


'''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     _UpperCamelCase      (		):
			UpperCAmelCase__  :					Optional[Any]        =     argparse.ArgumentParser()
			parser.add_argument("""--model_ckpt""" ,      type=UpperCamelCase__ ,      default="""microsoft/unixcoder-base-nine"""					)
			parser.add_argument("""--num_epochs""" ,      type=UpperCamelCase__ ,      default=5					)
			parser.add_argument("""--batch_size""" ,      type=UpperCamelCase__ ,      default=6					)
			parser.add_argument("""--gradient_accumulation_steps""" ,      type=UpperCamelCase__ ,      default=1					)
			parser.add_argument("""--freeze""" ,      type=UpperCamelCase__ ,      default=UpperCamelCase__					)
			parser.add_argument("""--learning_rate""" ,      type=UpperCamelCase__ ,      default=5e-4					)
			parser.add_argument("""--seed""" ,      type=UpperCamelCase__ ,      default=0					)
			parser.add_argument("""--lr_scheduler_type""" ,      type=UpperCamelCase__ ,      default="""cosine"""					)
			parser.add_argument("""--num_warmup_steps""" ,      type=UpperCamelCase__ ,      default=1_0					)
			parser.add_argument("""--weight_decay""" ,      type=UpperCamelCase__ ,      default=0.01					)
			parser.add_argument("""--output_dir""" ,      type=UpperCamelCase__ ,      default="""./results"""					)
			return parser.parse_args()


__A	=load('accuracy')







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[Any]        =     eval_pred
			UpperCAmelCase__  :					Dict        =     np.argmax(UpperCamelCase__ ,      axis=1					)
			return metric.compute(predictions=UpperCamelCase__ ,      references=UpperCamelCase__					)


class 		_snake_case					(     a__	):




					def __init__(      self							,							_lowerCamelCase):
								super().__init__()
								UpperCAmelCase__  :					int        =     trainer




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase):
								if control.should_evaluate:
											UpperCAmelCase__  :					List[Any]        =     deepcopy(_lowerCamelCase)
											self._trainer.evaluate(eval_dataset=self._trainer.train_dataset							,							metric_key_prefix="""train""")
											return control_copy







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					Union[str, Any]        =     get_args()
			set_seed(args.seed					)

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

			print("""Loading tokenizer and model"""					)
			UpperCAmelCase__  :					Tuple        =     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__  :					Union[str, Any]        =     model.config.eos_token_id

			if args.freeze:
						for param in model.roberta.parameters():
									UpperCAmelCase__  :					List[Any]        =     False

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

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

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

			UpperCAmelCase__  :					Tuple        =     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__  :					Dict        =     Trainer(
			    model=UpperCamelCase__ ,      args=UpperCamelCase__ ,      train_dataset=tokenized_datasets["""train"""] ,      eval_dataset=tokenized_datasets["""valid"""] ,      tokenizer=UpperCamelCase__ ,      data_collator=UpperCamelCase__ ,      compute_metrics=UpperCamelCase__ ,      )

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


if __name__ == "__main__":
		main()



'''simple docstring'''



import os







def     _UpperCamelCase      (		UpperCamelCase__ = "input.txt"					):

			with open(os.path.join(os.path.dirname(UpperCamelCase__					) ,      UpperCamelCase__					)					) as input_file:
						UpperCAmelCase__  :					Tuple        =     [
						    [int(UpperCamelCase__					) for element in line.split(""","""					)]
						    for line in input_file.readlines()
						]

			UpperCAmelCase__  :					Optional[Any]        =     len(UpperCamelCase__					)
			UpperCAmelCase__  :					Any        =     len(matrix[0]					)

			UpperCAmelCase__  :					Optional[int]        =     [[-1 for _ in range(UpperCamelCase__					)] for _ in range(UpperCamelCase__					)]
			for i in range(UpperCamelCase__					):
						UpperCAmelCase__  :					Any        =     matrix[i][0]

			for j in range(1 ,      UpperCamelCase__					):
						for i in range(UpperCamelCase__					):
									UpperCAmelCase__  :					Any        =     minimal_path_sums[i][j - 1] + matrix[i][j]

						for i in range(1 ,      UpperCamelCase__					):
									UpperCAmelCase__  :					Dict        =     min(
									    minimal_path_sums[i][j] ,      minimal_path_sums[i - 1][j] + matrix[i][j]					)

						for i in range(rows - 2 ,      -1 ,      -1					):
									UpperCAmelCase__  :					int        =     min(
									    minimal_path_sums[i][j] ,      minimal_path_sums[i + 1][j] + matrix[i][j]					)

			return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums					)


if __name__ == "__main__":
		print(f"""{solution() = }""")