add_argument("""--num_epochs"""				,      type=A				,      default=5   )
	parser.add_argument("""--batch_size"""				,      type=A				,      default=6   )
	parser.add_argument("""--gradient_accumulation_steps"""				,      type=A				,      default=1   )
	parser.add_argument("""--freeze"""				,      type=A				,      default=A   )
	parser.add_argument("""--learning_rate"""				,      type=A				,      default=5E-4   )
	parser.add_argument("""--seed"""				,      type=A				,      default=0   )
	parser.add_argument("""--lr_scheduler_type"""				,      type=A				,      default="""cosine"""   )
	parser.add_argument("""--num_warmup_steps"""				,      type=A				,      default=10   )
	parser.add_argument("""--weight_decay"""				,      type=A				,      default=0.01   )
	parser.add_argument("""--output_dir"""				,      type=A				,      default="""./results"""   )
	return parser.parse_args()


UpperCamelCase__   : Any					  = load("""accuracy""")




def    A_(			A   ):
	UpperCAmelCase_						,	UpperCAmelCase_								=     eval_pred
	UpperCAmelCase_								=     np.argmax(A				,      axis=1   )
	return metric.compute(predictions=A				,      references=A   )







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					def __init__(						self    :  str ,					__lowercase    :  Any		):






						'''simple docstring'''







						super().__init__()
						UpperCAmelCase_								=     trainer





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  int ,					__lowercase    :  Tuple ,					__lowercase    :  Dict ,					**__lowercase    :  Dict		):






						'''simple docstring'''







						if control.should_evaluate:
							UpperCAmelCase_								=     deepcopy(__lowercase		)
							self._trainer.evaluate(eval_dataset=self._trainer.train_dataset ,					metric_key_prefix="""train"""		)
							return control_copy




def    A_(			):
	UpperCAmelCase_								=     get_args()
	set_seed(args.seed   )

	UpperCAmelCase_								=     load_dataset("""codeparrot/codecomplex"""				,      split="""train"""   )
	UpperCAmelCase_								=     dataset.train_test_split(test_size=0.2   )
	UpperCAmelCase_								=     train_test["""test"""].train_test_split(test_size=0.5   )
	UpperCAmelCase_								=     DatasetDict(
	    {
	        """train""": train_test["""train"""],
	        """test""": test_validation["""train"""],
	        """valid""": test_validation["""test"""],
	    }   )

	print("""Loading tokenizer and model"""   )
	UpperCAmelCase_								=     AutoTokenizer.from_pretrained(args.model_ckpt   )
	UpperCAmelCase_								=     tokenizer.eos_token
	UpperCAmelCase_								=     AutoModelForSequenceClassification.from_pretrained(args.model_ckpt				,      num_labels=7   )
	UpperCAmelCase_								=     model.config.eos_token_id

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

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

	def tokenize(A   ):
		UpperCAmelCase_								=     tokenizer(example["""src"""]				,      truncation=A				,      max_length=1024   )
		UpperCAmelCase_								=     labels.straint(example["""complexity"""]   )
		return {
		    "input_ids": inputs["input_ids"],
		    "attention_mask": inputs["attention_mask"],
		    "label": label,
		}

	UpperCAmelCase_								=     train_test_validation.map(
	    A				,      batched=A				,      remove_columns=train_test_validation["""train"""].column_names				,      )
	UpperCAmelCase_								=     DataCollatorWithPadding(tokenizer=A   )

	UpperCAmelCase_								=     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_								=     Trainer(
	    model=A				,      args=A				,      train_dataset=tokenized_datasets["""train"""]				,      eval_dataset=tokenized_datasets["""valid"""]				,      tokenizer=A				,      data_collator=A				,      compute_metrics=A				,      )

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


if __name__ == "__main__":
				main()


