


"""simple docstring"""

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 lowerCAmelCase_   (					):






 """simple docstring"""







 __lowercase						=						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__					=load("accuracy")


def lowerCAmelCase_   (					UpperCamelCase__						:			Optional[int]			):






 """simple docstring"""







 __lowercase				,		__lowercase						=						eval_pred
 __lowercase						=						np.argmax(a__					, axis=1			)
 return metric.compute(predictions=a__					, references=a__			)





class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						Optional[Any]   ,							A_						:						Tuple				):






       '''simple docstring'''


       super().__init__()
       __lowercase						=						trainer






      def SCREAMING_SNAKE_CASE_						(		self						:						int   ,							A_						:						Optional[Any]   ,							A_						:						Dict   ,							A_						:						str   ,							**A_						:						str				):






       '''simple docstring'''


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






def lowerCAmelCase_   (					):






 """simple docstring"""







 __lowercase						=						get_args()
 set_seed(args.seed			)

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

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

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

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

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

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

 __lowercase						=						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"""					, )

 __lowercase						=						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()




def 			lowerCAmelCase__					(     a__:						Optional[int]			)    ->						Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase											=     len(a__			)
							for i in range(length - 1			):
														_UpperCAmelCase											=     i
														for k in range(i + 1     ,    a__			):
																					if collection[k] < collection[least]:
																												_UpperCAmelCase											=     k
														if least != i:
																					_UpperCAmelCase						,			_UpperCAmelCase											=     (collection[i], collection[least])
							return collection


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