



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__					:   List[str]													=					argparse.ArgumentParser()
    parser.add_argument("--model_ckpt"					,			type=lowerCAmelCase__					,			default="microsoft/unixcoder-base-nine"						)
    parser.add_argument("--num_epochs"					,			type=lowerCAmelCase__					,			default=5						)
    parser.add_argument("--batch_size"					,			type=lowerCAmelCase__					,			default=6						)
    parser.add_argument("--gradient_accumulation_steps"					,			type=lowerCAmelCase__					,			default=1						)
    parser.add_argument("--freeze"					,			type=lowerCAmelCase__					,			default=lowerCAmelCase__						)
    parser.add_argument("--learning_rate"					,			type=lowerCAmelCase__					,			default=5e-4						)
    parser.add_argument("--seed"					,			type=lowerCAmelCase__					,			default=0						)
    parser.add_argument("--lr_scheduler_type"					,			type=lowerCAmelCase__					,			default="cosine"						)
    parser.add_argument("--num_warmup_steps"					,			type=lowerCAmelCase__					,			default=10						)
    parser.add_argument("--weight_decay"					,			type=lowerCAmelCase__					,			default=0.01						)
    parser.add_argument("--output_dir"					,			type=lowerCAmelCase__					,			default="./results"						)
    return parser.parse_args()


lowerCAmelCase__					  =  load('''accuracy''')


def  __lowerCamelCase (       lowerCamelCase__						):

    """simple docstring"""


    lowercase__							,							lowercase__					:   Optional[int]													=					eval_pred
    lowercase__					:   Any													=					np.argmax(lowerCAmelCase__					,			axis=1						)
    return metric.compute(predictions=lowerCAmelCase__					,			references=lowerCAmelCase__						)

class 				snake_case__(_UpperCamelCase      ):

     """simple docstring"""






     def __init__(							self :     Dict				,       SCREAMING_SNAKE_CASE :     List[str]					):
         super().__init__()
         lowercase__					:   List[Any]													=					trainer







     def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     str				,       **SCREAMING_SNAKE_CASE :     List[str]					):



         if control.should_evaluate:
             lowercase__					:   Optional[int]													=					deepcopy(__lowerCAmelCase					)
             self._trainer.evaluate(eval_dataset=self._trainer.train_dataset				,       metric_key_prefix="train"					)
             return control_copy






def  __lowerCamelCase (       ):

    """simple docstring"""


    lowercase__					:   Optional[int]													=					get_args()
    set_seed(args.seed						)

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

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

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

    lowercase__					:   Optional[Any]													=					ClassLabel(num_classes=7					,			names=list(set(train_test_validation["train"]["complexity"]						)						)						)

    def tokenize(lowerCamelCase__						):
        lowercase__					:   Tuple													=					tokenizer(example["src"]					,			truncation=lowerCAmelCase__					,			max_length=1_024						)
        lowercase__					:   Optional[Any]													=					labels.straint(example["complexity"]						)
        return {
            "input_ids": inputs["input_ids"],
            "attention_mask": inputs["attention_mask"],
            "label": label,
        }

    lowercase__					:   str													=					train_test_validation.map(
        lowerCAmelCase__					,			batched=lowerCAmelCase__					,			remove_columns=train_test_validation["train"].column_names					,			)
    lowercase__					:   List[Any]													=					DataCollatorWithPadding(tokenizer=lowerCAmelCase__						)

    lowercase__					:   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"					,			)

    lowercase__					:   List[str]													=					Trainer(
        model=lowerCAmelCase__					,			args=lowerCAmelCase__					,			train_dataset=tokenized_datasets["train"]					,			eval_dataset=tokenized_datasets["valid"]					,			tokenizer=lowerCAmelCase__					,			data_collator=lowerCAmelCase__					,			compute_metrics=lowerCAmelCase__					,			)

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


if __name__ == "__main__":
    main()









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__					  =  {}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''NllbTokenizer''']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''NllbTokenizerFast''']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_nllb import NllbTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_nllb_fast import NllbTokenizerFast

else:
				import sys

				lowerCAmelCase__					  =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




