





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      (    ) ->Optional[Any]:
					lowerCamelCase__       =       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=0.0_1)
					parser.add_argument("--output_dir",     type=a,     default="./results")
					return parser.parse_args()


A_												=     load("accuracy")





def    __UpperCamelCase      (    a) ->Optional[Any]:
					lowerCamelCase__    ,		lowerCamelCase__       =       eval_pred
					lowerCamelCase__       =       np.argmax(a,     axis=1)
					return metric.compute(predictions=a,     references=a)

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	):
										super().__init__()
										lowerCamelCase__       =       trainer






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





def    __UpperCamelCase      (    ) ->Any:
					lowerCamelCase__       =       get_args()
					set_seed(args.seed)

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

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

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

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

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

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

					lowerCamelCase__       =       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=0.0_1,     metric_for_best_model="accuracy",     run_name="complexity-java",     report_to="wandb",     )

					lowerCamelCase__       =       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()








from typing import TYPE_CHECKING

from ...utils import _LazyModule


A_												=     {"tokenization_byt5": ["ByT5Tokenizer"]}


if TYPE_CHECKING:
						from .tokenization_byta import ByTaTokenizer
else:
						import sys

						A_												=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

